"The second relationship concerns with attitudes: “I have never joined a cleanup drive’; ‘I havenever attended a meeting related to ecology?. Although these statements are phrased in anegative manner but still, they do reflect a positive ecological behaviour. In this case, theoutcome 0.148, (p=0.000) which is positive and significant but particularly weak. The thirdrelationship relates environmental attitude with ecological behaviour. In this case, the constructof environmental attitude is formed by one dimension, which consists of two items pertaining tothat “I would donate a day’s pay to a foundation to help improve the environment and Isubscribe to ecological publications”. These findings produce positive but not significantrelationships (0.978 and p=0.115) (computed data).Similarly “I would donate a day’s pay to afoundation to help improve the environment and I make a special effort to buy products inrecyclable containers” These findings produce positive but not significant relationships (0.053and p=0.509>0.05) between the variables. Therefore this does not support hypothesis H3, whichstated that people with a major environmental attitude would have stronger environmental andbehavioural commitment. So the null hypothesis is rejected6.0 Conclusion:This study examines the influence of several factors on ecological behavior, proposes andvalidates a conceptual model. The results from the reproduced correlation matrix table6supportsome of our hypothesized relationships and, in particular, confirm significant influence ofenvironmental affect on ecological behaviour. In Kolkata, policy-makers and green marketershave recently become more aware of the damage being caused to the environment and theconsequences that has in its economy. So, new opportunities and challenges are arising in themarket. Thus, it is important that producers know how new consumers are going to behave andtake action to address their needs, emotions and attitudes.7.0 Future implication of researchThere is a scope of improving the proportion of explained variance of ecological behaviour andintroducing new measures within the model. One can include environmental knowledge16 variables as an important predictor (Kaiser et al., 1999a, b). Generally, the proposed frameworkhas worked correctly although there are asymmetric influences of ecological affect andenvironmental attitudes towards ecological behaviour that need to be explained. This will be apriority for future research.Reference:1. Arbuthnot, J. (1977) The roles of attitudinal and personality variables in the prediction ofenvironmental behavior and knowledge. Environmentand Behavior, 9, 217–232.2. Arbuthnot, J. &Lingg, S. (1975) A comparison of French and American environmentalbehaviours, knowledge and attitudes. International Journalof Psychology, 10, 275–281.3.Bagozzi, R.P. & Yi, Y. (1988) On the evaluation of structural equationmodels. Journal of theAcademy of Marketing Science, 16, 74–94.4Batson, C., Bolen, M., Cross, J. &Neuinger-Benefiel, H. (1986) Where isthe altruism in thealtruistic personality? Journal of Personality and Social Psychology, 50, 212–220.5Bhate, S. & Lawler, K. (1997) environmentally friendly products: factors that influence theiradoption. Technovation, 17, 457–465.6. Chan, R.Y.K. (1999) Environmental attitudes and behavior of consumers in China: surveyfindings and implications. Journal of International Consumer Marketing, 11, 25–52.7. Chan, R.Y.K. (2001) Determinants of Chinese? consumers? green purchasebehavior.Psychology and Marketing, 18, 389–413.8. Daniere, A.G. & Takahashi, L.M. (1999) Environmental behaviour in Bangkok, Thailand: aportrait of attitudes, values and behaviour. Economic Development and Cultural Change, 47,525–55717 Annexure:Table no-6-Reproduced correlation among variables Reproduced CorrelationsReproCorrln AF1 AF2 AF3 AF4 AF5 AF6 AF7 VC1 VC2 VC3 VC4 VC5 VC6 VC7 AC1 AC2 AC3 AC4 AC5 AC6 AC7 AC8 a .820 .106 .696 -.443 -.295 -.448 -.497 .396 -.722 -.639 -.672 -.672 -.542 .537 -.694 -.535 -.535 -.660 .052 .394 -.074 -.706AF1a AF2 .106 .890 .251 .222 .480 .135 -.260 -.409 .232 .013 .289 .289 .558 .108 .253 .205 .215 -.306 .040 -.401 .486 .172a AF3 .696 .251 .891 -.097 -.155 -.062 -.293 .556 -.384 -.212 -.370 -.370 -.332 .790 -.441 -.130 -.139 -.452 .188 .421 -.095 -.475a AF4 -.443 .222 -.097 .537 .319 .521 .277 -.118 .624 .649 .556 .556 .478 -.018 .510 .568 .575 .408 .209 -.149 .149 .480a AF5 -.295 .480 -.155 .319 .872 .556 .338 -.262 .330 .089 .259 .259 .428 -.080 .252 .124 .084 -.043 .178 -.198 .599 .185a AF6 -.448 .135 -.062 .521 .556 .766 .627 .103 .581 .567 .499 .499 .388 .123 .464 .533 .486 .379 .127 -.071 .138 .433a AF7 -.497 -.260 -.293 .277 .338 .627 .849 .182 .447 .407 .431 .431 .216 -.032 .454 .442 .363 .468 -.271 -.162 -.191 .478a VC1 .396 -.409 .556 -.118 -.262 .103 .182 .833 -.361 -.078 -.416 -.416 -.606 .646 -.455 -.145 -.185 -.083 .146 .644 -.381 -.435a VC2 -.722 .232 -.384 .624 .330 .581 .447 -.361 .907 .840 .902 .902 .778 -.269 .884 .838 .838 .636 -.085 -.521 .051 .871a VC3 -.639 .013 -.212 .649 .089 .567 .407 -.078 .840 959 .787 .787 .552 -.083 .740 .842 .857 .724 .100 -.220 -.123 .739a VC4 -.672 .289 -.370 .556 .259 .499 .431 -.416 .902 .787 .964 .964 .848 -.275 .960 .888 .880 .597 -.305 -.690 -.047 .955a VC5 -.672 .289 -.370 .556 .259 .499 .431 -.416 .902 .787 .964 .964 .848 -.275 .960 .888 .880 .597 -.305 -.690 -.047 .955a VC6 -.542 .558 -.332 .478 .428 .388 .216 -.606 .778 .552 .848 .848 .897 -.319 .847 .698 .699 .337 -.239 -.752 .222 .814a VC7 .537 .108 .790 -.018 -.080 .123 -.032 .646 -.269 -.083 -.275 -.275 -.319 .777 -.340 -.024 -.054 -.290 .151 .434 -.165 -.364a AC1 -.694 .253 -.441 .510 .252 .464 .454 -.455 .884 .740 .960 .960 .847 -.340 .970 .860 .848 .597 -.379 -.741 -.065 .971a AC2 -.535 .205 -.130 .568 .124 .533 .442 -.145 .838 .842 .888 .888 .698 -.024 .860 .921 .910 .597 -.239 -.495 -.198 .858a AC3 -.535 .215 -.139 .575 .084 .486 .363 -.185 .838 .857 .880 .880 .699 -.054 .848 .910 .911 .601 -.184 -.480 -.175 .844a AC4 -.660 -.306 -.452 .408 -.043 .379 .468 -.083 .636 .724 .597 .597 .337 -.290 .597 .597 .601 .718 -.064 -.198 -.240 .632a AC5 .052 .040 .188 .209 .178 .127 -.271 .146 -.085 .100 -.305 -.305 -.239 .151 -.379 -.239 -.184 -.064 .885 .624 .440 -.430a AC6 .394 -.401 .421 -.149 -.198 -.071 -.162 .644 -.521 -.220 -.690 -.690 -.752 .434 -.741 -.495 -.480 -.198 .624 .898 -.011 -.743a AC7 -.074 .486 -.095 .149 .599 .138 -.191 -.381 .051 -.123 -.047 -.047 .222 -.165 -.065 -.198 -.175 -.240 .440 -.011 .709 -.13718 AC8a-.706 .172 -.475 .480 .185 .433 .478 -.435 .871 .739 .955 .955 .814 -.364 .971 .858 .844 .632 -.430 -.743 -.137 .983R-.005 -.057 -.154 .084 -.027 -.036 .045 .036 .014 .019 .019 .022 .019 .048 -.005 -.017 .153 .025 .036 .038 .037e AF1s AF2 - - i -.005 .008 -.009 1.091E -.019 .057 9.194E -.029 -.028 -.039 -.039 -.025 -.044 -.018 .012 .041 .083 .041 -.018 -.049 .008d -5 -5u AF3 -.057 .008 .037 -.026 -.032 .060 .035 .003 .002 -.001 -.001 .006 -.111 -.013 -.017 .003 -.055 .021 -.027 -.007 .000a b AF4 -.154 -.009 .037 -.088 .161 -.076 -.073 -.131 -.068 -.050 -.050 -.036 -.040 -.056 -.048 -.012 -.099 -.112 -.094 -.124 -.052lAF5 - .084 1.091E -.026 -.088 -.078 .016 -.033 -.016 .026 .009 .009 .053 .022 .015 -.024 .027 .062 .067 .041 -.104 .008-5AF6 -.027 -.019 -.032 .161 -.078 -.156 -.004 -.065 -.018 -.020 -.020 -.053 -.007 -.016 -.010 -.026 .004 -.110 -.072 .014 -.028AF7 -.036 .057 .060 -.076 .016 -.156 .002 .025 -.010 -.013 -.013 .016 -.042 -.014 .023 .049 -.015 .093 .015 -.023 .015VC1 - .045 9.194E .035 -.073 -.033 -.004 .002 .048 .016 .022 .022 .020 -.130 .014 -.007 -.012 -.027 .024 -.033 .084 .020-5VC2 .036 -.029 .003 -.131 -.016 -.065 .025 .048 .023 .039 .039 .007 .017 .032 .013 -.043 -.019 .000 .054 .110 .026VC3 .014 -.028 .002 -.068 .026 -.018 -.010 .016 .023 .027 .027 .014 -.001 .011 -.009 -.014 -.031 .002 .024 .010 -.003VC4 .019 -.039 -.001 -.050 .009 -.020 -.013 .022 .039 .027 .036 .035 .013 .025 -.026 -.040 -.035 -.005 .042 .020 .007VC5 .019 -.039 -.001 -.050 .009 -.020 -.013 .022 .039 .027 .036 .035 .013 .025 -.026 -.040 -.035 -.005 .042 .020 .007VC6 .022 -.025 .006 -.036 .053 -.053 .016 .020 .007 .014 .035 .035 -.010 .026 -.068 -.020 -.039 .057 .049 -.087 .008VC7 .019 -.044 -.111 -.040 .022 -.007 -.042 -.130 .017 -.001 .013 .013 -.010 .024 .022 -.033 .047 -.050 .079 .030 -.003AC1 .048 -.018 -.013 -.056 .015 -.016 -.014 .014 .032 .011 .025 .025 .026 .024 -.022 -.038 .028 -.003 .040 .024 .015AC2 -.005 .012 -.017 -.048 -.024 -.010 .023 -.007 .013 -.009 -.026 -.026 -.068 .022 -.022 .045 .021 -.004 -.034 .084 .000AC3 -.017 .041 .003 -.012 .027 -.026 .049 -.012 -.043 -.014 -.040 -.040 -.020 -.033 -.038 .045 .010 .059 -.053 -.049 -.010AC4 .153 .083 -.055 -.099 .062 .004 -.015 -.027 -.019 -.031 -.035 -.035 -.039 .047 .028 .021 .010 .011 .004 .022 .034AC5 .025 .041 .021 -.112 .067 -.110 .093 .024 .000 .002 -.005 -.005 .057 -.050 -.003 -.004 .059 .011 .010 -.077 .014AC6 .036 -.018 -.027 -.094 .041 -.072 .015 -.033 .054 .024 .042 .042 .049 .079 .040 -.034 -.053 .004 .010 -.012 .01819 AC7 .038 -.049 -.007 -.124 -.104 .014 -.023 .084 .110 .010 .020 .020 -.087 .030 .024 .084 -.049 .022 -.077 -.012 .023AC8 .037 .008 .000 -.052 .008 -.028 .015 .020 .026 -.003 .007 .007 .008 -.003 .015 .000 -.010 .034 .014 .018 .023 Extraction Method:Principal ComponentAnalysis.a. Reproduced communalitiesb. Residuals are computed between observed and reproduced correlations. There are 47 (10.0%)non redundant residuals with absolute values greater than 0.05. 20 "