about the relevance and appropriateness of various forms of reputation measures and data (see Berens and Van Reil, 2004; Chun, 2005; Fryxell and Wang, 1994). This is mainly due to the breadth in potential respondent groups, their relationship to the firm as well as the nature of the assessments these respondents make. In other words, because reputations could be assessed using any stakeholders, or stakeholder groups’ perceptions about the firm, measurements of
corporate reputation may vary considerably on the basis of who’s perceptions are collected, how
or in what context these perceptions are collected, as well as what specifically about the firm’s
character, capabilities, favorability and so on, is explored. Fundamentally, market-based measures of corporate reputation are limited in their capacity to capture the corporate reputation concept because it is questionable whether they are, in fact, assessing reputation, or simply the firm’s capability of generating short-term financial returns. Walker (2010: 372) summarises the core issue with market-based methods by quoting Wartick (2002), in that “[c]orporate reputation should be measured as stakeholders’ perceptions – not factual representation”. Media representation techniques on the other hand scan the available media press/reports on organisations and through linguistic analysis, weight the media tone to generate an aggregate favourability score for sample firms (Deephouse, 2000). Media representations of reputation may capture only the perceptions of a medium non-representative of wider audiences.
Consequently, a third potential strategy to obtain corporate reputation data for this research
study was using large-scale survey methods such as Fortune or Reputation Quotient. Although
Reputation Quotient, like Fortune, asks for participant perceptions on a number of firm attributes and characteristics oriented to obtain a sense of the emotional appeal of the firm, its financial performance, its social responsiveness and so on – Reputation quotient is not widely accessible. Fortune data however, is similar in the sense that it too asks a number of participants for their
perceptions of various firms. Furthermore, Fortune’s WMAC survey asks participants to rate
firms on eight key attributes (described previously). This said, some scholars have pointed out that Fortune data has a significant financial orientation because its respondents are managers and market analysts (e.g., Fryxell and Wang, 1994). Due to this, some research suggests that
data collected in the WMAC survey tends to be “limited to measuring the extent to which a firm
is perceived as striving for financial goals” (Fryxell and Wang, 1994: 1) because this may be the most relevant dimension of reputational elements to this group of reputational assessors. Even so, it remains the most conceptually representative and widely source of reputation data.
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Table 3.1: Summary of dependent, independent and control variables
Variable name Values/ Measures Source(s)
(1) Corporate reputation 11-point scale (0= “poor”; 10= “excellent”) Fortune magazine,
WMAC survey
(2) Changes in corporate
reputation [LAG]
Changes in the reputation scores from one year to
another Author’s calculations
(3) ANY_EVENT
Whether or not there were any events of corporate irresponsibility acts in a given year
1= “yes”; 0= “no”
ASSET4, LexisNexis
(4) EVENT 1_Management
compensation
Whether management compensation controversies were identified in a year
1= “yes”; 0= “no”
ASSET4, LexisNexis
(5) EVENT 2_Shareholder rights
Whether shareholder rights controversies were identified in a year
1= “yes”; 0= “no”
ASSET4, LexisNexis
(6) EVENT 3_Earnings
restatements
Whether earnings restatements controversies were identified in a year
1= “yes”; 0= “no”
ASSET4, LexisNexis
(7) EVENT 4_Insider trading
Whether insider trading controversies were identified in a year
1= “yes”; 0= “no” ASSET4, LexisNexis
(8) EVENT 5_Accounting
controversies
Whether accounting controversies were identified in a year
1= “yes”; 0= “no” ASSET4, LexisNexis
(9) EVENT 6_Consumer_related
issues
Whether consumer related controversies were identified in a year
1= “yes”; 0= “no” ASSET4, LexisNexis
(10) EVENT 7_Product and service
quality
Whether product and or service related controversies were identified in a year
1= “yes”; 0= “no”
ASSET4, LexisNexis
(11) EVENT 8_Environmental
spills and pollution
Whether environmental spills and pollution controversies were identified in a year 1= “yes”; 0= “no”
ASSET4, LexisNexis
(12) EVENT 9_Product recalls Whether recall controversies were identified in a year 1= “yes”; 0= “no” ASSET4, LexisNexis
(13) EVENT 10_Intellectual
property
Whether intellectual property controversies were identified in a year
1= “yes”; 0= “no” ASSET4, LexisNexis
(14) EVENT 11_Public health
Whether public health controversies were identified in a year
1= “yes”; 0= “no”
ASSET4, LexisNexis
(15) EVENT 12_Taxation Whether taxation controversies were identified in a year 1= “yes”; 0= “no” ASSET4, LexisNexis
(16) EVENT 13_Anti-competition
Whether anti-competition controversies were identified in a year
1= “yes”; 0= “no” ASSET4, LexisNexis
(17) EVENT 14_Human rights
Whether human rights controversies were identified in a year
1= “yes”; 0= “no” ASSET4, LexisNexis
(18) EVENT 15_Child labour
Whether child labour controversies were identified in a year
1= “yes”; 0= “no” ASSET4, LexisNexis
(19) EVENT 16_Freedom of
association
Whether freedom of association controversies were identified in a year
1= “yes”; 0= “no” ASSET4, LexisNexis
(20) EVENT 17_Diversity and
opportunity
Whether diversity and opportunity controversies were identified in a year
1= “yes”; 0= “no”
ASSET4, LexisNexis
(21) EVENT 18_Wages and
working conditions
Whether wages and working conditions controversies were identified in a year
1= “yes”; 0= “no”
ASSET4, LexisNexis
(22) EVENT 19_Employee health
and safety
Whether employee health and safety controversies were identified in a year
1= “yes”; 0= “no”
ASSET4, LexisNexis
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Table 3.1: Summary of dependent, independent and control variables (continued)
Variable name Values/ Measures Source(s)
(24) HARM 1_Financial harm
Whether financial stakeholder harms were identified in a year
1= “yes”; 0= “no” LexisNexis
(25) HARM 2_ Physical harm
Whether physical stakeholder harms were identified in a year
1= “yes”; 0= “no” LexisNexis
(26) HARM 3_Emotional harm
Whether emotional stakeholder harms were identified in a year
1= “yes”; 0= “no”
LexisNexis
(27) HARM 4_Civil liberties harm
Whether civil liberties harms were identified in a year
1= “yes”; 0= “no”
LexisNexis
(28) HARM 5_Environmental harm
Whether environmental harms were identified in a year
1= “yes”; 0= “no”
LexisNexis
(29) INJURIES
Whether the firm has been associated with stakeholder injuries in a year
1= “yes”; 0= “no”
LexisNexis
(30) FATALITIES
Whether the firm has been associated with human fatalities in a year
1= “yes”; 0= “no” LexisNexis
(31) DECEPTION
Whether the firm is accused of deceiving stakeholders in a year
1= “yes”; 0= “no” LexisNexis
(32) DISCRIMINATION
Whether the firm is accused of discrimination in a year
1= “yes”; 0= “no” LexisNexis
(33) JOB LOSSES
Whether the firm has been associated with loss of employment in a year
1= “yes”; 0= “no”
LexisNexis
(34) EFFECT_UNDESIRABILITY
Whether firm actions are perceived as morally negative in a year
Continuous variable measured from LIWC
LexisNexis - Author’s calculations
(35) CULPABILITY
Whether the firm was solely responsible for a corporate irresponsibility event in a year 1= “yes”; 0= “no”
LexisNexis
(36) NON-COMPLICITY
Whether the stakeholders affected by the corporate irresponsibility event are perceived as vulnerable
1= “yes”; 0= “no” LexisNexis
(37) EFFECT_UNDESIRABILITY
AND CULPABILITY
Measures the presence of both observed effect undesirability and observed culpability
LexisNexis - Author’s calculations
(38) EFFECT_UNDESIRABILITY
AND NON-COMPLICITY
Measures the presence of both observed effect undesirability and observed non-complicity
LexisNexis - Author’s calculations
(39) NON-COMPLICITY AND
CULPABILITY
Measures the presence of both observed non- complicity and observed culpability
1= “yes”; 0= “no”
LexisNexis - Author’s calculations
(40) EFFECT_UNDESIRABILITY
AND CULPABILITY AND NON-
COMPLICITY
Measures the presence of observed effect undesirability and observed culpability and observed non-complicity
LexisNexis - Author’s calculations
(41) CORPORATE_LEVERAGE Ratio of long term debt to total assets DataStream
(42) RETURN ON ASSETS (ROA) Ratio of pre-tax profits to total assets DataStream
(43) FIRM_SIZE Logarithm of the value of total assets DataStream
(44) R&D INTENSITY (RDASS) Ratio of R&D expenditures to total assets DataStream
(45) SOCIAL SCORE
(SOC_SCORE)
Scores are a number between 0 and 100 that show
the firm’s social performance ASSET4
(46) ENVIRONMENTAL SCORE
(ENV_SCORE)
Scores are a number between 0 and 100 that show
68
Table 3.1: Summary of dependent, independent and control variables (continued)
Variable name Values/ Measures Source(s)
(47) CORPORATE GOVERNANCE SCORE
(CGV_SCORE)
Scores are a number between 0 and 100 that show the firm’s corporate governance performance
ASSET4
(48) Year 2006 1= “yes”; 0= “no” ASSET4
(49) Year 2007 1= “yes”; 0= “no” ASSET4
(50) Year 2008 1= “yes”; 0= “no” ASSET4
(51) Year 2009 1= “yes”; 0= “no” ASSET4
(52) Year 2010 1= “yes”; 0= “no” ASSET4
(53) Year 2011 1= “yes”; 0= “no” ASSET4
(54) Year 2012 1= “yes”; 0= “no” ASSET4
(55) Industrial sector SEC codes classification 1= “yes”; 0= “no” ASSET4
Using the ASSET4 dataset as a guide to observations of corporate irresponsibility for my sample, ASSET4 yielded a total of 4,542 observations for the sample firms for years 2003-2011. I then began a media search process (see Flammer, 2013) designed to both verify the ASSET4 dataset as well as provide the initial media reports to later explore for additional supplementary data. I used the LexisNexis search engine to identify media reports. I was able to specify results for specific organisation as well as the year in which the event occurred. Furthermore, I then searched for a number of key terms related to the underlying events as well as a list of broad search terms constructed to capture media reporting of corporate irresponsibility such as “bribery”, “lawsuit”, “outrage”, “misconduct”, “failed” and so on (see Appendix 3 and 4 for a full list of search terms used). I chose LexisNexis for its capabilities to specify search results as well as it drawing data from a wider range of reliable sources, i.e. both media press (e.g., Wall Street Journal, Financial Times) and corporate communications sources (online published corporate rhetoric). Following the process of identifying how many observations each event had in a year (if any), some of the event categories were identified in multiple event categories and were therefore recoded into a single event classification. Additionally, if searches resulted in the finding of media reporting of irresponsibility not included by ASSET4, these observations were added to the dataset. This process resulted in 3,696 confirmed incidents of corporate irresponsibility for further examination.
Following the media search process, I then extracted and processed the data from the articles identified. To reduce researcher subjectivity, I standardised the process of data-extraction by producing a pro forma. The pro forma included a mixture of quantitative and qualitative coding