Facebook as a tool for learning purposes: Analysis of the determinants leading to improved students’ learning

Abstract

The study described in this article aims to investigate the use of out-of-class communication between students and instructors using Facebook as a means of interaction for learning. How often/how much students use such an online social network and the clarification as to the reasons for online communication are the two exogenous variables used to measure the perceptions that students have about the impact of online social networking on their learning. How students perceive the quality of the content of student–instructor interaction on Facebook is treated as a mediating variable in the research model. How students perceive their performance is used as a proxy for their learning outcomes and is treated as the endogenous variable. A questionnaire was developed and used, and the analysis of the data gathered from the questionnaire was conducted using a measurement model and a structural model. The results of the study revealed that how often/how much students use an online social network and the reasons that they give for using online communication have a significant positive impact on how they perceive the quality of the content of student–instructor interaction on Facebook. In addition, the results demonstrated that the use of Facebook has a negative impact when it comes to the students’ perceptions of the quality of the content of student–instructor interaction on their perceived performance.

Keywords

class communication Facebook learning engagement social media social presence student interaction

Online social networking and its use in the classroom

The popularity of online social networking has increased dramatically in the past few years because of the advances in technology and because of the need for fast communications and interactions among social groups. With the inception of web 2.0 technology, online social networking has become a prominent communication tool particularly among youth, which includes students. Users tend to cultivate their virtual social relationships and virtual life on some common social networking websites such as Facebook, Twitter and MySpace. The academic value of Facebook is determined by the opportunities it provides to students as a means of fast interaction with classmates (Bishop-Russell et al., 2006). This interaction might be within the classroom or outside the classroom. Out-of-class communication consists of structured and unstructured student–instructor interaction, which is not part of the course requirements (Terenzini et al., 1999). Structured out-of-class communication refers to formal interactions such as emails or office visits, while unstructured out-of-class communication includes informal on/off campus interactions such as casual conversations before or after classes. No matter how structured or unstructured out-of-class communication is, engaging in out-of-class communication has some benefits for student retention and student academic and cognitive development as argued by Mansson et al. (2012) who state that students’ argumentativeness and assertiveness are positively related to their tendency to engage in out-of-class communication with their instructors. The frequency of interaction between students and instructors might be used to predict students’ learning outcomes on account of the intense relationship between the instructor and student (Young et al., 2011). In addition, out-of-class communication generally improves the language skills taught (Lau, 2013).

Garrison et al. (1999) first introduced the Community of Inquiry framework model which is a dynamic model that shows a strong relationship between collaborative constructivism and higher order learning outcomes in online environments (Akyol and Garrison, 2011). The three essential educational elements of the Community of Inquiry framework are social presence, teaching presence and cognitive presence. Within the online learning community, social presence has been defined as the ability of participants to project their personal characteristics and present themselves when they effectively connect to one another. The purposeful incorporation of specific social presence techniques into online critical inquiry had a positive impact on student perceptions of group interaction and online learning expectations (Mayne and Wu, 2011). The teaching presence has been described as the design and development of the educational experience (Mayne and Wu, 2011). Instructional orchestration (teaching presence) in online learning environment has different effects on cognitive presence, depending upon the learner’s online self-regulatory cognitions and behaviours (Shea and Bidjerano, 2012). The primary role of teaching presence is to assist the overall interaction of the learning community (Mayne and Wu, 2011). The influence of social presence and teaching presence on cognitive presence is therefore significant for a deeper and more meaningful understanding of important course concepts (Bangert, 2008). Deep learning would appear to be associated with the support of both social and teaching presence.

How often/how much students use such an online social network is a subject that, given the newness of the use of social networking sites into the classroom, is in its infancy. It is claimed that the more that students use it, however, the greater the benefit it may be to their learning (Yu et al., 2010). How often/how much students use such an online social network includes assessments of attitudinal changes in the participant, which taps the extent to which the participant is emotionally connected to the online social network and the extent to which the online social network is integrated into the participant’s daily activity (Ellison et al., 2007). More particularly, peer interaction has a considerable influence on the students’ cognitive and skill-based learning (Yu et al., 2010). Furthermore, computer-mediated communication is the element enhancing the users’ repeated access and return to a website (Lin, 2007).

Purposive value is the essential need that is widely adopted to determine the use of online social networks by individuals (Cheung and Lee, 2009). Purposive value discusses the value developed from obtaining some predetermined purposes of information and instrument. Students use available online communications tools such as email with their instructors for clarification of course policies, guidance on specific tasks such as papers and/or projects, and feedback on their performance (Waldeck et al., 2001). Young et al. (2011) state that when students use such communication with their instructor for the reasons of clarifying something, the student–instructor relationship may be developed. It is therefore important to look at how this relationship is developed via the use of an online social networking site such as Facebook. In addition to the relationship between student and instructor, how students view the quality of the content on such networking sites comes into play.

The quality of the content of a website is defined as how the user perceives the quality of information that the site provides (Lu and Lin, 2002). Lu and Lee (2010) state that experienced users or blog veterans demonstrate concerns about the quality of the content of blogs. Accordingly, the perceived quality of the content of a blog covers what the blog offers to users. Generally, users of an online social networking site seek and share many types of information of interest with one another. Online social networking sites enable a wide reach of the users’ message as well as the opportunity for personalised communication. Additionally, online social networking sites provide a space for discussions and feedback among the users. Users are also able to share and ‘like’ wall posts. Ledford and Anderson’s (2013) finding indicates that consumers, as users of online social networking sites, engage in active discussion on online social networking sites.

With specific learning goals, performance goals focus on getting a level of task proficiency or attaining a task outcome (DeShon and Gillespie, 2005; Seijts and Latham, 2005). Cognitive factors connected with students’ particular ability to process information and solve problems potentially impact students’ academic performance (Pitt et al., 2012). Yu et al. (2010) reveal that online social networking has both a direct and indirect influence on students’ performance as it relates to their ability to perform tasks and solve problems. Furthermore students’ use of online communication for the purpose of seeking clarification seems to help them to develop an interpersonal connection with their instructor, and the maintenance of a student–instructor relationship is seen to have an effect on academic success (Young et al., 2011). How students perceive the quality of the content on an online social networking site and also how they perceive the standard of the interaction with both their instructor and their peers are said to influence how well students perform in the course. Junco et al. (2010) find that encouraging the use of Twitter for educational purposes has a positive effect on students’ grades. However, a recent study concludes that the use of Facebook negatively affects grade point average (Kirschner and Karpinski, 2010). Paul et al. (2012) reveal that there is a negative relationship between time spent by students on online social networking and their academic performance owing to a lack of desire or willingness to use online social networking for learning purposes (Dahlstrom et al., 2011).

When students use such communication with their instructor for the reasons of clarifying something in the online social networking space/environment, this is taken to mean students’ questions to instructors on Facebook regarding lecture material, assignment guidance, course content and examinations as well as on the content that has been posted by students. This allows students to discuss course content, pose questions to and build relationships with their instructors. It is argued that the effectiveness of building such relationships is an integral part of the students’ learning process as well as affecting how they perceive their performance in the online social networking site. This can be achieved by incorporating the use of Facebook in the course requirements as a means of communication among the students and between students and instructors – something that has been researched but without incorporating the use of out-of-class communication in the course requirements (Terenzini et al., 1999). Although much of the out-of-class communication research has examined the extent to which student–instructor interaction fosters student learning, few have looked at the out-of-class communication between the instructor and student in the online social networking environment. The research described in this article investigates this important question.

There is a need to investigate the link, if any, between how often/how much students use such an online social network and whether or not there is a positive or negative influence of this on their perceptions of the quality of the social networking site itself. This is important given the impact of online social networking on their learning. How students perceive the quality of the content of student–instructor interaction on Facebook is treated as a mediating variable in the research model described in this article. How students perceive their performance is used as a proxy for their learning outcomes. It is also important to investigate the quality of the content and information exchanged among the users of the online social networking site, in this case, instructors and students and/or students and students. The effectiveness of the interaction between the students and the instructors should be reflected by the richness of the answers to questions posted on an online social networking site. It is to be expected that it is not how extensive the interaction on the online social networking site which might have positive effects on the students’ learning and outcomes but instead the quality of the responses from both the instructors and/or peers.

The study described in this article investigates whether the impact of the online social network, which includes how often/how much students use it and the reasons that they seek clarification for online communication between the instructor and student, influences the students’ perceptions of the quality of the content of the online social network when it comes to their learning. Additionally, the study will investigate how the mediating role of how the students perceive the quality of the content impacts the nature of the relationships in the online social network used, Facebook in this research, and the students’ perceptions of their performance. Based on the above, the following three hypotheses are put forward and are shown in the model in Figure 1:

Hypothesis 1. How often/how much students use the online social network is positively related to their perceptions of the quality of the content of the online social network when it comes to their learning.

Hypothesis 2. The extent of clarifications sought by students from their instructor in the social networking online learning environment is positively related to how they perceive the quality of the content of the online social network when it comes to their learning.

Hypothesis 3. How students perceive the quality of the content of the online social network when it comes to their learning has a positive influence on their perceived performance.

Figure 1.

Hypotheses and research model.

Methodology

Research design

The experiment was conducted on an introductory first semester Business Computing course at RMIT University. The fact that the students are in their first semester of tertiary education makes their learning process a challenging experience especially because they are used to rote learning, something which characterises most of the learning experiences in South-East Asia. Business Computing is a course which comprises 3.5 contact hours per week over 12 weeks (a 1.5-hour lecture and a 2-hour workshop per week).

Facebook was chosen as the out-of-class communication online social networking tool. A Facebook account is created for each student and groups are set by the instructors. The instructors also created the content outline which was used throughout the semester as a guide. Once the groups were set up, the link was provided for the student to join.

Facebook activity

Facebook was chosen as a platform for communication and interaction between instructor and students as well as among students to improve the overall learning experience and to increase the students’ engagement outside the classroom. Students were not allowed to use Facebook during the lectures and the workshops. Instead the tool was used as a means for students to get the extra support they needed outside the classroom. Only content and questions were posted on Facebook for the student to use as resources. A scenario of the students’ use of the tool can be described as follows. Every week at the start of each workshop, instructors spent approximately 30 minutes discussing ‘review questions’ which were available to students pertaining to the concept which they had covered in that week. The question took the form of two or three short answer questions which were designed to test the students’ critical thinking abilities regarding the materials covered and their abilities to apply them in hypothetical situations. In order to support students in this, the review questions were posted on the Facebook page in which students had the ability to comment on and form a discussion online.

Students tended to use the tool as a place to ask questions. They were likely to discuss diverging views which hopefully led to a greater understanding before the classroom schedule. Asking questions on Facebook became one of Facebook’s primary uses, and the answers came from other students or instructors.

Instructor and student involvement

Eight staff members were involved in the course. Three of them were involved on a daily basis to provide support for the students. They answer questions, post content, post questions or polls and engage in discussions with the students. One of the staff members was in charge of posting content as described in the social media roadmap. This was done to ensure a steady flow of content to the platform. The other staff members participated daily on an ad-hoc basis to post content and field questions.

A total of 417 students were enrolled in the Business Computing course. The Facebook page was made available to the students as an option, and 242 of them signed as members. This number represents 58% of the student population. Our monitoring of the use of the tool was passive most of the time and only a small percentage of the students actively contributed to discussions and answered questions. Posts were observed by the majority of members, as Facebook has the facility to display the number of people who has seen a post. The exception to this was polls which allowed students to quickly select an answer; these posts tended to get a higher rate of participation from the students.

Data collection

A survey approach was adopted and data collection was carried out between weeks 6 and 9 of the semester in 2013. Shepard (1996) states that student interaction with their instructors tend to dramatically increase as the assessment date nears, and in this course there was a midterm examination period and an assignment submission date in week 6 in the semester. An anonymous online survey questionnaire invitation message was sent to the 242 students who signed into Facebook and who had prior experience in using the Business Computing Facebook page. A total of 136 students completed the online survey, yielding a response rate of 56%. Students were encouraged to participate in the survey voluntarily with no expected rewards.

To minimise the common method bias influencing the research results, different scales were applied for the predictors and criterion measures (Podsakoff et al., 2003). A 5-point Likert scale was used to measure the exogenous variables and mediating variable, whereas a 7-point Likert scale was employed to measure the endogenous variable.

Measures

How often/how much students use the online social network and the extent of clarifications sought by students from their instructor in the social networking online learning environment were treated as two exogenous variables (Ellison et al., 2007; Steinfield et al., 2008; Yu et al., 2010). Five items were used to measure how often/how much students use the online social network. Accordingly, a five-item questionnaire was created to measure the extent to which the students were emotionally connected to Facebook and the extent to which Facebook was integrated into their daily activities.

In order to measure the extent of clarifications sought by students in their online communication, a four-item questionnaire was adopted from existing research (Waldeck et al., 2001; Young et al., 2011). In order to measure the perceived quality of the content of the online social network and how often/how much students use it, a three-item questionnaire was created using research by Lu and Lin (2002) and was further validated by Lu and Lee (2010) in order to measure blog stickiness. The perceived quality of the content of online social networking intensity was treated as a mediating variable. The students’ perceptions of the quality of the content cover what the up-to-date post offers to them and how they think of it.

We treated the students’ perceptions of their performance/proficiency as an endogenous variable which included a four-item questionnaire based on Chao et al. (1994) and was later tested by Yu et al. (2010). This construct measures the students’ perceived outcomes of task performance and problem solving after learning in interaction on Facebook.

Methods

The research adopted two analysis models, namely, the measurement model and the structural model. The former specifies the relationships between the observed indicators and the latent variables; the latter specifies the relationships among the latent variables. Structural equation modelling (SEM), a statistical technique which combines complex path analysis with latent variables (factors), was used to verify the proposed model. In addition, the AMOS 18 software package was applied for empirical results.

Results

The convergent validity and discriminant validity of measured variables which represent the theoretical latent construct of the data were tested to assess the measurement model for reflective constructs (Hulland, 1999). The convergent validity was tested by examining the composite reliability (CR) and the average variance extracted (AVE) from the measures (Hair et al., 1998).

The CR scores of the reflective constructs shown in Table 1 exceed the threshold of 0.70 (Nunnally, 1978). The AVE values (in Table 1) exceed the recommended cut-off value of 0.50 (Fornell and Bookstein, 1982). Taken together, the evidence provides initial support for the convergent validity of the construct measurement.

Table 1.

Assessment of convergent validity.

	Online social network intensity	Clarification reason	Perceived content quality	Perceived performance proficiency
Construct reliability (CR)	0.86	0.92	0.90	0.85
Average variance extracted (AVE)	0.65	0.79	0.80	0.68

The discriminant validity was tested by comparing the square roots of the AVE for each factor within the inter-construct correlations associated with that factor. Table 2 shows the discriminant validity analysis. All square roots of the AVE scores in the table are greater than the corresponding inter-construct correlation estimates. This means that the indicators have more in common with the construct they are associated with than they do with other constructs, demonstrating discriminant validity (Fornell, 1987).

Table 2.

Assessment of discriminant validity.

Variable	1	2	3	4
1. Online social network intensity	0.807
2. Clarification reason	0.432	0.890
3. Perceived content quality	0.377	0.396	0.894
5. Perceived performance proficiency	−0.276	−0.252	−0.287	0.824

Bold values on the diagonal are square roots of average variance extracted (AVE) value of constructs.

Table 3 presents the overall fit measures of the SEM model. The most widely used goodness-of-fit indices include Chi-square/df ratio (χ²/df), root mean square error of approximation (RMSEA), standardised root mean square residual (SRMR), incremental fit index (IFI), Tucker–Lewis index (TLI) and comparative fit index (CFI). Chi-square/df ratio should be less than 2 (Byrne, 1989). Values of RMSEA and SRMR should be less than the threshold value of 0.06 and 0.07, respectively (Yu, 2002). IFI, TLI and CFI should exceed 0.95 (Hu and Bentler, 1999), 0.96 and 0.96 (Yu, 2002), respectively. As shown in Table 3, the fitness measures are all within acceptable range, showing that the proposed researched model is supported by the data.

Table 3.

Fitness indices for the full model.

Measures	Recommended criteria	Results	Supporting literature
χ²/df	<2	1.50	Byrne (1989)
RMSEA	<0.06	0.06	Yu (2002)
SRMR	<0.07	0.05	Yu (2002)
IFI	>0.95	0.97	Hu and Bentler (1999)
TLI	>0.96	0.97	Yu (2002)
CFI	>0.96	0.97	Yu (2002)

RMSEA: root mean square error of approximation; SRMR: standardised root mean square residual; IFI: incremental fit index; TLI: Tucker–Lewis index; CFI: comparative fit index.

The analysis of the structural model explores the direction and significance of causal relationships between latent variables (Lou et al., 2000). As shown in Figure 2, how often/how much students use the online social network is positively related to their perceptions of the quality of the content of the online social network when it comes to their learning. This demonstrates that the quality of the content on the online social network is integral to the students’ learning (H1: β = 0.310, p = 0.001).

Figure 2.

Results of the structural model.

The results also demonstrate that the reason given as to why students seek clarification from their instructor in the social networking online learning environment is positively related to how they perceive the quality of the content of the online social network when it comes to their learning (H2: β = 0.439, p < 0.001).

However, contrary to the assumption, the results demonstrate that how students perceive the quality of the content of the online social network when it comes to their learning has a negative rather than a positive influence on their perceived performance (H3: β = −0.332, p < 0.001).

Discussion and conclusion

Online social media networking sites such as Twitter and Facebook play a vital role not only in terms of how university students learn within the classroom but also outside the classroom (Hwang et al., 2004; Morrow, 1999; Steinfield et al., 2008; Yu et al., 2010). In computer-mediated out-of-class communication, when students use email to communicate with the instructor in order to seek clarification, it is believed that this correspondence improves their predicted outcomes (Young et al., 2011). Regarding how students perceive the quality of the content of the online social network when it comes to their learning, although the study of Lu and Lee (2010) focused on the antecedents of blog stickiness, little was known about the quality of the content and how this impacts how students interact with their instructor and their peers. The results of the research described earlier in this article demonstrate strong evidence that their perceptions of the quality of the content of the online social network when it comes to their learning greatly impact how often/how much students use the online social network and also why students seek clarification from their instructor in the social networking online learning environment. Students perceived the quality of the content of the online social network as being an important element in making them interact and discuss learning materials on Facebook as suggested by Lu and Lee (2010) who state that users of a blog care about the quality of the content.

However, the results demonstrate that the students’ perceptions of the quality or otherwise of the content have a negative impact on how they perceive their performance or proficiency. The distraction of using Facebook for the purposes of learning (rather than for socialising or other non-study-related purposes) might be caused by the effect of an adversarial network which is negatively related to performance (Baldwin et al., 1997; Sparrowe et al., 2001). In addition, out of the majority of the 242 students in the study, only a few played an active role in initiating posts; most posts were passively seen by the majority of members (as was evidenced by Facebook’s ‘Seen By’ function). These results are in accordance with similar findings which demonstrate that a network formed on the basis of friendship is not considered to influence student performance (Yang and Tang, 2003). Others stress that the use of an online social network for the purpose of learning rather than socialising requires a lot of attention and desire (Dahlstrom et al., 2011; Paul et al., 2012) as well as an appropriate social network configuration (Morrison, 2002).

There are limitations to the study. First, this study does not establish how Facebook can be used to improve students’ learning outcomes. Second, the results did not demonstrate how the students’ perceptions of the quality of the content of the online social network and of their performance relate to any changes in either how they carry out their learning or to the outcomes of that learning. Third, this study was conducted with students from the same culture and social structure. Accordingly, different results may be achieved if the same study is carried out with students from different cultures and social structures. Fourth, a small percentage of the students from the sample actively contributed to discussions and answered questions. Fifth, results may be different with the use of online social networks that differ from those used in this study.

Social network services are bound to evolve and play a major role in education and are fast becoming an effective tool for learning. Current research supports this as using the Internet (Cotten, 2008; Gordon et al., 2007; Morgan and Cotten, 2003) and Facebook (Ellison et al., 2011) in certain ways has made their contribution to better psychosocial outcomes and using Twitter has resulted in better academic outcomes (Junco et al., 2010). Junco (2011) suggests that using social networking via tools such as Facebook and Twitter can positively be used in ways that are advantageous to students. Therefore, further research would investigate developing a comprehensive teaching design and supportive intervention that help students use online social networks for learning and to enhance their academic outcomes. Future research needs to identify some latent variables related to the psychological and sociological aspects given how important these are. This direction in research is also supported by Yu et al. (2010). In addition, further research is needed which entails looking at the most widely used social networks in order to find out which could be more suitable in different learning environments (learning environments vary) or when students are asked to do different tasks. Finally, research on identifying the best effective collaborative tool for interaction among group members indicates that the problems with available support for collaboration are in the diverse nature of the tools available in terms of what they claim to support and the quality of that support and that as a result organisations should build their own group support systems (El-Den, 2010). It is proposed here that tertiary educational institutions should invest in developing and/or outsourcing online social networking–like tools/group support systems which aim to help students in the online learning environment.

The results of the study described in this article highlight the implications for practice. Recent studies have emphasised the important role of social networking sites in sharing interests and knowledge. Online social networking, one of several forms of computer-mediated communication, might help individuals in reducing barriers to interaction and promoting self-exposure (Bargh et al. 2002; Tidwell and Walther, 2002). In addition, an online social network (e.g. Facebook) has been found to be able to provide greater benefits to those who exhibit low self-esteem (Ellison et al., 2007). However, there is a lack of study into what the impact of the content quality is and how it influences students’ learning outcomes from a pedagogical perspective. From a teaching perspective, online social networking directly impacts learning and social network sites have greatly extended the influence on different angles of student life in the era of web 2.0 technology. Feenberg (2003) suggests that a productive way of elaborating technology is as human controlled and value-laden.

Additionally, the quality of content needs to be improved. Seen from the viewpoint of education, the quality of the content is essential for students. But there is a difference, perhaps, between using online networking sites for leisure and learning, and so more needs to be done to better understand how to use such sites whether within or outside the classroom when it comes to learning. In addition, if there is complex information to be conveyed or understood, instructors need to work out whether or not it is better to do this through face-to-face interaction rather than via an online social networking site or any other technology because clarification of subtle matters is often clearer and easier via the former (Waldeck et al., 2001).

Online social networking already plays an integral role in education at the tertiary level. Students’ expertise in using online social networking in their social lives could certainly come into play when using this for the purposes of learning. Online social networking has deeply penetrated social life including university campuses and all other aspects of student life. Crook et al. (2008) point out that instructors should consider creative involvement that adopts a web 2.0 mentality. In other words, it is important to focus more on students’ engagement and outcomes that may be explained by the adoption of these technologies rather than the technology itself.

The explicit social network of the instructor, as well as the appropriate design of the social network itself, can help students to learn in the social network (Maglajlic and Gütl, 2012). In addition, course interfaces and course design usually mean that students have to use and make sense of a great deal of information, and so it is important that instructors and others design them well (Swan, 2003). Whether or not the use of tools such as Facebook and Twitter should be made obligatory either within or outside the classroom is something that each instructor will need to weigh up carefully, as there are both plus points and negatives associated with this. Learning through online discussion relies on the frequency, timeliness and nature of the messages posted (Hawisher and Pemberton, 1997; Shea et al., 2001; Wiener and Mehrabian, 1968), and so for those who choose to use such online networking sites thought needs to be given as to what might stimulate students to provide appropriate feedback and to engage with the subject matter (Yang and Tang, 2003).

Footnotes

Appendix 1 Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Author biographies

Mathews Nkhoma is Head of Department of Business Information Technology and Logistics at RMIT Vietnam. His major topics of research are as follows: zero day attacks and vulnerability research; information systems security; transparency in information security system design; network security investment model; ethical hacking and network defence; network security management; forensic computing and evidence recovery including mobile devices; cybercrime; identity theft, consumer protection, trust and confidence; and impact of ICT in education. Address: Department of Business IT and Logistics, RMIT Vietnam, 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam. [email: mathews.nkhoma@rmit.edu.vn]

Hiep Pham Cong is a Senior Lecturer at RMIT Vietnam. He has completed a Master’s degree in advanced information systems at University of New South Wales and an MBA (Executive) at RMIT Vietnam. He is actively conducting research in information management and information systems security management. Address: Department of Business IT and Logistics, RMIT Vietnam, 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam. [email: hiep.pham@rmit.edu.vn]

Bill Au is currently a Lecturer at RMIT Vietnam and has worked for a number of digital consultancies. His areas of research lie in e-learning and impact of social media in education. Address: Department of Business IT and Logistics, RMIT Vietnam, 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam. [email: bill.au@rmit.edu.vn]

Tri Lam, a research officer of business and management, works with undergraduate and postgraduate lecturers. His research interests are divided between learning and teaching in higher and further education and taking a critical approach to futures and policy studies. Address: Department of Business IT and Logistics, RMIT Vietnam, 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam. [email: tri.lam@rmit.edu.vn]

Joan Richardson is Deputy Head, Learning and Teaching. She has extensive teaching experience in the area of information systems, and her research focus is on the development of new technologies in business. Address: School of Business IT and Logistics, RMIT Australia, GPO Box 2476, Melbourne, VIC 3001, Australia. [email: joan.richardson@rmit.edu.vu]

Ross Smith is a Professor in the School of Business Information Technology, RMIT University, Melbourne, Australia. Ross has researched and published widely in systems methodologies, requirements engineering and knowledge management. Much of this research has sought to apply and further develop systems thinking to tackle the sort of messy, ill-structured problems which real life presents to managers, of all kinds and at all levels. Address: School of Business IT and Logistics, RMIT Australia, GPO Box 2476, Melbourne, VIC 3001, Australia. [email: ross.smith@rmit.edu.au]

Jamal El-Den is a Senior Research Fellow at the School of Engineering and IT, Charles Darwin University, Australia. His research interests are in Knowledge Management, Knowledge Transformation and Group-support Systems. Recently, his research focuses on the application of knowledge management on different areas of business, such as supply chain management, security, process development and business-process reengineering. Currently, he is on sabbatical leave at RMIT Vietnam. Address: School of Engineering and IT, Charles Darwin University, Ellengowan Drive, Darwin, NT 0909, Australia. [email: jamal.el-den@cdu.edu.au]

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