INVESTIGATING YOUTH PERCEPTIONS OF ENVIRONMENTAL ISSUES

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1 INVESTIGATING YOUTH PERCEPTIONS OF ENVIRONMENTAL ISSUES ABSTRACT Lee Kwee Fah Universiti Tunku Abdul Rahman, Malaysia Tuam Kwok Choon Universiti Tunku Abdul Rahman, Malaysia Environmental issues can harm the community and nature. Understanding of the environment is an important factor in changing human behavior towards a responsible social engagement with the environment. However, prior to this, there must be awareness of environmental issues. Despite the importance, a review of the literature shows few studies on this area. As a large proportion of the population comprise of youths, they have a major impact on the environment. Thus, this study attempts to investigate youth awareness of the environment, based on their perceptions. An online survey was conducted. Two hundred ninety eight responses were received. A total of thirty one scale items was tested using exploratory factor analysis employing principal component analysis with Varimax rotation. Twenty five items were reduced to four factors of youth perceptions of environmental issues. These four factors account for 55.2% of the total variance explained. JEL Classifications: Keywords: youth, perceptions, environment Corresponding Author s Address: leekf@utar.edu.my INTRODUCTION Environmental issues are hurting the community and nature. Unmanaged pollution, for example, can endanger lives and contribute to natural disasters. Dumping of organic waste, for example, can cause environmental hazard. The issues of global warming caused by pollutions and environmental damages have led to global concerns by governments of nations and if left unchecked, they will lead to planet destruction and lives. It is a great understatement to say that humans must be taught to live responsibly towards the surrounding environment and promote environmental protection for all living beings, creatures and organisms in this rare life supporting planet of this universe. The key words to environmental protection are sustainable development and global environmental sustainability. Understanding the environment is an important factor in changing human behaviour towards a more responsible social engagement with the environment. Education brings about the process of awareness and motivation to individuals to act and respond to the environment in a more responsible manner (Hungerford & Volk, 1980). In Malaysia, it is heartening to know that initiatives have been taken by some local universities to establish research groups to study the environment and behavior of children and youth (Abbas, 2010). Nevertheless, youth engagement is still lacking, as most of the participants of Malaysia s Youth Programme are limited to students of tertiary institutions, and participation by students in programmes initiated by political parties are strictly prohibited by law. Research on assessing the perception, awareness and concerns of youth towards environmental issues has been dearth. Thus, the researchers have decided to investigate this area of concern. LITERATURE REVIEW According to the theory of consumer behavior (TRA), a person s behavior is affected by his overall evaluation of a concept, i.e. that a consumer s buying behavior is positively affected by his/her positive evaluation of a given product brand (Peter et al., 1999). The theory also asserts that actual behavior is strongly influenced by one s intention which in turn is affected by one s attitude towards behavior and norm (Chau & Liqing, 2010). This is also applicable for youths and their attitudes and behaviour towards environmental issues. By extension, the degree of perception of youth towards factors that affect environmental issues will also predict their attitudes and civic behavior towards the issues. As perceptions are subjective and likely to be varied comprising of a number of items, this research sets out to investigate the factors that make up perceptions through an exploratory factor analysis. A study by Fien, Irene, Yencken, Sykes and Treadgust (2002) shows the impact and pervasiveness of global environmental concerns over local cultural influences in affecting youth attitudes towards the environment. It is the researchers belief that this is a global concern which likewise affects Malaysian youth. Youth range is defined as between the ages of years old in the 1997 National Youth Development Policy.

2 A great challenge in this study is to measure environmental knowledge of today s youth in a meaningful way. This attempt is to measure their knowledge based on different categories of perceptions. RESEARCH METHODOLOGY The study was designed to provide an understanding of youths perceptions of the government, the social community (local people), educational institutions and the business community towards environmental issues. Survey method was employed. Since there are no directly applicable questions, the survey questions are modified from a few studies which examined youth perceptions of the environment (Lee 2008, Schneider 1999, Crouch 2004). Data was collected using self-administered questionnaire (Appendix 1) via the internet. Since the internet is used extensively by young people, it is a suitable medium to conduct a survey on youth perceptions. The survey questions were posted online through Universiti Tunku Abdul Rahman s online polling system from in the month of May, A total of 306 responses were received. However, upon closer inspection 5 responses were removed as they were unusable due to illogical replies such as giving the same answer to every question. Thus, 301 responses were accepted. DATA ANALYSIS The original data set included 32 scale items which measure youth perceptions. Screening and cleaning of the data was done. One item (Question 34) was removed as a visual inspection of the data reveal that the overall respondents have misunderstood the question by giving replies that were inconsistent with the replies to the other questions. Next, frequencies analysis was run on SPSS. It reveals that there are 3 respondents who are not within the defined youth age limit of years old. Hence, these 3 are removed from the data for further analysis. The balance 298 responses were retained for further analysis. The data was checked for reliability using SPSS. The measurement for reliability, Cronbach s Alpha coefficient is.910 indicating excellent internal consistency (Appendix 2). Other related measures are also shown in Appendix 4. The Corrected Item-Total Correlation value for PerEI5 is showing a low value of.060 (less than.3) indicating that the item is measuring something different from the scale as a whole (Pallant 2011). Thus, this item may be considered for removal later on. Then factor analysis based on principal component analysis (PCA) was employed on the remaining 31 items which measure perception and the sample of 298 respondents using SPSS version 20. Prior to performing PCA, the suitability of the data for factor analysis was assessed. TABLE 1. KMO AND BARTLETT S TEST Kaiser-Meyer-Olkin Measure of Sampling Adequacy.883 Bartlett s Test of Sphericity: Approx. Chi-Square df 465 Sig The Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) value is.883 and the Bartlett s Test of Sphericity value is significant (p=0.000). Since the KMO value is.6 or above and Bartlett s test is significant (i.e. the Sig. value should be.05 or smaller), therefore factor analysis is appropriate. The anti-image correlation matrix is another measure of sampling adequacy (MSA). The diagonal elements in the anti-image correlation matrix are all above 0.5, thus the data matrix is suitable for factor analysis. A visual inspection of the Correlation Matrix table reveals that the correlation coefficients of most of the items are neither too small (<.1) or too large (>.9), indicating moderate partial correlations between the items. The only exception is PerEI5 which has a substantial number of small correlations (<.1) with other items with 13 out of 30 correlations (i.e %) <.1. PerEI5 also shows a substantial number of insignificant correlation (p<0.05, sig) with other items. Other items are generally significant. Therefore, all items except for PerEI5, are suitable for inclusion in factor analysis. To determine how many components (factors) to extract, Kaiser s criterion is used. Only components having an eigenvalue of 1 or more are considered. In the Total Variance Explained table (Appendix 3), the Initial

3 Eigenvalues column shows seven components with eigenvalues above 1 (8.682, 3.157, 2.022, 1.558, 1.213, 1.153, 1.056). These seven components explain a total of per cent of the variance. The unrotated component matrix did not have a completely clean set of factor loadings (there are substantial cross-loadings and did not maximize the loadings of each variable on one factor), hence a rotation technique is applied to improve the interpretation. In this case, the VARIMAX rotation is used later on and its impact on the overall factor solution will be described as well. The values of communality are also examined. The size of the communality is a useful index for assessing how much variance in a particular variable is accounted for by the factor solution (Hair et al 2010). No statistical guidelines indicate exactly what is large or small (Hair et al 2010). For practical considerations, it is decided that a level of.40 and above is used for communalities in this analysis since there are many variables being investigated. One item (PerEI4,.380) is below.40. However, as PerEI4 is quite close to.4, it is retained in the analysis. In summary, PerEI5 is deleted since a few tests demonstrate the unsuitability of including this variable in factor analysis. All other variables are retained for factor analysis. Next, we proceed to rotate the four-factor solution using Varimax rotation. The rotated factor solutions show that all the variables load on one factor only indicating no problem with cross loadings. However, the communality of a few items fell below the.40 cut-off. Based on this, it is decided that these five items be deleted namely PerLP8 (.370), PerEI3 (.367), PerEI4 (.312), PerBC2 (.295) and PerBC3 (.333). Then Varimax rotation is run again on SPSS. The results are displayed below. TABLE 2. TOTAL VARIANCE EXPLAINED Initial Eigenvalues Rotation Sums of Squared Loadings % of Cumulative % of Cumulative Component Total Variance % Total Variance % After the second Varimax rotation, the Total Variance Explained by the 4 components increased by 5% from % to % indicating improvement in the total explanatory power by the four factors after deletion of the five items mentioned above. The communalities of the remaining 25 items are all above the cut-off of 0.40 and still within the acceptable range (see Appendix 4). Therefore, the communalities of the 25 items are of sufficient size to warrant inclusion in the factor model. The Rotated Component Matrix is shown below. Each item load on one factor only at above This figure is considered significant as the sample size is 298 respondents (Hair et al 2010). With the simplified pattern of loadings, all communalities above 40 per cent, and the overall level of explained variance high enough, the 25-variable/four-factor solution is accepted, with the final step to describe the factors. TABLE 3. ROTATED COMPONENT MATRIX Component PerGov2.800 PerGov6.786 PerGov3.771 PerGov4.757 PerGov1.714 PerGov5.673 PerGov9.578 PerLP4.811 PerLP5.742 PerLP2.686 PerLP3.678 PerLP7.643 PerLP1.640 PerLP6.530

4 PerEn2.706 PerEn.665 PerGov8.658 PerGov7.620 PerEn1.608 PerEn4.533 PerBC1.771 PerBC5.734 PerEI1.670 PerBC4.620 PerEI2.538 Each factor is named based on the variables with significant loadings: 1. Factor 1 Perception of the Government. All the variables with significant loadings are related to the government i.e. on the government administration and policies. 2. Factor 2 Perception of the Local People. All the variables with significant loadings are related to the people who live in the same community as the respondent. 3. Factor 3 Perception of the Environment. All the variables with significant loadings are related to the physical environment. 4. Factor 4 Perception of the Organisation. All the variables with significant loadings are related to perceptions of the organization in which the respondent work/study. CONCLUSION Exploratory factor analysis was successfully conducted on the variables (scale items) obtained from the survey questionnaires on youth perceptions of the environment. The factor model shows significant loadings of the items on 4 factors indicating that youth perceptions of environmental issues can be represented by 4 categories. The four factors (categories) are Perception of the Government, Perception of the Local People, Perception of the Environment, and Perception of the Organisation. Twenty five items were reduced to four factors making interpretation of the variables much easier. The four factors account for 55.2% of the variance of the 25 items/variables, deemed sufficient in terms of total variance explained. It serves as a starting point for further analysis which may be conducted on the four factors to investigate whether or not there are any relationships between youth perceptions and attitudes towards the environment as well as their behavior towards environmental issues. REFERENCES Abbas, M.Y., Children, Youth & Environments (CYE): Lessons for Developing Countries, Procedia - Social and Behavioral Sciences, 2010, Vol. 38, pp Chau, V. S., & Liqing W.L.C. Ngai, The youth market for internet banking services: Perceptions, attitude and behavior, The Journal of Services Marketing, 2010, Vol. 24, pp doi: / Crouch, C. V., An investigation of perceptions, concerns, and awareness of environmental issues among American Indians, Oklahoma State University, ProQuest Dissertations and Theses. Fien, J., Irene Teh-Cheong Poh, A., Yencken, D., Sykes, H., & Treagust, D., Youth environmental attitudes in Australia and Brunei: implications for education, Environmentalist, 2002, Vol. 22, pp Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E., Multivariate data analysis (7 th ed), Pearson, N.J. Hungerford, H.R. and Volk L.V., Changing learner behavior through environmental education, Retrieved E E8-8C3D- 50EBE1847CB8%7D/Changing%20learner%20behavior%20-%20 H%20and%20V.pdf Lee, K., Opportunities for green marketing: young consumers, Marketing Intelligence & Planning, 2008, 26, Pallant, J., SPSS survival manual, McGrawHill, Australia. Schneider, S.A., The environmental concern of youth at a YMCA adventure camp, Southern Illinois University, ProQuest Dissertations and Theses.

5 APPENDIX 1 Questionnaire Items 1. Education Level 2. Marital Status 3. I am currently 1- Studying 2 - Working 3 - Unemployed 4. Annual gross income 5. Ethnicity 6. Sector you're currently in 1 - Public 2 - Private 7. I am a member of an environmental organization 1 - Yes 2 - No 8. Nationality 1 Malaysian 2 - Non-Malaysian Questions 9 40 are all in 5-point Likert Scale from 1 Strongly disagree to 5 Strongly agree 9. My government is doing a good job in protecting the environment (PerGov1) 10. There are sufficient laws and enactments in my country to protect the environment (PerGov2) 11. There is sufficient law enforcement in my country to protect the environment (PerGov3) 12. There is sufficient national budget allocation for the protection of the environment (PerGov4) 13. Our governmental departments and building structures are environmentally clean and green (PerGov5) 14. Our ministers are concerned about environmental issues affecting our country (PerGov6) 15. Our roads and streets are clean and green (PerGov7) 16. Our roads and streets are free of pollution (PerGov8) 17. My government supports and encourages recycling activities (PerGov9) 18. The young people of my community are civic-conscious towards the environment (PerLP1) 19. The elderly people of my community are civic-conscious towards the environment (PerLP2) 20. The people of my community do not litter on streets (PerLP3) 21. The people of my community participate in keeping the environment clean (PerLP4) 22. The people in my community maintain a green garden and green landscape (PerLP5) 23. My community organizes get-together community cleanup projects from time to time (PerLP6) 24. My people will participate and contribute to environmental watchdog organizations when asked to (PerLP7) 25. The people of my community do not practise open burning (PerLP8) 26. I come from an education institution which supports environmental issues (PerEI1) 27. The schools in my country are environmentally clean and green (PerEI2) 28. My course has syllabuses that cover environmental issues (PerEI3) 29. I actively participate in one or more student societies that address environmental issues (PerEI4) 30. I believe that educational institutions should be vocal about environmental issues (PerEI5) 31. The organization I work/study in places emphasis on managing the environment (PerBC1) 32. The shopping malls and supermarkets in my country organize events relating to managing the environment (PerBC2) 33. The small business premises in my business community are environmentally clean (PerBC3) 34. The market place is one of the least clean places in my community 35. My organization sets aside a budget to contribute to environmental issues annually (PerBC4) 36. I have a clean and green working environment in my workplace/learning institution (PerBC5) 37. The air quality in my country is in the range of good to excellent (PerEn1) 38. The rivers in my country are clean and not polluted (PerEn2) 39. The waste management (e.g. garbage disposal methods) in my country is efficient and effective (PerEn3) 40. The community and surroundings in my country have green landscapes (PerEn4)

6 APPENDIX 2 Reliability Statistics Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items Scale Mean if Item Deleted Item-Total Statistics Scale Corrected Variance if Item-Total Item Deleted Correlation Squared Multiple Correlation Cronbach's Alpha if Item Deleted PerGov PerGov PerGov PerGov PerGov PerGov PerGov PerGov PerGov PerLP PerLP PerLP PerLP PerLP PerLP PerLP PerLP PerEI PerEI PerEI PerEI PerEI PerBC PerBC PerBC PerBC PerBC PerEn PerEn PerEn PerEn

7 APPENDIX 3 Proceedings of the Australian Academy of Business and Social Sciences Conference 2014 Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Comp onent Total % of Variance Cumulative % Total % of Variance Cumula tive % Total % of Variance Cumula tive % Extraction Method: Principal Component Analysis.

8 APPENDIX 4 Communalities Initial Extraction PerGov PerGov PerGov PerGov PerGov PerGov PerGov PerGov PerGov PerLP PerLP PerLP PerLP PerLP PerLP PerLP PerEI PerEI PerBC PerBC PerBC PerEn PerEn PerEn PerEn Extraction Method: Principal Component Analysis.