Printer Friendly

New media and youth: differences in the use of social network sites between young men and women users.

1. Introduction

In the first 10 years of the millennium, the world probably witnessed the most exciting developments in human history. The mobile phones that we saw in science-fiction movies; which are wireless, are small enough to fit our pockets, and which we can't live without; are a part of our daily lives today. The global network system called internet made information, news and entertainment common property for all the people of the world. The global village that Mc Luhan predicted came into life through internet. In this world, letters are sent to the receivers in just a few seconds using email; ordinary people become publishers; celebrities are born on the Internet; people that never see each other in person are chatting and falling in love on the Internet. This progress is happening so fast that in the last 10 years, some parts of our real lives are getting equivalents in the virtual world. One of this is the friendship, and our friends.

The social networks reveal the way friendships are headed. The most popular social network site in Turkey, and in the world, is Facebook. The founder even has a movie about his life. The site has 450 million users. If Facebook were a country, it would be the third crowded after China and India; but it would be the most cosmopolite.

This study focuses on high school students use of Facebook in the light of Technology Acceptance Model. Gender differences on Facebook usage preferences constitutes the main Research topic.

2. Conceptual Framework and Literature Review

Technology Acceptance Model (TAM), was developed by Davis (1989) to understand the factors effecting people's intention to shop in internet. However, it is based on another theory, which is named as Theory of Reasoned Action. TRA was a socio-psychology based theory developed by, Fishbein and Ajzen (1975). According to TRA, social behaviors are related to individuals' attitudes; and the use of information technologies is affected by individual behavior (Davis, 1989; Lee et al., 2004:753; Dishaw et al., 2002: 1021) TRA, is developed by researchers to explain individuals' actions that is note by their free will; and it is used by researchers in lots of different studies (Olson and Zanna, 1993). Ajzen (1991) improved TRA and proposed the TPB (Theory of Planned Behavior) model, stating that the sole source of behavior is not free will, but some other factors come into play too, while behavior is shaped. However, the abstract concepts that are used in TRA, such as beliefs and values, made the theory weak. TAM was developed in response to the weaknesses in TRA and TPB theories.

After the model revealed, researchers tried to measure the limits of the model, and to see if it was really superior to TRA. Trying to improve the mode, Adams et al. made 5 different additions. They made changes when users accept word processors, graphics, spreadsheets, e-mail and v-mails. However, Davis applied TAM successfully when researching e-mail and text editors on 112 participants, in 1993. After Subramanian's similar studies in 1994, it is accepted that TAM is a simpler and useful model in researching the acceptance of computer technology, and it is superior to TRA. Adams, stated that Davis's Perceived Usefulness and Perceived Ease-of-use factors are valid in different information systems. Hendrickson et al. analysed these two variables in 1993 and in 1996 and concluded it is valid. Segars and Grover revealed the use of three variables in 1993, making the theory more effective. Szajna studied in 1994 that if TAM can measure future behaviors successfully. Davis and Venkatesh concluded that in addition to Szajna's findings, TAM is useful in gaining foresight about information system acceptance, in 1996. According to Igbaria et al. (1995), TAM is the most easy to use, simple and powerful model of information technology usage model. Similarly, Chau (1996) and, Saga and Zmud (1994) states that TAM is one of the most valid models about information technology usage.

This model is one of the most comprehensive models, and explains the acceptance of technology usage. According to Davis, to factors affect the acceptance:

1. Perceived Usefulness (PU)

2. Perceived Ease of Use (PEOU)

PU is the measure of the belief that by using a product, one can increase his or her job performance. PEOU refers to the degree to which users expect the technology does not require lots of effort (Davis, 1989:320). According to Keller (2005:302), PU is related to all types of increase in performance, and PEOU is related to learning the new technology with ease.

Gefen and Straub argued in 1997 that in accepting the information system, gender differences, social norms and structures has important effects on PU and PEOU. According to them, women are more affected by Subjective Norms and PEOU, while men are more affected by PU (Straub et al., 1997:5).

Venkatesh and Davis introduced TAM 2 in 2000 as the millennium version of TAM. According to them, external factors effecting the perception of usefulness are, experience, voluntariness, subjective norm, image, job relevance, output quality, and result demonstrability. The external variables of PEOU are antecedents (New technology as a game, sufficiency of new technology, external audit) and adaptations (Usability of new technology and its perception as entertaining). According to Venkatesh (2000) PEOU has a direct positive effect on the acceptance of information technology. In a study with Davis, they found out that Subjective Norms (SN) are effective on PEOU and behavioral intention (BI). Venkatesh, Morris and Ackerman's study in 2000 supported this finding. Especially, gender differences and social norms are the determining variables (Venkatesh et al., 2000). However, in another study of Venkatesh et al., in 2003, it is argued that SN does not affect BI. In the research after this, the number of correlations between PEOU and PU variables has been increased. For example, the relations between PEOU and past experience, education level, relations between workplace, and PU have been tried to found out. Added variables were:

3. Actual Usage (AU)

4. Attitude (A)

5. Subjective Norm (SN)

TAM, was highly criticized at first. Researchers tried to improve the model by adding different elements. Again, at first, actual usage (AU) was added to the model and proposed that BI can predict AU directly. AU is the frequency and intensity of individual's new technology usage. Generally, it is proposed that an individual's attitude to do something (A) can affect this person's behavior intention (BI) and in the end, this can lead to actual usage (AU) (Lee et al., 2007). However, according to this order, PU and PEOU variables can also affect A, and in the end, AU (Ma and Liu, 2004:60-63). Subjective Norms, compatibility, external factors are added in time. Compatibility variable affects PU and PEOU variables, which are affecting A, and in the end, BI. Subjective Norms are affected by the attitudes of people that are important to the individual (Ma et al., 2005). The effectiveness of Subjective norms is frequently argued by researchers (Azjen, 1988). Studies in different fields shows that especially children and teens are affected by important people to them, especially their friends (Aktas et al., 2011; Ugur, 2011; Gulerarslan, 2011). External factors are controllable and uncontrollable factors affecting technology use of the individual.

Although most of the studies show that PU and PEOU have a strong effect on BI, some researchers are comparing PU and PEOU in themselves. Most of the studies state that PEOU affects PU and PEOU is more effective than PU (Venkatesh, 2000; Teo, 2009). However, it must be noted that there are studies that claim the opposite (Adams et al., 1992; Ma and Liu, 2004). In addition to this, some studies state that PEOU and PU have separate effects on BI, in spite of themselves (Giner et al., 2009). In a study about women's attitudes toward shopping, PEOU was excluded and PU was used (Al-Magribi and Dennis, 2010).

Ventakesh and Bala proposed the TAM 3 model in 2008. The external factors defined as Individual Differences, System Characteristics, Social Influence and Facilitating Conditions have effects on PU and PEOU. PEOU affects PU and has effectiveness in the acceptance of both behaviors.

TAM has been used in many studies relating to information technology usage (Martinez-Torres et al., 2006). Some of these studies can explain the behaviors and usage intentions greatly. In some studies, TAM is used in various types of information technologies, including e-commerce (Gefen et al., 2003), internet usage (Shih, 2004), information technology usage of accountants (Ozer et al., 2010), usage of e-school software (Baglibel, et al., 2010), technology usage of scholars (Tarcan et al., 2010; Turan and Colakoglu, 2008), technology usage of secretaries (Turan and Cetinkaya, 2010), usage of new generation smartphones (Sek et al., 2010), text message usage (Ceccucci et al., 2010), and even usage of ATM's (Falorunso, 2010). In the meta-analysis study on TAM, King and He (2006) found 149 articles about TAM. This number shows the acceptance of TAM model.

3. Research Subject and Importance

Facebook has been the most popular tool for socializing in the recent years. Young or old, many people make different friends on Facebook. People can create virtual friendships and virtual relationships with other people far from their country. In some cases, these virtual friendships become real by meeting in person, attending to events, taking action or even getting married. In Tunis and Egypt, government's ban on other communication devices led people to get organized in platforms like Facebook. Facebook is getting stronger and stronger every day and it has become a part of our daily lives. Researchers are aware of these trends and researching every aspect of Facebook. Pempek et al (2009) studied collage students' Facebook experience, Kirshner and Karpinski (2010) researched Facebook and its relation to academic performance, Nosko et al. (2010) studied user profiles as a way to express themselves, Ross et al. (2009) researched Facebook usage and personal motivations. According to October-December 2010 data of SciVerse, 25 out of 13 most reviewed articles are about social networks, especially Facebook.

Facebook is a solution to teenagers who have communication problems in their puberty ages. The site is spreading among them rapidly. The age limit of 13 is not enough to stop teens and children to create accounts. Even elementary school children have Facebook accounts. Especially high school teenagers, who are in the process of developing their characters, must be researched. The criteria affecting the use of Facebook among girls and boys are important and must be researched.

4. Method, Universe and Sample

The method of the research is survey, which is suitable for TAM. Facebook is an Internet technology, so it is suitable to research based on TAM. In this study, in addition to demographics questions, we have asked questions under six topics. The first one is PU, second one is PEOU, third one is RELE, fourth one is attitude (A), fifth one is perceived performance (PP) and the last one is BI. Research questions are mostly descriptive. Because of this, there is no hypothesis. We tried to reveal differences between genders. The research universe consists of high school teenagers. Sample consists of the second and third year students of Anadolu Communications Business School (Anadolu Iletisim Meslek Lisesi) in Konya. All of the students in the classes have been surveyed. This school is selected because, it represents the high school teenagers and because there students are the communication professionals of the future, and determining their views are important. First year student are not included because they are new to the school. 120 questionnaires were filled, and out of these, 108 were usable. SPSS software used to process the data. The same person conducted the survey and entered the data, so there is no need to test this person's reliability.

5. Findings and Discussion
Table 1: Age

                      F         %

15                      3        2,8
16                     33       30,6
17                     65       60,2
18                      7        6,5
Total                 108      100,0

Statistics (Age)

N                  Valid         108
                   Missing         0
Mean                         16,7037
Median                       17,0000
Mode                           17,00
Std. Deviation                ,63037
Variance                        ,397
Range                           3,00
Minimum                        15,00
Maximum                        18,00
Sum                          1804,00

Table 2: Class

                  F       %       V %

          10      45     41,7     42,1
          11      62     57,4     57,9
          TOTAL  107     99,1    100,0
Invalid   ,00      1       ,9
TOTAL            108    100,0

Table 3: Gender

          F      %

Female   61    56,5
Male     47    43,5
TOTAL    108   100,0

Table 4: Do you have a computer in your house? X gender

                  Yes     No      Total

Gender   Female      49      12       61
                  80,3%   19,7%   100,0%
         Male        43       4       47
                  91,5%    8,5%   100,0%
Total                92      16      108
                  85,2%   14,8%   100,0%

80,3% of females and 91,5% of males have a computer in their house.
There is no significant relationship among genders
([chi square]=2,621, sd=1, p=,105).

Table 5: Do you have internet access in your house? X gender

                  Yes     No      Total

Gender   Female      41      20       61
                  67,2%   32,8%   100,0%
         Male        35      11       46
                  76,1%   23,9%   100,0%
Total                76      31      107
                  71,0%   29,0%   100,0%

67,2% of the females and 76,1% of the males have internet Access in their houses. Although the numbers are low relative to computer ownership, it can be said that most of the students have internet access ([chi square]=1,004, sd=1, p=,316).
Table 6: How many times accessing internet in a week x gender

                  Everyday    Every      Once in   Once in  Total
                             other day   3 days     week

Gender   Female        23           6        10       20       59
                    39,0%       10,2%     16,9%    33,9%    100,0%
         Male          31           4         4        7       46
                    67,4%        8,7%      8,7%    15,2%    100,0%
Total                  54          10        14       27      105
                    51,4%        9,5%     13,3%    25,7%    100,0%

Males stating "everyday" 67.4% and females are 39%. There is significant relationship between genders ([chi square]=8,943, sd=3, p=,030).
Table 7: How many hours is he or she using internet daily x gender

                  1-2 hours   3-4 hours    5-6    7 hours   Total
                                          hours   or more

Gender   Female         13          11       3         7        34
                     38,2%       32,4%    8,8%     20,6%    100,0%
         Male            4           5      13        13        35
                     11,4%       14,3%    37,1%    37,1%    100,0%
Total                   17          16      16        20        69
                     24,6%       23,2%    23,2%    29,0%    100,0%

Males spend more time on the internet. They use internet for 5-6 hours, or 7 hours or more. Females use internet for 1-2 hours or 3-4 hours ([chi square]=15,053, sd=3, p=,002).
Table 8: A member of Facebook x gender

                  Yes      No      Total

Gender   Female       52       9       61
                   85,2%   14,8%   100,0%
         Male         45       0       45
                  100,0%     ,0%   100,0%
Total                 97       9      106
                   91,5%    8,5%   100,0%

There is a significant correlation between genders, according to membership statistics ([chi square]=7,225, sd=1,p=,007).
Table 9: How many friends on Facebook x gender

                  0-50   51-100   101-200   201 or more   Total

Gender   Female      5       10        17            21       53
                  9,4%    18,9%     32,1%         39,6%   100,0%
         Male        2        5         9            30       46
                  4,3%    10,9%     19,6%         65,2%   100,0%
Total                7       15        26            51       99
                  7,1%    15,2%     26,3%         51,5%   100,0%

65,2% of males and 39,6% of females have more than 201 friends ([chi square]=6,540, sd=3, p=,088).
Table 10: Interacts with how many of his or her Facebook friends
in real life x gender

                  All    3/4     1/2     1/4 and less   Total

Gender   Female      3      15      23             13       54
                  5,6%   27,8%   42,6%          24,1%   100,0%
         Male        4      12      19             11       46
                  8,7%   26,1%   41,3%          23,9%   100,0%
Total                7      27      42             24      100
                  7,0%   27,0%   42,0%          24,0%   100,0%

It is interesting to note that only 8,7% of males and 5,6% of females interact with all of their Facebook friends ([chi square]=,386, sd=3, p=,943).
Table 11: Gender and Total Points T TEST

                Gender   N    Mean      Std. Deviation   Std. Error

FACE(PU)TOP     Female   61   17,0984         6,13108       ,78500
                Male     43   20,3721         5,68215       ,86652
FACE(PEOU)TOP   Female   61   15,6721         2,91388       ,37308
                Male     47   16,8723         2,83317       ,41326
FACE(RELE)TOP   Female   61   19,5574         6,25972       ,80148
                Male     45   22,2444         5,81204       ,86641
FACE(A)TOP      Female   59   16,3051         6,34702       ,82631
                Male     46   19,6957         6,01801       ,88731
FACE(PP)TOP     Female   61   8,6230          3,25148       ,41631
                Male     46   9,9783          2,67074       ,39378
FACE(SN)TOP     Female   61   12,9836         3,62166       ,46371
                Male     47   15,5745         3,11200       ,45393

The sig (2-tailed) in the T Test table is all lower than 0,05. According to this finding, there is significant relationships between PU, PEOU, RELE, A, PP and SN. According to the table, it can be said that males have a more positive attitude toward Facebook.
Table 12: Facebook's Perceived Usefulness According to Gender

PERCEIVED USEFULNESS                  [[chi square].sup.(chi square)]

PU1   Facebook helps to create                   9,581(a)
      better friendships.

PU2   Facebook makes it easy to                  5,797(a)
      communicate with my friends.

PU3   Facebook contributes to my                 12,216(a)
      school life.

PU4   It is easier for me to                     7,852(a)
      communicate with my friends
      through Facebook, than
      communicating in person.

PU5   Facebook helps met to know my              6,850(a)
      friends better.

PU6   Friendships that are created               11,723(a)
      by Facebook are more real
      than the old friendships.

PU7   Friendships that are created               5,717(a)
      by Facebook are more
      permanent than the old

PERCEIVED USEFULNESS                  sd.   p (sig.)

PU1   Facebook helps to create         4       ,048
      better friendships.

PU2   Facebook makes it easy to        4       ,215
      communicate with my friends.

PU3   Facebook contributes to my       4       ,016
      school life.

PU4   It is easier for me to           4       ,097
      communicate with my friends
      through Facebook, than
      communicating in person.

PU5   Facebook helps met to know my    4       ,144
      friends better.

PU6   Friendships that are created     4       ,020
      by Facebook are more real
      than the old friendships.

PU7   Friendships that are created     4       ,221
      by Facebook are more
      permanent than the old

PU1, PU3 and PU6 have significant relationship according to gender. Males believe that Facebook friendships are more real than real-life relationships. According to them, Facebook has a positive effect on school life.
Table 13: Facebook's Perceived Ease of Use According to Gender

PERCEIVED EASE OF USE                 [[chi square].sup.(chi square)]

PEOU1   It is easy to join Facebook              9,224(a)

PEOU2   It is easy to manage a                   7,856(a)
        Facebook profile and
        understand menus.

PEOU3   It is easy to add new                    6,600(a)
        friends in Facebook

PEOU4   I can have new friends                   11,233(a)
        without any trouble in

PERCEIVED EASE OF USE                 sd.   p (sig.)

PEOU1   It is easy to join Facebook     4       ,056

PEOU2   It is easy to manage a          4       ,097
        Facebook profile and
        understand menus.

PEOU3   It is easy to add new           4       ,159
        friends in Facebook

PEOU4   I can have new friends          4       ,024
        without any trouble in

In terms of perceived usefulness, PEOU4 question has a significant relationship. More of the males state that they can find new friends without any difficulty.
Table 14: Information Need Factor According to Gender

RELEVANCE OF INFORMATION NEEDS      [[chi square].sup.(chi square)]

RELE1   I learn about other                    5,602(a)
        people by using Facebook.

RELE2   I learn about my friends               4,162(a)
        by using Facebook.

RELE3   I learnt lots of things                1,427(a)
        that I didn't know about
        my friends by using

RELE4   By using Facebook, I have              11,232(a)
        accessed private
        information about my

RELE5   By using Facebook, I have              2,475(a)
        accessed information
        about the people i want
        to know.

RELE6   I know my friends better               4,324(a)
        by using Facebook.


RELE1   I learn about other          4       ,231
        people by using Facebook.

RELE2   I learn about my friends     4       ,385
        by using Facebook.

RELE3   I learnt lots of things      4       ,839
        that I didn't know about
        my friends by using

RELE4   By using Facebook, I have    4       ,024
        accessed private
        information about my

RELE5   By using Facebook, I have    4       ,649
        accessed information
        about the people i want
        to know.

RELE6   I know my friends better     4       ,364
        by using Facebook.

The only significant relationship is on RELE4 question. According to this, males are accessing private information of their friends more than females.
Table 15: Attitude According to Gender

ATTITUDE TOWARDS USING THE     [[chi square].sup.(chi square)]   sd.

A1   I like to use Facebook.               6,296(a)                4

A2   It is a pleasure to use               4,983(a)                4
     Facebook for me.

A3   Using Facebook is                     7,389(a)                4
     indispensable for me.

A4   I can't think about a                 7,276(a)                4
     life without Facebook.

A5   Facebook makes my                     20,031(a)               4
     friendships better.

A6   By using Facebook, I'm                18,634(a)               4
     creating new and better


A1   I like to use Facebook.      ,178

A2   It is a pleasure to use      ,289
     Facebook for me.

A3   Using Facebook is            ,117
     indispensable for me.

A4   I can't think about a        ,122
     life without Facebook.

A5   Facebook makes my            ,000
     friendships better.

A6   By using Facebook, I'm       ,001
     creating new and better

Male and female high school students differ in attitudes against Facebook. Both hare positive.
Table 16: Perceived Performance According to Gender

PERCEIVED PERFORMANCE           [[chi square].sup.(chi square)]   sd.

PP1   I'm using Facebook to                  8,147                 4
      improve my
      communication with my

PP2   I'm glad that Facebook               7,099(a)                4
      increases my knowledge
      about my friends.

PP3   I think Facebook                     3,287(a)                4
      increases my popularity
      among my friends.

PERCEIVED PERFORMANCE           p (sig.)

PP1   I'm using Facebook to        ,086
      improve my
      communication with my

PP2   I'm glad that Facebook       ,131
      increases my knowledge
      about my friends.

PP3   I think Facebook             ,511
      increases my popularity
      among my friends.

Although PP1, PP2 and PP3 do not present significant relationship among genders, it can be said that high school students believe that Facebook has a role in their personal performance and popularity.
Table 17: Perceived Subjective Norms According to Gender

SUBJECTIVE NORM              [[chi square].sup.(chi square)]   sd.

SN1   All my friends are                 11,465(a)               4
      using Facebook.

SN2   My close friends are               4,593(a)                4
      using Facebook.

SN3   People whom are                    19,865(a)               4
      important for me,
      expects me to use

SUBJECTIVE NORM              p (sig.)

SN1   All my friends are        ,022
      using Facebook.

SN2   My close friends are      ,332
      using Facebook.

SN3   People whom are           ,001
      important for me,
      expects me to use

There is a significant correlation between SN1 and SN3, which are listed under Subjective Norms title. More males than females believe that all of their friends are using Facebook. They also believe more than females that important people for them must have a Facebook account. High school youth are affected by their peers.


Facebook, which is a web site that became a part of our daily lives, is spreading fastest among youth. While spreading, it can be said that youth are advantaged in the acceptance of new technology. However, having a Facebook account is similar to having an e-mail address or mobile phone. High school teenagers are accessing Internet almost every day. Males spend more time than females, but the spent time is more than hours. Internet, which is a source of information and communication, is now used for entertainment and socialization.

Only a small portion of the students interact with all of their Facebook friends. 90% have a profile filled with people that they have never seen in person. Some even believe Facebook friendship is more real than the old friendships. For males, being on Facebook is a source of popularity. It is a tool to learn more about friends, and to learn more about the personal lives of other people. Even these students admit that Facebook does not help with their homework, school performance or their future jobs, a good portion of them believe that there is no life without Facebook.

Females are a little behind in Facebook compared to males, but they are close. They are careful about the number of their Facebook friends, and they do not think Facebook is indispensable. They are very cautious about Facebook.


Adams, D. A., R. R. Nelson, and P. A. Todd (1992), "Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication," MIS Quarterly, Volume 16, Number 2: 227-247.

Ajzen, I. (1985), "From Intentions to Actions: A Theory of Planned Behavior," Action Control: From Cognition to Behavior, Kuhl J. Beckmann J. (Eds.), Berlin, Heidelberg, New York: Springer-Verlag.

Ajzen, I. (1991), "The Theory of Planned Behavior," Organizational Behavior and Human Decision Processes, Volume 50, Number 2: 179-211.

Ajzen, I., and M. Fishbein (1980), Understanding Attitudes and Predicting Social Behavior. Prentice Hall, Englewood Cliffs.

Aktas, H., M. N. Ozupek, and H. Altunbas (2011), "Cocuklarin Marka Tercihleri ve Medya Tuketim Aliskanliklari," Selcuk Iletisim Dergisi, Cilt 6, Sayi 4: 115- 125.

Al-Maghrabi, T., and C. Dennis (2010), "Driving Online Shopping: Spending and Behavioral Differences Among Women in Saudi Arabia," International Journal of Business Science and Applied Management, Volume 5, Issue 1: 30-47.

Baglibel, M., M. Samancioglu, and M. S. Summak (2010), "Okul Yoneticileri Tarafindan E-Okul Uygulamasinin Genisletilmis Teknoloji Kabul Modeline Gore Degerlendirilmesi," Mustafa Kemal University Journal of Social Sciences Institute, Volume 7, Issue 13: 331-348.

Ceccucci, W., A. Peslak, and P. Sendall (2010), "An Empirical Study Of Behavioral Factors Influencing Text Messaging Intention," Journal of Information Technology Management. Volume 21, Number 1: 16-34.

Chau, P. Y. K. (1996), "An Empirical Assessment of a Modified Technology Acceptance Model," Journal of Management Information Systems--Special Section: Strategic and Competitive Information Systems, Volume 13, Issue 2: 185-204.

Davis, F. D. (1989), "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology," MIS Quarterly, Volume 13, No. 3: 319339.

--(1993), "User acceptance of Information Technology: System characteristics, user perceptions and behavioral impacts," International Journal of Man-Machine Studies, Volume 38, Number 3: 475-487.

Davis, F.D., R. P. Bagozzi, and P.R. Warshaw (1992), "Extrinsic and Intrinsic Motivation to Use Computers in The Workplace," Journal of Applied Social Psychology, Volume 22, Number 1: 1111-1132.

Davis, F. D., R. P. Bagozzi, and P. R. Warshaw (1989), "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, Volume 35, Number 8: 982-1003.

Dishaw, M. T., and D. M. Strong (1999), "Extending the Technology Acceptance Model with Task-Technology Fit Constructs," Information and Management, Volume 36, Number 1: 9-21.

Fishbein, M. and I. Ajzen (1975), Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, Boston: Addison-Wesley, Reading.

Folorunso, O., O. A. Ateji, and O. Awe (2010), "An Exploratory Study of the Critical Factors Affecting Acceptability, the Automated Teller (ATM) Machine in Nigeria," Annals, Computer Science Series, 8th Tome, 1st fasc.: 151-162.

Gefen, D., and D. W. Straub (1997), "Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model," MIS Quarterly, Volume 21, Number 4: 389-400.

Giner, J. L. C., V. Fernandez, and M. D. Boladeras (2009), "Framework for The Analysis of Executive Information Systems Based On The Perceived Usefulness And Perceived Ease of Use," Intangible Capital, Volume 5, Number 4: 370-386.

Guleraraslan, A. (2011), "Tuketici Olarak Cocuk ve Ailenin Satin Alma Kararlarina Etkisi," Selquk Iletisim Dergisi, Volume 6, Number 4:126-137.

Igbaria, M., J. Livari, and H. Maragahh (1995), "Why Do Individuals Use Computer Technology? A Finnish Case Study," Information and Management, No 29: 227-38.

Keller, C. (2005), "Virtual Learning Environments: Three Implementation Perspectives, Learning," Media and Technology, Volume 30, Number 3: 299-311.

King, W. R. and J. He (2006), "A Meta-Analysis of the Technology Acceptance Model," Information & Management, Number 43: 740-755.

Kirschner, P. A. and A. C. Karpinski (2010), "Facebook(R) and Academic Performance," Computers in Human Behavior, Volume 26, Issue 6: 1237-1245.

Lee, Y., K. A. Kozar, and K. R. T. Larsen (2003), "The Technology Acceptance Model: Past, Present, and Future," Communications of the Association for Information Systems, Number 12: 752-780.

Ma, Q. and L. Liu (2004), "Technology Acceptance Model: A Meta-Analysis of Empirical Finding," Advanced Topics in End User Computing, Mahmood, M.A. (ed.), Hershey, Idea Group Publishing: 112-128.

Martinez-Torres, M. R., S. L. T. Marin, Garcia F.B. Vazquez, M. A. Oliva and T. Torres (2008), "A Technological Acceptance of E-Learning Tools Used in Practical and Laboratory Teaching, According to the European Higher Education Area," Behavior & Information Technology, Volume 27, Number 6: 495-505.

Nosko, A., E. Wood, and S. Molema (2010), "All about Me: Disclosure in Online Social Networking Profiles: The Case of FACEBOOK," Computers in Human Behavior, Volume 26, Issue 3: 406-418.

Olson, J. M., and M. P. Zanna (1993), "Attitudes and attitude change," Annual Review of Psychology, Number 44: 117-154.

Ozer, G., M. Ozcan, and S. Aktas (2010), "Muhasebecilerin Bilgi Teknolojisi Kullaniminin Teknoloji Kabul Modeli (Tkm) Ile Incelenmesi," Journal of Yasar University: 3278-3293.

Pempek, T.A., Y. A. Yermolayeva, and S. L. Calvert (2009), "College Students' Social Networking Experiences On Facebook," Journal of Applied Developmental Psychology, Volume 30, Issue 3: 227-238.

Ross, C., E. S. Orr, M. Sisic, J. M. Arseneault, M. G. Simmering, and R. R. Orr, (2009), "Personality and Motivations Associated with Facebook Use," Computers in Human Behavior, Volume 25, Issue 2: 578-586.

Saga, V., and R. Zmud (1994), "The Nature and Determinants of Information Technology Acceptance, Routinization and Infusion," Diffusion, Transfer and Implementation of Information Technology North-Holland, Levine, L. (ed.), Amsterdam: 67-86.

Segars, A. H., and V. Grover (1993), "Re-Examining Perceived Ease of Use and Usefulness: A Confirmatory Factor Analysis," MIS Quarterly, Vol. 17 (4): 517- 525.

Sek, Y. W., S. H. Lau, K. K. Teoh, C. Y. Law, and S. B. Parumo, (2010), "Prediction of User Acceptance and Adoption of Smart Phone for Learning with Technology Acceptance Model," Journal Of Applied Sciences, Volume 10, Issue 20: 2395-2402.

Shih, H. P. (2004), "Extended Technology Acceptance Model of Internet Utilization Behavior," Information & Management, Number 41: 719-729.

Subramanian, G. H. A. (1994), "Replication of Perceived Usefulness and Perceived Ease of Use Measurement," Decision Sciences, Vol. 25, No. 5/6: 863- 874.

Szajna, B. (1996), "Empirical Evaluation of the Revised Technology Acceptance Model," Management Science, Volume 42, Number 1: 85-92.

Tarcan, E., E. S. Varol, and B. Toker, B. (2010), "A Study On The Acceptance of Information Technologies From The Perspectives of The Academicians In Turkey," Ege Academic Review, Volume 10, Number 3: 791-812.

Taylor, S. and P. Todd (1995), "Assessing IT Usage: The Role of Prior Experience," MIS Quarterly, Volume 19, Number 4: 561-570.

Teo, T. (2009), "Is There An Attitude Problem? Reconsidering The Role Of Attitude in the TAM," British Journal of Educational Technology, Volume 40, Number 6: 1139-1141.

Turan, A.H. and O. Cetinkaya (2010), "Burolarda Teknoloji Kabul Ve Kullanimi: Gelistirilmis Teknoloji Kabul Modeli Ile Bir Model Onerisi Ve Sekreterler Uzerinde Ampirik Bir Degerlendirme," Akademik Bakis Dergisi, Sayi 19: 1-16.

Turan, A. H. and B. E. Colakoglu (2008), "Yuksek Ogrenimde Ogretim Elemanlarinin Teknoloji Kabulu Ve Kullanimi: Adnan Menderes Universitesinde Ampirik Bir Degerlendirme," Dogus Universitesi Dergisi, Cilt 9, Sayi 1: 106-121.

Ugur, I. (2011), "Televizyon reklamlarindan Genclerin Etkilenme Bicimleri: Reklam Oyuncularinin Genclerin Imajlarinin Sekillenmesinde Etkisi," Selquk Iletisim Dergisi, Volume 6, Number 4: 101-114.

Venkatesh, V. (1999), "Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation," MIS Quarterly, Volume 23, Number 2: 239-260.

--(2000), "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, Volume 11, Number 4: 342-365.

Venkatesh, V., and H. Bala (2008), "Technology Acceptance Model 3 and a Research Agenda on Interventions," Decision Sciences, Vol. 39, No. 2: 273-315.

Venkatesh, V. and F. D. Davis (2000), "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, Volume 46, Number 2: 186-204.

Venkatesh, V., M. G. Morris, and P. L. Ackerman, P.L. (2000), "A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes," Organizational Behavior and Human Decision Processes, Volume 83, Issue 1: 33-60.

Venkatesh, V., M. G. Morris, G. B. Davis, and F. D. Davis (2003), "User Acceptance of Information Technology: Toward a Unified View," MIS Quarterly, Volume 27, Number 3: 425-478.


Selcuk University


Inonu University


Selcuk University


Selcuk University

Hasret Aktas graduated from the Faculty of Communication at Marmara University in 1990. For his Master's degree, awarded in 1996, he conducted research on the 'Political Communication'. His PhD research centered around the "Communication Politics in the Example of the Election Campaigns of Parties". He has three books about 'Political communication on the Net', 'Internet Advertising' and several articles about 'Child and Communication', 'Political Communication on the Net,' 'New Communication Technologies', ... Currently he is an assistant professor and a member of Selcuk University Communication Faculty.

Mevlut Akyol graduated from the Faculty of Communication at Selcuk University in 2001. For his Master's degree, awarded in 2004, he conducted research on the 'TV Advertising Technology'. His Ph.D. research was focused on the 'A Study on "Advergame" as a Means of Brand Communications: Its Role over Creating Brand Awareness' and he has several articles about new advertisement media and children. Currently he is an assistant professor and a member of Inonu University Communication Faculty.

Halit Kartal graduated from the Faculty of Communication Selcuk University in 2006. For his Master's degree, awarded in 2011, he conducted research on the 'The Use of Visual Language on Sports Broadcasting. He conducts research on Sports and Media, New Communication Technologies', Technological Acceptance Model on Television Broadcasting. Currently, he is a Research Assistant at Selcuk University, the Faculty of Communication.

Kazim Tolga Gurel completed his Master's degree and started research at Selcuk University. He received a doctorate degree from the same university. He has written four books so far--two of them have already been published. He has several articles about mass communication and political philosophy. Furthermore he has been writing about streets in Istanbul metropolis, postmodern life styles and opposite actions to global capitalism in newspapers and public journals. He is an activist too.
COPYRIGHT 2014 Addleton Academic Publishers
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2014 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Aktas, Hasret; Akyol, Mevlut; Kartal, Halit; Gurel, Kazim Tolga
Publication:Journal of Research in Gender Studies
Article Type:Report
Date:Jan 1, 2014
Previous Article:Television genres. A case study: marriage programs.
Next Article:What does the internet feed on?

Terms of use | Privacy policy | Copyright © 2022 Farlex, Inc. | Feedback | For webmasters |