Exploration of the factors that affect the adoption of social networking services by Generation Y in Chile/Exploracion de los factores que afectan a la adopcion de los servicios de redes social por la Generacion Y en Chile/Exploracao dos factores que afetam a adocao dos servicos de redes sociais pela Geracao Y no Chile.
The purpose of this paper is to study the adoption of social networking services (SNS) for a sample of members belonging to the Generation Y in Chile. The study used an adapted technology acceptance model (TAM) to measure the acceptance and use of SNS of the 173 respondents (111 women and 62 men). The basic TAM was enhanced with three antecedents of perceived usefulness: social identity, telepresence and altruism. Structural equation modeling has been applied to test the proposed model. Results show that a version of TAM model explains the adoption of SNS by Generation Y in Chile.
KEYWORDS / Altruism / Chile / Generation Y / Social Identity / Social Networking / Technology Acceptance / Telepresence
El proposito de este trabajo es estudiar la adopcion de los servicios de redes sociales (SRS) en una muestra de miembros pertenecientes a la Generacion Y en Chile. El estudio utilizo una adaptacion del modelo de aceptacion de tecnologia (TAM) para medir la aceptacion y el uso de los SRS en 173 encuestados (111 mujeres y 62 hombres). El modelo TAM basico fue enriquecido con tres antecedentes de la utilidad percibida: identidad social, telepresencia, y altruismo. La tecnica de modelado de ecuaciones estructurales se utilizo para probar el modelo propuesto. Los residtados muestran que la version del modelo TAM explica la adopcion de la SRS por la Generacion Y en Chile.
O proposito deste trabalho e estudar a adocao dos servicos de redes sociais (SRS) em uma amostra de membros pertencentes a Geracao Y no Chile. O estudo utilizou uma adaptacao do modelo de aceitacao de tecnologia (TAM) para medir a aceitacao e o uso dos SRS em 173 entrevistados (111 mulheres e 62 homens). O modelo TAM basico foi enriquecido com tres antecedentes da utilidade percebida: identidade social, telepresenca, e altruismo. A tecnica de modelagem de equacoes estruturais foi utilizada para provar o modelo proposto. Os resultados mostram que a versao do modelo TAM explica a adocao da SRS pela Geracao Y no Chile.
Generation y is considered to be a pioneer population with regards to new technologies, Internet or social networking services (SNS). These young people have been at the cutting edge of information technology (IT) services for two decades. To know how this generation adopts and acts in relation to the challenges of Web 2.0 and SNS is quite relevant in order to analyze the future development of these ITs. They integrate the biggest segment of SNS users and their study will guide managers' decisions for IT firms.
In short, the main objective of this paper is to study the adoption of SNS for a sample of members of Generation Y from Chile. In addition, two sub-objectives that drive the main one are: 1) to test a model based on the Technology Acceptance Model (TAM) of SNS in Generation Y, and 2) to evaluate the influence of personal features that may define Generation Y, such as telepresence, altruism and social identity and their relationship with the adoption of technology.
The field of marketing has used the theory of generations for the analysis of consumers through the characterization of the generations identified since 1900 and based it in cohorts of 20 years, known as G1, the Silent Generation, Baby Boomers, Generation X, Generation Y, and for people born from 2003 onwards, Generation Z. Each generation is stereotyped with a range of attitudinal and behavioral characteristics, based on research and experience in developed countries (Baum, 2011). In this context, Generation Y represents those born between 1978 and 1994 (Sheahan, 2005).
Ten years ago, Generation Y was considered a young market, resistant to advertising efforts. For example, in a sample of college students, the results of Wolburg and Pokrywczynski (2001) indicated that depictions in movies and television are rated significantly better than depictions in advertising. Nowadays, generational differences at work are small, at least with regard to work attitudes. Compared to members of Generation Baby Boomers and Generation X, members of Generation Y reported higher levels of overall company and job satisfaction, satisfaction with job security, recognition, and career development and advancement; however, they reported similar levels of satisfaction with pay and benefits, and the work itself and turnover intentions (Kowske et al., 2010).
The literature on how to manage Generation Y is very limited. Nevertheless, some authors propse some features of this generation. Costello et al. (2004) indicate that Generation Y prefers active or kinesthetic learning environments. Crampton and Hodge (2009) indicate that the members of Generation Y are mostly educated, well-traveled, less outcome focused, have a sense of morality and civic duty, believe that making a lot of money is less important, that their contributions to society and their role as parents are equally important, believe that a job is a contract, not a calling (work to live as opposed to live to work), seem to lack interpersonal skills, tend to lack patience, and are easily bored. Rondan-Cataluna and Martin-Ruiz (2010) indicate that Generation Y is more likely to believe that downloading music from P2P networks is less illegal.
Researchers agree that members of Generation Y are technologically savvy and they are more involved in purchase behavior over the Internet. Nusair et al. (2011) indicate that the Internet is considered a major player in the lives of Generation Y across the entire globe. Generation Y uses the Internet for a variety of activities including text messaging, social networks, podcasts, and blogs.
User acceptance of social networking services
Social networking services. SNS are defined as web-based services that enable individuals to build a public or semi-public profile within a bounded system, articulate a list of other users with whom they share a connection, and view and traverse their list of connections and those made by others within the system (Boyd and Ellison, 2007). Bagozzi (2007) argued that great part of human behavior is not best explained by an individual acting in isolation. In particular, the decision to use SNS represents a social phenomenon that depends largely on the interactions between users and the use of social technologies, and may make sense only when a group of individuals is willing to use and keep using the technology together (Cheung and Lee, 2010).
Technology acceptance model. Proposed by Fred Davis (1989), TAM explains the process of acceptance of information technology at the individual level. TAM posits that an individual's behavioral intention to use IT is determined by the perceived usefulness and ease of use. At the same time, perceived ease of use directly impacts perceived usefulness. Since then, several revisions and expansions have developed from the original model, including intrinsic and extrinsic factors. The most popular developments have been TAM2 (Venkatesh and Davis, 2000) and TAM3 (Venkatesh and Bala, 2008).
The TAM model has been widely used, revised and expanded in the literature (Venkatesh and Davis, 2000; Venkatesh and Morris, 2000; Venkatesh and Bala, 2008) and in particular in a Web 2.0 context (Ong and Lai, 2006; Kim et al., 2008; Arenas-Gaitan et al., 2011; MuftozLeiva et al., 2012;). Below, we focus on testing the SNS as novel systems by extending the conventional TAM with the new perceived constructs: social identity, altruism, and telepresence.
Social identity. Social identity has been proposed as a factor affecting the use of a particular technology in virtual communities (Casalo et al., 2010; Cheung and Lee, 2010; Kwon and Wen, 2010; Lee et al., 2010, 2011; Sombutpibool, 2011). In the SNS context, social identity is a positive perception of belonging to a community where people are motivated to interact socially with others, and this has a positive impact on both their intentions to use SNS (Cheung and Lee, 2010; Sombutpibool, 2011) and their usage of SNS (Lee et al., 2011).
Social Identity Theory has been used in the field of technology (Casalo et al., 2010). Previous research uses this theory to explain how a person identifies with others (Akkinen, 2005). Overall, the theory proposes that people develop a sense of self from the groups to which they belong (Hung et al., 2011) and a collective identity that contrasts with other identities in which the individual is unique and separate (Bhattacharya et al., 1995). Social identity of individuals in relation to their social group is characterized by three elements: group solidarity, conformity to internal rules, and discrimination against outgroups (Riedlinger et al., 2004). Social identity implies that the person believes that he or she belongs to a certain social group and that this membership has significant value (Tajfel et al., 1971; Hogg and Terry, 2000). As a result, a sense of unity among group members is developed.
Different from personal identity, social identity means the individual's position in a social group (Gecas et al., 1973). Strong social identity is known to be an important variable for self-goal setting. Social identity helps those with low self-esteem establish self-conception and also helps to avoid low goal-setting (Festinger, 1954; Baumeister, 1993; Brockner et al., 1998). Individuals with a higher social identity tend to perceive their groups in ways that distinguish themselves positively from external groups; these individuals prefer a group that provides them with a positive self-image.
The above implies that social identity may influence the perceived usefulness of SNS (Kwon and Wen, 2010). For example, a friend plans to use an SNS to call a reunion event. However, the use of an SNS is dependent on whether other friends are willing to use it. In addition, their usage experiences will create a norm for them to continuously use it for their other reunion events. Thus, it would be more appropriate to examine the decision to use online social networks using the intentional social action perspective (Cheung and Lee, 2010).
Altruism. Trivers (1971) explained that "altruistic behavior can be defined as behavior that benefits another organism, not closely related, while being apparently detrimental to the organism performing the behavior, benefit and detriment being defined in terms of contribution to inclusive fitness". Previous research indicates that altruism can be classified into two types: kin altruism and reciprocal altruism.
Kin altruism benefits a genetic relative's chances of survival or reproduction, at the expense of one's own chances. Kin altruism is frequently observed in nature. Hamilton (1964) indicated that the degree of relationship is an important parameter in predicting altruistic behavior, and this could be explained in terms of natural selection: altruistic behaviors arose from shared heritable variation.
Another mechanism for producing altruism is establishing trust between pairs of individuals who reciprocally help each other. This mechanism is named reciprocal altruism. In essence, it is an effort to explain the evolution of altruism among individuals who are not related. Specifically, reciprocal altruism is to help other individuals based on the belief that individuals who benefited will return such support in the future. Trivers (1971) suggests that reciprocal altruism can be selected even when the recipient is so distantly related to the organism performing the altruistic act that kin selection can be ruled out.
In information technology context, since altruistic people are willing to share knowledge to help others (Wasko and Faraj, 2005), altruism has been found as an important antecedent of use for certain types of information technologies related to knowledge. For examples, Lu and Hsu (2008) indicated that altruism is positively related to user attitudes towards answering questions in a Web 2.0 knowledge community. In addition, Prasarnphanich and Wagner (2009) identified altruism as a key success factor in the production processes of contents on Web 2.0. Finally, Hung et al. (2011) indicated that altruism is a relevant antecedent of perceived ease of use of an electronic knowledge repository.
In a SNS context, users can perform the two types of altruism and their altruistic behavior can positively affect the perceived usefulness of the system (Kwon and Wen, 2010).
Telepresence. Minsky (1980) coined the term telepresence to emphasize the possibility that individuals could feel the sense of being physically transported to a remote work place via teleoperation systems. Since then, several authors have referred to telepresence in order to explain the sense of being transported by technology (Sukoco and Wu, 2011). Steuer (1992) explains that presence is the direct experience of reality and telepresence is the simulated perception of direct experience. Kim and Biocca (1997) indicated that the concept of telepresence means that the individual feels like he is located in a remote place from where he currently is. In other words, telepresence describes the compelling sense of being present in a virtual environment.
According to Steuer (1992), telepresence has two determinants, interactivity and media richness. The author defines interactivity as "the extent to which a user can participate in modifying the form and content of a mediated environment in real time", and media richness as "the representational richness of a mediated environment's formal features; that is, the way in which an environment presents information to the senses". Later, studies supported this affirmation empirically (Sukoco and Wu, 2011).
In an Internet context, telepresence has been used both as a factor for the effectiveness of online advertisements (Klein, 2003; Sukoco and Wu, 2011) and as a factor with a positive effect on perceived usefulness of SNS (Kwon and Wen, 2010; Sombutpibool, 2011).
Figure 1 illustrates the research model based on the technology acceptance model (TAM), which relates the constructs of perceived usefulness (PU), perceived ease of use (PEOU) and utilization (USE) of social networking services (SNS). This TAM model is enriched with three constructs of perceived use: social identity, telepresence and altruism.
The TAM model has been used successfully in a range of contexts (Venkatesh and Morris, 2000) and particularly in a Web context (Kim et al., 2008; Arenas-Gaitan et al., 2011; Munoz-Leiva et al., 2012). Recently, some studies have used TAM in a SNS context (Cha, 2009; Kwon and Wen, 2010; Lee et al., 2011; Lorenzo-Romero et al., 2011; Sombutpibool, 2011). Based on these previous studies and considering the importance of contributing with new empirical data in a SNS context, and specifically on Generation Y, the following hypotheses are proposed:
H1: PEOU is positively related to PU in adopting SNS by Generation Y.
H2: PU is positively related to USE in adopting SNS by Generation Y.
H3: PEOU is positively related to USE in adopting SNS by Generation Y.
Previous studies suggest that social identity (SI) has a positive impact on intentions to use SNS (Cheung and Lee, 2010; Sombutpibool, 2011) and using SNS (Lee et al., 2011). In particular, SI is positively related to PU of SNS (Kwon and Wen, 2010). Besides, prior studies indicate that telepresence (TELE) has a positive effect on PU of SNS (Kwon and Wen, 2010; Sombutpibool, 2011). Furthermore, altruism (ALT) has been found as an important antecedent on the use of certain types of IT related to knowledge (Prasarnphanich and Wagner, 2009; Hung et al., 2011). Consequently, it has been suggested that ALT has a positive impact on PU of SNS (Kwon and Wen, 2010). Based on these results, the following hypotheses are proposed:
H4: SI is positively related to PU in adopting SNS by Generation Y.
H5: TELE is positively related to PU in adopting SNS by Generation Y.
H6: ALT is positively related to PU in adopting SNS by Generation Y.
Materials and Methods
The empirical research is based on a non-probabilistic and self-selection sampling method; therefore it is a convenience sample. Specifically, the data was collected in Chile from a sample of on-line questionnaires from January 14th, 2011 to March 15th, 2011. The exclusion of invalid questionnaires due to duplications or empty fields provided a final sample size of 173 SNS users who belong to Generation Y. Of the final sample, 111 respondents were women (64.2%) and 62 men (35.8%). The average ages of women and men interviewees were 21.9 (SD= 3.8) and 21.4 (SD= 3.2) years old, respectively. The majority (91.9%) of respondents had tertiary education.
The applied measurement scales have been tested in other studies. Specifically, to measure TAM constructs the scales proposed by Venkatesh and Bala (2008) have been adapted, and to measure SI, TELE and ALT the scales proposed by Kwon and Wen (2010) have been adapted. In all cases, a 7-point Likert-scale was employed.
Structural equation modeling (SEM) analysis using analysis of moment structures (AMOS; Arbuckle, 1994) was used to test the proposed research model.
Results of the descriptive statistics are shown in Table I. As a previous step to the structural model analysis it is necessary to analyze reliability and validity of the measurement model. We conducted both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Table II shows the six components obtained from EFA that explained 73.35% of the total variance. CFA was applied to test the adequacy of the measurement model. Reliability was evaluated by examining individual loads ([lambda]) of the measures with their respective latent variables; indicators with [lambda][greater than or equal to] 0.5 were accepted. Cronbach's a coefficient was used as the reliability index of the latent variables. In addition, composite reliability was calculated. The convergent validity of latent variables was assessed by examining the average variance extracted (AVE; Table III) and variables with AVE>0.5 were accepted (Fornell and Larcker, 1981). Discriminant validity of latent variables was tested by analyzing whether the square root of AVE of each one was greater than the correlations with the remaining latent variables; Table IV shows the results obtained.
Finally, path analysis was run in AMOS to test the hypotheses. Figure 2 shows results of the model. The ratio [chi square]/degrees of freedom of 2.5 and other goodness-of-fit statistics (GF= 0.862, AGFI= 0.823, CFI= 0.906, NFI= 0.854, and RMSEA= 0.076) indicated an appropriate goodness of fit of the predicted model. Based on these results hypotheses H1, H2, H4 and H5 are accepted. However, hypotheses H3 and H6 are not supported because the relationships hypothesized were not statistically significant.
Three main contributions of this study are highlighted. Firstly, a version of TAM model to explain the adoption of SNS by Generation Y has been successfully used, including dimensions that are more focused on the social interactivity of SNS.
Secondly, the finding of a strong and significant relationship in members of Generation Y between telepresence and perceived usefulness of SNS is remarkable. This can be interpreted as: the more interactivity and media richness, the more users' perception of utility.
Thirdly, a significant relationship between social identity and perceived usefulness was found, indicating an attitudinal characteristic of Generation Y to use Web 2.0. If members of Generation Y conceive the use of SNS as a way of belonging to a community where people are motivated to interact socially with others, they will show a higher perception of usefulness of its technology.
Considering that young people belonging to Generation Y have been at the vanguard of IT services over the past two decades and in the near future will remain a very important group of consumers of such technology, the following are two implications of this study.
Firstly, Generation Y prefers telepresence. Telepresence, in accordance with previous studies in age heterogeneous samples (Kwon and Wen, 2010; Sombutpibool, 2011), has a positive effect on the perceived usefulness of a SNS by members belonging to Generation Y. Study findings sup" port the conclusions of consulters that indicate the fact that telepresence is one of the opportunities for growth in services aimed towards young people.
Secondly, Generation Y is solidary. Members of Generation Y believe that their contribution to society is important. Study findings support this idea in SNS context; altruism and social identity (which includes group solidarity) are the exogenous variables most highly valued by respondents (see Table I). Therefore, we believe that this sense of solidarity can be an important clue to explore in future studies on the behavior of this generation.
This study has some limitations that guide future work. First, the validation of results requires a larger sample of individuals. Second, the use of a non-probabilistic sampling method limits the generalization of findings. Third, the study is cross sectional, a longitudinal study would be advisable to compare the different stages of the adoption of SNS.
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Received: 05/31/2012. Modified: 09/23/2013. Accepted: 09/26/2013.
Patricio Ramirez-Correa. Engineer in Informatics and Master in Managment, Universidad Catolica de Valparaiso, Chile. Doctor in Economics and Administration, Universidad de Sevilla, Spain. Professor, Universidad Catolica del Norte, Chile. Address: Escuela de Ciencias Empresariales, Universidad Catolica del Norte. Larrondo 1281, Coquimbo, Chile, e-mail: email@example.com
F. Javier Rondan-Catalufta. Bachelor of Science and Doctor in Business Administration, Universidad de Sevilla, Spain. Professor, Universidad de Sevilla, Spain, e-mail: firstname.lastname@example.org
Jorge Arenas-Gaitan. Bachelor of Science and Doctor in Business Administration, Universidad de Sevilla, Spain. Professor, Universidad de Sevilla, Spain, e-mail: email@example.com
TABLE I DESCRIPTIVE STATISTICS Latent variable Global mean Indicator Mean SD ALT1 6.0867 1.0391 ALT 6.2367 ALT2 6.2659 0.9079 ALT3 6.3584 0.9818 PEOU1 6.3353 0.8232 PEOU2 6.0694 0.8995 PEOU 6.2045 PEOU3 6.1676 0.9343 PEOU4 6.2543 0.8983 PU1 6.1792 1.1449 PU2 5.9422 1.3150 PU 6.1435 PU3 6.3931 0.9378 PU4 6.0925 1.1323 SI1 4.7399 1.6018 SI 5.0947 SI2 5.4971 1.2038 SI3 5.0578 1.5764 TELE1 4.2832 1.6898 TELE2 5.3468 1.4451 TELE 4.4896 TELE3 3.8035 1.7609 TELE4 2.6821 1.7971 USE1 6.0058 1.5039 USE 5.3220 USE2 5.2659 1.7182 USE3 4.7110 1.8732 ALT: altruism, PEOU: perceived ease of use, PU: perceived usefulness, SI: social identity, TELE: telepresence, USE: utilization of social networking services. TABLE II RESULTS FROM PRINCIPAL COMPONENT ANALYSIS Indicators ALT PEOU PU SI TELE USE ALT1 0.8397 0.0413 -0.0192 0.2040 0.1131 0.0525 ALT2 0.9095 0.0143 0.1712 0.1016 0.0990 0.0366 ALT3 0.8808 0.0305 0.1619 0.0492 0.1086 -0.0499 PEOU1 0.1248 0.7653 -0.0113 0.0052 -0.0021 0.3286 PEOU2 -0.0148 0.8550 0.1293 0.0397 0.0256 0.0290 PEOU3 0.0657 0.8960 0.0578 -0.0165 0.0229 0.0828 PEOU4 -0.0559 0.8055 0.1017 0.1634 0.0715 -0.0782 PU1 0.0939 0.1352 0.6462 0.1304 0.0958 0.3251 PU2 -0.0513 -0.0184 0.8025 0.1797 0.0676 0.1268 PU3 0.2335 0.0812 0.8177 0.0586 0.0133 0.1818 PU4 0.1099 0.1277 0.7131 0.1434 0.1478 0.1069 SI1 0.0184 0.0771 0.1908 0.7798 0.1208 0.1680 SI2 0.2417 0.0933 0.2109 0.7862 0.0691 0.1889 SI3 0.1547 0.0198 0.0868 0.8850 0.0580 0.1281 TELE1 0.1159 0.0087 0.1746 0.1525 0.6866 0.1068 TELE2 0.2607 0.0450 0.0743 0.1357 0.7087 0.0361 TELE3 0.0817 0.0832 0.0357 -0.0404 0.7998 0.0468 TELE4 -0.0873 -0.0227 0.0215 0.0286 0.7908 0.1662 USE1 0.0550 0.1487 0.3650 0.0835 0.1355 0.8187 USE2 0.0268 0.0563 0.1990 0.1779 0.1426 0.8958 USE3 -0.0570 0.1029 0.2091 0.3503 0.1617 0.7776 TABLE III CRONBACH'S [alpha], AVERAGE VARIANCE EXTRACTED (AVE) AND COMPOSITE RELIABILITY Cronbach's Composite Latent variable [alpha] AVE reliability ALT 0.8898 0.8173 0.9305 PEOU 0.8626 0.7049 0.9050 PU 0.8080 0.6350 0.8741 IS 0.8445 0.7618 0.9056 TELE 0.7658 0.5769 0.8446 USE 0.9093 0.8454 0.9425 ALT: altruism, PEOU: perceived ease of use, PU: perceive usefulness, SI: social identity, TELE: telepresence, USE: utilization of social networking services. TABLE IV DISCRIMINANT VALIDITY * ALT PEOU PU SI TELE USE ALT 0.9040 PEOU 0.0948 0.8396 PU 0.2768 0.2267 0.7969 SI 0.3058 0.1740 0.4117 0.8728 TELE 0.2756 0.1038 0.2738 0.2623 0.7595 USE 0.1122 0.2693 0.5398 0.4410 0.3165 0.9195 * The square root of AVE in the diagonal and correlations between constructs below the diagonal. ALT: altruism, PEOU: perceived ease of use, PU: perceived useful- ness, SI: social identity, TELE: telepresence, USE: utilization of social networking services.
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|Author:||Ramirez-Correa, Patricio; Rondan-Cataluna, F. Javier; Arenas-Gaitan, Jorge|
|Date:||Sep 1, 2013|
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