Social Capital in E-services Adoption

Depósito de Investigación de la Universidad de Sevilla https://idus.us.es/ This is the peer reviewed version of the following article: Rey-Moreno, M. and Medina- Molina, C. (2016), Social Capital in E-services Adoption. Psychol. Mark., 33: 1151-1158. https://doi.org/10.1002/mar.20952, which has been published in final form at https://doi.org/10.1002/mar.20952. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited." Social capital in e-services adoption Manuel Rey-Moreno, University of Seville Cayetano Medina-Molina, Centro Universitario San Isidoro April 2016 Sendcorrespondence to Manuel Rey-Moreno, University of Seville, Spain ([email protected]) and Cayetano Medina-Molina, Centro Universitario San Isidoro, Spain ([email protected]). 2 Abstract Several studies acknowledge social network use’s effect on university students’ learning process and that effect’s moderating role in e-learning platform adoption. In line with Social Capital Theory, this study analyzes the differences in e-Learning platform adoption between users of a generic social network, linked to bridging social capital, and users of a study-specific network, connectedtothe development of bonding social capital. The results show that a studies-linked social network has a more significant moderating effect on the relation between some antecedents of intention to use and effective use of e-learning platforms. Thus, this study advocates for the development and use of studies-linked social networks as a factor favoring the adoption of e-learning platforms. Keywords: Social capital; social network sites; bridging social capital; bonding social capital; UTAUT model 3 1. Introduction By overcoming the limitations of time and space,Information and Communication Technologies (ICT) transform the learning experience and increase the speed, flexibility, and efficiency of knowledge transfer (Uzunboylu et al., 2011). The most widespread application of this concept is the use of e-Learning platforms, an innovative tool that fosters students’ active participation, initiative, and critical thinking. The increasing popularity of e-Learning has brought a research interest in identifying the factors that underlie the successful implementations of ICT interactive tools. As with any other information system,this success depends on the learners` acceptance of these technologies and the social interaction amongthem (Hernández et al., 2011). To obtain real benefits in the learning process,students should actively use these digital learning tools (Sánchez-Franco et al., 2014). Research on the factors driving students` digital choice concerning whether to use technology for learning may help educators to enhance students’ use of technology for learning. The designers of e-Learning platforms are seeking complementary solutions to increase convenience and effectiveness on individual and collaborative learning (Sánchez-Franco et al., 2014). Recent generations of university students are assuming a greater control of their learning environments to achieve experiences that are more innovative and social. Social communication tools’ main purpose is to connect participants in virtual training tools. In this line, studies have noted that SNSs (Social Network Sites) can positively affectstudent involvement in the use of e-Learning platforms (Ainin et al., 2015), as 4 well as how students’ use of SNS moderatese-Learning platforms’ adoption (Medina &Rufín, 2014; Rufín&Medina, 2012). Although the literature has already established the incidence and moderating effect that the use of SNSs has on adopting e-Learning platforms, this studyanalyzesa difference in such effect depending on the type of users that employ the social network being generic or specific; that is, related to students’ studies. This study builds on two theories on e-Learning platform adoption: Social Learning TheoryandSocial Capital Theory. Social Learning Theory (Bandura, 1971) posits that students learn from interacting with others, whereas in Social Capital Theory (Putnam, 2000) these interactions occur in the individual’s social networks or connections. According to Putnam (2000), the social capital generated may depend on whether the student interacts with people whose profiles and interests are similar or not. Transferred to social networks, this study aims to discover a possible difference between adopting an e-Learning platform when the SNS is generic, associated with the development of bridging capital social(which arises when people with different backgrounds connect), and using a studies-specific SNS linked to bonding capital socialgeneration(which surgesbetween people with similar profiles). 2. Social networks and social capital in university education 2.1. Social network sites and higher education Online SNSs are environments that promote the support received from and the community participation of their members and thus facilitate the creation, 5 maintenance,and intensity of the feelings of social and individual identity, belonging, and community involvement (Sánchez-Franco et al., 2012; Sánchez-Franco et al., 2014). As these sites are particularly effective in promoting communication and collaboration, their use in educational settingsis possible (Park et al., 2014; Wang &Chiu, 2011; Zhan et al., 2011). Universities willing to have students that participate in their own learning must adapt their teaching strategies to their students’ lifestyles (Arteaga et al., 2014). The emergence of SNSs transforms the educational scenario, particularly the students’ acquisitionof knowledge and competences in an environment where the absence of social interaction has become the main barrier (Balakrishnan et al., 2015). An SNS allows users to concentrate and promote new learning experiences with their open material, easy-to-use supplements, and an effective support for collaboration and communication. SNSenables users with similar interests to form new educational spaces in communities where users can create and easily share content with others.Finally, SNSfosters personal learning environments, offering new pedagogical approaches to strengthen self-regulated learning (Balakrishnan et al., 2015). SNSs reinforce collaborative learning, involve participants in critical reasoning, and boost communication and writing skills. Studies suggest a positive correlation between SNS use and student involvement as a predictor of academic success, as well as helping students to adapt to the university's culture (Zhan et al., 2011). Different works prove the positive influencethat SNS use has on the student’s result (Ainin et al., 2015), measured as social acceptation and asthe attitude toward university life (Arteaga et al., 2014; Park et al., 2014). SNS usealsofavors users’ integration into 6 university life, endowing them with greater self-esteem, self-efficacy, and social influence (Arteaga et al., 2014; Balakrishnan et al., 2015). 2.2. Social capital in online university education From a sociocultural perspective, scholars should not examine learning as an isolated process which takes place in someone’s mind, but rather as participation ina broad range of social activities (Fauville et al., 2015). This social interaction can occurthrougha wide variety of formal and informal tools such as SNSs and is a prerequisite for collaborative learning. Social interactionalsoenhancescontent assimilation and fosters analytical and critical reasoning, as well as the competences of synthesis and evaluation linked to such reasoning(Khan et al., 2014). Recently, social capital has become one of the measuring elements of ICTs’ influence on communities. Social capitalis a relational resource comprising a wide variety of elements such as personal networks, social norms, values, trust, and shared psychological resources. The use of ICTs shores up the development of social capital in terms of facilitating knowledge sharing, entertainment and ensuring quality education (Aleksic-Maslac&Magzan, 2012). Social capital is the aggregation of the current or potential resources necessary for having a lasting network of more or less institutionalized relations through mutual recognition and respect (Mazzoni&Iannone, 2014). Social capital refers to the benefits stemming from social networks or connections with an individual or a group (Seale et al., 2015), and such social capital that stems from SNSs and in the rules of reciprocity associated with them (Tomai et al., 2010). University students consider SNSs to be tools 7 that, maintaining the social capital, can help them in their transition to university life (Arteaga et al., 2014; Mazzoni&Iannone, 2014). Son and Lin (2008) show how research on individual actors’social capital generation belong to one of two traditions. The first underlines the usefulness of social capital for these individual actors, whereasthe second focuses on social capital’s effect on social actors. Within the first category, Putnam (2000) distinguishes between bridging and bonding social capital. Bridging socialcapital arises when people fromdifferent backgrounds connectviaSNSscreating bridges between them.Bonding social capitalcanbe exclusive and results from the interactionamongpeoplewith similar profiles. These have very little diversity and generate strong personal connections. 3. Model and hypothesis 3.1. Identifying the Antecedents of e-Learning Platform Adoption The Unified Theory of Acceptance and Use of Technology (UTAUT)posits the existence of four key elements for technology acceptance and use. UTAUTstates that performance expectancy, effort expectancy, and social influence affect intention of use, while use intention and facilitating conditions are what determine how the technology is actually used (Chan et al., 2012). Chan et al. (2012) consider a range of antecedents of the UTAUT model buildingon the stages of introducing a new technology. In this perspective, the preparatory stage emphasizes awareness; the segmentation stage relates to compatibility and self-efficacy; the positioning stage relates to flexibility and aversion to personal interaction; finally, the execution stage results from trust, convenience, and assistance. 8 3.2. Use of social network sites as a moderating factor in e-learning platform acceptance E-Learning platform acceptance may depend on students' involvement and social interaction, which is key for the necessary exchange of knowledge to occur (Ma &Yuen, 2011). SNSs provide an active, collaborative audience for content developmentby enhancing communication and cooperation amongstudents (Zhan et al., 2011). The use of SNSs moderates the adoption of e-Learning platforms (Medina &Rufín, 2014; Rufín&Medina, 2012) and leads to greater levels of self-efficacy, selfesteem, and the students’ social acceptance (Ainin et al., 2015; Arteaga et al., 2014; Balakrishnan et al., 2015). The use of SNSs increases students’social capital at both the bridgingcapital and bonding capital level, though each of these has its particularities. Furthermore, studies have also proven the disparate effect that bonding and bridging social capital has on their users. SNSs thatlink people with different profiles increase the students’ comprehension and trust (Ainin et al., 2015). Regarding to bridging social capital, weak ties stand out, as a more distant relation usually builds on non-redundant information and resources. Therefore, a connection exists between intensity of use of the network and bridging social capital (Tomai et al., 2010). Students who frequently interact with people having a different background and experience develop a greater familiarity with them, attain a higher level of comprehension, and gain more trust (Ainin et al., 2015). In this line, sometimes students appreciate generic technologies more than specific e-Learning-support 15 The antecedents of Effort expectancy are Assistance (path: 0.3202, t-value: 4.2016) and Convenience (path: 0.5569, t-value: 8.6391), the antecedents of Performance expectancy are Compatibility (path: 0.4158, t-value: 3.3111) and Trust(path: 0.2715, t-value: 3.2245), the Facilitating conditions result from Self-efficacy (path: 0.1239, t-value: 1.9077), Assistance(path: 0.5083, t-value: 7.1787), and Convenience (path: 0.2522, t-value: 3.2988). The antecedent of Social influence is Awareness (path: 0.4307, t-value: 5.9042), that of Intention to use is Effort expectancy (path: 0.4817, t-value: 3.7193) and that of actual Use isIntention to use (path: -0.1310, tvalue: 2.2040) (Table3). 4.3. Interaction effect Table4. Interaction Effect Gen SNS Spe SNS Path Path tvalue Path tvalue API ->PerfExpec -0.2494 0.8915 -0.4278 1.6684* Assistance->FacCond 0.9798 1.2394 1.0402 1.2787 Assistance ->EffExpect 2.4097 4.1571*** 2.1580 3.9347*** Self-efficacy ->FacCond 0.3895 1.1624 0.5804 1.5539 Self-efficacy ->EffExpect 0.4828 2.0058* 0.6002 2.2462** Compatibility ->PerfExpec -0.9516 1.9104* -1.1721 2.3992** FacCond -> USE 1.5728 2.0264* 2.4851 3.8135*** Trust ->PerfExpec -0.6340 1.3391 -1.1405 2.5574** 16 Awareness ->SocialInfl 0.7242 0.7946 2.1306 1.9556* Convenience ->FacCond 0.5232 0.9376 0.6533 0.9694 Convenience ->EffExpec 0.2815 0.5356 1.0456 1.7910* EffExpec ->IntentUse 0.4859 0.4568 0.2250 0.2079 PerfExpec ->IntentUse -0.3808 0.4350 -1.2049 1.3859 Flexibility->PerfExpec 0.3099 0.5914 0.4933 0.9016 SocialInfl ->IntentUse -1.1736 3.5006*** -1.1552 4.1399*** IntentUse -> USE 0.1586 0.4323 0.9799 1.9727* To analyze how using a generic SNS or a specific SNS moderates e-Learning platform adoption, this study measures each of these networks through a continuous variable. The analysis method is the interaction effect, which, according to Rigdon et al. (1998), is a natural method to analyze moderator effects and the method recommended whenever possible (Henseler&Fassott, 2010). In this study(Table 4), the use of a generic SNS moderates the relation between Assistance and Effort expectancy (t-value: 4.1571), Self-efficacy and Effort expectancy (t-value: 2.0058), Compatibility and Performance expectancy (t-value: 1.9104), Facilitating conditions and Use (t-value:2.0264) and Social influence and Intention to use (3.5006). The use of a specific SNS also moderates the previously mentioned relations - Assistance and Effort expectancy, t-value: 3.9347; Self-efficacy and Effort expectancy, t-value: 2.2462; Compatibility and Performance expectancy, t-value: 2.3992; 17 Facilitating conditions and Use, t-value: 3.8135; Social influence and Intention to use, tvalue: 4.1399, Aversion to personal interaction relations and Performance expectancy (tvalue: 1.6684), Trust and Performance expectancy (t-value: 2.5574), Awareness and Social influence (t-value: 1.9556), Convenience and Effort expectancy (t-value: 1.7910) and Intention to use and Use (t-value: 1.9727). 5. Interpretation of theresults Table5.Relationships moderationsummary Relation Gen SNS Spe SNS API ->PerfExpec No Yes (-) Assistance->FacCond No No Assistance ->EffExpect Yes (+) Yes (+) Self-efficacy ->FacCond No No Self-efficacy ->EffExpect Yes (+) Yes (+) Compatibility ->PerfExpec Yes (-) Yes (-) FacCond -> USE Yes (+) Yes (+) Trust ->PerfExpec No Yes (-) Awareness ->SocialInfl No Yes (+) Convenience ->FacCond No No Convenience ->EffExpec No Yes (+) 18 EffExpec ->IntentUse No No PerfExpec ->IntentUse No No Flexibility->PerfExpec No No SocialInfl ->IntentUse Yes (-) Yes (-) IntentUse -> USE No Yes (-) Regarding the behavior of the antecedents in the UTAUT, Performance expectancy depends on Compatibility and Trust, but not on Flexibility and Aversion to personal interaction. Convenience and Assistance have a significant influence on Effort expectancy, but Self-efficacy does not. The antecedent of Social influence (Awareness) and those of Facilitating conditions (Self-efficacy, Convenience and Assistance) are significant.Of the antecedents of Intention to use, only Effort expectancy has a significant influence—Performance expectancy and Social influence do not. Finally, Intention to use has a significant influence on Use but Facilitating conditions do not. These results coincide with those proposed in previous works, in which Selfefficacy isnot an antecedent of Effort expectancy and Flexibility of Performance expectancy, although in this studythe influence of Convenience on Facilitating conditions and Compatibility on Performance expectancy differs. A previous work in the e-Learning area also proves the model’s behavior,Medina andRufín (2014) find a lack of significance of the role of Flexibility in Performance expectancy. Regarding the constructs initially incorporated into the UTAUT model, Intention to use derived from Effort expectancy but not fromPerformance expectancy and Social influence. These results agree with Van Dijk et al. (2008). Previous studies use this 19 modelization for blended learning environments in which the effect of the three antecedents of Intention to use (Effort expectancy, Performance expectancy and Social influence) issignificant (Medina &Rufín, 2014; Rufín&Medina, 2012). Finally,Intention to use determines actual Use of the e-Learning platform. Regarding type of SNS employed, the use of a generic SNS positively moderatesthe relation between Assistance and Effort expectancy, Self-efficacy and Effort expectancy and, finally, between Facilitating conditions and Use. On the contrary, use of generic SNSnegatively moderates the relation between Compatibility and Performance expectancy and Social influence and Intention to use. That is, the greater the use of the SNS, theweaker the influence of Compatibility on Performance expectancy and of Social influence on Intention to use. These results differ when the e- Learning platform works as support for classroom training. In this case, the SNS moderates the relation between the antecedents of Intention to use (Effort expectancy, Performance expectancy, and Social influence), as well the relation between Convenience and Facilitating conditions (Medina &Rufín, 2014). Using a specific SNS moderates all the previously mentioned relations of Aversion to personal interaction and Performance expectancy (with a negative sign), Trust and Performance expectancy (with a negative sign), Awareness and Social influence, Convenience and Effort expectancy, and Intention to use and Use. These findings support H1; that is, the type of SNS (generic or specific) moderates the adoption of an e-Learning platform, and the moderator effect is stronger when using an SNS which links people who have a similar profile. Although according to theoretical approaches generic SNSs could have a greater 20 effect on users, this study finds a stronger influence of using a specific SNS. Interacting daily with people with whom users can talk about study-related topics leads to a weaker influence ofAversion toward personal interaction on Performance expectancy. That is to say, the socialization occurring in the social networkcreatesalink between users which promotes real-life interactionwith their colleagues. According to Ainin et al. (2015), generic SNSs build up greater Trust, whereasspecific SNSs causeless impact of Trust on Performance expectancy. In the same way, even though a generic SNS generates greater Awareness (Ainin et al., 2015), using a specific SNS has a stronger impact on Social influence. Furthermore, Convenience creates a stronger effect on Intention to use. Finally, if a university that offers its services exclusively online uses a network, the use of a specific SNS moderates the relation between Intention to use and actual Use. This result agrees with Zhang (2014), who states that the use of a specific SNS produces a greater involvement of the students. 6. Conclusions and limitations If universities mean to have students involved in their learning processes, they must adapt their teaching strategies to their students’ lifestyles (Arteaga et al., 2014). Among the technologies that students have recently adopted, SNSs, in addition to their generalized acceptance, are highly relevant because they have modified how students establish and maintain their social relations. Thelink between social relations and learning is implicit: studying learning froma sociocultural perspective requires its conceptualization as a process stemming from a broad range of social activities 21 (Fauville et al., 2015). Students’ interaction is decisive forlearningsuccess, especially in online environments. This social interaction can occur in a wide variety of formal and informal tools, such as SNSs, and is a prerequisite for collaborative learning (Khan et al., 2014). The study of the nexus between the students’ learning and the management of the interactions amongthem has a broad tradition. Social Learning Theory (Bandura, 1971) proposes that students learn from interactions with others.These interactions can take place in the individual’s networks of social connections, according to Social Capital Theory (Putnam, 2000). These lines of thought lead to the analysis of the effect ofSNSs’ use on the learning process—with a positive effect in most cases—and on the adoptionof e-Learning platforms. Setting out from Social Capital Theory (Putnam, 2000), this study analyzes the difference of the moderatingeffectof e-Learning platform adoption depending on the type of SNS: generic SNS (which develops bridging social capital) or specific network (which generates bonding social capital). The hypothesis is that specific studies-linked SNS has a greater effect. The results attained have implications for organizations. As the great majority of students have incorporated generic SNSs into their lives, the only doubt of online institutions is whether they should develop a studies-linked SNSthemselves. Two important insights steam from the results. The first finding is the reduction of the impact of Aversion to personal interaction on Performance expectancy.Adjusting to the students’ way of interacting may bring about a greater feeling of belonging among them and, as far as possible, may help students develop a close relationship with the University staff. These days, creating 22 a base of students who identify themselves with their university is important, especially in the case of online universities. The second finding refers to the positive effect of the relation between the intention to use and actual use. Online Universities require an involvement with their learning process which reflects in a high rate of use of the university’s e-Learning platform. Therefore, incorporating a specific SNS causes the students to transfer their intention to use the platform into actual use to a greater extent. In conclusion, online universities must try to implement specific SNSs in addition to an e-Learning platform. Given that the students have already actively incorporated generic SNSs, creating these SNS is a complementary strategy which would enrich the students’ learning process. Regarding the work’s limitations, apart from those that are typical of the sample size, the fact that the actual use construct consists of a single item may condition the extrapolation of its results. 23 References Ainin, S., Naqushbandi, M.M., Moghavvemi, S. &Jaafar, N.I. (2015).Facebook usage, socialization and academic performance.Computers & Education, 83, 64–73. Aleksic-Maslac, K. &Magzan, M. (2012).ICT as a tool for building social capital in higher education.Computer-Wide Information Systems, 29, 272–280, 2012. Arteaga, R., Cortijo, V.,&Javed, U. (2014). Students` perceptions of Facebook for academic purposes.Computers & Education, 70, 138–149. Balakrishnan, V., Liew, T.K. &Pourgholaminerad, S. (2015). Fun learning with Edooware- A social media enabled tool. Computers & Education, 80, 39–47. Chan, F.K.Y., Thong, J.Y.L., Venkatesh, V., Brown, S.A., Hu, P.J-A.,& Tam, K.T. (2010). Modeling citizen satisfaction with mandatory adoption of an egovernment technology.Journal of the Association for Information Systems, 11, 519–549. Fauville, G., Dupont, S.,Von Thun, S.,&Lundin, J. (2015). Can Facebook be used to increase scientific literacy? A case study of the Monterrey Bay Aquarium Research Institute Facebook page and ocean literacy.Computers & Education, 82, 60–73. Henseler, J.,&Fassott, G. (2010). Testing moderating effects in PLS Path Models: An illustration of available procedures. In Esposito, V. et al. (eds): Handbook of Partial Least Squares.Berlin: Springer Handbooks of Computational Statistics. Hernández, B., Montaner, B.T., Sese, F.J.,&Urquizu, P. (2011). The role of social motivations in e-learning. How do they affect usage and success of ICT interactive tools? Computers in Human Behavior, 27, 2224–2232. 24 Khan, M.L., Wohn, D.Y.,& Ellison, N.B. (2014). Actual friends matter: An internet skills perspective on teens` informal academic collaboration on Facebook. Computers & Education, 79, 138–147. Ma, W.W.K.,& Yuen, A.H.K. (2011). Understanding online knowledge sharing: An interpersonal relationship perspective.Computers & Education, 56, 210–219. Mazzoni, E.,&Iannone, M. (2014). From high school to university: Impact of virtual networking sites on social capital in the transition of emerging adults. British Journal of Educational Technology, 45, 303–315. Medina, C.,&Rufín, R. (2014). Social Network sites as a moderating factor in e- Learning platform adoption.An extension, in LectureNotes in Computer Science, 8718. Springer, 277–287. Park, S.Y., Cha, S-B., Lim, K.,& Jung, S-H. (2014). The relationship between university student learning outcomes and participation in social network services, social acceptance and attitude towards school life. British Journal of Educational Technology, 45, 97–111. Putnam, R.D. (2000). Blowing alone. New York: Simon & Schuster.