UNDERSTANDING THE ROLE OF CULTURE IN ECO-
INNOVATION ADOPTION – AN EMPIRICAL CROSSCOUNTRY COMPARISON
Completed Research Paper
|
Sebastian Busse
Georg-August-University Göttingen
Chair of Information Management
Sustainable Mobility Research Group
Humboldtallee 3,
37073 Göttingen, Germany sbusse@uni-goettingen.de
|
Vujdan El
Khatib
Georg-August-University
Göttingen
Chair of Information
Management
Platz der Göttinger Sieben 5,
37073 Göttingen, Germany vkhatib@uni-goettingen.de
|
|
University of Freiburg
Information Systems Research
Platz der Alten Synagoge,
79085 Freiburg, Germany
tobias.brandt@is.uni-freiburg.de
|
Georg-August-University
Göttingen
Chair of Management
Information
Systems and Methods
Platz der Göttinger Sieben 5,
37073 Göttingen, Germany
jkranz@uni-goettingen.de
|
Lutz Kolbe
Georg-August-University
Göttingen
Chair
of Information Management
Platz
der Göttinger Sieben 5,
37073 Göttingen, Germany lkolbe@uni-goettingen.de
Abstract
In this paper
we merge research approaches from information systems, social and environmental
psychology, as well as innovation diffusion to investigate the effect of
cultural factors on the adoption of eco-innovations. Specifically, we conduct
an empirical study based on the Decomposed Theory of Planned Behavior and the
Value Belief Norm Theory to estimate how culture influences the intention to
adopt electric vehicles as a surrogate for eco-innovations. In our study we
find evidence that there exist major differences in adoption behavior of
eco-innovations between Germans and Chinese. Furthermore we were able to show
that in contrast to prior findings on innovation adoption, primary sources’
influence was the most important predictor of the intention to adopt electric
vehicles.
Keywords: Cross-cultural
differences, Environmental sustainability, Adoption
1
Introduction
The transportation sector
accounts for about one quarter of total greenhouse gas (GHG) emissions in the
United States and the European Union. Electric mobility can substantially
reduce these emissions by using energy from renewable sources to power
vehicles. On the way towards fully utilizing the potential of electric
mobility, information systems (IS) play an integral part. They can, for instance,
enable „green” charging through the implementation of intelligent charge
control systems or increase the efficiency of electric driving through
information systems within the vehicle, such as GPS or vehicle monitoring
systems (Brandt 2013).
Following the energy informatics research agenda by Watson et al. (2010), Brandt et al. (2012; 2013) have
already begun to analyze how IS can further enhance the benefits gained from
electric mobility. IS enables electric vehicles to be integrated into
residential energy management systems or to be aggregated to provide
substantial storage capabilities to the power grid. In their studies they were
able to show that IS is used at each stage to observe, evaluate, and coordinate
the behavior of the system, increasing energy efficiency and reducing waste.
The electric vehicle is just one
example out of a set of new products and services that help to satisfy and
express an individual’s environmental values. During the past decade, research
in Green IS, which seeks to reduce the environmental footprint of the IT sector
and others, has been another example of this development. These „eco-friendly”
goods and services have lower negative impacts on the environment than their
classic counterparts and are, therefore, called eco-innovations (Pujari 2006).
Yet, to date these products often do not find enough adopters to establish
themselves in their respective markets, despite their obvious individual and
collective benefits. Case in point: despite substantial subsidies around the
globe—with the US, Germany, and China all intending to bring one million
electric cars onto their streets by 2020—actual adoption of electric mobility
has been slow. However, achieving a critical mass of electric vehicles across
these culturally different influenced markets will be essential for
successfully implementing a business environment of additional services around
these vehicles. As information systems have the potential to enhance the
advantages and mitigate the disadvantages of eco-innovations, as shown by Brandt et al. 2012 for
the example of electric vehicles, it is important to first understand electric
vehicle adoption behavior in general to effectively outline and conduct future
IS research that best helps in mitigating the disadvantages.
In this paper, we therefore
adapt research approaches on technology usage and acceptance that are well
established within the IS community to explain the diverging role of
cross-cultural factors in the decisions of individuals to adopt electric
mobility as a surrogate for eco-innovations. Specifically, the paper has the
following objectives:
• We
introduce a comprehensive model to explain the intention of individuals to
adopt electric vehicles as a surrogate for eco-innovations. This provides a
starting point for future IS research to focus on issues that best supports
eco-innovations.
• We
shed light on how cross-cultural differences may affect these intentions,
which, among other factors, are influenced by attitudes and moral norms. This
is achieved by conducting a survey in Germany and China, two of the most
important, but at the same time culturally very distinctive, markets for
electric vehicles and breakthrough innovations in electric mobility.
In the following section we
will proceed by exploring work that is related to our research. Subsequently,
we will present the research model and the resulting hypotheses, after which we
specify our methodological approach. Afterwards, we present the results of our
survey. The last section concludes.
Background
Research on technology
adoption is seen as one of the most elaborated domains in IS research (Venkatesh et al. 2003). To
extend the IS knowledge base as well as identify and explain the root causes
influencing technology adoption, many different research models have been
applied. Most of these models originate from theories that have their roots in
social psychology and behavioral science, such as the Theory of Planned
Behavior (TPB) (Ajzen 1991) and the
Technology Acceptance Model (TAM) (Davis
1989; Davis et al. 1989).
Another popular theory in technology adoption research is Roger's (2003)
Innovation Diffusion Theory (IDT), which focuses on five antecedents of
technology adoption. Later, Tornatzky and
2
Klein (1982) showed that three
of the original five antecedents had the most consistently significant
relationship to innovation adoption: relative advantage, compatibility, and
complexity. Building upon the original TPB, Taylor and Todd (1995a; 1995b) decomposed
the belief structures and used Tornatzky and Klein's (1982) findings to
decompose the attitudinal belief.
The central difference between
eco-innovations and their „classical” counterparts is that some of their
benefits do not immediately concern the adopter. For instance, a sustainable
and environmentally friendly lifestyle, may not change the quality of life of
the person in question, but improve it for future generations. Hence,
eco-innovations cannot be judged using purely utilitarian measures, such as
financial benefits or the desire to „belong” to a particular group, but point
at a deeper moral motivation. For the special case of eco-innovation adoption, Melville (2010) elaborates on the critical
role IS can play in shaping beliefs about the environment and how this belief
may affect individual behavioral intention to adopt IS for sustainability.
Studying the adoption of a low-involvement eco-innovation, Kranz and Picot
(2011) provide evidence that intention to adopt is significantly influenced by
environmental concerns. In terms of high-involvement goods like electric
vehicles, Jansson (2011) and Jansson et al. (2010) combine environmental
psychology research (Stern 2000) and
Roger's (2003) diffusion of innovations literature to explore factors
influencing adoption intention. Their primary finding is that norms, novelty
seeking, and perceived innovation characteristics are the major drivers of high
involvement good eco-innovation adoption.
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 |
 |
 |
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Publication
|
 |
 |
 |
 |
 |
 |
 |
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Davis, Bagozzi and Warshaw (1989)
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X
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X
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X
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Taylor and Todd (1995b)
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X
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X
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X
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Karahanna, Agarwal and Angst (2006)
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X
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X
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Loch, Straub and Kamel (2003)
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X
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X
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X
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Straub, Keil and Brenner (1997)
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X
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X
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X
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Choi and Geistfield (2004)
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X
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X
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X
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Melville (2010)
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X
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X
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Kranz and Picot (2011)
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X
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X
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Jansson et al. (2010)
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X
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X
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Jansson (2011)
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X
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X
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Research
Contribution
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X
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X
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X
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X
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Table 1.Research Contribution
3
Another important stream in
technology-adoption research investigates the role of cultural values and has
found either direct (Elbeltagi et al. 2005;
Lee et al. 2007;
Veiga et al. 2001) or moderating effects
(Dinev et al. 2009; McCoy et al. 2005; Pavlou and Chai 2002) on adoption intention.
Findings suggest that cultural values of individualism, masculinity, power
distance, and uncertainty avoidance significantly affect behavioral intention
(i.e., intention to adopt) (Dinev et al. 2009).
Furthermore Straub et al. (1997) are
able to show that culture influences technology adoption, as they found
differing results when they conducted a survey based on TAM across three
countries. To date, research that integrates findings from cross-cultural
studies and eco-innovation adoption is limited. Therefore, we incorporate
cultural values implicitly by comparing the differences between potential
Chinese and German adopters. We have chosen these two countries as, on the one
hand, their cultural values differ significantly (see Table 5), and on the
other hand, both countries are important markets for electric vehicles.
The Concept of Culture
In the literature a
disagreement still exists in trying to define what culture means. Kroeber and
Kluckhohn (1952) identify 164 definitions of culture. House et al.'s (2004)
GLOBE study defines culture as „shared motives, values, beliefs, identities,
and interpretations or meanings of significant events that result from common
experiences of members of collectives that are transmitted across generations.
Hofstede defines culture as „the collective programming of the mind that
distinguishes the members of one group or category of people from others”
(Hofstede and Hofstede 2005). Further components of definitions of culture
include values, beliefs, norms, material components, symbols, heroes, rituals,
and practices, artifacts, and unconscious assumptions (Ferrante 2003; Hofstede
and Hofstede 2005; Schein 1999).
However, the most common
elements in definitions of culture are values and norms. These are shared
conceptions of what is good, right, appropriate, worthwhile, and important with
regard to human behavior (Ferrante 2003; Jacks and Palvia 2011). Another
distinctive mark of definitions of culture is levels of culture. These levels
are essential for culture studies and to what extent culture is examined.
Figure 1 illustrates these levels by means of the Onion Model (Karahanna et al. 2005).
Figure 1.Karahanna et al. (2005): Onion Model
4
Hofstede's research is still the
most utilized and widespread in cultural comparative studies (Zakour 2007).
Smith and Bond (1999) even consider it unrivaled, for instance with respect to
the survey population. Since 1968 Hofstede consulted 116,000 IBM employees,
which is a yet unbeaten number. Seventy-two countries participated in the
survey. However, limited to one organization only, the results are criticized
as unrepresentative. In addition, cultures develop and change over time and the
data collection has elapsed several decades. Hofstede's approaches only
consider culture on a national level; therefore, his culture term is not free
of stereotyping and does not consider the current research discourse in culture
sciences. Building on the results of Hofstede, the project GLOBE (Global
Leadership and Organizational Behavior Effectiveness Research Program) began a
new study in 1994 (House et al. 2004). This study is considered the youngest
culture research such that cultures are examined up to a very recent point in
time. GLOBE's survey population covers 17,370 persons and the interviewees are
limited to managers of medium-sized enterprises. Hence, the results are also
subject to some restrictions. However, the population is not limited to one
organizational culture, but comprises three different organizational
backgrounds. Therefore, the understanding of culture is closer to the current
research discourse.
Furthermore, House et al.’s
(2004) culture concept is not limited to a national level. They consider
culture as a dynamic concept that can change over time and has inner-cultural
differences. The GLOBE study examined 62 cultures across 59 countries as they
also consulted subgroups like East and West Germany. A special feature of the
GLOBE study lies within the examination of culture as practices and values.
Practices („as is”) explain the way things are done in a certain culture,
whereas values („should be”) describe how things should be done in specific cultures
(House et al. 2004). To explain particular attitudes and moral norms of Chinese
and German cultures, we use a selection of culture dimensions by House et al.
(2004).
The GLOBE study expresses
cultural differences in index scales. A selection of culture dimensions of the
GLOBE study is defined in Table 2. We focus our analysis on the following
selection of dimensions, as they are the most commonly used dimensions in
adoption literature: power distance,
in-group collectivism, uncertainty avoidance, and future orientation. Because of this, we have a good, justifiable
base for finding and explaining the role of culture in the context of adopting
eco-innovations.
Table 2.House et al. (2004) selection of relevant culture dimensions
|
|
Power distance
|
In-group collectivism
|
Uncertainty avoidance
|
Future orientation
|
|
Definition House et
al. (2004)
|
Society members’ expectations
concerning unequal power sharing and the extent to which members maintain
inequality in terms of power relations.
|
Degree to which individuals prefer
memberships in small groups such as the family or prefer to identify with the
collective rather than the individual sphere.
|
Society’s reliance on social norms and procedures to
alleviate the unpredictability of future events.
|
Social encouragement and rewarding of future oriented
behaviors by members.
|
Research Model and Hypotheses
In the following, we develop
our research model (see Figure 2). Traditional research on the adoption of
technological innovations emphasizes that the technology's characteristics
affect adoption or intention to adopt (Arts et al. 2011; Davis et al. 1989).
However, researchers were able to prove that moral norms resulting from an
individual’s ecological awareness also have a strong positive impact on
adoption intention, especially with eco-friendly goods and services such as
alternatively-fueled vehicles (Jansson et al. 2010, Jansson 2011).
Our model builds upon the Decomposed Theory of Planned
Behavior (DTPB) (Taylor and Todd 1995a), being a combination of the Theory of
Planned Behavior (TBP) (Ajzen 1991) and the Innovation Diffusion Theory
(Rogers 2003). All of these theories are
well-established in IS research and beyond
(Venkatesh et al. 2003). As
we examine the adoption of an innovation that has the potential to
5
substantially increase
environmental sustainability, we additionally incorporate the construct of
moral norms, originating from the Value Belief Norm Theory (VBNT) (Stern 2000),
into our model.
Figure 2.Eco-Innovation Adoption Model
Attitude, Perceived Behavioral Control, and Subjective
Norm
Fishbein and Ajzen (1975)
define attitude as the degree to which an individual assesses a behavior as
being favorable or not. In terms of eco-innovation adoption attitude, this
reflects an individual's view about whether an eco-innovation has reputedly
less harmful impacts on the environment and is thus more or less beneficial to
express the individual's green values (Jansson 2011).
Consistent with previous studies that found attitude to be a significant
predictor of intention (Pavlou and Chai 2002; Pedersen 2005), we
suggest that a positive attitude towards using eco-innovations is positively
related to the adoption intention. Hence, we contend:
H1a: Consumers’
attitude positively influences the intention to adopt an electric vehicle.
In terms of the influence of
attitude on the behavioral intention to adopt an eco-innovation such as
electric vehicles, recent studies have arrived at mixed results. While Pavlou and Chai (2002) were
able to show that collectivistic cultures tend to show a stronger effect of
attitude on the behavioral intention, a greater amount of researchers were able
to show the exact opposite trend (Chan and Lau 2002; Kacen and Lee 2002; Tan et
al. 2004; Tan et al. 2007). We, therefore, hypothesize:
H1b: Attitude is a stronger predictor for
the German sample than for the Chinese one.
Perceived behavioral control is
defined as „the ease or difficulty of
performing the behavior of interest“ (Ajzen 1991). The
variable „should be read as perceived
control over the performance of a behavior” that denotes the „subjective degree of control over
performance of the behavior itself” (Ajzen 2002). Thus, concerning
eco-innovation adoption, perceived behavioral control is related to the
individual's subjective degree of control over adopting and using the specific
innovation. We suggest that the greater the level of perceived behavioral
control, the higher the intention to adopt the eco-innovation (Kranz and Picot 2011; Pavlou and Chai 2002).
Hence we contend:
H2: Perceived
behavioral control positively influences the intention to adopt an electric
vehicle.
Taylor and Todd (1995a) suggest
assessing subjective norms from the two separate perspectives only if primary
influences (e.g., friends, family) and secondary influences (e.g., mass or
social media) are
6
assumed to differ. In terms of
the intention to adopt an electric vehicle, we expect that both sources of
social influence affect the adoption decision differently. Thus, we distinguish
between primary and secondary sources’ influence „to capture the nuances of the social environment” (Srite and Karahanna 2006), as
the diversity of potential primary and secondary influential sources in private
settings is an important adoption driver (Brown
et al. 2002). Another aspect relating specifically to eco-friendly
innovations is that adopting often means conforming to social norms rather than
to distinct environmental concerns (Bamberg
2003). Therefore, in accordance with previous findings (Kranz and Picot
2011; Venkatesh and Brown 2001), we assume that people receiving positive
messages or social pressure from primary or secondary sources are more likely
to have a strong behavioral intention to adopt electric vehicles. Furthermore,
focusing on Hofstede’s individualism/collectivism dimension, Srite and
Karahanna (2006) argue that the psychological concept of the self helps to
explain the impact of individualism/collectivism on the behavior. Individuals
from cultures with strong collectivistic values tend to consider their friends
and family's views on a technology before adopting it more than individuals
from individualistic cultures (Dinev et al.
2009; Pavlou and Chai 2002).
Furthermore, cultures with a higher power distance (House et al. 2004) have
been shown to adopt new technologies more readily in prior research (Dinev et al. 2009; Pavlou and Chai 2002).
Thus, we contend that for the Chinese sample, both subjective norm variables
should have a greater impact on intention to adopt, as the Chinese culture is
more collectivistic and has a greater power distance. Hence we hypothesize:
H3: Secondary
sources’ influence positively influences the intention to adopt electric
vehicles.
H4a: Primary sources’
influence positively influences the intention to adopt electric vehicles.
H4b:
Primary sources’ influence is a stronger predictor for the Chinese sample than
for the German one.
Attitudinal Beliefs
Relative advantage measures the
increased benefit of an innovation compared to existing products or services,
which can refer to financial benefits, avoidance of discomfort, social
prestige, or time savings (Rogers 2003).
Compared to conventional vehicles, customers regard the limited driving range
and higher purchasing costs as the major disadvantages of electric vehicles
(Sovacool and Hirsh 2009). Positive aspects include lower GHG emissions,
decreased noise emissions, and fuel savings (Peters and Hoffmann 2011, Moons et al. 2009). Across many contexts,
relative advantage or similar constructs, such as utilitarian outcome
(Venkatesh and Brown 2001) or perceived usefulness (Hsieh et al. 2005), have
been shown to influence adoption behavior positively (Hsieh et al.
2005; Taylor and Todd 1995a). Furthermore, uncertainty avoidance has been
shown to explain differences of technology adoption across different contexts
(De Mooij and Hofstede 2011; Tellis et al.
2003; Yeniurt and Townsend 2003). Literature demonstrates that cultures
with a higher level of uncertainty avoidance tend to adopt innovations that are
expected to work more reliably than other solutions. As a result, cultures with
higher levels of uncertainty avoidance are found to invest more in technology
innovations. Overall, we contend:
H5a:
Perceived relative advantage
positively affects the attitude towards the adoption of electric vehicles.
H5b:
Perceived relative advantage is a stronger predictor for the Chinese sample
than for the German one.
The perceived complexity and
the resulting required efforts for learning are dependent on adopters’
knowledge and willingness to learn (Litfin 2000). In
the context of electric vehicles, consumers have to learn using new interfaces
and displays and the general usage, such as handling of plugs and charging
(Peters and Dütschke 2010). A recent study also showed that the perceived
complexity of electric vehicles is greater before individuals actually test
electric vehicles (Moons et al. 2009).
Thus, electric vehicles are regarded as more complex to adopt if consumers do
not have „hands-on” experience. Furthermore, people belonging to
individualistic cultures tend to be more self-confident when using a new
technology (Thatcher et al. 2003) and evaluate the use of
new technological services such as mobile internet (Lee et al. 2007) to be less complicated in
comparison to people from collectivistic cultures. Hence, we expect
individualism-oriented Germans to be more self-confident about handling
electric vehicles in general than the Chinese. We therefore contend:
7
H6a:
Perceived complexity negatively
influences the attitude towards the adoption of electric vehicles.
H6b:
Perceived complexity is a weaker
predictor for the German sample than for the Chinese sample.
An innovation’s compatibility
is determined by its social and technological compatibility (Rogers 2003).
Correspondingly, the usage of an electric vehicle has to be compatible with an
adopter's lifestyle and consumption values (Au and Enderwick 2000) as well as
with technical facilities at the consumer’s home or workplace, such as the
possibility to charge the vehicle. If an innovation fits with both social and
technical norms and beliefs of an adopter, a positive influence on adoption
behavior has been found (Taylor and Todd 1995a; Van Slyke et al. 2010).
H7:
Perceived compatibility
positively affects the attitude towards the adoption of electric vehicles.
Moral Norms
In environmental psychology,
green values of potential adopters are often operationalized using Stern's
(2000) Value Belief Norm Theory (VBNT). This theory contends that moral norms
strongly affect the relationship between fundamental values and behavioral
intention because moral norms aggregate „personal
feelings of [...] responsibility to perform, or refuse to perform, a certain
behavior“ (Ajzen 1991) and thus amongst others expresses
the ecological awareness of an individual (Stern 2000). Moreover, this
ecological awareness was found to have a positive impact on the usage of
eco-friendly means of transport (Heath and
Gifford 2002; Nordlund and
Garvill 2003), the acquisition of lowinvolvement products (Minton and Rose 1997) and the willingness to pay
a premium for organic grocery products (Widegren 1998). In addition to these findings, Jansson et al. (2010) and Jansson (2011) showed
that the relationship also holds true for high-involvement goods, such as
alternative fuel vehicles.
In line with VBNT, we argue
that values, beliefs, and behaviors of individuals, groups, and institutions
are strongly influenced by their cultural surroundings and are further shaped
by the way the socio-cultural surrounding accepts them as legitimate.
Ecological awareness implies that an individual wants to conserve the
environment. At the same time, severe environmental damage tends to be a result
from longenduring environmental damage (e.g., continuously-emitted GHGs have a
long-lasting impact on the ozone layer). Therefore it is assumed that cultures
with a high degree of long-term orientation should show high degrees of
ecological awareness and thus a stronger moral norm than cultures that are
rather short-term oriented (Clayton 2012). Thus, we state:
H8a: Moral
norms positively affect the intention to adopt electric vehicles.
H8b: Moral
norms are a stronger predictor for the German sample than for the Chinese one.
Methodological Approach
Sample and Data Collection Procedure
We validated our research
model with data collected from an online survey. The survey was run in November
2012. To ease problems of understanding, we supplied the survey in both English
and Chinese. To account for construct equivalence, we chose a reversed
translation approach. Thus, the translation was performed by two English and
Chinese native speakers who translated the survey back and forth independently
from each other. Furthermore, before translating the survey, we conducted a
pretest in October 2012 with four IS scholars. According to their feedback, we
amended the wording and the order of some items. In addition, a short
definition of electric vehicles and information about the importance of IS to
enable an advanced electric mobility system were provided in the introduction
of the survey. Subjects were recruited via social networks and German and
Chinese university websites. Within two and a half weeks, 174 participants (nGerman
= 93, nChinese = 81) from a total of 252 people (response rate: .69)
who started the survey completed it successfully. In order to increase the
response rate, participants were
8
given the chance to win a
3-month subscription to a music streaming service. On average, the participants
were 24.3 years old; 76% were male, while 61.9% had a college degree and 38.1%
had a high school degree. We were not able to measure any significant
difference between male and female participants, making a bias caused by the
sample imbalance rather unlikely.
Measurement of Constructs
We followed standard
psychometric scale development procedures. Table 3 shows all scales used and
their sources together with descriptive statistics and psychometric properties.
All items were rated on reflective seven-point Likert scales with the anchors
„strongly agree” (1) and „strongly disagree” (7).
We assessed reliability and
validity for each reflective measure using the PLS approach (see Gefen and
Straub 2005). First, we checked convergent validity. Results indicate that all
items significantly loaded on their respective construct (.70 or higher).
Second, we calculated values for composite reliability (CR) and the average
variance extracted (AVE) to check reliability (Fornell and Larcker, 1981). For
each construct, CR values were larger than .70. In addition, the AVEs of all
constructs exceeded .50 (Straub et al. 2004). Third, we assessed discriminant
validity using the criterion of Fornell and Larcker (1981). For all constructs,
the shared variances between the variables were lower than the AVE values of
the respective constructs (see square root AVEs on the diagonal in Table 3).
Thus, discriminant validity could also be established. We also tested for
common method bias as independent and dependent variables were provided by the
same respondent. Both, the Harman’s single-factor test (Podsakoff et al. 2003) and the marker variable
test (Lindell and Whitney 2001) indicate that common
method bias was not a threat to the validity of our study.
Table 3. Correlations and Measurement Information
|
Var
|
Source
|
No. Mean Items
|
SD
|
CA
|
CR AVE 1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
|||
|
1 ATT
|
Davis (1989)
|
3
|
5.27
|
1.15
|
.68
|
.82
|
.61 .78
|
|
|
|
|
|
|
|
|
|
|
2 CLX
|
Taylor and Todd (1995a)
|
3
|
5.98
|
.87
|
.63
|
.72
|
.58
|
.08
|
.76
|
|
|
|
|
|
|
|
|
3 CMP
|
Taylor and Todd (1995a)
|
3
|
4.66
|
1.41
|
.85
|
.90
|
.76
|
.40
|
-.10
|
.87
|
|
|
|
|
|
|
|
4 INT
|
Mathieson (1991)
|
3
|
3.34
|
2.08
|
.89
|
.93
|
.81
|
.34
|
-.13
|
.20
|
.90
|
|
|
|
|
|
|
5 MN
|
Stern (2000)
|
4
|
2.84
|
1.99
|
.93
|
.95
|
.83
|
.32
|
-.32
|
.31
|
.35
|
.91
|
|
|
|
|
|
6 PBC
|
Taylor and Todd (1995a)
|
4
|
4.27
|
1.63
|
.66
|
.85
|
.74
|
.02
|
.11
|
.11
|
.06
|
.22
|
.86
|
|
|
|
|
7 PSI
|
Mathieson (1991)
|
3
|
2.74
|
1.72
|
.88
|
.92
|
.79
|
.27
|
-.20
|
.15
|
.52
|
.21 -.08
|
.89
|
|
|
|
|
8 RA
|
Taylor and Todd (1995a)
|
3
|
4.57
|
1.17
|
.69
|
.77
|
.62
|
.44
|
-.06
|
.40
|
.31
|
.30 .13
|
.23
|
.79
|
|
|
|
9 SSI
|
Brown and Venkatesh
(2005)
|
3
|
3.48
|
1.60
|
.70
|
.86
|
.76
|
.10
|
-.11
|
.20
|
.06
|
.33 .20
|
.10
|
.12
|
.87
|
|
Note:SD: standard deviation; CA:
Cronbach’s alpha; CR: composite reliability; AVE: average variance extracted;
ATT: Attitude; CLX: Complexity; CMP: Compatibility; INT: Intention to adopt;
MN: Moral Norm; PBC: Perceived Behavioral Control; PSI: Primary Sources'
Influence; RA: Relative Advantage; SSI: Secondary Sources' Influence. Diagonal
elements (in boldface) are the square root of AVE.
Results
To test the research model, we
used partial least squares (PLS) via the software SmartPLS version 2.0.M3
(Ringle et al. 2005). We have chosen variance-based PLS instead of
covariance-based structural equation modeling (SEM); As we split our complete
sample in two minor sub-samples (German and Chinese), PLS is particularly
suitable for this study as it is prediction-oriented and robust to relatively
small sample sizes
9
(Chin, 1998), and also better predicts and
identifies key „driver” constructs (Hair et al. 2011). To
calculate the significance of parameter estimates, a bootstrapping resampling
procedure was performed (n = 3,000 samples).
Table 4.Results of Model Estimation and Model Comparison
For conducting the group
comparisons we estimated the structural model for the Chinese and German
sub-samples separately. We tested whether the parameter estimates obtained for
both sub-samples significantly differ using t-tests suggested by Chin (2000). The
idea is to test whether the differences in the parameter estimates between the
two samples are different from zero. Overall, the proposed model receives ample
support. It accounts for 28% (complete sample), 33% (Chinese sample) and 43%
(German sample) of the variance of the attitude toward electric vehicles and
for 37% (complete sample), 79% (Chinese sample) and 38% (German sample) of the
variance of the intention to adopt electric vehicles. Results for each
hypothesized effect or group difference are provided in Table 4.
Discussion and Conclusion
Our study proposed and tested a
research model that integrated research from information systems, social and
environmental psychology, and innovation diffusion. Overall, our model was able
to successfully account for an ample amount of the variance in intention. We
could find differing varieties of the tested factors between the Chinese and
the German sample. According to Hofstede's study and the GLOBE study, the
countries are based on different national cultures. We propose several reasons
for the differences between intentions to adopt in Germany and China and take
House et al.'s GLOBE study (2004) as a reference. In the following, we shortly
want to draw some attention first to intriguing findings regarding the analysis
of the complete sample and underpin why the further analysis of the two
subsamples is of great importance to better understand adoption behavior. We
then focus on the differences between the two culturally different influenced
subsamples in more detail and discuss the role of culture in the context of the
results.
Focusing on the complete
sample, an intriguing finding of our study was that, in contrast with prior
findings on innovation adoption, primary sources’ influence was the most
important predictor of the intention to adopt electric vehicles. The
non-significant effect of perceived
behavioral control on
10
intention was unexpected.
Supporting this finding, Ajzen (1991) argues that a strong impact of other
constructs (e.g., PSI and attitude) may lessen the effect of perceived
behavioral control. Moreover, although secondary sources’ influence is regarded
as important in the early adoption stage, our study could not establish a
significant relationship. This may result from limited media interest in
electric vehicles.
Table 5.Summary of General Hypotheses and Results
|
No.
|
Hypothesis
|
Supported?
|
|
H1a
|
Consumers’ attitude
positively influences the intention to adopt an electric vehicle.
|
Yes
|
|
H2
|
Perceived behavioral
control positively influences the intention to adopt an electric vehicle.
|
No
|
|
H3
|
Secondary sources’
influence positively influences the intention to adopt electric vehicles.
|
No
|
|
H4a
|
Primary sources’ influence
positively influences the intention to adopt electric vehicles.
|
Yes
|
|
H5a
|
Perceived relative
advantage positively affects the attitude towards the adoption of electric
vehicles.
|
Yes
|
|
H6a
|
Perceived complexity
negatively influences the attitude towards the adoption of electric vehicles.
|
No
|
|
H7
|
Perceived compatibility
positively affects the attitude towards the adoption of electric vehicles.
|
Yes
|
|
H8a
|
Moral norms positively
affect the intention to adopt electric vehicles.
|
Yes
|
Furthermore, by pointing out
differences in significance between the complete sample and the individual
sub-samples for the same construct, our results provide clear evidence that
regarding different culturallyinfluenced subsamples individually is of great
importance to better understanding the adoption behavior in terms of
eco-innovation adoption.
Table 6.Summary of Culture-Specific Hypotheses and Results
|
No.
|
Hypothesis
|
Supported?
|
|
H1b
|
Attitude is a stronger
predictor for the German sample than for the Chinese one.
|
Yes
|
|
H4b
|
Primary sources’ influence
is a stronger predictor for the Chinese sample than for the German one.
|
Yes
|
|
H5b
|
Perceived relative
advantage is a stronger predictor for the Chinese sample than for the German
one.
|
No
|
|
H6b
|
Perceived complexity is a
weaker predictor for the German sample than for the Chinese sample.
|
No
|
|
H8b
|
Moral norms are a stronger
predictor for the German sample than for the Chinese one.
|
No
|
As mentioned before, we proposed
the hypothesis H1b that attitude is a stronger predictor for the German sample
than for the Chinese one. The items of our questionnaire determined e.g.,
whether the participants had a positive attitude towards electric vehicles and
whether they were open/prepared to spend money for them. We were able to find
support for this hypothesis as the two samples differ significantly in
answering behavior of the relevant items and we could only identify a strong
and significant path coefficient for the German sample. We base our
argumentation on the Chinese sample’s lack of a significant effect, thus
implying that the effect must at least be weaker than for the German sample.
The result can be explained by the culture dimension of future orientation,
which indicates to what degree cultures are caring for the environment.
Cultures with a high future orientation, such as in Germany, „have a strong capability and willingness to
imagine future contingencies, formulate future goal states, and seek to achieve
goals and develop strategies for meeting their future aspirations”
(Askanasy et al. 2004). In contrast, a culture such as the Chinese one, with
low future orientation or high present orientation, is free of past worries or
future anxieties (Askanasy et al. 2004).
Hypothesized in H4b, primary
sources’ influence is a stronger predictor for the Chinese sample than for the
German one. We were able to find support for this hypothesis as the two samples
also differ
11
significantly in answering
behavior of the related items, and we could only identify a strong and
significant path coefficient for the Chinese sample. In line with previous
rationale, we base our argumentation on the German sample lacking a significant
effect in this case, thus implying a weaker effect of this predictor for the
German sample than for the Chinese one. This result is not unexpected: as
mentioned before, primary sources comprise family and friends who are
influencing one's attitude. According to the GLOBE study, the result can be
explained with the individualism and collectivism dimension. In collectivistic
cultures, families and friends are the main drivers influencing the formation
of an individual’s opinion. Thus, for the Chinese, being significantly more
family oriented than Germans, primary sources' influence logically should be a
stronger predictor.
We further hypothesized in H5b
that the perceived relative advantage is a stronger predictor for the Chinese
sample than for the German one. Although our results were not able to fully
support this hypothesis, we can provide evidence that perceived relative
advantage is a strong predictor of electric vehicle adoption in the complete
sample, as well as in both sub-samples. The path coefficient of the Chinese
sample is indeed stronger than that of the German sample. However, the sample
comparison shows that the samples do not differ significantly. Regarding the
culture dimension uncertainty avoidance, Germany and China's scores for
uncertainty avoidance for practices is significantly higher than those of other
national cultures. House and Javidan (2004) demonstrated that these cultures
tend to enjoy scientific progress. We could not find a suitable explanation for
this result based on culture. We assume that the anxiety of using electric
vehicles still is great. Thus, uncertainty avoidance is more focused on
avoiding the risk related to using one (e.g., anxiety that the vehicle cannot
drive after a certain distance) than avoiding environmental pollution.
Furthermore, our results
clearly state that perceived complexity is of no importance to the adoption
decision for either the German sample or the Chinese one. Because electric
vehicles are usually assumed to be more complex than their conventional
counterparts, this might initially be an intriguing finding. However, the
non-significant effect of secondary sources' influence (media influence) might
be a reason why the interviewed candidates were not able to clearly estimate an
electric vehicle's degree of complexity.
Table 7.Indices of
Culture Dimensions of Results of the GLOBE Study (Germany and China)
Practices
(as is) Values
(should be)
|
|
Germany
|
China
|
Germany
|
China
|
|
Power distance
|
5.40
|
5.04
|
2,62
|
3.10
|
|
Collectivism
|
4.27
|
5.80
|
5.2
|
5.09
|
|
Uncertainty avoidance
|
5.19
|
4.94
|
3.63
|
5.28
|
|
Future orientation
|
4.11
|
3.75
|
5.04
|
4.73
|
Higher scores indicate a greater
expression of the culture dimension.
Our last hypothesis regarding
cultural differences in adoption behavior was that moral norms are a stronger
predictor for the German sample than for the Chinese one. According to the
results of the questionnaire, this hypothesis cannot be supported. To clarify
the result, cultures who have a higher power distance like the Chinese are more
accepting of inequality, rules, and given instructions. De Luque and Javidan
(2004) also found out that cultures with high uncertainty avoidance show less
tolerance for breaking rules. We assume that a construct such as moral norms is
therefore more fitting to the Chinese culture.
The following limitations
should be considered when interpreting the results. First, constructs such as
moral norms tend to be biased by social desirability. This especially holds
true for the Chinese culture, as the Chinese pay much attention to maintaining
the respect of others. Nevertheless, due to assured anonymity, social
desirability may not be a major concern. Second, the study’s sample is younger
and more educated than the general population. Hence, as in most non-randomized
surveys, there are issues concerning the generalizability to the entire
population of the results. However, as early adopters tend to be young and
educated, the results are reliable for this important market segment. Third, we
studied only
12
one particular eco-innovation.
Hence, future research should generalize the findings by examining other
eco-innovations.
Notwithstanding these
limitations, our paper contributes to IS research in three major ways: First,
we extended prior research by providing empirical evidence that culture does
have a strong impact on attitude and norms as well as intention to adopt
eco-innovations. For this, we used the GLOBE study as a basis, as it is the
most elaborated culture study within the current research discourse. Second,
the study determined that primary sources’ influence has a very strong impact
on intention to adopt ecoinnovations. Third, in cultures with a high degree of
collectivism, primary sources’ influence is a much stronger predictor for the
intention to adopt eco-innovations than in individualistic cultures. These
insights help the IS community to focus research on its vital role in enhancing
the advantages and mitigating the disadvantages of eco-innovations. From a
practitioner perspective, the results imply that advertising of new
eco-innovations should concentrate on the „greenness“ of the product to
emphasize environmental awareness in line with moral norms. Furthermore, for
markets in cultural regions with a high degree of collectivism, a new product
launch might be accompanied by a concentrated social media campaign to actively
influence individual’s social peers.
Acknowledgments
Tobias Brandt was supported by
a fellowship granted by the Foundation of German Business (sdw).
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16
Appendix
|
|
Table
8. Overview of Model Constructs and Items
|
||
|
Construct
|
Source
|
English
|
Chinese
|
|
Familiar with EV
|
Self-developed
|
Are you familiar with
electric vehicles?
|
您对电动汽车熟悉吗？
|
|
No, I do not know any electric vehicles
|
不，我对电动汽车一无所知
|
||
|
|
|
Yes, I have heard of electric vehicles
|
是, 我听说过电动汽车
|
|
|
|
Yes, I have tried an electric vehicle
|
是，我试开过一辆电动汽车
|
|
|
|
Yes, I own an electric vehicle
|
是，我拥有一辆电动汽车
|
|
|
|
Please indicate your level of agreement to the
following statements (strongly disagree, neutral, strongly agree)
|
请表明您对下面的说法的同意度(非常不同意，中立，非常同意)
|
|
Relative Advantage
|
Taylor and Todd (1995a);
Taylor and Todd
(1995b)
|
An electric vehicle will be of no benefit to me
The advantages of an electric vehicle
outweigh the disadvantages
|
电动汽车对我来说没有好处电动汽车的优点比缺点多
|
|
|
|
Overall, using an electric vehicle will be
advantageous
|
总共来说，使用电动汽车会有好处
|
|
Complexity
|
Taylor and Todd (1995a);
Taylor and Todd
(1995b)
|
It will be difficult to learn how to use an electric vehicle
An electric
vehicle will be easy to operate
|
很难学会驾驶一辆电动汽车。
使用一辆电动汽车很简单
|
|
|
|
It will be frustrating to learn how to use an
electric vehicle
|
学习使用电动汽车会让人沮丧
|
|
Compatibility
|
Taylor and Todd (1995a);
Taylor and Todd
(1995b)
|
Using an electric vehicle will fit well with how I use my
car
An electric vehicle is compatible with my lifestyle
|
驾驶电动汽车和我驾驶其他车一样。
电动汽车和我的生活方式是兼容的
|
|
|
|
An electric vehicle will fit well with my lifestyle
|
电动汽车很适合我的生活方式
|
|
Secondary Sources’ Influence
|
Brown and
Venkatesh (2005)
|
Media and advertising consistently recommend using
electric vehicles
|
媒体和广告一直推荐使用电动汽车
|
|
|
|
Media is full of reports, articles and news
suggesting buying electric
vehicles is a good idea
|
媒体上满是声称买电动车是个好主意的报告和文章
|
|
|
|
I read/saw news reports that using electric vehicles
is a good idea
|
我读过/看过声称使用电动车是好主意的新闻报告
|
|
|
|
Media and advertising consistently recommend buying
electric vehicles
|
媒体和广告一直推荐购买电动汽车
|
|
Primary Sources’ Influence
|
Mathieson (1991)
|
My friends and family would think that I should buy
an electric vehicle
|
我的朋友们和家庭认为我应该买一辆电动汽车
|
|
|
|
My friends and family would think that I should use
an electric vehicle
|
我的朋友们和家庭认为我应该使用一辆电动汽车
|
|
|
|
My friends and family think that we should all buy electric vehicles
|
我的朋友们和家庭认为我们全都应该买电动汽车
|
|
|
|
My friends and family think that we should all use electric
vehicles
|
我的朋友们和家庭认为我们全都应该使用电动汽车
|
|
Moral norm
|
Stern (2000)
|
I feel morally obliged to buy an electric vehicle
instead of a conventional
|
我感到有道德义务购买电动汽车, 而
|
17
|
|
|
vehicle
|
不是传统上的汽车
|
|
|
|
If I would buy a new vehicle, I would feel a moral
obligation to buy an electric one
|
如果我要买一辆新汽车，我会出于道德义务去买电动汽车
|
|
|
|
I would feel guilty not
using an electric vehicle
|
我如果不使用电动车我会感到有罪恶感
|
|
|
|
I feel a moral obligation to use an electric vehicle
|
我感到我有道德义务去使用一辆电动汽车
|
|
Perceived behavioral control
|
Taylor and Todd
(1995a);
|
I have the resources, knowledge and ability to purchase an
electric vehicle
I have the resources, knowledge and ability to
operate an electric vehicle
|
我有资源，知识和能力去购买一辆电动车
我有资源，知识和能力去使用一辆电动车
|
|
|
|
I would be able to purchase
an electric vehicle
|
我会有能力去购买一辆电动车
|
|
|
|
I would be able to operate
an electric vehicle
|
我会有能力去使用一辆电动车
|
|
Intention to use
|
Mathieson (1991)
|
I would buy an electric
vehicle rather than a conventional vehicle
|
我会更愿意买一辆电动汽车 而 不是传统车
|
|
|
|
My intention would be to use an electric vehicle
rather than a
conventional vehicle
|
我打算使用一辆电动汽车而不是传统车
|
|
|
|
I plan to buy an electric vehicle rather than a
conventional vehicle
|
我计划买一辆电动车，而不是传统车
|
|
Attitude towards use
|
Davis (1989)
|
The following questions
refer to your attitude towards electric vehicles
|
下面的问题针对您对电动汽车的态度
|
|
Using an electric vehicle is
a_____(bad/neutral/good) idea
|
使用一辆电动汽车是一个_____(坏/ 中立/好)主意
|
||
|
|
|
I think buying an electric vehicle is
a_______(bad/neutral/good) idea
|
我认为购买一辆电动汽车是一个
_____(坏/中立/好)主意
|
|
|
|
I_____(dislike/neutral/like) the idea of using an
electric vehicle.
|
我_____(不喜欢/中立/喜欢)使用电动车这个主意
|
|
|
|
Purchasing an electric
vehicle would be a _____(foolish/neutral/wise) idea
|
购买一辆电动汽车是一个(愚蠢 /中立
/高明 )的主意
|
18
Responses to Reviewers
for Manuscript ICIS-0878-2013:
“Understanding the Role
of Culture in Eco-Innovation Adoption –An Empirical Cross-Country Comparison”
Overall Comments
We
thank the associate editor and the reviewers for their helpful comments on the
previous version of the manuscript. A major revision was conducted to address
the limitations identified in the review process. Specifically, we have made
four major changes:
1. Clarified contribution to IS research
and practice
IS
plays a vital role in a (future) electric vehicle eco system. To enable an
elaborated electric mobility system, setting free the economic and
environmental potentials of electric vehicles, the automotive, energy and IT
industry / domains have to work hand in hand. We addressed these facts in the
introduction to our survey. In combination with eco-innovations, IS has the
potential to enhance positive characteristics (less emissions) and mitigate
disadvantaged (such as higher costs). Hence, there is a great research demand
in addressing IS’s role and potential to boost eco-innovation adoption. To
better direct IS research effort in this prosperous domain, we want to give
first insights in eco-innovation adoption behavior. To clarify this in the
manuscript, we added/changed the following paragraphs:
“On the way towards
fully utilizing the potential of electric mobility, information systems (IS)
play an integral part. They can, for instance, enable „green” charging through
the implementation of intelligent charge control systems or increase the
efficiency of electric driving through information systems within the vehicle,
such as GPS or vehicle monitoring systems (Brandt 2013).”
“As information systems have the
potential to enhance the advantages and mitigate the disadvantages of
eco-innovations, as shown by Brandt et al. 2012 for the example of electric
vehicles, it is important to first understand electric vehicle adoption
behavior in general to effectively outline and conduct future IS research that
best helps in mitigating the disadvantages.”
“We introduce a comprehensive model to
explain the intention of individuals to adopt electric vehicles as a surrogate
for eco-innovations. This provides a starting point for future IS research to
focus on issues that best supports eco-innovations.”
“These insights help the IS community
to focus research on its vital role in enhancing the advantages and mitigating
the disadvantages of eco-innovations.”
2. Clarified the difference between conventional and eco-innovations
As
addressed by reviewer #1 we further explained what differences exist between
conventional innovations and eco-innovations to underpin the uniqueness and
importance of our eco-innovation adoption model:
“The central difference
between eco-innovations and their „classical” counterparts is that some of
their benefits do not immediately concern the adopter. For instance, a
sustainable and environmentally friendly lifestyle, may not change the quality
of life of the person in question, but improve it for future generations.
Hence, eco-innovations cannot be judged
1
using purely utilitarian measures,
such as financial benefits or the desire to „belong” to a particular group, but
point at a deeper moral motivation.”
3. Resolved methodological issues
Furthermore,
reviewer #1 mentioned five methodological issues, which we have taken care of
in the latest version of our manuscript:
a) We used variance-based PLS over
covariance based SEM because we deal with relatively small sample sizes which
result from splitting the complete samples in two sub-samples (German and
Chinese). PLS is said to be robust to relatively small sample sizes (Chin 1998) and also better predicts and
identifies key „driver” constructs (Hair et al. 2011).
b) As we included two marker variables
in the survey, we also tested for common method bias. Both, the Harman’s
single-factor test (Podsakoff
et al. 2003) and the
marker variable test (Lindell and Whitney 2001) indicate that common method bias
was not a threat to the validity of our study. We added a respective comment to
our manuscript.
c) We agree with the comment that the
large share of male participants may have constituted a bias. Nevertheless,
recalculating the model with the two sub samples (male and female), we were not
able to measure any significant difference between male and female
participants, making a bias caused by the sample imbalance rather unlikely. We
added that respective comment to our manuscript.
d) As mentioned by reviewer #1 we
included the model constructs and their items as used in the questionnaire in
the appendix of our manuscript.
e) We
explicitly accounted for construct equivalence in our survey. To clarify this
we added the following paragraph to the manuscript:
“To account for construct equivalence, we chose a reversed
translation approach. Thus, the translation was performed by two English and
Chinese native speakers who translated the survey back and forth independently
from each other.”
We now supply both, English and Chinese item translation in
the appendix of our manuscript.
4. Eliminated typos and missing spaces
We
eliminated all typos and missing spaces in our manuscript.
Overall we want
to thank the AE and the reviewers for their thoughtful and instructive comments
which helped (and will help) to improve the quality of the paper significantly.
I hope that you will find your comments addressed in a satisfactory way.
The
author has requested enhancement of the downloaded file. All in-text references
underlined
in blue are linked to publications on ResearchGate.
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