Emotional Intelligence: A tangled web of definitions, predictors, outcomes, and models

Topic: Emotional Intelligence, Job Performance, Leadership

Publication: Industrial and Organizational Psychology: Perspectives on Science and Practice

Article: Emotional Intelligence: Toward Clarification of a Concept

Author: C. Cherniss

Selected commentary authors: Kaplan, Cortina, and Ruark (2010); Antonakis, J. & Dietz, J. (2010)

Reviewed by: Samantha Paustian-Underdahl


Images Emotional Intelligence (EI) has been one of the most popular topics studied throughout the history of I/O psychology. Given its popularity, it has been defined and measured in several different ways throughout time, leading to some confusion and controversy in the field. Cherniss (2010) argues that despite these multiple definitions and models, most researchers generally agree on what EI is: ‘‘the ability to perceive and express emotion, assimilate emotion in thought, understand and reason with emotion, and regulate emotion in the self and others’’ (Mayer, Salovey, & Caruso, 2000, p. 396). Despite a common definition, some researchers model EI in different ways, with some arguing that EI is a kind of intelligence, meaning it is a set of related abilities like reasoning, problem-solving, and the processing of information (Mayer, Caruso, & Salovey, 1999) while others describe EI as a set of competencies or Emotional and Social Competencies (ESC), which are competencies that are clearly linked to EI (i.e., the perception, expression, understanding, and regulation of emotion in oneself and others). Cherniss (2010, p. 116) believes that there will always be a gray area around EI, however there he proposes one view of EI that encompasses multiple perspectives, “

that the core EI abilities, such as emotional perception, provide the foundation for emotional and social competencies such as ‘‘influence’’ or ‘‘stress tolerance.’’


Another concern amongst scholars is the validity of EI measures. Cherniss (2010) proposes that measures of EI can be divided into different categories: ability tests, self-report measures, and alternative measures. Amongst these categories of tests, Cherniss believes the MSCEIT (an ability test) has the strongest support for content validity and reliability, the Schutte’s self-report emotional intelligence test (SREIT) has high reliability (amongst self report tests), and multi-rater or “360” assessments are a promising alternative to these self-report and ability measures. While some of the EI tests are supported in the literature, there are several limitations including weak discriminant and divergent validity. Thus, Cherniss (2010) believes that new measures should be developed that take into account the context in which they will be used. Cherniss proposes that researchers and practitioners should consider more ecologically valid, behavior-based assessment strategies such as assessment centers, event-based interviews, and role-plays.


Finally, Cherniss (2010) discusses complications related to outcomes of EI and ESC. Recent research suggests that EI is positively associated with job performance. One study found a correlation of .43 between company rank and EI, and a correlation of .35 between merit salary increase percentage and EI as measured by the MSCEIT in a group of analysts and clerical employees (Lopes, Grewal, Kadis, Gall, & Salovey, 2006). However, Cherniss (2010) believes that, in many situations, certain ESCs may be stronger predictors of performance than EI. Further, social context is likely to moderate the relationship between EI or ESC and outcomes.


Selected Commentaries:

Kaplan, Cortina, and Ruark (2010) commend Cherniss for beginning to “disentangle the jumble” of models and definitions of EI. However, these authors criticize the general approach that has been taken by most IO psychologists in studying EI. They believe that much of the focus of EI, up to this point, has been on its predictive value, regardless of the outcomes it may or may not predict. They suggest a practical approach to studying EI by following an outcome-driven strategy. Instead of trying to determine ‘‘how well EI predicts,’’ a more useful strategy for organizational researchers will be to start with the outcome of interest and then work backwards to identify those particular socioemotional constructs that predict specific dimensions of that outcome. Their approach is outlined below:


Step 1: Carefully identify organizational phenomena and outcomes in which emotions and emotionally relevant processing are most relevant and impactful (like supportive leadership, participative decision making, etc).


Step 2: Explicitly define and map out the dimensionality of that outcome. Researchers of EI should (a) make distinctions among the particular components of EI, (b) make distinctions among the specific types or dimensions of the outcome domain, and (c) consider the moderating role of contextual factors.


Step 3: Identify the predictor variables that are most likely to explain or account for the specific dimensions of the outcome of interest. Kaplan et al (2010) agree with Cherniss who argued that focusing on any one definition or conceptualization of EI, to the exclusion of other important socioemotional variables, is likely to result in a failure to appropriately capturing all relevant predictors, thereby resulting in a less than optimal prediction of organizational outcomes.


Antonakis and Dietz (2010) agree with Cherniss that emotions are important for many organizational phenomena, however, they disagree with Cherniss regarding the incremental validity (or lack thereof) of EI and ESC over and above IQ (general intelligence) and personality tests. Antonakis and Dietz (2010) also raise concerns regarding Cherniss’s take on EI and ESCs:


They believe that there are important conceptual problems in both the definition of ESC and the distinction of ESC from EI, (b) that Cherniss’s interpretation of neuroscience findings as supporting the constructs of EI and ESC is outdated, and (c) that his interpretation of the famous marshmallow experiment as indicating the existence of ESCs is flawed.


In summary, Antonakis and Dietz (2010) generally believe that there is not considerable support for many of Cherniss’s arguments about EI. They feel that the only way EI research can move forward successfully would be to commit firmly to the ability definition of EI (e.g, Mayer, Caruso, & Salovey, 2000) and its consequences—then there is no need to include ESCs as they may unnecessarily complicate our understanding of EI.


Focal article:

Cherniss, C. (2010). Emotional intelligence: Toward clarification of a concept. Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 110–126.


Antonakis, J.& Dietz, J. (2010). Emotional Intelligence: On Definitions, Neuroscience, and Marshmallows, Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 165-170.

Kaplan, S., Cortina, J. & Ruark, G. (2010). Oops. . . . We Did It Again: Industrial Organizational’s Focus on Emotional Intelligence Instead of on Its Relationships to Work Outcomes. Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 171–177.



Lopes, P. N., Grewal, D., Kadis, J., Gall, M., & Salovey, P. (2006). Evidence that emotional intelligence is related to job performance and affect and attitudes at work. Psichothema, 18, 132–138.

Mayer, J. D., Caruso, D. R., & Salovey, P. (1999). Emotional intelligence meets traditional standards for an intelligence. Intelligence, 27, 267–298.

Mayer, J. D., Salovey, P., & Caruso, D. R. (2000). Models of emotional intelligence. In R.J. Sternberg (Ed.), Handbook of intelligence (2nd ed., pp. 396–420). New York: Cambridge University Press.