# Advertising strategy and its effectiveness on consumer online search in a defaming product-harm crisis

Sungha Jang (Kansas State University, Manhattan, Kansas, USA)
Jinsoo Kim (Rhode Island College, Providence, Rhode Island, USA)
Reo Song (California State University, Long Beach, California, USA)
Ho Kim (University of Missouri, St Louis, Missouri, USA)

ISSN: 1355-5855

Publication date: 11 June 2018

## Abstract

### Purpose

Actual product-harm crises pose significant challenges to firms, but so can defaming product-harm crises, which are defined as crises caused by false or malicious rumors made by consumers or competing firms. Unlike typical product-harm crises, in defaming product-harm crises, the truth often emerges only after substantial damage has been done to the victim firm. Thus, crisis management strategies in these two cases may be different. The paper aims to discuss these issues.

### Design/methodology/approach

Using a defaming product-harm crisis that involved two competing firms, this paper examines how the firms changed their advertising strategies and how the changes affected consumers’ online search behavior regarding the two firms.

### Findings

The analyses show that after the crisis, the offending firm sensitively reacted to its own and the victim firm’s advertising levels, but the victim firm did not react to the offending firm’s advertising as it had previously. The effectiveness of advertising on consumers’ online search weakened for both firms after the crisis.

### Originality/value

The paper provides a new insight about marketing strategies and their effectiveness in the product-harm crisis literature.

## Keywords

#### Citation

Jang, S., Kim, J., Song, R. and Kim, H. (2018), "Advertising strategy and its effectiveness on consumer online search in a defaming product-harm crisis", Asia Pacific Journal of Marketing and Logistics, Vol. 30 No. 3, pp. 705-724. https://doi.org/10.1108/APJML-03-2017-0056

### Publisher

:

Emerald Publishing Limited

## Introduction

A product-harm crisis is a discrete event in which a product is found to be defective and therefore dangerous to at least part of the product’s customer base (Cleeren et al., 2017). Incidents involving Firestone Tire (2000), Kraft Peanut Butter (2007), Mattel (2007), Domino’s Pizza (2009), Toyota (2010), and Volkswagen (2015) are just a few recent examples in which customers and the companies were seriously imperiled by faulty products (Ackman, 2001; Clifford, 2009; Consumer Reports, 2016; Goldman and Reckard, 2007; van Heerde et al., 2007; USA Today, 2004). Such crises are seemingly increasing in number due to ever-changing market environments, greater product complexity, closer scrutiny by business-monitoring organizations and government regulators, and stronger customer demands for high-quality and safe products (Ahluwalia et al., 2000; Berman, 1999; Dawar and Pillutla, 2000). A product-harm crisis endangers the well-being of customers and is a devastating threat to companies (Dawar and Pillutla, 2000; van Heerde et al., 2007); it can negatively affect sales, advertising effectiveness, and firm value (Chen et al., 2009; Cleeren et al., 2013; van Heerde et al., 2007; Zhao et al., 2011).

Accordingly, many researchers have examined the antecedents and consequences of product-harm crises and developed advertising and pricing strategies to provide managerial insights on these proliferating crises (Cleeren et al., 2017). Prior studies have drawn insights mainly based on one or two fictitious product-harm cases in lab experiments (e.g. Whelan and Dawar, 2016) or on product recalls publicly announced in empirical settings (e.g. Liu et al., 2017). Existing studies on product-harm crises are still limited in that because product recalls are caused by the focal firms only, studies focus on a few dominant industries (e.g. automobile or consumer packaged goods (CPG) industries), and recall information is mostly available in developed countries due to stronger regulations and law enforcement. These facts suggest a gap in the literature, including a variety of causes of product-harm crisis in various industries and geographic areas (Cleeren et al., 2017). We aim to fill at least a part of this gap by studying a crisis caused by defamation, in which two Korean bakery firms are involved.

#### Figure 2

Main estimation results before and after the product-harm crisis

#### Figure 3

Cumulative impulse response functions for advertising before and after the product-harm crisis

Advertising amount ($1,000) Month 2005 2006 2007 2008 2009 2010 2011 2012 2013 A. Firm P 1 0 0 0 0 0 38 17 24 1,246 2 0 0 20 0 27 73 32 116 112 3 0 0 0 49 23 4 323 14 83 4 0 0 0 0 0 0 169 1,390 2,652 5 0 9 584 681 856 1,123 252 1,306 1,644 6 0 0 529 88 856 1,175 22 30 1,468 7 0 0 446 0 283 422 28 10 99 8 0 0 168 237 1 458 12 0 8 9 24 939 0 374 951 338 34 1 0 10 4 645 0 16 772 321 1,232 1,386 209 11 312 323 610 242 357 17 714 85 19 12 1,540 744 967 1,391 1,271 2,184 1,610 1,725 1,978 Total 1,880 2,659 3,324 3,077 5,398 6,153 4,443 6,088 9,519 B. Firm C 1 304 351 0 0 0 40 63 5 1,283 2 277 235 0 0 272 0 92 44 4 3 57 108 666 0 232 42 9 45 516 4 128 0 443 0 243 2 7 49 1,538 5 167 324 454 1 235 18 8 32 383 6 0 438 0 729 1 65 6 3 1 7 0 337 177 758 13 0 9 3 0 8 0 0 47 0 0 0 1,129 10 4 9 0 0 14 0 901 0 349 639 0 10 133 424 10 523 644 0 4 216 13 11 335 244 208 389 31 487 3 1,329 32 12 303 224 1,231 1,197 1,023 1,164 1,310 3,405 654 Total 1,705 2,685 3,250 3,598 3,596 1,818 2,989 5,779 4,429 ## Table II Advertising model Before the crisis (2005-2010) After the crisis (2011-2013) Dependent variable Independent variable Estimate SE Estimate SE AdPt Intercept1 3.007 2.774 1.390 4.589 AdPt-1 0.147 0.109 0.126 0.159 AdCt-1 −0.053 0.129 0.285 0.172 AdPt-12 0.512*** 0.114 0.548*** 0.166 AdCt-12 0.289** 0.137 0.136 0.173 AdCt Intercept2 6.729** 2.880 0.228 4.075 AdPt-1 −0.080 0.113 −0.120 0.141 AdCt-1 0.208 0.134 0.419*** 0.153 AdPt-12 0.266** 0.118 0.418*** 0.147 AdCt-12 0.035 0.142 0.219 0.153 Accumulative impulse AdPt (SE) AdCt (SE) AdPt (SE) AdCt (SE) AdPt+12 1.69 (0.213) 0.209 (0.274) 1.618 (0.381) 0.662 (0.476) AdCt+12 0.146 (0.247) 1.306 (0.319) 0.196 (0.477) 1.831 (0.606) BIC 9.94 10.760 Univariate R2 0.409/0.127 0.404/0.441 Chow Test χ2 (df =13): 9.756 (p=0.713) Notes: *p<0.1; **p<0.05; ***p<0.01 ## Table III Firm P’s advertising and keyword search model Before crisis (2007-2010) After crisis (2011-2013) Dependent variable Independent variable Estimate SE Estimate SE AdPt Intercept1 4.764 8.806 10.361 11.249 AdPt-1 0.100 0.153 0.272* 0.151 KwPt-1 −0.001 1.502 −1.220 1.755 AdPt-12 0.679*** 0.137 0.614*** 0.160 KwPt Intercept2 1.524 1.253 3.412*** 0.834 AdPt 0.041** 0.021 −0.0004 0.009 AdPt-1 −0.011 0.020 −0.007 0.008 KwPt-1 0.200 0.204 0.116 0.097 KwPt-12 0.555*** 0.196 0.311*** 0.097 Accumulative impulse AdPt (SE) KwPt (SE) AdPt (SE) KwPt (SE) AdPt+12 1.790 (0.282) −0.001 (2.272) 2.007 (0.437) −1.924 (0.794) KwPt+12 −0.008 (0.02) 1.805 (0.154) −0.011 (0.021) 1.458 (0.185) BIC 6.07 5.383 Univariate R2 0.464/0.407 0.355/0.278 Chow Test χ2 (df=11): 26.968 (p=0.0046) Notes: *p<0.1; **p<0.05; ***p<0.01 ## Table IV Firm C’s advertising and keyword search model Before crisis (2007-2010) After crisis (2011-2013) Dependent variable Independent variable Estimate SE Estimate SE AdCt Intercept1 36.670** 15.345 25.721 18.712 AdCt-1 0.369** 0.178 0.483*** 0.146 KwCt-1 −6.204* 3.265 −5.075 4.236 AdCt-12 0.013 0.124 0.321** 0.150 KwCt Intercept2 1.347** 0.604 0.673 0.457 AdCt 0.012*** 0.004 −0.0003 0.003 AdCt-1 0.003 0.006 −0.006** 0.003 KwCt-1 0.081 0.111 0.172** 0.080 KwCt-12 0.628*** 0.072 0.667*** 0.082 Accumulative impulse AdCt (SE) KwCt (SE) AdCt (SE) KwCt (SE) AdCt+12 1.489 (0.401) −9.147 (3.605) 2.404 (1.32) −12.727 (9.162) KwCt+12 0.011 (0.011) 1.558 (0.134) −0.015 (0.018) 1.965 (0.24) BIC 3.31 3.108 Univariate R2 0.094/0.759 0.289/0.681 Chow Test χ2 (df=11): 28.08 (p=0.0031) Notes: *p<0.1; **p<0.05; ***p<0.01 ## References Ackman, D. 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## Corresponding author

Sungha Jang is the corresponding author and can be contacted at: sjang@ksu.edu