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This article provides a review and conceptual comparison between self‐report and performance‐based measures of emotional intelligence. Analyses of reliability, psychometric properties, and various forms of validity lead to the conclusion that self‐report techniques measure a dispositional construct, that may have some predictive validity, but which is highly correlated with personality and independent of intelligence. Although seemingly more valid, performance‐based measures have certain limitations, especially when scored with reference to consensual norms, which leads to problems of skew and restriction of range. Scaling procedures may partially ameliorate these scoring weaknesses. Alternative approaches to scoring, such as expert judgement, also suffer problems since the nature of the requisite expertise is unclear. Use of experimental paradigms for studying individual differences in information‐processing may, however, inform expertise. Other difficulties for performance‐based measures include limited predictive and operational validity, restricting practical utility in organizational settings. Further research appears necessary before tests of E1 are suitable for making real‐life decisions about individuals.

This article provides a commentary on the article “Emotional intelligence, leadership effectiveness, and team outcomes” by Prati, Douglas, Ferris, Ammeter, and Buckley (2003). The role of emotional intelligence (EI) as a construct in organizational behavior is addressed by discussing (a) the boundary conditions of theories in organizational behavior; (b) the relative importance of EI, g and personality in leadership effectiveness; (c) whether EI is needed for leadership effectiveness; (d) the degree EI is a unique construct versus a part of normal psychological functioning; (e) the relationship between EI and levels of analyses in organizations; and (f) whether EI is important for charismatic leadership. This discussion concludes with a cautionary note about premature excitement over the use of EI in the workplace.

This paper aims to understand what drives people – their motivations, autonomous learning attitudes and learning interests – to volunteer as mentors for a program that helps families to ideate technological solutions to community problems.

A three-phase method was used to build volunteer mentor profiles; elicit topics of interest and establish relationships between those. The mentor profiles were based on self-assessments of motivation, attitude toward lifelong learning and self-regulated learning strategies. The topics of interest were elicited through content analysis of answers to reflection questions. Statistical methods were applied to analyze the relationship between the interests and the mentor profiles.

Bottom-up clustering led to the identification of three mentor groups (G1 “low”; G2 “high” and G3 “medium”) based on pre-survey data. While content analysis led to identifying topics of interest: communication skills; learning AI; mentoring; prototype development; problem-solving skills; working with families. Analyzing relationships between mentor profile and the topics of interest, the group G3 “medium,” with strong intrinsic motivation, showed significantly more interest in working with families. The group with the overall highest scores (G2 “high”) evidenced also substantial interest in learning about AI, but with high variability between members of the group.

The study established different types of learning interests of volunteer mentors and related them to the mentor profiles based on motivation, self-regulated learning strategies and attitudes toward lifelong learning. Such knowledge can help organizations shape the volunteering experience to provide more value to volunteers. Furthermore, the reflection questions can be used by volunteers as an instrument for reflection and by organizations to elicit the learning interests of volunteers.