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Stakeholder Tracking and Analysis: The RepTrak® System for Measuring

Corporate Reputation

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Stakeholder Tracking and Analysis: The RepTrak® System for Measuring Corporate Reputation

Charles J. Fombrun Reputation Institute, New York, NY, USA

Leonard J. Ponzi Reputation Institute, New York, NY, USA

William Newburry Florida International University, Miami, Fl, USA

ABSTRACT ‘Reputation’ is increasingly recognized for its influence in creating stakeholder support and engagement with companies. Both researchers and practitioners would therefore benefit from having a rigorous instrument to measure reputations and the ability to develop predictive modeling of repu- tation’s impact on stakeholder outcomes. The RepTrak® System evolved from studies conducted by Reputation Institute since 2000 to provide a systematic tool for tracking and analyzing stake- holder perceptions that could help companies better manage their reputation and its effects on stake- holder behaviors. Prior research has demonstrated the validity of the RepTrak® Pulse as a short form measure of ‘corporate reputation’. This study reports empirical tests developed to validate the seven dimensions that the RepTrak® System uses to predict corporate reputation and stake- holder support. Although these seven dimensions have been verified internally by Reputation Institute, this methodology and its validation have not been reported publicly, limiting researchers’ and practitioners’ abilities to use the seven dimensions in their respective efforts. To shed light on the model’s structure, this paper

reports tests conducted to validate the measures empirically across five stakeholder groups in six countries. Multivariate analyses confirm the exis- tence and stability of the seven underlying dimen- sions in the factor structure, each of which is constructed from a set of 3–4 underlying attributes. The modeling therefore validates the RepTrak®

scorecard as a tool for measuring and tracking multi- stakeholder perceptions of companies. Corporate Reputation Review (2015) 18, 3–24. doi:10.1057/crr.2014.21

KEYWORDS: corporate reputation; cross-cultural; reputation measurement; scale development; stakeholder management

INTRODUCTION ‘Reputation’ is increasingly recognized for its influence on stakeholder support and engagement with companies (Fombrun, 1996, 2012). Both researchers and practi- tioners would therefore benefit from having a rigorous instrument to measure reputations and the ability to develop predictive models

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of reputation’s impact on stakeholder out- comes. Recognizing a growing need by both practitioners and academics for a better con- ceptual and empirical tool for assessing and managing reputation – and the lack of vali- dated instruments for doing so – Reputation Institute launched a global project in 1998 to understand and measure the diverse factors associated with corporate reputation. The first measurement instrument that resulted from our initial exploration was the Reputation Quotient (RQ), a six-dimension scale con- structed from 20 attributes (Fombrun et al., 2000). The four-attribute RepTrak® Pulse measure was pulled out of the RQ in 2005 and used to create a separate measure of a person’s emotional attachment to a company (eg, Christian, 1959). Ponzi et al. (2011) demonstrated the reliability and validity of the RepTrak® Pulse scale as a measure of reputation, and since 2005 it has been exten- sively tested and shown to have high face and content validity (Sarstedt et al., 2013).

The full RepTrak® System was created in 2005–2006 to provide executives with an analytical instrument that could be used, not only to track and assess stakeholder perceptions of companies, but that would also enable a more comprehensive under- standing of the underlying informational drivers of reputation that elicit emotional attachment. The system is based on measur- ing a company’s overall reputation using the RepTrak® Pulse and decomposing that emotional attachment into an underlying set of dimensions and attributes, and predicting their effects on stakeholder support.

The rigorous methodological under- pinnings of the model and its validation have not been reported publicly to date, limiting the ability researchers and practitioners have to use the dimensions in their work. The purpose of this paper is to report empirical tests done to validate the seven dimensions of the RepTrak® System and predict corporate reputation and stakeholder support across five stakeholder groups in six countries.

The RepTrak® System recognizes the fact that a company’s overall reputation is rooted in the perceptions of its stakeholders (Newburry, 2010), each of which responds to different signals or informational inputs (Spence, 1973; Prabhu and Stewart, 2001; Basdeo et al., 2006). By examining the kinds of informational inputs that influence stake- holder perceptions of a company, we can better predict the dimensions that are likely to trigger stakeholders’ emotional reactions of admiration, liking and trust toward a firm – its reputation.

Stakeholder management is an important component of corporate strategy in general (eg, Freeman, 1984; Donaldson and Preston, 1995) and the study of corporate reputation in particular (Fombrun, 1996; Fombrun, 2012). Social-constructionists view reputa- tion as a composite of different types of sta- keholder perceptions of a firm (Rindova and Martins, 2012). They note that percep- tions of firms come from many sources, many of which would not be considered ‘valid signals’ by traditional economic the- ory (Rindova and Martins, 2012). Signaling theory (Spence, 1973) relies on information economics to discuss the behavior of inter- acting actors under conditions of informa- tion asymmetry and uncertainty. In a marketplace, sellers send signals to buyers through strategic actions such as prices, warrantees or return policies to demonstrate the quality of their products and other firm competencies (Basdeo et al., 2006). More broadly, companies send signals to their various stakeholder groups in order to influence how they are perceived (Prabhu and Stewart, 2001; van Riel, 2012). How- ever, these stakeholders also receive signals from other sources, such as formal media (eg, newspapers, TV; Van Den Bogaerd and Aerts, 2014; Mason, 2014), social media (eg, blogs; Fan et al., 2013), friends and industry competitors, all of which influence the perceptions individuals have of firms, and in turn, their reputations.

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The strategic objective of corporate com- munication is to align stakeholders with the goals of the organization (van Riel, 2012) in order to ensure that stakeholders develop a sense of trust in a company (Srivastava and Chakravarti, 2009; Van Der Merwe and Puth, 2014). However, stakeholders respond to different signals, making it critical for practitioners to understand and communicate across the multiple facets of companies to which stakeholders are exposed, hence the need for a multidimensional framework for measuring reputation internationally.

In the next section, we examine seven types of information signals that past literature suggests as influencers on the formation of corporate reputation, and which make up the dimensions of the RepTrak® System. Since reputation assessments have been shown to vary across stakeholder groups (van Riel and Fombrun, 2007; van Riel, 2012) and across national and other environmental contexts (eg, Michaelis et al., 2008; Deephouse et al., 2009; Brammer and Jackson, 2012), we assess the reliability and validity of the seven dimensions by examining the stability of the model when tested across five stakeholder groups in multiple industries, in six countries, specifically: key opinion leaders (in Brazil), the general public (in the United States), investors (in Spain), physicians (in Switzerland) and customers of the insurance industry (in Denmark and Sweden). While it would be difficult and costly to test the universal validity of the model across all stakeholder groups and research settings, by demonstrating the validity of the RepTrak® System across a diverse set of respondents, industries and countries, we sug- gest that the system has the potential for generalizability.

Overall, the research we report in this paper makes the following contributions. First, we validate the seven-dimensional RepTrak® framework derived from prior literature that addresses the need to manage stakeholders in general and on each of these seven reputation dimensions in particular.

Second, we empirically demonstrate the soundness of the model as an instrument that can be applied across stakeholders, industries and countries. Third, we suggest that because the RepTrak® System is a tool that was rig- orously developed and validated, it has prac- tical relevance for tracking and analyzing the reputations of companies globally.

REPUTATION AND ITS DIMENSIONS Prior literature suggests that a distinction can be made between stakeholder assessments based on a generalized view of reputation and assessments based on specific dimensions. Lange et al. (2011) conducted a literature review from which they identified three major reputation conceptualizations: being known, being known for something and generalized favorability. ‘Being known’ and ‘generalized favorability’ are broad percep- tions of a company. By contrast, ‘being known for something’ suggests a dimensional basis for reputation measurement. Consistent with this interpretation, Lange et al. (2011: 166–167) noted that the critical distinction between ‘being known for something’ and the ‘generalized favorability’ dimension ‘is that the latter reflects the perceiver’s approach – avoidance reactions to the generalized global perceptions of the firm, while the former reflects perceiver expectations for particular desired or undesired organizational attributes or outcomes’. The distinction between a company’s overall reputation and specific reputation dimensions is also analogous to the distinction between a company’s overall brand and its product brands (eg, Smith et al., 2010), and between general and specific brand ima- ges (Sonnier and Ainslie, 2011).

Building on the international qualitative work already completed by Reputation Institute to develop the RQ instrument (Gardberg and Fombrun, 2002; Gardberg, 2006), Reputation Institute extended the research by conducting a wide range of interviews with reputation managers, senior

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communications managers and functional heads (eg, human resources, marketing and finance) of global companies headquartered around the world. These executive inter- views were supplemented by consumer focus groups, a number of which were run in the United States, Europe and Asia, as well as in nine countries of Latin America (Carreras et al., 2013). This broad range of stakeholder interviews and focus groups conducted between 1999 and 2006 were used to iden- tify the seven dimension structure of corpo- rate reputation. We review these dimensions below in terms of their conceptual roots. We then empirically validate the RepTrak®

model for analyzing corporate reputation based on these seven dimensions.

Products/Services Most stakeholders know of a company from its product and service offerings in the mar- ketplace, and its reputation is likely to be influenced by perceptions of its product brands (Rao et al., 1999; Smith et al., 2010). Some are more familiar, others less so, depending on the particular characteristics of the company’s touch points with specific sta- keholders. All stakeholders, and customers more so than others, can be expected to develop perceptions of a company based on its products and services – the quality of its offering, the price at which it sells, its per- ceived value, the customer support provided and the belief in the company’s willingness to stand behind its products and services (Dawar and Parker, 1994; Lange et al., 2011). Signals from the marketplace can also color the impressions that non-customer stakeholders have of any company – and so the degree to which those stakeholders will experience the company as admirable, likeable, trustworthy and well regarded. Game theory models posit that reputations, in fact, are built mainly from investments companies make to increase pro- duct quality (Milgrom and Roberts, 1986). RepTrak®’s ‘products/services’ dimension

therefore assesses perceptions of a company’s offerings based on whether they are thought to be high in quality, in value and service, and in their ability to meet customers’ needs.

Innovation As an important firm asset (Fang et al., 2011), innovation inherently relates to doing some- thing new or differently, and so readily gen- erates an emotional reaction of respect and admiration for the innovator, and therefore reputation. Research confirms that there is a relationship between innovation and reputa- tion, and recognizes that positive regard is often dependent upon effective commu- nication about an innovation (Courtright and Smudde, 2009). Companies that adapt quickly to change, launch new products and develop new ideas are more likely to earn respect and admiration – and many publica- tions such as Forbes, Bloomberg and Business Week compile and publish rankings of inno- vative companies, thereby conveying infor- mation that adds visibility to innovators. These publications signal to all observers about a company’s innovativeness – thereby adding to their reputation. RepTrak®’s ‘innovation’ dimension assesses perceptions of a company as innovative and adaptive.

Workplace Our qualitative research suggests that most stakeholders like and respect companies that maintain good workplaces. Research asserts that satisfied employees are more likely to commit to long-term involvement, less likely to turn over and so more likely to act as ambassadors of the company and give a good employer a favorable rating. In turn, a firm’s reputation as a good workplace is critical in recruiting a high-quality workforce (Alniacik et al., 2012; Nolan et al., 2013). Various publications highlight how companies treat their employees. Fortune regularly releases ‘The 100 Best Companies to Work For’ and

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Forbes publishes a list of ‘The 25 Best Places to Work’. Both broadcast workplace signals into the reputational marketplace that add visibility to employers who treat their employees well. Special interest stakeholders are regularly influenced by segment-specific publications publishing lists and information about the employment practices of compa- nies, such as Working Mother Magazine’s ‘100 Best Companies for Working Moms’ and the Human Rights Campaign’s ‘Corporate Equality Index’ that assesses the diversity policies of employers. Newburry et al. (2014) used the workplace dimension of reputation to examine issues related to foreignness and internationalization as they impact the attractiveness of employers in Latin America. Similarly, Martin et al. (2011) examined the relationship between employer branding and reputation. Signals that convey information about how fairly a company treats employees are likely to generate trust and respect among most stakeholders – and so contribute to building favorable reputations for those companies. RepTrak®’s ‘workplace’ dimen- sion assesses perceptions of a company’s practices in maintaining an environment that shows concern for employees, and for treat- ing and rewarding them fairly and equitably.

Governance Davis (2005: 143) defined corporate govern- ance as the ‘structures, processes and institu- tions within and around organizations that allocate power and resource control among participants’. Given the growing complexities of multinational firms, governance is increas- ingly recognized as a key issue for firms (eg, Kim et al., 2011; Ghosh and John, 2009). Having adequate governance structures in place to manage corporate reputation is recognized as a key component of reputation management (Casado et al., 2014). Stake- holders are regularly exposed to information about a company’s governance whether from media, from auditors or from government

agencies. The more a company is perceived as ethical and transparent, the more likely it is to generate admiration and trust in the minds of most stakeholders – and hence to build repu- tation because ‘… the corporation tends to be viewed less as property and more as a public entity with a broad range of responsibilities to creditors, workers, the public, and others’ (Soleimani et al., 2014: 4). Companies them- selves often become signatories to institutional codes of conduct to signal to stakeholders their principles and commitments, generate confidence that their internal practices are sound – and thereby build reputation. RepTrak®’s ‘governance’ dimension assesses stakeholder perceptions of a company as ethical, fair and transparent.

Citizenship Qualitative inputs suggest that stakeholders tend to respect and admire a company for their good deeds (eg, Orlitzky and Swanson, 2012). Moreover, empirical evidence suggests that corporate citizenship is a legitimacy building strategic asset (Sridhar, 2012) that leads to various forms of company support (Aaron et al., 2012), and can even provide a buffer that protects firms in times of crisis (Mio and Fasan, 2012). As such, companies com- monly expend significant dollar amounts and marketing efforts to promote these activities and thereby build up a company’s image (Gottschalk, 2013; Morris et al., 2013; Vlachos et al., 2013). Empirically, corporate social performance has been one of the main corre- lates of corporate reputation (Lange et al., 2011). By acting responsibly and commu- nicating about it, companies signal that they are good citizens, deserving of praise, and thereby build trust and reputation. Good citizenship is itself a multidimensional construct, one com- monly understood to encompass notions of environmental sustainability and responsible behavior (Tichy et al., 1997). Past research suggests that corporate citizenship can cut both ways (eg, Koschate-Fischer et al., 2012).

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Some stakeholders credit companies for act- ing like good citizens and view it as a form of relationship marketing, while others see it as a distraction and an unnecessary drain on corporate resources. RepTrak®’s ‘citizenship’ dimension assesses stakeholder perceptions of a company as environmentally friendly, a supporter of good causes and a positive con- tributor to society.

Leadership CEOs can be important catalysts for generat- ing admiration and trust with stakeholders (Flatt et al., 2013; Halff, 2013). Studies of celebrity CEOs (Treadway et al., 2009), star CEOs (Wade et al., 2008) and CEO Brands (Bendisch et al., 2013) defend the importance of leadership in conveying a company’s suc- cess and performance to the financial com- munity and other stakeholders. Moreover, research confirms that managers do differ in their strategic abilities (Goldfarb and Yang, 2009). Appealing leaders attract favorable media coverage and investor endorsements, thereby signaling to all stakeholders the cred- ibility of the company’s activities, increasing confidence and trust in the company, and thereby building corporate reputation. Pub- lished rankings of CEOs induce favorable perceptions of a company’s leaders, and can build an appealing halo for the company itself (Gaines-Ross, 2002; Westphal and Deephouse, 2011). RepTrak®’s ‘leadership’ dimension is intended to assess perceptions of leaders as excellent and visionary managers, and strong endorsers of their companies.

Performance A common signal that influences how stake- holders assess companies is ‘financial perfor- mance’. Although stakeholders place different expectations on organizations, strong financial performance is in part the consequence of satisfying these diverse objectives (Donaldson and Preston, 1995;Waddock and Graves, 1997;

Walsh et al., 2003). Past and current profit- ability are important signals to investors about the company’s operating success. It also signals the likelihood of continuing profitability – indicating a company with strong future pro- spects for growth. Expectations of future profitability are important to all valuation models – and therefore are a powerful signal about the strength of a company’s business model. Profitability and growth prospects have been shown to influence ratings of the ‘world’s most-admired’ companies (Fombrun and Shanley, 1990), and have been consistent correlates of reputation in other academic studies (Lange et al., 2011). RepTrak®’s ‘per- formance’ dimension is therefore based on a set of attributes that assess stakeholder percep- tions of a company’s overall financial perfor- mance, profitability and growth prospects.

Table 1 describes the seven-dimension model and 23 attributes of the RepTrak®

scorecard. In the next section, we examine the validity of this model across a varied set of stakeholders, industries and countries.

METHODOLOGICAL APPROACH To explore the cross-stakeholder validity of the RepTrak® System, we selected five studies conducted in 2010–2011. Each study was selected from past studies con- ducted by Reputation Institute that used a common survey instrument with sufficient sample size across different stakeholder groups and countries. Although no finite number of samples could demonstrate uni- versal validity of the dimensions across all stakeholders and countries, by selecting a diverse set of stakeholders and countries, we seek here to demonstrate that the seven- dimension structure has reasonable validity across a broad range of respondents and geographical settings. In each study, struc- tural equation modeling (SEM) was then applied to examine the reliability and validity of the multidimensional frame- work. SEM has several advantages over

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more traditional techniques, particularly when the model to be evaluated is not directly observable.

The hypothesized model we are testing postulates that corporation reputation can be measured using 23 observed variables that load into a first-order structure that consists of seven latent variables labeled as Products/ Services, Innovation, Workplace, Govern- ance, Citizenship, Leadership and Perfor- mance. These seven dimensions describe a single second-order factor structure measur- ing ‘Reputation’.

A review of the theoretical SEM-related literature on corporate reputation has revealed a number of different constructed measures with a mix of formative and reflec- tive models. A discussion of this literature is

beyond the scope of this paper. However, our data-led methodological approach poin- ted us to a ‘reflective’ specified model, which refers to the case that observable variables (or attributes) are ‘reflective’ or representative of a defined reputation construct – in our case, the RepTrak® Pulse. As reflective, the over- all model should be unidimensional and the items correlated. The model is graphically illustrated in Figure 1.

The next section describes the sample and data cleaning procedures we applied. We then present the results of the first and second-order confirmatory factor analyses conducted using AMOS v22.0 software. A maximum likelihood estimation model was adopted because it provided the most stable results for our sample sizes.

Table 1: The RepTrak® System: Dimensions and Attributes of Reputation

Dimension Attribute

Products & Services Offers high quality products and services Products & Services Offers products and services that are a good value for the money Products & Services Stands behind its products and services Products & Services Meets customer needs Innovation Is an innovative company Innovation Is generally the first company to go to market

with new products and services Innovation Adapts quickly to change Workplace Rewards its employees fairly Workplace Demonstrates concern for the health and well-being of its employees Workplace Offers equal opportunities in the workplace Governance Is open and transparent about the way the company operates Governance Behaves ethically Governance Is fair in the way it does business Citizenship Acts responsibly to protect the environment Citizenship Supports good causes Citizenship Has a positive influence on society Leadership Has a strong and appealing leader Leadership Has a clear vision for its future Leadership Is a well-organized company Leadership Has excellent managers Performance Is a profitable company Performance Delivers financial results that are better than expected Performance Shows strong prospects for future growth

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SAMPLE AND DATA Five stakeholder data sets were selected for their ability to represent a cross-section of commonly measured stakeholders, industries and countries (see Table 2):

● Study 1: A representative sample of the general public in the United States.

● Study 2: A sample of key opinion leaders in Brazil.

● Study 3: A sample of investors and custo- mers assessing banks in Spain.

● Study 4: A sample of doctors assessing a pharmaceutical company in Switzerland.

● Study 5: A sample of customers in Denmark and Sweden assessing insurance companies.

Products

Innovation

Workplace

Governance

Citizenship

Leadership

Performance

High Quality

Strong and Appealing Leader

Clear Vision of Future

Well Organized

Excellent Managers

Profitable

Good Financial Results

Strong Growth Prospects

Concern for Employees’ Well -Being

Equal Opportunities in Workplace

Open and Transparent

Behaves Ethically

Fair in Doing Business

Protects Environment

Supports Good Causes

Positive Influence on Society

Good Value

Stands Behind

Meets Customer Needs

Innovative

First to Market

Adapts to Change

Rewards Employees Fairly

Corporate Reputation

e8

e6

e7

e5

e4

e9

e2

e1

e3

e10

e11

e13

e14

e12

e15

e17

e16

e18

e19

e21

e20

e23

e22

e24

e28

e29

e30

e27

e26

e25 λ1

λ6

λ7

λ5

λ4

λ3

λ2

Figure 1: Hypothetical model of corporate reputation

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Respondents in the five stakeholder samples were randomly selected from a larger sample set in each country, except for Study 3 in which a study sponsor provided a customer list. Respondents were screened for their familiarity with each of the companies and then asked to assess familiar companies on each of the 23 attributes of the RepTrak®

scorecard, scored on 7-point Likert scales ranging from ‘Does not describe well’ to ‘Describes very well’. Respondents also had the option to reply ‘not sure’ to questions posed.

Treatment of Missing Values Table 2 also shows the sample sizes and missing values in the initial data sets. Listwise deletion was applied to records with missing values in Studies 1, 2, 3 and 5. In Study 4, to deal with the smaller sample size, missing values were replaced with imputed values using EM (Dempster et al., 1977), an algo- rithm that produces acceptable results even

when underlying normality assumptions do not hold or missing data are not completely random (Little, 1992). With a minimum of 20 records per variable, the five data sets were sufficiently large to achieve statistical power (McQuitty, 2004; Schreiber et al., 2006).

Treatment of Outliers To legitimate multivariate analyses, all vari- ables in the model were presumed to follow normality assumptions. Each data set was therefore examined for violations of normal- ity. Outliers often contribute significantly to departures from normality and to distortions of the covariance matrix. Deleting outliers lowers multivariate skew and kurtosis. Kline (2005) recommends removing outliers if they reduce absolute values of skew to less than 3.0 and kurtosis to less than 10. These viola- tions of multivariate normality can also be evaluated in one step by examining Mardia’s multivariate kurtosis. The analysis identified outliers by calculating Mahalanobis distances

Table 2: Summary of Sample and Data Collection Methodologies

Stakeholder groups Study 1 Study 2 Study 3 Study 4 Study 5

General public Key opinion leaders

Bank customers & investors

Physicians Insurance buyers

Markets The United States

Brazil Spain Switzerland Denmark and Sweden

Year of study 2011 2010 2007 2007 2011 DC method Online CATI CATI Online Online Company list 150 10 10 10 9 Sample source Panel Panel Client List Panel Panel Initial sample size 4,652 1,291 2,328 585 3,300 Sample size after listwise deletion

1,835 564 580 585a 942

Sample size after outliers 1,813 538 532 557 942 Final sample size 1,813 538 532 300b 500b

aIncludes EM imputed values bRandom sample

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for each record in the data set. The larger the distance, the more likely the record was to violate multivariate normality. We deleted observations with distance greater than 100. Table 3 depicts the results of the assessment of the multivariate normality of each stakeholder sample and overall improvement from removing outliers. Since any remaining non-normality could lead to overestimation of χ2 fit statistics, potentially leading to false rejection of any model tes- ted, we also report additional fit statistics to assess each of the multivariate models tested using SEM.

The final samples analyzed in this paper consisted of 1,813 random respondents drawn from the US general public assessing the 150 largest US companies; 538 key opi- nion leaders in Brazil assessing 10 of Brazil’s largest companies; 532 bank clients and investors in Spain assessing Spain’s 10 largest banks; 300 physicians in Switzerland assessing the world’s 10 largest pharmaceutical com- panies; and 500 insurance clients in Denmark and Sweden assessing 9 insurance providers.

DATA ANALYSES AND RESULTS The results are presented in three parts. First, we examine the reliability and validity of each of the hypothesized dimensions across

the data sets using exploratory factor analysis. Second, we carry out a first-order con- firmatory factor analysis on each of the studies to test for the validity of the hypo- thesized seven-dimension factor structure of corporate reputation. Third, we conduct a second-order confirmatory factor analysis to justify the dimensional hierarchy of corporate reputation.

Establishing Reliability An exploratory factor analysis using princi- pal components was run on the 23 attri- butes. The researchers selected an equamax non-orthogonal rotation because of its properties to equally distribute explained variance and clarify the underlying struc- ture of unexplored data sets (Hair et al., 2006). Tables 4 and 5 depict diagnostic measures of each study, which included KMO, Bartlett’s test and Communalities. The measures were examined and found to be acceptable.

An examination of the factor-rotated structures in each study revealed a pattern matrix that matched the hypothesized RepTrak® framework, and attributes asso- ciated with Innovation, Workplace and Pro- ducts/Services loaded consistently on the hypothesized dimensions. Two pairs of

Table 3: Assessment of Multivariate Normality

Study All cases Final sample after removal of outliers

Skew Critical ratio of skew

Kurtosis Critical ratio of kurtosis

Skew Critical ratio of skew

Kurtosis Critical ratio of kurtosis

1 −0.77 −21.43 0.55 7.69 −0.73 −12.73 0.32 2.79 2 −1.58 −23.22 2.76 20.24 −1.63 −15.45 3.19 15.11 3 −1.00 −19.61 1.10 10.79 −0.89 −8.34 1.00 4.70 4 −1.00 −19.61 1.10 10.79 −0.89 −8.34 1.00 4.70 5 −0.11 −2.64 0.05 0.64 −0.16 −1.49 0.08 0.38

Overall improvement 15% 46% 26% 48%

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dimensions cross-loaded within individual studies, namely the Performance and Lea- dership attributes, and the Citizenship and Governance attributes. However, all four of

these dimensions were well-defined when the five data sets were pooled across stake- holders. The total explained variance ranged from 83 to 90 percent across the five studies.

Table 4: Exploratory Factor Analysis Diagnostics

EFA Diagnostic

Study 1 Study 2 Study 3 Study 4 Study 5

Kaiser–Meyer–Olkin measure of sampling adequacy (KMO)

0.990 0.988 0.972 0.960 0.983

Bartlett’s test of sphericity Approx. χ2

62,862.029 16,557.721 12,987.239 7,576.367 29,537.866

d.f. 253 253 231 253 253 Sig. 0.000 0.000 0.000 0.000 0.000

Table 5: Communalities

Communalities – Extraction

Study 1 Study 2 Study 3 Study 4 Study 5

Adapts quickly to change 0.851 0.838 0.890 0.871 0.884 Behaves ethically 0.902 0.849 0.897 0.794 0.845 Clear vision for its future 0.900 0.854 0.858 0.834 0.895 Employee well-being 0.879 0.890 0.867 0.848 0.900 Environmentally responsible 0.895 0.973 0.869 0.824 0.844 Excellent management 0.888 0.893 0.805 0.896 0.892 Fair in the way it does business 0.893 0.850 0.786 0.889 0.919 First to market 0.954 0.840 0.888 0.827 0.858 High quality 0.896 0.875 0.844 0.898 0.899 High-performing 0.946 0.863 0.800 0.869 0.880 innovative 0.880 0.834 0.849 0.880 0.888 Meets customer needs 0.892 0.879 0.854 0.790 0.895 Offers equal opportunities 0.906 0.903 0.822 0.793 0.892 Open and transparent 0.891 0.928 0.887 0.817 0.821 Positive influence on society 0.887 0.863 0.876 0.818 0.892 Profitable 0.971 0.977 0.938 0.848 0.963 Rewards employees fairly 0.909 0.889 0.868 0.846 0.881 Shows growth prospects 0.878 0.875 0.834 0.882 0.896 Stands behind 0.897 0.859 0.888 0.836 0.887 Strong and appealing leader 0.878 0.828 0.839 0.890 0.894 Supports good causes 0.871 0.893 0.894 0.867 0.944 Value for money 0.887 0.861 0.885 0.812 0.909 Well organized 0.894 0.873 0.831 0.893 0.889 Extraction method: Principal component analysis.

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In each study, we examined the relia- bility of the seven dimensions of the hypothesized model using Cronbach’s α (Nunnally and Bernstein, 1994). Seven Cronbach’s α coefficients were calculated for each study, and a total of 35 tests exam- ined. α coefficients ranged from a low of 0.84 (Physicians-Innovation dimension) to a high of 0.96 (Insurance-Product dimen- sion). Cronbach’s α’s on each test were all above 0.7, indicating strong scale reliability (Nunnally and Bernstein, 1994). In addi- tion, we tested the effect of deleting an attribute on each dimension scale. All dimension alpha coefficients were higher with all of the hypothesized dimension attributes included than with any of the attributes excluded, further supporting scale reliability. These analyses provide support for the internal consistency of the dimen- sional structure of the RepTrak® System.

Finally, the Exploratory Factor Analysis structure and loadings were examined. Table 6 depicts the standard estimates for each study factor loadings and correlations. The results illuminate that the overall struc- ture is unidimensional and the items are cor- related. Each study structure and attribute factor loadings on each dimension provided adequate support for the convergent and discriminant validity of the model.

Confirming Internal Validity: The First- Order Factor Model A first-order confirmatory factor analysis was run to examine the variance shared by observed variables with the latent or unob- served variables that were hypothesized to explain ‘Corporate Reputation’. The aim of these analyses was to demonstrate that the dimensions of each first-order model con- verge. We carried out a first-order con- firmatory factor analysis with maximum likelihood on the seven-factor measurement model illustrated in Figure 2.

The first-order confirmatory factor analy- sis produced an acceptable fit across all five studies. While the literature is specific with regards to the fit indices, to assess how well the models represented the data, we followed Hair et al.’s (2010) suggestion regarding a mix of fit indices, and examined χ2, one incremental fit test (ie, Comparative Fit Index, CFI), one goodness-of-fit index (ie, Trucker-Lewis Index, TLI), and one badness-of-fit index (ie, Root Mean Square Error of Approximation, RMSEA). These indices have been shown in past research to demonstrate very little random variation due to sample size, number of parameters, model misspecification or method of estimation (Fan et al., 1999). The χ2 test statistic is sig- nificant and is discussed below. A CFI of 0.90

Table 6: Factor Loadings and Correlations for Each Study

Factor loadings & correlations

Study 1 Study 2 Study 3 Study 4 Study 5

Corporation Reputation & RepTrak® Pulse 0.848 0.848 0.873 0.864 0.871 Products & Services 0.978 0.976 0.993 0.948 0.960 Innovation 0.989 0.989 0.994 0.906 0.941 Workplace 0.960 0.962 0.949 0.845 0.926 Governance 0.970 0.969 0.989 0.915 0.993 Citizenship 0.977 0.983 0.977 0.881 0.968 Leadership 0.997 0.997 0.975 0.946 0.966 Performance 0.982 0.975 0.935 0.909 0.932

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and above testifies to strong scale uni- dimensionality. A CFI and TLI above 0.90 indicates convergent validity. RMSEA values between 0.05 and 0.08 are considered acceptable. Table 7 presents the fit statistics we examined across the five data sets and demonstrates a robust model fit.

The most commonly cited fit index in the literature is the χ2 test statistic.

However, as mentioned earlier, there are several problems with solely relying on this index. The χ2 test statistic is problematic when used with data that are not multi- variate normal; it is extremely sensitive to sample size, and also affected by the num- ber of parameters in the model (Schermelleh-Engel et al., 2003; Satorra and Bentler, 2001). In a large sample, a χ2

Products

Innovation

Workplace

Governance

Citizenship

Leadership

Performance

High Quality

Strong and Appealing Leader

Clear Vision of Future

Well Organized

Excellent Managers

Profitable

Good Financial Results

Strong Growth Prospects

Concern for Employees’ Well-Being

Equal Opportunities in Workplace

Open and Transparent

Behaves Ethically

Fair in Doing Business

Protects Environment

Supports Good Causes

Positive Influence on Society

Good Value

Stands Behind

Meets Customer Needs

Innovative

First to Market

Adapts to Change

Rewards Employees Fairly e8

e6

e7

e5

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e9

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e1

e3

e10

e11

e13

e14

e12

e15

e17

e16

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e23

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Figure 2: Validating dimensionality using first-order confirmatory factor analysis

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test almost always leads to rejection of the model. To this end, the p-values associated with the computed χ2 did not exceed 0.05 for each of the five stakeholder groups. However, it is often noted that ‘incon- sistency among indices is common, and having the chi-square as the outlier is par- ticularly common’ (Eagle et al., 2001: 13). As such, we concluded that our overall set of analyses demonstrate strong model fit.

Convergent Validity To assess convergent validity, we examined the loadings of each factor and the average variance extracted (AVE) of the scale. Across studies, each observed variable had a significant loading on its respective latent construct ( P< 0.001) with values ranging from 0.78 to 0.94. AVE represents the var- iance in the independent variables accoun- ted for by each latent variable. Across the five studies, AVE was greater than 0.7, which exceeds the recommended 0.5 benchmark value (Fornell and Larcker, 1981), and confirms the measure’s con- vergent validity.

Discriminant Validity Establishing discriminant validity is required for latent variables analysis. Fornell and

Larcker (1981) argue that lack of dis- criminant validity creates uncertainty about whether the results confirm a hypothesized structure or whether the results demon- strate statistical inconsistencies (Farrell, 2010). To operationalize discriminant validity, we compared AVE with the amount of shared variance (Farrell, 2010). Shared variance is the amount of variance that the latent variable is able to explain in another latent variable, and is calculated as the square of the correlation estimates or factor loadings.

An appropriate test for discriminant valid- ity involves demonstrating that the AVE of observed attributes of a dimension is sig- nificantly larger than the shared variance of the dimensions in the model (Fornell and Larcker, 1981). Table 8 shows that the AVE of each dimension was indeed greater than its shared variance with any other dimension across all studies, with one exception out of 420 tests (Study 1, Innovation< –>Perfor- mance), thereby supporting the discriminant validity of the model.

Establishing Construct Validity: The Second-Order Factor Model The seven endogenous constructs (the repu- tation dimensions) are viewed as mediating variables into a single second-order latent variable. Second-order factor models were therefore developed from the underlying first-order models. The purpose of these second-order models is to provide external construct validity by confirming that the RepTrak® framework maintains the hypo- thesized second-order factor structure by converging on the latent reputation construct variable (Spector, 1992). We used the pre- viously validated RepTrak® Pulse measure of corporate reputation as the external depen- dent variable (Ponzi et al., 2011). Figure 3 describes the model specification whose construct validity we sought to establish.

Table 7: Goodness-of-Fit Indices of First- Order Factor Models

Study χ2 d.f. CFI TLI RMSEA VE

1 1574.0*** 209 0.98 0.97 0.06 0.83 2 416.8*** 209 0.99 0.99 0.04 0.80 3 758.7*** 209 0.96 0.95 0.07 0.78 4 808.8*** 209 0.92 0.90 0.10 0.74 5 586.7*** 209 0.98 0.97 0.06 0.82

***P-value< 0.000

Note: χ2 Chi-Square, d.f. Degrees of Freedom, CFI

Comparative Fit Index, TLI Trucker–Lewis, RMSEA Root

mean Error of Approx., VE Variance Extracted

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Table 8: Discriminant Validity of First-Order Confirmatory Factor Analysis

SH 1 SH 2

Dim A Dim B Dim A on Dim A

Dim B on Dim A

Dim B on Dim B

Dim A on Dim B

Dim A on Dim A

Dim B on Dim A

Dim B on Dim B

Dim A on Dim B

Products < –> Innovation 0.458 0.247 0.553 0.387 0.731 0.599 0.293 0.095 Products < –> Governance 0.539 0.279 0.561 0.283 0.310 0.127 0.671 0.569 Products < –> Citizenship 0.508 0.229 0.593 0.323 0.720 0.540 0.300 0.106 Products < –> Workplace 0.525 0.205 0.610 0.303 0.589 0.296 0.539 0.254 Products < –> Leadership 0.455 0.269 0.560 0.386 0.219 0.043 0.758 0.692 Products < –> Performance 0.523 0.263 0.524 0.330 0.176 0.002 0.689 0.831 Innovation < –> Governance 0.573 0.354 0.497 0.190 0.348 0.146 0.655 0.506 Innovation < –> Citizenship 0.475 0.302 0.532 0.305 0.633 0.465 0.358 0.169 Innovation < –> Workplace 0.513 0.268 0.555 0.256 0.551 0.292 0.540 0.264 Innovation < –> Leadership 0.453 0.480 0.382 0.343 0.248 0.049 0.760 0.917 Innovation < –> Performance 0.391 0.310 1.183 0.402 0.466 0.176 0.621 0.377 Governance < –> Citizenship 0.484 0.304 0.538 0.379 0.655 0.498 0.335 0.143 Governance < –> Workplace 0.524 0.249 0.577 0.327 0.614 0.400 0.443 0.203 Governance < –> Leadership 0.551 0.303 0.512 0.292 0.269 0.069 0.743 0.609 Governance < –> Performance 0.553 0.165 0.582 0.288 0.476 0.159 0.632 0.363 Citizenship < –> Workplace 0.500 0.301 0.532 0.325 0.561 0.299 0.539 0.252 Citizenship < –> Leadership 0.512 0.335 0.484 0.294 0.505 0.240 0.595 0.312 Citizenship < –> Performance 0.497 0.199 0.579 0.316 0.419 0.101 0.682 0.395 Workplace < –> Leadership 0.595 0.362 0.457 0.221 0.582 0.233 0.595 0.254 Workplace < –> Performance 0.563 0.243 0.517 0.257 0.538 0.118 0.661 0.305 Leadership < –> Performance 0.408 0.237 0.553 0.428 0.261 0.071 0.671 0.607

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SH 3 SH 4 SH 5

Dim A Dim B Dim A on Dim

A

Dim B on Dim

A

Dim B on Dim

B

Dim A on Dim

B

Dim A on Dim

A

Dim B on Dim

A

Dim B on Dim

B

Dim A on Dim

B

Dim A on Dim

A

Dim B on Dim

A

Dim B on Dim

B

Dim A on Dim

B

Products < –> Innovation 0.601 0.215 0.617 0.248 0.685 0.546 0.292 0.005 0.541 0.196 0.643 0.321 Products < –> Governance 0.639 0.249 0.585 0.202 0.335 0.059 0.742 0.425 0.512 0.280 0.499 0.336 Products < –> Citizenship 0.690 0.246 0.580 0.136 0.450 0.136 0.641 0.298 0.562 0.195 0.623 0.283 Products < –> Workplace 0.687 0.194 0.572 0.140 0.460 0.133 0.750 0.263 0.538 0.114 0.726 0.311 Products < –> Leadership 0.599 0.220 0.610 0.257 0.420 0.139 0.693 0.329 0.489 0.142 0.684 0.371 Products < –> Performance 0.655 0.223 0.541 0.194 0.452 0.118 0.687 0.276 0.575 0.133 0.638 0.277 Innovation < –> Governance 0.686 0.215 0.611 0.126 0.353 0.031 0.761 0.429 0.647 0.312 0.491 0.186 Innovation < –> Citizenship 0.726 0.290 0.535 0.082 0.659 0.714 0.206 0.002 0.655 0.279 0.554 0.180 Innovation < –> Workplace 0.728 0.212 0.544 0.074 0.533 0.222 0.677 0.185 0.608 0.260 0.599 0.236 Innovation < –> Leadership 0.650 0.233 0.579 0.184 0.284 0.004 0.796 0.561 0.582 0.236 0.589 0.266 Innovation < –> Performance 0.656 0.246 0.514 0.191 0.278 0.005 0.760 0.620 0.594 0.186 0.594 0.244 Governance < –> Citizenship 0.512 0.168 0.644 0.289 0.220 0.002 0.733 0.728 0.573 0.365 0.499 0.246 Governance < –> Workplace 0.518 0.098 0.639 0.283 0.477 0.136 0.752 0.341 0.629 0.320 0.549 0.209 Governance < –> Leadership 0.463 0.063 0.714 0.347 0.419 0.105 0.719 0.417 0.505 0.276 0.574 0.324 Governance < –> Performance 0.494 0.029 0.695 0.317 0.426 0.040 0.758 0.405 0.607 0.209 0.582 0.198 Citizenship < –> Workplace 0.605 0.164 0.582 0.223 0.459 0.196 0.707 0.340 0.570 0.232 0.626 0.268 Citizenship < –> Leadership 0.505 0.045 0.727 0.307 0.543 0.166 0.656 0.266 0.535 0.287 0.551 0.319 Citizenship < –> Performance 0.478 0.012 0.711 0.339 0.555 0.084 0.717 0.250 0.585 0.198 0.589 0.256 Workplace < –> Leadership 0.482 0.037 0.740 0.261 0.572 0.233 0.607 0.339 0.630 0.245 0.572 0.228 Workplace < –> Performance 0.475 0.011 0.717 0.275 0.638 0.178 0.628 0.261 0.650 0.193 0.582 0.201 Leadership < –> Performance 0.207 0.046 0.663 0.644 0.200 0.007 0.756 0.677 0.450 0.181 0.592 0.384

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Table 9 summarizes the goodness-of-fit indices for the five second-order factor models that were tested. Each model pro- vides support for the hypothesis that the seven dimensions of the RepTrak® System are components of a second-order con- struct that can be described as ‘Corporate Reputation’.

In sum, these analyses confirm the under- lying dimensional structure of corporate reputation hypothesized in the RepTrak®

System. The results are persuasive because they verified the dimensionality of the cor- porate reputation measure across five stake- holder groups, in multiple industries and across six geographies. The results therefore provide robust empirical support for the reliability and validity of the RepTrak® Sys- tem and provide researchers and practitioners

the confidence they need to apply the model for measuring corporate reputations across stakeholders, industries and geographies.

Products

Innovation

Workplace

Governance

Citizenship

Leadership

Performance

High Quality

Strong and Appealing Leader

Clear Vision of Future

Well Organized

Excellent Managers

Profitable

Good Financial Results

Strong Growth Prospects

Concern Employee Well-Being

Equal Opportunity Workplace

Open and Transparent

Behaves Ethically

Fair in Doing Business

Protects Environment

Supports Good Causes

Positive Influence on Society

Good Value

Stands Behind

Meets Customer Needs

Innovative

First to Market

Adapts to Change

Rewards Employees Fairly

Corporate Reputation

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e18

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e21

e20

e23

e22

e24

e28

e29

e30

e27

e26

e25

λ1

λ6

λ7

λ5

λ4

λ3

λ2

Good Overall Reputation

Good Feeling About

Trust

Admire and Respect

e31

e32

e33

e34

RepTrak Pulse

r

ξ1 ξ2

Figure 3: Establishing construct validity using second-order confirmatory factor analysis

Table 9: Goodness-of-fit Indices of Second Order Factor Models

Study χ2 d.f. CFI TLI RMSEA

1 2218.7*** 313 0.98 0.97 0.06 2 777.6*** 316 0.98 0.98 0.05 3 1241.3*** 314 0.95 0.94 0.08 4 964.7*** 311 0.93 0.92 0.08 5 845.9*** 312 0.97 0.97 0.06

***p< 0.000

Note: χs2 Chi-square, d.f. Degrees of Freedom, CFI

Comparative Fit Index, TLI Trucker–Lewis, RMSEA Root

mean Error of Approximation

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DISCUSSION AND CONCLUSIONS Researchers and practitioners have struggled for years to measure rigorously the reputa- tions of companies (Dowling and Gardberg, 2012). The need for a rigorous comparative measure has been made abundantly clear in conferences and journals for decades. Despite extensive conversations and debate, there remains a lack of consensus about the dimensionality and structure of the construct ‘corporate reputation’ (see, eg, Barnett and Pollock, 2012).

The analyses reported in this paper took a point of departure in various studies that have relied on Reputation Institute’s RepTrak®

System for measuring corporate reputations. By applying multivariate analyses to five representative data sets, we provide robust evidence of the reliability, internal validity and external validity of the RepTrak® Sys- tem’s seven dimensions and 23 attributes.

A first important theoretical implication of this study is the explicit attention given to validating a framework that incorporates both the seven dimensions of reputation and its generalized representation (the RepTrak®

Pulse). Doing so should prove invaluable for future academic research by enabling a more thorough understanding of corporate repu- tation and its influences. It suggests that the emotional appeal of a company – its reputa- tion – can be partly explained by the infor- mational content of the signals to which stakeholders respond.

By demonstrating the effectiveness of the signaling process (eg, Spence, 1973; Prabhu and Stewart, 2001), the model invites researchers to develop more fine-grained theories to explain how specific dimensions of reputation influence outcomes. While a few such studies have been conducted (D’Souza et al., 2013; Newburry et al., 2014; Vidaver-Cohen and Brønn, 2013), we see considerable opportunity to complement the vast majority of past research that has focused narrowly on the generalized reputation con- struct. Future research should identify the

specific impact that the seven dimensions of reputation have on outcomes of interest, including stakeholders’ supportive intent (eg, Newburry, 2010) and its manifestation in a company’s sales, risk profile and financial performance (eg, Smith et al., 2010).

From a practitioner standpoint, a validated management tool with predictive ability is paramount to tracking and managing stake- holder perceptions and relationships. The RepTrak® System provides practitioners a toolbox of complementary measures for both tracking and analysis of reputations and for linking to outcomes. Of particular value to managers is the demonstrated validity of the RepTrak® System to measure cross-industry, cross-stakeholder and cross-country percep- tions of stakeholders. Multinational compa- nies need this capability to address the diversity of the global environment in which they operate. Consistent tracking of stake- holder perceptions is one thing. The ability to use the RepTrak® System to predict profitable pathways for improving business outcomes, however, is another. With a vali- dated tool in hand, practitioners are in a bet- ter position now to build predictive models to explain where best to allocate their scarce resources in order to optimize outcomes.

This study is not without its limitations, of course. First, our validation relied on five studies of stakeholders in six countries. Given the increasing recognition of the importance of national and other environmental contexts (eg, Prabhu and Stewart, 2001; Michaelis et al., 2008; Berger and Fitzsimons, 2008), future studies should validate the measure with additional stakeholder groups in other cultural contexts. Given known differences between Western and Eastern cultures (eg, Chen et al., 2005), validation of the framework with respondents in Asia is partic- ularly needed. Second, future research should also examine links between RepTrak®’s dimensions and their direct and indirect impacts on supportive behaviors and business outcomes (Newburry, 2010), consistent with

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similar studies on brand value (eg, Goldfarb et al., 2009). Finally, studies of additional industries and stakeholders could further confirm the generalizability of the mea- surement system.

Overall, this paper has put forward a robust validation of the RepTrak® System for measuring reputations, one that practi- tioners can rely upon to track their reputa- tions on a continuous basis, to analyze the underlying dimensional drivers of their reputations and to link to outcome measures of interest to their businesses, such as sup- portive intent, sales, investor and employee churn, and financial performance (Fombrun, 1996). Given the increasingly recognized importance of reputation to all organizations, the availability of a robust measurement tool such as RepTrak® can help improve how we manage, not only reputational issues, but the intangible economic asset that a corporate reputation represents.

Acknowledgements The authors thank Professor Naomi Gard- berg, Sebastian Taciak, RI Manager – Global Research & Analytics, CRR Editor Cees van Riel and three anonymous reviewers for their helpful comments and suggestions on previous drafts of this paper.

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