6 CONCLUSSIONS
6.3 Limitations and possibilities for future research
In this study, respondents were told the aim of the study was to investigate the effect of values on pro-environmental air-travel related behaviour intentions. There is therefore the possibility that responses may be affected by social desirability bias which may encourage people to respond positively to questions related to sensitive topics like environmental protection (Budeanu 2007). Chung and Monroe (2003) suggest ensuring and providing assurances regarding the confidentiality of responses could reduce social desirability bias and this was made clear to participants in the invitation to take part in this study.
Data for the exogenous and endogenous variables in this study was gathered at the same time and as a result there is the risk that results could be affected by common method bias. While Harman’s single factor test was used to test for common method bias, this method has been criticised as it does not control for method effects and it has also been argued that the emergence of multiple factors does not indicate the absence of common method bias (Sharma, Crawford and Yetton 2009). However, some of the characteristics of this study’s questionnaire may reduce the impact of common method bias. For example, scales for values and past offsetting behaviour had a different number of scale points compared to those used for the endogenous variables. Additionally, while scales for past flying behaviour and the endogenous variables in this study were all measured using seven point scales, a very wide variety of anchor labels were used. For example, for the 32 items assessing the nine endogenous constructs, 16 different labels were used and these labels also differed from those used to assess the exogenous variables in this study. Podsakoff, Mackenzie and Podsakoff (2012) describe how these remedies can be effective in minimising common method bias.
A limitation that affects the generalisability of this study is the sample, which consisted solely of university employees. Compared to the Finnish population, there is a lack of young adults (under 30 years old) as well as over 60 year olds. The sample also has a much smaller proportion of male respondents than female and as a result of being university employees on average are likely to be more highly educated than the general Finnish population.
Additionally, much of the travel undertaken by the sample for this study is likely work rather than leisure related and as a result, consideration of individual costs related to self-enhancement values may be more relevant to study participants than those related to openness to change
considerations. This could lead to results from this study underestimating the effect that openness to change values could have on tourists intentions to act more environmentally.
Reproducing the results in a more representative population would better justify using the findings from this study as a basis for behavioural interventions aiming to reduce dependency on flying or for the marketing of carbon offsets.
While the majority of constructs in this study were measured using three to five items and demonstrated good reliability and validity, the two past behaviour constructs were each measured with a single item. Despite single item measures being easily implemented in PLS-SEM the use of single item measures goes against PLS-PLS-SEM’s concept of consistency at large (Hair et al. 2012) which can result in the underestimation of structural model relations.
Additionally the assumption of perfect reliability associated with single item latent variables in SmartPLS 3 is unreasonable in behavioural studies. Future studies could look to develop multi-item habit scales for use in the measurement of habitual flying and the habit of not purchasing carbon offsets when flying.
Findings from this study were based on a sample of 196 responses. While this was well over the minimum sample size recommended by Barclay et al. (1995) of ten times the number of paths pointing at a single latent variable in the inner model, this rule does not take into consideration other factors that can affect estimation power. A sensitivity analysis, which allows one to determine what effect size a study can detect at a certain power level, given the study’s sample size and specified alpha (Faul, Erdfelder, Lang and Buchner 2007), was conducted using G*Power 3.1. The result of this analysis showed that for a sample size of 196, effect sizes (f²) of 0.032 and above can be detected with a power of 0.8 and alpha level of 0.05.
There is therefore the risk of type II errors with regards to non-significant paths with smaller corresponding effect sizes such as SID INTII or SE PBC, meaning that as a result of the small sample size, analysis may fail to detect a significant relationship where in fact one exists.
Therefore until the findings of this study have been replicated, especially with larger samples, they should only be viewed as preliminary (Levers-Landis, Burant and Hazen 2011).
Some studies suggest pro-environmental behaviour may be related to socio-demographic variables including gender, age and income (Dolnicar and Leisch 2008b; Gilg et al. 2005) although other studies have found socio-demographic variables poor predictors of attitudes and moral norm concepts (Ebreo et al. 2002-2003) and that while socio-demographic variables
explained historical energy use or consumption, energy saving measures and changes in consumption were explained by psychological measures and socio-demographic variables had no effect.(Abrahamse and Steg 2009; Brandon and Lewis 1999). In this study a second limitation resulting from the relatively small sample was that the ability to test for differences in parameter estimates resulting from group specific differences like gender, age or income using multi-group analysis was restricted. This is because once the sample was split to represent differences in gender, age or income the group sizes were very small – less than recommended by Barclay et al. (1995) - and estimates based on these small samples are likely to deliver estimates that cannot be used for valid practical conclusions (Nitzl 2014). Replicating this study with a much larger sample would enable the testing of whether and how respondents’ socio-demographic characteristics affect parameter estimates.
In this thesis respondents’ behaviour intentions were of interest. However, while intentions have been found to be the end of an individual’s conscious choice process (Bamburg and Schmidt 2003, Bamburg and Möser 2007) not everyone acts in accordance with their intentions (Conner and Armitage 1998). This may be because as Ajzen (1985) explains, intentions may change between the time of measurement and when actions are performed, while behaviour may also be influenced by factors which people may have limited control over. For example factors such as available information, skills and abilities, willpower, time and opportunity could affect the extent to which intentions lead to behaviour (Ajzen 1985), while strong habits can also reduce the extent to which intentions lead to behaviour (Verplanken and Wood 2006). A longitudinal study could provide insight into the extent to which pro-environmental air-travel related behaviour intentions result in actually engaging in pro-environmental behaviours and enable the investigation of the extent to which habits and a lack of control affect air-travel related behaviours.
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