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dates are June 25, 2008 September 16, 2008; November 24, 2008; March 31, 2009; July 1, 2009; September 30, 2009; and January 7, 2010 respectively.
Table 3.3 Summary of Operational Variables
VARIABLE SUB
VARIABLE DIMENSION
INDICATOR SCALE
Green Marketing
Strategies Vaccaro,
2009: 323 1.Product
Strategy X1
PVC-free and
BFR-free models product systems
on the market Both PVC-free and
BFR-free products
double points Ordinal
2.Production Strategy
X2 Commitment
to reduce
GHG emissions from a
company’s own
operations with
timelines Commitment
to reduce
GHG emissions from own
operations by at least 20 by 2012
Ordinal
3.Distributio nmarket
Strategy X3
Provides effective voluntary
take- back where no EPR
laws Free, easy and global
take-back for
all products
in all
countries where
products are sold Ordinal
4.Promotion Strategy
X4 Disclosure
of carbon
footprint GHG emissions
of company’s own operations and two
stages of
the product
supply chain
Disclosure of ISO 14064-certified GHG
emissions from
company’s own
operations and those of at least two supply
chain stages Ordinal
Economic Sustainability
Fauzi et al., 2010: 1346
Stock Price Y
Stock price
quotation based on Greenpeace’s
Guide to Greener Electronics
timeline Quarterly stock price
quotation in
NASDAQ from June 2008 to January 2010
Ratio
E. Data Analysis Technique
Data analysis is “the application of reasoning to understand the data that have been gathered” Babin and Griffin, 2008. In addition, regression analysis will be used
to test hypotheses formulated for this study. Four independent variables –product, production, distributionmarket, and promotion strategies- are included to analyze
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their influence towards dependent variable, namely stock price. Multiple regressions will determine the relationship between dependent and independent variables, the
direction of the relationship, the degree of the relationship and strength of the relationship. Multiple regression are most sophisticated extension of correlation and
are used to explore the predict ability of a set of independent variables on dependent variable. Four hypotheses then generated, which then give direction to assess the
statistical relationship between the dependent and independent variable. To obtain the best model of research, researcher should perform other pre-
tests. The tests are classical assumption test and regression analysis, which comprises of hypothesis test.
1. Classical Assumption Test a. Multicollinearity Test
Multicollinearity test aims to test if there are correlation inter-independent variables in regression model Santoso, 2010: 203. A good regression model should
not account correlation amongst the independent variables Santoso, 2010: 204. If independent variables correlate one to another, it indicates these variables are not
orthogonal Ghozali, 2006: 96. Orthogonal variable is independent variable of which the correlation value among independent variables equals to zero Ghozali, 2006: 96.
To detect if multicollinearity happens in regression model, Ghozali 2006: 96 suggest researcher to consider the following:
R
2
value of an estimation of empirical regression model is high, but partially any independent variables are not significant influencing dependent one.
