The Theory of Planned Behaviour - Essay Example

? Business Research Methods I. Literature Review The Theory of Planned Behaviour First proposed by Icek Ajzen in 1985, this theory is in turn based on the earlier Theory of Reasoned Action posited by both Ajzen and Fishbein (1980) in the course of their studies on attitude and behaviour. As such, both theories work quite well to explain behavioural intent, as well as to predict both human behaviour and attitude (Ajzen 1980). The general idea behind these theories is that certain actions are viewed in certain ways by certain people, and their perception of that action in concert with existing social norms in turn dictate how likely they are to follow through with it. Generally speaking, a course of action that one intends to do will almost always be done (Ajzen and Fishbein 1975). The key addition present in the later Theory of Planned Behaviour as explained by Miller (2005) is that perceived behavioural control now comes into play. The reason behind said addition is that while people may really want to follow through on a certain course of action, they may lack the confidence or control to do so, or are otherwise being held back by other variables – to quote the Bible, ‘the spirit may be willing, but the flesh is weak’. An instance where this comes to play can easily be seen in the case of someone who tries and fails to diet. Only those who are disciplined enough can actually succeed in this endeavour; all too often, it is more likely that the person will eventually succumb to the temptation of bingeing. Clearly in this case, one needs the behavioural control to say ‘no’ to the seeming siren call of the buffet table and to focus on his diet. In other words, it is not just one’s personal attitudes and perceptions that now decide what one is going to do. While these still count for something, and in fact are still the deciding factor in whether or not a certain action will be done, social pressures and one’s sense of control will now count for something as well. In this way, one’s decisions are no longer solely dependent on his own perceptions and feelings on the situation. Instead, one will now take into account how society views the respective choices at hand, and how well he can commit or follow through on a given outcome (Cooke and Sheeran 2004). The latter part is especially important – which is the reason for its addition to the theory in the first place. As mentioned by the aforementioned Bible verse, we may not always follow through on an action that we are inclined towards (Armitage and Conner 2001). Otherwise, for instance, individuals should have been able to instantly follow through on their intent to take a certain course of action. Someone who desired to quit smoking should definitely be able to do so immediately, but may be hampered by doubts on whether or not he will be able to quit – if this happens, he really will not be able to quit. Not only that, but one’s perceived and therefore actual behavioural control can also be swayed by the perceptions and opinions of those around him, which may sometimes manifest, among other things, as peer pressure (Ajzen 1985). As anyone in real life will admit, relatives, friends and coworkers inadvertently sway their decisions in voicing out their own viewpoints on the matter. That said, some of these individuals will have more influence on decisions concerning certain areas; for example, decisions regarding one’s family will of course be influenced most by relatives and family, as they are the ones directly concerned. And consequently, the biggest influence on one’s career moves will be his bosses and colleagues. Social norms, on the other hand, are much more varied, being described by Schulz et al (2007) as being either descriptive or injunctive in nature. Descriptive norms describe the behaviour of a particular group, and include stereotypical statements such as how x out of y people prefer a certain brand, or something similar. Meanwhile, injunctive norms are more subtle, and refer to certain cues or signals about how a culture approves of or frowns upon behaviour and other similar things. These different kinds of norms are also definite factors in individual behaviour, although how exactly depends on the situation. Stavrova et al (2011) also expound further by differentiating injunctive norms as being either societal or personal. As the designations imply, societal injunctive norms refer to what society thinks of certain behaviour, while personal ones instead refer to the opinions of a few. This means that, among other things, being in a situation viewed by members of one’s community as being ‘bad’ tends to make one feel bad. Applied to real life, this could help explain the Japanese preoccupation with test scores; since their culture highly emphasizes test scores, students born into that culture do their absolute best – and then some – to get the highest grades possible. Failure to do so results in depression that, in extreme cases, drives them to suicide. This theory has been applied to a number of studies, each of which sought to determine the likelihood that certain people will follow through with certain acts. Examples of these include health behaviour (Hardeman et al 2002) and dieting (Bagozzi et al 2004). Said theory can also be tested among different cultures and in different contexts. Common methods of observing the theory at work generally include asking what one’s loved ones would think of a certain behaviour (French et al 2007), and are generally met with varying degrees of success. The theory, however, also acknowledges that while the three components of attitudes, social norms and behavioural control are universal, the exact weight each one carries will vary from person to person. Ambivalent attitudes (Conner et al 2003) may not always affect behaviour, and subjective norms may be more influential when it comes to collectivist rather than individualistic cultures. Personality traits also come into play, as noted by Sheeran and Orbell (2000), while Armitage et al (1999) in turn observed the effect one’s mood has on his behaviour. For instance, when someone is in a bad mood, he is more likely to make decisions on a whim, as opposed to thinking things through – something much more likely to happen when that individual is in a good mood. The difficulty of the task involved can also be a factor (Bansal and Taylor 2002), as can demographics (Conner et al 2003). II. Interpretation of Findings Reliability Analysis for Behavioural Intention Table 1. Descrptive Statistics: Behavioural Intention. Mean Std. Deviation I intend to use my mobile phone to send a text whilst driving in the forthcoming month (Int1) 5.49 1.92 I am likely to use my mobile phone to send a text whilst driving in the forthcoming month (Int2) 4.77 2.22 Behavioural Intention 5.13 1.97 There were two statements measuring behavioural intention. The first statement measures the extent of truth to intent to use one’s mobile phone to text while driving in the forthcoming month. The mean is 5.49 (sd=1.92), suggesting that it leans towards being false. Moreover, the likelihood of using one’s mobile phone to text while driving was rated with 4.77 on average (sd=2.22) indicating that it leans towards being unlikely. Table 2. Inter-Item Correlations: Behavioural Intention. Int1 Int2 Int1 Pearson Correlation 1.00 0.81**   Sig. (2-tailed)   0.00 Int2 Pearson Correlation 0.81 1.00   Sig. (2-tailed) 0.00   ** Correlation is significant at the 0.01 level (2-tailed). The truth behind using one’s phone to text in the forthcoming month is significantly and positively correlated with the likelihood to use the phone to text within the same period (r=0.81, p=.00). Table 3. Cronbach’s Alpha: Behavioural Intention. Cronbach's Alpha N of Items 0.89 2.00 Computing for the internal consistency of the Behavioural Intention scale, Cronbach’s alpha for the two items is high at 0.89, indicating high reliability. Reliability Analysis for Attitudes Table 4. Descriptive Statistics: Attitudes. Mean Std. Deviation Harmful-Beneficial (Att1) 2.32 1.35 Dangerous-Safe (Att2) 1.96 1.27 Bad-Good (Att3) 1.89 1.17 Worthless –Valuable (Att4) 2.97 1.73 Unenjoyable-Enjoyable (Att5) 2.89 1.65 Wrong thing to do-Right thing to do (Att6) 1.54 1.01 Attitudes 2.25 1.07 The attitudes of the respondents on texting while driving have been assessed. The extent to which it is perceived as harmful garnered a mean of 2.32 (sd=1.35), indicating leaning towards being harmful. It was also gauged as learning towards being dangerous (X=1.96, sd=1.27), worthless (X=2.97, sd=1.73), unenjoyable (X=2.89, sd=1.65) and being wrong (X=1.54, sd=1.01). Table 5. Inter-Item Correlations: Attitudes. Att1 Att2 Att3 Att4 Att5 Att6 Att1 r 1.00 0.66** 0.55** 0.58** 0.46** 0.37**   p   0.00 0.00 0.00 0.00 0.00 Att2 r 0.66** 1.00 0.69** 0.45** 0.44** 0.62**   p 0.00   0.00 0.00 0.00 0.00 Att3 r 0.55** 0.69** 1.00 0.48** 0.57** 0.63**   p 0.00 0.00   0.00 0.00 0.00 Att4 r 0.58** 0.45** 0.48** 1.00 0.50** 0.43**   p 0.00 0.00 0.00   0.00 0.00 Att5 r 0.46** 0.44** 0.57** 0.50** 1.00 0.45**   p 0.00 0.00 0.00 0.00   0.00 Att6 r 0.37** 0.62** 0.63** 0.43** 0.45** 1.00   p 0.00 0.00 0.00 0.00 0.00   ** Correlation is significant at the 0.01 level (2-tailed). Each of the items measuring attitudes is significantly and positively correlated with the other 5 items measuring attitudes towards texting while driving. Table 6. Cronbach’s Alpha: Attitudes. Cronbach's Alpha N of Items 0.86 6.00 The Cronbach’s alpha measuring the internal consistency of the six items indicates high internal consistency of the scale, at 0.86. Reliability Analysis for Subjective Norms Table 7. Descriptive Statistics: Subjective Norms. Mean Std. Deviation People who are important to me would approve/disapprove of me responding to a text message whilst driving (SN1) 5.78 1.47 People who are important to me would use their phone to text whilst driving (SN2) 4.27 1.86 The people in my life who’s opinions I value text/do not text whilst driving (SN3) 4.69 1.83 Many people like me respond to text messages whilst driving (SN4) 3.74 2.15 It is expected of me to respond to a text whilst driving (SN5) 5.99 1.37 Subjective Norms 4.89 1.15 On the approval of people important to them on texting while driving, the average indicates disapproval (X=5.78, sd=1.47). On the truth of their loved ones texting while driving, the average indicates neutrality as it leans towards “4.00” which represents a middle, neutral response (X=4.27, sd=1.86). Moreover, the texting while driving behaviour of people whose opinions are valued by the respondents slightly leaned towards “not texting” (X=4.69, sd=1.83). On the number of people like the respondent who text while driving, the mean indicates neutrality (X=3.74, sd=2.15). Finally, expectations on responding to a text while driving leaned towards being false (X=5.99, sd=1.37). Table 8. Inter-Item Correlations: Subjective Norms. SN1 SN2 SN3 SN4 SN5 SN1 r 1.00 0.31** 0.28** 0.06 0.17*   p   0.00 0.00 0.50 0.05 SN2 r 0.31** 1.00 0.68** 0.33** 0.19*   p 0.00   0.00 0.00 0.03 SN3 r 0.28** 0.68** 1.00 0.33** 0.27**   p 0.00 0.00   0.00 0.00 SN4 r 0.06 0.33** 0.33** 1.00 0.22**   p 0.50 0.00 0.00   0.01 SN5 r 0.17* 0.19** 0.27 0.22** 1.00   p 0.05 0.03 0.00 0.01   ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). The inter-tem correlations of the statements measuring Subjective Norms indicate that each statement is at least significantly and positively correlated with 3out of the 4 other statements which measure the construct. Table 9. Inter-Item Correlations: Subjective Norms. Cronbach's Alpha N of Items 0.67 5.00 Cronbach’s alpha for the Subjective Norms scale shows acceptable internal consistency, garnering a value of 0.67. Reliability Analysis for Perceived Behavioural Control Table 10. Descriptive Statistics: Perceived Behavioural Control. Mean Std. Deviation For me responding to a text whilst driving in the forthcoming month would be easy/difficult (PBC1) 3.60 2.13 If I wanted to send a text whilst driving in the forthcoming month I could definitely true/false (PBC2) 2.51 1.83 How much control do you believe you have to stop yourself responding to a text whilst driving in the forthcoming month: No control / complete control (PBC3) 5.96 1.62 It is mostly up to me whether or not I respond to a text whilst driving in the forthcoming month: strongly agree / strongly disagree (PBC4) 1.63 1.50 Perceived Behavioural Control (4 items) 3.44 1.22 Perceived Behavioural Control (3 items) 2.59 1.47 On the items of Perceived Behavioural Control, the item on responding to a text whilst driving in the forthcoming month is rated neutrally (X=3.60, sd=2.13), while wanting to send a text whilst driving in the forthcoming month was leaning towards being true (X=2.51, sd=1.83). The extent of control that the respondent believes that the respondent has to stop oneself from responding to a text whilst driving leaned towards having complete control (X=5.96, sd=1.62). The average to “It is mostly up to me whether or not I respond to a text whilst driving in the forthcoming month: strongly agree / strongly disagree” leaned towards agreement (X=1.63, sd=1.50). Table 11. Inter-item Correlations: Perceived Behavioural Control. PBC1 PBC2 PBC3 PBC4 PBC1 r 1.00 0.70** 0.18** 0.21**   p   0.00 0.03 0.01 PBC2 r 0.70** 1.00 0.08 0.34**   p 0.00   0.37 0.00 PBC3 r 0.18** 0.08 1.00 0.01   p 0.03 0.37**   0.95** PBC4 r 0.21 0.34 0.01 1.00   p 0.01 0.00 0.95   ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). The inter-item correlations for Perceived Behavioural Control show that 3 out of the 4 statements significantly and positively correlate with at least 2 out of 3 of the other statements. However, one statement, “How much control do you believe you have to stop yourself responding to a text whilst driving in the forthcoming month” only correlates with one other statement. This indicates that it may possibly be taken out of the subscale to increase its internal consistency. Table 12. Cronbach’s Alpha: Perceived Behavioural Control. Cronbach's Alpha N of Items 0.59 4.00 The original Cronbach’s alpha for the 4 items of Perceived Behavioural Control is 0.59, which suggests that it has not met the 0.60 cut-off for acceptable internal consistency. Table 12. Cronbach’s Alpha for Three Items: Perceived Behavioural Control. Cronbach's Alpha N of Items 0.69 3.00 Taking out Item 3 from the scale, the internal consistency of Perceived Behavioural Control is 0.69. Application of the Theory of Planned Behaviour Table 13. Model Summary for Attitudes, Subjective Norms, and Perceived Behavioural Control Regressed against Behavioural Intention.         Model R R Square Adjusted R Square Std. Error of the Estimate 1.00 0.62 0.39 0.3730 1.56 a Predictors: (Constant), pbcnew, sn, att 37.30% of the variance in behavioural intention is accounted for by the three independent variables of attitudes, subjective norms, and perceived behavioural control. Table 14. One-way ANOVA for Attitudes, Subjective Norms, and Perceived Behavioural Control Regressed against Behavioural Intention. Model Sum of Squares df Mean Square F Sig. 1.00 Regression 209.06 3.00 69.69 28.57 0.00   Residual 331.75 136.00 2.44       Total 540.81 139.00       a Predictors: (Constant), pbcnew, sn, att b Dependent Variable: int The one-way ANOVA for the regression model which uses the three independent variables suggest that it is able to predict behavioural intentions on texting while driving. Table 15. Beta Coefficients for Attitudes, Subjective Norms, and Perceived Behavioural Control Regressed against Behavioural Intention. Model Unstandardized Coefficients   Standardized Coefficients t Sig.     B Std. Error Beta 1.00 (Constant) 3.62 0.89   4.09 0.00   att -0.49 0.15 -0.27 -3.33 0.00   sn 0.29 0.14 0.17 2.12 0.04   pbcnew 0.46 0.10 0.34 4.55 0.00 a Dependent Variable: int The beta coefficients for the three independent variables indicate that the more negative one’s attitudes are towards texting while driving, the greater is the intention of committing this act (B=-0.27, p=.00). Moreover, the stronger are subjective norms of significant others related to texting while driving, the more probable it is for the respondent to commit the same. Additionally, the greater is the perceived control, the more likely it is for the respondent to text while driving (B=0.34, p=0.00). All three independent variables figured as predictors of behavioural intentions of texting while driving Additional Contribution of Past Behaviour to the TPB Model Table 16. Model Summary with the Addition of Past Behaviour. Model R R Square Adjusted R Square Std. Error of the Estimate 1.00 0.64 0.41 0.3878 1.54 a Predictors: (Constant), Past, sn, att, pbcnew The adjusted R square for the original model is 37.30%, while the addition of past behaviour increase the coefficient of determination to 38.78%. This indicates that past behaviour has contributed an additional 1.48% to the explanatory power of the model, in predicting behavioural intention. Table 17. One-way ANOVA for New Model. Model Sum of Squares df Mean Square F Sig. 1.00 Regression 219.25 4.00 54.81 23.01 0.00   Residual 321.55 135.00 2.38       Total 540.81 139.00       A Predictors: (Constant), Past, sn, att, pbcnew B Dependent Variable: int The new model garnered a significant F-value (F=23.01,p=0.00), indicating that there are independent variables in the model that can predict behavioural intention. Table 17. Beta Coefficients for New Model. Model Unstandardized Coefficients   Standardized Coefficients t Sig.     B Std. Error Beta 1.00 (Constant) 3.02 0.92   3.28 0.00   att -0.46 0.15 -0.25 -3.09 0.00   sn 0.26 0.14 0.15 1.90 0.06   pbcnew 0.36 0.11 0.27 3.16 0.00   Past 0.77 0.37 0.17 2.07 0.04 a Dependent Variable: int           The beta coefficients for the new model suggests that attitudes are still negatively correlated with behavioural intention (B=-0.25, p=0.00) indicating that as attitudes become more negative, the more likely is it for the respondent to have intentions of texting while driving. Moreover, subjective norms are marginally and positively correlated with behavioural intentions (B=0.27, p=.06), while increasing perceived behavioural control also strengthens these (B=0.27, p=.00). Past behaviour of texting while driving is also significantly and positively correlated with intentions of texting while driving (B=0.17, p=.04). III. Discussion The outcomes of the present study lends further empirical support to the Theory of Planned Behaviour, where it was shown that 37% of the variability in behavioural intentions in texting while driving is explained by the TPB variables of attitudes, subjective norms, and perceived behavioural control. The study further lends support to the idea that certain people view acts in a particular light, which then dictates how likely they are to act in that manner, specific to the behaviour of texting while driving. That said, when one intends to do a certain act, he almost always does (Ajzen, 1980; Ajzen and Fishbein, 1975). The outcomes on perceived behavioural control suggest that as the control is perceived more strongly, the more likely are the respondents to text while driving. These are consistent with the findings of Miller (2005), which suggests that intent to perform a certain act or behave in a certain manner does not always result in one actually doing so. For instance, a respondent may want to text while driving, but if social control for such behaviour is strong, the person will more likely have second thoughts about committing this action. In short, while personal attitudes and perceptions still count for a lot in the decision process, and in fact still have the final say in one’s decision, these are now compounded by social pressures and the individual’s own sense of control. Rather than decisions being based solely on one’s own perceptions and feelings, one must now take into account the existing social norms in his community, as well as his own perception on whether or not he can see things through (Cooke and Sheeran, 2004). The latter is especially important, which is exactly why it was added to the TPB. - because people do not always do an act they claim they will do (Armitage and Conner 2001). If the opposite were true, people who decide on texting while driving should be able to follow through with it in an instant. Oftentimes one finds himself doubting whether or not he can commit to his chosen path – should this happen, the answer will always be no. Moreover, attitudes also significantly influence one’s behavioural intentions, whereby the degree of negativity attached to texting while driving influences whether or not such action is carried out. These are consistent with the assertions of Ajzen (1985) and Armitage and Conner (2001). Additionally, one’s behavioural control can also be affected by the perceptions and opinions of others, which is what peer pressure falls under (Ajzen 1985), which is also supported by the outcomes of the study where subjective norms were found to be positively correlated with behavioural intentions to text while driving. Most people have experienced having their relatives, friends and coworkers swaying their decisions just by airing out their own opinions. Of course, these people will all have varying degrees of influence depending on the context, such as one’s career moves being influenced by one’s coworkers, and family decisions being influenced by family. As the study shows, the opinions and behaviours of one’s significant others influence one’s intent to text while driving. Meanwhile, social norms are described by Schulz et al (2007) as being descriptive or injunctive. Respectively, these norms refer to the behaviour to which a particular set of people is inclined to, and cues and signals that a culture will either approve of or reject. For instance, while statements on what 9 out of 10 persons like fall under the former, the latter is instead more implicit, and can usually be picked up through observation rather than being outright stated. Both these factors will undoubtedly come into play in one’s individual behaviour. How ever, the extent to which they do may vary from situation to situation. In the case of texting while driving, such norms do substantially influence the intent to text while driving Stavrova et al (2011) further note that societal injunctive norms refer to how behaviour is perceived by society as a whole. That is, the society judges acts, behaviours and viewpoints depending on the teachings imparted to them by their political, spiritual and community leaders. It may be possible that norms that have to do with texting while driving are implicit and yet are potent because these are upheld by people who are significant to the respondents. Moreover, it should also be noted that certain behaviour may be viewed differently in different cultures – that is, a behaviour looked down upon in a certain culture may be tolerated in another, and may yet be condoned in yet another culture. In the UK culture, society’s norms are determinants of whether its members actually text while driving. The Theory of Planned Behaviour has seen use by a number of studies that sought to examine how likely people are to act in certain ways. For instance, Hardeman et al (2002) focused on health behaviour, while Bagozzi et al (2004) instead examined how a specific group of people view dieting and their inclination towards it. Not only can the context vary, so too can the context in which the study is applied change, as in the present study who found support for TPB as regards texting behaviour while driving. Regardless of culture or context, however, there are certain surefire ways of observing the theory in action. For instance, one can ask his respondents what his loved ones think of a particular behaviour and act, though this may be variably successful depending on a number of factors (French et al, 2007). Additionally, the present study lends evidence to the idea that no matter what happens, the three components of attitudes, social norms and behavioural control will always be present. The extent of their presence and influence, however, may not be the same depending on the persons involved. Additionally, as explained by Conner et al (2003), ambivalent attitudes may sometimes have limited influence on one’s behaviour. Subjective norms, in turn, tend to be more influential in collectivist rather than individualistic cultures. These are not consistent with the study’s outcomes, whereby UK respondents were shown to be affected or influenced by subjective norms in so far as texting while driving is concerned. References Ajzen, I, (1985). From intention to action: A theory of planned behaviour. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behaviour, pp. 11-40. Springer-Verlag, New York. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Prentice-Hall, Englewood Cliffs, NJ. Armitage, C. J., Conner, M., & Norman, P. (1999). ‘Differential effects of mood on information processing: Evidence from the theories of reasoned action and planned behaviour’, European Journal of Social Psychology, vol. 29, pp. 419-433. Bagozzi, R. P., Moore, D. J., & Leone, L. (2004). ‘Self-control and the self-regulation of dieting decisions: The role of prefactual attitudes, subjective norms, and resistance to temptation’, Basic and Applied Social Psychology, vol. 26, pp. 199-213. Bansal, H. S., & Taylor, S. F. (2002). ‘Investigating interactive effects in the theory of planned behaviour in a service-provider switching context’, Psychology & Marketing, vol. 19, pp. 407-425. Conner, M., Smith, N., & McMillan, B. (2003). ‘Examining normative pressure in the theory of planned behaviour: Impact of gender and passengers on intentions to break the speed limit’, Current Psychology, vol. 22, pp. 252-263 Cooke, R., & Sheeran, P. (2004). ‘Moderation of cognition-intention and cognition-behaviour relations: A meta-analysis of properties of variables from the theory of planned behaviour’, British Journal of Social Psychology, vol. 43, pp. 159-186. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behaviour: An introduction to theory and research. Addison-Wesley, Reading, MA. French, D. P., Cooke, R., McLean, N., Williams, M., & Sutton, S. (2007). ‘What do people think about when they answer theory of planned behaviour questionnaires?: A "think aloud" study’, Journal of Health Psychology, vol. 12, pp. 672-687. Hardeman, W., , M., Johnston, D., Bonetti, D., Wareham,N. J., & Kinmonth, A. L. (2002). ‘Application of the theory of planned behaviour in behaviour-change interventions: A systematic review’, Psychology and Health, vol. 17, pp. 123-158. Miller, K. (2005). Communications theories: Perspectives, processes, and contexts, McGraw-Hill, New York. Armitage, C. J., & Conner, M. (2001). ‘Efficacy of the theory of planned behaviour: A meta-analytic review’, British Journal of Social Psychology, vol. 40, pp. 471-499. Schulz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2007). ‘The constructive, destructive, and reconstructive power of social norms’, Psychological Science, vol. 18, pp. 429-434. Sheeran, P., & Orbell, S. (2000). ‘Self-schemas and the theory of planned behaviour’, European Journal of Social Psychology, vol. 30, pp. 533-550. Stavrova, O., Schlosser, T., & Fetchenhauer, D. (2011). ‘Are the unemployed equally unhappy all around the world? The role of the social norms to work and welfare state provision in 28 OECD countries’, Journal of Economic Psychology, vol. 32, pp. 159-171. Appendix 1 – Selected SPSS Tables Int 1 Frequency Percent Valid Percent Cumulative Percent true 10.00 7.14 7.14 7.14 2.00 4.00 2.86 2.86 10.00 3.00 14.00 10.00 10.00 20.00 4.00 7.00 5.00 5.00 25.00 5.00 15.00 10.71 10.71 35.71 6.00 24.00 17.14 17.14 52.86 false 66.00 47.14 47.14 100.00 Total 140.00 100.00 100.00   Int 2 Frequency Percent Valid Percent Cumulative Percent likely 19.00 13.57 13.57 13.57 2.00 9.00 6.43 6.43 20.00 3.00 19.00 13.57 13.57 33.57 4.00 12.00 8.57 8.57 42.14 5.00 10.00 7.14 7.14 49.29 6.00 21.00 15.00 15.00 64.29 unlikely 50.00 35.71 35.71 100.00 Total 140.00 100.00 100.00   Att 1 Frequency Percent Valid Percent Cumulative Percent harmful 56.00 40.00 40.00 40.00 2.00 27.00 19.29 19.29 59.29 3.00 23.00 16.43 16.43 75.71 4.00 26.00 18.57 18.57 94.29 5.00 6.00 4.29 4.29 98.57 6.00 2.00 1.43 1.43 100.00 Total 140.00 100.00 100.00   Att 2 Frequency Percent Valid Percent Cumulative Percent dangerous 73.00 52.14 52.14 52.14 2.00 29.00 20.71 20.71 72.86 3.00 18.00 12.86 12.86 85.71 4.00 15.00 10.71 10.71 96.43 5.00 2.00 1.43 1.43 97.86 6.00 2.00 1.43 1.43 99.29 safe 1.00 0.71 0.71 100.00 Total 140.00 100.00 100.00   Att3 Frequency Percent Valid Percent Cumulative Percent bad 74.00 52.86 52.86 52.86 2.00 30.00 21.43 21.43 74.29 3.00 19.00 13.57 13.57 87.86 4.00 14.00 10.00 10.00 97.86 5.00 2.00 1.43 1.43 99.29 good 1.00 0.71 0.71 100.00 Total 140.00 100.00 100.00   Att4 Frequency Percent Valid Percent Cumulative Percent worthless 43.00 30.71 30.71 30.71 2.00 19.00 13.57 13.57 44.29 3.00 17.00 12.14 12.14 56.43 4.00 37.00 26.43 26.43 82.86 5.00 13.00 9.29 9.29 92.14 6.00 6.00 4.29 4.29 96.43 valuable 5.00 3.57 3.57 100.00 Total 140.00 100.00 100.00   Att5 Frequency Percent Valid Percent Cumulative Percent unenjoyable 46.00 32.86 32.86 32.86 2.00 15.00 10.71 10.71 43.57 3.00 20.00 14.29 14.29 57.86 4.00 37.00 26.43 26.43 84.29 5.00 14.00 10.00 10.00 94.29 6.00 6.00 4.29 4.29 98.57 enjoyable 2.00 1.43 1.43 100.00 Total 140.00 100.00 100.00   Att 6 Frequency Percent Valid Percent Cumulative Percent wrong 96.00 68.57 68.57 68.57 2.00 26.00 18.57 18.57 87.14 3.00 10.00 7.14 7.14 94.29 4.00 6.00 4.29 4.29 98.57 6.00 1.00 0.71 0.71 99.29 right 1.00 0.71 0.71 100.00 Total 140.00 100.00 100.00   SN1 Frequency Percent Valid Percent Cumulative Percent Strongly Approve 1.00 0.71 0.71 0.71 2.00 5.00 3.57 3.57 4.29 3.00 5.00 3.57 3.57 7.86 4.00 20.00 14.29 14.29 22.14 5.00 14.00 10.00 10.00 32.14 6.00 32.00 22.86 22.86 55.00 Strongly Disapprove 63.00 45.00 45.00 100.00 Total 140.00 100.00 100.00   SN2 Frequency Percent Valid Percent Cumulative Percent Completely True 8.00 5.71 5.71 5.71 2.00 22.00 15.71 15.71 21.43 3.00 22.00 15.71 15.71 37.14 4.00 25.00 17.86 17.86 55.00 5.00 23.00 16.43 16.43 71.43 6.00 15.00 10.71 10.71 82.14 Completely False 25.00 17.86 17.86 100.00 Total 140.00 100.00 100.00   SN3 Frequency Percent Valid Percent Cumulative Percent Text 10.00 7.14 7.14 7.14 2.00 6.00 4.29 4.29 11.43 3.00 20.00 14.29 14.29 25.71 4.00 32.00 22.86 22.86 48.57 5.00 19.00 13.57 13.57 62.14 6.00 20.00 14.29 14.29 76.43 Do not text 33.00 23.57 23.57 100.00 Total 140.00 100.00 100.00   SN4 Frequency Percent Valid Percent Cumulative Percent Completely True 24.00 17.14 17.14 17.14 2.00 32.00 22.86 22.86 40.00 3.00 14.00 10.00 10.00 50.00 4.00 22.00 15.71 15.71 65.71 5.00 7.00 5.00 5.00 70.71 6.00 16.00 11.43 11.43 82.14 Completely False 25.00 17.86 17.86 100.00 Total 140.00 100.00 100.00   SN5 Frequency Percent Valid Percent Cumulative Percent Completely True 1.00 0.71 0.71 0.71 2.00 4.00 2.86 2.86 3.57 3.00 3.00 2.14 2.14 5.71 4.00 15.00 10.71 10.71 16.43 5.00 12.00 8.57 8.57 25.00 6.00 34.00 24.29 24.29 49.29 Completely False 71.00 50.71 50.71 100.00 Total 140.00 100.00 100.00   PBC1 Frequency Percent Valid Percent Cumulative Percent Easy 31.00 22.14 22.14 22.14 2.00 21.00 15.00 15.00 37.14 3.00 24.00 17.14 17.14 54.29 4.00 18.00 12.86 12.86 67.14 5.00 13.00 9.29 9.29 76.43 6.00 9.00 6.43 6.43 82.86 Difficult 24.00 17.14 17.14 100.00 Total 140.00 100.00 100.00   PBC 2 Frequency Percent Valid Percent Cumulative Percent Definitely True 62.00 44.29 44.29 44.29 2.00 23.00 16.43 16.43 60.71 3.00 17.00 12.14 12.14 72.86 4.00 20.00 14.29 14.29 87.14 5.00 5.00 3.57 3.57 90.71 6.00 3.00 2.14 2.14 92.86 Definitely False 10.00 7.14 7.14 100.00 Total 140.00 100.00 100.00   PBC3 Frequency Percent Valid Percent Cumulative Percent No Control 4.00 2.86 2.86 2.86 2.00 5.00 3.57 3.57 6.43 3.00 6.00 4.29 4.29 10.71 4.00 11.00 7.86 7.86 18.57 5.00 7.00 5.00 5.00 23.57 6.00 25.00 17.86 17.86 41.43 Complete Control 82.00 58.57 58.57 100.00 Total 140.00 100.00 100.00   PBC 4 Frequency Percent Valid Percent Cumulative Percent Strongly Agree 107.00 76.43 76.98 76.98 2.00 16.00 11.43 11.51 88.49 3.00 3.00 2.14 2.16 90.65 4.00 2.00 1.43 1.44 92.09 5.00 1.00 0.71 0.72 92.81 6.00 5.00 3.57 3.60 96.40 Strongly Disagree 5.00 3.57 3.60 100.00 Total 139.00 99.29 100.00   System 1.00 0.71       140.00 100.00     Past Behaviour Frequency Percent Valid Percent Cumulative Percent yes 110.00 78.57 78.57 78.57 no 29.00 20.71 20.71 99.29 3.00 1.00 0.71 0.71 100.00 Total 140.00 100.00 100.00   Read More