The influence of negative publicity on brand equity: attribution, image, attitude and purchase intention

Mingzhou Yu (University of Western Australia, Crawley, Australia)
Fang Liu (University of Western Australia, Crawley, Australia)
Julie Lee (University of Western Australia, Crawley, Australia)
Geoff Soutar (University of Western Australia, Crawley, Australia)

Journal of Product & Brand Management

ISSN: 1061-0421

Publication date: 16 July 2018



This study aims to understand the influence of negative publicity on brand image, brand attitude and brand purchase intention. Specifically, the study examines the role of attribution (or brand blame) and information characteristics in Chinese consumers’ responses to negative publicity.


The study used a quasi-experimental approach involving two negative publicity scenarios (mild and high severity) and a sample of 203 young and educated Chinese consumers. Partial least squares was used to test the hypotheses.


A common assumption is that negative brand information has a negative influence on all aspects of a brand. However, this study finds that brand blame and information severity have differential effects on consumer evaluations of the affected brand. Specifically, brand blame negatively impacted attitudes and purchase intentions, but not brand image. In contrast, information severity negatively impacted brand image, but not attitudes or intentions. Further, the relations between brand image and brand attitudes and intentions depended on the level of information severity. In the mild-severity condition, brand image positively influenced attitudes and intentions, but not in the high-severity condition.

Research limitations/implications

Future research should examine consumer responses to negative publicity across different media and product categories. Cross-cultural studies should also be explored in the future.

Practical implications

When a brand encounters negative publicity, its marketer or brand manager should assess to what extent various brand equity components are influenced by negative publicity before adopting any cognitive-based or imagery-based communication strategies.


This paper contributes to the limited and fragmented literature on consumer response to negative publicity by examining the impact of consumer’s attributions of blame to the brand under conditions of mild and severe negative information on a range of important brand-related outcomes. Specifically, the authors find that negative publicity has a different impact on brand image, brand attitudes and intentions to purchase. The authors suggest that brand managers use this information to guide their marketing communications.



Yu, M., Liu, F., Lee, J. and Soutar, G. (2018), "The influence of negative publicity on brand equity: attribution, image, attitude and purchase intention", Journal of Product & Brand Management, Vol. 27 No. 4, pp. 440-451.

Download as .RIS



Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


Practitioners and researchers need to understand how consumers respond to negative brand information. The need for this understanding has become increasingly important with the ease of accesses to negative brand information on the internet in general and on social media in particular (East et al., 2007). For example, in 2016, the US Bureau of Consumer Financial Protection received more than 150,000 complaints about financial goods and services (Dataworld, 2016). In the same year, the Chinese State Administration for Industry and Commerce received about 1.7 million complaints about products and services; an increase of nearly 30 per cent compared with 2015 (Xinhua, 2017). There is little doubt that consumers are exposed to negative information.

It is a widely held belief that negative brand information negatively influences all aspects of a brand, yet it is unclear if this is the case. Most previous research suggested negative brand information has a negative effect on attitude towards a brand and/or purchase intention (Ahluwalia and Gürhan-Canli, 2000; Dawar and Pillutla, 2000; Dentoni et al., 2011; Ullrich and Brunner, 2015). However, Berger et al. (2010) found negative online reviews increased book sales if an author was not well-known, and Ein-Gar et al. (2012) found a small amount of negative information improved consumers’ views about a brand if they had previously received positive descriptions about that brand. Both of these studies suggested more research is needed to better understand the responses that are likely to be triggered by negative brand information. In this paper, we investigate the role of consumer attribution and information severity in an attempt to increase this understanding.

When exposed to negative publicity, people often go through an attribution process in which they think about what caused the negative publicity and who should be blamed (Laufer et al., 2005). Despite considerable research, only a handful of studies (Rosenthal and Schlesinger, 2002; Whelan and Dawar, 2016) have examined consumer attribution (or blame) in a negative publicity context. Weiner (1983) claimed people may blame different parties (e.g. attribute an incident to a brand itself or to other parties such as consumers) when exposed to the same piece of negative brand information.

Consumer attribution is likely to influence consumers’ attitudes and behaviours towards affected goods and services (Laczniak et al., 2001). However, attribution may influence components of brand equity (e.g. brand attitude and brand image) differently (Shimp and Andrews, 2013). Brand image is thought to be more enduring than attitudes (Chelminski and Coulter, 2011) and, as such, may be less influenced by negative information than other aspects, such as brand attitude (Park and Lee, 2009). No prior research compared the influence negative publicity had on brand attitude and on brand image.

Further, most negative publicity studies have focused on Western consumers, with little attention being given to consumers in other cultures. This may be important, as there is evidence that cultural differences influence consumer responses to negative information. For example, Laufer et al. (2005) found consumers from low, as compared with high, uncertainty avoidance countries were more likely to blame a brand for negative brand information, which the authors attributed to consumers feeling more threatened by ambiguous negative brand information. Further, Turnbull et al. (2000) found consumers from high, as compared with low, uncertainty avoidance cultures were more reluctant to make a purchase decision after being exposed to negative publicity. The current study aims to untangle the effects negative publicity has on brand image, brand attitudes and brand intentions, by examining consumer attributions after an exposure to negative brand information, in a Chinese context.

Conceptual development

Brand and negative publicity

Brands are an organization’s most important asset, which explains why there has been considerable research into branding and related topics. Such research has examined brand equity, which is the total value of a brand, although this has been defined and measured in different ways (Aaker, 1991; Keller, 1993; Baalbaki and Guzman, 2016). Thus, Aaker’s (1991) brand equity model had five components (brand awareness, brand associations, perceived quality, brand loyalty and other proprietary assets), whereas Keller’s (1993) customer-based brand equity framework had two important equity dimensions (brand awareness and brand image). More recently, Baalbaki and Guzman (2016) suggested a consumer-perceived consumer-based brand equity scale with four key dimensions (quality, preference, social influence and sustainability).

Brand equity can be influenced by positive communications, such as advertising, which help people develop favourable brand perceptions, thereby enhancing brand value (Shimp and Andrews, 2013). Advertising also increases brand recall and recognition (Keller et al., 2011) and has continuing and accumulative effects on consumer-based brand equity (Wang et al., 2009). Other forms of communication, including positive word-of-mouth (WOM), can also help people develop positive perceptions of a brand and build favourable purchase intentions (Bambauer-Sachse and Mangold, 2011; Jalilvand and Samiei, 2012).

However, brands also have to deal with negative brand information, as consumers access a wide variety of brand information through traditional and new media, including television, radio, online news forums, webcasts and smartphone apps. With the advent of the internet and the explosion of social media, the speed of information spread has increased dramatically, especially for negative brand information (Ward and Ostrom, 2006). As a result, people confront negative brand information more frequently than they did even a decade ago (Eisingerich et al., 2011).

Negative publicity, which is the “non-compensated dissemination” of negative information in major media channels (e.g. newspapers and TV) (Sherrell and Reidenbach, 1986), is a key part of negative brand information. In general, negative publicity has more serious effects than negative rumours or negative WOM because such information is usually confirmed and verified by authorities (Kim et al., 2007). As negative publicity is often disseminated through major media, it is generally believed to be more credible than rumours and negative WOM (Bond and Kirshenbaum, 1998) and is easier to accept (Ahluwalia and Gürhan-Canli, 2000).

Two types of negative publicity have been identified (performance-related and value-related negative publicity) (Dean, 2004). Performance-related negative publicity is negative brand information about functional aspects of a brand (e.g. product quality), whereas value-related negative publicity is negative brand information about business practices or ethical issues (e.g. child labour). Recent research suggests consumers may have a stronger negative response to value-related negative brand information (Liu and Sweeney, 2011; Pullig et al., 2006).

Consumer attribution

After being exposed to negative brand information, people often go through a process of deciding who should be held responsible or blamed for the negative brand information, which is called an attribution process (Weiner, 1983). Attribution theory is concerned with people’s tendency to look for causal relationships (Kelley, 1971). It is most often used to understand people’s thinking about the causes of a past event or incident they or others experienced (Kelley, 1971). The causes for an outcome are “internal attribution” (one’s own characteristics or personality) and “external attribution” (forces outside the individual). People seem to attribute good outcomes internally (e.g. I won the competition because I worked hard) and bad outcomes externally (e.g. I lost the competition because the evaluator marked unfairly) (Griffin et al., 2008).

Reynolds et al. (2006) applied the attribution theory when studying consumer search failure and found consumers often blame service providers (e.g. website design) rather than themselves (their computer ability), which is termed the “blame” factor. Leong et al. (2008) used the attribution theory to study people’s animosity towards a foreign country. They found that, 10 years after the 1997 Asian Financial Crisis, the more that people attributed the crisis externally (e.g. the financial crisis was caused by the pressure from the US on the local currency), the greater their animosity towards the foreign country (e.g. the US), which led to them being less likely to purchase products or brands from that country. However, there are also individual differences in attributions. For instance, Yoon (2013) found more analytical consumers are likely to attribute the cause of their negative consumption experience to the brand rather than to the retailer, which results in a reduction in brand purchase intention. Those who find it difficult to attribute blame are more likely to switch to another brand than are people who had a strong belief about who should be held accountable (Mattila and Ro, 2008).

Past research also suggested consumers’ attribution for product-related or service-related negative publicity has a strong impact on post-purchase evaluations (Sparks and Callan, 1997; Senecal and Nantel, 2004) and WOM behaviours (Swanson and Kelley, 2001). Thus, if a product or service failure was attributed to a brand, consumers were more likely to spread negative WOM (Curren and Folkes, 1987). Further, if a consumer believed that a brand should be fully responsible for the negative incident, they were less likely to repurchase its products (Folkes et al., 1987).

The reputation of an affected brand can also influence the blame factor. If a well-established brand is involved in a negative publicity incident, people tend to place more blame on brand users (e.g. blaming affected consumers for exaggerating or dramatizing the damage). In contrast, if the affected brand is not well-established, consumers tend to blame the brand itself (Laczniak et al., 2001; Laufer and Coombs, 2006). The severity of negative brand information is also important. A number of studies have found people are likely to place more blame on the brand when the negative brand information is more severe (e.g. causing injury or death rather than causing inconvenience or minor health problems) (Robbennolt, 2000; Laufer et al., 2005). Laufer and Coombs (2006) found people who blamed a brand for a negative publicity incident were less likely to purchase that brand’s products.

Negative brand information

The limited research undertaken into the impact of negative brand information suggests it has a stronger influence on consumers than does positive brand information (Mizerski, 1982; Mahajan et al., 1984; Ito et al., 1998; Wangenheim, 2005). It seems negative brand information attracts more attention than positive brand information because it is more “diagnostic or informative” (Skowronski and Carlston, 1989; Maheswaran and Meyers-Levy, 1990). Thus, people tend to categorize a product as having lower quality after being exposed to negative brand information. In contrast, if they are exposed to positive or neutral brand information, they are less motivated to assess relevant product attributes (Herr et al., 1991).

The current study focused on two key brand equity components (brand image and brand attitude) in examining blame attributions. The study also examined the influence brand image and brand attitude had on brand purchase intention in a negative information situation. Brand attitude reflects a consumer’s evaluation of a brand, which is an important component of brand equity (Keller, 1993, 2016). Brand attitude is one of the most frequently studied constructs in negative brand information research (Ullrich and Brunner, 2015; Um and Kim, 2016), and most previous studies have found negative brand information has a direct, significant and negative effect on consumers’ overall attitudes towards the affected brand (Ahluwalia and Gürhan-Canli, 2000; Dentoni et al., 2011).

A number of factors influence the extent to which brand attitude is influenced by exposure to negative brand information. For instance, the impact negative online reviews have on brand attitudes intensifies with the proportion of negative comments and the writing quality of such reviews (Lee et al., 2008). Different types of negative brand information have different effects on consumer attitude towards an affected brand. For example, although performance-related and value-related negative publicity harm brand attitude (Dutta and Pullig, 2011), value-related negative publicity has a greater impact (Liu and Kanso, 2011). In addition, negative publicity may not influence brand attitudes if a consumer’s prior brand attitude is held with a high level of certainty (i.e. they are loyal to the brand) (Pullig et al., 2006).

Further, negative information about a brand’s celebrity endorsers (Amos et al., 2008; Um and Kim, 2016) or a brand extension (Zhang and Taylor, 2009) can also influence brand attitudes. When a celebrity endorser is involved in a negative issue, consumers’ views of the endorser become less positive, which impacts brand attitudes (Murray and Price, 2012). It seems the more severe the negative publicity about a brand extension, the stronger is the negative impact on attitude towards the parent brand (Zhang and Taylor, 2009).

Brand image is a consumer’s perception of specific brand attributes (e.g. whether the brand is innovative, stylish or fashionable) and is an important component of brand equity (Keller, 1993; Magnusson et al., 2014). Some studies have found negative brand information damages brand image (Romeo, 1991; Dean, 2004; DeCarlo et al., 2007; Raju et al., 2009). More specifically, brand image is likely to be damaged by negative brand information associated with a brand’s endorser (White et al., 2009), brand founder (Zhu and Chang, 2012) or brand extension (Ng, 2010). The damage to brand image is likely to influence purchase intention (Wu, 2011). However, different types of negative brand information (e.g. negative publicity or negative WOM) seem to influence brand image differently (Urban, 2005). For instance, Bailey (2007) found negative publicity associated with a brand’s endorser had a significant influence on a new brand’s image, but not on a well-established brand’s image. Similarly, if consumers already held strong positive beliefs about a brand, their positive perceptions of the brand are less likely to be changed by negative publicity (John and Park, 2016).

Very little research has examined the influence attribution has on brand image. An exception is Louie and Obermiller (2002), who found the blame consumers attached to a celebrity brand endorser influenced brand image. For example, if a consumer believed a celebrity should be held responsible for the negative issue, the consumer would have less favorable perceptions of the brand image than when the celebrity was not believed to be responsible.

The impact negative publicity has on purchase intention, an important outcome of brand equity, has also been studied (Yoon, 2013; Beneke et al., 2015). Numerous studies suggest negative brand information (such as negative publicity or negative WOM) has a negative effect on consumers’ purchase intentions (Wyatt and Badger, 1984; Huang and Chen, 2006) and/or their actual purchase behaviour (Mahajan et al., 1984; Liu, 2006). The impact negative brand information has on purchase intention appears to be consistently negative across industries, including goods-dominated industries, such as books, clothing, sunglasses, cameras and films (Griffin et al., 1991; Skowronski and Carlston, 1989; Huang and Chen, 2006; Wyatt and Badger, 1984), and service-dominated industries, such as restaurants, health spas and beauty salons (Weinberger and Dillon, 1980; Cheng et al., 2006).

As discussed earlier, attribution may influence consumers’ purchase intentions. Laufer and Coombs (2006) found people who blamed a brand for a negative incident were less likely to purchase their products. Similarly, Leong et al. (2008) found the more consumers attributed an issue externally, the greater was their resistance to purchasing relevant products or brands.

Characteristics of negative information

The characteristics of the negative information may also influence the attribution process. This process has been explained by the discounting–augmenting principle (Phares and Wilson, 1972). Discounting refers to people’s tendency to attach less importance to a potential cause of some behaviour when other potential causes are also present, whereas augmentation refers to people’s tendency to attach greater importance to a potential cause of behaviour if the behaviour occurs despite the presence of other causes (Kelley, 1971). Further, if negative brand information is more severe (e.g. likely to have severe consequences), consumers may attribute the incident more to the brand than to other factors.

The severity of the information may also influence consumers’ motivation to process the information (Chiou et al., 2013). For example, Keller and Block (1996) found people who were exposed to high-fear-appeal negative brand information increased their elaboration, chose to take defensive actions and displayed more negative attitude changes than did those exposed to low-fear-appeals. They suggested exposure to low-fear-appeal negative brand information may not be strong enough to motivate elaboration on negative details and may lead to a lower likelihood of taking defensive actions and, consequently, produce fewer negative attitude changes.

Previous research has also shown information severity is related to the information diagnosis level. Herr et al. (1991) found more severe negative brand information was more diagnostic, leading to a more negative evaluation of the brand. However, if the information was not from a credible source, its diagnostic power was reduced (Pan and Chiou, 2011). Severity of the information also influenced consumer responses to an affected brand. For example, people’s attitude towards a brand was less favourable when they were exposed to severe negative brand information than when they were exposed to mild negative brand information (Zhang and Taylor, 2009). Severity may also impact brand image. Gendel-Guterman and Levy (2017) found mild negative publicity influenced product-related image but did not influence store-related image. However, when negative publicity became extreme, product-related and store-related image were both affected. Further, when the negative brand information was extreme (very serious), consumers were more likely to search for information about the brand. However, if the negative brand information was mild, consumers generally had little interest in searching for or sharing information (Shaw and Steers, 2000).

Based on this discussion, it can be hypothesized that:


Consumers’ attribution of negative publicity to a brand has a negative effect on brand image after a negative publicity exposure.


The perceived severity of the negative publicity has a moderating effect on the relationship between attribution and brand image.


Consumers’ attribution of negative publicity to a brand has a negative effect on brand attitude after a negative publicity exposure.


The perceived severity of the negative publicity has a moderating effect on the relationship between attribution and brand attitude.


Consumers’ attribution of negative publicity to a brand has a negative effect on purchase intention after a negative publicity exposure.


The perceived severity of the negative publicity has a moderating effect on the relationship between attribution and purchase intention.


Brand image has a positive effect on intention to purchase after a negative publicity exposure.


The perceived severity of the negative publicity has a moderating effect on the relationship between brand image and purchase intention.


Brand attitude has a positive effect on intention to purchase after a negative publicity exposure.


The perceived severity of the negative publicity has a moderating effect on the relationship between brand attitude and purchase intention.


Brand image has a positive effect on brand attitude after a negative publicity exposure.


The perceived severity of the negative publicity has a moderating effect on the relationship between brand image and brand attitude.

These hypotheses suggested a research model that can be seen in Figure 1. The study that was undertaken to examine this model is discussed in subsequent sections.

Research method

Design and stimuli

China was selected for this study, as it is one of the world’s largest and fastest-emerging economies (Rugman and Verbeke, 2004; Yin and Choi, 2005) and has a highly competitive market. Further, although Chinese consumers are not surprised by negative brand information in their daily life, research into negative brand information in China is scarce (Liu and Kanso, 2011). In the current study, a food product was chosen, as such products are often affected by negative brand information in China (Liu and Yu, 2013). Previous consumer research has suggested past experience may be important (Arora and Huber, 2001). Thus, the study adopted one of the best-known local juice brands.

Two types of negative publicity (mild- and high-severity) were designed based on a qualitative review of past negative news about food in China. The mild-severity condition included a piece of negative news from a major newspaper in Shanghai suggesting the juice brand contained excessive amounts of auramine (a chemical often used as an industrial colouring material) that might cause a slight stomach ache. The high-severity condition included a piece of negative news from the same medium that suggested the juice brand contained excessive amounts of auramine that might cause cancer. To control for the believability of the news, both conditions stated that the Chinese Association of Consumer Protection (the government authority that deals with consumer complaints) had confirmed the reported news was true.

Twenty-two undergraduate students from a university in Shanghai were invited to participate in a pre-test in order to assess the severity of the conditions on a five-point scale (1 being the lowest and 5 being the highest). A Mann–Whitney test, which was used owing to the small sample size, found a significant difference between the mild- and high-severity scenarios in the expected direction (Meanlow = 3.1 and Meanhigh = 4.6, p < 0.001). Thus, the manipulation seemed to be successful. After the pre-test, the participants were informed that the news they had exposed to was fictional.

Sample selection

Consumers’ responses to negative publicity may be different from those of non-consumers. Keller (1993) found people who had never used a brand were more likely to have negative views of that brand after a negative brand information exposure. However, Winchester and Romaniuk (2008) found that, after exposure to negative publicity, current consumers were more likely to have negative evaluations of a brand and to share negative views than were non-consumers. Consumers also often receive positive updates and messages from a company through mobile or internet sources, whereas non-consumers have less opportunity to receive such positive information. Therefore, current consumers are likely to be exposed to more positive brand information, and this may impact on their responses to negative brand information (Winchester and Romaniuk, 2008). Current consumers were chosen as the focus in this study, as they are more likely to affect a brand’s sales than non-consumers, especially in a frequently purchased category.


Chen et al.’s (2014) two-item attribution measure (e.g. the problem was caused by consumers/the problem was caused by the brand) was used, with higher scores implying greater blame was attributed to the brand. Brand image was measured using a ten-item scale adapted from previous studies (e.g. unsuccessful/successful) (Roth, 1992; Park, 2009; Messner and Reinhard, 2012), with higher scores implying a more positive brand image. Brand attitude was measured through six items (e.g. unfavourable/favourable) adapted from previous studies (Liu et al., 2012), with higher scores implying a more positive attitude. Purchase intention was measured through three items adapted from previous research (e.g. “I intend to purchase the brand”) (He et al., 2014), with higher scores implying a greater intention to purchase.

Data collection and analyses

Data collection

Three hundred and seventy-six responses were obtained from third-year business students attending a university in Shanghai, China. However, only responses from the 203 students who were current consumers of the juice brand were examined. Of these, 102 (50 per cent) were exposed to the mild condition and 101 (50 per cent) were exposed to the severe condition. In each condition, the students completed the questionnaires independently after reading the negative news. After completing the questionnaires, all of the participants were informed that the news was fictional.

Data analyses

The hypotheses were tested through a partial least squares (PLS) approach, which was used because of its ability to handle skewed data, model latent variables, assess measurement and estimate structural models (Chin, 2001). The WarpPLS computer package (Kock, 2015) was used to test the model. Before estimating a model, it is necessary to ensure the constructs have acceptable measurement properties. Thus, unidimensionality, reliability and convergent and discriminant validity were assessed by using Fornell and Larcker’s (1981) and Rivard et al.’s (1997) recommendations.

Table I shows the constructs’ measurement properties for the mild and high severity scenarios. The lowest loading was 0.77 (in the purchase intention construct), which was higher than Rivard et al.’s (1997) suggested 0.50 minimum. The construct reliability coefficients were all above 0.79 and higher than Fornell and Larcker’s (1981) suggested 0.70 minimum. Moreover, all of the average variance extracted (AVE) scores were higher than 0.65 and higher than Fornell and Larcker’s (1981) suggested 0.50 minimum.

The lowest square root of any of the AVE scores in the mild scenario was 0.81, whereas the highest correlation between any of the two constructs was 0.49. Similarly, the lowest square root of any of the AVE scores in the severe scenario was 0.80, whereas the highest correlation between any of the two constructs was 0.35, supporting discriminant validity between the constructs in both scenarios (Fornell and Larcker, 1981). The R2 coefficients for the endogenous constructs in the model ranged from 0.01 (brand image) to 0.29 (brand attitude), suggesting some of these constructs were reasonably well explained by the model, although brand image clearly was not.

The model’s usefulness was determined by examining three main indicators: average path coefficient (APC), average R squared (ARS) and Tenenhaus’ GoF measure (GoF) (Tenenhaus et al., 2005; Wetzels et al., 2009). According to Kock (2015), a model’s fit can be considered satisfactory if the GoF exceeds 0.25 and the p-values for the APC and ARS coefficients are less than 0.05. As can be seen in Table II, the model meets these requirements, suggesting it was worth looking at the results in detail.

Hypothesis testing

In a PLS analysis, path coefficients are standardized regression coefficients that can be used to test the significance of relationships between constructs (Loureiro et al., 2012). However, the standard errors in a PLS analysis are generally determined through bootstrapping procedures and, consequently, do not rely on the normality assumptions that underlie such a test in ordinary least-squares regression analysis. The results obtained can be seen in Table III.

The initial analysis used both groups (N = 203). In this case, the path coefficient suggested attribution did not influence brand image (β = −0.08, p = 0.11). Hence, H1a was not supported. However, attribution did have a significant direct influence on brand attitude (β = −0.28, p < 0.01) and on purchase intention (β = −0.16, p = 0.01), supporting H2a and H3a. In other words, the more a consumer blamed the brand, the less favourable was their brand attitude and their purchase intention. There was a significant and positive relationship between brand image and brand attitude (β = 0.20, p < 0.01), supporting H6a. There were also significant and positive relationships between brand image and brand purchase intention (β = 0.19, p < 0.01) and between brand attitude and brand purchase intention (β = 0.24, p < 0.01), supporting H4a and H5a.

Each of the paths was then estimated in each of the two conditions (i.e. the mild and the severe message), and these results are also shown in Table III. As can be seen in the table, there were some differences between the coefficients, but a series of tests to examine the differences between them that used Satterthwaite’s (1946) approach, as suggested by Kock (2014), found there was only one statistically significant difference, which was the path between brand image and brand attitude. In this case, there was a strong positive relationship in the mild severity case (β = 0.45, p < 0.01), but the relationship was not significant in the high severity case (p = 0.46). Consequently, H6b was supported. This result might be owing to the very severe nature of the message (cancer causing) that influenced brand image so heavily that the subsequent relationship was disturbed. Indeed this seemed to be the case, as the mean of the brand image in the mild severity case was 4.46 but 2.29 in the high severity case, a difference that was significant well beyond the 0.001 level. Further, the standard deviation in the high severity case was only 0.38 (compared with 1.32 in the mild severity case), suggesting these negative views were strongly held across most of the respondents.

Summary and discussion

The major objective of the present study was to examine the influence the attribution of negative publicity had on brand image, brand attitude and purchase intention. Consumers’ attribution of fault to a brand had a significant and negative impact on their brand attitude and purchase intentions, irrespective of the severity of the negative publicity. In other words, the more a consumer believed the brand should be blamed, the less favourable were their brand attitudes and purchase intentions, supporting Wu and Lo’s (2009) suggestion that attitude influences purchase intention when negative publicity is encountered.

Unlike brand attitude, this study suggested brand image was not influenced by consumer attribution. This finding, on one hand, supports our conceptual assumption that attribution does not influence the different components of brand equity in the same way. As respondents were consumers of the brand, this may also suggest brand image (once established) is more enduring than brand attitude and that customers’ image of a brand may not be easily influenced by external factors (Shimp and Andrews, 2013). On the other hand, the finding raises questions about consumers’ processing of negative information. For example, is it because of the characteristics of the product or the information provided or is it because of people’s personal processing style (e.g. analytical or holistic)? Previous research finds attribution may be dispositional or situational (Yoon, 2013), which suggests future studies should examine the interaction between attribution, products and consumers.

The findings on information severity were mixed. The severity of the negative publicity did not appear to influence the relationships between attribution and brand attitudes or between attribution and purchase intentions. This suggests a consumer’s brand attitudes and purchase intentions may be influenced negatively even if the negative publicity encountered is not severe. However, another important finding was that the relationship between brand image and brand attitude changed with the severity of the negative publicity. In the mild negative publicity scenario, brand image had both direct and indirect (via brand attitude) effects on purchasing intentions. In other words, in the mild negative publicity situation, the established brand image had an important role in predicting buying intentions. Conversely, in the severe negative publicity situation, brand image did not have any significant effect on brand attitudes or purchase intentions. This finding is particularly interesting, as it suggests consumers’ evaluation of brands may go through central route processing, which are cognitively-based (brand attitude) rather than imagery-based (brand image). These findings also suggest communications should be pinpointed to the brand aspects that are more significantly impacted by negative publicity.

When a brand suffers negative publicity, marketers should allocate resources to the communication channels that can effectively communicate with consumers. Although preventing negative publicity is ideal, once it happens, marketers should post positive information online to minimize further damage, such as negative WOM. Marketers need to engage in public relations with the media. Better public relations can help a brand obtain media support when negative issues arise. Even after consumers are exposed to negative publicity, managers should use public relations campaigns to reassure consumers of the brand’s quality and value. In recent years, well-known brands, such as McDonald’s, have developed risk management plans to deal with potential problems (Tybout and Roehm, 2009), and the possibility of negative publicity needs to be included in such plans. Without effectively managing negative publicity, the brand is likely to face more serious consequences, such as brand hate (Hegner et al., 2017).

Overall, the results support the notion that the attribution of negative publicity has a significant impact. However, attributions influenced brand attitudes and purchase intentions more than it did on brand image. More research is needed to help marketers better understand their target markets and, therefore, better manage negative information (Pee, 2016).

Limitation, future studies and conclusion

As always there are some limitations to note. Although the sample included current consumers of the product examined, they were university students, who are younger and better educated than most Chinese. Hence, their responses to negative publicity may not be the same as those from other groups in the Chinese market. Nonetheless, young well-educated consumers are a key segment for most product categories because of their customer lifetime value. Therefore, it is important for marketers to understand how such consumers respond to negative publicity. It would also be useful to extend the study to examine potential customers to see if they respond differently to similar information.

The current study examined only one product (a juice). As product type (i.e. service or good) can influence consumers’ response to negative publicity, future studies should include services to see if negative publicity’s impact is different in such contexts. Further, brand image may be multidimensional and these dimensions may be influenced differently by negative information. For example, Liu and Sweeney (2011) found that, when consumers were exposed to negative publicity, functional brand image (e.g. quality and durability) was more seriously affected than was symbolic image (e.g. fashionable and stylish). Future research should examine this construct in more detail. Future research should also examine different types of negative publicity, as Liu and Kanso (2011) found values-related negative publicity impacted negatively on brand image and brand attitude, whereas performance-related publicity did not.

As discussed in previous sections, brand equity has been measured in different ways. Thus, future research can investigate how negative publicity is influenced by different brand equity components other than brand image and brand attitude. For example, Baalbaki and Guzman (2016) recently suggested “social influence” and “sustainability” were brand equity dimensions. It would be interesting to look at the impact negative publicity has on these dimensions.

Future studies should also examine the influence of cultural and personal factors. It has long been recognized that culture is important in explaining behaviour (Hsieh et al., 2004), suggesting consumers’ attribution may be influenced by their cultural backgrounds. Nisbett et al. (2001) found Easterners (e.g. Chinese consumers) and Westerners (e.g. American consumers) evaluated negative brand information (e.g. brand extension failures) differently. When they were exposed to two pieces of negative brand information, Eastern consumers found a position between them. However, Western consumers preferred to evaluate each independently. Further, Laufer et al. (2005); and Swan and Zou (2012) found consumers from a high-uncertainty avoidance culture were more likely to blame brands. The blame factor may also be influenced by personality. For example, Monga and John (2008) found analytical consumers were more likely to blame the brand and change their attitudes and beliefs about a brand after being exposed to negative brand information. Further, a number of recent studies have found the influence negative publicity has on consumer attitudinal and behavioural responses are moderated by the strength of the brand–consumer relationship (Ullrich and Brunner, 2015; Um and Kim, 2016). Future research is also needed in this area.


The conceptual model

Figure 1

The conceptual model

The constructs’ measurement properties

Construct Composite reliability (mild/high) AVE score (mild/high) R2 (mild/high) Lowest loading/weight (mild/high)
Attribution 0.79/0.80 0.66/0.66 0.87/0.92
Brand image 0.97/0.85 0.76/0.74 0.03/0.01 0.81/0.93
Brand attitude 0.95/0.95 0.75/0.76 0.29/0.09 0.79/0.87
Purchase intention 0.88/0.84 0.71/0.65 0.18/0.13 0.77/0.83

Mild = mild-severity condition; high = high-severity condition

The model fit indicators

Condition APC (good if p < 0.05) ARS (good if p < 0.05) GoF (small ≥0.1, medium ≥0.25, large ≥0.36) R2 for purchase intent
Mild 0.24, p < 0.01 0.17, p = 0.02 0.36
High 0.14, p = 0.03 0.21, p = 0.01 0.34
Overall 0.19, p < 0.01 0.10, p = 0.03 0.27 0.17

Mild = mild-severity condition; high = high-severity condition

Comparisons between the mild- and high-severity scenarios

Path Coefficient (overall) p Coefficient (mild) p Coefficient (high) p Moderation (Satterthwaite)
Attribution to brand image (H1b) −0.08 0.11 −0.17 0.04 −0.07 0.24 0.23
Attribution to brand attitude (H2b) −0.28 <0.01 −0.22 <0.01 −0.31 <0.01 0.25
Attribution to purchase intention (H3b) −0.16 0.01 −0.24 <0.01 −0.07 0.25 0.10
Brand image to purchase intention (H4b) 0.19 <0.01 0.17 0.04 0.07 0.24 0.22
Brand attitude to purchase intention (H5b) 0.24 <0.01 0.17 0.03 0.33 <0.01 0.08
Brand image to brand attitude (H6b) 0.20 <0.01 0.45 <0.01 0.01 0.46 <0.01*

Mild = mild-severity condition; high = high-severity condition


Aaker, D.A. (1991), Managing Brand Equity: Capitalizing on the Value of a Brand Name, The Free Press, New York, NY.

Ahluwalia, R. and Gürhan-Canli, Z. (2000), “The effects of extensions on the family brand name: an accessibility-diagnosticity perspective”, Journal of Consumer Research, Vol. 27 No. 3, pp. 371-381.

Amos, C., Holmes, G. and Strutton, D. (2008), “Exploring the relationship between celebrity endorser effects and advertising effectiveness”, International Journal of Advertising, Vol. 27 No. 2, pp. 209-234.

Arora, N. and Huber, J. (2001), “Improving parameter estimates and model prediction by aggregate customization in choice experiments”, Journal of Consumer Research, Vol. 28 No. 2, pp. 273-283.

Bailey, A.A. (2007), “Public information and consumer skepticism effects on celebrity endorsements: studies among young consumers”, Journal of Marketing Communications, Vol. 13 No. 2, pp. 85-107.

Baalbaki, S. and Guzman, F. (2016), “A consumer-perceived consumer-based brand equity scale”, Journal of Brand Management, May, Vol. 23 No. 3, pp. 229-251.

Bambauer-Sachse, S. and Mangold, S. (2011), “Brand equity dilution through negative online word-of-mouth communication”, Journal of Retailing and Consumer Services, Vol. 18 No. 1, pp. 38-45.

Beneke, J., De Sousa, S., Mbuyu, M. and Wickham, B. (2015), “The effect of negative online customer reviews on brand equity and purchase intention of consumer electronics in South Africa”, International Review of Retail, Distribution and Consumer Research, Vol. 26 No. 2, pp. 1-31.

Berger, J., Sorensen, A. and Rasmussen, S. (2010), “Positive effects of negative publicity: when negative reviews increase sales”, Marketing Science, Vol. 29 No. 5, pp. 815-827.

Bond, J. and Kirshenbaum, R. (1998), Under the Radar: Talking to Today’s Cynical Consumer, Wiley, New York, NY.

Chelminski, P. and Coulter, R. (2011), “An examination of consumer advocacy and complaining behavior in the context of service failure”, Journal of Services Marketing, Vol. 25 No. 5, pp. 361-370.

Chen, Q., He, Y. and Alden, D. (2014), “Social presence in service failure: why it might not be a bad thing”, Customer Needs and Solutions, Vol. 1 No. 4, pp. 288-297.

Cheng, S., Lam, T. and Hsu, C. (2006), “Negative word-of-mouth communication intention: an application of the theory of planned behavior”, Journal of Hospitality & Tourism Research, Vol. 30 No. 1, pp. 95-116.

Chin, W.W. (2001), PLS-Graph User’s Guide, CT Bauer College of Business, University of Houston, Houston.

Chiou, J.S., Chi-Fen Hsu, A. and Hsieh, C.-H. (2013), “How negative online information affects consumers’ brand evaluation: the moderating effects of Brand attachment and source credibility”, Online Information Review, Vol. 37 No. 6, pp. 910-926.

Curren, M.T. and Folkes, V. (1987), “Attributional influences on consumers’ desires to communicate about products”, Psychology and Marketing, Vol. 4 No. 1, pp. 31-45.

Dataworld (2016), “Complaints received by Bureau of Consumer Financial Protection about financial products and services from 2012-2016”, available at:

Dawar, N. and Pillutla, M. (2000), “Impact of product-harm crises on brand equity: the moderating role of consumer expectations”, Journal of Marketing Research, Vol. 37 No. 2, pp. 215-226.

Dean, D.H. (2004), “Consumer reaction to negative publicity”, Journal of Business Communication, Vol. 41 No. 2, pp. 192-211.

DeCarlo, T.E., Laczniak, R., Motley, C. and Ramaswami, S. (2007), “Influence of image and familiarity on consumer response to negative word-of-mouth communication about retail entities”, Journal of Marketing Theory and Practice, Vol. 15 No. 1, pp. 41-51.

Dentoni, D., Tonsor, G., Calantone, R. and Peterson, H. (2011), “Animal welfare’ practices along the food chain: how does negative and positive information affect consumers”, Journal of Food Products Marketing, Vol. 17 Nos 2/3, pp. 279-302.

Dutta, S. and Pullig, C. (2011), “Effectiveness of corporate responses to brand crises: the role of crisis type and response strategies”, Journal of Business Research, Vol. 64 No. 12, pp. 1281-1287.

East, R., Hammond, K. and Wright, M. (2007), “The relative incidence of positive and negative word of mouth: a multi-category study”, International Journal of Research in Marketing, Vol. 24 No. 2, pp. 175-184.

Ein-Gar, D., Shiv, B. and Tormala, Z.L. (2012), “When blemishing leads to blossoming: the positive effect of negative information”, Journal of Consumer Research, Vol. 38 No. 5, pp. 846-859.

Eisingerich, A.B., Rubera, G., Seifert, M. and Bhardwaj, G. (2011), “Doing good and doing better despite negative information? The role of corporate social responsibility in consumer resistance to negative information”, Journal of Service Research, Vol. 14 No. 1, pp. 60-75.

Folkes, V.S., Koletsky, S. and Graham, J. (1987), “A field study of causal inferences and consumer reaction: the view from the airport”, Journal of Consumer Research, Vol. 13 No. 4, pp. 534-539.

Fornell, C. and Larcker, D. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.

Gendel-Guterman, H. and Levy, S. (2017), “Consumer response to private label brands’ negative publicity: a relational effect on retailer’s store image”, Journal of Product & Brand Management, Vol. 26 No. 2, pp. 204-222.

Griffin, M., Babin, B. and Attaway, J. (1991), “An empirical investigation of the impact of negative public publicity on consumer attitudes and intentions”, Advances in Consumer Research, Vol. 18 No. 1, pp. 334-341.

Griffin, R.J., Yang, Z., ter Huurne, E., Boerner, F., Ortiz, S. and Dunwoody, S. (2008), “After the flood anger, attribution, and the seeking of information”, Science Communication, Vol. 29 No. 3, pp. 285-315.

He, Z., Zhai, G. and Suzuki, T. (2014), “The immediate influence of a food safety incident on Japanese consumers’ food choice decisions and willingness to pay for safer food”, Human and Ecological Risk Assessment, Vol. 20 No. 4, pp. 1099-1112.

Hegner, S.M., Fetscherin, M. and van Delzen, M. (2017), “Determinants and outcomes of brand hate”, Journal of Product & Brand Management, Vol. 26 No. 1, pp. 13-25.

Herr, P.M., Kardes, F. and Kim, J. (1991), “Effects of word-of-mouth and product-attribute information on persuasion: an accessibility-diagnosticity perspective”, Journal of Consumer Research, Vol. 17 No. 4, pp. 454-462.

Hsieh, M.H., Pan, S. and Setiono, R. (2004), “Product-, corporate-, and country-image dimensions and purchase behavior: a multicountry analysis”, Journal of the Academy of Marketing Science, Vol. 32 No. 3, pp. 251-270.

Huang, J.H. and Chen, Y. (2006), “Herding in online product choice”, Psychology and Marketing, Vol. 23 No. 5, pp. 413-428.

Ito, T.A., Larsen, J., Smith, N. and Cacioppo, J. (1998), “Negative information weighs more heavily on the brain: the negativity bias in evaluative categorizations”, Journal of Personality and Social Psychology, Vol. 75 No. 4, pp. 887-900.

Jalilvand, M.R. and Samiei, N. (2012), “The effect of electronic word of mouth on brand image and purchase intention: an empirical study in the automobile industry in Iran”, Marketing Intelligence & Planning, Vol. 30 No. 4, pp. 460-476.

John, D.R. and Park, J.K. (2016), “Mindsets matter: implications for branding research and practice”, Journal of Consumer Psychology, Vol. 26 No. 1, pp. 153-160.

Keller, K.L. (1993), “Conceptualizing, measuring, and managing customer-based brand equity”, Journal of Marketing, Vol. 57 No. 1, pp. 1-22.

Keller, K. (2016), “Reflections on customer-based brand equity: perspectives, progress, and priorities”, AMS Review, Vol. 6 Nos 1/2, pp. 1-16.

Keller, K.L., Parameswaran, M. and Jacob, I. (2011), Strategic Brand Management: Building, Measuring, and Managing Brand Equity, Pearson, Chennai.

Keller, P.A. and Block, L. (1996), “Increasing the persuasiveness of fear appeals: the effect of arousal and elaboration”, Journal of Consumer Research, Vol. 22 No. 4, pp. 448-459.

Kelley, H.H. (1971), Attribution in Social Interaction, General Learning Press, New York, NY.

Kim, S.H., Carvalho, J. and Cooksey, C. (2007), “Exploring the effects of negative publicity: news coverage and public perceptions of a university”, Public Relations Review, Vol. 33 No. 2, pp. 233-235.

Kock, N. (2014), “Advanced mediating effects tests, multi-group analyses, and measurement model assessments in PLS-based SEM”, International Journal of e-Collaboration, Vol. 10 No. 1, pp. 1-13.

Kock, N. (2015), WarpPLS 5.0 User Manual, ScriptWarp Systems, Laredo, TX.

Laczniak, R.N., DeCarlo, T.E. and Ramaswami, S.N. (2001), “Consumers’ responses to negative word-of-mouth communication: an attribution theory perspective”, Journal of Consumer Psychology, Vol. 11 No. 1, pp. 57-73.

Laufer, D. and Coombs, W.T. (2006), “How should a company respond to a product harm crisis? The role of corporate reputation and consumer-based cues”, Business Horizons, Vol. 49 No. 5, pp. 379-385.

Laufer, D., Gillespie, K., McBride, B. and Gonzalez, S. (2005), “The role of severity in consumer attributions of blame: defensive attributions in product-harm crises in Mexico”, Journal of International Consumer Marketing, Vol. 17 Nos 2/3, pp. 33-50.

Lee, J., Park, D.H. and Han, I. (2008), “The effect of negative online consumer reviews on product attitude: an information processing view”, Electronic Commerce Research and Applications, Vol. 7 No. 3, pp. 341-352.

Leong, S.M., Cote, J.A., Ang, S.H., Tan, S.J., Jung, K., Kau, A.K. and Pornpitakpan, C. (2008), “Understanding consumer animosity in an international crisis: nature, antecedents, and consequences”, Journal of International Business Studies, Vol. 39 No. 6, pp. 996-1009.

Liu, F. and Kanso, A. (2011), “The effect of negative publicity in brand and product evaluation: an empirical study”, American Academy of Advertising Asia-Pacific Conference, Brisbane.

Liu, F. and Sweeney, J. (2011), “A study on brand negativity publicity: performance and value relevance”, Australia and New Zealand Marketing Academy Conference, Perth.

Liu, F. and Yu, M. (2013), “Subject, information, product category, and media relevance: Chinese consumers’ motivation in processing negative information”, Australian and New Zealand Marketing Academy Conference, Auckland.

Liu, F., Li, J., Mizerski, D. and Soh, W. (2012), “Self-congruity, brand attitude, and brand loyalty: a study on luxury brands”, European Journal of Marketing, Vol. 46 Nos 7/8, pp. 922-937.

Liu, Y. (2006), “Word of mouth for movies: its dynamics and impact on box office revenue”, Journal of Marketing, Vol. 70 No. 3, pp. 74-89.

Louie, T.A. and Obermiller, C. (2002), “Consumer response to a firm’s endorser (dis) association decisions”, Journal of Advertising, Vol. 31 No. 4, pp. 41-52.

Loureiro, S.M.C., Ruediger, K.H. and Demetris, V. (2012), “Brand emotional connection and loyalty”, Journal of Brand Management, Vol. 20 No. 1, pp. 13-27.

Magnusson, P., Krishnan, V., Westjohn, S.A. and Zdravkovic, S. (2014), “The spillover effects of prototype brand transgressions on country image and related brands”, Journal of International Marketing, Vol. 22 No. 1, pp. 21-38.

Mahajan, V., Muller, E. and Kerin, R.A. (1984), “Introduction strategy for new products with positive and negative word-of-mouth”, Management Science, Vol. 30 No. 12, pp. 1389-1404.

Maheswaran, D. and Meyers-Levy, J. (1990), “The influence of message framing and issue involvement”, Journal of Marketing Research, Vol. 27 No. 3, pp. 361-367.

Mattila, A.S. and Ro, H. (2008), “Discrete negative emotions and customer dissatisfaction responses in a casual restaurant setting”, Journal of Hospitality & Tourism Research, Vol. 32 No. 1, pp. 89-107.

Messner, M. and Reinhard, M.A. (2012), “Effects of strategic exiting from sponsorship after negative event publicity”, Psychology and Marketing, Vol. 29 No. 4, pp. 240-256.

Mizerski, R.W. (1982), “An attribution explanation of the disproportionate influence of unfavorable information”, Journal of Consumer Research, Vol. 9 No. 3, pp. 301-310.

Monga, A.B. and John, D.R. (2008), “When does negative brand publicity hurt? The moderating influence of analytic versus holistic thinking”, Journal of Consumer Psychology, Vol. 18 No. 4, pp. 320-332.

Murray, D. and Price, B. (2012), “When sports stars go off the rails: how gender and involvement influence the negative publicity of sport endorsers”, International Journal of Business Research, Vol. 12 No. 2, pp. 84-93.

Ng, S. (2010), “Cultural orientation and brand dilution: impact of motivation level and extension typicality”, Journal of Marketing Research, Vol. 47 No. 1, pp. 186-198.

Nisbett, R.E., Peng, K., Choi, I. and Norenzayan, A. (2001), “Culture and systems of thought: holistic versus analytic cognition”, Psychological Review, Vol. 108 No. 2, pp. 291-310.

Pan, L.Y. and Chiou, J.S. (2011), “How much can you trust online information? Cues for perceived trustworthiness of consumer-generated online information”, Journal of Interactive Marketing, Vol. 25 No. 2, pp. 67-74.

Park, C. and Lee, T.M. (2009), “Information direction, website reputation and eWOM effect: a moderating role of product type”, Journal of Business Research, Vol. 62 No. 1, pp. 61-67.

Park, S.H. (2009), The Antecedents and Consequences of Brand Image: Based on Keller’s Customer-Based Brand Equity, Unpublished PhD Thesis, The Ohio State University, OH.

Pee, L.G. (2016), “Negative online consumer reviews”, International Journal of Market Research, Vol. 58 No. 4, pp. 545-567.

Phares, E.J. and Wilson, K.G. (1972), “Responsibility attribution: role of outcome severity, situational ambiguity, and internal and external control”, Journal of Personality, Vol. 40 No. 3, pp. 392-406.

Pullig, C., Netemeyer, R.G. and Biswas, A. (2006), “Attitude basis, certainty, and challenge alignment: a case of negative brand publicity”, Journal of the Academy of Marketing Science, Vol. 34 No. 4, pp. 528-542.

Raju, S., Unnava, H.R. and Montgomery, N.V. (2009), “The moderating effect of brand commitment on the evaluation of competitive brands”, Journal of Advertising, Vol. 38 No. 2, pp. 21-36.

Reynolds, K.E., Folse, J.A.G. and Jones, M.A. (2006), “Search regret: antecedents and consequences”, Journal of Retailing, Vol. 82 No. 4, pp. 339-348.

Rivard, S., Poirier, G., Raymond, L. and Bergeron, F. (1997), “Development of a measure to assess the quality of user-developed applications”, ACM SIGMIS Database, Vol. 28 No. 3, pp. 44-58.

Robbennolt, J.K. (2000), “Outcome severity and judgments of responsibility: a meta‐analytic review1”, Journal of Applied Social Psychology, Vol. 30 No. 12, pp. 2575-2609.

Romeo, J.B. (1991), “The effect of negative information on the evaluations of brand extensions and the family brand”, Advances in Consumer Research, Vol. 18 No. 1, pp. 399-406.

Rosenthal, M. and Schlesinger, M. (2002), “Not afraid to blame: the neglected role of blame attribution in medical consumerism and some implications for health policy”, The Milbank Quarterly, Vol. 80 No. 1, pp. 41-95.

Roth, M.S. (1992), “Depth versus breadth strategies for global brand image management”, Journal of Advertising, Vol. 21 No. 2, pp. 25-36.

Rugman, A.M. and Verbeke, A. (2004), “A perspective on regional and global strategies of multinational enterprises”, Journal of International Business Studies, Vol. 35 No. 1, pp. 3-18.

Satterthwaite, F. (1946), “An approximate distribution of estimates of variance components”, Biometrics Bulletin, Vol. 2 No. 6, pp. 110-114.

Senecal, S. and Nantel, J. (2004), “The influence of online product recommendations on consumers’ online choices”, Journal of Retailing, Vol. 80 No. 2, pp. 159-169.

Shaw, J. and Steers, W. (2000), “Negativity and polarity effects in gathering information to form an impression”, Journal of Social Behavior and Personality, Vol. 15 No. 3, pp. 399-412.

Sherrell, D.L. and Reidenbach, R.E. (1986), “A consumer response framework for negative publicity: suggestions for response strategies”, Akron Business and Economic Review, Vol. 17 No. 2, pp. 35-44.

Shimp, T. and Andrews, J.C. (2013), Advertising Promotion and Other Aspects of Integrated Marketing Communications, Cengage Learning, Boston, MA.

Skowronski, J.J. and Carlston, D.E. (1989), “Negativity and extremity biases in impression formation: a review of explanations”, Psychological Bulletin, Vol. 105 No. 1, pp. 131-142.

Sparks, B.A. and Callan, V.J. (1997), “Service breakdowns and service evaluations: the role of customer attributions”, Journal of Hospitality & Leisure Marketing, Vol. 4 No. 2, pp. 3-24.

Swan, K.S. and Zou, S. (2012), Interdisciplinary Approaches to Product Design, Innovation, & Branding in International Marketing, Emerald Group Publishing, Bingley.

Swanson, S.R. and Kelley, S.W. (2001), “Attributions and outcomes of the service recovery process”, Journal of Marketing Theory and Practice, Vol. 9 No. 4, pp. 50-65.

Tenenhaus, M., Vinzi, V.E., Chatelin, Y.M. and Lauro, C. (2005), “PLS path modeling”, Computational Statistics & Data Analysis, Vol. 48 No. 1, pp. 159-205.

Turnbull, P.W., Leek, S. and Ying, G. (2000), “Customer confusion: the mobile phone market”, Journal of Marketing Management, Vol. 16 Nos 1/3, pp. 143-163.

Tybout, A.M. and Roehm, M. (2009), “Let the response fit the scandal”, Harvard Business Review, Vol. 87 No. 12, pp. 82-88.

Ullrich, S. and Brunner, C.B. (2015), “Negative online consumer reviews: effects of different responses”, Journal of Product & Brand Management, Vol. 24 No. 1, pp. 66-77.

Um, N.H. and Kim, S. (2016), “Determinants for effects of negative celebrity information: when to terminate a relationship with a celebrity endorser in trouble”, Psychology & Marketing, Vol. 33 No. 10, pp. 864-874.

Urban, G. (2005), Don’t Just Relate-Advocate! A Blueprint for Profit in the Era of Customer Power, Prentice Hall, Upper Saddle River, NJ.

Wang, F., Zhang, X.P.S. and Ouyang, M. (2009), “Does advertising create sustained firm value? The capitalization of brand intangible”, Journal of the Academy of Marketing Science, Vol. 37 No. 2, pp. 130-143.

Wangenheim, F.V. (2005), “Postswitching negative word of mouth”, Journal of Service Research, Vol. 8 No. 1, pp. 67-78.

Ward, J.C. and Ostrom, A.L. (2006), “Complaining to the masses: the role of protest framing in customer-created complaint web sites”, Journal of Consumer Research, Vol. 33 No. 2, pp. 220-230.

Weinberger, M.G. and Dillon, W.R. (1980), “The effects of unfavorable product rating information”, Advances in Consumer Research, Vol. 7 No. 1, pp. 528-532.

Weiner, B. (1983), “Some methodological pitfalls in attributional research”, Journal of Educational Psychology, Vol. 75 No. 4, pp. 530-543.

Wetzels, M., Odekerken-Schröder, G. and Van Oppen, C. (2009), “Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration”, MIS Quarterly, Vol. 33 No. 1, pp. 177-195.

Whelan, J. and Dawar, N. (2016), “Attributions of blame following a product-harm crisis depend on consumers’ attachment styles”, Marketing Letters, Vol. 27 No. 2, pp. 285-294.

White, D.W., Goddard, L. and Wilbur, N. (2009), “The effects of negative information transference in the celebrity endorsement relationship”, International Journal of Retail & Distribution Management, Vol. 37 No. 4, pp. 322-335.

Winchester, M. and Romaniuk, J. (2008), “Negative brand beliefs and brand usage”, International Journal of Market Research, Vol. 50 No. 3, pp. 355-375.

Wu, J. (2011), “The effects of brand crisis on brand evaluation and consumer’s willingness of brand relationship rebuilding: the moderating effect of self-image congruence”, Information Systems for Crisis Response and Management conference in Harbin, China, 2011, pp. 303-307.

Wu, S.I. and Lo, C.L. (2009), “The influence of core-brand attitude and consumer perception on purchase intention towards extended product”, Asia Pacific Journal of Marketing and Logistics, Vol. 21 No. 1, pp. 174-194.

Wyatt, R.O. and Badger, D.P. (1984), “How reviews affect interest in and evaluation of films”, Journalism Quarterly, Vol. 61 No. 4, pp. 874-878.

Xinhua (2017), “Chinese consumer complaints hit record high in 2016”, available at:

Yin, E. and Choi, C.J. (2005), “The globalization myth: the case of China”, Management International Review, Vol. 45 No. 1, pp. 103-120.

Yoon, S. (2013), “Do negative consumption experiences hurt manufacturers or retailers? the influence of reasoning style on consumer blame attributions and purchase intention”, Psychology & Marketing, Vol. 30 No. 7, pp. 555-565.

Zhang, L. and Taylor, R.D. (2009), “Exploring the reciprocal effect of negative information of brand extensions on parent brand”, Marketing Management Journal, Vol. 19 No. 1, pp. 1-15.

Zhu, D.H. and Chang, Y.P. (2012), “Negative publicity effect of the business founder’s unethical behavior on corporate image: evidence from China”, Journal of Business Ethics, Vol. 117 No. 1, pp. 1-11.

Corresponding author

Fang Liu can be contacted at:

About the authors

Mingzhou Yu (Stanley) has recently received his PhD in Marketing from the University of Western Australia, Perth, Australia. His research interests include advertising, brand management and cross-cultural consumer behaviour. Mingzhou has taught a number of marketing units, including advertising and promotion. He had worked as a Data Analyst at the Shanghai Securities prior to his academic pursuit.

Fang Liu (PhD in Marketing) is an Associate Professor in Marketing at the Business School of the University of Western Australia (UWA). Dr Liu’s research, teaching and consulting areas centre on marketing communications, branding, consumer psychology and cross-cultural consumer behaviour. She has a wide range of publications in these areas. Dr Liu had extensive experience in international trade and marketing and had worked for some of the largest international trade companies in China prior to her academic career.

Julie Lee is a Professor of Marketing at the University of Western Australia (UWA). She received her PhD from the University of Illinois at Urbana-Champaign. Her research focuses on values theory, measurement and application in the tourism and consumer behaviour contexts. She has published widely, including the book Marketing across Cultures (co-authored with J.C. Usunier, 2013), which is in its sixth edition and has been translated into several languages. She has been awarded over $2.5m in competitive grants.

Geoff Soutar graduated in Economics from the University of Western Australia (UWA) and undertook doctoral training at Cornell University before returning to teach at the UWA, from 1973 to 1986. He was Foundation Professor of Management at Curtin University of Technology from 1986 to 1994 and Executive Dean of the Faculty of Business and Public Management at Edith Cowan University from 1994 until 1999. He was also Director of the Graduate School of Management at UWA from 2000 until 2007 and Head of the Marketing Discipline Group at UWA from 2008 to 2016. Soutar has been a consultant to a large number of private and public sector organizations in Australia and internationally and has been active in research across a wide area, publishing more than 200 research papers in journals and in book chapters, as well as a number of research monographs, across a wide range of management and marketing areas and presenting more than 300 papers at seminars and conferences. His present research interests include cross-cultural decision-making, new product and service development and the marketing of services, especially educational and tourism services. He has a particular interest in service quality and its impact on organizational success, from which evolved a long-term study of consumption value and its impact on people’s willingness to buy and their subsequent satisfaction or dissatisfaction.