rcpsRevista Colombiana de PsicologíaRev. Col. Psicol.0121-54692344-8644Universidad Nacional de ColombiaColombiarevpsico_fchbog@unal.edu.co10.15446/rcp.v32n1.92739Monographic Section: Advances in Judgement and Decision MakingGreen Products Purchase Intention in Chilean Consumers: Comparing Three
Models Using Structural EquationsIntención de Compra de Productos Ecológicos en Consumidores Chilenos:
Comparación de Tres Modelos por Ecuaciones Estructuraleshttps://orcid.org/0000-0001-7247-7113SANDOVAL-DÍAZJOSÉ*jsandoval@ubiobio.clhttps://orcid.org/0000-0003-0155-7348LANGDONPATRICIO NEUMANN**Universidad del Bío-Bío, Chillán, Chile /
Centro de Estudios de ÑubleUniversidad del Bío-BíoChileUniversidad del Bío-Bío, Chillán, ChileUniversidad del Bío-BíoChileCentro de Recursos Hídricos para la Agricultura
y Minería (CRHIAM, Chile)Centro de Recursos Hídricos para la Agricultura
y MineríaChile
Universidad del Bío-Bío, Chillán, Chile / Centro de Estudios de Ñuble.
https://orcid.org/0000-0001-7247-7113
Universidad del Bío-Bío, Chillán, Chile / Centro de Recursos Hídricos para la
Agricultura y Minería (CRHIAM, Chile). https://orcid.org/0000-0003-0155-7348
Correspondence concerning this article should be addressed to José
Sandoval-Díaz, Universidad del Bío Bío, Centro de Estudios de Ñuble. e-mail:
jsandoval@ubiobio.clEnero-Junio20233218310113062022130720222023Universidad Nacional de ColombiaEsta obra está bajo una Licencia Creative Commons
Atribución-NoComercial-SinDerivar 4.0 Internacional.Abstract
The objective of this study was to compare three models: the theory of reasoned
action (TRA), the theory of planned behavior (TPB), and its extended form
(ETPB), to predict the intention of Chilean consumers for purchasing green
products. Five hundred people were surveyed, establishing an inter-rater
reliability analysis, and a discriminant, convergent and construct validity
using three structural equation models (SEM). The results showed that the ETPB
model is useful to explain the consumption behavior intention for green products
based on a triple bottom line, whose main contribution is the indirect effect of
environmental concern (EC) on the model. The study found that of the three
variables, perceived behavioral control (PBC) was the strongest predictor of
purchase intention (PI), followed by attitude (ATT). In conclusion, both the
ETPB model and the green products purchase intention instrument contribute to
broaden the studies about consumer behavior in sustainable marketing in Chile.
Resumen
El objetivo del presente estudio fue comparar los modelos de teoría de acción
razonada (TAR), teoría de comportamiento planificado (TCP) y su forma extendida
(TCPE), para predecir la intención de compra de productos ecológicos en
consumidores chilenos. Se encuestaron 500 personas y se estableció un análisis
de fiabilidad interjuez, validez discriminante, convergente y de constructo a
través de modelos de ecuaciones estructurales (SEM). Los resultados señalan que
el modelo TCPE es útil para explicar la intención de comportamiento de consumo
ante productos verdes con base en una triple línea de fondo, cuya principal
contribución es el efecto indirecto de preocupación ambiental (PA) sobre el
modelo. De las tres variables, se evidencia que el control conductual percibido
(CCP) era la predictora más fuerte de la intención de compra (IC), seguida de la
actitud (ACT). En conclusión, tanto el modelo TCPE como el instrumento de
intención de compra de productos verdes contribuyen a la ampliación de estudios
sobre el comportamiento del consumidor en marketing sustentable
en Chile.
Keywordsgreen productspurchase intentiontheory of planned behaviorenvironment scalestructural equation modelingPalabras claveproductos verdesintención de comprateoría de comportamiento planificadoescalas ambientalesmodelo de ecuaciones estructuralesHow to cite this articleSandoval-Díaz, J., & Neumann, P. (2023). Green products purchase
intention in Chilean consumers: Comparing three models using structural
equations. Revista Colombiana de Psicología, 32(1), 83-101.
https://doi.org/10.15446/rcp.v32n1.92739
<bold>Introduction</bold>
DURING THE last few decades, care and concern for the environment have turned into
one of the main priorities for sustainable development at a global scale. This is
reflected in the growing number of international policies oriented towards the
protection of the environment —with the Sustainable Development Goals of the United
Nations (UN) as a prime example (UN, 2015)— and an increasing public
interest in adopting a lifestyle that generates the lowest possible environmental
impact (Vita et al., 2019).
In this context, much of the environmental impact of the population is associated
with consumption behaviors and the processes used for the elaboration of consumed
products. In other words, most of the environmental impact is not caused directly by
household activities, but generated during the production of consumption goods. For
example, in a recent study Yin
et al. (2020) observed that the consumption of goods such as food, clothes
and services represents around 64% of the household carbon footprint in Beijing.
Similarly, Vanham, Gawlik and
Bidoglio (2019) found that in Hong Kong households, the indirect use of water
associated with the consumption of food is around 15 times higher than direct water
consumption. This trend has been observed in practically all the regions of the
world, with environmental footprints related to consumption accounting for more than
84% of total environmental impacts of the population (Tukker et al., 2016). In the case of
Latin America, this situation is also characterized by a significant inequality
between countries, as happens within the socioeconomic sectors of these nations (Zhong et al., 2020).
Moreover, at an operational level, a more sustainable pattern of consumption can be
achieved by encouraging the purchase and utilization of green or ecological
products. Even though there are diverse definitions of what is understood by “green
products”, a simple yet clear definition is provided by Sdrolia and Zarotiadis (2018), who, based
on an extensive review and acknowledging the fact that no product is free of
environmental burdens, define a green product as any product (tangible and
intangible) that minimizes its environmental impact (direct or
indirect) during its entire life cycle and is subject to current scientific and
technological status. Considering that both production and contextual elements
regarding consuming decisions can have a significant effect on the environment,
understanding the sociocultural and psychosocial factors of a green product purchase
decision process becomes of great importance for the development of multiscale
sustainable strategies (Singh
& Gupta, 2013). In this line, behavioral scientific studies are
fundamental to develop strategies to inform consumers about the environmental
consequences of their consumption decisions, guiding them on the basis of Life Cycle
Assessment (LCA) and other environmental analysis tools towards more sustainable
attitudes, practices, and behaviors (Polizzi et al., 2016; Steg & Vlek, 2009).
Considering the above, even though pro-environmental behaviors such as green product
consumption has been studied in Euro-American and Asian countries (Alzubaidi et al., 2020; Gatersleben et al., 2014; Paul et al., 2016) to the
best of our knowledge there is a lack of studies focusing in Latin American
consumers, specifically the Chilean population.
Chile is characterized by having one of the highest environmental impacts per capita
in Latin America, with an estimated carbon footprint of 6.24 tons of CO2
equivalent per year per person in 2018, compared with an average of 4.95 tons of CO
2 equivalent per year per person in the Latin American and Caribbean region (The World Bank, 2020).
Regarding environmental concern, according to data from a recent international study
(Électricité de France [EDF],
2019), 80% of the respondents perceive a bad environmental situation (above
the world average, which is 54%). Likewise, 65% of the Chileans are “much more
concerned” about this issue than they were five years ago, a figure that reaches a
world average of 37% (EDF,
2019). However, this perception of the relevance of environmental issues has
not been translated to an operational characterization of the psychosocial factors
that influence the adoption of sustainable practices, such as attitudes and
intentions of ecological behaviors (Gifford, 2014). This is an important step
for the successful implementation of environmental policies that require
societal-wide adoption.
In line with these antecedents, the last reports of the Intergovernmental Panel on
Climate Change (IPCC, 2018
; 2019) highlight the
relevance of directing the research efforts to better comprehend and potentially
modify the behavior intentions towards more sustainable lifestyles. According to
Ajzen (2002), if people evaluate the objective behavior as positive (attitude),
there will be a higher intention (motivation) for the actual performance of the
behavior. This main idea drives the development of different theoretical-conceptual
models that can be used to understand the consumer’s purchase behavior of green
products (Albayrak et al., 2013
).
Theory of reasoned action [TRA] (Fishbein & Ajzen, 1975).
The TRA model is utilized to assess non-rutinary decisions, using
psychological/cognitive processes to comprehend the contextual decision making of
consumers (Han & Kim, 2010).
Its central principle is behavioral intention (BI), understood as
the disposition towards the performance of the behavior in question (Ajzen, 1985). According to
this model, the purchase intention of green products would indicate
up to what point consumers would be disposed/prepared to buy green products or to
adopt more sustainable options/choices (Paul et al., 2016). In operational terms,
BI is determined by both a) attitudes (ATT), or evaluations and
beliefs about this behavior; and b) subjective norms (SN), or the
normative reasoning related to the social pressure to engage in behavior expected by
the group of belonging (Figure 1).
Theory of reasoned action.
Note: ATT = attitude; SN = subjective norm; PI =
purchase intention; PB = purchase behavior.
Theory of planned behavior (TPB) (Ajzen, 1985). When restrictions
over actions cannot be predicted based only on intention (TRA model), the perceived
behavioral control (PBC) allows us to examine the influence of personal,
social, and non-volitive determinants over the purchase intention, something that
greatly increases the predictability of the TRA model (Han et al., 2010). In the case of green
products consumption, the TPB model evaluates the potential relationship between the
behavior intention and the psychological/cognitive determinants (Jebarajakirthy & Lobo, 2014)
(Figure 2).
Theory of planned behavior.
Note: ATT = attitude; SN = subjective norm; PBC =
perceived behavioral control; PI = purchase intention; PB = purchase
behavior.
Extended theory of planned behavior (ETPB) (Paul et al., 2016). Although
TPB presents a vast amount of empirical evidence backed by diverse fields and areas
(Ajzen, 2006), for Paul et al. (2016) the
context where potential ecological consumers are located (defined by factors such as
country and social group) can condition the voluntary control over behaviors. Thus,
incorporating to the model the environmental concern (EC) as a
determining factor provides a better understanding of green products purchase
intention in developing countries (Paul et al., 2016). EC is defined as the
set of people’s values, feelings, and beliefs for the environment (and its
socio-natural assemblage), their emerging problems, and their individual and
collective willingness to solve them (López-Miguens et al., 2014) (Figure 3). Considering the role that context can
have over ecological consumption, it is likely that consumers in developing
countries do not have or perceive a voluntary control over decisions related to
green products purchase. Therefore, the applications of this type of models should
be validated case to case (Paul
et al., 2016).
Extended theory of planned behavior.
Note: EC = environmental concern; ATT = attitude; SN =
subjective norm; PBC = perceived behavioral control; PI = purchase
intention; PB = purchase behavior.
Against this background, the overall objective of this study is to compare the
empirical adequacy of TRA, TPB, and ETPB models for green product purchase intention
in Chilean population. In terms of the relevance of this work, it is important to
note that psychological research on this topic is still incipient in Latin America (Medina, 2021), therefore
it is necessary to develop and validate instruments and methodologies concordant
with the main socioenvironmental issues that exist at regional and national levels (Reveco-Quiroz et al., 2022).
To achieve this, we analyzed the reliability and validity of the GP (green products)
scale in Chilean population, as the models based on these theories are country
specific and difficult to understand outside their original context (Lee & Green, 1991; Paul et al., 2016).
Finally, we expect that the results of this study provide relevant knowledge about
the determining factors of pro-environmental behavior in Chilean consumers. This
information, combined with the results of LCA studies and other environmental impact
assessment tools, should form the basis for the successful implementation of
policies that encourage sustainable consumption.
<bold>Methodology</bold>
This was a non-experimental, cross-sectional, descriptive, and correlational study
that adapted and assessed the psychometric properties of a scale (León & Montero, 2015).
In particular, it examined the adaptability, reliability, and validity of a scale
that measures the intention to purchase green products (Paul et al., 2016).
<bold>Participants</bold>
The criteria used to select the sample size (n = 480) was
established based on the recommendation of Hair, Anderson, Tatham, & Black (2010) considering a
minimum of 15-20 participants per item for the realization of the structural
equations model (SEM). The original scale for the determining factors of green
products purchase intention (Paul et al. 2016) is made of 24
items. In this regard, we used a non-probabilistic sample by selecting 500
participants, which had to meet the following inclusion criteria: a) being 18 or
more years old, b) residing in the Ñuble region (Chile), and c) voluntarily
agreeing to participate in this study. The socio-demographic characteristics of
the participants are detailed in Table 1
.
<italic>Socio-demographic characteristics of the sample divided by
sex</italic>
Femalen (%)
Malen (%)
Totaln (%)
Total
328
(65.6)
172
(34.4)
500
(100)
Residence
Urban
285
(57)
150
(30)
435
(87)
Rural
43
(8.6)
22
(4.4)
65
(13)
Age
18-30
109
(21.8)
80
(16)
189
(37.8)
31-40
52
(10.4)
26
(5.2)
78
(15.6)
41-50
87
(17.4)
29
(5.8)
116
(23.2)
51
or more
80
(16)
37
(7.4)
117
(23.4)
Educational
LevelCompleted
Primary
55
(11)
26
(5.2)
81
(16.2)
Secondary
171
(34.2)
97
(19.4)
268
(53.6)
University
102
(20.4)
49
(9.8)
151
(30.2)
Income
One
minimum income *
203
(40.6)
81
(16.2)
284
(56.8)
Two
minimum incomes
53
(10.6)
31
(6.2)
84
(16.8)
Three
minimum incomes
27
(5.4)
18
(3.6)
45
(9.0)
Four
or more minimum incomes
45
(9)
42
(8.4)
87
(17.4)
Employment
Unemployed
and/or studying
131
(26.2)
52
(10.4)
183
(36.6)
Simple
jobs
59
(11.8)
33
(6.6)
92
(18.4)
Technical
jobs
57
(11.4)
39
(7.8)
96
(19.2)
Professional
jobs
72
(14.4)
41
(8.2)
113
(22.6)
Highly
specialized jobs
9
(1.8)
7
(1.4)
16
(3.2)
Note: Since March 1, 2020 the gross amount
is $318,000 Chilean pesos, equivalent to $476.56 dollars
(Law No. 21.112).
<bold>Instruments</bold>
Sociodemographic survey. A sociodemographic characteristics
instrument was elaborated. It included: a) place of residence, b) gender, c)
age, d) educational level, e) monetary income, and f) employment, and was based
on the survey format of the National Socio-Economic Characterization Survey
(CASEN in Spanish) (Ministerio
de Desarrollo Social y Familia, 2018).
Green products scale. This instrument consists of 24 items derived
from the extended theory of planned behavior proposed by Paul et al. (2016). The scale is
subdivided into dimensions and consists of: a) three items of attitude
(for example, “I like the idea of purchasing green products”); b) four items of subjective
norm (for example “People who are important to me think that I should
purchase green products”); c) seven items of perceived behavioral
control (for example, “I believe I am capable of purchasing green
products”); d) five items of environmental concern (for
example, “I am very concerned about the environmental problems”); and e) five
items of purchase intention (for example, “I will consider
buying less polluting products in the future”). A five-point Likert scale was
used for each item, where 1 = strongly disagree and 5 = strongly
agree. Regarding Cronbach’s alpha internal consistency, the five
dimensions (constructs) of the original study presented high values in the range
of α 0.79 and α 0.91.
<bold>Ethical considerations</bold>
The validation of the scale is part of the research project DIUBB 186009 3/I
“Consumo sustentable de bolsas de transporte de productos en el comercio:
evaluación ambiental del ciclo de vida asociado a los patrones del
comportamiento de la población”, which was revised and approved by the Ethics
Committee of the Universidad del Bío-Bío Research Directorate.
<bold>Procedures</bold>
Down below we present the procedure performed to assess the psychometric
properties of the scale:
Instrument administration. Data were collected by undergraduates of
psychology and natural resources engineering programs, who were trained to apply
the instruments considering the following aspects: a) clarification of
objectives, b) an informed consent, c) confidentiality, d) application time, and
e) emerging questions. The instrument was individually applied to householders
or their spouses, provided that they met the inclusion criteria and were willing
to participate in the study. The application was performed in a single moment
between July and December 2018 in the respondents’ residence and had an average
duration of 20 minutes. Before filling out the instrument, the participants read
and signed an informed consent that contained information about the research and
its objectives.
Statistical analysis. Different types of statistical analysis were
implemented. They are described below:
Inter-rater reliability. Fleiss’ kappa coefficient
was initially applied to the analysis of content validity agreement, a variable
used for more than two raters (Falotico & Quatto, 2015).
However, this coefficient could present biased results and not be a good
indicator of inter-rater agreement, as evidenced by the prevalence of higher
categories over the lower ones (Abad et al., 2011). For this reason,
the Randolph’s free-Marginal multirater kappa (2005) was used, with Gwet’s variance
formula (2010),
which was obtained using an online kappa calculator (Randolph, 2008). Regarding the
interpretation of this coefficient, its minimum value is 0 and its maximum is 1,
with the following strength of agreement ranges: a) 0.00 = poor, b) 0.10–0.20 =
slight, c) 0.21–0.40 = fair, d) 0.41–0.60 = moderate, e) 0.61–0.80 =
substantial, f) 0.81–1.0 = Almost perfect (Landis & Koch, 1977).
Descriptive statistics and internal consistency reliability.
Originally, a descriptive analysis was carried out for each item and its
respective dimension. Subsequently, the omega coefficient (ω) was used in the
internal consistency analysis for the five dimensions of the scale (McDonald, 1999). The
corrected item-total correlation was examined through the free graphic software
JASP version 0.11.1.
Convergent and discriminant validity.
On the one hand, for convergent validity, Bagozzi, Yi and Phillips (1991)
recommend that the standardized factor loadings, at a subscale-item level,
should be significant and present values higher than 0.5, as well as a composite
reliability higher than 0.7 (Hair et al., 2010). On the other
hand, for discriminant validity, Fornell and Lacker (1981) suggest
verifying if the square root of the average variance extracted of each dimension
is higher than the square of the correlations between sub-dimensions.
Structural Equations Modeling (SEM).
A confirmatory factor analysis (CFA) was carried out to compare the theoretical
models about the structure of green products purchase behavior (Hair et al., 2010),
using the Mplus v8 software. For the CFA, the robust weighted least squares
method (ULSMV) through polychoric correlations was used to correct possible
problems related to the normal distribution. To contrast model validity and to
make a comparison between rival models (Hair et al., 2010), the following
goodness fit indexes were calculated: a) χ² (chi-square), b) χ2 /df
(coefficient of χ² divided by the degrees of freedom), c) robust comparative fit
index (CFI), d) (Tucker-Lewis index) TLI, and e) root mean square error of
approximation (RMSEA). According to Browne and Cudeck (1993) and Hair et al. (2010), a
model is adequate when it presents the following indexes ≥ 0.90, χ²/df
between 2 and 5 and RMSEAs ≤ 0.08.
<bold>Results</bold>
<bold>Procedures of adaptation and validity of content of the scale</bold>
The original version of the scale was first translated to Spanish by the authors
of this article and an English-Spanish translator. This process involved
carrying out a semantic adaptation of its 24 items while trying to preserve the
sense and clarity of the original questions. The scale was later sent to four
faculty members of Chilean universities with five or more years of experience in
teaching and research in the environmental field and a master’s or doctorate
degree. These judges, who were intentionally selected, belonged to different
disciplines: psychology n = 2, sociology n =
1, and environmental engineering n = 1. They received an e-mail
with the instrument and the assessment form in June 2018 and a request for
submission within two weeks. The four judges agreed voluntarily and free of
charge to participate in the content validation of the instrument.
The assessment of the scale, grouped into the four original dimensions, was
conducted for each item using the experts’ assessment form proposed by Escobar-Pérez and
Cuervo-Martínez (2008) that includes the areas of sufficiency
, clarity, coherence, and relevance,
and establishes four evaluation options: does not meet the criterion, low level,
moderate, and high level. The assessment also considered the judges’ qualitative
observations for each item (Cassepp-Borges et al., 2010).
After tabulating the judges’ responses, the study determined the agreement degree
between them through the kappa coefficient. Regarding the assessment of the
adapted instrument, the calculation of the kappa coefficient considered the
proportion of possible agreements that occurred in each dimension and estimated
the strength of agreement magnitude. This was interpreted from slight (1), fair
(3), and substantial (1) by the group of judges, as detailed in Table 2.
<italic>Agreement coefficient of each dimension of the scale</italic>
Dimensions
Free-marginal kappa
IC 95%
Strength of agreement Interpretation (Landis & Koch,
1977)
Attitude
0.36
0.05–0.66
Fair
Subjective norm
0.21
0.11–0.32
Fair
Perceived behavioral control
0.14
0.06–0.22
Slight
Environmental concern
0.79
0.64–0.95
Substantial
Purchase intention
0.28
0.13–0.42
Fair
Afterwards, the strength of agreement between pairs of experts was calculated.
This revealed a substantial level of agreement in the environmental
concern subscale, followed by purchase intention and
attitude, with consistencies that ranged from slight to substantial. In
contrast, the subscales that presented the greatest disagreement were perceived
behavioral control and subjective norm, as detailed in Table 3.
<italic>Agreement coefficient by dimension between pair of judges</italic>
Dimensions
Free-marginal-kappa–agreement by pair of judges
1-2*
1-3
1-4
2-3
2-4
3-4
Attitude
0.60
0.47
0.47
0.20
0.47
0.07
Subjective norm
0.08
0.08
0.38
0.59
0.08
0.08
Perceived behavioral control
-0.15
0.07
0.10
0.58
0.21
0.03
Environmental concern
0.75
0.75
0.75
0.83
0.83
1
Purchase intention
0.08
0.08
0.42
0.83
0.25
0.42
Note: Number of each judge (judge 1, judge 2, judge 3, and
judge 4).
Finally, a substantial strength of agreement was found in clarity,
while the area related to sufficiency presented a moderate
strength of agreement. Relevance presented a fair strength of
agreement and coherence showed a slight agreement. This is
explained by the redundancy of certain items according to the judges (for
example: similarities in the three attitude items, as well as the generality of
item number 20 regarding environmental impact). This strength of agreement is
represented in Table 4.
<italic>Kappa coefficient in the four assessment areas of the judges </italic>
Areas
Free-marginal-kappa
IC95%
Strength of agreement Interpretation (Landis
& Koch, 1977)
Sufficiency
0.56
0.02–1.00
Moderate
Clarity
0.51
0.36–0.66
Substantial
Coherence
0.12
-0.01–0.26
Slight
Relevance
0.37
0.23–0.51
Fair
Once the content was validated by this expert assessment and the qualitative
recommendations were added, the writing of four items of the instrument was
adjusted (1, 2, 3, and 20). Subsequently, a pilot test was conducted to assess
the applicability/viability of the scale in a sample of 30 psychology students
from the quantitative research methodology class and seven natural resources
engineering students from the life cycle assessment class, both groups from the
Universidad del Bío-Bío. This data was used to refine instructions, the
application format, time, and other aspects that were later included. Appendix 1 presents the final version of
the instrument used.
<bold>Descriptive statistics and internal consistency reliability</bold>
The assessment of the items’ averages, ranged between 2.68 and 4.53 and with
standard deviations between 0.06 and 1.01, proofs the nonoccurrence of a “floor”
(1) or “ceiling” (5) effect in any of the analyzed items. The initial
item-subscale correlations were: a) attitude (between r =
0.63 and r = 0.68), b) subjective norm
(between r = 0.50 and r = 0.76), c) perceived
behavioral control (between r = -0.22 and r =
0.53), d) environmental concern (between r =
0.45 and r = 0.63), and e) purchase intention
(between r = 0.57 and r = 0.74). Only item 14
of the perceived behavioral control subscale obtained a value
lower than 0.30 (r = -0.22), therefore, this item was deleted,
obtaining new item-scale correlations which ranged from r =
0.44 to r = 0.59. Regarding the subscale means, the dimensions
of a) environmental concern (M = 4.48,
SE = 0.22), b) attitude (M = 4.47, SE
= 0.08), and c) purchase intention (M = 4.24,
SE = 0.25) obtained the highest values, which meant a greater presence of these
variables in the sample. The factors that presented the lowest means were the
dimensions of c) subjective norm (M = 3.81, SE
= 0.06) followed by d) perceived behavioral control (M
= 4.02, SE = 0.40). Descriptive statistics and internal consistency coefficients
are summarized in Table 5.
<italic>Descriptive statistics and internal consistency of items and
dimensions of the green products scale (n = 500) </italic>
Note: ATT = attitude; SN = subjective norm; PBC =
perceived behavioral control; EC = environmental concern; PI =
purchase intention; λ = factor loadings; AVE = average variance
extracted.
Subscale/item
M(SD)
Asymmetry
kurtosis
McDonald’s Omega ω
λ
AVE
Composite reliability
Attitude
4.47(0.08)
-1.07
1.50
0.81
ATT1
4.48(0.64)
-1.86
1.60
0.703
0.833
ATT2
4.53(0.63)
-1.54
4.05
0.754
0.851
0.719
0.8845
ATT3
4.38(0.70)
-1.01
1.14
0.739
0.859
Subjective norm
3.81(0.06)
-4.30
-0.03
0.85
SN1
3.81(0.90)
-0.40
-0.23
0.798
0.867
SN2
3.87(0.87)
-0.37
-0.38
0.787
0.899
0.745
0.9210
SN3
3.82(0.98)
-0.80
0.34
0.866
0.898
SN4
3.73(0.96)
-0.40
-0.37
0.782
0.784
Perceived behavioral control
4.02(0.40)
-0.29
-0.03
0.79
PBC1
4.40(0.68)
-0.97
1.04
0.723
0.827
PBC2
4.33(0.77)
-1.17
1.61
0.744
0.838
PBC3
4.40(0.70)
-1.38
3.18
0.724
0.818
0.456
0.8216
PBC4
3.74(0.98)
-0.66
-0.06
0.773
0.589
PBC5
3.50(1.01)
-0.32
-0.76
0.791
0.353
PBC6
3.76(0.92)
-0.63
0.03
0.779
0.454
Environmental concern
4.48(0.22)
-1.02
0.98
0.76
EC1
4.23(0.77)
-1.09
1.82
0.730
0.152
EC2
4.25(0.78)
-1.40
3.05
0.754
-0.794
EC3
4.57(0.68)
-1.80
3.81
0.717
-0.767
0.376
0.6828
EC4
4.66(0.59)
-1.90
4.28
0.674
-0.529
EC5
4.67(0.58)
-1.69
2.25
0.729
-0.635
Purchase intention
4.24(0.25)
-0.63
0.09
0.86
PI1
4.44(0.70)
-1.53
4.03
0.839
0.602
PI2
4.31(0.73)
-1.06
1.86
0.817
0.750
0.564
PI3
3.81(0.90)
-0.50
-0.09
0.849
0.842
0.9151
PI4
4.30(0.71)
-1.02
1.79
0.819
0.710
PI5
4.33(0.79)
-1.32
2.40
0.804
0.825
<bold>Convergent and discriminant validity </bold>
Two approaches were used for the convergent validity analysis. The first one was
based on Table 5, even though all the
items presented statistically significant factor loadings regarding their
dimensions. According to the criterion of Bagozzi et al. (1991) only 22 items
obtained factor loadings over 0.5, except for PBC5 and EC1. The second approach
was the average extracted variance (AEV), with values over 0.5 for the
dimensions of attitude, subjective norm, and purchase
intention, and lower values for perceived behavioral control
and environmental concern. These values were complemented by
composite reliability, obtaining values over 0.7 for all dimensions, except for environmental
concern (Hair et
al., 2010). Finally, in the case of discriminant validity, all the
dimensions were higher than the square of the correlations (Table 6).
<italic>Discriminant validity </italic>
Note: The diagonal values correspond to the square
root of AEV for each dimension.
Dimensions
ATT
SN
PBC
EC
PI
Attitude
(ATT)
0.848
Subjective
norm (SN)
0.190
0.863
Perceived
behavioral control (PBC)
0.282
0.195
0.676
Environmental
concern (EC)
0.244
0.056
0.156
0.613
Purchase
intention (PI)
0.292
0.132
0.384
0.382
0.751
<bold>Model comparison: TRA, TPB, and ETPB </bold>
The construct validity of the three models was assessed through confirmatory
factor analysis, hereinafter called CFA (Lloret-Segura et al., 2010). Following
the rival models strategy (Hair
et al., 2010), an assessment was conducted on the global fit criteria: a)
absolute RMSEA, b) incremental CFI and TLI, and c) parsimony (χ2/
df) for the three models (Table 7
).
<italic>Description of the models and their adequacy criteria </italic>
Indexes FIT & R2
Recommended values
TRA
TPB
etpb
χ2
139.254
329.494
495.825
gl
46
122
209
χ2/gl
2-5
3.027
2.700
2.372
CFI
≥0.9
0.977
0.960
0.965
TLI
≥0.9
0.967
0.950
0.958
RMSEA
≤0.08
0.064
0.058
0.052
R2
ATT
0.633
0.529
0.551
SN
0.275
0.102
0.319
PBC
____
0.471
0.747
EC
____
___
0.820
First, the TRA model presented the following values of unsatisfactory fits (χ
2 = 249.705; df = 51; p < 0.001; χ2/
df = 4.896; CFI = 0.952; TLI = 0.937; RMSEA = 0.088). In order to
improve the model’s fit, it was re-specified based on the residual modification
indices of the items related to the same construct (Lei & Wu, 2007) in the following
pairs of correlations belonging to the same dimension: a) i6 and i4, b) i6 and
i5, c) i22 and i20, d) i23 and i21, and e) i24 and i23. Once the model was
re-specified (Figure 4), an adequate fit
was obtained (χ2 = 139.254; df = 46; p < 0.001; χ2/
df = 3.027; CFI = 0.977; TLI = 0.967; RMSEA = 0.064).
Second, the TPB model presented the following values of unsatisfactory fits (χ
2 = 613.383; df = 129; p < 0.001; χ
2/df = 4.754; CFI = 0.907; TLI = 0.890; RMSEA = 0.087).
The model was re-specified, adding the following correlations: a) i13 and i12,
b) i24 and i23, c) i6 and i4, d) i6 and i5, e) i11 and i9, f) i11 and i12, and
g) i11 and i13. Once the model was re-specified (Figure
5), an adequate fit was obtained (χ2 = 329.494; df =
122; p < 0.001; χ2/df = 2.700;
CFI = 0.960; TLI = 0.950; RMSEA = 0.058).
Finally, the results of the extended TPB presented the following values of
unsatisfactory fits: χ2 = 886.897; df = 221;
p < 0.001; χ2 /df = 4.013; CFI = 0.919;
TLI = 0.907; RMSEA = 0.078. The model was re-specified by adding the theoretical
relationship between ATT and PBC and the empirical correlations between
residues: a) i13 and i12, b) i19 and i18, c) i22 and i21, d) i20 and i19, e) i6
and i4, f) i6 and i5, g) i20 and i18, h) i11 and i9, i) i10 and i8, j) i13 and
i11, and k) i12 and i11. Once the model was re-specified (Figure 6), an adequate extended fit was obtained (χ2
= 495.825; df = 209; p < 0.001; χ2
/df = 2.372; CFI = 0.965; TLI = 0.958; RMSEA = 0.052).
<bold>Discussion</bold>
Our findings indicate that the extended TPB presents a better structural fit than TPB
and TRA for the prediction of green products purchase intention in the studied
sample of Chilean population, despite psychometric differences inside the
environmental concern subscale. Likewise, this study confirmed the evidence of
validity and reliability for the green products scale in the studied population.
In line with the study by Paul
et al. (2016), the ETPB model is useful to explain the consumption behavior
intention for green products based on a triple bottom line. Its main contribution is
the indirect effect of EC, validating the initial hypothesis that positive PBC and
ATT results in a higher probability of green products PI. Even though we observed
that PBC and ATT are the main predictive variables, SN does not present a relevant
influence over the intention of green product purchase, similar to previous studies
in Filipino (Tarkiainen &
Sundqvist, 2005), Indian (Paul et al., 2016), and Indonesian (Asih et al., 2020)
population. In other words, the purchase intention of green products in the studied
Chilean population is mainly explained by personal values and beliefs over the
environment, and not by the social pressure or the potential approval of significant
persons. Nevertheless, it is important to mention that in previous studies using the
TPB model, subjective norm has been observed to be the variable
with the lowest weight, both at a general level (Ajzen, 1991), and in applications to green
marketing (Paul et al., 2016
; Tarkiainen & Sundqvist,
2005).
Our results also suggest that the instrument presents an adequate reliability in the
five assessed dimensions. Only environmental concern showed a fair
range of internal consistency, while the other sub-dimensions are in the range of
substantial and almost perfect. In conceptual terms, future studies should
strengthen the operationalization of the environmental concern
variable, incorporating indicators linked to moral obligation (values and ethic) and
emotions, as these represent influential elements over the attitudes and personal
intentions of green product purchase and consumption (Ghose & Chandra, 2020). Regarding
methodology, it is necessary to offer new evidence of reliability for the
application of test-retest. This a relevant step to refine and broaden the results.
In terms of validity, the results showed evidence based on the convergent,
discriminant, and confirmatory factor structure content. Regarding the first one,
the information provided by the inter-rater agreement assessment suggests that the
agreement ranges fluctuate between fair and substantial, except for the perceived
behavioral control sub-scale. Even though subsequent intra-item and
inter-scale modifications were made —they presented psychometric validity and
reliability— future studies should incorporate more sensitive operationalization
elements for measuring the variable related to the locus of control.
In relation to convergent and discriminant validity, the study found that the perceived
behavioral control and environmental concern sub-scales
were the only ones with marginal values lower than expected according to the average
variance extracted. This was complemented by the composite reliability analysis,
which showed lower than expected values only for the environmental concern sub-scale.
In this regard, it has been shown that the level of environmental concern depends
on the consumers’ home country; being developed countries the ones with greater
levels of concern (Singh &
Gupta, 2013). However, this is inconsistent with the high levels of environmental
concern shown by the Chilean population (EDF, 2019). A possible explanation for
this inconsistency is the type of sample used by Paul et al.’s research (2016), where a high level
of education was used as a control measure for a better understanding of the
situation (Hedlund, 2011
).
Finally, it is necessary to explain the limitations of this study. The first one is
related to the characteristics of the sample, which differ from the original study
in terms of educational level and income. This can be solved with a stratified
probability sampling in future research. At the same time, even if there is no
concluding evidence with respect to the role of the sociodemographic characteristics
over environmental concern, it is important to study different cultural, religious,
and economical contexts (Ghose
& Chandra, 2020). Furthermore, the replication of this study in other
Latin American countries where population and environmental concern are growing is
also desirable (Sandoval Díaz
et al., 2020), as it would allow for a deeper understanding of the
sociocultural and environmental determinants of green product purchase intention (Hazaea et al., 2022).
Second, even though the theory of planned behavior is the most utilized model in
literature to analyze the personal and behavioral factors of green product
purchasing, future studies should incorporate aspects related to the type of product
(and their publicity attributes) and social, environmental, and emotional factors
that influence the consumers (Hazaea
et al., 2022).
The third limitation of this study, also shared by Paul et al.’s investigation (2016), is the fact that
green products are considered as a whole. Future research should specify the type of
product and include attributes such as recyclability, organic production methods,
low embodied carbon, etc. Finally, it is expected that the incorporation of
knowledge about the psychosocial variables involved in the green products purchase
process can facilitate the communication of relevant environmental information to
consumers. Even though tools such as LCA can provide detailed and objective
information about the environmental profile of products, the way in which that
highly technical data is communicated to the population is relevant to effectively
direct their behavior towards more sustainable practices (Polizzi et al., 2016). Our results
suggests that the communication of this information should be oriented towards
influencing purchase intention through the environmental concern/attitude pathway
and highlights the importance of considering the population’s perceived limitations
about their potential for buying green product, including sociocultural, economic,
educational, and other relevant factors affecting behavior (Klöckner, 2015). Given the lack of
studies of concurrent and prospective validity, it is important to advance in a
culture of integral evaluation to generate models that could adequately predict
purchase intention, with results that could be incorporated in decision-making
processes such as the design and implementation of policies that requires a
societal-wide adoption in order to reduce the environmental impact of consumption.
Examples of these policies include limitations on single-use products (such as
carrier bags), subsidies to promote energy-efficient appliances, strategies to
promote car-sharing, and mechanisms to foster the adoption of sustainable means of
transportation, among others (Klöckner,
2015). Given the increasing relevance of these policies in Chile, such as the
Ley 21.368 (2021) that
limits the handing over of single-use plastics in commerce, understanding the
psychological aspects related to their adoption by the population is of paramount
relevance for a successful implementation.
Overall, the results of this study show that the ETPB theoretical constructs are
relevant for the studied population. This implies that in order to effectively
communicate environmental information collected from LCA and other environmental
analysis tools, communication strategies should include messages oriented to
influence cognitive-affective, contextual, and sociocultural factors related to the
purchase decision process.
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https://doi.org/10.1016/j.jenvman.2020.110979110979Notes
Research project DIUBB 186009 3/I “Consumo sustentable de bolsas de transporte de
productos en el comercio: evaluación ambiental del ciclo de vida asociado a los
patrones del comportamiento de la población”, Universidad del Bío Bío.
<bold>Appendix 1</bold>
The applied scale with its adapted and validated content.
Strongly
disagree
Disagree
Not
agree or disagree
Agree
Strongly
agree
1
I
like the idea of buying green products.
2
Purchasing
green products is a good practice.
3
I
have positive feelings towards the purchase of green
products.
4
People
who are important to me think that I should purchase
green products.
5
People
whose opinion I value prefer that I purchase green
products.
6
My
friend’s positive opinions can influence me to
purchase green products.
7
People
who are important to me want me to purchase green
products.
8
I
believe am capable of purchasing green products.
9
If
it were up to me, I am confident that I would
purchase green products.
10
I
believe I will be capable of purchasing green
products in the future.
11
I
have the resources, time and willingness to purchase
green products.
12
Green
products are generally available in the shops where
I do my shopping.
13
I
will probably have many opportunities to purchase
green products.
14
I
feel that purchasing green products is not within my
control.
15
I
am very concerned about the environmental problems.
16
I
would be willing to reduce my consumption in order
to protect the environment.
17
Major
political changes are necessary to protect the
environment.
18
Major
social changes are necessary to protect the
environment.
19
The
laws to protect the environment should be enforced
more strongly.
20
I
will consider buying less-polluting products in the
future.
21
I
will consider switching to green products for
ecological reasons.
22
I
plan to spend more money on green products than
conventional products.
23
I
expect to purchase green products in the future due
to their lower environmental impact.
24
I
definitely want to purchase products with a lower
environmental impact in the future.
<bold>Appendix 2 </bold>
En el siguiente listado de afirmaciones seleccione la
opción que mejor te represente. Tenga claro, que NO
existen respuestas correctas ni incorrectas, tan sólo se quiere
conocer su opinión. Por favor, marque con una X la
opción que mejor lo represente.
<bold>Escala de productos verdes</bold>
Muy
en desacuerdo
En
desacuerdo
Ni
en desacuerdo Ni de acuerdo
De
acuerdo
Muy
de acuerdo
1
Me
gusta la idea de comprar productos verdes.
2
Comprar
productos verdes es una buena práctica.
3
Tengo
sentimientos favorables hacia comprar productos
verdes.
4
Las
personas que son importantes para mí piensan que
debiera comprar productos verdes.
5
Las
personas cuya opinión valoro prefieren que compre
productos verdes.
6
Las
opiniones positivas de mis amigos pueden
influenciarme a comprar productos verdes.
7
Las
personas que son importantes para mí quieren que
compre productos verdes.
8
Creo
que soy capaz de comprar productos verdes.
9
Si
dependiera de mí, confío en que compraría productos
verdes.
10
Creo
que soy capaz de comprar productos verdes en el
futuro.
11
Poseo
los recursos, tiempo y voluntad para comprar
productos verdes.
12
Habitualmente
hay productos verdes disponibles en los lugares
donde compro.
13
Probablemente
tendré muchas oportunidades para comprar productos
verdes.
14
Siento
que comprar productos verdes no está dentro de mi
control.
15
Estoy
muy preocupado por las problemáticas
medioambientales.
16
Estaría
de acuerdo con disminuir mi consumo con tal de
proteger el medio ambiente.
17
Cambios
políticos importantes son necesarios para proteger
el medioambiente.
18
Cambios
sociales importantes son necesarios para proteger el
medioambiente.
19
Las
leyes que buscan proteger el medioambiente deben ser
aplicadas con mayor fuerza.
20
Consideraré
comprar productos menos contaminantes en el futuro.
21
Consideraré
cambiarme a productos verdes por razones ecológicas.
22
Planeo
gastar más dinero en productos verdes en vez de
productos convencionales.
23
Espero
comprar productos verdes en el futuro debido a su
menor impacto ambiental.
24
Definitivamente
deseo comprar productos con menor impacto ambiental
en el futuro.