Background:
Social media have not only changed the way people interact in their everyday lives but have also entered academia. Researchers are starting to use general social media platforms (e.g., Twitter, Facebook) as well as specialized tools (e.g., ResearchGate, Mendeley) for the creation, management and dissemination of scholarly work. The adoption of social media by scholars faces a variety of facilitators and barriers. Known obstacles include lack of time, lack of recognition of social media activities for getting a promotion or funding, and information overload (Acord & Harley 2013; CIBER 2010; Gruzd, Staves, & Wilk 2012; Nicholas et al. 2011; Ponte & Simon 2011), while uptake is facilitated by peer influence and collaborating with scholars from other institutions (Acord & Harley 2013; CIBER 2010; Gruzd, Staves, & Wilk 2012; Procter et al. 2010). It varies across disciplines and age or level of career progression (CIBER 2010; Gruzd, Staves & Wilk 2011, 2012; Holmberg & Thelwall 2014; Nicholas et al. 2014; Pearce 2010; Procter et al. 2010; Van Noorden 2014). Maintaining or creating ties with other scholars, promoting and disseminating their research, staying informed, communicating with other scholars and sharing information (CIBER 2010; Gruzd & Goertzen 2013; Gruzd, Staves, & Wilk 2012; Nicholas et al. 2014; Pearce 2010) were identified as main motivations of scholarly social media use.
Although collaboration is essential for science and has been associated with higher impact (Larivière et al. 2015; Sonnenwald 2007), the link between social media use and collaborative research practices remains largely unexplored.
Objective:
The objective of our study is to examine how scholars use social media at different stages of the research process in the context of an inter-institutional collaborative research project. More specifically, the study aims to:
(1) provideanin-depthanalysisofusepracticesofsocialmediaateachstageofaninter-institutional collaborative research project; and
(2) explorethefactorsofadoptionofsocialmediabyteamsofresearchersininter-institutional collaborative research projects.
Methods:
A mixed-methods sequential exploratory design (Creswell & Plano Clark 2011) will be used. The first phase will consist in a multiple case study. Four to eight inter-institutional research teams from various disciplines will be selected. The results of in-depth interviews conducted with each team member will be combined with quantitative bibliometric and altmetric data in order to provide a rich and contextualized description of the individual and collective factors that affect the adoption–or non-adoption—of social media in this context. The results of this first phase will inform the second phase consisting in a Canada-wide survey of researchers, allowing for a more complete picture of the phenomenon under study.
Results:
The project is still in its early stages. Initial efforts were devoted to defining the theoretical framework. To understand why and how a team of researchers use social media, we examined prominent technology adoption models—the Diffusion of Innovation Theory (Rogers 1983), the Technology Acceptance Model (TAM) (Davis 1989), and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003)—as well as models focusing on work teams—the Adaptive Structuration Theory (AST) (DeSanctis & Poole 1994) that outlines the factors influencing technology appropriation by groups within an organization, and Constantine’s (1993) organizational reference paradigms that outlines the characteristics of four different group “personalities”. To these, we added the Theory of Information Sharing of Constant, Keisler and Sproull (1994), enriched by Jarvenpaa and Staples (2000), which will allow us to better understand the collaborative personality of each team member, in the context of a specific research team, something that is not included in traditional technology adoption models.
Future Work:
In order to complete the first phase of our research, the next steps will be to define the selection criteria for the multiple case study, and then recruit research teams that have received funding from a Canadian research
References:
Acord, S. K., & Harley, D. (2013). Credit, time, and personality: The human challenges to sharing scholarly work using Web 2.0. New media & society, 15(3), 379-397. doi: 10.1177/1461444812465140
CIBER. (2010). Social media and research workflow. London: London’s Global University. Retrieved from http://ciber-research.eu/download/20101111-social-media-report.pdf
Constant, D., Kiesler, S., & Sproull, L. (1994). What’s mine is ours, or is it? A study of attitudes about information sharing. Information Systems Research, 5(4), 400-421. doi: 10.1287/isre.5.4.400
Constantine, L. L. (1993). Work organization: Paradigms for project management and organization. Communications of the ACM, 36(10), 35-43.
Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research (2nd ed.). Los Angeles: SAGE Publications.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. doi: 10.2307/249008
DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organization Science, 5(2), 121-147. Retrieved from http://www.jstor.org/stable/2635011
Gruzd, A., & Goertzen, M. (2013). Wired academia: Why social science scholars are using social media. In Proceedings of the 46th Hawaii International Conference on System Sciences (HICSS) (pp. 3332-3341). doi: 10.1109/HICSS.2013.614
Gruzd, A., Staves, K., & Wilk, A. (2011). Tenure and promotion in the age of online social media. Proceedings of the American Society for Information Science and Technology, 48(1), 1-9.
Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340-2350. doi: j.chb.2012.07.004
Holmberg, K., & Thelwall, M. (2014). Disciplinary differences in Twitter scholarly communication. Scientometrics, 1-16. doi: 10.1007/s11192-014-1229-3
Jarvenpaa, S. L. et Staples, D. S. (2000). The use of collaborative electronic media for information sharing: An exploratory study of determinants. Journal of Strategic Information Systems, 9, 129-154.
Larivière, V., Gingras, Y., Sugimoto, C. R., & Tsou, A. (2015). Team size matters: Collaboration and scientific impact since 1900. Journal of the Association for Information Science and Technology, 66(7), 1323- 1332. doi: 10.1002/asi.23266
Nicholas, D., Watkinson, A., Rowlands, I., & Jubb, M. (2011). Social media, academic research and the role of university libraries. The Journal of Academic Librarianship, 37(5), 373-375.
Pearce, N. (2010). A study of technology adoption by researchers. Information, Communication & Society, 13(8), 1191-1206. doi: 10.1080/1369118100366360
Ponte, D., & Simon, J. (2011). Scholarly communication 2.0: Exploring researchers’ opinions on Web 2.0 for scientific knowledge creation, evaluation and dissemination. Serials Review, 37(3), 149-156.
Procter, R., Williams, R., Stewart, J., Poschen, M., Snee, H., Voss, A., & Asgari-Targhi, M. (2010). Adoption and use of Web 2.0 in scholarly communications. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1926), 4039-4056.
Rogers, E. M. (1983). Diffusion of innovations. New York: Free Press.
Sonnenwald, D. H. (2007). Scientific collaboration. Annual Review of Information Science and Technology, 41(1), 643-681. doi : 10.1002/aris.2007.1440410121
Van Noorden, R. (2014). Online collaboration: Scientists and the social network. Nature, 512(7513), 126-129.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478.
Background:
When participating online, individuals draw on the limited cues they have available to create for themselves an imagined audience (Litt, 2012). Such audiences shape users’ social media practices, and thus the expression of identity online (Marwick & boyd, 2011). While institutions encourage scholars to go online (Mewburn & Thompson, 2013), and many scholars perceive value in online networks themselves (Veletsianos, 2016), limited research has explored the ways that scholars conceptualize online audiences.
Objective:
In this research we posed the following questions: (1) how do scholars conceptualize their audiences when participating on social media, and (2) how does that conceptualization impact their self-expression online? By answering these questions, we aim to provide a more nuanced picture of scholars’ social media practices and experiences.
Methods:
We employed a qualitative approach to this study. We recruited participants through a variety of methods: invitations to participate via email, through postings on blogs and social media, and through snowball sampling. From 42 responses, we selected 16 participants who represented a range of academic disciplines and roles (mean age = 41.6; S.D = 8.1; 12 self-identified as female, 3 as male, and 1 as transgender). Data were generated from two sources: semi-structured interviews with each participant, and examination of the social media spaces they used (e.g. blogs, Facebook, Twitter). Data were analyzed using the constant comparative approach (Glaser & Strauss, 1967). In particular, as we read a piece of data (e.g., a sentence, a paragraph) we assigned codes to indicating perceived audiences and impacts on expression of identity. Next, new data (e.g., another paragraph), were either assigned one of the pre-existing codes or assigned a new code that was created to describe the data. When new codes were created, data are re-read to examine whether the new codes could be assigned to them. Eventually, the process of constantly comparing codes and data lead to a list of codes describing the data, which were compiled into themes.
Results:
Participants identified four specific groups as composing their social media audiences: (1) academics, (2) family and friends, (3) groups related to one’s profession, and (4) individuals who shared commonalities with them. Interviewees felt fairly confident that they had a good understanding of the people and groups that made up their audiences on social media, but distinguished their audiences as known and unknown. The known audience included those groups and individuals known to interviewees personally. The unknown audience consisted of members whom participants felt they understood much about but did not know personally. Interviewees reported using their understanding of their audience to guide their decisions around what, how or where to share information on social media. All participants reported filtering their social media posts. This action was primarily motivated by participants’ concerns about how postings would reflect on themselves or others.
Implications:
The audiences imagined by the scholars we interviewed appear to be well defined rather than the nebulous constructions often described in previous studies (e.g. Brake, 2012; Vitak, 2012). While scholar indicated that some audiences were unknown, none noted that their audience was unfamiliar. This study also shows that a misalignment exists between the audiences that scholars imagine encountering online and the audiences that higher education institutions imagine their scholars encountering online. The former appear to imagine finding community and peers and the latter imagine scholars finding research consumes (e.g., journalists).
References:
Brake, D. R. (2012). Who do they think they’re talking to? Framings of the audience by social media users. International Journal of Communication, 6, 1056–1076.
Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory. Chicago: Aldine Publishing.
Gruzd, A., Staves, K., Wilk, A. (2012) Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28 (6), 2340–2350
Litt, E. (2012). Knock, Knock. Who's There? The Imagined Audience. Journal of Broadcasting & Electronic Media, 56(3), 330-345,
Marwick, A. E & boyd.d. (2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New media & society, 13(1), 114-133
Mewburn, I. & Thomson, P. (2013) Why do academics blog? An analysis of audiences, purposes and challenges. Studies in Higher Education, 38(8), 1105-1119.
Veletsianos, G. (2016). Networked Scholars: Social Media in Academia. New York, NY: Routledge.
Vitak, J. (2012). The impact of context collapse and privacy on social network site disclosures. Journal of Broadcasting & Electronic Media, 56(4), 451-470.
Background:
Twitter is one of the most popular social media platforms inside and outside academia. Researchers use Twitter to search for and disseminate research, communicate, network, and engage with a broader audience. In the academic context, Twitter is most notably used to improve one’s visibility (Haustein, Bowman, Holmberg, Peters & Larivière, 2014; Holmberg, Bowman, Haustein & Peters, 2014; Van Noorden, 2014). Twitter activity has been receiving increasing attention by academia as a potential indicator of research impact, alongside other social media-based metrics (altmetrics). Several studies suggest that Twitter might reflect public outreach and science communication activities, not measured by traditional bibliometric indicators (Haustein et al., 2014).
Objective:
To further investigate Twitter use in academia as an extension of scholars’ scientific network and as a diffusion channel, this paper looks at the Twitter activity of a specific community: information science schools and their faculty members. More specifically, we aim to answerthe following questions: 1) How much do information science schools and their faculty members use Twitter? 2) How are the members of this community connected on Twitter? 3) What do they tweet about?
Methods:
A list of the 30 North American iSchools was obtained from the website ischools.org. Using the schools’ website, we retrieved the list of all faculty members, and then searched for their Twitter account. We found a Twitter account for the 30 iSchools and 267 (33%) of the 803 faculty members. Using the Twitter API, we retrieve the date of creation, the number of tweets, the tweets content1 and the list of followers for each account found.
Results:
Preliminary results show a large disparity in the Twitter presence and activity of iSchools, with faculty members’ presence per institution ranging from 7% to 84% and activity ranging from 56 tweets/year to 19,067 tweets/year. Figure 1 shows the highly skewed distribution of tweets and followers where, in a Pareto-esque fashion, 20% of Twitter users send more than 80% of the tweets (left) and 20% have more than 80% of the total number of followers (right). 1 Only the last 3,200 tweets for each account are available from the Twitter API.
We applied the Blondel et al. (2008) community detection algorithm to the follower-followee network of scholars and institutions, and found eight distinct communities, mostly linked to institutional affiliation. Despite those clusters, the network is characterized by a high density of interconnections, which is not surprising given that following someone on Twitter does not require a high level of engagement. Furthermore, the most active and followed Twitter users are not central in this network, indicating that they are followed by people outside of the iSchools community. This suggests that the Twitter network is more than a simple extension of the professional network.
Looking at the 50 most frequently used hashtags by the iSchools community (Figure 2), based on the 132,638 tweets collected (24% of all tweets), one can see that library and information science related topics are among the most discussed. However, other frequently used hashtags relate to the general political and social context, showing that the iSchools community members also use Twitter to discuss topics that are not necessarily related to their work.
Conclusion:
The aim of this work was to assess the Twitter presence and activity of iSchools and their faculty members in order to determine whether it is used by scholars to expand their network and as a diffusion channel. Our results show that the Twitter network is not limited to professional connections and despite the fact that only a minority of faculty members (about 34%) have a Twitter account, these users reach a large number of people from outside the scientific community. This highlights Twitter’s potential as a tool for public outreach. In order to provide a more thorough description of the iSchools faculty members’ activity on Twitter, and to see how Twitter affordances (e.g., mentions, retweets, hashtags) are used, further studies could look at tweets content more systematically. Such studies could help understand to what extent this type of social media activity actually reflects scientists’ public outreach.
References:
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E. (2008) Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment (10), P10008.
Haustein, S., Bowman, T.D., Holmberg, K., Peters, I., and Larivière, V. (2014). Astrophysicists on Twitter: An in-depth analysis of tweeting and scientific publication behavior. Aslib Journal of Information Management, 66(3), 279–296.
Holmberg, K., Bowman, T.D., Haustein, S., and Peters, I. (2014). Astrophysicists’ conversational connections on twitter. PloS ONE, 9(8), e106086–e106086.
Van Noorden, R. (2014). Online collaboration: Scientists and the social network. Nature, 512
Background:
Social media penetration has compelled higher education and organisations to consider the role of social media in various novel pedagogical learning settings (Linna et al, 2015; Pettersson et al 2014). We explored social media communication and perceptions of undergraduate engineers involved in an industrially focused annual worldwide competition to design and develop a racing car, where students are allocated project management and team leadership responsibilities (Gargiulo & Benassi, 2000; Gällstedt, 2003; Zika-Viktorsson, A., Sundström, P., & Engwall, M, 2006).
Objective:
The aim of the research was to understand the views of engineering students who are highly centralised in social media networks, and if they perceive the nature of their work differently than less central team members.
Methods:
During the project design phase, data from various sources were extracted from one academic institution. These data sources included meeting minutes, Computer Aided Design (CAD) files, project reports and Facebook communication in relation to the project’s Facebook page. This data extraction occurred between the 04-June-2013 and the 17-April-2015. Additionally, students periodically completed a questionnaire (consisting of 42 questions) about a range of project related factors. This WIP paper focuses on the analysis of the student Facebook communication in relation to the survey answers. Using the degree centrality figures for students from their Facebook network we compared these results to the survey answers using Spearmans rank correlation coefficient (small sample, unknown linear relationship, unknown number of outliers). There were 35 students who conversed via Facebook and of those a total of 23 students also completed the survey.
Results:
We found that from the 42 questions, three questions showed a significant correlation between centrality ranking and higher answers being given in a scale from 5 (very high) to 1 (very low). The first of these significant correlations concerned individual workload with the higher centralised individuals also perceiving that their workload was higher (Rho = 0.42264). There was also a significant positive correlation with highly centralised individuals and perception of group conflict (Rho = 0.51224) and group delays (Rho = 0.61839). These findings suggest that highly centralised students perceive that their workload is higher than that of their peers. They also appear to have a holistic view of the project, identifying conflicts and potential delays in the overall group as they bring different components of the engineering project together. It is also notable that the persons identified as having the highest centrality scores were the student project manager and team leaders.
Future Work:
The sample size in this study is small (23 individuals), but it is envisaged that the same analysis will be conducted with the 2015/16 cohort of design engineering students. This comparison between year groups will ascertain if the presented significant findings occur year-on-year rather than falsely significant. Furthermore, we wish to compare other available data sources such as the network of collaborations on project documents and CAD files, with the questionnaire results and Facebook communication to understand the digital footprint of the project manager and how, long term, we can digitally support them.
References:
Gargiulo, M., and Benassi, M. (2000) "Trapped in your own net? Network cohesion, structural holes, and the adaptation of social capital." Organization science 11.2: 183-196.
Gällstedt, M. (2003) "Working conditions in projects: perceptions of stress and motivation among project team members and project managers." International Journal of Project Management 21.6: 449-455.
Linna, P., Aramo-Immonen, A., Saari, M., Turunen, J., Jussila, J., Joel-Edgar, S., Huhtala, M. (2015) Assessment of Social Media Skills Among Vocational Teachers in Finland. EduLearn, Barcelona, Spain.
Pettersson, E., Aramo-Immonen, H., Jussila J.J. (2014) Social media utilization in b2b networks’ organisational learning – review and research agenda proposal. Journal of Mobile Multimedia, Vol. 10, No.3&4 (2014) 218 – 233
Zika-Viktorsson, A., Sundström, P., & Engwall, M. (2006) "Project overload: An exploratory study of work and management in multi-project settings." International Journal of Project Management 24.5: 385-394.
Background:
Social media offers qualitative researchers volume, richness and the promise of direct access to the lived experience of the individual. However, the scale and complexity of social media data presents a “little big data” challenge in terms of data collection, aggregation and interpretation (Esomar 2014). Social media identity production is located within an online infrastructure that reproduces a world “out there” (Pridmore & Lyon 2011) and involves the curation of numerous data fragments within ongoing episodic narration. Established qualitative data collection and analysis can struggle to capture fully this longitudinal and iterative process. We contend that new methods of qualitative data collection and analysis are needed to capture the longitudinal adjustment to social norms; self-censorship and the translation of the self into content in order to provide a holistic understanding of the online identity production.
Objective:
Our paper synthesises filmic methods and consumer research theory to develop an innovative methodology, which captures the interactive process-based nature of social media identity production.
Methods:
We implement a four-phase mixed-methods methodology, which forms a prism of reflexive data collection and analysis. We recruited professional filmmakers to construct films composed of a discrete chosen participant’s social media data. In the first phase, the professional filmmakers acted as expert interpreters and constructors of narrative. These filmmakers constructed a biopic of selected research subjects using longitudinal data (words, pictures, speech and music) extracted from social media platforms. The filmmakers were not informed of the research agenda and focussed solely upon the subject’s online identity as enacted on social media. The filmic process involved the synthesis and aggregation of data into a themed narrative. Second, the subject responded to the filmic representation of their online identity. Third, the filmmaker responded to the subject response. Fourth, the final films and accounts by participants and film makers were analysed.
Results:
We report on our experience of using this method and argue that this approach enhances understanding of the disclosed and the (re)-interpreted self within social media. Our work shows that social media identity production involves reconciliation with the temporal self, harmonizing multiple selves and reconciling the dichotomous public/private self. The resultant findings advance understanding of celebritisation and marketisation of the self in contexts where public and private merge in increasingly challenging ways.
Future Work:
We plan to synthesise film production and interpretive consumer research theory to propose a robust methodological framework for the presentation and analysis of social media activity. Our work will provide a nuanced and reflexive template for research into digital narrative construction and self presentation.
References:
Esomar (2014) Big Data and the Future of Qualitative Research, RW Connect, Retrieved from http://rwconnect.esomar.org/big-data-and-the-future-of-qualitative-research/, Accessed 2016-01-15
Pridmore, J., & Lyon, D. (2011). Marketing as Surveillance: Assembling Consumers as Brands, In D. Zwick & J. Cayla, (Eds), Inside Marketing (pp. 115-136). Oxford: Oxford University Press.
Background:
The growing use of social media has brought both opportunities and challenges to organizations. One of these challenges is the uncontrolled spread of content on social media that could potentially damage the reputation of the organization and its members (e.g. Meijer & Kleinnijenhuis, 2006). Social identity theory can be used to explain how organizations and their members cope with such phenomena (e.g. Petriglieri, 2011). Some organizations attempt to counter negative outcomes by imposing social media standards on their members, as one of the many ways in which the organization tries to enforce their employees’ loyalty. Employees, however, are then confronted with various characteristics of what Coser (1974) and Peterson and Uhnoo (2012) refer to as a ‘greedy institution’.
Objective:
In this paper, we explain how police officers – who strongly identify with their organization and profession – cope with both threats to their organizational and professional identity emerging from external pressure (e.g. social media content) as well as demands of total commitment from their organization.
Methods:
For this study, a mixed method approach is used. Data analysis is based on 32 semi-structured interviews and focused probes (following a Q-sorting experiment) in a large police region in The Netherlands. The interviews (which lasted from 35 minutes to one and a half hour) were executed by two researchers, tape-recorded and transcribed verbatim.
Results:
In line with social identity theory (Tajfel & Turner, 1979), police officers try to maintain a positive social identity. As their identity is threatened by, for example, social media content, they engage in identity management strategies.
Contrary to what was expected based on social identity theory, police officers – who strongly identify with their organization, and even stronger with their profession – do not frequently choose an identity management strategy that actively protects their social identity in case of an identity threat. Instead of positively distinguishing their threatened identity, they, for example, conceal their police identity in private times or condemn the condemners (Petriglieri, 2011). This can be explained by the finding that the Dutch Police shows striking similarities with Coser’s (1974) description of a greedy (highly demanding) institution. The police organization wants to ensure that police employees actively protect the organizational image in case of a threat and refrain from possible image threatening behavior. In order to avoid negative organizational image and subsequent legitimacy losses, appropriate behavior is enforced by the organization under the pretext of:
“Don’t forget… everything you do is under a magnifying glass. You, literally, live in a glass house. For the smallest mistake you might make, you could be reprimanded or even fired.” (R8)
Our focused interviews reveal that tensions arise when police employees at the same time try to protect their professional identity and their ‘personal space’ when their professional identity is threatened in private times. Police employees, then, perceive a threat not merely as an identity threat, but also as a threat to their work-private boundary. The struggle police employees experience because they, on the one hand, want to speak up to protect their threatened and so much ‘beloved’ professional identity, and on the other hand want to enjoy their off-time, is well reflected in the following quote:
“You are proud of your work (…). So, every now and then, I do get tossed back and forth, because on the one hand, you don’t like that people always talk negative about your work, but on the other hand, you don’t always feel like ending up in discussions. (…) In first instance, I would try to keep my mouth shut, but eventually, um, if I do fall for the provocation, I would defend it.“ (R21)
In accordance, police officers not merely use identity management strategies to protect their threatened identity. They also use these strategies as boundary mechanisms: to enhance vigilance against total intrusions of their personal identity.
Future Work:
Data analysis is already at an advanced stage. A first draft of a paper will be presented at the conference. Feedback will be welcomed. The paper is one of the studies in the PhD project of the first author.
References:
Coser, L. A. (1974). Greedy institutions: Patterns of Undivided Commitment. New York: Free press.
Meijer, M. M. & Kleinnijenhuis, J. (2006). News and corporate reputation: Empirical findings
from the Netherlands. Public Relations Review, 32, 341-348.
Peterson, A. & Uhnoo, S. (2012). Trials of loyalty: Ethnic minority police officers as ‘outsiders’
within a greedy institution. European Journal of Criminology, 9 (4), 354-369.
Petriglieri, J. L. (2011). Under threat: responses to and the consequences of threats to
individual identities. Academy of Management Review, 36 (4), 641-662.
Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin,
& S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33-47).
Monterey, CA: Brooks Cole.
Background:
With the advent of the Web, the way bank customers perform various banking activities has changed over the years which has brought about the concept of social media banking. This refers to the use of social media as a form of delivery channel for banking services which may include balance inquiries, account opening and fund transfers. Banking using social media channels is relatively new and any idea that is perceived as new should be considered an innovation worthy enough to be studied.
Objective:
The objective of the study is to investigate and compare social media banking acceptance and adoption across two countries namely Nigeria and England and to identify what factors have significant effects on consumer attitudes and behavioural intentions towards social media banking.
Methods:
The study will use the Unified Theory of Acceptance and Use of Technology (UTAUT) model developed by Venkatesh et al (2003). This model has been selected because it incorporates a range of variables from several models that have been used to understand technology acceptance and adoption. Venkatesh et al compared and summarized eight existing models of user acceptance theories and based on their results, they refined the eight models and merged it into an integrated single model which captures elements of the different models.
The UTAUT model was developed from theories in sociology and psychology and these theories are namely the Theory of Reasoned Action (TRA) (Ajzen and Fishbein, 1980), the Technology Acceptance Model (TAM) (Davis, 1986), the Theory of Planned Behaviour (TPB) (Ajzen, 1985), the Motivational Model (MM) (Ryan and Deci, 2000), Decomposed Theory of Planned Behaviour (DTPB) (Taylor and Todd, 1995), the Model of PC Utilization (MPCU) (Thompson et al, 1991), the Diffusion of Innovation theory (DOI) (Rogers, 2003), and the Social Cognitive Theory (SCT) (Bandura, 1986).
A mixed method approach will be used for this study. Quantitative data will be collected using the survey method while a semi structured interview will also be conducted to get an in depth understanding of the users experience and other factors that may not be evident through the use of quantitative methods. The target population will be bank customers who use social media banking tools and data analysis would be done using statistical techniques using an appropriate software. The study will be exploratory and deductive in nature as an existing conceptual framework will be used (Bryman and Bell, 2007).
Results:
The study will enable us find out what factors determine usage behaviour and what constructs (performance expectancy, effort expectancy, social influence and facilitating conditions) significantly affect the acceptance and adoption of social media banking in both countries.
Future Work:
Future research would investigate cross culture effects on the adoption of social media banking as well as the effects of demographics on the acceptance of social media banking.
References
Ajzen, I. and Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs: Prentice-Hall.
Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behaviour (pp. 13–37).
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, N.J.: Prentice-Hall.
Bryman, A., and Bell, E. (2007). Business Research Methods. 2nd edn. Oxford: Oxford University Press, USA.
Davis, F. (1986). Technology acceptance model for empirical testing new end-user information system: theory and results. MIT Sloan School of Management.
Rogers, E. (2003). Diffusion of innovations. New York: Free Press.
Ryan, R., and Deci, E. (2000). Self-Determination Theory and the facilitation of intrinsic motivation, social development and well-being. American psychologist, 55(1), pp.68-78.
Taylor, S., and Todd, P. (1995). Decomposition and cross-over effects in the theory of planned behaviour: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), pp.137-155.
Thompson, R., Higgins, C., and Howell, J. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), pp.125-143.
Venkatesh, V., Morris, M., Davis, G., and Davis, F. (2003). User acceptance of information technology: Toward a unified view, MIS Quarterly, 27(3) pp.425-478.
Venkatesh, V., and Zhang, X. (2010). Unified theory of acceptance and use of technology: U.S. vs. China, Journal of Global Information Technology Management. 13(1), pp.5-27.
