International Journal of Psychology, 2015 DOI: 10.1002/ijop.12159
Students’ online collaborative intention for group projects: Evidence from an extended version of the theory of planned behaviour Eddie W. L. Cheng1 and Samuel K. W. Chu2 1
Department of Social Sciences, The Hong Kong Institute of Education, Tai Po, N.T., Hong Kong of Education, Division of Information and Technology Studies, The University of Hong Kong, Pokfulam, Hong Kong
2 Faculty
G
iven the increasing use of web technology for teaching and learning, this study developed and examined an extended version of the theory of planned behaviour (TPB) model, which explained students’ intention to collaborate online for their group projects. Results indicated that past experience predicted the three antecedents of intention, while past behaviour was predictive of subjective norm and perceived behavioural control. Moreover, the three antecedents (attitude towards e-collaboration, subjective norm and perceived behavioural control) were found to significantly predict e-collaborative intention. This study explored the use of the “remember” type of awareness (i.e. past experience) and evaluated the value of the “know” type of awareness (i.e. past behaviour) in the TPB model. Keywords: The theory of planned behaviour; E-collaborative intention; Past experience; Past behaviour.
Group projects provide a mean for students to work together to construct knowledge through social networking (Hung & Yuen, 2010). Other than using face-to-face meetings, students can work collaboratively online to conduct their group work. Online collaboration (or e-collaboration) allows students to not only share their information and views, but also do their projects from anywhere and at any time. As group work involves a social cognitive process that governs choices of action, it is crucial to identify what factors affect students’ intention to work together via Internet (i.e. students’ e-collaborative intention). This research therefore aims at examining an extended version of the theory of planned behaviour (TPB) in explaining students’ intention to collaborate online for their group projects. In the original TPB, three antecedents are proposed to predict behavioural intention: (a) attitude towards the behaviour, (b) subjective norm and (c) perceived behavioural control (Ajzen, 1991). The theory has been widely used to study behavioural intention across various
disciplines (e.g. Baker, Al-Gahtani, & Hubona, 2007; d’Astous, Colbert, & Montpetit, 2005), including college students’ mobile learning intention (Cheon, Lee, Crooks, & Song, 2012). In this research, attitude towards e-collaboration is defined as the degree to which a student has a positive or negative feeling about working online together. Subjective norm refers to a student’s perception of the important others’ opinions. Perceived behavioural control is a student’s perceived ease or difficulty when involving e-collaboration. According to Ajzen (1991, 2014), a feedback loop that forms from prior behaviour to present cognitions is hidden in the TPB model. Previous studies have supported the effect of earlier behaviour on the three antecedents of behavioural intention (e.g. Carr & Sequeira, 2007; d’Astous et al., 2005). Therefore, in this study, the extended version of the TPB model adds two distal variables, which are past behaviour and past experience. They are what Ochsner (2000) refers
Correspondence should be addressed to Eddie W. L. Cheng, Department of Social Sciences, The Hong Kong Institute of Education, 10 Lo Ping Road, Tai Po, N.T., Hong Kong. (E-mail: [email protected]). The first author has been involved in literature review, model design, questionnaire design, data collection and analysis and draft of the article. The second author has been involved in part of the literature review, model design, questionnaire design and revising the draft. This research was financially supported by The Hong Kong Institute of Education with grant numbers 03571 and R6403. The authors would like to thank the editor and the anonymous reviewers for their constructive comments on earlier versions of the article.
© 2015 International Union of Psychological Science
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CHENG AND CHU
to as two distinct types of awareness (i.e. know and remember). Past behaviour refers to the “know” type of awareness, in which students express whether they are familiar with online collaboration. On the other hand, past experience refers to the “remember” type of awareness, which brings to students’ mind about the details of their behaviour routines, such as information sharing, online discussion, and so on. As Taylor and Todd (1995) mentioned, previous experience may make knowledge and skills easily accessible in memory so that experience users may perceive IT usage to be more positive than inexperienced users. Moreover, when students conduct group projects via Internet, group mates with online experience may provide those without experience with more support and encouragement. Through such a socialisation process within group (Minocha & Thomas, 2007), students’ intention to participate in e-collaboration would improve. Past behaviour is commonly measured by the frequency of performing the behaviour, such as whether one has ever owned a business (Carr & Sequeira, 2007), how often music downloading applications had been used (d’Astous et al., 2005), and so on. It is usually employed as the single measure of prior behaviour in the extant literature. In this research, past experience is also included as Ochsner’s (2000) typology is adopted. Past experience is seldom used to study behavioural intention. The inclusion of this specific measure may disclose a more holistic view of the role of prior behaviour in the deliberative reasoning process. As noted by Jacoby, Yonelinas, and Jennings (1997), the term familiarity, which gives rise to the sense of pastness through the automatic process, is different from the experience that involves recollection of specific details. However, Ochsner (2000) supplemented that the “know” and “remember” types of awareness (or the so called behaviour familiarity and recollection processes respectively) are not necessary to be mutually exclusive, but are two separate processes of recognition memory. In other words, they are conceptualised independently and form two distinct variables. As shown in Figure 1, the theoretical model proposes that past behaviour and past experience are predictors of attitude towards e-collaboration, subjective norm and perceived behavioural control, which in turn affect e-collaborative intention. All relationships are proposed to be positive.
Attitude toward e-collaboration
Past experience
Subjective
E-collaborative
norm
intention
Past Perceived
behavior
behavioral control
Figure 1. The hypothesised model.
questionnaire. Students, who studied for their bachelor degree in one higher education institution in Hong Kong, were invited to take part in this research. They must have experience in conducting group projects and gave answers based on a recently completed group project. A survey pack (a cover letter and the questionnaire) was used. Follow-ups were administered to secure a higher response rate. As a result, 230 responses were received. An analysis of the demographic profile of the respondents reveals that approximately 25% were males (n = 57), while 75% were females (n = 173). They were aged from 18 to 30 (mean = 21). Respondents were asked about the number of group projects they completed in last semester. The results were quite diverse. Most students had around 2–6 projects (n = 156), while the mean was around 3.8. To test whether gender, age and the number of group projects, acting as extraneous variables, have an impact on the dependent variables (Reynolds, Simintiras, & Diamantopoulos, 2003), t-statistic and correlation tests were performed. The results indicate that they were independent of the latent variables, except for the relationship between age and perceived behavioural control, which was just modest (ρ = .13, p < .05). Therefore, they were not included in further analyses. Measures The latent variables were measured using multi-item scales, which, except for past experience, were developed based on Baker et al. (2007), Carr and Sequeira (2007) and Cheon et al. (2012), whose original measures have acceptable reliability.
METHOD Attitude towards e-collaboration Participants and procedure The research was conducted quantitatively. A paper-and-pencil survey was administered and a self-report questionnaire was designed. A pilot test was launched for the purpose of revising the measures and the
It was measured using four items with a 7-point bipolar adjective scale. A sample item is: I feel that working collaboratively via Internet with group mates for the group project is “extremely unhelpful (1) ↔ extremely helpful (7).” Subjective norm was measured using four © 2015 International Union of Psychological Science
STUDENTS’ ONLINE COLLABORATIVE INTENTION
3
TABLE 1 Medians, means, standard deviations and bivariate correlations
1. Attitude towards e-collaboration 2. Subjective norm 3. Perceived behavioural control 4. E-collaborative intention 5. Past experience 6. Past behaviour
Median
Mean
SD
1
2
3
4
5
5.00 5.00 5.50 5.33 5.50 5.67
5.08 4.97 5.44 5.16 5.35 5.49
0.92 0.89 0.97 0.96 0.90 1.08
0.49** 0.47** 0.72** 0.55** 0.29**
0.55** 0.49** 0.38** 0.25**
0.48** 0.36** 0.46**
0.67** 0.30**
0.35**
Note: Spearman bivariate correlation (ρ). SD = Standard deviation. * p < .05. ** p < .01.
items with a 7-point Likert scale from strongly disagree (1) to strongly agree (7). A sample item is: If those people (who are important to me) think that I should work with group mates via Internet for the group project, I would do it. Perceived behavioural control was measured using two items with a 7-point Likert scale from strongly disagree (1) to strongly agree (7). A sample item is: I believe that I am able to work with group mates via Internet for the group project. E-collaborative intention was measured using three items with a 7-point scale from extremely improbable (1) to extremely probable (7). A sample item is: I intend to work with group mates via Internet for the group project. Past behaviour was measured using three items where subjects were asked to rate based on a 7-point Likert scale from “strongly disagree (1)” to “strongly agree (7).” A sample item is: Prior to this group project, I did not conduct group projects via Internet (reverse-scored). Past experience was measured with six items where subjects were asked to rate based on a 7-point Likert scale from “strongly disagree (1)” to “strongly agree (7)”. A sample item is: I shared information with group mates via Internet for the group project. Data analysis The partial least squares (PLS) approach to structural equation modelling (SEM) was used to examine both the measurement and structural models. It is a non-parametric method and is appropriate for situations where multivariate normality of data is violated (Hair, Hult, Ringle, & Sarstedt, 2014; Urbach & Ahlemann, 2010). The software tool used for PLS-SEM was WarpPLS. Bootstrap samples were set at 100 as suggested by Kock (2011, p. 6), who argued that “higher numbers of re-samples lead to negligible improvements in the reliability of P values.” RESULTS Table 1 not only presents the median, mean and standard deviation of each latent variable, but also Spearman rank © 2015 International Union of Psychological Science
TABLE 2 Indicators’ outer loadings and cross loadings
Past behaviour Past experience Attitude towards e-collaboration Subjective norm Perceived behavioural control E-collaborative intention
Outer loadings
Cross loadings
0.79 ↔ 0.81 0.65 ↔ 0.81 0.86 ↔ 0.90 0.83 ↔ 0.87 0.74 ↔ 0.85 0.84 ↔ 0.84
0.01 ↔ 0.52 0.19 ↔ 0.61 0.13 ↔ 0.64 0.13 ↔ 0.64 0.11 ↔ 0.57 0.14 ↔ 0.50
correlations, which indicate that the six latent variables were all significantly correlated. As shown in Table 2, each indicator’s outer loading on the respective latent variable was higher than its cross loadings on other latent variables. Moreover, the results shown in Table 3 indicate that for each latent variable, the average variance extracted (AVE) was much higher than the highest squared correlation (HSC) of that latent variable with other latent variables. Thus, the above tests provide evidence for the constructs’ discriminant validity. In addition, the AVE values of the latent variables were in the range between 0.53 and 0.76, which were higher than the threshold of 0.50 (Hair et al., 2014), representing acceptable levels of convergent validity. The composite reliability of the scales ranged from 0.83 to 0.91, which show that the measurement model presents acceptable reliability. As shown in Table 4, the variance inflation factor (VIF) of the predictor variables ranged from 1.12 to 1.60 (within the acceptable range between 0.20 and 5), showing that collinearity is not a problem in the structural model. Moreover, for assessing model fit, three indices were used, which were average path coefficient (APC), average R-squared (ARS) and average variance inflation factor (AVIF) (Kock, 2011). The values for both APC and ARS should be under 2 and statistically significant at the 0.05 level, while the value for AVIF should be lower than 5 (or ideally below 3.3) (Berglund, Lytsy, & Westerling, 2013; Kock, 2011). The structural model demonstrated a good fit to the data, with APC = 0.29 (p < 0.01), ARS = 0.33 (p < 0.01) and AVIF = 1.3 (which also showed low overall collinearity).
4
CHENG AND CHU TABLE 3 Results summary for reflective measurement model testing
Past behaviour Past experience Attitude towards e-collaboration Subjective norm Perceived behavioural control E-collaborative intention
AVE
Composite reliability
Cronbach’s alpha
HSC
0.64 0.53 0.72 0.61 0.71 0.76
0.84 0.87 0.91 0.86 0.83 0.91
0.72 0.82 0.87 0.79 0.59 0.84
0.19 0.43 0.52 0.29 0.29 0.52
Note: AVE = average variance extracted; HSC = highest squared correlation of a latent variable with any other latent variable. TABLE 4 PLS-SEM results Structural model
β
DV: e-collaborative intention Attitude towards e-collaboration Subjective norm Perceived behavioural control DV: attitude towards e-collaboration Past experience Past behaviour DV: subjective norm Past experience Past behaviour DV: perceived behavioural control Past experience Past behaviour
R2
Adjusted R2
0.57**
0.56**
0.63** 0.13* 0.09*
1.43 1.59 1.60 0.33**
0.33**
0.55** 0.06
1.16 1.16 0.16**
0.15**
0.32** 0.15**
1.16 1.16 0.28**
0.29** 0.35**
VIF
0.27** 1.12 1.12
Note: β = beta coefficient; DV = dependent variable; PLS = partial least squares; R = coefficient of determination; SEM = structural equation modelling; VIF = variance inflation factor. * p < .05. ** p < .01.
DISCUSSION This research aims at identifying what factors affect students’ e-collaborative intention. For this aim, a hypothesised model was developed and examined. Overall, the results indicate that the dependent variables were significantly explained by the respective independent variables. The theory is helpful to explain students’ intention to work online collaboratively for their group projects. It is worth mentioning that past experience was found to predict attitude towards e-collaboration, subjective norm and perceived behavioural control significantly, while past behaviour significantly predicted subjective norm and perceived behavioural control except for attitude towards e-collaboration. Moreover, the three antecedents (attitude towards e-collaboration, subjective norm and perceived behavioural control) were found to be significantly related to e-collaborative intention. Referring to the magnitudes of the relationships, past experience appears to be more valid than past behaviour in explaining the relationships. Past experience explains more variance of attitude towards e-collaboration and subjective norm than past behaviour, except for the opposite finding for perceived behavioural control. As said
before, past behaviour and past experience are two different senses of pastness. They were described as “frequency” and “recollection” of behaviour, respectively. Previous research has found that past behaviour contributes to the prediction of attitude, subjective norm and perceived behavioural control (e.g. Carr & Sequeira, 2007; d’Astous et al., 2005). In contrast, past experience is rarely defined operationally. This research confirms its influential role in a TPB model. As noted by Ouellette and Wood (1998), past behaviour (as well as past experience) is likely to be controlled by deliberative reasoning processes such that past behaviour could not directly predict later behaviour. However, it is important to recollect what has been done so that similar behaviour can be performed in the future when students are willing to do so. Students’ reflections on their group projects (including their use of web technology and their online collaboration) are crucial to improve their memory of how they behave previously (Storch, 2005). Moreover, through repetitive and continued practices, a non-habitual behaviour becomes routinised and appears to function independent of intention. It is because conscious awareness would diminish when the behaviour is increasingly performed. Yet, such behaviour must be “well-practiced and performed in © 2015 International Union of Psychological Science
STUDENTS’ ONLINE COLLABORATIVE INTENTION
stable contexts” (Ouellette & Wood, 1998, p. 65). In group projects, the contexts vary in terms of group mates, teachers, the project nature, difficulty levels, and so on, thus the model that specifies past behaviour and past experience as precursors of the antecedents of intention has been empirically supported in this research. Limitations could not be avoided, especially in exploratory studies. First, social desirability is considered in self-report questionnaires. Owing to limited resources, it was not possible to have access to the respondents with more than one time. However, this research attempted to reduce some intervenes due to social desirability biases by assuring the anonymity of respondents, random order of the items of the latent variables, use of reverse-scored items, and so on. Second, to address non-response bias, this research has taken effective steps, including careful questionnaire design, explanation of the research, assurance of anonymity and reminder letters, to improve the response rate (Rogelberg & Stanton, 2007). For testing non-response bias, this study followed the suggestion by Rogelberg and Stanton (2007) to compare those who responded to certain demographic questions with those who did not (acting as a proxy for non-respondents). The t-test results indicate that there was no significant difference between the two groups on the six latent constructs, thus non-response bias was trivial in this research. Manuscript received August 2014 Revised manuscript accepted January 2015
REFERENCES Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50, 179–211. Ajzen, I. (2014). The theory of planned behaviour is alive and well, and not ready to retire: A commentary on Sniehotta, Presseau, and Araújo-Soares. Health Psychology Review, . Retrieved from 10.1080/17437199.2014.883474.. Baker, E. W., Al-Gahtani, S. S., & Hubona, G. S. (2007). The effects of gender and age on new technology implementation in a developing country: Testing the theory of planned behaviour (TPB). Information Technology & People, 20(4), 352–375. Berglund, E., Lytsy, P., & Westerling, R. (2013). Adherence to and beliefs in lipid-lowering medical treatments: A structural equation modelling approach including the necessity-concern framework. Patient Education and Counseling, 91, 105–112. Carr, J. C., & Sequeira, J. M. (2007). Prior family business exposure as intergenerational influence and entrepreneurial
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intent: A theory of planned behaviour approach. Journal of Business Research, 60, 1090–1098. Cheon, J., Lee, S., Crooks, S., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behaviour. Computers & Education, 59(3), 1054–1064. d’Astous, A., Colbert, F., & Montpetit, D. (2005). Music piracy on the web—How effective are anti-piracy arguments? Evidence from the theory of planned behaviour. Journal of Consumer Policy, 28, 289–310. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modelling (PLS-SEM). Thousand Oaks, CA: SAGE. Hung, H.-T., & Yuen, S. C.-Y. (2010). Educational use of social networking technology in higher education. Teaching in Higher Education, 15(6), 703–714. Jacoby, I. L., Yonelinas, A. P., & Jennings, J. M. (1997). The relation between conscious and unconscious (automatic) influences: A declaration of independencies. In J. D. Cohen & J. W. Schooler (Eds.), Scientific approaches to consciousness. Carnegie Mellon Symposia on cognition (pp. 13–47). Hillsdale, NJ: Erlbaum. Kock, N. (2011). Using WarpPLS in e-collaboration studies: Descriptive statistics, settings, and key analysis results. International Journal of e-Collaboration (IJeC), 7(2), 1–18. Minocha, S., & Thomas, P. (2007). Collaborative learning in a wiki environment: Experiences from a software engineering course. New Review of Hypermedia and Multimedia, 13(2), 187–209. Ochsner, K. N. (2000). Are affective events richly recollected or simply familiar? The experience and process of recognising feelings past. Journal of Experimental Psychology: General, 129(2), 242–261. Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behaviour predicts future behaviour. Psychological Bulletin, 124(1), 54–74. Reynolds, N. L., Simintiras, A. C., & Diamantopoulos, A. (2003). Theoretical justification of sampling choices in international marketing research: Key issues and guidelines for researchers. Journal of International Business Studies, 34, 80–89. Rogelberg, S. G., & Stanton, J. M. (2007). Understanding and dealing with organizational survey nonresponse. Organizational Research Methods, 10, 195–209. Storch, N. (2005). Collaborative writing: Product, process, and students’ reflections. Journal of Second Language Writing, 14, 153–173. Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561–570. Urbach, N., & Ahlemann, F. (2010). Structural equation modelling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40.
Students’ online collaborative intention for group projects: Evidence from an extended version of the theory of planned behaviour Eddie W. L. Cheng1 and Samuel K. W. Chu2 1
Department of Social Sciences, The Hong Kong Institute of Education, Tai Po, N.T., Hong Kong of Education, Division of Information and Technology Studies, The University of Hong Kong, Pokfulam, Hong Kong
2 Faculty
G
iven the increasing use of web technology for teaching and learning, this study developed and examined an extended version of the theory of planned behaviour (TPB) model, which explained students’ intention to collaborate online for their group projects. Results indicated that past experience predicted the three antecedents of intention, while past behaviour was predictive of subjective norm and perceived behavioural control. Moreover, the three antecedents (attitude towards e-collaboration, subjective norm and perceived behavioural control) were found to significantly predict e-collaborative intention. This study explored the use of the “remember” type of awareness (i.e. past experience) and evaluated the value of the “know” type of awareness (i.e. past behaviour) in the TPB model. Keywords: The theory of planned behaviour; E-collaborative intention; Past experience; Past behaviour.
Group projects provide a mean for students to work together to construct knowledge through social networking (Hung & Yuen, 2010). Other than using face-to-face meetings, students can work collaboratively online to conduct their group work. Online collaboration (or e-collaboration) allows students to not only share their information and views, but also do their projects from anywhere and at any time. As group work involves a social cognitive process that governs choices of action, it is crucial to identify what factors affect students’ intention to work together via Internet (i.e. students’ e-collaborative intention). This research therefore aims at examining an extended version of the theory of planned behaviour (TPB) in explaining students’ intention to collaborate online for their group projects. In the original TPB, three antecedents are proposed to predict behavioural intention: (a) attitude towards the behaviour, (b) subjective norm and (c) perceived behavioural control (Ajzen, 1991). The theory has been widely used to study behavioural intention across various
disciplines (e.g. Baker, Al-Gahtani, & Hubona, 2007; d’Astous, Colbert, & Montpetit, 2005), including college students’ mobile learning intention (Cheon, Lee, Crooks, & Song, 2012). In this research, attitude towards e-collaboration is defined as the degree to which a student has a positive or negative feeling about working online together. Subjective norm refers to a student’s perception of the important others’ opinions. Perceived behavioural control is a student’s perceived ease or difficulty when involving e-collaboration. According to Ajzen (1991, 2014), a feedback loop that forms from prior behaviour to present cognitions is hidden in the TPB model. Previous studies have supported the effect of earlier behaviour on the three antecedents of behavioural intention (e.g. Carr & Sequeira, 2007; d’Astous et al., 2005). Therefore, in this study, the extended version of the TPB model adds two distal variables, which are past behaviour and past experience. They are what Ochsner (2000) refers
Correspondence should be addressed to Eddie W. L. Cheng, Department of Social Sciences, The Hong Kong Institute of Education, 10 Lo Ping Road, Tai Po, N.T., Hong Kong. (E-mail: [email protected]). The first author has been involved in literature review, model design, questionnaire design, data collection and analysis and draft of the article. The second author has been involved in part of the literature review, model design, questionnaire design and revising the draft. This research was financially supported by The Hong Kong Institute of Education with grant numbers 03571 and R6403. The authors would like to thank the editor and the anonymous reviewers for their constructive comments on earlier versions of the article.
© 2015 International Union of Psychological Science
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CHENG AND CHU
to as two distinct types of awareness (i.e. know and remember). Past behaviour refers to the “know” type of awareness, in which students express whether they are familiar with online collaboration. On the other hand, past experience refers to the “remember” type of awareness, which brings to students’ mind about the details of their behaviour routines, such as information sharing, online discussion, and so on. As Taylor and Todd (1995) mentioned, previous experience may make knowledge and skills easily accessible in memory so that experience users may perceive IT usage to be more positive than inexperienced users. Moreover, when students conduct group projects via Internet, group mates with online experience may provide those without experience with more support and encouragement. Through such a socialisation process within group (Minocha & Thomas, 2007), students’ intention to participate in e-collaboration would improve. Past behaviour is commonly measured by the frequency of performing the behaviour, such as whether one has ever owned a business (Carr & Sequeira, 2007), how often music downloading applications had been used (d’Astous et al., 2005), and so on. It is usually employed as the single measure of prior behaviour in the extant literature. In this research, past experience is also included as Ochsner’s (2000) typology is adopted. Past experience is seldom used to study behavioural intention. The inclusion of this specific measure may disclose a more holistic view of the role of prior behaviour in the deliberative reasoning process. As noted by Jacoby, Yonelinas, and Jennings (1997), the term familiarity, which gives rise to the sense of pastness through the automatic process, is different from the experience that involves recollection of specific details. However, Ochsner (2000) supplemented that the “know” and “remember” types of awareness (or the so called behaviour familiarity and recollection processes respectively) are not necessary to be mutually exclusive, but are two separate processes of recognition memory. In other words, they are conceptualised independently and form two distinct variables. As shown in Figure 1, the theoretical model proposes that past behaviour and past experience are predictors of attitude towards e-collaboration, subjective norm and perceived behavioural control, which in turn affect e-collaborative intention. All relationships are proposed to be positive.
Attitude toward e-collaboration
Past experience
Subjective
E-collaborative
norm
intention
Past Perceived
behavior
behavioral control
Figure 1. The hypothesised model.
questionnaire. Students, who studied for their bachelor degree in one higher education institution in Hong Kong, were invited to take part in this research. They must have experience in conducting group projects and gave answers based on a recently completed group project. A survey pack (a cover letter and the questionnaire) was used. Follow-ups were administered to secure a higher response rate. As a result, 230 responses were received. An analysis of the demographic profile of the respondents reveals that approximately 25% were males (n = 57), while 75% were females (n = 173). They were aged from 18 to 30 (mean = 21). Respondents were asked about the number of group projects they completed in last semester. The results were quite diverse. Most students had around 2–6 projects (n = 156), while the mean was around 3.8. To test whether gender, age and the number of group projects, acting as extraneous variables, have an impact on the dependent variables (Reynolds, Simintiras, & Diamantopoulos, 2003), t-statistic and correlation tests were performed. The results indicate that they were independent of the latent variables, except for the relationship between age and perceived behavioural control, which was just modest (ρ = .13, p < .05). Therefore, they were not included in further analyses. Measures The latent variables were measured using multi-item scales, which, except for past experience, were developed based on Baker et al. (2007), Carr and Sequeira (2007) and Cheon et al. (2012), whose original measures have acceptable reliability.
METHOD Attitude towards e-collaboration Participants and procedure The research was conducted quantitatively. A paper-and-pencil survey was administered and a self-report questionnaire was designed. A pilot test was launched for the purpose of revising the measures and the
It was measured using four items with a 7-point bipolar adjective scale. A sample item is: I feel that working collaboratively via Internet with group mates for the group project is “extremely unhelpful (1) ↔ extremely helpful (7).” Subjective norm was measured using four © 2015 International Union of Psychological Science
STUDENTS’ ONLINE COLLABORATIVE INTENTION
3
TABLE 1 Medians, means, standard deviations and bivariate correlations
1. Attitude towards e-collaboration 2. Subjective norm 3. Perceived behavioural control 4. E-collaborative intention 5. Past experience 6. Past behaviour
Median
Mean
SD
1
2
3
4
5
5.00 5.00 5.50 5.33 5.50 5.67
5.08 4.97 5.44 5.16 5.35 5.49
0.92 0.89 0.97 0.96 0.90 1.08
0.49** 0.47** 0.72** 0.55** 0.29**
0.55** 0.49** 0.38** 0.25**
0.48** 0.36** 0.46**
0.67** 0.30**
0.35**
Note: Spearman bivariate correlation (ρ). SD = Standard deviation. * p < .05. ** p < .01.
items with a 7-point Likert scale from strongly disagree (1) to strongly agree (7). A sample item is: If those people (who are important to me) think that I should work with group mates via Internet for the group project, I would do it. Perceived behavioural control was measured using two items with a 7-point Likert scale from strongly disagree (1) to strongly agree (7). A sample item is: I believe that I am able to work with group mates via Internet for the group project. E-collaborative intention was measured using three items with a 7-point scale from extremely improbable (1) to extremely probable (7). A sample item is: I intend to work with group mates via Internet for the group project. Past behaviour was measured using three items where subjects were asked to rate based on a 7-point Likert scale from “strongly disagree (1)” to “strongly agree (7).” A sample item is: Prior to this group project, I did not conduct group projects via Internet (reverse-scored). Past experience was measured with six items where subjects were asked to rate based on a 7-point Likert scale from “strongly disagree (1)” to “strongly agree (7)”. A sample item is: I shared information with group mates via Internet for the group project. Data analysis The partial least squares (PLS) approach to structural equation modelling (SEM) was used to examine both the measurement and structural models. It is a non-parametric method and is appropriate for situations where multivariate normality of data is violated (Hair, Hult, Ringle, & Sarstedt, 2014; Urbach & Ahlemann, 2010). The software tool used for PLS-SEM was WarpPLS. Bootstrap samples were set at 100 as suggested by Kock (2011, p. 6), who argued that “higher numbers of re-samples lead to negligible improvements in the reliability of P values.” RESULTS Table 1 not only presents the median, mean and standard deviation of each latent variable, but also Spearman rank © 2015 International Union of Psychological Science
TABLE 2 Indicators’ outer loadings and cross loadings
Past behaviour Past experience Attitude towards e-collaboration Subjective norm Perceived behavioural control E-collaborative intention
Outer loadings
Cross loadings
0.79 ↔ 0.81 0.65 ↔ 0.81 0.86 ↔ 0.90 0.83 ↔ 0.87 0.74 ↔ 0.85 0.84 ↔ 0.84
0.01 ↔ 0.52 0.19 ↔ 0.61 0.13 ↔ 0.64 0.13 ↔ 0.64 0.11 ↔ 0.57 0.14 ↔ 0.50
correlations, which indicate that the six latent variables were all significantly correlated. As shown in Table 2, each indicator’s outer loading on the respective latent variable was higher than its cross loadings on other latent variables. Moreover, the results shown in Table 3 indicate that for each latent variable, the average variance extracted (AVE) was much higher than the highest squared correlation (HSC) of that latent variable with other latent variables. Thus, the above tests provide evidence for the constructs’ discriminant validity. In addition, the AVE values of the latent variables were in the range between 0.53 and 0.76, which were higher than the threshold of 0.50 (Hair et al., 2014), representing acceptable levels of convergent validity. The composite reliability of the scales ranged from 0.83 to 0.91, which show that the measurement model presents acceptable reliability. As shown in Table 4, the variance inflation factor (VIF) of the predictor variables ranged from 1.12 to 1.60 (within the acceptable range between 0.20 and 5), showing that collinearity is not a problem in the structural model. Moreover, for assessing model fit, three indices were used, which were average path coefficient (APC), average R-squared (ARS) and average variance inflation factor (AVIF) (Kock, 2011). The values for both APC and ARS should be under 2 and statistically significant at the 0.05 level, while the value for AVIF should be lower than 5 (or ideally below 3.3) (Berglund, Lytsy, & Westerling, 2013; Kock, 2011). The structural model demonstrated a good fit to the data, with APC = 0.29 (p < 0.01), ARS = 0.33 (p < 0.01) and AVIF = 1.3 (which also showed low overall collinearity).
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CHENG AND CHU TABLE 3 Results summary for reflective measurement model testing
Past behaviour Past experience Attitude towards e-collaboration Subjective norm Perceived behavioural control E-collaborative intention
AVE
Composite reliability
Cronbach’s alpha
HSC
0.64 0.53 0.72 0.61 0.71 0.76
0.84 0.87 0.91 0.86 0.83 0.91
0.72 0.82 0.87 0.79 0.59 0.84
0.19 0.43 0.52 0.29 0.29 0.52
Note: AVE = average variance extracted; HSC = highest squared correlation of a latent variable with any other latent variable. TABLE 4 PLS-SEM results Structural model
β
DV: e-collaborative intention Attitude towards e-collaboration Subjective norm Perceived behavioural control DV: attitude towards e-collaboration Past experience Past behaviour DV: subjective norm Past experience Past behaviour DV: perceived behavioural control Past experience Past behaviour
R2
Adjusted R2
0.57**
0.56**
0.63** 0.13* 0.09*
1.43 1.59 1.60 0.33**
0.33**
0.55** 0.06
1.16 1.16 0.16**
0.15**
0.32** 0.15**
1.16 1.16 0.28**
0.29** 0.35**
VIF
0.27** 1.12 1.12
Note: β = beta coefficient; DV = dependent variable; PLS = partial least squares; R = coefficient of determination; SEM = structural equation modelling; VIF = variance inflation factor. * p < .05. ** p < .01.
DISCUSSION This research aims at identifying what factors affect students’ e-collaborative intention. For this aim, a hypothesised model was developed and examined. Overall, the results indicate that the dependent variables were significantly explained by the respective independent variables. The theory is helpful to explain students’ intention to work online collaboratively for their group projects. It is worth mentioning that past experience was found to predict attitude towards e-collaboration, subjective norm and perceived behavioural control significantly, while past behaviour significantly predicted subjective norm and perceived behavioural control except for attitude towards e-collaboration. Moreover, the three antecedents (attitude towards e-collaboration, subjective norm and perceived behavioural control) were found to be significantly related to e-collaborative intention. Referring to the magnitudes of the relationships, past experience appears to be more valid than past behaviour in explaining the relationships. Past experience explains more variance of attitude towards e-collaboration and subjective norm than past behaviour, except for the opposite finding for perceived behavioural control. As said
before, past behaviour and past experience are two different senses of pastness. They were described as “frequency” and “recollection” of behaviour, respectively. Previous research has found that past behaviour contributes to the prediction of attitude, subjective norm and perceived behavioural control (e.g. Carr & Sequeira, 2007; d’Astous et al., 2005). In contrast, past experience is rarely defined operationally. This research confirms its influential role in a TPB model. As noted by Ouellette and Wood (1998), past behaviour (as well as past experience) is likely to be controlled by deliberative reasoning processes such that past behaviour could not directly predict later behaviour. However, it is important to recollect what has been done so that similar behaviour can be performed in the future when students are willing to do so. Students’ reflections on their group projects (including their use of web technology and their online collaboration) are crucial to improve their memory of how they behave previously (Storch, 2005). Moreover, through repetitive and continued practices, a non-habitual behaviour becomes routinised and appears to function independent of intention. It is because conscious awareness would diminish when the behaviour is increasingly performed. Yet, such behaviour must be “well-practiced and performed in © 2015 International Union of Psychological Science
STUDENTS’ ONLINE COLLABORATIVE INTENTION
stable contexts” (Ouellette & Wood, 1998, p. 65). In group projects, the contexts vary in terms of group mates, teachers, the project nature, difficulty levels, and so on, thus the model that specifies past behaviour and past experience as precursors of the antecedents of intention has been empirically supported in this research. Limitations could not be avoided, especially in exploratory studies. First, social desirability is considered in self-report questionnaires. Owing to limited resources, it was not possible to have access to the respondents with more than one time. However, this research attempted to reduce some intervenes due to social desirability biases by assuring the anonymity of respondents, random order of the items of the latent variables, use of reverse-scored items, and so on. Second, to address non-response bias, this research has taken effective steps, including careful questionnaire design, explanation of the research, assurance of anonymity and reminder letters, to improve the response rate (Rogelberg & Stanton, 2007). For testing non-response bias, this study followed the suggestion by Rogelberg and Stanton (2007) to compare those who responded to certain demographic questions with those who did not (acting as a proxy for non-respondents). The t-test results indicate that there was no significant difference between the two groups on the six latent constructs, thus non-response bias was trivial in this research. Manuscript received August 2014 Revised manuscript accepted January 2015
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