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Invariance of the Reputation Emotional Index RepTrak Pulse: A Study Validation on Generational Change

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Abstract

Reputation takes on special relevance for companies and organizations at a time when the traditional model of short-term capitalism is being redefined and focused on moving from short term to the creation of long-term value and creating value for all stakeholders. This approach requires demonstrating with metrics the benefits that long-term creation produces and precisely, a key performance indicator (KPI) that addresses this need of demonstrating empirically the long-term value is reputation. Nowadays, the rising value of company-intangible assets and resources such as reputation has increased academic interest in the methodological properties of such metrics which are being object of monitoring and management. One of these properties is the invariance of the metric between the different demographic groups composing the population. The invariance of the corporate intangible metrics is an essential requirement for a correct interpretation of reputation results surveys, especially in those indices such as RepTrak Pulse that measure overall reputation on a broad demographic spectrum. The populational invariance guarantees that individual RepTrak Pulse scores might be aggregated to other population samples because everyone is expressing the same underlying feeling. The classic tests about reliability and validity of the RepTrak Pulse metric do not deal with this issue and therefore, the invariance analysis becomes a complementary and necessary test that allows legitimately to make use Pulse scores at populational level. In 2011, Ponzi, Fombrun and Gardberg demonstrated that the RepTrak Pulse was valid and reliable in several countries. However, they did not verify the population invariance of this metric. Four years later, this deficiency was corrected, demonstrating the invariance of the Reptrak Pulse by sex, age, and educational level by Alloza (2015). The populational invariance guarantees that Pulse scores variations reflect real feeling changes and not changes due to variations in the demographic composition of survey samples. Our current research aims to reinforce the robustness of RepTrak Pulse index by checking the degree of invariance with respect to a variable that often goes unnoticed in the Pulse follow-up, namely case of the generational group. The generational effect should not be confused with that of age, even though they are related. Generations imply specific ways of understanding and reacting to the world that could interact the notion of Pulse shared by each generational group and could invalidate the temporal comparison. Thus, here we examine the degree of invariance that Reptrak Pulse has by generation. To separate the effect between generation and age the “time-lag method” was applied to the invariance analysis in two cross-sectional samples (in 2006 and 2017). Data proceeding from 9000 interviews were analyzed in Spain, France, Germany, Canada, Brazil and Japan. Our results show that RepTrak Pulse is invariant by generation; thus, generational changeover does not interfere with the interpretation of the temporal trajectory of this overall reputation construct.

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Notes

  1. The RepTrak System was developed in the framework of collaboration between the Corporate Reputation Forum, a predecessor of the “Corporate Excellence Center for Reputation Leadership Foundation” and the Reputation Institute (Alloza, 2015).

  2. In addition to be the first year that the Reputation Institute systematically gathers Pulse information at an international level.

  3. Example: For Boomers, the individual “i” response to the item "I trust" would be equal to the value of the Pulse for this individual multiplied by its slope "b3_Boo" and added the interceptor "t3_Boo" \({\text{Trust}}_{{i\_{\text{Boomer}}}} = t3_{{\_{\text{Boo}}}} + b3_{{\_{\text{Boo}}}} *{\text{Pulse}}_{{\_{\text{Boo}}_{i} }}\)

    The same for another individual response from group Millennial \({\text{Trust}}_{{i\_{\text{Millennial}}}} = t3_{{\_{\text{Mil}}}} + b3_{{\_{\text{Mil}}}} *{\text{Pulse}}_{{\_{\text{Mil}}_{i} }}\)

    Be the latent mean the same in both groups 0 \(\overline{{{\text{Pulse}}_{{\_{\text{Boo}}}} }} = \overline{{{\text{Pulse}}_{{\_{\text{Mil}}}} }} = 0\)

    and reaches zero, the average of each item (although different in both groups) will indicate the same latent mean, only when their interceptors are the same \(\overline{{{\text{Trust}}_{{\_{\text{Boo}}}} }} - t3_{{{\text{Boo}}}} = {\text{Boomer}}\;{\text{Pulse}}\;{\text{average}} = 0 = {\text{Millennial}}\;{\text{Pulse}}\;{\text{average}} = \overline{{{\text{Trust}}_{{\_{\text{Mil}}}} }} - t3_{{{\text{Mil}}}}\)

    Indeterminacy, the latent value depends on two terms, the way to determine it would be setting the interceptors to equality. Therefore, the item means will indicate the same latent variable in both groups if the interceptors are the same.

  4. Some authors indicate a last source of invariance called strict invariance or error invariance. Nevertheless, this last type of invariance would be irrelevant in the comparison between latent ones: “… is recognized by most researchers as difficult to achieve and not really necessary to test differences in factor structure or latent means” (Schmitt & Kuljanin 2008, p. 212).

  5. The Gamma Hat index has been calculated from the RMSEA:\({\text{Gamma}}\;{\text{Hat}} = \frac{k}{{k + (2*{\text{RMSEA}}^{2} )}}\)

  6. This procedure works in several phases through a process of sequential release of slopes until it locates the one that corresponds to the optimal indicator that will act as the reference indicator. The first step consists of restricting the all the item weights of the latent variable to the equality between groups. To identify the model, the variance of the latent zero is fixed. Its rejection would imply that some of the loads are not equal between groups. In the next phase, the load with the highest modification index is released, which would most reduce the value of 2 or adjustment of the model to the data (how much the adjustment (2) would improve if the equality constraint were released) and the new model partially invariant is estimated. If it is rejected in turn, the slope with highest modification index is released again and adjusted until an acceptable model is reached and the optimal indicator to be restricted is identified (Yoon and Millsap 2007).

  7. Recall that in this country, qualified informants are younger.

  8. In the verification of scalar invariance, we have been more rigorous, and we have assessed the greater loss of adjustment, with respect to the configural model, the one taken as a valid reference. In this way, type I error, the probability of making a mistake when stating that the loss is significant, is much smaller, so that we have greater confidence when corroborating the scalar invariance of the metric.

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Acknowledgements

We would like to thank the consulting firm, Reputation Institute (currently named, The RepTrak Company), for providing the data and for the full autonomy and independence we have had to carry out this research. We would like to thank Dr Clara Alloza for her helpful comments and editing of this article.

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  1. Corporate Excellence Centre for Reputation Leadership, 25. 2ª, 28004, C/Carranza, Spain

    Ángel Alloza-Losana & Enrique Carreras-Romero

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  1. Ángel Alloza-Losana
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Correspondence to Ángel Alloza-Losana.

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Alloza-Losana, Á., Carreras-Romero, E. Invariance of the Reputation Emotional Index RepTrak Pulse: A Study Validation on Generational Change. Corp Reputation Rev 24, 143–157 (2021). https://doi.org/10.1057/s41299-020-00099-w

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