Scholarly article on topic 'Towards Understanding the Cynicism of Social Networking Sites: An Operations Management Perspective'

Towards Understanding the Cynicism of Social Networking Sites: An Operations Management Perspective Academic research paper on "Computer and information sciences"

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Abstract of research paper on Computer and information sciences, author of scientific article — Dhanya Jothimani, Abhay Kumar Bhadani, Ravi Shankar

Abstract With the advent of Internet and high diffusion of mobile phones, Social Networking Sites (SNS) have become the new source of entertainment, information and business support system. Easy accessibility of SNS through various devices is one of the major reasons for addiction among the young people. In this research, the researchers have made an attempt to identify various factors that lead to abandoning the usage of these sites by an individual. The factors are identified through a survey conducted among the SNS users (primarily students). Using Interpretive Structural Modelling, a hierarchical model has been developed to understand the interrelationship among the identified factors. Further, MICMAC analysis conducted classifies the factors into four groups based on their dependence and driving power: dependent, independent, linkage and autonomous factors. The analysis highlights that poor academic performance and fear of losing opportunities during placements are the key factors.

Academic research paper on topic "Towards Understanding the Cynicism of Social Networking Sites: An Operations Management Perspective"

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Procedía - Social and Behavioral Sciences 189 (2015) 117 - 132

XVIII Annual International Conference of the Society of Operations Management (SOM-14)

Towards understanding the cynicism of social networking sites: An operations management perspective

Dhanya Jothimania*, Abhay Kumar Bhadanib, Ravi Shankara

aDepartment of Management Studies, Indian Institute of Technology Delhi bBharti School of Telecommunication Technology and Management, Indian Institute of Technology Delhi

Abstract

With the advent of Internet and high diffusion of mobile phones, Social Networking Sites (SNS) have become the new source of entertainment, information and business support system. Easy accessibility of SNS through various devices is one of the major reasons for addiction among the young people. In this research, the researchers have made an attempt to identify various factors that lead to abandoning the usage of these sites by an individual. The factors are identified through a survey conducted among the SNS users (primarily students). Using Interpretive Structural Modelling, a hierarchical model has been developed to understand the interrelationship among the identified factors. Further, MICMAC analysis conducted classifies the factors into four groups based on their dependence and driving power: dependent, independent, linkage and autonomous factors. The analysis highlights that poor academic performance and fear oflosing opportunities during placements are the key factors.

©2015TheAuthors.Published byElsevierLtd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-reviewunderresponsibilityofthescientific committee of XVIII Annual International Conference of the Society of Operations Management (SOM-14).

Keywords: Social Networking Sites; Interpretive Structural Modelling; Social Media, Deactivation; Addiction

* Corresponding author. Tel.: +91-11-2659-6421. E-mail address: dhanya.jothimani@dms.iitd.ac.in

1877-0428 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the scientific committee of XVIII Annual International Conference of the Society of Operations

Management (SOM-14).

doi: 10.1016/j.sbspro.2015.03.206

1. Introduction

With social media becoming pervasive, Social Networking Sites (SNS) have become one of the indispensable communication mediums. In the last decade, there has been an explosion in the number of SNS. Various SNS that emerged include Facebook, Twitter, Linkedln and Quora. As of June, 2014, Facebook, being available in more than 70 languages, is one of the most popular social networking sites with 1.32 billion monthly active users (Facebook Statistics 2014) followed by Twitter with 0.645 billion active users (Twitter Statistics 2014).

SNS enable people to share personal as well as professional information on a global platform through text, images and videos. People can comment, share, re-share, and "like" various posts. Today, SNS have become a platform for companies to advertise and market their products and services. It also acts as a mode for convergence of information from various sources like news alerts, advertisement, promotional campaigns, video sharing and simple & distributed gaming. Development of various applications enables the accessibility of SNS in different platforms like mobile devices. As per (Facebook Statistics 2014), 1.07 billion users access Facebook through their mobiles.

Despite several advantages of SNS, there are certain problems like lack of concentration, underperformance at workplace, anxiety, sleep disorder, cyber bullying, lack of social interaction, lack of physical activities, poor team work and many others. Many researchers Alabi (2013), Andreassen, Torsheim, Brunborg, and Pallesen (2012), Balci and Golcu (2013), Deters and Mehl (2012), Marcial (2013), Saslow, Muise, Impett, and Dubin (2012), Sherman (2011), Toma and Hancock (2013), and Van Iddekinge, Lanivich, Roth, and Junco (2013)) worldwide have attempted to study addiction behavior and understand its impact on personal life. To the best of knowledge of the researchers, none of the studies focused on the factors leading to the discontinued use of the SNS accounts.

Recently, a trend of abandoning/quitting/deactivating the SNS account is being observed. For instance, Quit Facebook Day was observed on May 31, 2010. As a result, over 40,000 users quit using Facebook but it didn't result in the collapse of Facebook (Quit Facebook Day, 2010). A study by Boyd and Ellison (2007) focused on developing an understanding of the usage of SNS and its importance in one's social life. Here, the researchers have tried to identify the factors affecting the discontinued usage of SNS and establish the interrelationship among them using survey among 60 students. The interrelationship among the factors is analyzed using Interpretive Structural Modelling (ISM). In the next section, different factors that are identified as a part of the exploratory phase of this study are discussed.

2. Factors Influencing deactivation of SNS

2.1. Collection and Shortlisting of factors

The collection and shortlisting of the factors was carried out in two phases: (i) survey, and (ii) rating of factors. The process was carried out as a part of classroom activity for post-graduate students. Students who had deactivated their accounts at least once (either temporarily or permanently) were considered for the study. This resulted to a total of 60 students, of which 10 (17%) were females and 50 (83%) were males. The average age of the participants was 24 years.

Phase 1: Survey: The objective of this phase is to identify various factors that lead to deactivation of SNS accounts by the students. They were asked to suggest the reasons (minimum: 2 and maximum: 10), which in their opinion, would lead to abandoning the usage of SNS. The survey was conducted online using Google Form. The responses were collected after 2 days of circulation of the survey form. Based on the responses, similar reflection of opinions was grouped together to define a factor. For instance, factors like "decreased social and physical activity times" and "Decrease in interaction with friends" have been combined to form single factor "decrease in real-life communities". Further, the factors were validated with the help of literature. At the end of this phase, a list of 16 factors (Table 1) was identified.

Phase 2: Rating of factors: In this phase, the students were presented with the list of 16 factors obtained in previous step and were briefed about each factor. They were asked to rate the factors on 7-point Likert Scale (7 indicating very high importance and 1 indicating very low importance). Questions like "Importance of peer pressure on deactivation of accounts in SNS" and "Importance of depression on deactivation of accounts in SNS" were posed

to them. The relevant descriptive statistics are presented in Table 1, where the factors are listed in the decreasing order of their significance. Four factors, namely, huge information to process (2.9500), memory corrosion (2.9000), peer pressure (2.4000) and changing interface of site (2.1250) were dropped at the end of this phase.

Table 1. Descriptive Statistics of Factors.

s. No Factor Mean Standard Deviation

1 Poor Academic Performance 6.47500 0.67889

2 Fear ofLosing Opportunities during Placements 6.20000 0.60764

3 Social Escape 6.10000 0.59052

4 Ignoring Academic Responsibilities 6.07500 0.94428

5 Relatedness Dissatisfaction 6.07500 0.79703

6 Relationship Problems 5.82500 0.98417

7 Decrease in Real-Life Communities 5.47500 0.75064

8 Depression 5.37500 0.83781

9 Disturbance in Sleep-Cycle 4.72500 1.06187

10 Mental Preoccupation 4.60000 0.90014

11 Threat of Botnets 4.27500 1.37724

12 Cyberbullying 3.52500 1.08575

13 Huge Information to Process 2.95000 0.98580

14 Memory Corrosion 2.90000 0.49614

15 Peer Pressure 2.40000 0.87119

16 Changing Interface ofthe Site 2.12500 0.68641

2.2. Description of factors

The 16 factors obtained at the end of phase 1 are explained in this section.

Poor Academic Performance: Students might show some withdrawal behavior like absenteeism and turnover (Van Iddekinge et al., 2013; Andreassen et al., 2012; Kuss & Griffiths, 2011). They lose their tolerance and patience to study and may procrastinate even a small work. The tendency to work in a team or a group decreases. This can be related to staying up till late night playing games or chatting in the SNS. They prefer to be socially connected to their friends during lectures. This affects their concentration in the class.

Fear of Losing Opportunities during Placements: There is a co-existence of friends' group and work group in the SNS. Users generally share their opinion in the SNS without knowing that they are being monitored by their peers from workplace. Here, the workplace relates to the companies or institutes where students carry out their summer/winter internships and short-term projects. Any negative sentiment posted in the site would cost them opportunities during placements (Wilson, Gosling, & Graham, 2012; Karl, Peluchette, & Schlaegel, 2010b, 2010a; Kluemper & Rosen, 2009; Bohnert& Ross, 2010; Binder, Howes, & Sutcliffe, 2009). Apart from processing the employers' data, the concept of talent analytics and HR analytics relate to mining the information from social media to hire or shorltlist students for interview and being hired. The SNS addiction kills creativity, critical thinking and evaluation and communication skills, which provide a student competitive edge over others (Bloem, 2013).

Social Escape: Users prefer communicating with their friends through SNS instead of utilizing their social and physical activity times. Few (introvert) people prefer expressing their views freely in SNS platform (Kuss & Griffiths, 2011). They are comfortable with SNS till positive responses are received on their posts/opinions. Negative responses can make them anxious and embarrassed (Balci & Golcu, 2013; Baker & White, 2010).

Ignoring Academic Responsibilities: With increased usage of SNS, people tend to withdraw themselves from the physical world and become passive towards their personal and academic life (Balci & Golcu, 2013; Baker &

White, 2010, Kuss & Griffiths, 2011; Alabi, 2013; Young, 1998). This could lead to ignoring academic responsibilities like submission of assignments and ill preparedness towards exams. Addiction to SNS infact reduces the patience of the students to work in a team for a group related activity.

Relatedness Dissatisfaction: Relatedness is defined as an individual's need to stay connected with his/her network. Social interaction can be through one of the mediums: face-to-face, mobile, internet or SNS. Interaction by means of technology makes one feel closer to his/her best friend. When a problem within real-life communities is not resolved through SNS, it causes relatedness dissatisfaction (Deters & Mehl, 2012).

Relationship Problems: SNS may lead to conflicts in any relationship - be it within friends circle or work group. There could be miscommunications between friends in SNS, which may lead to problem in real world relationship (Andreassen et al., 2012; Kuss & Griffiths, 2011). SNS can lead to problems in romantic relationships. A person can stalk his/her partner by monitoring his/her activities in SNS. This increases the surveillance behavior and leads to jealousy and obsessive behaviour (Saslow et al., 2012; Anderson et al., 2012; Muise, Christofides, & Desmarais, 2009; Marcial, 2013). Recent up gradation of "last seen " feature in the SNS reinforces the surveillance behavior.

Decrease in Real-Life Communities: Most times, the users are completely engaged in the SNS activities. There is a decrease in the time they spend on physical and social activities with their friends, which are crucial for real-life interaction (Baker & White, 2010; Balci & Golcu, 2013; Andreassen et al., 2012; Kuss & Griffiths, 2011). This affects interpersonal skills, which are needed during placements, thus causing depression.

Depression: Depression is one of the most common SNS addiction behaviors (Marcial, 2013). Most of the SNS like Google+, Facebook and Twitter have provision for the users to share their opinion as "status update" or "tweet". A user posts a status update or tweet as an initiation of social interaction. Another feature of SNS is instant messaging or direct message which lets people to have one-to-one chat or group chat with their friends. Lack of response from his/her friends to status updates and/or direct message increases his/her feelings of loneliness, which ultimately leads to depression (Deters & Mehl, 2012; Burke, Marlow, & Lento, 2010). Lack of interaction with the real-life friends and online friends leads to depression.

Disturbance in Sleep-Cycle: Excessive use of SNS causes disturbance in the sleep-cycle (Andreassen et al., 2012; Marcial, 2013). Advancement of technology enables the easy access of SNS like Facebook and Twitter through mobile. This not only affects the sleep-cycle but affects the entire work cycle of the day. This results in students either missing their classes or going late for the lectures.

Mental Preoccupation: Frequent usage of SNS keeps an individual mentally occupied (Baker & White, 2010; Balci & Golcu, 2013; Kuss & Griffiths, 2011). They remain in front of computer or mobile almost all day accessing SNS (Balci & Golcu, 2013; Kuss & Griffiths, 2011) either playing games or waiting for responses on their posts. Anxiousness and eagerly awaiting response or feedback ("likes" or "comments" on the posts) are the two common behaviors after every status update. This leads to the mood swings and decreases tolerance capacity (Baker & White, 2010; Balci & Golcu, 2013). Sometimes, in order to hide the addiction behaviour, users tend to deactivate their SNS accounts.

Threat of Botnets: Fake profiles are created in SNS. These profiles invite a user to be "friends" or "follow" them and access their private information. 81 million fake profiles are created in Facebook (Facebook Statistics, 2014). Further, they spread malware by making the users to click on few links. Victims fall prey to this trick without their own knowledge. Link would be posted on their space or their friends' space in the SNS thus propagating this phenomenon (Wilson et al., 2012; Thomas & Nicol, 2010). For instance, when the Malaysian Airlines Flight MH370 went missing in March 2014, it caught everyone's attention and people were interested in the recent updates about the missing plane. Hackers shared a video "Missing Malaysia Airlines Flight MH370 plane found in Bermuda Triangle!" via Twitter and Facebook to spread malwares. There were posts asking the users to "share" the video before viewing it (Eleftheriore-Smith, 2014).

Cyberbulling: The constantly upgraded design of SNS encourages content sharing and improves the overall user experience (Wilson et al., 2012; Burke, Marlow, & Lento, 2009). But this modification in design is accompanied with certain changes in the privacy settings. In most cases, the users are unaware about the changes in the privacy settings and they become the victim of certain potential privacy risks. Few of the privacy risks include involuntary sharing of personal information publicly, unwanted message by strangers, vulnerability to gossips and stalkers and use of information by third party application developers (in case of games and other applications) (Debatin, Lovejoy, Horn, & Hughes,2009; Boyd & Ellison, 2007; Taraszow, Arsoy, Shitta, & Laouris, 2008; Anderson et al., 2012). For instance, when graph search was introduced in Facebook, the settings of all posts were made "public" (i.e., posts can be seen by people other than one's own network). Any public post can be used by any user to defame

a person.

Huge Information to Process: Every 20 minutes, 1 million links are being shared in Facebook (Facebook Statistics, 2014). 9100 tweets are being posted every second (Twitter Statistics, 2014). This shows that information is being generated at an exponential rate in SNS. People are unable to process this information, be it related to product or news, at right time (Bloem, 2013). This affects the decision-making ability of the people when the right kind of information does not reach the right kind of people at right time (Sherman, 2011; Young, 2004). Information in SNS is highly manipulative. For instance, recently there was a tweet saying Syrian President Bashar al-Assad was killed. Two more tweets after the initial tweet confirmed the death of the president. Within an hour of this tweet, the oil prices rose bySl (Wills, 2012). Manipulative nature of SNS leads to lack of trust in information.

Memory Corrosion: In the era of Internet and SNS, any information (related to a personality, product or event) that is needed is just "one-click away". This results in decline in use of the long-term memory. For instance, birthday reminder feature of a SNS reminds us about the birthday of our near and dear ones! (Bloem, 2013)

Peer Pressure: Social influence not only influences the usage of SNS but also abandoning the usage of SNS (Andreassen et al., 2012). Word-of-mouth through friends, like usage of SNS as a source of distraction and the privacy policies of SNS, plays a major role in social influence. Observation of deactivation of SNS account by friends could influence an individual to deactivate his/her SNS account.

Changing Interface of the Site: Design of the SNS are constantly upgraded to improve the overall user experience and to attract new users (Wilson et al., 2012). This leads to changes in privacy settings about which the users are not notified. But sudden drastic changes may not attract or please few people. Facebook recently changed its site design. There is a reduction in the space for news feed and number of advertisements suggested by Facebook is increasing. In another case, Twitter is planning to adopt "Facebook like" interface for users to upload photo albums. This might lead to deactivation of the SNS account either temporarily or permanently.

3. Interpretive Structural Modelling

A system is comprised of various elements and these elements interact with each other. The complex relationships among these elements can be handled using Interpretive Structurally Modelling (ISM). Using ISM, the order and direction among the system variables can be accomplished. The terms factors, elements and variables are used interchangeably. A system can be well-explained by the direct and indirect relationships among the variables than the individual elements. The relationship among the factors is decided by decision makers or experts, hence it is known as group decision-making technique. These relationships are represented in the form of graph, thus, making ISM comprehendible. The relationship is framed into a systemic hierarchical model. ISM has been used in various disciplines like logistics and supply chain management (Raj, Shankar, & Suhaib, 2008; Ramesh, Banwet, & Shankar, 2010), technology transfer (Singh & Kant, 2007), information security management (Chander, Jain, & Shankar, 2013) and knowledge management (Singh & Agrawal, 2003). In this paper, ISM is used to build the hierarchical relationship among the factors leading to the decreased usage of SNS.

The process ofISM methodology can be enumerated below:

(a) The factors affecting the system under consideration are identified with the help of survey or group decision-making process. Here, abandoning the usage of SNS represents the system under consideration. The factors identified with the help of survey and literature (as explained in previous section) represent the system elements.

(b) A contextual relationship among factors is established.

(c) Pairwise relationships among the identified factors for abandoning the usage of the SNS are established to develop a Structural Self-Interaction Matrix (SSIM).

(d) From SSIM, a binary matrix called Initial Reachability matrix is developed. This matrix is checked for transitivity (i.e., if an element X is related to Y and Y is related to Z, then X is necessarily related to Z) to obtain Final Reachability Matrix.

(e) The obtained final reachability matrix is divided into different levels.

(f) Based on the relationships in the reachability matrix, a directed graph (also known as digraph) is drawn. The transitive links are removed.

(g) ISM is obtained from the resultant digraph by replacing the element nodes with statements of factors.

(h) Necessary modifications are made, in case of any conceptual inconsistency in the developed ISM model.

The above steps are presented in Figure 1.

Fig. 1. Flow Chart ofthe Methodology

3.1. Structural Self-Interaction Matrix

First 12 factors listed in Table 1 are considered for the development ofISM Model. Expert opinion can be used to obtain the contextual relationship among the factors. Consensus methodologies especially brainstorming and Nominal Group Technique (NGT) were used for identifying the contextual relationships among the factors leading to the deactivation of the SNS account. Nominal Group Technique is one of the methodologies to obtain both qualitative and quantitative data in a group. For further reading on NGT, readers are suggested to refer (Gallagher, Hares, Spencer, Bradshaw, & Webb, 1993; Georgeakopoulos, 2009). Since this work explores the factors affecting the decreased usage of SNS, the respondents are students. Majority users of SNS lie in the age group of 18-30 years. Hence, this justifies students as the choice of experts. The same set of 60 students (same as survey) were posed questions like "Does Relationship problems lead to Poor performance at workplace", "Does disturbance in sleep-cycle lead to fear oflosingjob opportunities at work place" etc.

Based on the contextual relationship among the factors, the direction of relationship between factors (i and/) is denoted with the help of four symbols: V, A, X and O. When factor i affects factor/ but not otherwise, then the relationship is denoted by V. A is used when factor/ influences i, but not vice-versa. When both factors i and j affect each other, then the relationship between them is represented by X. Finally, O represents that there is no relation between factors i andj.

The use of the symbols V, A, X and O in SSIM (Table 2) can be explained with help of following example:

(i) Factor 3 affects factor 9. "Depression" leads to "Relatedness Dissatisfaction". Thus, the relationship between factors 3 and 9 is denoted by "V" in the SSIM.

(ii) Factor 7 affects factor 6. "Mental Preoccupation" affects "Ignoring Academic Responsibilities". Thus, the relationship between factors 6 and 7 is denoted by "A" in the SSIM.

(iii) Factors 1 and 12 affect each other. The factor 1, "Cyberbullying" and factor 12, "Threat of Botnets" affect each other. Thus, the relationship between factors 1 and 12is denoted by "X" in the SSIM.

(iv) No relationship exists between factors 5 (fear of losing opportunities during placements) and 12 (threat of botnets) and hence the relationship between these factors is denoted by "O" in the SSIM.

Following the above notations for similar contextual relations, the SSIM is developed for all 12 factors leading to abandoning the usage of SNS.

Table 2. Structural Self Interaction Matrix.

S.No Factors 12 11 10 9 8 7 6 5 4 3 2 1

1 Cyberbullying X o o O V V O o V V o

2 Decrease in Real life communities O V V O o A V o o X

3 Depression A o V V V A V V A

4 Disturbance in Sleep Cycle A o o o o X o o

5 Fear oflosing opportunities during placements O A o o X A o

6 Ignoring Academic Responsibilities O o X X V A

7 Mental Preoccupation A o o o V

8 Poor Academic Performance O A A A

9 Relatedness Dissatisfaction O o o

10 Relationship Problems o X

11 Social Escape o

12 Threat of Botnets

3.2. Reachability Matrix

Initial reachability matrix is a binary matrix formed from SSIM by substituting V, A, X and O by 1 and 0 appropriately. The rules for substitution are provided in Table 3. Table 4 represents the initial reachability matrix obtained for the identified factors

Table 3. Rules for conversion of SSIM to initial reachability matrix. Entry in SSIM Entry in Initial Reachability Matrix

i'j) (Ji)

Table 4. Initial Reachability Matrix.

Factors 1 2 3 4 5 6 7 8 9 10 11 12

1 1 0 1 1 0 0 1 1 0 0 0 1

2 0 1 1 0 0 1 0 0 0 1 1 0

3 0 1 1 0 1 1 0 1 1 1 0 0

4 0 0 1 1 0 0 1 0 0 0 0 0

5 0 0 0 0 1 0 0 1 0 0 0 0

6 0 0 0 0 0 1 0 1 1 1 0 0

7 0 1 1 1 1 1 1 1 0 0 0 0

8 0 0 0 0 1 0 0 1 0 0 0 0

9 0 0 0 0 0 1 0 1 1 0 0 0

10 0 0 0 0 0 1 0 1 0 1 1 0

11 0 0 0 0 1 0 0 1 0 1 1 0

12 1 0 1 1 0 0 1 0 0 0 0 1

As explained in Step (d) of the ISM methodology, transitivity check is carried out for the initial reachability matrix to obtain the final reachability matrix. In Table 5, the dependence power (DP) and the driving power (Dr.P) of each factor are shown. The total number of factors that affects factory represent the dependence power of factory. The driving power of a factor is defined as the total number of factors (including itself) that it may affect. Both dependence power and driving power play a major role in classifying the factors into four groups of dependent, autonomous, independent and linkage factors.

Table 5. Final Reachability Matrix.

Factors 1 2 3 4 5 6 7 8 9 10 11 12 Dr.P

1 1 1* 1 1 1* 1* 1 1 1* 1* 1* 1 12

2 0 1 1 0 1* 1 0 1* 1* 1 1 0 8

3 0 1 1 0 1 1 0 1 1 1 1* 0 8

4 0 1* 1 1 1* 1* 1 1* 1* 1* 1* 0 10

5 0 0 0 0 1 0 0 1 0 0 0 0 2

6 0 0 0 0 1* 1 0 1 1 1 1* 0 6

7 0 1 1 1 1 1 1 1 1* 1* 1* 0 10

8 0 0 0 0 1 0 0 1 0 0 0 0 2

9 0 0 0 0 1* 1 0 1 1 1* 1* 0 6

10 0 0 0 0 1* 1 0 1 1* 1 1 0 6

11 0 0 0 0 1 1* 0 1 1* 1 1 0 6

12 1 l* 1 1 1* 1* 1 1* 1* 1* 1* 1 12

DP 2 6 6 4 12 10 4 12 10 10 10 2

3.3. Level Partitions

The reachability set and the antecedent set for each factor are identified from final reachability matrix (Warfield, 1974). The reachability set for a particular element constitutes the element itself and other elements, which it may affect. The antecedent set for an element consists of an element itself and other elements, which may affect them. Subsequently, the intersection of antecedent and reachability sets for all elements is derived to obtain the intersection set. The element(s) for which the reachability and the intersection sets are the same, is (are) given the

top most position in the ISM hierarchy. This means that these elements would not affect any other variable above their own level. Top level elements are removed from the remaining variables and are not considered for further iteration process. From Table 6, the elements occupying level I are factors 5 and 8. Hence, these would occupy the top most position of the ISM model. These factors are discarded in Table 7 for further iteration. Level of each element is determined by continuing this process. The identified elements along with their respective levels aid in building the digraph and the ISM model. The iteration of factors for level partition are shown in tables 6 to 10.

Table 6. First Iteration.

Factor Reachability Set Antecedent Set Intersection Set Level

1 {1,2,3,4,5,6,7,8,9,10,11,12} {1,12} {1,12}

2 {2,3,5,6,8,9,10,11} {1,2,3,4,7,12} {2,3}

3 {2,3,5,6,8,9,10,11} {1,2,3,4,7,12} {2,3}

4 {2,3,4,5,6,7,8,9,10,11} {1,4,7,12} {4,7}

5 {5,8} {1,2,3,4,5,6,7,8,9,10,11,12} {5,8} I

6 {5,6,8,9,10,11} {1,2,3,4,6,7,9,10,11,12} {6,9,10,11}

7 {2,3,4,5,6,7,8,9,10,11} {1,4,7,12} {4,7}

8 {5,8} {1,2,3,4,5,6,7,8,9,10,11,12} {5,8} I

9 {5,6,8,9,10,11} {1,2,3,4,6,7,9,10,11,12} {6,9,10,11}

10 {5,6,8,9,10,11} {1,2,3,4,6,7,9,10,11,12} {6,9,10,11}

11 {5,6,8,9,10,11} {1,2,3,4,6,7,9,10,11,12} {6,9,10,11}

12 {1,2,3,4,5,6,7,8,9,10,11,12} {1,12} {1,12}

Table 7. Second Iteration.

Factor Reachability Set Antecedent Set Intersection Set Level

1 {1,2,3,4,6,7,9,10,11,12} {1,12} {1,12}

2 {2,3,6,9,10,11} {1,2,3,4,7,12} {2,3}

3 {2,3,6,9,10,11} {1,2,3,4,7,12} {2,3}

4 {2,3,4,6,7,9,10,11} {1,4,7,12} {4,7}

6 {6,9,10,11} {1,2,3,4,6,7,9,10,11,12} {6,9,10,11} II

7 {2,3,4,6,7,9,10,11} {1,4,7,12} {4,7}

9 {6,9,10,11} {1,2,3,4,6,7,9,10,11,12} {6,9,10,11} II

10 {6,9,10,11} {1,2,3,4,6,7,9,10,11,12} {6,9,10,11} II

11 {6,9,10,11} {1,2,3,4,6,7,9,10,11,12} {6,9,10,11} II

12 {1,2,3,4,6,7,9,10,11,12} {1,12} {1,12}

Table 8.Third Iteration.

Factor Reachability Set Antecedent Set Intersection Set Level

1 {1,2,3,4,7,12} {1,12} {1,12}

2 {2,3} {1,2,3,4,7,12} {2,3} III

3 {2,3} {1,2,3,4,7,12} {2,3} III

4 {2,3,4,7} {1,4,7,12} {4,7}

7 {2,3,4,7} {1,4,7,12} {4,7}

12 {1,2,3,4,7,12} {1,12} {1,12}

Table 9.Fourth Iteration.

Factor Reachability Set Antecedent Set Intersection Set Level

1 {1,4,7,12} {1,12} {1,12}

4 {4,7} {1,4,7,12} {4,7} IV

7 {4,7} {1,4,7,12} {4,7} IV

12 {1,4,7,12} {1,12} {1,12}

Table lO.Fifth Iteration.

Factor Reachability Set Antecedent Set Intersection Set Level

1 {1,12} {1,12} {1,12} V

12 {1,12} {1,12} {1,12} V

3.4. Development of Conical Matrix

The elements in the same level are clubbed across rows and columns to develop a matrix called the conical matrix, which is presented in Table 11.

Table 11. Conical Matrix.

Factors 5 8 6 9 10 11 2 3 4 7 1 12

5 1 1 0 0 0 0 0 0 0 0 0 0

8 1 1 0 0 0 0 0 0 0 0 0 0

6 1 1 1 1 1 1 0 0 0 0 0 0

9 1 1 1 1 1 1 0 0 0 0 0 0

10 1 1 1 1 1 1 0 0 0 0 0 0

11 1 1 1 1 1 1 0 0 0 0 0 0

2 1 1 1 1 1 1 1 1 0 0 0 0

3 1 1 1 1 1 1 1 1 0 0 0 0

4 1 1 1 1 1 1 1 1 1 1 0 0

7 1 1 1 1 1 1 1 1 1 1 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1

12 1 1 1 1 1 1 1 1 1 1 1 1

3.5. Development of ISM-basedModel

From the conical matrix and the final reachability matrix, a systemic and structural model is developed. An arrow pointing from i to j shows the relationship (if any) that exists between i and j. The ISM model for the factors leading to the deactivation of SNS is shown is Figure 2. It is observed from Figure 2 that factors namely, cyberbullying (Factor 1) and threat to botnets (factor 12) form the base of the ISM hierarchy. These are the driving factors that lead to abandoning the usage of SNS. Fear of losing opportunities at workplace (Factor 5) and Poor performance at workplace (Factor 8) are the dependent variables, which ultimately lead to deactivation of the SNS accounts. These factors have positioned in the top of the hierarchy.

Fig. 2. ISM Model offactors leading to deactivation of SNS accounts

4. MICMAC Analysis

A system consists of various elements. The interaction among these elements can be explained in two ways: direct relationship and indirect relationship analysis using the multiplication properties of matrices. In direct relationship analysis, the direct relationships between the variables are considered to construct a matrix called direct relationship matrix M. In this matrix, the diagonal entries are zero and the transitive links are ignored. The direct relationship matrix developed based on final ISM model is shown in Table 12. The number of interactions in the column and rows are summed to obtain dependence (DP) and driving power (Dr.P) of the variables, respectively. The dependence and driving power of element 2 are 2 and 3, respectively. Though this approach reveals the maximum direct impact but the impact of the hidden relationships on the system is not known. Hence, the indirect relationships are analyzed using MICMAC analysis.

The principle of Matrice d'Impacts croises-multiplication appliqué an classment (cross-impact matrix multiplication applied to classification), abbreviated as MICMAC, is based on multiplication properties of matrices (Saxena, Sushil, & Vrat, 1990; Sharma, Gupta, & Sushil, 1995). To explain indirect relations, consider three system elements, say,p, q and r. If elementp influences q and q influences r, then p would influence r. The elementsp and r are said to have indirect relation. MICMAC analysis provides the impact of numerous indirect relations affecting the system. Direct matrix, M is taken as the input for MICMAC analysis. When the matrix is squared, then second order relationship is obtained. Similarly, when the matrix is multiplied n times, then influencing paths of nth order are obtained. The process is terminated when the hierarchy of dependence and driving power reaches a stable stage. M9 multiplication matrix is shown in Table 13. At the stable stage, the power by which the matrix is raised to, is known as the length of the circuit. In Table 14, it is observed that the ranks of dependence and driving power are same at both M9 and M11. This represents that their hierarchy is stabilized at M9 and it gets repeated atM11. Thus, the length of the circuit is 9. Similarly, in Table 13, ei2 (second row and third column) represents the path length influencing the relationship between factors 1 and 2. Similar to the direct relationship matrix, the driving and dependence power for each factor are obtained. As the name suggests, based on the MICMAC analysis, the elements are classified into four categories: dependent, independent, linkage and autonomous factors. The results are

diagrammatically represented in Figure 2, where dependence and driving power are represented along the x-axis and y-axis, respectively.

Table 12. Direct Relationship Matrix.

Factors 1 2 3 4 5 6 7 8 9 10 il 12 Dr.P Rank

1 0 0 0 1 0 0 0 0 0 0 0 1 2 2

2 0 0 1 0 0 0 0 0 0 0 0 0 1 3

3 0 1 0 0 0 1 0 0 0 0 0 0 2 2

4 0 0 1 0 0 0 0 0 0 0 0 0 1 3

5 0 0 0 0 0 0 0 1 0 0 0 0 1 3

6 0 0 0 0 0 0 0 1 1 1 0 0 3 1

7 0 1 0 0 0 0 0 0 0 0 0 0 1 3

8 0 0 0 0 1 0 0 0 0 0 0 0 1 3

9 0 0 0 0 0 1 0 0 0 0 0 0 1 3

10 0 0 0 0 0 1 0 0 0 0 1 0 2 2

11 0 0 0 0 0 0 0 0 0 1 0 0 1 3

12 1 0 0 0 0 0 1 0 0 0 0 0 2 2

DP 1 2 2 1 1 3 1 2 1 2 1 1

Rank 3 2 2 3 3 1 3 2 3 2 3 3

Table 13.1ndirect Relationship Matrix.

Factors 1 2 3 4 5 6 7 8 9 10 11 12 Dr.P Rank

1 0 8 0 1 22 45 0 0 0 0 22 1 99 4

2 0 0 1 0 0 0 0 33 21 33 0 0 88 5

3 0 1 0 0 33 55 0 0 0 0 33 0 122 2

4 0 0 1 0 0 0 0 33 21 33 0 0 88 5

5 0 0 0 0 0 0 0 1 0 0 0 0 1 8

6 0 0 0 0 0 0 0 55 34 55 0 0 144 1

7 0 1 0 0 12 21 0 0 0 0 12 0 46 7

8 0 0 0 0 1 0 0 0 0 0 0 0 1 8

9 0 0 0 0 21 34 0 0 0 0 21 0 76 6

10 0 0 0 0 33 55 0 0 0 0 34 0 122 2

11 0 0 0 0 0 0 0 33 21 34 0 0 88 5

12 1 0 8 0 0 0 1 34 24 34 0 0 102 3

DP 1 10 10 1 122 210 1 189 121 189 122 1

Rank 6 5 5 6 3 1 6 2 4 2 3 6

REGION II: INDEPENDENT VARIABLES

+ Depreiiau

Threat if bsiiEtL i CytertuJlins

D«Ki3cia ies] life ajicjciiui ties DisturtsnrE ill i l??p r^'dt

REGION HI: LLVKAGE VARIABLES

I ¡us rill? Acfldtnsit: Et=]»iiLibiliti EL

* E rl n ri 3 m Ji i F priibl riL:

* Broaa] Eit-spt

♦ ReI^IeIuell diss tir fsrbsii

?ii EUts] p-rKTOLp ätiSIlFllll siii:tbu

RE CION I: AUT ONOUOU5 VARIABLES

REGION IV: DEPENDENT VARIABLES

Fta r if Ii opps rtiniiliE luria? FJ s : auentL

Für Acadämir Firfi ITBSIirE

KM 1J(J

DepEDdEDCEPlNl'er

Fig. 3. MICMAC Analysis

5. Result and Discussion

In this research, the objective of ISM model is to develop hierarchical relationship among the factors leading to deactivation of SNS accounts. Various factors leading to the deactivation of SNS accounts are classified with the help of driving power and dependence power obtained using MICMAC analysis. From MICMAC analysis, it is observed that mental preoccupation and addiction (Factor 7) falls under the category of autonomous variables (Region I in Figure 3). These variables have low dependence power as well as low driving power. Though they are generally considered to be disconnected from the system, they play an important role. Addiction to several features of SNS leads to mental preoccupation. Several researchers have focused on addiction behaviors, hence it can be concluded that though mental preoccupation and addiction is an autonomous variable but it helps to understand the phenomenon of deactivation of SNS accounts, predominantly by students.

Next cluster consists of independent variables (Region III in Figure 3), which have high driving power and low dependence power. These factors form the base of the systemic hierarchical structure (Figure 2). Factors like cyberbulling, threat of botnets, disturbance in sleep-cycle, depression and decrease in real-life communities fall under this region. In this region, "depression" has relatively high driving power and high dependence power compared to others. Remaining factors (threat of botnets, cyberbulling, and decrease in real life communities and disturbance in sleep cycle) in this region influence this factor. Activities like cyberbulling, cyberstalking and installation of malware through SNS lead to disturbance in sleep cycle, which ultimately leads to depression. When the interaction with friends or family members decreases, it again leads to depression and vice-versa. Though users have little control over the changing interface of the SNS and change of privacy settings, but they can try to make their SNS profile private or protected. They can be selective in accepting the requests in SNS. Students can minimize their time spent on SNS (chats and games) and increase their interactions with friends. This would refresh their minds and let them focus on academics.

Table 14: Ranks of driving and dependence power obtained using MICMAC analysis

Fa M M3 M5 M7 Ms M10 M"

ct D R D R D R D R Dr R D R Dr. R D R Dr R D R D R D Ran D R D R

or r a P a r a P a P a P a P a P a .P a P a r. a P k r. a P a

P n n P n n n n n n n n P n P n n

k k k k k k k k k k k k k

1 2 2 1 3 5 3 1 5 13 3 1 6 36 3 1 6 99 4 1 6 1 3 1 6 2 4 1 6

9 6

0 7

2 1 3 2 2 4 4 4 4 12 4 6 5 33 4 8 5 88 5 1 5 1 6 1 5 2 5 1 5

0 2 1 3 2

2 2

3 2 2 2 2 6 2 4 4 17 2 6 5 46 2 8 5 12 2 1 5 2 1 1 5 3 2 1 5

2 0 3 1 2 2

2 1

4 1 3 1 3 4 4 1 5 12 4 1 6 33 4 1 6 88 5 1 6 1 6 1 6 2 5 1 6

2 3

2 2

5 1 3 1 3 1 6 4 4 1 7 14 4 1 7 4 4 1 8 1 3 1 8 1 4 1 8 3 3

3 2 8 3

2 9 2

6 3 1 3 1 8 1 1 1 21 1 29 1 55 1 7 1 14 1 2 1 1 2 3 2 3 1 5 1

0 9 4 1 9 2 7 5

0 9 0 7 3

7 1 3 1 3 2 5 1 5 6 6 1 6 17 6 1 6 46 7 1 6 8 7 1 6 1 7 1 6

8 2

8 1 3 2 2 1 6 7 2 1 7 23 2 1 7 6 2 1 8 1 2 1 8 3 1 1 8 5 2

8 8 3 0

9 2 9

9 1 3 1 3 4 4 5 3 11 5 16 3 29 5 4 3 76 6 1 4 1 5 2 3 1 6 3 4

5 2 4 1 9 2

1 4 0 9 0

10 2 2 2 2 6 2 7 2 17 2 23 2 46 2 6 2 12 2 1 2 2 1 3 1 3 2 5 2

8 2 8 3 3 2 0

9 2 2 1 9

11 1 3 1 3 4 4 4 4 12 4 14 4 33 4 4 4 88 5 1 3 1 6 1 4 2 5 3 3

3 2 2 8 3 3

2 2 9 2 2

12 2 2 1 3 4 4 1 5 12 4 1 6 36 3 1 6 10 3 1 6 1 4 1 6 2 3 1 6

2 4 7

5 8

Dr.P and DP represent Driving Power and Dependence Power, respectively

The next cluster (Region IV in Figure 3) consists of variables having high dependency power and high driving power. These variables are called linkage variables. Factors like relatedness dissatisfaction, ignoring academic responsibilities, relationship problems and social escape fall under this category. Any disturbance in this region affects the entire system. In this analysis, these factors are influenced by those in region II and influence the factors in region I. Hence they act as the connecting link between other factors in the system. From ISM model (Figure 2), it can be seen that these factors are interrelated amongst themselves. When a person has a problem and/or is depressed, he or she tries to share with his or her online friends. But when it is not solved by them, it leads to problem in their relationship. This is again reflected in lack of fulfillment of academic responsibilities, thus leading to poor academic performance. Students should share their problems with their near and dear ones face-to-face rather than communicating online. This would try to save their time as well as unwanted misunderstandings.

The last cluster (Region II in Figure 3) comprises of the dependent variables, which have high dependency power and low driving power. Poor performance at workplace and fear of losing opportunities at workplace are the dependent variables. The factors at top of hierarchy represents that they are result of actions of remaining factors. Poor academic performance is result of various factors mentioned above. Since academic performance is basis on which the students are shortlisted for further placement processes, it denies them an opportunity. Apart from poor

academic performance, lack of creativity and team effort also affect any tasks in group discussion and personal interview. Opinions shared in the SNS could be monitored by the hiring specialists. The students should be aware their social media activities.

Further, a correlation is observed between the systemic hierarchical structure (Figure 2) and the descriptive statistics of the factors obtained using survey (Table 1). For instance, the factors "Poor academic performance" and "Fear of losing opportunities at workplace" having mean 6.475 and 6.200, respectively appear at the top of the ISM hierarchy. Similarly, the factors "Threat of botnets" and "Cyberbulling" appearing at the bottom of the ISM hierarchy have mean 4.275 and 3.525, respectively. This suggests that the developed ISM model is consistent.

6. Conclusion

In this paper, the factors influencing the decreased usage of SNS have been identified. ISM has served the purpose of finding the hierarchical relationship among the factors. The factors were collected from the students and were validated using existing literature. Poor academic performance and fear of losing opportunities during placements are found to be the major factors that lead to deactivation of SNS accounts by students. The factors occupying the lowest level of the hierarchical model are threat of botnets and cyberbulling. The correlation observed in the ISM model and the descriptive statistics of factors increases the practical usability of the model. Though the students who deactivated their SNS accounts were considered to be experts but the study did not take into the consideration of the opinions of the social media experts.

However, the limitation of this study is the qualitative nature of ISM where expert opinion is used to establish the contextual relationships between the factors. The complexity of ISM model increases with increase in number of factors to a problem. As a part of future work, a hybrid ISM-ANP can be used to prioritize the factors. The hierarchical relationship obtained using ISM would serve as an input to ANP. The factors can be measured based on questionnaire survey and Structural Equation Modelling. Similar type of study can be conducted to understand why certain companies block social networking sites in their premises.

References

Alabi, O. F. (2013). A survey of Facebook addiction level among selected Nigerian University undergraduates. New Media and Mass Communication, 10, 70-80.

Anderson, B., Fagan, P., Woodnutt, T., & Chamorro-Premuzic, T. (2012). Facebook psychology: Popular questions answered by research.

Psychology of Popular Media Culture, 1 (1), 23-37. Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development ofa Facebook addiction scale. Psychological Reports, 110 (2), 501-517.

Baker, R. K., & White, K. M. (2010, November). Predicting adolescents' use of social networking sites from an extended theory of planned

behavior perspective. Computers in Human Behavior, 26(6), 1591-1597. Balci, S., & Golcu, A. (2013). Facebook addiction among University students in Turkey: Selcuk university example. Journal of Turkish Studies, 34, 255-278.

Binder, J., Howes, A., & Sutclie, A. (2009). The problem of conflicting social spheres: Effects of network structure on experienced tension in social network sites. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 965-974). New York, NY, USA: ACM.

Bloem, S. D. J. (2013). The dark side of social media: Alarm bells, analysis and the way out (Tech. Rep.). Research Institute forNew Technology. Bohnert, D., & Ross, W. H. (2010). The influence ofsocial networking web sites on the evaluation ofjob candidates. Cyberpsychology, Behavior,

and Social Networking, 13(3), 341-347. Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13 (1), 210-230.

Burke, M., Marlow, C., & Lento, T. (2009). Feed me: Motivating newcomer contribution in social network sites. In Proceedings of the SIGCHI

conference on human factors in computing systems (pp. 945(954). New York, NY, USA: ACM. Burke, M., Marlow, C., & Lento, T. (2010). Social network activity and social well-being. In Proceedings of the SIGCHI conference on human

factors in computing systems (pp. 1909(1912). NewYork, NY, USA: ACM. Chander, M., Jain, S. K., & Shankar, R. (2013). Modeling of information security management parameters in Indian organizations using ISM and

MICMAC approach. Journal of Modelling in Management, 8 (2), 171-189. Debatin, B., Lovejoy, J. P., Horn, A.-K., & Hughes, B. N. (2009). Facebook and online privacy: Attitudes, behaviors, and unintended

consequences. Journal of Computer-Mediated Communication, 15(1), 83-108. Deters, F. G., & Mehl, M. R. (2012). Does posting Facebook status updates increase or decrease loneliness? An online social networking experiment. Social psychological and personality science, 4 (5), 8579-586.

Eleftheriore-Smith, L-M. (2012). "Missing Malaysia Airlines Flight MH370 plane found in Bermuda Triangle!" hackers profiting from viral Facebook links. (2014, March), http://www.independent.co.uk/life-style/gadgets-and-tech/news/missingmalaysia-airlines-flight-mh370-plane-found-in-bermuda-triangle-facebooklinks-are-proting-hackers-9194660.html. (Accessed on: March 23, 2014)

Facebook statistics. (2014, January), http://www.statisticbrain.com/facebookstatistics/. (Accessed on: March 10, 2014)

Gallagher, M., Hares, T., Spencer, J., Bradshaw, C., & Webb, I. (1993). The nominal group technique: A research tool for general practice? Family practice, 10 (1), 76-81.

Georgakopoulos, A. (2009). Teacher effectiveness examined as a system: Interpretive modelling and facilitation sessions with U.S. and Japanese students. International Education Studies, 2 (3), 60-76.

Karl, K., Peluchette, J., & Schlaegel, C. (2010a). A cross-cultural examination of student attitudes and gender differences in Facebook profile content. International Journal of Virtual Communities and Social Networking, 2 (2), 11-31.

Karl, K, Peluchette, J., & Schlaegel, C. (2010b). Who's posting Facebook faux pas? A cross-cultural examination of personality differences. International Journal of Selection and Assessment, 18(2), 174-186.

Kluemper, D. H., & Rosen, P. A. (2009). Future employment selection methods: Evaluating social networking web sites. Journal of Managerial Psychology, 24 (6), 567-580.

Kuss, D. J., & Griths, M. D. (2011). Online social networking and addiction-A review of the psychological literature. International Journal of Environmental Research and Public Health, 8 (9), 3528-3552.

Marcial, D. E. (2013). Are you a Facebook addict? Measuring Facebook addiction at a Philippine University. International Proceedings of Economics Development and Research, 66(3), 12-15.

Muise, A., Christodes, E., & Desmarais, S. (2009). More information than you ever wanted: Does Facebook bring out the green-eyed monster of jealousy? CyberPsychology, Behavior, and Social Networking, 12 (4), 441-444.

Quit Facebook Day. (2010). http://www.quitfacebookday.com/. (Accessed on March 20, 2014)

Raj, T., Shankar, R., & Suhaib, M. (2008). An ISM approach for modelling the enablers of flexible manufacturing system: The case for India. International Journal of Production Research, 46(24), 6883-6912.

Ramesh, A., Banwet, D. K, & Shankar, R. (2010). Modeling the barriers ofsupply chain. Journal of Modelling in Management, 5 (2), 176-193.

Sage, A. (1977). Interpretive structural modeling: Methodology for large-scale systems (A. Sage, Ed.). McGraw-Hill, New York, NY.

Saslow, L. R., Muise, A., Impett, E. A., & Dubin, M. (2012). Can you see how happy we are? Facebook images and relationship satisfaction. Social Psychological and Personality Science, 4 (4), 411-418.

Saxena, J. P., Sushil, & Vrat, P. (1990). Impact of indirect relationships in classification of variables - A MICMAC analysis for energy conservation. Systems Research, 7(4), 245-253.

Sharma, H., Gupta, A., & Sushil. (1995). The objectives of waste management in India: A futures inquiry. Technological Forecasting and Social Change, 48 (3), 285-309.

Sherman, E. (2011). Facebook addiction: Factors influencing an individual's addiction. Unpublished master's thesis, University of Massachusetts Boston.

Singh, D., & Agrawal, D. P. (2003). CRM practices in Indian industries. International Journal of CRM, 5, 241-257.

Singh, M. D., & Kant, R. (2007). Knowledge management barriers: An Interpretive Structural Modeling approach. In 2007 IEEE International Conference on Industrial Engineering and Engineering Management (p. 2091-2095).

Taraszow, T., Arsoy, A., Shitta, G., & Laouris, Y. (2008). How much personal and sensitive information do Cypriot teenagers reveal in Facebook? InProceedings from 7th European Conference on E-learning (p. 871-876).

Thomas, K, & Nicol, D. (2010). The Koobface botnet and the rise of social malware. In 5th International Conference on Malicious and Unwanted Software (MALWARE) (p. 63-70).

Toma, C. L., & Hancock, J. T. (2013). Self-affirmation underlies Facebook use. Personality and Social Psychology Bulletin, 39 (3), 321-331.

Twitter statistics. (2014, January), http://www.statisticbrain.com/twitterstatistics/. (Accessed on: March 10, 2014)

Van Iddekinge, C. H., Lanivich, S. E., Roth, P. L., & Junco, E. (2013). Social media for selection? Validity and adverse impact potential of a Facebook based assessment. Journal of Management, 1-25.

Warfield, J. N. (1974). Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man and Cybernetics, SMC-4 (1), 81-87.

Wills, A (2014). Twitter Death Rumor Leads to Spike in Oil Prices. (2012, August), http://mashable.com/2012/08/07/twitter-rumor-oil-price/. (Accessed on: March 23, 2014)

Wilson, R. E., Gosling, S. D., & Graham, L. T. (2012). A review of Facebook research in the Social Sciences. Perspectives on Psychological Science, 7(3), 203-220.

Young, K. S. (1998). Internetaddiction: The emergence ofanew clinical disorder. CyberPsychology & Behavior, 1 (3), 237-244.

Young, K. S. (2004). Internet addiction: A new clinical phenomenon and its consequences. American Behavioral Scientist, 48 (4), 402-415.