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Closing the green gap: the impact of environmental commitment and advertising believability.

Green advertising is used to promote products, services, or companies that counter or reduce environmental harm. This style of advertising is an essential part of consumer marketing, upon which companies are increasing their advertising spending (Zinkhan & Carlson, 1995). Increased advertising of green products has created demand for these environmentally conscious goods (Menon & Menon, 1997). This trend reflects consumers' growing interest in environmental protection as they become increasingly aware that what they buy impacts the biosphere (e.g., Baek, Yoon, & Kim, 2015). In line with this trend, advertising managers commonly assume that consumers will accept advertising messages in which green claims are made, and will be drawn to purchase the green products or services. Past researchers have generally reported positive correlations between environmental attitude and environmental behavior (Roberts & Bacon, 1997).

However, other researchers have questioned this assumption by showing a weak or no relationship between consumers' green attitude and their green purchase behaviors, which indicates that green advertisements may spark green acceptance but not necessarily generate actual green purchase behavior (Hanas, 2007; Yoon, Kim, & Baek, 2016). Collectively, in the literature it is suggested that many consumers may accept green claims, but fewer intend to act subsequently on their accepted beliefs. This gap between how consumers intend to behave regarding green living and how they actually behave has been termed the green gap (Fowler & Close, 2012). To advertisers of green products, the discrepancy between what consumers believe and what they intend to purchase signals that many dollars are wasted on green advertising, particularly if the advertising enhances message acceptance but fails to stimulate actual intention to purchase (Fishbein & Ajzen, 1975). Our aim in this study was to better understand why this discrepancy occurs and to find ways to close the green gap.

Our objective was to identify factors that can enhance the consistency between message acceptance and purchase intention. Specifically, we argued that the level of consumer commitment to environmentally friendly behaviors would largely determine the strength of acceptance-intention consistency, and that advertising believability would further enhance this consistency. To test this proposition, we developed a model in which we incorporated the demographic factors that have been identified in the literature as green-relevant variables: that is, gender, age, education, and marital status. We introduced our variables of interest--environmental commitment, and message believability--to the model separately as layers that boost consistency to demonstrate that these two variables would contribute to the strength of the acceptance-intention relationship, uniquely and respectively, above and beyond the demographic variables.


Consumers' Commitment to Environmental Behaviors

Consumers may show various reactions to green advertising, depending on the level of their environmental commitment. In studies linking the effectiveness of green advertising to green commitment it has been reported that environmentally committed consumers are more receptive to green advertising than are noncommitted consumers (e.g., Kim & Shin, 2010). These findings indicate that environmentally committed consumers are intrinsically motivated to process green messages, but that noncommitted consumers are relatively extrinsically motivated in their green message processing. Drawing on these findings, we defined environmental commitment operationally in terms of consumers' previous engagement in proenvironmental behaviors.

Researchers have often found the attitude-behavior link among environmentally conscious consumers. In this regard, they have reported that environmental consciousness guides people to make greener purchase decisions (Peattie, 2001; Schlegelmilch, Bohlen, & Diamantopoulos, 1996), and that environmentally conscious consumers are apt to change their purchasing behaviors in order to improve the environment (Roberts, 1996). Directly relevant to our conceptualization, Fazio, Zanna, and Cooper (1978) demonstrated that attitudes formed through direct behavioral experience better predict later behavior than do attitudes formed through indirect experience. Applying the findings reported by Fazio et al. to the context of our study, we reasoned that this implies that there would be a stronger relationship between the message acceptance and purchase intention of environmentally committed consumers with past practices of proenvironmental behaviors than there would be among noncommitted consumers. Therefore we hypothesized that:

Hypothesis 1: The relationship between green message acceptance and green purchase intention will be stronger among environmentally committed consumers than among noncommitted consumers.

Advertising Message Believability

Advertisers' environmental claims can provide informative bases for consumers to process cognitively in making purchase decisions (Mobley, Painter, Untch, & Unnava, 1995). Although green advertising offers informative benefits, consumers are becoming increasingly skeptical about its credibility and usefulness (e.g., Carlson, Grove, & Kangun, 1993; Kangun & Polonsky, 1995; Pfanner, 2008). Some of this skepticism can be attributed to the context and presentation of green-themed advertisements. Specifically, green-marketing environmental information has often been criticized for being ambiguous (Pfanner, 2008), misleading (Davis, 1993), or exaggerated (Garfield, 1991). Similarly, researchers have found that many green advertisements reflect a shallow or moderate greenness, denoting a lack of substantiveness, comprehensiveness, and credibility (Kilbourne, 1995).

From this perspective, advertising believability is an important variable in terms of consumer purchase decisions; thus, in addition to environmental commitment, advertising believability is essential for successful consumer persuasion (Hoyer & MacInnis, 2004). Researchers have shown that, characteristically, green consumers are skeptical about advertising claims (Shrum, McCarty, & Lowrey, 1995; Yoon, Choi, & Song, 2011). This indicates that green consumers who are interested in buying green products may be receptive to green advertising, but that they may evaluate advertising messages carefully. As a result, when they perceive a green message to be highly believable, they may find it relevant, and feel confident and certain about their attitude toward the green message.

Thus we sought evidence to establish whether or not relevance, confidence, and certainty spill over into the relationship between message acceptance and purchase intention. Indeed, persuasion literature findings attest that these variables influence the attitude-behavior relationship. For example, Snyder and Kendzierski (1982) found that increasing the relevance of attitude was effective for inducing individuals to translate attitude into corresponding behaviors. Likewise, Fazio and Zanna (1978) showed that people who were led to believe that they held their attitudes confidently displayed greater attitude-behavior consistency than did people who were led to believe that they lacked confidence in their attitudes. Therefore we proposed the following hypothesis:

Hypothesis 2: Among consumers who are environmentally committed, the relationship between green message acceptance and green purchase intention will be stronger among those who perceive the message as believable than it will among those who perceive the message as less believable.


Participants, Stimuli, and Procedure

Participants (N = 231) were adults aged between 18 and 65 years, of whom 135 (58.4%) were women and 95 (41.1%) were men (one participant's gender is unknown; .4%) who were approached and asked to participate in various public places in South Korea (e.g., shopping malls). They were randomly assigned to two product conditions: shampoo (n = 109) and yogurt (n = 122). We used two print mock advertisements with fictitious brand names--Purity for shampoo and Good Morning for yogurt--to control for the potential influences of brand familiarity and prior brand attitudes.

Each advertisement contained a visual that was a product shot (a bottle of shampoo or yogurt); the main section of advertising copy appeared above the product image, and the copy subsection appeared below the image. In the main copy the product benefits were described and the green message appeared in the subcopy: "While your hair/body regains its natural vitality, our planet gets healthier as well. A healthy environment is our most-treasured asset. Purity/ Good Morning is an environmentally friendly, biodegradable/organic product that contains no chemicals or toxins." Participants were told that the study was being conducted to determine public interest in a new brand of shampoo or a drinkable yogurt.


Participants rated items in the various measures on a 7-point scale. There were six items for message acceptance with response options ranging from strongly disagree (1) to strongly agree (7). A sample item is: "I think Purity is better than other shampoos."; [alpha] = .92. There were two questions to assess purchase intention; e.g., "If the product is available in the market, will you buy it?" that were rated from absolutely (7) to not at all (1); [alpha] =.94. The three items for message believability were rated on a scale ranging from unbelievable (1) to believable (7); [alpha] = .93; e.g., "The purity ad is ..." There were 14 items to measure proenvironmental behaviors on a scale ranging from never (1) to always (7); e.g., "I choose eco-labeled products."; [alpha] = .90.

Data Analysis

We employed a linear model to estimate the relationship between purchase intention (Y) and message acceptance (X). We used the standardized coefficient. To account and control for the effects of respondents' demographic background on their purchase intention, we formulated a multiple linear model with four explanatory variables on gender (G), age (A), education (E), and marital status (M). The baseline model for all participants could be expressed in equation form as follows:

[gamma] = [[beta].sub.0] - [[beta].sub.1]X + [[beta].sub.2]G + [[beta].sub.3]A + [[beta].sub.4]E + [[beta].sub.5]M + [epsilon], (1)

where the parameters [[beta].sub.1], i = 0, 1, ..., 5 would be the regression coefficients to be estimated and the error term [epsilon] was assumed to have a normal distribution with mean zero and constant variance. It should be noted that [[beta].sub.1] was the standardized coefficient indicating the linear relationship of interest. The baseline model (1) would provide the estimate of the overall relationships among purchase intention and demographic variables.

To test H1, the participants were first classified into two groups. [G.sub.j] for j = 1, 2, with low and high proenvironmental behaviors, respectively. We defined an indicator variable I to be 0 for participants in [G.sub.1] and 1 for those in [G.sub.2]. We made no assumption about the effects of participants' demographic background on their purchase intention; that is, we expected the two groups to differ in intercept and slope only for the main predictor. Then, the difference in the relationship (slope) could be detected by the following regression model with an interaction term, which would be an expansion of model (1):

[gamma] = [[beta].sub.0] - [[beta].sub.1]X + [[alpha].sub.0]] + [[upsilon].sub.1]X + I + [[beta].sub.2]G + [[beta].sub.3]A - [[beta].sub.1]E + [[beta].sub.5]M - [epsilon], (2)

where the [[alpha].sub.1], i = 0, 1 would be the regression coefficients added by the indicator variable. However, if the effects of participants' demographic background on their purchase intention varied across groups, the difference in the relationship could be assessed by two separate models, which was equivalent to applying model (1) to each of the two groups, respectively. We were not pursuing the effect of demographic variables, so we simplified model (2) as follows:

[gamma] = [[beta].sub.0] - [[beta].sub.1]X + [[alpha].sub.0]] + [[upsilon].sub.1]X + I + D[delta] - [epsilon], (3)

where D[delta] = [[beta].sub.2]G + [[beta].sub.3]A + [[beta].sub.4]E + [[beta].sub.5]M denoted all the effects from the four demographic variables.

It should be noted that model (3) defined two regression lines. For the low-environmental-commitment group, I = 0, and thus model (3) would indicate a straight line with intercept [[beta].sub.0] and slope [[beta].sub.1]. For the high-environmental commitment group, I = 1 and then model (3) would become:

[gamma] = ([beta][upsilon] + [[upsilon].sub.0]) + ([[beta].sub.1] - [[upsilon].sub.1])X + D[delta] + [epsilon], (4)

which would be a straight line with intercept [beta][upsilon] - [[upsilon].sub.0] and slope [[beta].sub.1] + [[alpha].sub.1]. The slope [[beta].sub.1] of model (4) relating purchase intention to message acceptance would not depend on environmental commitment. However, the slope [[alpha].sub.1] would depend on environmental commitment. The parameter [[alpha].sub.1] would reflect the difference in the relationship associated with changing from the low-commitment group to the high-commitment group, whereas the parameter [[alpha].sub.0] would reflect the change in the mean response, which was outside our interest in this study. If the estimate of [[alpha].sub.1] were statistically significant at 5% level, H1 would be supported. In the first part of the results section, to test H1, we reported the estimates of two parameters on slope: [[beta].sub.1] and [[alpha].sub.1].

To test H2, participants with high environmental commitment were further classified into two subgroups by the second factor: advertising message believability (low- vs. high-believability groups). This variable was incorporated into the model as another indicator variable J:

Y = [[beta].sub.0] + [[beta].sub.1]X + [[alpha].sub.0]I + [[alpha].sub.1]X * I + [[gamma].sub.0]J + [[gamma].sub.1]X * I + [[gamma].sub.2]I * J + [[gamma].sub.3]I * J * X + D[delta] + [epsilon], (5)

where the [[gamma].sub.1]; i = 0, 1, 2, 3 were the regression coefficients added by the indicator variable, and J was 0 and 1 for low- and high-believability group, respectively. The interaction term [[gamma].sub.3]I * J * X would indicate that changing from low to high environmental commitment, where I = 1, would have an effect on the slope depending on the value of message believability, where J = 0 or 1.

For the group of participants with high environmental commitment and low message believability, model (5) could be simplified to model (6) where I = 1 and J = 0.

[gamma] = ([[beta].sub.[upsilon]] + [[upsilon].sub.0]) + ([[beta].sub.1] - [[upsilon].sub.1])X + [delta]D + [epsilon], (6)

On the other hand, for the group of participants with high environmental commitment and high message believability for green advertisement messages, where I = J = 1, model (5) would become:

[gamma] = ([[beta].sub.[upsilon]] + [[upsilon].sub.0] + [[gamma].sub.0] - [[gamma].sub.2]) + ([[beta].sub.1] - [[upsilon].sub.2] + [[gamma].sub.1] + [[gamma].sub.3])X + [delta]D + [epsilon], (7)

It should be noted that the change in slope from model (6) to (7) is [[gamma].sub.1] + [[gamma].sub.3], reflects the difference in the relationship associated with changing from low to high believability among those who have relatively high environmental commitment. Therefore, H2 would be supported if the estimate of either [[gamma].sub.1] or [[gamma].sub.3] were statistically significant at 5% level. In the first part of the results section, to test H2, we have reported the estimates of the four parameters on slope: [[beta].sub.1], [[alpha].sub.1], [[gamma].sub.1] and [[gamma].sub.3].


First, using the baseline model (1) when we estimated the overall relationship between purchase intention and message acceptance for all participants it was .71 (SE = 0.05) and was statistically strongly significant (p < .01). Second, we examined whether or not product type, shampoo versus yogurt, influenced this relationship. The estimate of linear relationship, in model (1) was almost the same for both products: .71 (SE = 0.07,p < .01) for shampoo (n = 105) and .71 (SE = 0.07, p < .01) for yogurt (n = 118). As the two 95% confidence intervals for the relationship overlapped, we concluded that there was no difference in the relationship by product type, and thus we combined the data (n = 223) for further analyses.

To test H1, we classified the participants into two groups by their environmental commitment using the median value 4.69. Figure 1 visually contrasts the linear relationship of the low-commitment group with that of the high-commitment group. As shown in Table 1, the averages of message acceptance and purchase intention of the high-commitment group were higher than those of the low-commitment group.

The slope (commitment level) was significant (p < .05) but the intercept was not (p = .35). This indicates that the overall averages of purchase intention between the two groups were the same, but as the level of environmental commitment changed the linear association between message acceptance and purchase intention changed. For those who were highly committed, purchase intention depended more strongly on their message acceptance (Figure 1). Thus H1 was supported.


To test H2, we classified the high-commitment group into two subgroups by the median value 4.00 of their message believability. Figure 2 visually contrasts the linear relationships of the high-commitment/low-message-believability group with the high-commitment/high-message-believability group. In Table 2 the descriptive information is offered. The averages of message acceptance and purchase intention of the high-commitment/high-message-believability group were much higher than those of the high-commitment/low-message-believability group.

Environmental commitment and message believability, when combined, strengthened the relationship between message acceptance and purchase intention. To reiterate, for the high- (vs. low-) commitment group the slope changed by .20 (H1). Moreover, for the high- (vs. low-) commitment group, the relationship increased to the level of .54 (the estimate of [[gamma].sub.3] in Table 2) when high (vs. low) message believability was included in the calculation. For those who were highly committed to environmental protection, message believability considerably strengthened their dependency on message acceptance to enhance their purchase intention. Thus H2 was supported.



We investigated how consumer and message characteristics affect the consistency between consumers' message acceptance and their purchase intention. Our data show that in our study, for the participants who were committed to environmentally friendly behaviors, as measured by their past proenvironmental behaviors, there was a tighter relationship between accepting green advertising messages and intending to purchase green products. Moreover, among participants who were highly committed to environmentally friendly behaviors, the relationship was further strengthened by including the believability of the message for the individual in the model.

With this study we have made important theoretical contributions by illuminating solutions for the green gap phenomenon. Broadly, we have added to the literature on the attitude-behavior relationship by demonstrating that both dispositional and message factors strengthen thought-action connections. A key contribution is that although, previously, researchers have focused exclusively on attitudes as they affect specific target behaviors and corresponding behavioral intentions (Baek et al., 2015; Fazio & Zanna, 1978), we have shifted the focus from general attitudes to the acceptance of advertising messages and resulting product purchase intention. Compared with general attitudes, such constructs are more relevant in the study of advertising (e.g., Yoon, Oh, Song, Kim, & Kim, 2014).

For advertising practitioners, we have offered straightforward strategic implications: to maximize the conversion rate of a green advertising campaign (the percentage of viewers of advertisements who become product buyers), marketers should use highly truthful messages and target those who are already committed to environmentally friendly behaviors. Nevertheless, this is not to say that advertisers should not attempt to cultivate environmentally noncommitted consumers, but they should recognize that many will fail to act on green-friendly persuasion; green advertising may buy their minds but may fail to buy their actions. Perhaps an alternative approach to persuading noncommitted consumers is to raise their green consciousness while they are processing the green message, such as by using priming, a subtle persuasive technique shown to momentarily boost green consciousness (see Tate, Stewart, & Daly, 2014). For example, Oh, Yoon, Vargas, & Wyer (2011) showed that consumers evaluate environmentally friendly products more favorably when their self-construal is temporarily activated and is compatible with associated benefits provided in the advertisement. Similarly, green messages may be crafted to activate green consciousness, in turn increasing the likelihood that the noncommitted consumer will become a green buyer. Future researchers may examine this possibility.

We omitted one potential moderator in our current study: the concreteness of the green message. Some researchers contended that concrete claims are more persuasive than abstract claims for positively influencing green behaviors (Chan & Lau, 2004; Chan, Leung, & Wong, 2006). Expanding this line of thought, concrete green messages, such as product-related claims, may be more effective than abstract claims, such as image-related claims, for smoothing the transition between acceptance and purchase. At least two theories lend credence to this prediction. First, in the theory of reasoned action, Fishbein and Ajzen (1975) assert that for there to be a strong relationship between measures of attitudes and behavior it is necessary for the measures to be compatible in terms of their specificity. According to Fishbein and Ajzen's framework, the more concrete the green message, the greater the compatibility between message acceptance and purchase intention. Second, concrete claims appear to be linked to higher believability (Ford, Smith, & Swasy, 1990; Hoch & Ha, 1986; Pechmann, 1996). The concreteness of a green claim may make the green message more believable, which, as the current findings show, in turn results in a tighter relationship between message acceptance and purchase intention.

Future researchers might also address the salience of rewards that consumers enjoy from green consumption, whether these rewards are intrinsic or extrinsic (Seligman, Fazio, & Zanna, 1980). For example, buying green products may be intrinsically rewarding for consumers who feel that green consumption will help preserve the biosphere. On the other hand, buying green may be extrinsically rewarding for consumers who feel that green consumption enhances their social status when others see them using green products. When advertisements make intrinsic rewards salient, message believability might directly impact the relationship between message acceptance and purchase intention. But when advertisements make extrinsic rewards salient, the impact of message believability on the acceptance-intention relationship might be absent. The actual greenness of the advertised product--that is, whether or not the product really helps the environment--may be important only to intrinsically motivated consumers who choose to buy green to benefit the planet, and less meaningful to extrinsically motivated consumers who use green products to impress others.

Our results are useful with respect to the application of green advertising messages that have specific goals. Nevertheless, some reflections regarding the study design must be noted for future research. First, we forcibly exposed our study participants to the advertisements; therefore measures of immediate response may have affected the study's validity. Second, the results reported here reflect only consumer responses to low-involvement products, for which people devote little time, thought, and energy to the purchase process, and do not consider responses to high-involvement products. Third, the messages we used in our study were formulated to convey product attributes; therefore the wording may not have been sufficiently explicit, which may have affected participants' reactions. Some participants might have preferred more detailed environmental messages that explicated how consuming a product can benefit the environment. We note, however, that any such preference cannot readily account for results obtained, because it is likely that all consumers would be affected, not just those who were highly committed to environmental causes. Finally, we selected study participants using a nonprobability sampling method, which may have limited the generalizability of our findings.

Yeonshin Kim

Myongji University

Sangdo Oh

Yonsei University

Sukki Yoon

Bryant University

Hwashin Hyun Shin

Queen's University

Yeonshin Kim, Department of Business Administration, Myongji University; Sangdo Oh, Division of Business Administration, College of Government and Business, Yonsei University; Sukki Yoon, College of Business, Department of Marketing, Bryant University; Hwashin Hyun Shin, Department of Mathematics and Statistics, Queen's University.

This work was supported by the 2013 Research Fund of Myongji University.

Correspondence concerning this article should be addressed to: Sangdo Oh, Division of Business Administration, College of Government and Business, Yonsei University, 1, Yonseidae-gil, Wonju-si, Kangwon-do, Republic of Korea. Email:


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Table 1. Linear Regression Results for Hypothesis 1

Environmental                       Low          High
commitment                      commitment    commitment
(low vs. high)                   (n = 109)     (n = 114)

Message acceptance    M (SD)    3.79 (1.06)   4.35 (1.30)
Purchase intention    M (SD)    3.48 (1.33)   4.18 (1.63)

Difference in        Estimate       SE          p value
relationship *
  [[beta].sub.1]       0.58        0.08          .001
  [[alpha].sub.1]      0.20        0.10          .044

Note. * applying model (4).

Table 2. Descriptive Statistics and Linear Regression
Results for Hypothesis 2

Message believability              High commitment   High commitment
(low vs. high)                     and low message   and high message
within the high-                    believability     believability
commitment group                      (n = 41)           (n = 73)

Message acceptance       M (SD)      3.33 (1.01)       4.92 (1.08)
Purchase intention       M (SD)      2.76 (1.30)       4.99 (1.19)

Difference in           Estimate         SE              p value
relationship *
  [[beta].sub.1]          0.51          0.12               .001
  [[alpha].sub.1]        -0.10          0.17               .576
  [[gamma].sub.1]        -0.26          0.18               .148
  [[gamma].sub.3]         0.54          0.23               .023

Note. * applying model (7).
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Author:Kim, Yeonshin; Oh, Sangdo; Yoon, Sukki; Shin, Hwashin Hyun
Publication:Social Behavior and Personality: An International Journal
Article Type:Report
Date:Mar 1, 2016
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