Print version ISSN 0121-5051
Innovar vol.21 no.39 Bogotá Jan./May 2011
William Newburry* & M. Abrahim Soleimani**
* Associate Professor, Florida International University. Department of Management and International Business, Miami. E-mail: email@example.com
** PhD Candidate, Florida International University. Department of Management and International Business, Miami. E-mail: Abrahim.Soleimani@fiu.edu
Recibido: julio de 2010 Aprobado: noviembre de 2010
This study uses signaling theory to investigate industry -firm- and individual-level determinants of individual-level corporate reputation assessments in the context of Latin America. In a hierarchical linear model, we test our theory using 76,419 individual evaluations of 80 companies in five Latin American countries collected by the Reputation Institute in conjunction with the Foro de Reputación Corporativa. Results show that across our Latin American sample, reputations of firms in the telecom and energy industries are significantly lower than those of manufacturing firms. Additionally, we find consistent evidence across marginalized groups (e.g., women, lower social class, education and income) that they assess telecom industry reputations relatively higher than their less marginalized counterparts do. Results are mixed with regards to marginalized group assessments of firms from other service industries. Additionally, counter to expectations, we do not find evidence that firm size or financial performance impact reputation assessments.
corporate reputation, signaling theory, marginalization, service industries, Latin America
El presente estudio se basa en la teoría de señales para la investigación de la industria -firma-, y en la valoración individualizada de determinantes para niveles individuales de reputación corporativa, dentro del contexto latinoamericano. Nuestra teoría se testeó a través de un modelo linear jerárquico usando 76,419 evaluaciones individuales de 80 compañías, en cinco países latinoamericanos, que fueron recopiladas por el Reputation Institute, en conjunto con el Foro de Reputación Corporativa. A través de toda esta muestra latinoamericana, los resultados arrojaron que la reputación de las firmas que pertenecen a las industrias de energía y de telecomunicaciones, es significativamente más baja que la de las firmas manufactureras. Adicionalmente, se encontró evidencia consistente entre grupos marginados (ej., mujeres, clases sociales bajas, con poca educación e ingresos) puesto que tienden a valorar de manera más alta la reputación de la industria de telecomunicaciones que otros grupos en mejores condiciones. Estos resultados son mixtos si se consideran las valoraciones que se han obtenido sobre otras industrias de servicio, entre grupos marginales. Además, y contrario a las expectativas, no se encontró evidencia de que el tamaño de la firma o su desempeño financiero, produzca un impacto en la forma de valorar su reputación.
reputación corporativa, teoría de señales, marginalización, industrias de servicios, América Latina.
Cette étude se base sur la théorie des signaux pour la recherche de l'industrie et sur l'évaluation individualisée d'éléments déterminants pour les niveaux individuels de réputation corporative, dans le contexte latino-américain. Notre théorie a été testée par un modèle linéaire hiérarchique utilisant 76,419 évaluations individuelles de 80 compagnies, dans 5 pays latino-américains, rassemblés par le Reputation Institute, en association avec le Foro de Reputación Corporativa ». A partir de cet échantillon latino-américain, les résultats ont signalé que la réputation des firmes qui appartiennent aux industries d'énergie et de télécommunications est beaucoup plus basse que celle des firmes manufacturières. Il a également été évident que parmi les groupes marginaux (par exemple, les femmes, les classes sociales peu élevées, avec peu d'éducation et de ressources) les personnes ont tendance à évaluer la réputation de l'industrie de télécommunications à un niveau plus élevé que d'autres groupes en meilleures conditions. Ces résultats sont mixtes si l'on considère les évaluations obtenues dans d'autres industries de service, parmi les groupes marginaux. De plus, contrairement aux expectatives, il n'a pas été démontré que la grandeur de l'entreprise ou son développement financier a un effet sur le mode d'évaluation de sa réputation
réputation corporative, théorie des signaux, marginalisation, industries de services, Amérique Latine
O presente estudo baseia-se na teoria de sinais para a pesquisa da indústria -firma-, e na valorização individualizada de determinantes para níveis individuais de reputação corporativa, dentro do contexto latino americano. Nossa teoria foi testada através de um modelo linear hierárquico usando 76,419 avaliações individuais de 80 companhias, em cinco países latino-americanos, que foram compiladas pelo Reputation Institute, em conjunto com o Fórum de Reputação Corporativa. Através de toda esta mostra latino-americana, os resultados demonstraram que a reputação das firmas que pertencem às indústrias de energia e de telecomunicações, é significativamente mais baixa que a das firmas manufatureiras. Adicionalmente, encontrou- se evidência consistente entre grupos marginalizados (ex., mulheres, classes sociais baixas, com pouca educação e receita) posto que tendem a valorizar de maneira mais alta a reputação da indústria de telecomunicações que outros grupos em melhores condições. Estes resultados são mistos se consideram-se as valorizações que se obtiveram sobre outras indústrias de serviço, entre grupos marginalizados. Além disso, e contrário às expectativas, não se encontrou evidência de que o tamanho da firma ou seu desempenho financeiro, produza um impacto na forma de valorizar sua reputação.
reputação corporativa, teoria de sinais, marginalização, indústrias de serviços, América Latina.
Corporate reputation refers to the overall knowledge and esteem about a corporation held by the public (Fombrun, 1996), and is well established as a significant interest area in the strategy field (Fombrun & Shanley, 1990). As emerging market investment gains scholar and practitioner interest (Meyer, 2004), a better understanding of the factors influencing firm reputations in this context is becoming increasingly useful and important. Responding to trade liberalization starting in the late 1980s, MNC investment into Latin America increased five-fold in the 1990s to a peak of US$108 billion (Casanova, 2005; Robles et al., 2003), changing the competitive dynamics between firms in the region (Dasu & de la Torre, 1997), and increasing the relevance of examining reputation perceptions in this region. Given this research need, this manuscript examines individual- and firm-level predictors of firm reputation in five Latin American countries to discover which firm and individual characteristics are associated with firm reputations. Using signaling theory (e.g., Basdeo et al., 2006), we suggest that certain industry and firm-level factors will serve as signals which are interpreted differently by individuals with different demographic characteristics when making reputation evaluations.
While interest in Latin America has increased, it has not been uniform across industries. Mirroring the major foreign direct investment patterns in the region, which were largely influenced by deregulation and privatization (e.g., Casanova, 2005; Dasu & de la Torre, 1997; Robles et al., 2003), this examination focuses on investment in several service industries, including telecommunications, retail, energy provision and banking. These industries vary significantly regarding the degree to which individuals interact with companies, along with the nature of these interactions. As such, factors affecting reputation assessments could differ significantly, and thus, the previous questions will be examined in terms of whether reputation assessments differ across different service industries.
Theoretically and empirically examining the largely understudied Latin American region is an important goal in itself. Emerging markets have gained significant interest amongst international business and management scholars (e.g., Chen et al., 2009; Meyer, 2004; London & Hart, 2004). However, the vast majority of academic work in this area has focused on China. Greater knowledge of non-Chinese emerging markets would benefit both academics, in terms of refining and expanding existing theories, as well as managers, who are increasingly being drawn to these regions. Greater knowledge of Latin America may also provide insights into the future of the neighboring United States, as the origin of what is now the largest U.S. minority group, with Hispanics accounting for 14.8% of the U.S. population as of 2006 (U.S. Census Bureau, 2009).
In this manuscript, we next briefly review relevant literature and develop hypotheses predicting a respondent's assessment of firm reputation. We then present the study methodology, followed by our results. Our analysis is based upon 76,419 individual evaluations of 80 companies in five Latin American countries collected by the Reputation Institute in conjunction with the Foro de Reputación Corporativa. We conclude with some general discussion and implications.
Corporate reputation has been examined in many academic disciplines. However, there have been long debates on its definition and measurement. Fombrun and Van Riel (1997) regarded corporate reputation as ambiguous. Since then, significant work has helped clarify this concept by studying literature differences and similarities to come up with a clearer corporate reputation definition. According to Fombrun and Rindova's (1996) cross-disciplinary literature review, one reason for this conceptual ambiguity is reputation's usage by scholars in different disciplines: economics (Ross, 1977; Weigelt & Camerer, 1988), accounting (Sveiby, 1997), sociology (DiMaggio & Powell, 1983; Shaphiro, 1987), strategy (Caves & Porter, 1977; Freeman, 1984), and marketing (Dowling, 1986; Kennedy, 1977) among others. Therefore, corporate reputation has been defined and used from disciplinary lenses.
Bennett and Kottasz (2000) assembled a list of 16 corporate reputation definitions. Barnett et al., (2006) then took this list and added results of their review of studies from 2000 to 2003 to cluster definitions into three categories: awareness, assessment, and assets. The awareness cluster includes a group of scholars (Balmer, 2001; Roberts & Dowling, 2002) who define corporate reputation as perception of audience. Another group of scholars (Deephouse, 2000; Gotsi & Wilson, 2001) looks at reputation as a judgment and estimation, forming the assessment cluster. Finally, the assets cluster includes definitions considering reputation as something of value to a firm (Drobis, 2000; Goldberg et al., 2003). Barnett et al. (2006, p. 34) concluded by proposing a definition for corporate reputation: "Observers' collective judgments of a corporation based on assessments of the financial, social, and environmental impacts attributed to the corporation over time".
The lack of consensus on a corporate reputation definition has led to the development of measurement tools, which differ in three ways. First, they differ in their underlying definitions of corporate reputation. Second, the groups who assessed reputation differ in order to be consistent with the underlying theory of the measurement tool. Finally, the items and dimensions in the measurement tools differ. A wide range of reputation measures have been developed, such as Fortune's Most Admired Companies (MAC), the RepTrack Pulse (Fombrun & Van Riel, 2004), and the Corporate Personality Scale (Davies et al., 2003). In this study, as we aim to investigate factors that impact the general population's assessment of corporate reputation (versus, for example, financial analysts), we adopt the RepTrack Pulse corporate reputation measure to investigate the hierarchical effects of corporate reputation stimuli on perceptions of the general public.
Signaling theory (Spence, 1973) is an information economic theory that discusses the behavior of interacting factors under information asymmetry and uncertainty conditions. In signaling theory, two parties interact with each other: agents or sellers and principles or buyers. The environment is uncertain and agents have information that principles do not have. For example, in a marketplace, there are sellers of a product competing with each other that have information about the quality of their products, which is not necessarily available to buyers. Therefore, sellers try to send signals such as price, warrantees, or return policies to buyers to demonstrate higher product quality.
Signaling is a dynamic process. Initially, agents (e.g., sellers) send a signal and principles (e.g., buyers) receive and translate the signal to separate (distinguish) agents and make the transaction (e.g., buy). After the transaction, principles get feedback (of product quality), learn through this experience and are better able to separate agents next time. This learning process from agents to principles and vice versa continues until a state of equilibrium is achieved. Equilibrium occurs in two types: separating and pooling (Boulding & Kirmani, 1993). In separating equilibrium, the value of a strategy and its cost lead the agents to choose different actions. For example, maintenance companies can adopt a three-day guarantee strategy to show the quality of their services, which is a valuable strategy for buyers to distinguish between companies. If it takes five days to deliver the service, the cost of a three-day guarantee would be more than its benefits. Therefore, a low-quality service company would not adopt this strategy. In other words, in separating equilibrium, principles can distinguish between agents. Pooling equilibrium is a condition where both low and high quality agents can choose the same strategy and principles cannot distinguish between them. Both service companies can deliver their services in three days in this condition. Therefore, only if separating equilibrium occurs, strategy can serve as a signal that helps principles to distinguish between agents (Boulding & Kirmani, 1993).
Signaling theory has been widely used in management, marketing, and finance contexts, including studies on board of director structure (Miller & Triana, 2009), corporate social performance (Turban & Greening, 1997), insider stock trading (Sanders & Boivie, 2004), labor markets (Spence, 1973), organizational reputation (Behrend et al., 2009), advertising (Ippolito, 1990), new product introduction (Akerlof, 1970), price (Milgrom & Roberts, 1986), warranties (Lutz, 1989; Spence, 1977), and initial public offerings (IPO) (Certo et al., 2001). However, one factor that has not been sufficiently studied is the multi-level impacts of signals. Accordingly, this study intends to investigate not only the individual effects of signals, but also the hierarchical effects of them.
In studying multi-level determinants of corporate reputation, three sets of hypotheses are suggested. The first set refers to industry-level factors informing corporate reputation assessments, while the second set investigates firmlevel corporate reputation drivers. Finally, a third set looks at moderating impacts of individual-level variables. By doing so, we attempt to disentangle multi-level effects on corporate reputation perceptions.
Industry has been established as an important component of reputation management (e.g., Newburry, 2010; Winn et al., 2008). In the Latin American context, certain industries, such as telecom, financial and energy, have received increased scrutiny, mirroring the major investment trends in the region (e.g., Casanova, 2005). Accordingly, industry may have systematic effects on individual evaluations of corporate reputation in this context. While the degree of these effects may vary by industry, we suggest that in industries where fast changes to the market are occurring due to deregulation and privatization, individuals will have lesser abilities to assess the reputations of individual firms, and accordingly will be more likely to evaluate companies based upon collective industry reputations (e.g., Winn et al., 2008), even though these may still be emerging as well. Company-level information cues upon which to make supportive behavior decisions may not yet be available, or may be heavily discounted due to rapid change.
Past research has noted that individuals with complex knowledge structures about a topic are more likely to include both unfavorable and favorable attributes in their descriptions of others than those with simple ones are (Gardberg & Newburry, 2010; Linville, 1982). Thus, this research has found that individuals with less knowledge about a firm are less likely to punish the firm with negative actions such as boycotting (e.g., Gardberg & Newburry, 2010; Klein et al., 2004). Service industries are often characterized as having numerous differences from manufacturing, including greater knowledge intensity, intangible products, and customer presence for part or all of service production (Aharoni, 1996; Boddewyn et al., 1986; Bowen et al., 1989; Erramilli & Rao, 1993). Building on this past literature, we suggest that given inherent differences between manufacturing and service industries, individuals from the public are more likely to be knowledgeable about manufacturing industries, where physical products can be much more easily evaluated. Thus, firms within these industries may be more highly evaluated -particularly in rapidly changing economies where individuals may have had less chance to experience services. Accordingly, we suggest the following hypothesis:
H1: Reputation assessments of firms within recently deregulated service industries will be lower than those of firms within manufacturing industries are.
Previous scholars have distinguished credence goods and services from other types of goods (e.g. Darby & Karni, 1973; Emons, 2001; Siegel & Vitaliano, 2006). For credence goods, the quality of a good or service is not easily identifiable by the purchaser through normal use (Darby & Karni, 1973). Thus, the purchaser relies greatly on the seller to provide information regarding what product is needed, along with the quality of the product (Emons, 2001). Additionally, for credence goods, some consumers use external indicators related to a company, such as the degree to which the company harms the environment, when evaluating purchasing decisions (Feddersen & Gilligan, 2001). Since easily verifiable performance measures are not available for credence goods, overall industrylevel signals may be more important to the reputation evaluations of firms selling these goods and services than for other types of goods. However, in cases where industry signals are unclear due to a rapidly changing environment, these industries might be most vulnerable to negative reputations. By contrast, for experience goods and services or goods where the quality is directly visible, reputation assessment decisions may be less a function of externally evaluated attributes of a firm and more a function of actual firm characteristics. Accordingly, we suggest that service industry reputations will be particularly negative for credence service goods, as compared to experience goods. Thus, we hypothesize:
H2: Reputations assessments of service firms that sell credence goods will be lower than those of other service firms are.
As prior research has established financial performance and firm size as important predictors of corporate reputation, in the following paragraphs we examine the effects of these two variables in the Latin American context.
Financial performance. Under conditions of information asymmetries between a firm and its stakeholders, stakeholder perceptions are formed based on the signals they receive from a firm's past and current actions. One such signal is the financial performance. McGuire et al., (1988) found that return on assets was significantly correlated with reputational rankings of firms. To the general public, stronger financial results reflects a superior business model, more effective management, better resource deployment, more productive personnel, and better overall fit between resources and strategies. Therefore, high financial performance affects the public's perception in favor of firms (Fombrun & Shanley, 1990). Investors and creditors, on the other hand, translate good accounting and financial performance to indicate a company's healthy and wellmanaged standing and that a company is able to bring positive results in the future (Helm, 2007). Gabbioneta et al. (2007) showed that higher financial performance leads security analysts to a more favorable disposition towards firms. In total, financial signals affect stakeholders' perceptions in a consistent way and in return the collective representation of the firm. Therefore, we suggest:
H3: There is a positive relationship between financial performance and corporate reputation.
Firm size. Another firm-level factor affecting corporate reputation is size. Large firms have financial resources, talented employees, advanced technology, and effective networks that can help them create and support a favorable reputation. However, size is a double-edged sword and larger firms are more visible to their stakeholders and therefore more subject to scrutiny of the public, government, and regulatory bodies. While favorable corporate reputation is a critical firm resource and a source of competitive advantage (Barney, 1991; Deephouse, 2000; Fombrun, 1996), building it is a slow and incremental process (Hall, 1993) and a firm may lose its favorable reputation quickly due to a negative event, crisis, or deregulation (Carter & Ruefli, 2006). Large and more visible firms are more covered by media and therefore, their stakeholders are better and faster informed about them. In addition, people not only expect more from larger corporations, but also trust them less. In sum, we suggest that firm size provides a negative signal in terms of predicting corporate reputation, and accordingly, the following hypothesis:
H4: There is a negative relationship between firm size and corporate reputation.
Marginalization is "the process by which established or emerging elites create socioeconomic relations of superior versus subordinate/dependent through manipulations of labor and distributions of social resources" (Arnold, 1995, p. 88). Strong evidence suggests that certain demographic groups may be more or less advantaged within Latin American society, based upon gender (Heath et al., 2005; Skidmore & Smith, 2005), income, social class (Gomez & Sanchez, 2005; Martinez, 2005), and education level (Koljatic & Silva, 2006). These advantages reinforce each other through societies that emphasize social contacts (Dávila & Elvira, 2005; Weaver, 2000) and family-owned businesses (Fogel, 2006; Perkins et al., 2010). This status of being marginalized could lead to differential levels of identification with firms (e.g., Gardberg & Newburry, 2010; Newburry et al., 2006). Accordingly, being part of a marginalized group could systematically impact individual-level reputation assessments by limiting the types of information signals available to an individual to make such an assessment. Herein, we suggest that more marginalized individuals are more likely to identify with firms where they have had frequent experience with a good or service than with firms where they have had less close contact. Within the context of our prior discussion of credence versus experience goods, as experience goods are more easily evaluated that credence goods, this suggests that marginalized individuals will have relatively more positive assessments of these goods than their credence counterparts will. Accordingly, we suggest:
H5: Marginalized individuals are more likely to evaluate experience goods higher than credence goods.
We utilize a sample of individual perceptions of companies from the following countries studied by the Reputation Institute in association with The Foro de Reputación Corporativa: Argentina, Chile, Brazil, Mexico, and Peru. The analyses contained herein are based upon data collected for this project between January 2007 and July 2008, which is part of a larger ongoing study. Questionnaire items were based upon the Reputation Institute's reputation survey, which aims to measure multiple aspects of corporate reputations. Respondents were identified from the respondent pool of a well-established data collection agency operating in all of the study countries. To be eligible for the study, potential respondents were screened to determine if they met the minimal requirement of being at least somewhat familiar with the focal company of evaluation based on their ability to answer 3 out of 4 general questions regarding the company (Asher, 2004). The population of respondents was also targeted to be representative of the general population of each country in terms of gender and age. Questionnaires were administered via computer assisted telephone interviews (CATI ) in the local language of the respondent (Spanish or Portuguese). For this analysis, this procedure produced 76,419 usable responses.
Corporate Reputation is measured using the following four general items developed by the Reputation Institute to gage a firm's overall reputation. Multiple authors have found that items measuring general perceptions tend to have greater face validity across cultures than more specific culturally derived items (Newburry et al., 2008; Scandura, Williams & Hamilton, 2001). Respondents evaluated the items on a 7-point scale, where "1" indicates "I strongly disagree" and "7" indicates "I strongly agree". Respondents also had the option to indicate "Not Sure". LIS REL analyses confirmed the unidimensionality, convergent validity and fit of the scale items (Alpha = .933). As interpretation of items can vary across cultures (e.g., Gardberg, 2006), the factor structure for the four items was evaluated in each country and found to be equivalent.
The items contained in this measure are:
- [Company] has a good overall reputation.
- [Company] is a company I have a good feeling about.
- [Company] is a company that I trust.
- [Company] is a company that I admire and respect.
Industry effects (H1/2) were tested for the Telecom, Retail, Financial and Energy Provision industries. As the telecom and retail industries involve much closer interaction with the service provided, these were considered experience services for our examination, while the financial and energy provision industries were considered credence services.
Company data were obtained primarily from company annual reports, and supplemented when needed with data from the Economatica, Mergent and Hoover's Online databases. Return on Assets (H3) indicates the 2006 ratio of net income over total assets for each company, and it was used to measure financial performance. Firm Sales (H4) indicates the 2006 sales for a company, and it was used to examine firm size effects.
To examine marginalization effects (H5), we computed multiplicative interactions between gender, social class, income and education and the four service industries indicated above. Gender is dichotomous, coded "1" for females and "0" for males. Since appropriate indicators of Social Class varied by nation, different measures were used in each country, and then, responses were standardized to allow inclusion of different nations in the same dataset (Craig & Douglas, 2000). Thus, this variable is coded "1" for lower class, "2" for lower middle class, "3" for upper middle class, and "4" for upper class. As living standards differ significantly between sample nations, different scales were also used in each nation to examine Household Income. Responses were standardized across nations, with the variable coded as: "1" for low income, "2" for mid-level income and "3" for affluent. Education is categorical, coded "1" if less than a basic education (e.g. elementary school) ranging to "7" if completed graduate school.
While the HLM nature of the analysis (see next subsection) controls for fixed effects at the country and company levels, the following individual-, company- and countrylevel controls were added. Respondent Age is categorical, ranging from "1" if under 18 to "10" if over 60. Familiarity with a company was measured using the item, "I am familiar with [Company]", with responses ranging from 1 (Not at all familiar) to 4 (Very familiar). As respondents met a familiarity threshold to be included in the sample, responses for this variable are highly biased towards the top end of the scale. International Scope measures a respondent's perception of the scope of a firm's international activities, based upon the item: "[Company] has an international scope", with responses ranging from 1 (Does not describe well) to 7 (Describes very well). Unemployed measures whether the respondent was employed by a firm at the time of the study.
At the company level, Foreign HQ is a dichotomous variable, coded "1" if a firm's headquarters was located outside the country where a firm's reputation was being evaluated and "0" if located in the focal country. Local Years is measured as the number of years that a firm had operated in a particular market at the time of survey administration.
GDP per capita (GDPpc) measures the gross domestic product for each country divided by the population. Additional controls were considered for Hofstede's (2001) cultural dimensions, since these have been shown to impact reputation assessments (Deephouse et al., 2009). However, these were not included due to the limited variance across the study countries, most of which was already captured by the GDPpc variable. Strong correlations of three dimensions with GDPpc also created collinearity problems.
As our analysis involved a hierarchical data structure with nested data (Hitt et al., 2007), we used the hierarchical linear modeling program HLM 6 (Raudenbush et al., 2004) to compute a model with three data levels (individual within company within country). The approach of examining reputation from multiple analysis levels is consistent with Barnett and Hoffman (2008), who recently noted the multilevel nature of reputation. While ordinary least squares (OLS ) regression is often used for analyses of this type, OLS does not account for the interdependence of individual-level data being nested within higher levels of observation. To minimize multicollinearity, we standardized and centered the direct effects prior to creating the moderating effects, with the exception of the binary variables in a manner consistent with Joshi et al., (2006).
Table 1 presents descriptive statistics and correlations for our study variables. A few high correlations are worth noting-education with social class, local years with the telecom industry, and banking with ROA . While multicollinearity analyses suggest that these high correlations are not significantly impacting our results, they should nonetheless be kept in mind when interpreting the findings. All study firms sold products or services, and operated facilities in the host country where they were evaluated.
Table 2 presents the basic models used to test our direct effect hypotheses 1 through 4. All models have significant Chi-square statistics (p<.001). Model 1 presents the analyses using the full data sample. Models 2 through 6 divide the sample by country. While the number of companies evaluated in each country makes it difficult to ascertain conclusions from the country-specific models, these models do allow us to see the consistent negative coefficients of our four service industries across markets.
Hypothesis 1 suggested that reputation assessments of firms within recently deregulated service industries would be lower than those of firms within manufacturing industries. Within Model 1, we find mixed support for this hypothesis. While all four service industry coefficients have negative signs, consistent with our hypothesis, only two are significantly negative (telecom and energy provision) when compared to the omitted other industry dummy variable, while a third (banking) is marginally significant.
Hypothesis 2 suggested that reputation assessments of service firms that sell credence goods will be lower than those of other service firms will. Again, within Model 1, we find mixed support for this hypothesis. While both of the credence industries (energy and banking) had significant or marginally significant negative coefficients, telecommunications (an experience industry) had a highly significant (p<.001) coefficient as well.
Hypotheses 3 suggested a positive relationship between financial performance and corporate reputation, while hypothesis 4 suggested a negative relationship between firm size and corporate reputation. Contrary to expectations, neither of these hypotheses was supported in Model 1.
Table 3 presents the basic models used to test our marginalization interaction effect suggested in hypothesis 5. All models have significant Chi-square statistics (p<.001). To reduce multicollinearity problems between variables, interactions with each demographic variable (female, social class, education and income) are examined in separate models.
Hypothesis 5 stated that marginalized individuals are more likely to evaluate experience goods higher than credence goods. The two experience-based industries examined in our study were the telecom and retail industries, while the two credence industries were energy provision and banking. We see our strongest result for the telecom industry interactions, which consistent with our hypothesis, are highly significant across Models 7 through 10. In Model 7, females are relatively more likely to rate telecom firm reputations higher than males. In Models 8 through 10, persons of lower social class, education and income are more likely to rate telecom firm reputations higher than their less marginalized counterparts.
With respect to our other experienced -based industry, retail, the results are mixed- the female-retail interaction is significant in the anticipated positive direction (p<.01), while it is not significant for the other interactions. Regarding our credence industries, we find two significant interactions for the banking industry, with female (p<.05) and education (p<.05), and none for the energy provision industry.
Discussion and Conclusion
This study investigated industry -firm- and individual-level determinants of individual-level corporate reputation assessments. In a hierarchical linear model, we tested our theory using 76,419 individual evaluations of 80 companies in five Latin American countries. Overall, our study results demonstrated that across our sample, firm reputations in the telecom and energy industries are significantly lower than those of manufacturing firms. Additionally, we found consistent evidence across marginalized groups (i.e., women, lower social class, education and income) that they assess telecom industry reputations relatively higher than their less marginalized counterparts. Results were mixed with regards to marginalized group assessments of firms from other service industries. Additionally, counter to expectations, we did not find evidence that firm size or financial performance impact reputation assessments.
Looking at our individual hypotheses, our first hypothesis suggested that reputation assessments of firms within recently deregulated service industries would be lower than those of firms within manufacturing industries. While not conclusive, our results were consistent with this hypothesis as the coefficients of our four-service industry variables were all in the predicted negative direction, although with mixed significance levels. These results build upon past scholars who have noted how service industries differ significantly from manufacturing (e.g., Aharoni, 1996; Boddewyn et al., 1986; Bowen et al., 1989; Erramilli & Rao, 1993) by demonstrating a measurable difference in the evaluations of these firms within the Latin American context.
Our second hypothesis attempted to disentangle the reputation assessments of service firms by classifying them based upon the literature regarding credence versus experience goods and services (e.g. Darby & Karni, 1973; Emons, 2001; Siegel & Vitaliano, 2006). While we found mixed support for this hypothesis, we believe that demonstrating systematic differences in reputation assessments across service industry segments contributes to a relatively new literature regarding industry reputations (e.g., Barnett & Hoffman, 2008; Winn, MacDonald & Zietsma, 2008). Future work might delve more deeply into the characteristics of service industries, such as the types of goods and services they produce, which cause some service industries to differ from others.
Our third hypothesis suggested that financial performance and corporate reputation would be positively related. This was perhaps the most surprising result of the study, since the financial performance-reputation relationship is highly established in many contexts in the reputation literature (e.g., Fombrun & Shanley, 1990; McGuire et al., 1988). Multiple explanations may exist for this result. One possibility is that most studies of this relationship have occurred in developed world contexts, where corporate financial performance may be more easily visible to individuals to use in their reputation assessments. In developing and emerging markets, such as the Latin American markets studied herein, performance information or even a uniform understanding of what constitutes firm performance may be lacking. A second contributing factor to our lack of results is that our reputation measure was based upon assessments of the general population, as opposed to many past studies using the Fortune reputation measures or other similar measures, which are evaluated by company analysts. The general population may have a much broader view of the components that contribute to a company's reputation.
Our fourth hypothesis examined the relationship between firm size and corporate reputation. Our lack of significant results here may be driven by our sample, which primarily consisted of large firms. A sample with more breadth may produce different results.
Finally, our marginalization interaction results are worth commentary. We found strong results that marginalized demographic groups, whether based upon gender, social class, income or education, rated telecom industry firms higher than their less marginalized counterparts. Of the four service industries in our study, the telecom industry is probably the one where individuals have the closest direct contact with the provided service, given the ubiquitous nature of telephone usage around the globe. Thus, this result is consistent with the expectation that more marginalized groups make reputation assessments to a greater degree based on their direct experiences than less marginalized groups, who may have better access to less direct information upon which to make reputation assessments. Accordingly, the telecom interaction results provide additional evidence that the difference between credence and experience goods matters in terms of reputation assessments. However, our other marginalization-service industry interactions were less conclusive, and thus, these results suggest that differences between service industries deserve much greater attention in future research.
Past research has suggested that the marginalization of certain demographic groups in society impacts their attitudes towards firms (e.g., Gardberg & Newburry, 2010). This manuscript's results further help elucidate how marginalized groups may differ from their less marginalized counterparts in the Latin American context.
Overall, previous signaling studies have investigated the effects of different signals on various desired output factors. In this study, we have attempted to contribute to this literature by examining reputation signals in a hierarchical manner so as not only to understand the individual effect of each signal, but also the hierarchical effects of them. We found strong effects related to both industry and individual- level variables, with relatively weak firm-level results. The combination of these results suggests that the reputations of firms are driven by much more than the characteristics of firms themselves, and that firms need to pay attention to factors beyond their direct control to effectively manage their reputations.
We also acknowledge limitations in our study, which provide opportunities for future research. First, corporate reputation is an aggregate of the public's perceptions about different aspects of a company, which we only examined using a general overall scale. Future studies can investigate the effect of other aspects associated with reputation such as quality of leadership, corporate governance, workplace attractiveness and corporate citizenship. Second, there is a need for deeper and wider investigation of industry differences and their effects on corporate reputation. Third, there is a lack of studies on country-level determinants of corporate reputation. More detailed analysis of differences between the countries in our study could explain more about the mechanisms underlying corporate reputation. In addition, it is interesting to examine the effects of other country-level factors such as culture, media effectiveness or penetration, and international involvement. Fourth, our reputation assessments were limited to a short timeframe. Future studies could examine reputation assessments over a longer timeframe to gain a more longitudinal picture. Fifth, as Latin American MNCs gain prominence (e.g., Martinez et al., 2005), the analyses contained herein could be further developed to examine differences between reputation signals of Multilatinas and foreign firms. This is important since developing nation MNCs typically display different characteristics than developed nation counterparts (e.g., Cuervo-Cazurra & Genc, 2008; Cuervo-Cazurra, 2008; Perkins et al., 2010).
In summary, this study aimed to partially fill gaps in the corporate reputation literature in two ways. Theoretically, there is a lack of studies analyzing industry- and individual- level antecedents of corporate reputations, and we help fill this gap by finding significant industry- and individuallevel effects, in addition to more commonly studied firmlevel variables. Methodologically, this study contributes by using multi-level variables to predict corporate reputation. Using multi-level analysis, this study recognizes the interdependence among variables at different levels and captures more accurate effects of lower level variables on our criterion variable. Therefore, this study suggests further application of multi-level analysis.
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 We gratefully acknowledge the support of The Reputation Institute and The Foro de Reputación Corporativa. In particular, we thank Charles Fombrun, Leonard Ponzi, and Sebastian Taciak of the Reputation Institute (New York), Fernando Prado, Pilar Villegas and Beverly Nannini of the Reputation Institute (Spain), ángel Alloza of BBVA and Sofia Fernández of Telefónica. Additionally, we wish to thank Delmary Salgado (FIU) for research assistance.
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