CHAPTER 1: INTRODUCTION
2.7 STRATEGY TO MANAGE AND RESPONSE TO NEGATIVE EWOM
2.7.1 What a company (candidate) can do to manage the EWOM
Because there did not have any research talking about manage online firestorm, this
knowledge persuasion propagation
afEirmation
Virtual Circle
study selected the research about managing EWOM to observe the possible answer.
Litvin et al. (2008), focusing on hospitality and tourism, refer to two major categories to manage electronic WOM: informational and revenue generation. (1) From an informational perspective, they mentioned that marketers should gather discussion and feedback created online. Gathering information helps marketers enhance customer satisfaction by improving the product, solving problems, discovering the experience of customers, analyzing competitive strategies and monitoring company image. (2) Conversely, from a revenue generating perspective, they mentioned that marketers spread good WOM of a company through email, websites, blogs, virtual communities, newsgroups, or product review sites for themselves, allowing potential customers to see good comments about their company. Additionally, they can spread negative WOM about their competitors. Because the technology is easily accessible to everyone and is difficult for customers to detect, some companies will choose to use the Internet to harm others or improve themselves. Although such revenue-generating strategies are positive proactive marketing activities, these behaviors create some ethical concerns. Many such marketing activities can be classified as ‘stealth marketing’, which Neisser (2004) defines as ‘employing tactics that engage the prospect without them knowing they are being marketed’.
Haywood (1989) creates a comprehensive list of what a company can do to manage word of mouth:
(1) Listen actively and question effectively
Talking with customers face-to-face is the most useful approach for a company to understand their problems.
(2) Take appropriate action
A senior manager should contact customers or suppliers on weekly basis to provide the impression that they are respected by the company.
(3) Focus on a customer/constituent orientation
Customer/ constituent orientation is based on a commitment to service and quality.
(4) Deliver on promises
When a company advertises, it makes promises. Staff must be thoroughly aware and prepared to meet customer expectations.
(5) Manage after the service
Businesses should consider all customers potential opinion leaders and ask regular
customers to join a customer panel or even build personal connections with them, making them feel that they are important.
(6) Target opinion leaders
Opinion leaders often have many people who read their comments, so it is important to focus closely on their comments.
(7) Work with suppliers
Suppliers often contact client companies, so businesses should avoid addressing such transactions casually.
(8) Cooperate with competitors
Friendly competition can generate positive customer reactions and WOM.
(9) Help people who are seeking information
A company must ensure that customers can obtain correct and precise information every time they seek it.
(10) Generate interest and discussion through advertisements
Make customers feel that advertisements are linked with their life and can appeal to their sympathy.
(11) Train employees and managers to become more effective communicators
Managers and employees should constantly be gathering information inside and outside the company and proactively spread it to interested people.
(12) Plug communication leaks
The entire company should have a comprehensive system to prevent confidential information leaking to the public media or competitors.
(13) Determine what others are saying
A company should be open minded to gather information from different people and domains such as advisers, consultants, colleges, attending industry conferences or creating development programs.
There are some similarities between the findings of the studies by Haywood (1989) and Litvin et al. (2008). This study decided to use the framework provided by Litvin et al. (2008) because it is more structured and facilitates concise analysis. This study can see that what Haywood mentioned is categorized in a framework of
‘informational perspectives’ (Litvin et al., 2008). All the suggestions help a company gather opinion from customers to help it make improvements to enhance customer satisfaction and company reputation. In addition, revenue generating is another strategy to enhance the company image with customers. Marketers can automatically
spread good WOM through email, websites, blogs, virtual communities, newsgroups, or product review sites to help potential customers encounter positive WOM.
From an information perspective, during the campaign, candidates in Taiwan usually would have their own electoral office, Facebook account, advertising and press conferences to offer information, gather suggestions from voters or answer voters’
questions. They would use these opinions to adjust their public political views, and do what supporters want them to do while avoiding negative opinion or competing with competitors.
In addition, from a revenue generating perspective, candidates in Taiwan spread good WOM through newsgroups and virtual communities to let potential voters see good comments from and about them. However, some candidates tend to spread negative WOM about their competitors. Such behavior is of some ethical concern; therefore, most candidates would not admit to such behavior to avoid angering voters.
2.7.2 How a company (candidates) respond to a crisis
Per our definition, NWOM means that dissatisfied customers share their complaints to others to harm the company. Complaining or telling others about an unsatisfactory experience through the Internet will reach many potential customers; furthermore, if NWOM triggers an online firestorm, it will cause a crisis for the company; for example, customers switching brand loyalty, and company image will suffer tremendous harm, possibly even causing a failure. Therefore, the question is the following: how can a company address negative WOM to restore or maintain its image?
Similar to Coombs’s crisis communication strategy (CCS) (1998), Benoit (1997) refer to research by Dutton (1986) and Fink et al. (1971) indicating that image is critical to corporate, government or any non-profit groups; therefore, he created an image restoration discourse to help corporations create responses during image crises. The basics of understanding the image restoration discourse include clearly defining an
‘attack’ that induces a response or a corporate crisis. There are two characteristics of
‘attack’:
1. People’s perception is more important than reality. Whether a company is truly responsible for an offensive action does not matter; if people believe the company
should be responsible for it, then the company must face the resulting crisis.
2. Whether the action is offensive is decided by the public.
Therefore, unless people believe that an action is offensive and should be blamed on a specific corporation, the corporate image would not be at risk.
Image restoration discourse (Benoit, 1997) focuses on what message a corporation can convey when it suffers a crisis. The theory provides five categories of image restoration strategies to help a corporation address a crisis and repair its image.
Table 2.7.2-1. The description of Image restoration discourse
Strategy Definition Variant Definition
Denial Refuse to admit the corporate had done the accused offensive action.
Fully deny Comprehensive deny that corporate has do any offensive action.
Evade Corporate can claim that their behavior is
response to another’s offensive action to rationalize their behavior.
Defeasibility Corporate claim that they lack the relevant information or fail to control essential element of event to illustrate their mistake.
Accident Corporate would try to convince that their action is by accidentally to
lighten the image harm.
Good intention Corporate could claim that their behavior is
Bolstering Corporate could use positive bolstering to offset the negative image or WOM of company.
Minimize Corporate could try to minimize the affection of the offensive action they done.
Differentiation Corporate could compare the behavior with some
Transcendence Corporate could explain their action to more
Compensation Corporate could offer money, service or product to compensate people and offset the negative image.
Corrective action
Corporate could make a commit that they would fix the situation before crisis and promising the event would not happened in the future.
None None
Mortificatio n
Corporate could apologize for their fault and beg for forgiveness of public.
None None
This study chose to use Benoit’s image restoration discourse to analyze what strategy a candidate might use to respond to an online firestorm because image restoration discourse clearly classifies responses. Thus, it can help the content of our analysis be more accurate.
Chapter 3: Methodology
3.1 Research approach
This research used content analysis as the research methodology. This study collected data on several online firestorm events that happened during the nine-in-one political election to understand how candidates reacted in responding to online firestorms.
Content analysis is a research method that facilitates making valid inferences from collected data, then surfacing knowledge, new insights, or a practical guide as a research contribution (Krippendorff, 1980). Hsieh and Shannon (2005) mentioned that content analysis is a widely used method for qualitative research; it has been used in communication, journalism, sociology, psychology and business (Neuendorf, 2002).
3.2 Data collection 1. Electronic news
The data were primary selected from electronic news and celebrity Fan pages or websites. Most of the news can be accessed online, and most electronic news provides a forum for people to discuss. Therefore, through searching the news online, this study can obtain not only news of the candidates but also opinions from the viewers.
In addition, some candidates do not only respond to online firestorms through the public media; some have their own fan pages or websites to respond to the public.
Therefore, this can be one data resource.
2. Online firestorm events
This research focuses on the online firestorm events that happened during a nine-in-one political election launched in Taiwan; therefore, this study must collect data on the online firestorm events. However, this study initially wants to ensure that the event with NWOM actually triggered the online firestorm. Therefore, this study needs a measurement to help us determine which events are actually online firestorms.
For such a measurement, this study must clearly examine which factors cause an online firestorm. In a review, Pfeffer et al. (2014) summarized seven factors that cause opinions to spread: speed and volume, binary choices, network clusters and echo chambers, unrestrained information flow, lack of diversity, cross-media dynamics and network-triggered decision processes. Because ‘binary choices’ is a design characteristic of social platforms, it is not a helpful factor for our measurement.
In addition, ‘Network clusters and echo chambers’, ‘unrestrained information flow’,
‘lack of diversity’ and ‘network-triggered decision processes’ phenomena are seen only in people’s social platforms, they are also difficult to measure. Therefore, this study decided to use ‘cross-media dynamics’ and ‘speed and volume’ to design our measurement.
After experiencing the online firestorm event, if there were dissatisfied people spreading NWOM online, then an increasing number of people would encounter the information. As the network-triggered decision processes this study discussed occurred, people who encountered the news would want to ascertain what really happened and make up their mind. Many decision makers are not passive; they actively seek information (Haywood, 1989), and one of their primary information resources is the Internet. According to our research, INSIGHTXPLORER (2014) and Global Views Monthly (2013) both indicate that the primary Internet behavior in Taiwan is searching for information or news. Therefore, this study can believe that an event keyword being searched for many times provides the important characteristics
‘speed and volume’; thus, this study can consider this event the trigger of the online firestorm.
To determine the searching popularity of the specific keywords, this study use Google Trend to see the situation about how specific keywords be searched during the specific timespan and period. Google Trends is a service provided by Google to observe the volume of searches and news articles handled by their search engines.
Choi and Varian (2012) noted that the information Google presents is not the real searching volume; it is a relative number that provides a comparison with the other days a keyword has been searched. Google uses the numbers 1 to 100 to present keyword search volumes; the day that was searched the most frequently in a particular timespan is assigned a value of 100.
Based on the social sharing literature, this study know that people tend to share information shortly after an event happens—over 50% even on the same day (Rimé et al., 1992). Therefore, this study assumes that an online firestorm will form within no more than least five days. In addition, there are many news stories during the political election; therefore, to avoid news overlap altogether, five days is suitable for the measurement. This study analyzes Google Trend of the online firestorm cases mentioned in table 2.6-2. to make our measurements.
Table 3.2-1. Google Trend analyze on table 2.6-2. online firestorm cases
Event Keyword Average Google
Trend values five
ING-DiBa 53.6 25,30,100,67,46
2011/11/21
Qantas Twitter event
Qantas 74.6 82, 79,76,71,65
From the table, this study can see that the lowest average Google Trend value of the cases is 49.6, and the lowest Google Trend value happened during an online firestorm is 60. Therefore, this study wants to make a measurement to measure the online firestorm:
1. The Google Trend value of the keyword five days after the events should have at least one day up to 60.
2. The average Google Trend values five days after events should be at least 49.
This study assumes that if the event satisfies one of the conditions, then it can be considered an online firestorm. Therefore, this study selected some famous NWOM events that happened during the nine-in-one political election to check whether the NWOM triggered an online firestorm. The search area was set to Taiwan, and the search time limit was set to include the month of the event.
Table 3.2-2. Google Trend analyze of NWOM cases in Taiwan political election
Events Date of
The Taipei candidate, 2014/9/6 Ko 37.2 25,29,60,46,26
Wen-Je Ko (Ko), who was previously a doctor, was accused of a tendency toward sexism.
Ko was accused of money laundering (MG149 so the behavior is illegal.
2014/10/
12
Ko 58.6 59,50,62,60,62
Ko said that rejecting company “Ting-Shin” was similar to witch burning in medieval times. The witch-burning choice of words was attacked as being a poor analogy that did not express people’s
2014/7/15 Lien 35.8 28,54,38,37,22
prosperous. accused of not being able to understand living with hardship.
2014/8/7 Lien 34.2 34,46,40,28,23
Lien posted a video asking that if we were a rich man, what would we want to do?
The video mentioned that some people would want to have fun every day, and mentioned if we were as rich as Lien we would not want to run for Taipei mayor. However, this video caused him to be accused of not understanding citizens at all.
2014/9/11 Lien 57.8 63,74,45,55,52
Lien performed a working activity in the night-market.
He was accused of showing off.
2014/9/18 Lien 76.4 69,66,87,62,98
Lien said that Buddha was a prince before, but he chose to liberate all sentient beings. This statement resulted in him being
2014/11/2 Lien 23.8 22,41,19,18,19
accused of being shameless by describing himself as Buddha.
Lien was accused of eavesdrop on Ko’s office.
2014/11/5 Lien 58.2 24,23,97,89,58
Lien’s father criticize Ko’s firestorms. In the next section, this study will focus on the events that conformed to the online firestorm measurement.
3.3 Data analysis
1. Image restoration strategies
This study would use the image restoration strategies from Benoit (1997) to analyze the respond strategy of candidates.
Table 3.3-1. The categorizes of Image restoration strategies
Strategy Variant
Mortification None
2. Using Opview insight clowd service to collect the online comments
Opview social WOM database service launched by eLand technologies corporate from Taiwan, this software can collect the comments from the several social platforms including Facebook, Plurk, news, blogs, BBS, and analysis the comments or news to judge the EWOM is positive or negative. This software can help us analyze the negative and positive WOM patterns of specific key word everyday, and show the data in graphic form. This software service has been used by several public media in Taiwan (Opview, 2013), therefore this study can believe the credibility.
3. Using Opview data to the formula to analyze the online firestorm
Rimé et al.(1992) mentioned that over 50% people tend to share information shortly after an event happens, some even spread the news on the same day. Since there are many news stories during the political election, in order to avoid news overlap altogether, five days is suitable for the measurement. Therefore, this study assumes that an online firestorm will form within no more than least five days.
This study assumes that some online firestorm cases need a “boiling period” to cause the online firestorm; later, events come to a peak that this study calls the “explode period”. After the “explode period”, events gradually receive little interest from the public; this study called this time the “faded period”. However, some online firestorm cases are too intense; they skipped the “boiling period” and jumped directly to the
“explode period”. This study develops a formula to describe the developing pattern of an online firestorm.
Given specific keyword, Opview would collect amounts of NWOM and PWOM from several social platforms and calculate the quantity. This study use “Wen-Je Ko” as a keyword in the cases of Ko; and use ”Sheng-Wen Lien” as a keyword in the cases of Lien. This study assumes that NWOM amounts of the dayi is di, assume i is a positive integer. The average NWOM comments value of the first five days after events is A .
A = d1+ d2+ d3+ d4+ d5 5
If d1< A , then day one is “boiling period”.
If di< A and di−1to d1< A, then i is “boiling period”.
If di ≥ A,
then i is “explode period”.
Otherwise, i is ”faded period”.
The effectiveness of responses would be judged by the exploded days, this study assumes that an effective response strategy can help the events exploded for only several days and faded soon. However, the inappropriate response would deteriorate the situation and exploded for long time, causing the online firestorm be more severe and harm the brand of candidate.
Chapter 4: Data Analysis
Online political election firestorms can result from three factors: fault, defect, and counterforce slander. To conduct the analysis, this study thus select six cases, which are presented in table 3.2-2, that correspond to the online firestorm standard. These six cases include: Ko was accused of a tendency toward sexism, Ko said that rejecting company “Ting-Shin” was similar to witch burning and Ko was accused of involving in human organs trading; Lien’s father criticized Ko’s grandfather is a japanization person, Lien donated one hundred thousand NT dollars to Kaohsiung gas explosion and Lien was accused of eavesdrop on Ko’s office. This study separates these six cases in three situations: fault, defect and counterforce slander. Faults occur when candidates lie, break the law or do something that society deems unacceptable.
Defects occur when candidates do or say something inappropriate and offend the public, and such situations are shaped by candidate personalities and opinions.
Counterforce slander is defined as accusatory remarks from a competitor. In this section, this study describes online firestorm events, candidate responses and use a formula to show how long each event exploded to determine which responses are most effective. In addition, this study also examines the cases to identify which features differ from online conventional business firestorms.
4.1. Fault
In this part, this study selected two cases both candidates say something society unacceptable to see which responses is more effective in this situation.
4.1.1. Ko was accused of a tendency toward sexism
On 6 September 2014, Ko stated that a female Jiayi candidate, Chen Yie-Chen, was
On 6 September 2014, Ko stated that a female Jiayi candidate, Chen Yie-Chen, was