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Dear readers!

This is the scientific collected book containing the results of the First International Conference called «Behavioral Economics: New Approaches», which took place from January 1 0 to February 28, 201 3. During the Conference the following issues were discussed: using of Crowdsource technology and Emotional Intelligence in different areas of science and life, also interpersonal (emotional) factors in the economy. The initiator of the conference was Crowdintell team: A. Kotin – curator, E. Viktorova – organization of conference, E. Levina – layout and design of this scientific collected book. The official website of the conference is The conference brought together scientists, teachers, practitioners and graduate students who developed scientific and practical projects in behavioral economics. They all come from Russia, Ukraine, the USA, Australia, Switzerland and other countries. The Organizing Committee would like to thank the participants of the First International Conference «Behavioral Economics: New Approaches»: P. Salovey, G. Asmolov, M. Brackett, D. Caruso, R. Corcoran, G. Gignac, V. Ivanov, S. Jennings, A. Korobova, D. Kostrov, R. Manocha, J. Mayer, S. Mullainathan, B. Palmer, S. Rivers, C. Stough, R. Thaler, R. Tormey and etc.

For all the questions and requests for cooperation please contact us at: Yours sincerely, CrowdIntell Team

Перед Вами научный сборник, изданный по итогам первой международной конференции «Поведенческая экономика: новые подходы», проходившей в период с 1 0 января по 28 февраля 201 3 года. В рамках конференции обсуждались следующие проблемы: применение технологий краудосорсинга и эмоционального интеллекта в разных областях науки и жизни, межличностные (эмоциональные) факторы в экономике. Инициатором проведения конференции стала команда Crowdintell: А. Котин – куратор проекта, Е. Викторова – организация и проведение конференции, Е. Лёвина – верстка и дизайн сборника. Официальный сайт конференции В работе конференции приняли участие ученые, преподаватели, практикующие специалисты, аспиранты, разрабатывающие научнопрактические проекты в области поведенческой экономики, из России, Украины, США, Австралии, Швейцарии и других стран. Оргкомитет конференции выражает благодарность участникам первой международной конференции «Поведенческая экономика: новые подходы»: П. Саловею, Г. Асмолову, М. Брэкету, Дж. Гигнаку, С. Дженнингсу, В. Иванову, Д. Карузо, Р. Коркоран, Д. Кострову, А. Коробовой, Дж. Майеру, Р. Маноху, С. Мулэйнетену, Б. Палмеру, С. Риверсу, К. Стоу, Р. Тейлору, Р. Тормей и другим.

По всем интересующим вопросам и пожеланиям по сотрудничеству пишите по адресу: Искренне ваши, Команда CrowdIntell

is a psychologist at the University of New Hampshire. He is a personality psychologist. He co-developed a popular model of emotional intelligence with Dr. Peter Salovey. He is one of the authors of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT), and has developed a new, integrated framework for personality psychology, known as the Systems Framework for Personality Psychology. John D. Mayer

is the Provost and the Presidentelect of Yale University. He is also the Chris Argyris Professor of Psychology. Salovey has authored or edited thirteen books translated into eleven languages and published more than 350 journal articles and essays, focused primarily on human emotion and health behavior. Peter Salovey

is a co-founder of the EI Skills Group. He is also the Special Assistant to the Dean of Yale College (at Yale University, a p/t administrative position). David R. Caruso


Мы обращаемся к вопросам достоверности теста на определение эмоционального интеллекта Мейера-Саловея-Карузо (MSCEIT), поставленным Маулом (201 2). Чтобы проиллюстрировать свою точку зрения, мы отвечаем на вопросы касаемо нашей модели и объясняем, почему преимущества методов MSCEIT противопоставляются исследованию и многие верные факты, которые показывает MSCEIT, могут быть на самом деле не совсем правомерны, повторно анализируя стандартизацию теста, где N = 5,000. Также мы приводим результаты четырех последних статей, которые доказывают взаимосвязь между MSCEIT и другими тестами.


We address concerns raised by Maul (201 2) regarding the validity of the Mayer-SaloveyCaruso Emotional Intelligence Test (MSCEIT). We respond to requests for clarifications of our model, and explain why the MSCEIT’s scoring methods stand up to scrutiny and why many reported reliabilities of the MSCEIT may be underestimates, using reanalyses of the test’s standardization sample of N = 5,000 to illustrate our point. We also organize findings from four recent articles that provide evidence for the MSCEIT’s validity based on its relations with other tests. Maul (201 2) reviews the validity of an ability measure of emotional intelligence: the MayerSalovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey, Caruso, & Sitarenios, 2003). He explores what the MSCEIT most likely measures and whether its technical attributes are adequate for the job. Maul accepts the premise that emotional intelligence (EI) is worth discussing and that tasks can be constructed to measure mental abilities in emotion-related reasoning. The MSCEIT is based on a model of emotional intelligence that specifies four specific “branches” (of a hierarchy) of problemsolving: perceiving emotion accurately, using emotions to facilitate thought, understanding emotions, and managing emotion (Mayer & Salovey, 1 997). The MSCEIT produces an index of EI for each of the four branches (e.g., understanding emotions), as well as an overall emotional intelligence quotient (EIQ) score. Each of the branches is represented by two different tasks, and each task is represented by multiple items. According to Maul, our theoretical statements regarding emotional intelligence “set reasonable boundaries on the target domains of each of the four proposed branches of EI” (Maul, 201 2, p. 398). In our response, we will provide some requested clarifications and new evidence addressing some of the questions Maul has raised concerning the MSCEIT’s validity. In his discussion of the MSCEIT, Maul

expressed concerns about: (a) a need for clarification in portions of our theory; (b) the adequacy of the test’s scoring system; (c) the content representation, reliability, and generalizability of the test scores; (d) the correlations between the test’s scores and other measures that assess abilities in the emotional intelligence domain; and (e) correlations between the scores and criteria that are predicted theoretically. We will organize our comments according to these areas, and include findings so recent that neither Maul’s article nor our own recent reviews included them (Mayer, Roberts, & Barsade, 2008; Mayer, Salovey, & Caruso, 2008). Issues of Theoretical Clarity

Although Maul notes that our theory of emotional intelligence “set reasonable bounds” on EI, he also raised issues that he would like to have clarified (Maul, 201 2, p. 398). For example, regarding the perceiving emotion area, the MSCEIT asks test- takers to identify emotional content in faces (one task) and landscapes and designs (a second task). Regarding this branch, Maul asked us to clarify why we include landscapes and designs. To respond, many forms of visual stimuli connote certain emotions: Barren landscapes often connote sadness, sharp angles are associated with anger, and squiggly lines potentially indicate humor and joy (Buck, 1 984; Hevner, 1 935; Jansson-Boyd, 2011 ; Kastl & Child, 1 968; Rosenhan & Messick, 1 966; Windhager et al., 2008). Collectively, the ability to perceive these seemed (to us) to represent expertise in perceiving emotion and factor analyses indicated that the ability to perceive such emotional connotations was related to the overall MSCEIT test scores. Regarding the understanding emotions area, Maul asks what we meant in describing understanding as “appreciat[ing]

[. . .] emotional meanings” (Maul, 201 2, p. 398). By appreciating meanings we meant that a person who possessed emotional knowledge could understand emotional word meanings and concepts, understand the situations and other events that bring about emotions, and evaluate emotions according to their hedonic tone, and their moral value in a given context. Regarding managing emotion, Maul would like us to clarify what we meant by “personal understanding and growth” (Maul, 201 2, p. 398). We believe, in this regard, that people apply emotional reasoning so as to understand themselves and to develop more mature relationships with others. Because such positive development can occur in multiple ways depending upon the individual’s personal goals and context, we did not specify exactly how a person would carry this out (see, e.g., Helgeson, Reynolds, & Tomich, 2006; Ryan, Huta, & Deci, 2008; Ryff & Singer, 2008). Our aim for the MSCEIT was limited to assessing whether a person knows how to manage their own and others’ emotions, assuming that they often will do so to promote their own and others’ positive development. Of the four areas, Maul expresses his greatest reservations about the using emotion measures, raising concerns that we may not have distinguished the branch well enough, and indicating that the tasks we employ to assess “using” may not reflect our theory (cf., Joseph & Newman, 201 0, p. 55). We believe it is worth distinguishing between the often naturallyoccurring emotions that people use to guide their thinking (using emotion) versus managing emotional states themselves (managing emotion). Using one’s emotions occurs when, for example, a person who feels sad decides it is a good time to undertake some detailed proof-reading, or when a person employs different perspectives on a problem, that were brought about by transitions from one mood to another (which prompts alternative viewpoints; e.g., Blanchette & Richards, 201 0; Bower, 1 981 ; Mayer, Gaschke, Braverman, & Evans, 1 992). By contrast, managing emotions involves changing the feeling states

themselves, as when a sad person manages her attention so as to keep her sadness within manageable boundaries, or decides to cheer herself up. The “managing” branch measures people’s abilities to change their own and others’ emotions through active coping mechanisms—either intrapsychically or through interpersonal acts. Issues of Scoring the MSCEIT Item Generation was Guided by Theory

Maul (201 2) argues that good theories of emotion reasoning are necessary for constructing good items in the EI domain, in part so as to ensure that there are correct and incorrect answers to choose from for each question. In developing the MSCEIT we were guided by many such theories. For example, we used detailed theories of the affective lexicon to construct items for the understanding area of the test (Clore, Ortony, & Foss, 1 987; Plutchik, 2000), and also employed theories for the management, perception, and using areas (e.g., Isen, Daubman, & Nowicki, 1 987; Rosenhan & Messick, 1 966). Our theoretically informed approach surely added to the quality of the overall test, and it is one reason (as we will show) that there is considerable evidence for the MSCEIT’s validity. The Scoring System is Adequate

Regarding the MSCEIT, Maul suggests that “support for the adequacy of the scoring system [. . .] does not seem sufficient” (Maul, 201 2, p. 397). The MSCEIT gives the user a choice to employ one of two scoring keys for the test, which are extremely similar: one based on a consensus among 21 experts and the other based on a consensus among 5,000 testtakers (Mayer, Salovey, & Caruso, 2002). Consensus scoring, common to both keys, works as follows: Consider a multiple choice item where 70% of those 21

emotions experts identified “b” as a correct answer. The score of a participant who chose “b” would be incremented by .70 (and if the respondent chose “d” and 1 0% of the experts had done so, the score would be incremented .1 0). The MSCEIT also can be scored using the consensus of the general standardization sample. These two methods produced highly similar scoring keys: The correlation between the weights of alternatives calculated based on the experts versus those calculated using the test-taker sample varied from r =.88 to .91 depending upon the subsample studied (all Ns > 2,000). Moreover, the two scoring keys generated branch, area, and total scores with rs = .96 to .98—so high as to be nearly indistinguishable from one another. These high correlations result whether using the first approximately 2,000 participants studied or the full normative sample of 5,000 individuals (Mayer et al., 2002, p. 34, Table 5.1 2; Mayer et al., 2003). Maul suggested that perhaps the experts were not very expert. The group of 21 individuals consisted of 1 0 men and 9 women (2 did not identify their gender); all were members of the International Society for Research on Emotion, whose expertise involved research and scholarship on emotions. The group included 1 6 professors and lecturers, 2 “researchers,” and 2 doctoral students (and one non-identified) with a median age of 38. We consider this adequate evidence of their expertise. Despite their diversity (from North America, Europe, and the Middle East), their level of agreement as to answers was also higher than that in the general sample (Mayer et al., 2003). Given that the expert group was highly credentialed, it is worth briefly exploring why they agreed so highly with the general sample as to the correct answers. First, much emotion knowledge reflects common use of an emotion language. Almost everyone knows, for example, that fear arises in response to a threat. In such instances, experts can help identify the common consensus, but so can a scoring key that simply employs information from how a general sample responds. Second, emotional information is a domain best

modeled by fuzzy logic (or probabilistic computation), in which multiple conditions may apply and more than one correct answer is possible. A statement that a person “is angry” can indicate a range of possible levels of anger depending upon the context, as well as many different outcomes of such anger—to let the anger pass, to express it, to reframe it. Consequently answers to emotional problems often involve a lack of certainty, and are dependent on emphasis (“how much anger?”). In such instances, consensus across a group of people who have everyday language skills and who all experience emotions may approximate expert opinion quite closely. We have argued that consensus is a legitimate means of determining correct answers and it has been used in other contexts, such as the measurement of traditional intelligence (e.g., the comprehension subscale of the Wechsler Adult Intelligence Scale [WAIS]-III). The WAIS-III Technical Manual (Psychological Corporation, 1 997) suggests that a form of expert consensus was employed for subtests such as vocabulary, similarities, and comprehension: Two team members collected potential responses (e.g., definitions of a vocabulary word), placed them into groups, and examined the discrepancies among their groupings. The manual explains how, at a later stage, “team members had to agree on the grouping of responses and [. . .] evaluated the quality of the responses and assigned a score value (0, 1 , or 2) to each [. . .] on the basis of the accuracy of the response” (Psychological Corporation, 1 997, p. 37). We would have been delighted to reference all the response alternatives on the MSCEIT to an authoritative reference book. In 2000, however, there was no widely agreed upon dictionary of emotional meanings. As that changes, veridical scoring becomes more possible.

Issues of Construct Representation and Reliability The MSCEIT Representation




Maul (201 2) argued that, because the MSCEIT does not test each and every emotional

within the verbal-comprehension domain is generally regarded as sufficient. Similarly, the MSCEIT is designed to sample important skills within each of the theory’s four branches rather than to measure them exhaustively. That said, the MSCEIT was designed as a relatively brief assessment so as to encourage its use and thereby accumulate evidence regarding its validity. The fact that the test employs two tasks per branch (rather than more) means that it is less useful for some research purposes than others (i.e., discovering the covariance structure of EI). Reports of Reliability at or Close to Those Found in the Test Manual

intelligence skill specified in our theory, the MSCEIT suffers from concept underrepresentation. Because human skills are diverse in most intellectual domains, it is neither advisable nor possible to measure all possible skills therein. An intelligence test that measures verbal-comprehension intelligence, for example, does not do so by representing all such skills – that would require including the abilities to sound out words, to know their roots, to find rhymes, to know how to write a sentence, to be able to deliver a persuasive speech, to manipulate mathematical symbols in equations, and so forth. Prudent sampling

Maul reports ranges of Cronbach’s coefficient alpha estimates of the MSCEIT’s total and branch reliabilities that are a bit lower than those described in the test manual. The MSCEIT is a composite of diverse individual tasks (two tasks represent a branch). Although items are homogeneous (i.e., of parallel form) within a task, the items are heterogeneous across tasks by design: The diversity of items and item response methods was intentionally built into the test so as to maximize the test’s overall validity. For example, the items address emotion expressed in a face in one task (faces) and the meaning of emotion blends in another task (blends). The heterogeneous items dictate, in important ways, how internalconsistency reliability estimates ought to be calculated: Such estimates are best based on a division of the test into two or more equivalent forms (i.e., roughly parallel). To construct equivalent forms of the MSCEIT, one can take half of the items of each task

and place them on one form, and place the remaining half of the items on the other form. This way, items representing each task can be found on each split of the test—making them equivalent. When we reanalyzed the standardization data for the MSCEIT (N = 5,000) and calculated the reliability in this way, the reliability was r = .93, as shown in the first column of Table 1 (using the general-consensus reliability estimates that Maul discusses). Split-halves of the same data set can be created that are non- equivalent. In such cases the estimated reliability of the MSCEIT will be far lower. SPSS, for example, uses a default split-half approach that takes the first half of all the test items (based on the order of variables entered) and compares them with the second half of the test items. If a pair of MSCEIT tasks on a given branch had equal numbers of items (in fact, the tasks vary somewhat in length), the SPSS program would estimate the branch reliability by comparing items on one task to the non- equivalent items on the second task, yielding non-equivalent forms. For the overall MSCEIT, SPSS would compare the first four tasks with the last four, again yielding nonequivalent forms. We also analyzed the standardization sample in this way. The

estimated reliability for the overall test was r = .80; branch values also were lower (Table 1 , column 2). A basic assumption of coefficient alpha is homogeneity of items. Oversimplifying slightly, coefficient alpha provides an estimate of reliability that approximates the average of many haphazardly chosen splithalf estimates. For a heterogeneous test such as the MSCEIT, this means that, in essence, reliability estimates based on equivalent split-half form will be averaged with far more numerous haphazard splits of items. Cronbach (1 951 ) noted that, as a consequence, the alpha reliability estimate would be low relative to a split half using equivalent half tests or, in his words, alpha may estimate an inappropriately low reliability for a test even though a given half of the test “may nonetheless have a high correlation with a carefully planned equivalent form” (Cronbach, 1 951 , pp. 300, 307). Coefficient alpha’s estimate will be still lower if it is calculated on only a few parts (Osburn, 2000), where the number of parts is represented as K. Regarding the MSCEIT, if the alpha coefficient for each of the four branches is calculated at the level of the two tasks that make it up (i.e., K =

2), and the overall MSCEIT is calculated based on the eight tasks (i.e., K = 8), it would lead to a considerable underestimate of the MSCEIT’s overall reliability at α = .75, as compared to the r = .93 of the equivalent forms (see Table 1 , column 3). As K grows larger, as would happen for an alpha estimated at the item level, alpha provides less of an underestimate, as shown in Table 1 in the rightmost columns. Most reports of MSCEIT coefficient alphas do not specify whether they are at the task level or not; this information would be helpful to report in the future. Even so, judging by our standardization sample, the alpha based on K = 24 to 42 items for individual tasks appears to underestimate the branchlevel reliabilities somewhat. We repeated these analyses for the expert-scored data (which yields reliability estimates about .01 to .03 lower throughout); the same pattern of underestimated reliability held. Our point is that researchers who report alphas for the MSCEIT thereby underestimate the test’s reliability as a consequence of violating the coefficient’s assumptions in ways known to lead to such underestimates. A better estimate of the MSCEIT’s reliability can be obtained through equivalent-forms split-half estimates (Cronbach, 1 951 ) and stratified alphas (Osburn, 2000; cf. Tellegen & Briggs, 1 967). Another appropriate reliability estimate is the test–retest reliability estimate; the MSCEIT test–retest reliability is r = .86 (Brackett & Mayer, 2003). Recent Evidence Indicates the MSCEIT Correlates Well with Other EI Ability Measures

Most importantly, if the MSCEIT correlates in a reasonable fashion with similar other ability measures intended to assess emotional intelligence, then the MSCEIT “works” in some fundamental way, regardless of other technical issues. Correlations between the MSCEIT and several similar performance-based measures of EI not included in Maul’s review are shown in Table 2.

The left-hand columns of Table 2 include the reported MSCEIT reliabilities of the studies, the middle columns indicate the criterion scales and their reliabilities, and the far right columns show the correlations of the scores with those criterion scales. The MSCEIT correlated differently with different groups of criteria. The MSCEIT was least correlated with three measures of emotional perception: vocal 1 (vocal tone; Roberts et al., 2006), the Japanese and Caucasian Brief Affect Recognition Test (JACBART; Matsumoto et al., 2000), and “facial blends” (Austin, 201 0). Of these measures, vocal 1 has a reliability of r = .45, facial blends is of unknown reliability, and neither scale correlates highly with the MSCEIT. Because the validity of the two criterion scales is uncertain, these results are difficult to interpret. The JACBART, however, is a widely employed test with demonstrated validity. The correlations between the MSCEIT and the JACBART scale are no higher than r = .1 8 (Table 2, right-hand side). It is not clear why the correlation between these criteria scales and the perception branch should be so low (essentially zero), but as we have acknowledged elsewhere (Mayer et al., 2008), it suggests that the perception branch of the MSCEIT may be insufficient to measure this skill. In fact, the MSCEIT understanding branch exhibits higher correlations with these scales, at r = .23, .1 8, and .1 8. The MSCEIT did correlate, however, with an apparently similar measure, the Reading the Mind in the Eyes test (Baron- Cohen, Wheelwright, Hill, Raste, & Plumb, 2001 ). Baron- Cohen’s test involves looking at a picture of a person’s eyes and trying to estimate what the person is feeling. The MSCEIT strategic area (understanding and managing areas) shown in the “total” column, correlated r = .56 with the Reading the Mind test. We have suggested elsewhere that the Reading the Mind test may measure emotional understanding as well as perception because of the advanced vocabulary used on it (Mayer, Panter,& Caruso, 201 2).

Another pair of criterion measures was developed to measure emotional understanding and management: MacCann and Roberts’s Situational Test of Emotional Understanding and Situational Test of Emotional Management (STEU and STEM; MacCann & Roberts, 2008). The STEU is scored veridically: The authors claim there is a correct and incorrect answer for each question, determined by theory (Roseman, 2001 )—an approach that Maul says he prefers. The STEM uses an expert consensus. In the study cited here, the alpha reliabilities of the STEU and STEM were r = .48 and .67 (lower than the original reports), which were low enough, especially regarding the STEU, to meaningfully depress their correlations with the MSCEIT (discussed next). The STEU measures appraising emotions, a form of emotional understanding not directly measured by the MSCEIT. It can reasonably be considered a new task in the understanding area and, consistent with that idea, it exhibits a subscale-like correlation with the understanding area of r = .44 (Austin, 201 0). The STEM and MSCEIT management branches are related r = .30. The factor structures of the STEM and STEU are complex, which may help explain why the STEM correlated more highly with MSCEIT understanding, r = .40, than with the management branch (Ferguson & Austin, 201 0). Finally, Maul (2011 ) compared the MSCEIT to an earlier test of emotional intelligence, the Multifactor Emotional Intelligence Scale (MEIS), a 1 2-task measure of EI which has several similar tasks but no item overlap with the MSCEIT (Mayer, Salovey, & Caruso, 1 997). The MSCEIT and MEIS correlated r = .72 (Maul, 2011 ). Overall, the new findings reported in Table 2 make the case that the MSCEIT correlates meaningfully with a variety of ability based criteria of EI. The MSCEIT correlates in meaningful fashions with the Reading the Mind test, the STEU, the STEM, and the MEIS. Collectively, they provide encouraging support of the adequacy of the MSCEIT’s relations with similar ability measures, excepting relatively pure measures of emotional perception. When

Maul (201 2, p. 399) wrote “evidence [is] still scant on this topic,” he had not included these most recent findings. Criterion Correlations More Generally Support the Validity of the MSCEIT

A test’s validity should be supported by theoretically-expected correlations with a variety of criteria. The recent Annual Review article on EI found several trends in criterion correlations that are predictable by theory: in particular, that EI enhances social relationships both among friends and among colleagues at work (Mayer et al., 2008). Maul basically agrees that “MSCEIT scores are associated with [. . .] other psychological variables and positive outcomes in a manner fairly consistent with the idea that the MSCEIT measures emotional intelligence” (Maul, 201 2, p. 400). General Conclusion

In this comment we did not reply to all of Maul’s criticisms of the MSCEIT, but only to those we regarded as most crucial. Taking into account the new findings we have reported (as well as previous findings), the argument for the MSCEIT’s overall validity is growing and arguably quite strong, notwithstanding the technical imperfections that are a part of any reallife form of measurement, and acknowledging that improvements in the MSCEIT and measurement in the area are desirable. References

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93646 Mayer, J. D., & Salovey, P. (1 997). What is emotional intelligence? In D. J. Sluyter (Ed.), Emotional development and emotional intelligence: Educational implications (pp. 3–34). New York, NY: Basic Books. Mayer, J. D., Salovey, P., & Caruso, D. R. (1 997). Emotional intelligence test. Needham, MA: Virtual Knowledge. Mayer, J. D., Salovey, P., & Caruso, D. R. (2002). Mayer-Salovey-Caruso emotional intelligence test (MSCEIT) user’s manual. Toronto, Canada: Multi-Health Systems. Mayer, J. D., Salovey, P., & Caruso, D. R. (2008). Emotional intelligence: New ability or eclectic traits? American Psychologist, 63, 503–51 7. doi:1 0.1 037/0003-066X.63.6.503 Mayer, J. D., Salovey, P., Caruso, D. R., & Sitarenios, G. (2003). Measuring emotional intelligence with the MSCEIT V2.0. Emotion, 3, 97–1 05. doi:1 0.1 037/1 528-3542.3.1 .97 Osburn, H. G. (2000). Coefficient alpha and related internal consistency reliability coefficients. Psychological Methods, 5, 343–355. doi:1 0.1 037/1 082-989X.5.3.343 Plutchik, R. (2000). A psychoevolutionary theory of emotion. In R. Plutchik (Ed.), Emotions in the practice of psychotherapy: Clinical implications of affect theories (pp. 59–79). Washington, DC: American Psychological Association. doi:1 0.1 037/1 0366-004 Psychological Corporation. (1 997). WAIS-III, WMS-III technical manual. San Antonio, TX: Author. Roberts, R. D., Schulze, R., O’Brien, K., MacCann, C., Reid, J., & Maul, A. (2006). Exploring the validity of the Mayer-SaloveyCaruso emotional intelligence test (MSCEIT) with established emotions measures. Emotion, 6, 663–669. doi:1 0.1 037/1 528-3542.6.4.663 Roseman, I. J. (2001 ). A model of appraisal in the emotion system: Integrating theory, research, and applications. In K. R. Scherer, A. Schorr, and T. Johnstone (Eds.), Appraisal processes in emotion: Theory, methods, research (pp. 68–91 ). New York, NY: Oxford University Press. Rosenhan, D., &

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Source: Yale

Center for Emotional

Intelligence: 0085

Doctoral Student in London School of Economics.

The Help Map was the first use of Ushahidi in Russia to coordinate assistance between victims of this summer’s wildfires and citizens who wished to help them. Almost 200,000 people visited the platform and left more than 1 ,600 messages. When the emergency situation passed and the wildfires were stopped (rather by rains than by firefighters), the interest in the platform significantly decreased. The motivation of its participants decreased as well. Everyone was back to normal life. This brought a number of questions. Do we need Ushahidi at all after the situation has normalized and, if yes, how to make Ushahidi-based projects sustainable in the post-emergency period? And, more generally, how to maintain a volunteer-based organization in the long term? Ethan Zuckerman suggests a Virtual-Person to Person-Virtual (VPV – “virtual, person to person, then virtual again”) model for development of networked projects: «People discover the community online, and connect based on their sense of shared identity and values with the people already participating. They come together, face to face, either at the biennial meetings we run or at the other

people’s conferences That, in turn, builds the trust and relationships we need to survive working together for the next months or years until we see each other face to face». Most of the people who joined to the Help Map project knew each other only from Google Groups and Skype discussions. At the end of September 201 0, a meeting took place in the center of Moscow. The meeting was a move from the first “V” to “P” in expectation of further “V”-based cooperation. The participants discussed the past and the future of the platform and brainstormed ideas about continuation of Help Map, as well as about other possible uses of Ushahidi that the core team could support. At the same time, it was clear that Ushahidi-based projects could not be sustainable based only on the core team of Help Map. Therefore, meeting the colleagues in person was only the first step. The second step was looking for new partners by introducing the project to new audiences and engaging them. One of the best partners could be students and

universities. A lectures about social and political aspects of crowdsourcing and Help Map as a case study were presented at the political science departments of Moscow universities – the Moscow State University and the Higher School of Economics. The discussion was focused on a question of whether ICT could create new platforms for governance and civil society. Another type of discussion about Help Map took place at theDepartment of Psychology of the Moscow State University. This time the lecture was focused on the evolutionary role of ICT and crowdsourcing as tools that can facilitate network cooperation and mutual aid. Lectures at the universities were not only the opportunity for a discussion but also for finding partners for future projects, both on an institutional level and among students. Universities can be a place for creating experimental platforms that will make possible the students’ engagement in crowdsourcing projects and research. Not only universities expressed interest in Ushahidi and Help Map. The Civic Chamber of the Russian Federation, a governmental body that incorporates leaders of various civil society organizations, held a special meeting devoted to “options for coordination of volunteer activities based on the case study of an Internet project Help Map.” Representatives of the chamber, leaders of NGOs who took part in firefighting and volunteers attended the meeting. One of the major questions that was asked at the Civic Chamber meeting was if Help Map would like to create its own non-profit organization that would continue to work on crowdsourcing platforms for facilitation of volunteer activities. The issue of whether a network-based project could be facilitated through the creation of an organizational structure is controversial. Projects such as Help Map are powerful because they are based on self-organized networks, and trying to transform them into organized structures might threaten their

networked nature. Another threat that could be caused by this type of transformation is the bureaucracy that every non-profit organization in Russia has to deal with. Later on, another aspect of this question was raised during meetings with people from the e-government community. Should the government support development of crowdsourcing projects for emergency situations? Can the resources, cooperation with government structures, and outreach assistance make projects like Help Map more efficient? Or, the other way around, will it threaten the independent selforganized people-to-people nature and reduce the motivation of volunteers to take part in it? There are no evident answers to these questions so far. It’s clear, however, that it depends on a political context and the degree of trust between the government and its citizens. Meetings such as the one at the Civic Chamber contributed to raising awareness of the role of IT in general and crowdsourcing in particular in emergency situations. The official summary of the discussion that was published by the Civic Chamber was titled “Help Map will continue to develop”. Another channel for cooperation are local NGOs. The Help Map team met with the local environmental groups. The discussion was focused on the possible transformation of Help Map for wildfires into a map that would collect information about any forest-related issues (e.g., monitoring of violations, coordination of restoration). At the same time, it would improve preparedness to provide help in emergency situation in the future (according to forecasts of the Russian environmentalists, wildfires next summer can be even worse than the ones this year). Ushahidi has already caused some chain effect in Russia. A prominent Russian blogger Alexey Navalny is working on a project aimed at monitoring problems with the roads and forcing the authorities to fix these problems. One of the NGOs is

considering launching a platform that would monitor violations in regard to military service in Russia. Another group is working on a platform that would collect information about various types of citizens’ rights violations. Yet another possible direction is the incorporation of crowdsourcing practices within e-government. Despite some concerns and skepticism, we may still hope that platforms such as Ushahidi can play a role in bridging the gap between the government and citizens. The Russian e-gov efforts have already showed a few interesting and inspiring projects like, which, in a user-friendly way, tell people about the public procurement and main government contractors.

Source: 0/1 0/23/russiapost-emergency-sustainability-ofcrowdsourcing-projects/#comment-81 5363447 Posted 23 October 201 0

Marc A. Brackett,

Ph.D., is Director of the Yale Center for Emotional Intelligence, a Faculty Fellow in the Edward Zigler Center in Child Development and Social Policy, and a Research Scientist in the Department of Psychology at Yale University. Susan E. Rivers,

a research scientist in Yale University’s Department of Psychology and the associate director of Yale’s Health, Emotion, and Behavior Laboratory. is the Provost and the President-elect of Yale University. He is also the Chris Argyris Professor of Psychology. Salovey has authored or edited thirteen books translated into eleven languages and published more than 350 journal articles and essays, focused primarily on human emotion and health behavior. Peter Salovey


Эта статья представляет собой краткий обзор возможностей использования модели эмоционального интеллекта и включает обсуждение пользы концепции, как для образования, так и для практической деятельности. Мы рассматриваем четыре вида эмоциональных способностей, которые включают эмоциональный интеллект и методы ассесмента, разработанные для оценки концепции в целом. Основной целью является обеспечение обзора исследования, описывающего взаимосвязи элементов эмоционального интеллекта. Мы описываем известные исследования касаемо функционирования эмоционального интеллекта человека как внутри личности, так и во внешней среде применительно к образованию и работе. Факты указывают на следующее: работа обеспечивает – высокую зарплату, идеальное местоположение и большие возможности для развития. Существуют факторы, заставляющие вас чувствовать не совсем комфортно при увольнении с работы и движении дальше. Что Вы сделаете? Проигнорируйте чувство и выберите то, что кажется логическим выходом или рискнете, разочаровывая семью? Возможно, при принятии решений вы будете учитывать как эмоциональные, так и рациональные аспекты? Решение проблем мудро, руководствуясь не только умом, но и прислушиваясь к своим чувствам, интуиции как раз является частью

того, что мы именуем эмоциональным интеллектом (Майер & Саловей, 1 997; Саловей & Майер, 1 990). Установление взаимосвязи между эмоциями и интеллектом было относительно ново, в том время, когда впервые ввели данную теоретическую модель около двадцати лет назад (Саловей & Майер, 1 990; Гарднера, 1 983 ⁄1 993). Среди исследуемых вопросов как ученых, так и обычных людей интересовали следующие: является ли эмоциональный интеллект врожденным, можно ли его корректировать? Можно ли его повышать с помощью обучения? Это новый вид интеллекта или он представляет собой выборку из уже существующих концепций? Какими способами можно достоверно оценивать эмоциональный интеллект? Что привносит эмоциональный интеллект в повседневную жизнь человека? В каких случаях эмоциональный интеллект влияет на умственное здоровье, межличностные взаимоотношения, каждодневные решения, ситуации в повседневной жизни и работе? В этой статье мы предоставляем краткий обзор теории эмоционального интеллекта, включающий краткую дискуссию о том, как и почему концепция использовалась в различных ситуациях (образование, работа). Поскольку в настоящее время данная область знания перенасыщена различными статьями, книгами по тематике и руководствами, в том числе по развитию эмоционального интеллекта, также потому что методы оценки стали чрезвычайно разнообразными, мы также разъясняем свою точку зрения по данному вопросу. Заключительной целью настоящей статьи является актуальный обзор в области исследования характерных особенностей эмоционального интеллекта людей в различных ситуациях: индивидуально, в обществе, обручении и на рабочем месте.


This article presents an overview of the ability model of emotional intelligence and includes a discussion about how and why the concept became useful in both educational and workplace settings. We review the four underlying emotional abilities comprising emotional intelligence and the assessment tools that that have been developed to measure the construct. A primary goal is to provide a review of the research describing the correlates of emotional intelligence. We describe what is known about how emotionally intelligent people function both intra- and interpersonally and in both academic and workplace settings. The facts point in one direction: The job offe you have in hand is perfect – great salary, ideal location, and tremendous growth opportunities. Yet, there is something that makes you feel uneasy about resigning from your current position and moving on. What will you do? Ignore the feeling and choose what appears to be the logical path, or go with your gut and risk disappointing your family? Or, might you

consider both your thoughts and feelings about the job in order to make the decision? Solving prob- lems and making wise decisions using both thoughts and feelings or logic and intuition is a part of what we refer to as emotional intelligence (Mayer & Salovey, 1 997; Salovey& Mayer, 1 990). Linking emotions and intelligence was relatively novel when first introduced in a theo- retical model about twenty years ago(Salovey & Mayer, 1 990; but see Gardner, 1 983 ⁄ 1 993). Among the many questions posed by both researchers and laypersons alike were: Is emotional intelligence an innate, nonmalleable mental ability? Can it be acquired with instruction and training? Is it a new intelligence or just the repackaging of existing constructs? How can it be measured reliably and validly? What does the existence of an emotional intelligence mean in everyday life? In what ways does emotional intelligence affect mental health, relationships, daily decisions, and academic and workplace performance?

In this article, we provide an overview of the theory of emotional intelligence, including a brief discussion about how and why the concept has been used in both educational and workplace settings. Because the field is now replete with articles, books, and training manuals on the topic – and because the definitions, claims, and measures of emotional intelligence have become extremely diverse – we also clarify definitional and measurement issues. A final goal is to provide an up-to-date review of the research describing what the lives of emotionally intelligent people ‘look like’ personally, socially, academically, and in the workplace. What is Emotional Intelligence?

Initial conception of emotional intelligence Emotional intelligence was described formally by Salovey and Mayer (1 990). They defined it as ‘the ability to monitor one’s own and others’ feelings and emotions, to discriminate among them and to use this information to guide one’s thinking and actions’ (p. 1 89). They also provided an initial empirical demonstration of how an aspect of emotional intelligence could be measured as a mental ability (Mayer, DiPaolo, & Salovey, 1 990). In both articles, emotional intelligence was presented as a way to conceptualize the relation between cognition and affect. Historically, ‘emotion’ and ‘intelligence’ were viewed as being in opposition to one another (Lloyd, 1 979). How could one be intelligent about the emotional aspects of life when emotions derail individuals from achieving their goals (e.g., Young, 1 943)? The theory of emotional intelligence suggested the oppo- site: emotions make cognitive processes adaptive and individuals can think rationally about emotions. Emotional intelligence is an outgrowth of two areas of psychological research that emerged over forty years ago. The first area, cognition and affect, involved how cognitive and emotional processes interact to enhance thinking (Bower, 1 981 ; Isen, Shalker, Clark, & Karp, 1 978; Zajonc, 1 980). Emotions like anger, happiness, and fear, as well as mood states,

preferences, and bodily states, influence how people think, make decisions, and perform different tasks (Forgas & Moylan, 1 987; Mayer & Bremer, 1 985; Salovey & Birnbaum, 1 989). The second was an evolution in models of intelligence itself. Rather than viewing intelligence strictly as how well one engaged in analytic tasks associated with memory, reasoning, judgment, and abstract thought, theorists and investigators began considering intelligence as a broader array of mental abilities (e.g., Cantor & Kihlstrom, 1 987; Gardner, 1 983 ⁄ 1 993; Sternberg, 1 985). Sternberg (1 985), for example, urged educators and scientists to place an emphasis on creative abilities and practical knowledge that could be acquired through careful navigation of one’s everyday environment. Gardner’s (1 983) ‘personal intelligences,’ including the capacities involved in accessing one’s own feeling life (intrapersonal intelligence) and the ability to monitor others’ emotions and mood (interpersonal intelligence), provided a compatible backdrop for considering emotional intelligence as a viable construct. Popularization of emotional intelligence

The term ‘emotional intelligence’ was mostly unfamiliar to researchers and the general public until Goleman (1 995) wrote the best-selling trade book, Emotional Intelligence: Why it can Matter More than IQ. The book quickly caught the eye of the media, public, and researchers. In it, Goleman described how scientists had discovered a connection between emotional competencies and prosocial behavior; he also declared that emotional intelligence was both an answer to the violence plaguing our schools and ‘as powerful and at times more powerful than IQ’ in predicting success in life (Goleman, 1 995; p. 34). Both in the 1 995 book and in a later book focusing on workplace applications of emotional intelligence (Goleman, 1 998), Goleman described the construct as an

array of positive attributes including political awareness, self-confidence, conscientiousness, and achieve- ment motives rather than focusing only on an intelligence that could help individuals solve problems effectively (Brackett & Geher, 2006). Goleman’s views on emotional intelligence, in part because they were articulated for ⁄ to the general public, extended beyond the empirical evidence that was available (Davies, Stankov, & Roberts, 1 998; Hedlund & Sternberg, 2000; Mayer & Cobb, 2000). Yet, people from all professions – educators, psychologists, human resource professionals, and corporate executives – began to incorporate emotional intelligence into their daily vernacular and professional practices. Definitions and measures of emotional intelligence varied widely, with little consensus about what emotional intelligence is and is not. Alternative models of emotional intelligence

Today, there are two scientific approaches to emotional intelligence. They can be characterized as the ability model and mixed models (Mayer, Caruso, & Salovey, 2000). The ability model views emotional intelligence as a standard intelligence and argues that the construct meets traditional criteria for an intelligence (Mayer, Roberts, & Barsade, 2008b; Mayer & Salovey, 1 997; Mayer, Salovey, & Caruso, 2008a). Proponents of the ability model measure emotional intelligence as a mental ability with performance assessments that have a criterion of correctness (i.e., there are better and worse answers, which are determined using complex scoring algorithms). Mixed models are so called because they mix the ability conception with personality traits and competencies such as optimism, self-esteem, and emotional self-efficacy (see Cherniss, 201 0, for a review). Proponents of this approach use self-report instruments as opposed to performance assessments to measure emotional intelligence (i.e., instead of asking people to demonstrate how they perсeive an emotional expression accurately, self-report measures ask people to judge and report how good they are at perceiving others’

emotions accurately). There has been a debate about the ideal method to measure emotional intelligence. On the surface, self-report (or selfjudgment) scales are desirable: they are less costly, easier to administer, and take considerably less time to complete than performance tests (Brackett, Rivers, Shiffman, Lerner, & Salovey, 2006). However, it is well known that self-report measures are problematic because respondents can provide socially desirable responses rather than truthful ones, or respondents may not actually know how good they are at emotion-based tasks – to whom do they compare themselves (e.g., DeNisi & Shaw, 1 977; Paulhus, Lysy, & Yik, 1 998)? As they apply to emotional intelligence, self- report measures are related weakly to performance assessments and lack discriminant validity from existing measures of personality (Brackett & Mayer, 2003; Brackett et al., 2006). In a metaanalysis of 1 3 studies that compared performance tests (e.g., Mayer, Salovey, & Caruso, 2002) and self-report scales (e.g., EQ-i; Bar-On, 1 997), Van Rooy, Viswesvaran, and Pluta (2005) reported that performance tests were relatively distinct from self-report measures (r = 0.1 4). Even when a self-report measure is designed to map onto performance tests, correlations are very low (Brackett et al., 2006a). Finally, self-report measures of emotional intelligence are more susceptible to faking than performance tests (Day & Carroll, 2008). For the reasons described in this section, we assert that the ability-based definition and performance-based measurement of emotional intelligence should be preferred. This makes it possible to both operationalize the construct distinctly and assess its unique contribution to important life outcomes over and above personality attributes. This viewpoint is supported by researchers not associated with any of the established measures of emotional

intelligence (e.g., Matthews, Zeidner, & Roberts, 2002). The focus for the remainder of this article, therefore, is on the ability model of emotional intelligence. A more thorough review of the validity of both ability and mixed models of emotional intelligence can be found in a recent meta-analysis (O’Boyle, Humphrey, Pollack, Hawver, & Story, 201 0). The Mayer and Salovey Model of Emotional Intelligence

The Mayer and Salovey (1 997) model of emotional intelligence defines four discrete mental abilities (also referred to as ‘branches’) that comprise emotional intelligence: (i) perception of emotion, (ii) use of emotion to facilitate thought, (iii) understanding of emotion, and (iv) management of emotion. These four inter-related abilities are arranged hierarchically such that more basic psychological processes (i.e., perceiving emotions) are at the base or foundation of the model and more advanced psychological processes (i.e., conscious, reflective regulation of emotion) are at the top. Empirical demonstrations of whether the higherlevel abilities are dependent, to some extent, upon the lower-level abilities, have yet to be conducted. Here, we provide a brief description of the four abilities, which are described more fully elsewhere (Mayer & Salovey, 1 997; Mayer et al., 2008a,b). The first branch, ‘Perception of emotion,’ includes the ability to identify and differen- tiate emotions in the self and others. A basic aspect of this ability is identifying emotions accurately in physical states (including bodily expressions) and thoughts. At a more advanced level, this ability enables one to identify emotions in other people, works of art, and objects using cues such as sound, appearance, color, language, and behavior. The ability to discriminate between honest and

false emotional expressions in others is consid- ered an especially sophisticated perceiving ability. Finally, appropriately expressing emotions and related needs represents more complex problem solving on this branch. The second branch, ‘Use of emotion to facilitate thinking,’ refers to harnessing emotions to facilitate cognitive activities such as reasoning, problem solving, and interpersonal communication. A basic aspect of this ability is using emotions to prioritize thinking by directing attention to important information about the environment or other people. More advanced skills involve generating vivid emotions to aid judgment and memory Figure 1. Graphical

representation of the Mayer-Salovey-Caruso model of Emotional Intelligence.

processes, and generating moods to facilitate the consideration of multiple perspectives. Producing emotional states to foster different thinking styles (e.g., people’s thinking is more detailoriented, substantive, and focused when in sad versus happy

moods) constitutes an especially high level of ability on this branch. The third branch, ‘Understanding and analyzing emotions,’ includes comprehension of the language and meaning of emotions and an understanding of the antecedents of emotions. Basic skill in this area includes labeling emotions with accurate language as well as recognizing similarities and differences between emotion labels and emotions themselves. Interpreting meanings and origins of emotions (e.g., sadness can result from a loss, joy can follow from attaining a goal) and understanding complex feelings such as simultaneous moods or emotions (feeling both interested and bored), or blends of feelings (e.g., contempt as a combination of disgust and anger) represent more advanced levels of understanding emotion. Recognizing transitions between emotions (e.g., sadness may lead to despair which may lead to devastation) is an especially sophisticated component of this branch. The fourth branch, ‘Reflective regulation of emotions,’ includes the ability to prevent, reduce, enhance, or modify an emotional response in oneself and others, as well as the ability to experience a range of emotions while making decisions about the appropriate- ness or usefulness of an emotion in a given situation. Basic emotion regulation ability involves attending to and staying open to pleasant and unpleasant feelings, while more advanced ability involves engaging or detaching from an emotion depending on its per- ceived utility in a situation. Monitoring and reflecting on one’s own emotions and those of others (e.g., processing whether the emotion is typical, acceptable, or influential) also represents more complex problem solving within this branch.

emotion are the Diagnostic Analysis of Nonverbal Accuracy Scales (DANVA and DANVA-2; Nowicki & Duke, 1 994). Elsewhere, these and other measures are described in detail (Brackett & Geher, 2006; Mayer et al., 2008a,b). A comprehensive performance test of emotional intelligence is the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT; Mayer et al., 2002) for adults and the Mayer–Salovey–Caruso Emotional Intelligence Test, Youth Version (MSCEITYV; Mayer, Salovey, & Caruso, 2005) for adolescents (ages 1 2–1 7). These are considered performance tests because they require individuals to solve tasks pertaining to each of the four abilities defined by the theory (Mayer, Salovey, Caruso, & Sitarenios, 2003). The adult version of the MSCEIT has eight tasks (two for each of the four branches), as depicted in Figure 1 . The test takes about 45 minutes to complete and yields scores for each of the four branches and a total score. Here, we provide a brief overview of the adult version of the test. More detailed descriptions of both the adult and youth versions of the tests can be found elsewhere (Rivers, Brackett, & Salovey, 2008). The first branch, Perceiving Emotions, is measured by asking respondents to identify the emotions expressed in photographs of people’s faces (Faces) as well as the feelings suggested by artistic designs and landscapes (Pictures). For example, in the Faces task, par- ticipants are presented with a picture of a person expressing a basic emotion like joy.

Measuring emotional intelligence

Below the picture is a list of five emotions; the test-taker is asked to rate on a five-point scale how much of each particular emotion is expressed in the picture.

There are a number of published performance tests that measure distinct components of emotional intelligence (i.e., one or more of the branches of Mayer and Salovey’s model, but not all branches). For example, two frequently used measures of perceptual accuracy of

The second branch, Using Emotion to Facilitate Thought, is measured by two tests that assess people’s ability to describe emotional sensations and their parallels to other sen- sory modalities using a nonfeeling vocabulary (Sensations) and identify

the feelings that might facilitate or interfere with the successful performance of various cognitive and behavioral tasks (Facilitation). For example, the task measuring Sensations presents participants with a sentence asking them to imagine feeling an emotion such as guilt. Participants are then given a list of adjectives pertaining to other sensory modalities (e.g., warm, blue, and sour) and are asked to rate on a five-point scale from ‘Not Alike’ to ‘Very Much Alike’ how much the feeling of guilt is similar to the adjectives. The third branch, Understanding Emotion, is measured by two tasks that pertain to a person’s ability to analyze blended or complex emotions (Blends) and to understand how emotional reactions change over time or how they follow upon one another (Changes). For example, a question on the Blends task presents a statement such as ‘Anticipation and joy often combine to form`’. Participants are then presented with a list of response alternatives and choose the most appropriate. The fourth branch, Managing Emotions, has two subtests that assess how participants would manage their own emotions (Emotion Management) and how they would manage the emotions of others (Social Management). For example, the Social Management task asks participants to read a vignette about another person, and then determine how effective several different courses of action would be in coping with emotions in the vignette. Participants rate a number of possible actions ranging from ‘Very ineffective’ to ‘Very effective.’ On the MSCEIT, better and worse answers are determined by consensus or expert scoring. Consensus scores reflect the proportion of people in the normative sample (over 5,000 people from North America) who endorsed each MSCEIT test item. Expert norms were obtained from 21 investigators, including psychologists and philosophers who were members of the International Society for Research on Emotion (ISRE). These scientists

and scholars provided their expert judgment on each of the test’s items based on findings from the professional literature on emotion. Scores are weighted by the proportion of the normative or expert sample that provided the same answer. Full-scale MSCEIT scores based on both the consensus and expert norms correlate quite highly, r = 0.91 (Mayer et al., 2003). Generally, correlations with various outcomes are replicated across the two scoring methods as well. The MSCEIT is reliable at the full-scale level and at the area and branch levels (Mayer et al., 2003), but it should not be scored at the level of individual tasks. Mayer, Salovey, Caruso, and Sitarenios (2001 b) and Mayer et al. (2003, 2008a,b) claim that the MSCEIT meets the criteria for a test of intelligence because: (i) it has a factor structure congruent with the four branches of the theoretical model; (ii) the four abilities have expected convergent and discriminant validity (Brackett & Mayer, 2003; Gil-Olarte, Palomera Martin, & Brackett, 2006; Mayer, Salovey, & Caruso, 2004; Lopes, Salovey, & Straus, 2003; Van Rooy et al., 2005; Warwick & Nettelbeck, 2004); that is, they are statistically independent from other well established constructs (including personality traits), are meaningfully related to other mental abilities such as verbal intelligence, and are associated with conceptually-related constructs such as empathy; (iii) emotional intelligence develops with age and experience, and finally; (iv) the abilities are measured objectively. The MSCEIT has been criticized on a number of grounds (for reviews see Matthews et al., 2002; Rivers et al., 2008). Here, we point out a few valid concerns about the test. First, the MSCEIT was designed as an easy-to-administer test that can be completed using either paper-and-pencil or online versions. This structure does not

allow for the direct assessment of certain skills such as the appropriate expression of emotion and the ability to regulate emotions in realtime, which would require either sophisticated techonology or experimental conditions. Thus, the MSCEIT may be more closely related to crystallized intelligence (the ability to use skills and knowledge) rather than fluid intelli- gence (the capacity to think logically and problem-solve) (Farrelly & Austin, 2007). Sec- ond, certain dimensions on the MSCEIT, like the perception of emotion, have a small number and range of facial expressions. The test also taps a limited scope of non-verbal channels; it does not capture gesture, voice, or posture (O’Sullivan & Ekman, 2004). With respect to scoring, both consensus and expert methods have their limitations. Day (2004) questioned whether high EI individuals know what everyone else knows about emotion or know more about emotion. It may be that agreement with the consensus reflects average emotional intelligence, not high emotional intelligence. MacCann, Roberts, Matthews, and Zeidner (2004) found that emotion ability measures using veridical scoring (i.e., tasks that have a true or real answer as opposed to those that are rated as more or less effective according to a consensus; Geher & Renstrom, 2004) might be ideal because they converge better with other ability measures than those using consensusbased scoring. Emotional Intelligence in Everyday Life

Even though the Adult Version of the MSCEIT was published in 2002 and the Youth Version is still under development, a number of studies have provided evidence supporting the validity of both tests. The findings with adults, in particular, indicate that the MSCEIT is measuring something different than other intelligence and personality assessments, and that it predicts psychological constructs and behavior above and beyond existing measures of intelligence and personality (see Cherniss, 201 0; Mayer et al., 2008a,b, for reviews). Scores on the test are associated with relevant outcomes across multiple dimensions, including

cognitive and social functioning, psychological well being, psychopathology, academic performance, and leadership and other behaviors in the workplace. In this section we provide an overview of studies that demonstrate the validity of both versions of the test. Relation to cognitive abilities

According to the ability model of emotional intelligence, each ability influences how individuals utilize emotions to facilitate thinking or regulate emotions to focus on important information. For these reasons, emotional intelligence is hypothesized to correlate moderately with other intelligences, like verbal-propositional intelligence (Mayer & Salovey, 1 997). A recent meta-analysis of 1 8 studies that used the MSCEIT and its predecessor test, the Multifactor Emotional Intelligence Scale (Mayer, Caruso, & Salovey, 1 999) supports these hypotheses. Van Rooy et al. (2005) reported correlations in the 0.30 range between MSCEIT scores and assessments of both verbal and spatial intelligence. Other studies have shown that MSCEIT scores correlate moderately (rs = 0.20–0.50) with verbal SAT scores (Brackett, Mayer, & Warner, 2004; David, 2005), WAIS-III scores (Lopes et al., 2003), ACT scores (O’Connor & Little, 2003), reasoning ability (O’Connor & Little, 2003), academic giftedness (Zeidner, ShaniZinovich, Matthews, & Roberts, 2005), and measures of general intelligence (e.g., GilOlarte et al., 2006). In general, scores on the test correlate more highly with measures of crystallized rather than fluid intelligence. The Understanding of Emotion domain on the MSCEIT tends to have the strongest relationship to measures of general cognitive function (rs = 0.40–0.60). This is not surprising as this subtest relies on knowledge of a sophisticated emotional vocabulary (Lopes et al., 2003). MSCEIT scores are related to the amount of cognitive effort employed to solve

problems (generally) and performance on emotionladen social problems, in particular. In one study, individuals with higher MSCEIT scores used less cognitive effort while solving emotion-laded problems, as assessed by patterns in theta and alpha frequency bands of electroencephalographic activity of the brain (Jausovec, Jausovec, & Gerlic, 2001 ). In another study, individuals with higher MSCEIT scores solved social problems that were affective in content more quickly than those with lower scores (Reis et al., 2007). These studies provide preliminary evidence for the neural correlates of emotional intelligence. Mental health and well being

The most common complaints that lead people to psychotherapy are anxiety and depres- sion. The skills associated with emotional intelligence, therefore, should help individuals to deal effectively with unpleasant emotions and to promote pleasant emotions in order to promote both personal growth and well being. MSCEIT scores correlate (rs = 0.1 0– 0.40) with psychopathologies that have roots in emotional disturbances, including depression, social anxiety disorder, and schizophrenia. David (2005) reported negative correlations between MSCEIT scores and depression and anxiety. O’Connor and Little (2003) showed that MSCEIT scores correlated negatively with anxiety. Gardner and Qualter (2009) found a relationship between MSCEIT scores and Borderline Personality Disorder (BPD) criteria in a large sample of non-clinical adults. MSCEIT scores also were lower among inpatients diagnosed with major depressive disorder, substance abuse disorder, and BPD when they were compared to a matched control group sample (Hertel, Schutz, & Lammers, 2009). In another study, patients with schizophrenia performed significantly worse than controls on the MSCEIT. Among the patients, lower MSCEIT scores also were associated with higher negative and disorganized symptoms, as well as worse community functioning (Kee et al., 2009). On the positive side, among college students, MSCEIT scores correlated positively with measures of psychological well being

(Brackett & Mayer, 2003; Lopes et al., 2003). It also appears that individuals with higher MSCEIT scores are more likely to seek psychotherapy in times of need (Goldenberg, Matheson, & Mantler, 2006). Rivers et al. (201 0) conducted an initial validity test of the MSCEIT-YV using student and teacher reports of academic, social, and personal functioning on the Behavior Assessment System for Children (BASC; Reynolds & Kamphaus, 1 992). Students scoring higher on the MSCEIT were less likely to be rated by their teachers as having externalizing problems (e.g., hyperactivity, aggression, conduct problems), internalizing problems (e.g., anxiety, depression), and school problems. The association between MSCEIT scores and school problems was particularly high (r = )0.57), indicating that students with higher emotional intelligence may have better attention skills and fewer learning problems. Finally, in a study with 54 adolescents recruited from both psychiatric clinics and the community, MSCEIT scores were shown to moderate the association between sexual abuse and both suicidal ideation and attempts (Cha & Nock, 2009). It may be that emotional intelligence is a protective factor for serious psychological problems among adolescents. Social functioning

Emotional intelligence is postulated to promote positive social functioning by helping individuals to detect others’ emotion states, adopt others’ perspectives, enhance communication, and regulate behavior. Indeed, people with higher MSCEIT scores tend to be more socially competent, to have better quality relationships, and to be viewed as more interpersonally sensitive than those with lower MSCEIT scores (Brackett, Warner, & Bosco, 2005; Brackett et al., 2006a; Lopes, Salovey, Coˆ te´, & Beers, 2005; Lopes et al., 2003, 2004). Most of these associations remain statistically significant (rs in the 0.30 range) even after controlling for

established personality traits such as neuroticism and general intelligence. Mayer–Salovey–Caruso Emotional Intelligence Test scores have been associated positively with self-perceived supportive relationships with friends and parents, and negatively associated with antagonistic and conflictual relationships with close friends (Lopes et al., 2004). For example, college students with higher MSCEIT scores were viewed by their peers as more interpersonally sensitive and prosocial (Lopes et al., 2005). Higher MSCEIT scores were associated strongly (rs > 0.50) with judges’ positive ratings of ‘the ability to work well with others’ and ‘overall social competence’ using a videotaped ‘getting acquainted’ social interaction, but for men only (Brackett et al., 2006a). Finally, MSCEIT scores correlated significantly with secure attachment styles, which reflect emotional closeness to others as well as feeling comfortable both depending on others and having others depend on oneself (Kafetsios, 2004). Emotional intelligence should facilitate successful navigation through the emotionladen situations one encounters in romantic relationships. In one study, dating and mar- ried couples with higher MSCEIT scores reported more satisfaction and happiness in their relationships (Brackett et al., 2005). Moreover, if both partners had low MSCEIT scores, relationship quality was lower and both conflict and maladaptive relationship behaviors were higher than when both partners had high MSCEIT scores (Brackett et al., 2005). Individuals may even select partners initially based on similarity of emotional intelligence scores (Brackett, 2006), although evidence for such a phenomenon may reflect the possibility that partners’ emotional intelligence converges over time. How emotional intelligence contributes to relationship quality and satisfaction is still unknown; longitudinal research will provide insights. Among teenagers, those lower in emotional intelligence were rated in one study as more aggressive than others and tended to engage in more conflictual behavior than their

counterparts who scored higher in emotional intelligence (Mayer, Perkins, Caruso, & Salovey, 2001 a; Rubin, 1 999). Middle school students’ MSCEIT-YV scores were correlated positively with teacher ratings of adaptive skills including social skills and leadership ability. Emotional intelligence scores correlated with student self-reports of the same outcomes. Finally, MSCEIT-YV scores correlated positively with student reports of having healthy social relationships, high self-reliance, and betterquality relationships with their parents (Rivers et al., 201 0). Emotional intelligence has been negatively associated with maladaptive lifestyle choices. Lower MSCEIT scores among male college students were related to higher levels of drug and alcohol use as well as stealing and fighting (Brackett et al., 2004; Mayer et al., 2004). Inner-city adolescents’ smoking also was related to lower MSCEIT scores (Trinidad & Johnson, 2002). It appears that emotional intelligence may help individuals both to navigate their social worlds more effectively and make better choices about engaging in selfdestructive behavior. Academic performance

Emotional intelligence is hypothesized to aid in prioritizing thinking and to enable one to manage emotions in anxiety-provoking situations, such as taking standardized tests. Evidence supporting the role of emotional intelligence in academic settings is mixed. Some studies show positive associations (Barchard, 2003; Brackett & Mayer, 2003), whereas others show no links at all (O’Connor & Little, 2003; Rode et al., 2007). In two studies with college students, MSCEIT total scores and grades were correlated modestly (Barchard, 2003; Brackett & Mayer, 2003). However, the correlations in these studies became nonsignificant once verbal intelligence scores were controlled. A study with high school students in Spain, however, demonstrated

the incremental validity of emotional intelligence in the predication of grades. Scores on the MSCEIT, which was administered at the start of the academic year, correlated with final grades after controlling for both personality and academic intelligence (GilOlarte et al., 2006). Among middle school students, MSCEIT scores correlated significantly with year-end academic and behavior grades after controlling for scores on verbal standardized tests (prs = 0.34, 0.21 , respectfully) (Rivers et al., 201 0). The findings from middle and high school students may be stronger than those using college student samples due to a restricted range of grades among college student samples. Although more research is necessary to unpack whether (and how) emotional intelligence relates to academic performance, it appears that emotional intelligence may influence other aspects of student performance in school. Students scoring higher on the MSCEIT-YV were less likely to be rated by their teacher as having school problems, including attention and learning problems. Students scoring higher on the MSCEIT-YV also were less likely to report negative attitudes toward school and toward their teachers (Rivers et al., 2008). Workplace performance

Emotional intelligence is hypothesized to influence the success with which employees interact with colleagues, the strategies they use to manage conflict and stress, and overall job performance (Ashkanasy & Daus, 2005; Lopes, Coˆ te´, & Salovey, 2006a). Preliminary findings with the MSCEIT suggest that emotional intelligence positively contributes to several aspects of workplace performance. In a health insurance company, analysts and clerical employees from the finance department with higher MSCEIT scores had higher company rank and received greater merit pay increases than employees with lower MSCEIT scores. Employees with higher emotional intelligence also received better peer and ⁄ or supervisor ratings of interpersonal facilitation, stress tolerance, and leadership potential than those with lower emotional

intelligence (Lopes et al., 2006b). Similarly, among middle and high school teachers, MSCEIT scores were associated positively with job satisfaction and negatively with burnout. These associations were mediated by teacher reports of experiencing positive emotions in school and their perceived support from their school principal (Brackett, Palomera, Mojsa, Reyes, & Salovey, 201 0a). Emotional intelligence has been associated with the extent to which managers conduct themselves in ways that are supportive of the goals of the organization, according to the ratings of their supervisors (Coˆ te´ & Miners, 2006). MSCEIT scores for 38 manufacturing supervisors’ managerial performance correlated positively with managerial performance ratings by nearly 1 ,300 employees (Kerr, Garvin, Heaton, & Boyle, 2006). MSCEIT scores of senior executives predicted leadership effectiveness as rated by managers (Rosete & Ciarrochi, 2005). With few exceptions, most of the associations in the above studies remained statistically significant after controlling for age, gender, education, verbal ability, and personality traits. More thorough discussions on the role of emotional intelligence in the workplace, including both job performance and leadership, can be found elsewhere (Ashkanasy & Daus, 2005; Coˆ te´, Lopes, Salovey, & Miners, 201 0; O’Boyle et al., 201 0). Conclusion and Future Directions

Scientific findings on emotional intelligence support the notion that emotions are func- tional when the information they provide is attended to, interpreted accurately, integrated into thinking and behavior, and managed effectively. According to emotional intelligence theory, the cognitive, physiological, and behavioral changes that accompany emotional responses are adaptive – these changes prepare us to respond to the event that

caused the emotion to occur (Lazarus, 1 991 ). The theory also asserts that emotions serve important social functions, conveying information about other people’s thoughts, intentions, and behavior (Ekman, 1 973; Keltner & Haidt, 2001 ). Indeed, the ability to integrate emotional information into cognitive activities is essential to effective functioning across the life course (Damasio, 1 994). Think back to the scenario that opened this article. You had a choice: stay in your current job or accept a new one that has great benefits. Logically, the choice was obvious – accept the new job. But you felt uneasy about this choice. How could you integrate the information from this feeling to make a wise decision? Recognizing that the feeling is a discomforting one may prompt you to reflect upon the aspects of your current job that are unsatisfying, as well as the aspects of the prospective job that may make it not as desirable as it seems. In your current job, perhaps you are not recognized often for your contributions. In the new job, perhaps you will be required to travel more often and thus lose valuable time with your family. Understanding the causes and consequences of the uneasiness is informative to both managing the feeling and making a decision. Maybe the uneasiness is connected to apprehension about having to establish yourself in a new place with a new group of colleagues. In this case, asking a respected colleague or mentor for a pep talk might be sufficient to reduce your nervousness. If the cause is the increased tra- vel, then talking with your spouse and children about the implications of the new job might help you to manage the emotion and also help you make a choice. For individuals with high emotional intelligence, the above process may happen automatically and regularly. For many others, it is likely that formal learning opportunities will be necessary to acquire this problem-solving skill. Ideally, skill development in this area begins early, and is on-going. Other research we have conducted shows that the emotion knowledge and skills that comprise emotional intelligence can be taught and developed (Brackett, Rivers, Reyes, & Salovey,

201 0b). Our school-based prevention programs, called The RULER Approach, are designed to provide skill-building opportunities for students, teachers, school leaders, and family members to develop the skills of recognizing, understanding, labeling, expressing, and regulating emotions (the RULER skills) in order to make better decisions, form and maintain mutually supportive relationships, behave in prosocial ways, and regulate their feelings in order to experience greater well being. Findings from a randomized-controlled experiment testing The RULER Approach suggest that it creates a more positive learning climate (Brackett et al., 201 0b). RULER classrooms were rated as having more interactions reflecting positive relationships and respect; more prosocial behavior; greater enthusiasm about learning; fewer instances of bullying between students; less frequent expression of anger or frustration by teachers. Teachers in RULER classrooms were also more supportive of students, encouraging them to be autonomous in their learning and to share their ideas. Other research shows that an emotionally positive learning climate is a primary precursor to both academic engagement and achievement (Reyes et al., 201 0). Thus, how educators and students feel, and how they utilize and respond to their feelings, influences the school environment in ways that support learning and development. A recent meta-analysis examining the impact of social and emotional learning programming shows that a systematic process for promoting the social and emotional development of students is the common element among schools that report an increase in academic success, improved quality of relationships between teachers and students, and a decrease in problem behavior (Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, forthcoming). Applications of emotional intelligence theory extend beyond the classroom – we

have created training programs for businesses, medical professionals, and parents. Each of these applications strives to develop the skills of emotional intelligence. Empirical investigations examining whether adults can raise their emotional intelligence are under- way. What we know about emotional intelligence suggests that the construct is operationalized best as a set of mental abilities involving emotion-based problem solving measured with performance tests, as opposed to a set of traits and perceived abilities measured with self-report batteries. Preferring ability models makes it possible to both develop valid performance assessment tools and analyze the extent to which the construct contributes unique variance to a person’s behavior. Although research in this field is in its incipient stages, what we have learned thus far is promising: emotional intelligence can be measured objectively, it predicts important life outcomes, and it appears that the skills that comprise the construct can be learned. Over the next few decades, the field will advance as researchers continue to test and revise emotional intelligence theory and assessments, conduct validation studies, and create professional development programs. References

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Source: Yale

Center for Emotional Intelligence: 0085

Roisin P. Corcoran,

Department of Psychology, Yale University, New Haven, CT, USA. Roland Tormey,

Centre de Recherche de d’Appui pour la Formation et ses Technologies (CRAFT), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. Аннотация

Хотя есть сведения, что эмоциональный интеллект педагога важен для адаптации ученика и его обучения, а для самих учителей в управлении эмоциональной стороной своей профессии, мало известно об уровнях эмоционального навыка практикующих педагогов и в период их профессионального становления. Используя модель эмоционального интеллекта Майера и Саловея и тест MSCEIT, данное исследование показывает уровень развития эмоциональных навыков будущих педагогов. Результаты показывают, что уровень эмоциональных навыков будущих педагогов ниже среднего, но с существенными различиями у студентов в области эмоционального интеллекта вцелом. В статье исследуются значения результатов для подготовки будущих педагогов.


Although there is evidence that teacher emotional intelligence is important for pupil adjustment and learning and for teachers in managing the emotional demands of their work, little is known about the levels of emotional skill of teachers and beginning teachers. Using Mayer and Salovey’s emotional intelligence (EI) model and the MSCEIT test of EI, this study investigates how emotionally skilled student teachers are (N ¼ 352). Results show lower than average levels of EI among student teachers, but with important differences between students and

across emotional skill areas. The implications of the findings for pre-service teacher education are explored. 1. Introduction

The decades since the early 1 980s have seen something of an “emotional revolution” in psychology (Sutton & Wheatley, 2003, p. 328) and by the mid to late 1 990s this had impacted on teacher research and teacher education research. The special edition of the Cambridge Journal of Education (edited by Nias, 1 996) along with several articles by Hargreaves

(1 998, 2000) attracted some much needed attention to the area. As a result, there is a growing body of literature looking at the emotional context of teaching, learning, and learning to teach (Boyle, Borg, Falzon, & Baglioni, 1 995; Bullough, Knowles, & Crow, 1 991 ; van Dick & Wagner, 2001 ; Emmer, 1 994; Erb, 2002; Evelein, Korthagen, & Brekelmans, 2008; Hargreaves, 1 998, 2000; Helsing, 2007; Hoekstra & Korthagen, 2011 ; Intrator, 2006; Kyriacou, 1 987, 1 998; Lortie, 1 975; Meyer, 2009; Rosiek, 2003). There has also been some focus on the sort of teacher education needed to support the development of teachers who can utilise emotions effectively (Intrator, 2006; Rosiek, 2003; Whitcomb, Borko, & Liston, 2008). Much of the existing research on teacher emotions takes a broadly qualitative and descriptive approach to emotions; describing the range, depth and contexts in which emotions are experienced, managed and displayed. While there is evident value in such studies, there are also clear limitations. Recognising that teaching and beginning teaching are emotionally charged experiences does not actually tell us how competent teachers and beginning teachers are in productively working with and problemsolving using emotional information and in which areas they most need to develop their skills. That is the issue which this paper addresses with respect to pre-service teacher education. In the next section the emotional intelligence (EI) framework developed by Mayer and Salovey (1 997) is described, its importance with respect to teachers’ work is assessed and potential questions such as the relationships between gender and emotional intelligence and between emotional intelligence and different entry pathways into pre-service teacher education are identified. Thereafter the methodology used in the research is described and then the findings from a large study on EI in pre-service teacher education students are reported. The discussion section identifies which emotional skill areas are most

and least problematic for the student teachers studied and which need to be addressed in their teacher education programs. 2. The emotional intelligence framework and teachers’ work

The question as to how levels of emotional skills or competence can be measured is one that is fraught with difficulty. Indeed, some would argue that “the intangible emotional and empathic qualities which make a ‘good teacher’ from the viewpoint of the students cannot be measured” (Constanti and Gibbs [2004, p. 247]; cited in O’Connor, 2008, p. 11 7) while others (Hargreaves, 1 998) argue that to see emotions in terms of ‘skills’ is to decouple the emotional from its social context. At the same time, teacher educators are concerned with student skills (as well as with an understanding of the social contexts of learning) and so some account of emotional skills cannot be completely neglected. Measuring emotional skills is what emotional intelligence (EI) models (Bar-On, 1 997b; Goleman, 1 995; Mayer & Salovey, 1 997) focus on. The concept was originally coined by Peter Salovey and John D. Mayer (1 990) as a way of recognising that emotions and emotional information were an important part of problem solving and adaptation in everyday life. The term was broadly popularised by Daniel Goleman’s (1 995) book entitled, Emotional Intelligence, Why it can matter more than IQ. It is worth noting that there is, as of yet, little agreement as to how emotional competences are to be understood, made operational or measured (Humphrey, Curran, Morris, Farrell, & Woods, 2007). Indeed, despite the shared use of the term by different research groups, EI is today understood in what can be characterised as two broadly different ways: ● As a restricted set of mental abilities involving the processing of emotional information (Mayer & Salovey, 1 997; Salovey & Mayer, 1 990) and being

assessed through the use of a test of emotional problem solving and skills known as the MayereSaloveyeCaruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey, & Caruso, 2002a); ● As a broad range of personality traits, skills and abilities (Bar-On, 1 997b; Goleman, 1 995) assessed through self-report or 360○ -evaluation models (not unlike a personality inventory) such as the EQ-i (Bar-On, 1 997a), and the ECI (Boyatzis, Goleman, & Hay/McBer., 1 999; Boyatzis, Goleman, & Rhee, 2000). A complete evaluation of the strengths and weakness of each model is provided elsewhere (Corcoran, & Tormey, 201 2b), however it should be noted that the Mayer and Salovey model of EI has a number of distinct strengths that make it suitable for work in this area. First, the more specific and limited focus of Salovey and Mayer on cognitive skills of processing emotional information means that their concept of EI measures something different than what is already measured through broad personality-type variables (Mayer, Roberts, & Barsade, 2008; O’Connor, & Little, 2003). Second, the MSCEIT tests a set of skills directly rather than relying on selfreport or 360○-evaluation mechanisms, something which adds significantly to the sense that it is a valid measure (Dunning, Heath, & Suls, 2004). Put simply, “one’s perceived intelligence is considerably different from one’s actual intelligence” (Mayer, Salovey, & Caruso, 2004, p. 203). Salovey and Mayer have defined emotional intelligence as “the ability to perceive and express emotions, to understand and use them, and to manage emotions so as to foster personal growth” (Salovey, Bedwell, Detweiler, & Mayer, 2000, p. 506), and have used this definition to develop a framework of skills or abilities which can be tested to give rise to an overall measure of emotional intelligence (EIQ) as well as four branch scores, each representing a class of skills. The four categories utilized are: ● Perception, Appraisal, and Expression of

Emotion (PEIQ); ● Using Emotion to Facilitate Thinking (FEIQ); ● Understanding and Analysing Emotional Information (UEIQ); and ● Regulation of Emotion (MEIQ). It should be noted that the term “intelligence” can sometimes still be interpreted as referring to innate or fixed abilities, despite significant evidence that social and environmental factors impact both upon the cognitive skills that people develop and upon the skills that are valued and required in their social setting (Neisser et al., 1 996). In this study, no assumptions are made about emotional intelligence being innate or fixed. Rather, the EI model is seen as providing a framework for conceptualising emotional skills, and the MSCEIT as a way of testing those skills. There are good grounds for seeing a high level of EI as a valuable part of the teachers’ skills set. For example, EI plays an important role in the development of prosocial behaviour, better social functioning and quality interpersonal relationships with peers and teachers (Brackett, Mayer, & Warner, 2004; Lopes et al., 2004; Mayer et al., 2008). Teachers who are more skilled at regulating their emotions tend to report less burnout and greater job satisfaction; they also experience greater positive affect while teaching and receive more support from the principals with whom they work (Brackett, Palomera, Mojsa-Kaja, Reyes, & Salovey, 201 0). Developing teachers’ emotional skills can create a more positive and effective learning environment which is important for the motivation and productivity of both teachers and students (Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, 2011 ; Jennings & Greenberg, 2008; RimmKaufman, Fan, Chiu, & You, 2007; Sutton & Wheatley, 2003). Furthermore, the emotional skills of teachers’ influences student conduct, engagement, attachment to school, and academic performance (Baker, 1 999; Duckworth, Kirby, Gollwitzer, & Oettingen, in press; Hawkins, 1 999;

Schaps, Battistich, & Solomon, 1 996; Wentzel, 2002). The ability to perceive emotion in self and others has repeatedly been identified as important for teachers (Helsing, 2007; Intrator, 2006; Rosiek, 2003; Whitcomb et al., 2008). Recognising that different emotional states can facilitate or hinder different cognitive processes (Isen, Daubman, & Nowicki, 1 987; Palfai & Salovey, 1 993) also means that the ability to regulate emotions in the self and others is likely to have importance for teachers (Corcoran & Tormey, 201 2b). Such competence is likely to be as important for pre-service teachers who are engaged both in the emotional processes of learning and of teaching at the same time, and for whom beginning teaching has been described as akin to an emotional whirlpool (Erb, 2002) marked by a “dramatic range of intense emotions and passions” (2006, p. 235). Yet despite all this, there is relatively little research on teachers and on beginning teachers using the emotional intelligence framework. While Byron (2001 ), reported that novice teachers scored no differently on measures of emotional intelligence than the normative sample, Brackett et al. (201 0) found that the mean emotion regulation ability score for a sample of secondary school teachers in England was about .5 of a standard deviation lower than those reported in the normative sample. Similarly Corcoran and Tormey (201 0) found that the overall EIQ score of a sample of Irish teachers was about .3 of a standard deviation below interna- tional norms. It is difficult to draw firm conclusions from this small number of studies, carried out with relatively small samples. For example, research using the MSCEIT with larger data sets has found that females have, on average, slightly higher EQI than males (though of course this describes only the averages and so there are many males with EIQ scores higher than many females) (Mayer, Salovey, & Caruso, 2002b, p. 32). A number of studies (Briton & Hall, 1 995; Fischer & Manstead, 2000) suggest that women are stereotyped as being the more emotional sex, and believed to

experience and express emotions more often. Some empirical studies have found women to be more intensely expressive of most positive and many negative emotions than were men (Brody & Hall, 2000, p. 344), with the possible exception of anger (Coats & Feldman, 1 996; Dimberg & Lundquist, 1 990). At the same time, Barrett, Robin, Pietromonaco, and Eyssell (1 998) found no gender differences in reported experiences when men and women report how they feel in the moment (“how anxious do you feel right now?”), as opposed to reporting from hindsight (“how anxious were you doing last week’s exam?”), or at a global level (“how often do you feel sad or depressed?”). Hochschild (2003, pp. 1 64e1 70) argues that if there are differences between men and women in the management of emotion these are related to both gendered differences in expectations of masculinity and femininity and to differences in power and status, with women being required to manage emotion to a greater degree because they are more likely to be in positions of lower status. It is hard to know what this might mean with respect to potential gender differences in emotional skills among student teachers where females and males share a common status of student teacher, but may at the same time differ both in their gender socialisation and in gendered expectations of emotional display rules. Existing studies are too small to draw on empirical data in addressing such questions. Existing samples have also been too small to allow other important differences to be explored. With pre-service teachers, for example, much attention has been paid in the last decade to the perceived need for a range of pathways into the teaching profession. A study based on nine-countries (Conway, Murphy, Rath, & Hall, 2009) highlights that many countries allow for entry into teaching through either a concurrent or a consecutive teacher education route, while in some countries

deregulation has led to entry through workplace-based qualifications or through emergency licenses. Entry onto teacher education programs is often based on academic criteria alone and critics of this system argue that the entry criteria do not necessarily emphasise the compe- tences needed for effective teaching (OECD, 2005). Some posit that “personal suitability data” should accompany academic criteria and suggest “it is desirable to seek to recruit more mature students with varied work experience” which would enrich the teaching profession (Coolahan, 2003, p. 22). As it stands, the existing data on emotional intelligence does not allow such issues to be addressed; that is, whether there are differences between preservice teachers depending on whether they are selected by academic criteria alone or through combining academic criteria with other “suitability”- type criteria. In summary, there is a growing body of literature on teacher emotions, much of it qualitative, descriptive and contextual. There is evidence that a high level of emotional skill is of benefit to teachers and their pupils, but there is limited evidence about the levels of emotional intelligence or skill that pre-service teachers have. It is also unclear as to whether or not student teachers’ emotional intelligence levels are associated with gender or with the route of entry into pre-service teacher education. The EI model developed by Mayer and Salovey and the MSCEIT provide a conceptual framework for making sense of what are the specific emotional skills that student teachers have or lack. This in turn has implications for what should be addressed in teacher education. 3. Methodology

The question which is at the core of this study is: How competent are student teachers in (a) perceiving emotions in self and others, (b) using emotions to facilitate particular types of thought (c) understanding emotional information and (d) managing emotion? In order to answer this question, the MSCEIT

V2.0 was admin- istered to a diverse group of Irish post-primary student teachers (N ¼ 352). Given that the MSCEIT was being used in a new context a confirmatory factor analysis was conducted to test the structural validity of the four branch model used in the MSCEIT in the Irish context. 3.1. Participants

Student teachers, from the third year of a four-year undergraduate program (UG) and from a one-year graduate diploma (GD) program in an Irish university, were invited to participate in this study aimed at assessing their level of emotional intelligence. Of the students that applied, undergraduate students were selected using a stratified random sampling technique. Since numbers in the graduate diploma program were lower, all graduate diploma students who agreed to participate in the research were selected. The MSCEIT was administered to both groups of students. This entire process was then repeated the following year. The second cohort (in cycle two) were students on the same education courses at the same point in their studies and drawn from the same applicant pool as the previous participants. This gave MSCEIT (emotional intelligence) data for 352 students in total (see Table 1 ), 205 female and 1 47 male. Given the gender imbalance in the college courses, the male student teachers were largely drawn from the engineering, construction and technology programs and female students were largely drawn from physical education, science, languages, music and business programs. The Organization for Economic Cooperation and Development (OECD) report entitled, Teachers matter: Attracting, developing and retaining effective teachers points to the high social status and competitiveness for entry traditionally enjoyed by the teaching career in Ireland, Finland and Korea (OECD, 2005). Entry to the UG teacher education programs

(concurrent model in


teacher education) that participated in the study is largely by academic grades only, although entry into the GD programs (consecutive model in secondary teacher education) is also based on performance at an interview. A number of other countries e Austria, Australia, the Czech Republic, England, Finland, Israel, the Netherlands, Northern Ireland, Scotland, the Slovak Republic, Sweden and Wales e similarly offer both models in preparing secondary school teachers (OECD, 2005). However, entry routes to pre-service teacher education programs are a frequent international debate (Cochran-Smith & Fries, 2006; OECD, 2005). 3.2. Instrument

The MSCEIT V2.0 was administered to all 352 student teachers. The MSCEIT is composed of 1 41 -items which make up eight ‘task’ scales, which in turn make up the four ‘branch’ scales that form the basis of the Mayer and Salovey model of EI. According to Mayer et al. (2002b, p. 70) it “provides an estimate of a person’s ability by having them solve problems. The MSCEIT asks you to solve problems about emotions, or problems that require the use of emotion”. The MSCEIT yields a number of different scores, and as with other intelligence tests, MSCEIT scores are constructed so that the average score for the population would be expected to be 1 00, with a standard deviation of 1 5. The available scores include a total Emotional Intelligence score (EIQ) and four branch scores which are Perceiving Emotions (PEIQ), Facilitating Thought (FEIQ), Understanding Emotions (UEIQ) and

Managing Emotions (MEIQ). Scores are represented numerically, but also a score range is provided to help interpret the results. Scores between 90 and 1 09 are considered to indicate that the person is ‘competent’ in that skill area, with higher scores indicating that the person is more skilled while lower scores indicate that they should consider improving their capacity in that area. Reliability is assessed using split-half analyses for the EIQ and four-branch scores. A score of .7 or higher is regarded as showing a reliable measure. Mayer et al. (2004, p. 201 ) report reliability scores of .93 for the EIQ score and between .76 and .91 for the four-branch scores. It should be noted that while the validity and reliability of the measure is strong at the level of a global score (EIQ) and at the level of the four branch scores (PEIQ; FEIQ; UEIQ; MEIQ), below the branch score level the reliability of the test is diminished (Mayer et al., 2002b) and so it is only the general score and the branch scores which are reported upon here, as in the MSCEIT literature more generally. The test-retest reliability was .86 for the MSCEIT total score after a three-week period (Brackett & Mayer, 2003, p. 11 52). As has been noted above, the MSCEIT can be regarded as a more valid measure of emotional intelligence than other tests because the MSCEIT actually assesses skills as opposed to a person’s perception of their skills (Roberts, Zeidner, & Matthews, 2001 , p. 200). Likewise, the clear correspondence between the definition of emotional intelligence and the test structure means the test can also be regarded as having a high degree of content validity. The factorial or structural validity of the measure has been assessed using confirmatory factor analysis. This confirms that the overall EIQ score and the four branch scores are both good fits for the MSCEIT V2.0 and its predecessors (MEIS and MSCEIT V1 .1 ; Mayer, Salovey, Caruso, &

Sitarenios, 2003, p. 1 04). In other words, factor analysis supports the idea that the test is measuring four different abilities which can meaningfully be clustered together to represent a single measure that is EIQ. 3.3. Confirmatory factor analysis

Confirmatory Factor Analysis (CFA) was performed using AMOS. Based on previous recommendations (Cole, 1 987; Marsh, Balla, & McDonald, 1 988), the indices selected to assess goodness-of-fit were as follows: the Goodness-of-Fit Index (GFI) and the Adjusted Goodness-of-Fit Index (AGFI) (Jöreskog & Sörbom, 1 981 ), the Normed Fit Index (NFI) and the Non-Normed Fit Index (NNFI) (Bentler & Bonett, 1 980), and the Root Mean Squared Error of Approximation (RMSEA). The criteria used to indicate good fit, based on several evaluations (Anderson & Gerbing, 1 984; Cole, 1 987; Marsh et al., 1 988; McDonald & Marsh, 1 990), include the following: GFI > .85, AGFI > .80, NFI > .90, NNFI > .90, and RMSEA < .08. Using expert scoring, the model consisting of four branch scores e Perceiving Emotions (PEIQ), Facilitating Thought (FEIQ), Understanding Emotions (UEIQ) and Managing Emotions (MEIQ) e produced highly acceptable goodness-of-fit indices (GFI ¼ .98, AGFI ¼ .96, NFI ¼ .92, NNFI ¼ .92, RMSEA ¼ .05). The area level structure e scores from the four branch scores combine into two area scores e was also supported by the goodness-of-fit indicators (GFI ¼ 1 .00, AGFI ¼ .98, NFI ¼ .99, NNFI ¼ .98, RMSEA ¼ .04). This confirms that, in the Irish context, the four branch structure of the MSCEIT remains valid. 3.4. Limitations

The results presented in this paper need to be interpreted in a context of some important conceptual and methodological limitations. First, much of the existing research in the area of emotions in education is qualitative and

descriptive. This makes obvious sense, given that meaning is central to the experience of emotion (Denzin, 1 984). Recognising the dominance of qualitative approaches, Sutton and Wheatley (2003) conclude that multiple measures research is needed to gain a more complete picture of teachers’ emotions. This paper does not match quantitative with qualitative data within the study; however, it clearly complements the qualitative data that are found in the literature. Second, while the validity and reliability of the MSCEIT is well established, quantitative models for assessing emotional skills, like the MSCEIT, are not without limitations. As with any quantitative test, one must be cautious to avoid drawing overstated inferences from the scores. The MSCEIT is designed to assess the four areas of skill included in the Mayer and Salovey definition of emotional intelligence. It is not a measure of ‘niceness’, personal warmth or moral behaviour. It does not measure personality-type variables associated with emotion (such as emotional stability or optimism). Nor does it indicate whether or not a person is likely to use their competences in any given interaction. Third, these data have been collected in an Irish context. As with all quantitative tests there are questions about the applicability of the MSCEIT to different ethnic or national groups. It was normed based on an expert group drawn from an international panel, with participants from a number of continents, but primarily based in the US. As such, while the test constructors have made an effort to ensure that the test is not culturally biased, it is still open to question as to whether or not the norms are genuinely transferable across different ethnic groups or national origins (Sue, 1 999). The test was reviewed for cultural applicability before its use and no issues with language were reported by participants during the testing. The CFA which was carried out also indicates that the factor

structure remains valid in an Irish

and the results are reported in Table 4.

4.2. Differences between undergraduate and graduates in the sample

context. 4. Results 4.1. Overall scores obtained by all student teachers

The means and standard deviations for total EIQ and each of the four skills e PEIQ, FEIQ, UEIQ and MEIQ e are reported in Table 2. The average total EIQ score for the 352 students on the MSCEIT was within the competent range, but is more than .5 of a standard deviation below the expected average score of 1 00. The average scores for the four skills were also within the competent range and below the expected average of 1 00. As Table 3 shows, the students’ average scores were significantly below the expected mean in the case of all four branch scores and in their overall EIQ score. A within-subjects analysis of variance (ANOVA) permitteddetermined of whether differed with respect to the four branch scores. Mauchly’s test indicated that the assumption of sphericity had been met (c2(5) ¼ 7.68, p > .05). Results indicated that student teachers differed significantly with respect to each of the four branch scores, F(3, 1 053) ¼ 9.1 9 (p < .0001 ). Post-hoc comparisons were employed to determine the nature of these differences

Despite the differences between the entry and qualifications of the undergraduate and graduate elements of the sample, there are very little differences between the scores obtained by students in undergraduate and graduate diploma courses; the means and standard deviations are reported in Table 5. It is notable that, contrary to what one might have expected, the score for graduates (who are on programs that require both high academic attainment and an interview for entry) is actually lower than the undergrad- uates scores (who enter on the basis of academic achievement alone) for EIQ, PEIQ and MEIQ. The two group means for total EIQ and each of the four EI skills are compared using independent samples t-tests (results of Levene’s test for equality of variances indicates equal variances may be assumed for both groups in each test). The difference (3.49) is only significant for PEIQ at the .05 level, t(350) ¼ 2.43, p < .05, 95% CI [.67, 6.30]. This indicates the mean score for the ‘perceiving emotions’ skill area is statistically significantly higher for undergraduate students than graduate students. 4.3. Differences female students




There are differences in the mean scores obtained by male and female students within the sample, the means and standard devi- ations are reported in Table 6. Results indicate that the mean scores for female student teachers are higher across all skill areas. Inde- pendent samples t-tests indicate that the mean scores for males for

each of the EI skills are statistically

significantly lower than the mean scores for females; results are reported in Table 7. Given that the student’s course of study was related to their gender (with male students more likely to be studying to be engineering, or technology teachers and female students more likely to be studying to be language, music, PE or science teachers) it is worth questioning how this ‘gender effect’ relates to their course of study. Indeed, there were differences between the scores of students depending on their course of study (data not presented due to space constraints and as they are not crucial to the argument presented here). A twoway independent ANOVA was used to examine the interaction between gender and school subjects. Results indicate a significant main effect (at the p ¼ .05 level) for gender on EIQ, controlling for school subjects, is found, F(1 ,336) ¼ 6.52, p ¼ .011 . The effect for school subjects on EIQ, controlling for gender, was found to be notable, but not significant, F(8, 336) ¼ 1 .78, p ¼ .08. A non-significant interaction effect between college course and gender on EIQ is found, F(6, 336) ¼ 1 .87, p ¼ .086. A significant main effect for gender on FEIQ, controlling for school subjects, is also found, F(1 , 336) ¼ 8.07, p ¼ .005. 5. Discussion

The evidence highlighted earlier suggests that having a high level of emotional competence is

likely to be of positive benefit to teachers and their pupils. The few studies that exist on this topic show contradictory findings as to whether student teachers’ levels of emotional intelligence are broadly in line with expected averages (Byron, 2001 ) or below average (Corcoran, & Tormey, 201 2a,b; Brackett et al., 201 0). This study, based on a reasonably large data set, shows that the pre-service student teachers studied have levels of emotional intelligence below the norm for the wider population. If anything, the evidence here is even more stark than in other studies, with the mean average score for the sample of student teachers being more than .5 of a standard deviation below the average for the wider population, and, for male student teachers, being .8 of a standard deviation below the average for the wider population e below the range that is described as ‘competent’. The gender differences are greater in this sample than would be expected in the wider population (Mayer et al., 2002b, p. 32). The pattern of student scores on each of the four components is also associated with gender: female students perform about average at using emotion to facilitate thinking and at regulation of emotion, but less well in the other two components. Male students perform about the same (92 or 93 on average) in all four skill areas. As was noted above, the emotional skills of teachers have been found to influence student conduct, engagement, attachment to school, and academic performance (Baker, 1 999; Hawkins, 1 999; Schaps et al., 1 996; Wentzel, 2002). It is therefore a cause for some concern that student teachers on average show lower levels of emotional intelligence.

As was noted above, one response to this finding might be to look to alternative entry routes into teacher education which are based in part on “personal suitability data” (Coolahan, 2003, p. 22). The data here would not suggest that interviewing students is an appropriate way of achieving this. It is notable that the comparatively lower levels of EI for student teachers holds true irrespective of whether the student teachers in question are on a graduate program (in which acceptance on the program requires both high academic attainment and a strong performance at interview) or an undergraduate program (in which acceptance on the program is based only on high academic attainment). Given the frequent international debate concerning entry routes to teacher education programs, these are important findings. Given the comparatively lower levels of emotional intelligence found among student teachers it is worth considering including a focus on such emotional competences within pre-service teacher education programmes. Again, the data is helpful here as it highlights the skill areas that are in need of most attention. The Mayer and Salovey model of emotional intelligence identifies four skill areas, and each of them will now be looked at in turn. The first skill area was the perception, appraisal, and expression of emotion (PEIQ). This area has frequently been identified as important for teachers and as Whitcomb et al. (2008, p. 269) have commented: “A quality of attentiveness to both our selves and our students is central”. Because the MSCEIT is a test of emotional skills “in abstract” rather than in specific teaching situations it is not possible to be too precise as to how a lower level of skill in any of the four areas would be evident in a teacher’s work, however a lower level of skill in this area might be likely to be seen in a teacher failing to recognise their own emotional state and the way it is impacting upon their behaviour. It may also mean that they have a decreased sense of being in control of their own emotions. It is likely to mean a teacher

who has trouble in picking up on emotional cues which might alert them to students who are bored, frustrated, angry, excited and so on. This is likely to impact upon both learning and on the social and behavioural environment in the classroom. The evidence here is that student teachers in this study were, on average, within the competent range, but were significantly below the expected average in this area (Table 3), and were worse at this than at a number of other skill areas (Table 4). Despite the fact that the graduate students had all been chosen through interviews, and the undergraduates had not, the undergraduates actually out- performed the graduates on this competence (Table 5). Male students did worse than female students on this area of skill (Table 7) although the gap between them was not as large as in other skill areas. How can student teachers develop their capacity to recognise emotions in self and others? Activities such as asking students to identify their own emotional state and how and where they feel that emotion in their body can begin to raise awareness of their own emotional state and its physical impact upon them. This can be combined with an emotional diary activity that can ask them to note what emotion(s) they are feeling at different times in the day or week and how they experience that emotion physiologically (Caruso & Salovey, 2004, p. 92). By sharing such diary entries with each other, students may start to broaden their awareness of emotions in other people. Engaging in and debriefing each other when involved in role-playing activities in which they seek to act out non-verbally a particular emotional response to a scenario may also be valuable activities to help develop this skill area. The role of meditation in aiding an awareness of emotions has been highlighted (Whitcomb et al., 2008), while Hoekstra and Korthagen (2011 ) have highlighted that feedback to teachers on the emotions they appear to be displaying in a coaching context can aid the awareness of emotions.

The second skill area was using emotions to facilitate thinking (FEIQ). Skills in this area are likely to be important in enabling the kind of emotional scaffolding that Rosiek (2003) identifies as an important part of teachers’ pedagogic content knowledge. This skill area also involves the ability to generate emotions required to facilitate particular thinking activities. While the students teachers in this study were, on average, significantly below the expected mean score of 1 00 they were significantly stronger in this area than in perceiving emotions and in understanding emotional changes (Table 4); this is largely attributable to the fact that the average score for female students for this skill was at the population average, however, the score for male students remained notably lower. This is the skill area which saw the largest gap between the performance of male and female students. How might this ability be developed in student teachers? For student teachers, an awareness of the different types of thinking activities that contribute to learning (Krathwohl, 2002) are probably already addressed in their programs. In doing this it would also be possible to broaden this to include a focus on how different sorts of emotions can facilitate or hinder different types of thinking (Isen et al., 1 987; Palfai & Salovey, 1 993). Caruso and Salovey (2004, p.11 0) suggest that people can also learn to generate emotions through linking into past emotional memories and through using techniques used in drama to develop what they refer to as their “emotional imagination.” The third skill area is understanding and analysing emotional information (UEIQ). Given the characterisation of the student teacher experience as a whirlpool of ever changing emotions (Erb, 2002) and of the classroom as an emotionally dynamic space in which different emotions are always ebbing and flowing (Intrator, 2006) it would seem that an understanding of how a given emotion is likely to change in response to different events would be an important ability for student teachers. As with perceiving emotions, however, this is an area of comparative weakness for the student

teachers. This was the area in which they scored lowest of all (Table 3), and their scores on this area was statistically significantly lower than for using emotions to facilitate thinking and for emotion regulation (Table 4). Again, male student teachers scored lower than female student teachers and this difference is statistisignificant. How might student teachers develop their abilities in this area? The Plutchik Circumplex (Plutchik, 1 994, 2001 ) is a useful framework for enabling student teachers to begin to conceptualise the intensification of emotions as well as for developing their emotional vocabulary e something which is seen to be associated with this skill area (Caruso & Salovey, 2004, p. 1 23). Role plays and scenario work in which they are asked to enact or discuss what emotions are likely to be experienced in a given situation and how a new event may affect those emotions may also be a valuable learning experience. The fourth skill area is the regulation of emotion (MEIQ); according to Koole (2009, p. 5), the “tremendous increase in research volume has rendered the study of emotion regulation one of the most vibrant areas in contemporary psychology”. Hargreaves (1 998, 2000) draws on Hochschild’s concept of emotional labour to describe the processes for teachers in regulating their emotional displays with both pupils and with parents, although the regulation of emotion goes beyond regulating what emotion you show (Gross, 1 998a, 1 998b; Gross & Thompson, 2007). Emotion regulation involves applying the evaluation process to emotion itself and may be located in the regulator (teachers’ may try to calm themselves down, for example) or may involve extrinsic regulation, that is, the regulation of the emotions of others’ (teachers’ may try to calm their students down, for example). It has been argued, for example, that the culture of teaching requires or should require teachers to care (Noddings, 1 992) and to have passion (Fried, 1 995) love, sympathy, concern

(Oplatka, 2007). One important aspect of emotion regulation for teachers is working with stress. The stress that teachers experience has been recognised as an international phenomenon, with studies on teacher stress having been conducted in Canada (Klassen, 201 0), France (Pedrabissi, Rolland, & Santinello, 1 993), Italy (Pisanti, Gagliardi, Razzino, & Bertini, 2003), the Netherlands (de Heus & Diekstra, 1 999), China (Hui & Chan, 1 996), Australia (Pithers & Soden, 1 998), and many other developed countries (Boyle et al., 1 995; van Dick & Wagner, 2001 ; Kyriacou, 1 987, 1 998). In one study, Travers and Cooper (1 993) found that more than thirty percent of British teachers perceived their jobs as stressful with reports of increasing pressure. While Borg (1 990) found about as many as a third of the teachers surveyed in various studies around the world reported that they regarded teaching as highlystressful. Stress and negative affect interfere with self-regulation (Holm-Denoma, Joiner, Vohs, & Heatherton, 2008; Keel, Baxter, Heatherton, & Joiner, 2007; Sinha, 2007; Sinha et al., 2008). Self-regulatory failure is a core feature of many social and mental health problems (DeWall et al., 2011 ; Gruber, Harvey, & Gross, in press; Gyurak, Gross, & Etkin, 2011 ; Heatherton, 2011 ; Heatherton & Wagner, 2011 ; Kober & Ochsner, 2011 ; Williams, Bargh, Nocera, & Gray, 2009). It is therefore not surprising that stress and poor emotion management continually rank as the primary reasons why teachers become dissatisfied with the profession and end up leaving their positions (Darling- Hammond, 2001 ). Teachersâ&#x20AC;&#x2122; with high emotion regulation scores (MEIQ) tend to report less burnout and job satisfaction; they also experience greater positive affect while teaching and receive more support from the principals with whom they work (Brackett et al., 201 0). Although the average MEIQ for the student teachers was significantly below the expected average (Table 3), this was an area of comparative strength for them; their score on this area was significantly higher than their score for perceiving emotions and for understanding emotions (Table 4). Again, as was the case with using emotions, this can be

attributed to the fact that the average score for female students was almost half (.44) of a standard deviation higher than the score for male students. There were no notable differences between undergraduate and graduate students on this skill. This skill area involves allowing oneself to be open to emotions and to use the information that emotions convey in a judicious way. How can student teachers develop their skills in this area? The regulation of emotion involves the use of strategies such as a person acting upon or changing their own physiological state, as well as the use of cognitive strategies (Caruso & Salovey, 2004; pp. 1 34e1 55). Student teachers can learn to better regulate their emotions using a range of strategies including cognitive reframing of a situation, relaxation techniques, reflection or visualization, for example (for further discussion see Corcoran, & Tormey, 201 2b). Evidence suggests that having a range of strategies is important because some strategies are more helpful in particular situations than others and there is no one strategy that works always. For example, aggregated evidence suggests that suppression is cognitively and socially costly (Butler et al., 2003; Gross, 2002; Srivastava, Tamir, McGonigal, John, & Gross, 2009), while reappraisal can be helpful in particular situations because the individual is deeply processing information (Gross & John, 2003; Mauss, Cook, Cheng, & Gross, 2007; McRae, Heller, John, & Gross, 2011 ; Richards & Gross, 2000). However, which strategy is preferentially engaged switches from predominantly reappraisal at the lower emotion intensity to predominantly distraction at the higher intensity (Sheppes, Scheibe, Suri, & Gross, 2011 ). In addition, the effectiveness of reappraisal is moderated by emotion intensity where distraction is not. Also distraction may be more difficult to self generate in a contextually sensitive fashion and reappraisal may be able to be cued more appropriately in particular contexts than distraction which is cued very early in the

emotion regulation process (Sheppes & Gross, 201 2; Suri, Sheppes, & Gross, 201 2). These findings suggest that which strategy works best varies as a function of context, one dimension of which is emotion intensity (Sheppes & Gross, 2011 ). Therefore, it is important to teach teachers and student teachers to have a richer array of regulatory processes so they can flexibly implement them in a situation specific fashion. This also means that student teachers must be taught to think about the match between the strategy and the situation/context in order to effectively regulate their own and otherâ&#x20AC;&#x2122;s emotions. Indeed, there is now a very solid evidence base to support the view that such skills can be learnt and effectively used (Sheldon, 2011 ), including recent research using functional magnetic resonance imaging (fMRI) methods which suggests that specific strategies (namely reinterpretation of the external world vs. distancing oneself from it) may be differentially susceptible to training (Ochsner, 2011 ). It is worth reflecting a little on the gendered nature of the data presented here. In this study male students scored on average lower than female students though again, the ranges in scores means that there are many males which have higher levels of emotional intelligence than many females. The gender differences in this sample were wider than those in the normative sample used for the MSCEIT. It may be that this is in part a function of the particularly gendered culture of the subjects which the students were preparing to teach (with male student teachers largely drawn from the engineering construction and technology programs and female students largely drawn from physical education, science, languages, music and business programs) e there was a notable if marginally non-significant effect for school subjects when gender was controlled for. Over and above that, it may also reflect a cultural difference in gendering of emotional skills between the Irish and US samples (a significant effect for gender remained when school subject was controlled for). This suggests that it is worth thinking about emotional skills not only as a skill of the

individual, but as something which may well be woven into gendered and subjectspecific social expectations. The data presented here, drawn from a relatively large study of emotional intelligence in pre-service teacher education suggests that student teachers may have comparatively lower levels of emotional intelligence (though of course, the range of scores means that some will have levels which are above the expected average for the wider population). While there is an active debate on whether personal suitability data can effectively complement academic factors in entry into teacher education, the evidence presented here does not support the contention that using interview data to supplement academic data makes a difference to the emotional intelligence of the teacher candidates admitted. The data presented here refers to the emotional skills of student teachers measured in abstract, and does not allow for clear statements to be made about how they use these abstract skills in the situated reality of classroom life. This may mean a need to develop contextspecific versions of the test (Corcoran, & Tormey, 201 2a). At the same time, there are clear grounds for suggesting that preservice teacher education should pay attention to developing emotional competences in student teachers. The data presented here suggests that for many students all four skill areas will be important, but that (a) perceiving emotions in self and others and (b) understanding and analysing emotional information may be particularly weak areas for many student teachers. 6. Conclusion

Emotions matter in learning, in teaching and in learning to teach. If student teachers are to develop the emotional competence that might allow them to work with the emotional dimensions of pupil learning e and the emotional dimensions of their own process of learning to teach e then we need

a conceptual framework which would allow us to identify particular sets of necessary skills and to put in place activities which would enable learners to develop and utilize that competence. Given that stress and poor emotion management continually rank as the primary reasons why teachers become dissatisfied with the profession and end up leaving their positions (Darling-Hammond, 2001 ), and given that social and emotional skills are associated with success in many areas of life, including teaching, student learning, quality relation- ships, and academic performance (Durlak et al., 2011 ) the case for a focus on development of emotional competences in pre-service teacher education seems very strong (Palomera, FernandezBerrocal, & Brackett, 2008; Weare, & Gray, 2003). The evidence here suggests that, on average, student teachers may need help in all four of the competence areas that we have described, however it does also suggest that teacher education programs might need to place a particular emphasis on the skills of perceiving emotions in self and others, and of understanding emotional changes and progressions. The data also suggests that male students, on average, are weaker than female students at using emotions to facilitate thinking and at regulation of emotion (though such averages should not obscure the variances in scores in both male and female students). This finding directs attention to the way in which emotional skills are developed in and embedded in gendered social experiences and possibly also in subcultures of particular school subjects. This highlights the need to see these emotional skills as embedded in social contexts. Hargreaves (1 998) warns that emotional experiences are intrinsically social, and that a focus on â&#x20AC;&#x153;skillsâ&#x20AC;? can distract from the contextual and social dimensions of emotional experiences. However, this research suggests that the answer is not to refuse to conceptualise emotions in terms of skills or competence, but instead to see a focus on emotional intelligence as a useful counterpart to qualitative research which describes the

range, depth and experience of emotions in institutional and social contexts. A general model of teacher emotions will not be framed in either/or term, but rather in terms of both. An emotional intelligence framework does not offer a general theory of teacher emotions, and it does not address the social, institutional, cultural and labour process dimensions of teacher emotions. It does, however, offer a framework for making sense of what emotional competence teachers and student teachers need, for assessing to what extent they have required skills and for using that information in designing and reforming teacher education programs, and, at the moment, that is precisely what we need. Acknowledgment

This research was based on Roisin P. Corcoranâ&#x20AC;&#x2122;s doctoral dissertation and was written with the support of a Postgraduate Award from the Irish Research Council for the Humanities and Social Sciences (IRCHSS). The authors also wish to acknowledge the support of Tom Geary and Jim Gleeson for facilitating this research, and the Ubuntu Network which also supported this project as part of a study of the role of emotional intelligence in enabling student teachers to engage with overseas development issues and moral education. The authors wish to thank the Teaching and Teacher Education editors and reviewers for their helpful comments and suggestions. References

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Исследование структурного фактора методики самооценки уровня эмоционального интеллекта за авторством Н. Шутте на основе поверочного факторного анализа Аннотация

До настоящего времени исследование в области оценки эмоционального интеллекта (SSREI; Schutte и др., 1 998) основывалост на 4 факторах: оптимизм, социальные навыки, эмоциональная регуляция и использование эмоций. Однако в модели эмоционального интеллекта, на которой базируется SSREI (Саловей & Мейер, 1 990) никогда не рассматривался ряд элементов моделирования факторного анализа. Таким образом, в данной работе мы исследовали насколько CFA подходит другим моделям, сравнивая четырех-факторную модель, о которой пишет Саклофский, Остин и Минский (2003) и шести-факторную модель эмоционального интеллекта Саловея и Мейера.


Research to-date on the dimensionality of the Schutte Self-Report Emotional Intelligence (SSREI; Schu- tte et al., 1 998) scale appears to support a four-factor interpretation, corresponding to Optimism, Social Skills, Emotional Regulation and Utilization of Emotions. However, the model of EI upon which the SSREI is based (Salovey & Mayer, 1 990) has never been considered when determining the number of fac- tors to extract/model in the factor analyses. Thus, in this investigation, we examined the CFA fit of several models, comparing the four-factor

model reported by Saklofske, Austin, and Minski (2003), and the six- factor model of EI described by Salovey and Mayer (1 990). The CFA results indicated that two of the six dimensions of the Salovey and Mayer (1 990) model of EI could not be identified, independently of first-order general and acquiescent factors. Specifically, while ‘appraisal of emotions in the self’, ‘appraisal of emotions in others’, ‘emotional regulation of the self’, and ‘utilizing emotions in problem solving’ were identified, ‘emotional regulation of others’ and ‘emotional expression’ were not. The results are discussed in light of how the

SSREI could be potentially improved for the purposes of measuring the dimensions within the Salovey and Mayer model (1 990). 1. Introduction

Although a few exploratory/confirmatory factor analytic investigations exist in the literature that have examined the factor structure of the Schutte Self-Report Emotional Intelligence (SSREI; Schutte et al., 1 998) scale, none of the investigations have considered the EI model upon which the sale is based, when deciding the number of factors to extract/model. Schutte et al. (1 998) based the SSREI on the original Salovey and Mayer’s (1 990) theory of emotional intelligence, because they believed that the original model better characterized an individual’s present level of emotional development. As reported by Schutte et al. (1 998), Salovey and Mayer’s (1 990) original model included three categories: (1 ) ‘appraisal and expression of emotions’; (2) ‘regulation of emotions’; and (3) ‘utilization of emotions in solving problems’. However, within these three categories are subcategories. Specifically, the appraisal aspect of the ‘appraisal and expression of emotions’ category can be divided into ‘appraisal of emotions in the self’ and ‘appraisal of emotions in others’. Similarly, the ‘regulation of emotions’ category can be subdivided into ‘regulation of emotions in the self’ and ‘regulation of emotions in others’. Finally, and perhaps less clearly, ‘utilization of emotions’, can be subdivided into four components: (1 ) ‘flexible planning’, ‘creative thinking’, ‘redirected attention’ and ‘motivation’. Thus, in total, there are 1 0 first-order categories of EI within the Salovey and Mayer (1 990) model, as described by Schutte et al. (1 998). However, four of these categories are subsumed by the more nebulous ‘utilisation of emotions’ dimension. Thus, it could be argued that there are six primary dimensions within Salovey and Mayer’s (1 990) model of EI. Schutte et al. (1 998) generated an initial pool of 62 items to reflect all of the categories of Salovey and Mayer’s (1 990) model of EI, and then performed a principal components analysis on

the 62 item questionnaire, which was administered to 346 individuals. Based on their interpretation of a scree plot, Schutte et al. (1 998) extracted four components and rotated the components, orthogonally. However, Schutte et al. (1 998) reported that only the first component had an appreciable number of component loadings greater than .40, which was the criterion they used to demarcate a significant loading from a non-significant loading. Perplexingly, however, Schutte et al. (1 998) reported that all of the categories of the Salovey and Mayer (1 990) model were represented within the first component. Consequently, only the 33 items that exhibited component loadings greater than .40 on the first component were retained for the purpose of forming the published version of the questionnaire. Schutte et al.’s (1 998) component analysis can be criticized on a number of levels. First, it would have been more appropriate to perform a factor analysis on the 62 items, rather than a component analysis. Based on a comprehensive simulation study, Widaman (1 993) demonstrated that greater factor structure accuracy (i.e., loadings and factor inter-correlations) can be achieved with factor analysis, in contrast to principal component analysis, to the extent that the factor loadings are expected to be smaller, rather than larger. Thus, in the case of analysing items, which tend to be relatively unreliable, it would have been especially advisable to perform factor analysis, rather than components analysis. Further, because items tend to be relatively unreliable, the demarcation criterion of .40 used by Schutte et al. (1 998) for the purpose of identifying significant factor loadings may have been too strict, even for principal components analysis, which tends to overestimate the factor reliability of the items in the analysis (Widaman, 1 993). Moreover, as argued by Petrides and Furnham (2000),1 the components/factors should have been rotated obliquely, not orthogonally. Theoretically, the categories of

Salovey and Mayer’s (1 990) model of EI would be expected to exhibit positive inter- correlations. Orthogonal rotations preclude any correlations between components/factors (Gorsuch, 1 983). Further, and perhaps most surprisingly, given the results reported in Schutte et al. (1 998), an orthogonal rotation will preclude the existence of a general factor (Jensen, 1 998, p. 66). Schutte et al. (1 998) reported (p. 1 71 ) the eigenvalues for the rank ordered components as 1 0.79, 3.58, 2.90, and 2.53. These values appear to be derived from an unrotated solution. In all likelihood, an orthogonal rotation of the components would have spread the common variance relatively evenly from the first component, probably a general component, to the other compo- nents retained in the analysis. Thus, it is very possible that the component that Schutte et al. (1 998) interpreted, and ultimately used as the basis for creating the current SSREI inventory, was a general component, derived from an unrotated factor solution. Another argument in favour of the general component interpretation resides in the fact that the published version of the SSREI inventory includes items from all of the broad dimensions of Salovey and Mayer’s (1 990) model of EI. Had Schutte et al. (1 998) interpreted the rotated component solution, it is very doubtful that any of the components would have contained component loadings of .40 from such a multifarious collection of items, which just happen to represent all of the categories from the Salovey and Mayer’s (1 990) model of EI. Since the publication of Schutte et al. (1 998), a few published papers have investigated the item- level factor structure of the SSREI. For instance, Petrides and Furnham (2000) sought to test the hypothesis, via CFA, that the SSREI inventory measured a single, general factor of EI, based on a sample of 260 university students. The general factor model was associated with close-fit index values that indicated that the general factor model was unacceptable (e.g., CFI = .51 , RMSEA = .1 05). Consequently, Petrides and Furnham (2000) followed their CFA analysis with an unrestricted (exploratory) principal components

analysis. Based on Petrides and Furnham’s judgement, four components were extracted and rotated both orthogonally and obliquely. The components were found to correlate below j.30j. It is unclear whether Petrides and Furnham (2000) present the factor solution for the oblique or orthogonal rotations. The four components extracted by Petrides and Furnham (2000) corresponded to Optimism, Appraisal of Emotions, Social Skills, and Utilisation of Emotions. Saklofske et al. (2003) performed an unrestricted, exploratory principal components analysis on the extraction of four components (oblique rotation). The factor solution obtained by Saklofske et al. (2003) corroborated closely the factor solution obtained by Petrides and Furnham (2000), with the four components corresponding to Optimism, Appraisal of Emotions, Utilisation of Emotions, and Social Skills. However, when Saklofske et al. (2003) attempted to test the four-fac- tor model via CFA, the model was found to be poor fitting. Unfortunately, Saklofske et al. (2003) do not report the specific fit CFA close-fit values for the four-factor model. Hakanen’s (2004) EFA investigation into the dimensionality of the SSREI suffers from the same limitations reported above: component analysis and orthogonal rotation. Given that two previous CFA investigations failed to support the four-factor model within the SSREI, it was decided that a further investigation into the factor structure of the SSREI was merited. However, in contrast to Petrides and Furnham (2000) and Saklofske et al. (2003), it was hypothesized that the SSREI would conform more closely to the theoretical sixfactor model, rather than the four-factor model. Further, because the SSREI inventory is extremely unbalanced (30 positively keyed items and only three negatively keyed items), it was hypothesized that it would be necessary to model a first-order acquiescence factor, in conjunction with the substantive factors, for

the purpose of achieving satisfactory model fit. Several investigations have demonstrated the existence of an acquiescence factor, when analysing selfreport questionnaires via CFA at the item level (see Billiet & McClendon, 2000). Prior to statistical testing, we analyzed and categorized, qualitatively, the 33 items of the SSREI inventory for the purpose of classification into the conceptual categories of Salovey and Mayer’s (1 990) model of EI. Based on our qualitative analysis, the six substantive categories were identified within the SSREI inventory. The corresponding items for each category are listed in Table 1 . It can be seen that two of the dimensions only have two items each: ‘appraisal of emotions in the self ’ (AES) and ‘emotional expression’ (EE). Further, five items could not be readily classified into any of the six theoretical dimensions. Thus, we sought to test the hypothesis, via CFA, that the six-factor model was a more plausible model of the dimensions within the SSREI inventory, in comparison to the fourfactor model, currently advocated by several investigators (e.g., Petrides & Furnham, 2000; Saklofske et al., 2003). Further, we hypothesized the existence of a first-order

acquiescence factor, in conjunction to the six first-order substantive factors. 2. Method 2.1. Participants

The sample consisted of 367 participants (1 07 males, 257 females, 3 unreported), ranging in age from 1 5 to 78 (Mean = 38.3 years, SD = 1 3.7). The participants were obtained from the general population across the states of Victoria

and New South Wales (Australia) via advertisements. 2.2. Materials

The Schutte Self-Report Emotional Intelligence (SSREI) scale by Schutte et al. (1 998) is comprised of 33, 5-point Likert scale items, three of which are negatively keyed. Previous investigations have found the total scores on the SSREI to be acceptably internally consistent (e.g., .90; Schutte et al., 1 998). 2.3. Procedure

Once informed consent was obtained from the participants, test booklets were provided, which included the SSREI inventory and a scannable answer sheet. The participants were given an unlimited amount of time to complete the paper based inventory, and they received a small monetary reward for participating. 2.4. Data analytic strategy

The CFA analyses (AMOS 5.0) will be based on the 28 items selected in the introduction above. The first model that will be tested (model 1 ) will be the hypothesized sixfactor model based on Salovey and Mayer’s (1 990) framework of EI. The approach to modeling the six-factors will be based on a nested CFA modeling strategy. Specifically, rather than model the covariance between the six fac- tors as an oblique factor model or a higher-order general factor model, a nested factors modeling approach will be used (Gustafsson & Balke, 1 993). Thus, all 28 items will be specified to load onto a firstorder general factor, directly. Further, all 25 of the positively keyed items will be specified to load onto a first-order

acquiescence factor. A graphical depiction of the first model that will be tested can be seen in Fig. 1 . For the purposes of model comparison, a single general factor model, and a general factor model with an acquiescence factor will also be tested via CFA. It is hypothesized that the hypothesized sixfactor model will be associated with practically better fit, in comparison to the general factor model, and the general factor model with an acquiescence factor. Finally, a nested factors model consistent with the EFA findings of Saklofske et al. (2003) will be tested. Thus, in comparison to the hypothesized six-factor model reported above, the Saklofske et al. (2003) model will consist of four nested factors, corresponding to Optimism, Appraisal, Utilization, and Social. For the purposes of fair comparison, the Saklofske et al. (2003) four-factor model will also be based on only 28 items, as described above for the hypothesized six-factor model. It is

hypothesized that the six-factor model will be associated with practically better model fit than the Saklofske et al. (2003) fourfactor model. A graphical depiction of the Saklofske et al. (2003) model can be seen in Fig. 2. All CFA analyses will be based on a Pearson covariance matrix and Maximum Likelihood Estimation (MLE). Models will identified by constraining the latent variables to 1 .0. In the case of a latent variable defined by only two observed variables, the factor loadings will be constrained to equality, in accordance with Little, Lindenberger, and Nesselroade (1 999, pp. 204â&#x20AC;&#x201C;205). In accordance with Hu and Bentler (1 999), a combination approach will be used to evaluate model fit. Specifically, two absolute close-fit indices (SRMR and RMSEA) and two incremental close-fit indices were chosen (TLI and CFI). Also in accordance with Hu and Bentler (1 999), models will be evaluated as good fitting, when the absolute fit indices (SRMR and RMSEA) are <.06 and the

incremental fit indices (TLI and CFI) are approximately .95 or larger. Rather than compare the fit of models statistically, a practical significance difference test will be used based on TLI values. In accordance with Vandenberg and Lance (2000), a model with a TLI value .01 larger than another model will be considered practically better fitting. 3. Results

Average absolute levels of item skew and kurtosis were —.91 and .93, respectively, which is within the acceptable range for CFA analysis using maximum likelihood estimation (Muthen & Kaplan, 1 985). A variant (see DeCarlo, 1 997) of Small’s omnibus test of multivariate normality was rejected (VQ3(66) = 11 39.1 3, p < .001 ); however, the majority (72%) of the multivariate non-normality was due to skewness (Q1 (33) = 81 6.85, p < .001 ), rather than kurtosis (VQ2(33) = 322.28, p <

.001 ), indicating that the MLE CFA results would likely be minimally affected by the multivariate non-normality. A null CFA model hypothesizing no covariation between the 28-items was rejected v2 ¼ 3734.85, p < .001 , RMSEA = .1 59, SRMR = .278, TLI = .000, CFI = .000), indicating that the inter-item covariation was appropriate for factor analysis. The hypothesized nested factors model with a first-order general factor, a first-order acquies- cence factor, and nested firstorder ERS, ERO, EE, AEO, AES, and UEPS factors produced a ð299Þ ¼ 561 .1 6, p < .001 . However, as can be seen in Table 2 (model 1 ), the factor loadings from the ERO and EE factors were not significant statistically. Further, an excessively large standard error (i.e., 8.69) was associated with item 30’s unstandardized factor loading of 1 .33. Finally, item 30 and 20’s residual variances were associated with negative

values of —1 .33 and —.1 4, which is unacceptable in an adequate CFA model. Thus, despite the decent close-fit index values (e.g., RMSEA = .050 and CFI = .922), the hypothesized model was not considered acceptable. A modified hypothesized model was tested which was identical to the hypothesized model, with the exception that the nested ERO and EE factors were removed from the model. The modified hypothesized model produced a v2 ¼ 570.1 2, p < .001 , and all of the standard errors were within an acceptable region, indicating that the model was potentially acceptable. In contrast to the original model, only one item’s residual variance was associated with a negative value. To test the hypothesis that the negative error variance may be due to sampling fluctuations, item 20’s residual error variance was constrained to .0001 , in accordance with Chen, Bollen, Paxton, Curran, and Kirby (2001 ). The modified model with the constrained error variance yielded a ð306Þ ¼ 570.72, p < .001 , which was not statistically significantly worse fitting than the previous non-constrained model, indicating that the negative error variance was likely due to sampling fluctuations, rather than a fundamentally inappropriate model specification. An examination of the modification indices indicated that there was some unmodeled covariance between the AES and AEO factors. Because a positive association between these two factors was reasonable theoretically (i.e., were from the same domain of ‘appraisal of emotions’), the modified model was retested with the addition of a covariance link between the AES and AEO factors, which produced a ð305Þ ¼ 559.40, p < .001 . The correlation between the AES and AEO factors was estimated at .28 (CR = 3.65, p < .001 ). This final model was associated with absolute close-fit index values indicating good model fit (SRMR = .043 and RMSEA = .049), while the incremental close-fit index values indicated marginally good model fit (CFI = .924 and TLI = .906). An examination of the standardized residual covariances and modification index values (i.e., Lagrange multiplier) did not suggest any conspicuously

large and/or meaningful changes to the model. As can be seen in Table 2 (model 2), all of the factor loadings were positive and statistically significant, with the exception of only three items on the acquiescence factor. A graphical depiction of the modified hypothesized model is presented in Fig. 3. For the purposes of model comparison, a general factor model was tested, which produced a ð35Þ ¼ 1 358.83, p < .001 , RMSEA = .091 , SRMR = .079, CFI = .699, TLI = .675, indicating very poor model fit. A TLI difference test indicated substantial practical improvement in fit, favoring the modified hypothesized model, in comparison to the general factor model (DTLI = .231 ). Similarly, a general factor model with an acquiescence factor produced a v2 ¼ 949.63, p < .001 , RMSEA = .074, SRMR = .064, CFI = .81 4, TLI = .784, indicating poor fit, based on the close-fit indexes. A TLI difference test indicated substantial practical improvement in fit in favor of the modified hypothesized model tested above, in comparison to the gen- eral + acquiescence factor model (DTLI = .1 22). The Saklofske et al. (2003) four-factor (28-item) model produced a v2 ¼ 61 4.84, p < .001 . Although the absolute close-fit index values indicated good model fit (SRMR = .050, RMSEA = .055), the incremental close-fit index values indicated that the model could not be ac- cepted (TLI = .880, CFI = .906). Further, as can be seen in Table 3, the Optimism factor had a mix of positive and negative factor loadings. In particular, items 22 and 9 exhibited factor loadings of —.32 and —.33 on the Optimism factor. It will be noted that items 22 and 9 were the items that were used to form the Appraisal of Emotions in the Selffactor in the hypothesized factor modeling above. Thus, the Saklofske et al. (2003) four factor (28-item) model could not be accepted. It will also be noted that the modified hypothesized model tested above

was practically significantly better fitting than the Saklofske et al. (2003) fourfactor (28-item) model (DTLI = .026).

acquiescence factor. Within the TAS-20 (Bagby, Parker, & Taylor, 1 994), a ‘diffcult describing feelings’ dimension has

4. Discussion

The results of the CFA analyses suggested that the hypothesized sixfactor model of EI proposed by Salovey and Mayer, 1 990 could not be completely recovered by the Schutte Self-Report Emotional Intelligence scale (Schutte et al., 1 998). Specifically, a nested factors model with a firstorder general factor, and four nested factors corresponding to Appraisal of Emotions in the Self, Appraisal of Emotions in Others, Emotional Regulation of the Self, and Utilization of Emotions in Problem Solving were identified, in conjunction with a first-order acquiescence factor. There was no evidence to suggest the existence of an independent Emotional Expression or Emotional Regulation of Others factors. With only two items, the ‘appraisal of emotions in the self’ (AES) factor should be considered weak. At this stage, the two items from the AES factor should probably be considered a starting point for the potential development of a more complete subscale/factor. The addition of five or six internally consistent items would be considered a substantial step toward developing a stronger AES factor, and a more complete SSREI inventory. In contrast to the two items which formed the AES ‘‘factor’’, the two items hypothesized to form an ‘emotional expression’ (EE) factor did not demonstrate any unique covariance, independently of the general EI factor and the

emerged in several factor analytic investigations (e.g., Haviland & Reise, 1 996; Parker, Taylor, & Bagby, 2003), suggesting that an EE dimension deserves some serious consideration within a comprehension EI inventory. Despite the face validity of including an EE dimension within an EI framework, Mayer and Salovey’s, 1 997 revised model of EI does not include a unique dimension specifically relevant to the expression of emotions (instead, it is amalgamated within the ‘appraisal of emotions’ dimension). The exclusion of a specific emotional expression dimension, which appears to have received greater relevance in the original model (Salovey & Mayer, 1 990), may be due to the likely forbidding difficulties of measuring emotional expression from an ability model perspective. A mixed model perspective (i.e., self-report) is not plagued by the same problem, and, thus, future attempts to

model a more comprehensive emotional expression factor within the SSREI is encouraged. When tested via CFA, the four-factor model reported by Saklofske et al. (2003) was found to be less well fitting than the modified

hypothesized model based on Salovey and Mayer’s (1 990) original model of EI. In particular, the two ‘appraisal of emotions in the self’ items loaded negatively on the ‘optimism’ factor, independently of the general factor and the acquiescence factor, supporting the contention that these two items should be modeled as a separate factor. The observation of a negative association between items 22 and 9 and the ‘optimism’ factor also highlights one of the advantages of modeling a general factor as a first-order factor, within a nested factors modeling framework, because a comparable higher-order model and/or first-

order oblique model failed to identify this effect (CFA results available upon request). That is, in the presence of a general factor, items 22 and 9 loaded positively (.68 and .75, respectively) on the ‘optimism’ factor, not because of any unique association between items 22 and 9 and the specified

‘optimism’ factor, but simply because of the variance shared by all of the items with a second-order general factor. Although Saklofske et al. (2003) reported CFA model fit to a comparable degree that was found in this study for their four-factor model (i.e., CFI = .90), it should be emphasized that Saklofske et al. (2003) modified their four-factor model, in a mechanical way, according to the modification indices (i.e., residual correlation matrix and Lagrange multiplier). The practice of

following mechanically the information provided by modification indices has been sternly criticised by several SEM researchers (e.g., Bollen, 1 990; MacCallum, Roznowski, & Necowitz, 1 992). Unfortunately, Saklofske et al. (2003) did not specify how the model was modified, specifically. However, the degrees of freedom associated with their final model (455) suggests that a very large number of modifications (i.e., 34) were required to achieve a CFI criterion of .90. Although item 21 , ‘I have control over my emotions,’ was listed as ‘uncategorized’ within the qualitative analysis, it is believed that emotional control should be considered a possible valid dimension of self-report emotional intelligence. It is argued here that emotional control should be conceptualized as a more reactive, inhibitory process. For example, controlling ones temper, until a more appropriate time to express oneself presents itself. In contrast, ERS should be considered more proactive. For instance, conscientiously surrounding oneself with people who makes one feel good. Thus, although the emotional control item was not included in the SSREI CFA model recommended here, it is believed that emotional control should be part of a future SSREI inventory to potentially capture adequately a comprehensive model of EI. The potential development of a revised SSREI in accordance with the CFA findings in this investigation is considered a valuable objective, because it is one of the few EI inventories in the public domain. Further, in contrast to the most popular ability based model test (i.e., MSCEIT; Mayer, Salovey, & Caruso, 2000), which is plagued by the absence of a valid scoring protocol, low internal consistency, and emphasis on maximal performance rather than typical performance (see Matthews, Zeidner, & Roberts, 2002), self-report inventories such as the SSREI do appear to offer advantages with respect to scoring, reliability, emphasis on typical performance, as well as the opportunity to complement an EI assessment with 360° feedback. Although not without it’s own problems (e.g., susceptibility to social desirable

responding), the possibility of developing a self-report EI inventory with some validity should still be considered a potentially viable alternative to ability based model tests. References

Bagby, R. M., Parker, J. D. A., & Taylor, G. J. (1 994). The twenty-item Toronto Alexityhymia Scale. I. Item selection and cross-validation of the factor structure. Journal of Psychosomatic Research, 38(1 ), 23–32. Billiet, J. B., & McClendon, M. J. (2000). Modeling acquiescence in measurement models for two balanced set of items. Structural Equation Modeling, 7, 608–628. Bollen, K. A. (1 990). A comment on model evaluation and modification. Structural Equation Modeling, 25(2), 1 81 –1 85. Chen, F., Bollen, K. A., Paxton, P., Curran, P. J., & Kirby, J. B. (2001 ). Improper solutions in structural equation models: Causes, consequences, and strategies. Sociological Methods and Research, 29(4), 468–508. DeCarlo, L. T. (1 997). On the meaning and use of kurtosis. Psychological Methods, 2(3), 292–307. Gorsuch, R. L. (1 983). Factor analysis. Hillsdale, NJ: Lawrence Erlbaum. Gustafsson, B. (1 993). General and specific abilities as predictors of school achievement. Multivariate Behavioral Research, 28(4), 407–434. Hakanen, E. A. (2004). Relation of emotional intelligence to emotional recognition and mood management. Psychological Reports, 94, 1 097–11 03. Haviland, M. G., & Reise, S. P. (1 996). Structure of the twenty-item Toronto Alexithymia Scale. Journal of Personality Assessment, 66(1 ), 11 6–1 25. Hu, L., & Bentler, P. M. (1 999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1 ), 1 –55. Jensen, A. R. (1 998). The g factor. Westport, CT: Praeger.

Kaiser, H. F. (1 960). The application of electronic computers to factor analysis. Psychometrika, 20, 1 41 –1 51 . Kaiser, H. F. (1 970). A second generation Little Jilly. Psychomtrika, 35(4), 401 –41 5. Little, T. D., Lindenberger, U., & Nesselroade, J. R. (1 999). On selecting indicators for multivariate measurement and modeling with latent variables: When ‘‘good’’ indicators are bad and ‘‘bad’’ indicators are good. Psychological Methods, 4, 1 92–211 . MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1 992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111 (3), 490–504. Matthews, G., Zeidner, M., & Roberts, R. (2002). Emotional intelligence: Science and myth. Cambridge: MIT Press. Mayer, J., & Salovey, P. (1 997). What is emotional intelligence? In P. Salovey & D. Sluyter (Eds.), Emotional development and emotional intelligence: Implications for educators (pp. 3–31 ). New York: Basicbooks, Inc. Mayer, J. D., Salovey, P., & Caruso, D. R. (2000). The Mayer, Salovey, and Caruso emotional intelligence test: Technical manual. Toronto, ON: MHS. Muthen, B., & Kaplan, D. (1 985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematic and Statistical Psychology, 38, 1 71 –1 89. Parker, J. D. A., Taylor, G. J., & Bagby, R. M. (2003). The 20-item Toronto Alexithymia Scale––III. Reliability and factorial validity in a community population. Journal of Psychosomatic Research, 55, 269–275. Petrides, K. V., & Furnham, A. (2000). On the dimensional structure of emotional intelligence. Personality and Individual Differences, 29, 31 3–320. Saklofske, D. H., Austin, E. J., & Minski, P. S. (2003). Factor structure and validity of a trait emotional intelligence measure. Personality and Individual Differences, 34, 707–721 . Salovey, P., & Mayer, J. D. (1 990). Emotional intelligence. Imagination, Cognition, and

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Marketing Specialist, sr. instructor Dept. of World Economics and Economic Theory, VolSTU, fellow partner in Crowdintell.

Emotions are the kind of data. (David Caruso)

During the last ten years, emotional intelligence (EI) has attracted the attention of a lot of people, such as entrepreneurs, executives and headhunters. Why is There So Much Interest in EI?

In 1 995, journalist Daniel Goleman published his book "Emotional Intelligence: Why It Can Matter More Than IQ Âť, where he told us that our emotions play a much greater role in thinking, decision making and personal success than generally recognized. Plus, in 2002, psychologist Daniel Kahneman was awarded the Nobel Prize in economics, proved the influence of psychological (emotional) characteristics for decision making in an uncertain situation (risk). We act not as a rational being, but as so-called computers and tend to be intuitive (emotional); and this is not pathological, but rather the norm. What is EI and Who Invented It?

Emotional intelligence (EI) is the ability to identify, assess, and control the emotions of oneself, of others, and of groups. The term EI was coined by American scientists Peter Salovey and Jack Mayer in 1 990. Then, together with David Caruso, researchers suggested a model of emotional intelligence and a model of abilities, which were new and exciting. What kind of abilities are spoken of here? First of all, are the abilities of perception, because emotions contain the information about us, other people and about the world. Emotions are the kind of data that makes it important to define exactly what we and others feel. Our emotions (mood) determine our thought processes and, therefore, our behavior (including loyalty) (see Fig. 1 ). We think and behave quite differently, when in a bad mood, as opposed to a good mood. How to Identify the Level EI?

Actually there are three ways to measure EI: The first is self-assessment. 80% of people consider themselves wiser than the


The second - 360-degree assessment, when you appraise of others, they estimate of you and you measure of yourself. Finally, the third - specific tests, such as MSCEIT - Mayer-Salovey-Caruso Emotional Intelligence Test. Since founders of EI consider that emotions can be interpreted unambiguously, the test has right and wrong answers. Since founders of EI consider that emotions can be interpreted unambiguously, the test has right and wrong answers. What Kind of Test is the MSCEIT?

The test consists of facial images and questions, offered to evaluate how strongly each feeling is expressed? And we need to estimate how happy, confused or angry the person is. The 5 possible responses (linguistic variables) are, 1 - "a complete lack of emotion," 2 - "there is practically no emotion," 3 - "emotion is barely noticeable," 4 - "visible

emotion," 5 - "extreme manifestation". The test results are expected to give an idea of the general EI level. After processing the test results, a person gets a 2-dimensional matrix with an EI level for each of the following skills. It is interesting that the MSCEIT results often offer a surprise. Sometimes there is a difference in how people assess their skills and the evaluation they get by the test. This is logical. If a person doesn’t pay any attention to a conversation, because of their partner’s expression of boredom or sadness during their communication, he may be sure the dialog was successful and the goal was attained. Eventually the person can be surprised that the goal was not met. But why is it so? This is because the person hasn’t seen (estimated) the reaction of his conversation partner. He hasn’t noticed boredom, disagreement, disappointment, or any other emotions. He didn't have a good handle of emotions and wasn't able to identify them. According to the MSCEIT test, brainiacs (with high IQ) with high EI rate are better socialized, can get along with people better, and rely on them to attain particular goals. Executives can have a better understanding of their staff, achieve target results in a shorter period, can motivate other better, visualize goals and describe them to staff. Does MSCEIT Estimate EI Exactly?

According to the theory of Daniel Kahneman and Amos Tversky, people make up their decisions in conditions of uncertainty (decision under risk) and they often rely not on rational, but on intuitive (emotional) perceptions. This is so called heuristics in human decision-makers. We will try to apply it to the results of the MSCEIT test. When a person undergoes the test (you need approximately 40 minutes), he is thinking and concentrating (rational behavior). In this case the person has all of the available information which he could need to solve a problem, but not in an unpredictable situation (risk). He may not be able to apply his knowledge, to identify emotions. Conclusion: The test had shown a high rate of EI, but in reality the person doesn’t have these skills. If the MSCEIT test has shown a low EI score, most likely this is really so. How come? If a person isn't passing the test (situation of certainty) and can’t identify people’s emotions, this means, most likely, he won't be able to do so in real life (situation of uncertainty). Also, the hypothesis of Peter Salovey, John Mayer and David Caruso, that emotions as type of data could be determined univocal, is highly questionable. This issue requires special elaboration. Therefore There are Many Open Issues…

* Is the person with a high EI (according to the test) always effective in working with emotions in real life? * Is the person with a high EI more preferable as an employee, because he or she will have more loyalty to corporate policies in posse? * Does it make sense to test the EI of an employee before and after recruitment, and will the person demonstrate a higher EI in the first case? * Is there a normal correlation (i.e. conforms normal distribution) between the loyalty of an applicant and the brand of a particular organization? * If a real community (target audience, group) is provided with the EI test, which is divided into logical parts (for example, how to identify, understand, and evaluate emotions) and then

combined into one test, will the result show the EI rate of a Customer’s Advocate? While forming the target group, of real people, we select them according to criteria such as age, gender, educational level, religion, nationality and etc. supposing their behavioral reactions will be similar or identical (which is desirable). Notice: Customer’s Advocate it is a average consumer, whose characteristics (data) are deduced in the process of his or her identification with a group of real consumers. The reactions of real consumers are described in the Prospect theory of Daniel Kahneman and Amos Tversky, based on a representativeness heuristic. References

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Source: Gignac,

G., Palmer, B.R., Manoch, R., & Stough, C. An examination of the factor structure of the schutte self-report emotional intellignce (SSREI)scale via confirmatory factor analysis.Personality & Individual Differences, 39, 1 029-1 042.

Иванов Валерий

Доктор экономических наук, профессор. Ученый в области менеджмента, управления проектами, инновационных систем, маркетинга, логистики, корпоративных финансов, государственного и муниципального управления. Создал научную школу: формирование эффективных систем управления.

Коробова Анна

Начальник отдела ИТ, префектура Зеленоградского АО г.Москвы. Под непосредственным руководством созданы все элементы электронного и мобильного Зеленограда на уровнях префектуры и управ округа, включая формирование системы «одного окна», реализован эксперимент по созданию центра обслуживания населения и организаций в режиме «одного окна», результаты которого одобрены постановлением Правительства Москвы от 21 .08.2007 №730-ПП. Создана и впервые в России внедрена эффективная автоматизированная система регулирования пассажирских перевозок и контроля проезда на городском транспорте. Annotation:

The method proposed by authors applies to crowdsourcing technologies from international information space. That implies the phased use of full cycle management system of global creativity, improvement of foreign economic relations for manufactures, providing of competitiveness for the activities at the sphere of foreign economics and development of innovation clusters. Аннотация.

Авторами предложена методика применения технологий краудсорсинга из международного информационного пространства на основе поэтапного использования инструментов полноцикличной системы управле-

ния глобальной креативностью, налаживания внешнеэкономических производственных связей, обеспечения конкурентоспособности внешнеэкономической деятельности и развития инновационных кластерных зон.

Актуальной научно-практической задачей развития российской экономики является её коренная модернизация в целях повышения конкурентоспособности на основе интеграционных инновационных проектов, реализуемых правительством РФ, например, таких как создание иннограда «Сколково» и формирование особых экономических зон технико-внедренческого типа. Проведенное исследование международного опыта создания современных интеллектуальных экономик США, Ирландии, Китая и Индии [1 , с.284] показывает, что ключевым критерием успеха модернизации мировой экономики является внедрение моделей, обеспечивающих существенное и массовое использование научных знаний в производстве товаров и услуг широкой номенклатуры в различных областях деятельности. Для этого обеспечено вовлечение широких научно-образовательных и производственных сил в процессы генерации новых знаний и креативных идей с последующим их распространением. Ключевой проблемой функционирования существующих наукоемких предприятий является отсутствие на уровне региональной администрации эффективной комплексной системы управления, позволяющей создать необходимую инфраструктуру формирования инновационных кластеров, коммерциализировать результаты научных исследований, получить преимущества в международной конкуренции, интегрироваться в глобальные процессы. Созданная сегодня для этого в регионах России инфраструктура, преимущественно решает задачи предоставления инноваторам недвижимости на комфортных условиях, но не позволяет обеспечить эффективное целевое инкубирование малых предприятий, создание технологий, востребованных на глобальном рынке. SWOT-анализ реализации инновационного потенциала России указывает на новые возможности для конкурентоспособной внешнеэкономической деятельности (ВЭД) с использованием подходов к процессам глобализации [1 , с.264]. Во-первых, современные технологии Интернет, вступление

России в ВТО практически стирают границы ведения внешнеэкономической деятельности и международного сотрудничества. Технологическое развитие привело к появлению глобальной деловой сети, как фактора производства, нового института рыночной экономики, сокращению трансакционных издержек. Развитие ВТО, постепенная либерализация торговли товарами и услугами, рынков капитала, ограничение политики протекционизма сделали мировую торговлю более свободной. В результате, удаленность партнеров друг от друга перестала быть решающим препятствием для их производственного и научнотехнического сотрудничества. Во-вторых, Интернет выравнивает условия международной конкуренции для крупных и малых компаний. Это нашло отражение в т.н. «парадоксе Нейсбитта»: «Чем выше уровень глобализации экономики, тем сильнее ее мельчайшие участники» [9]. Парадокс Нейсбитта имеет несколько аспектов. В условиях современных технологий, ТНК вынуждены персонализировать свои товары и услуги, то есть действовать как небольшие компании, и наоборот малые компании могут расширять свою ВЭД, увеличивая сбытовые ареалы до международных масштабов на основе отлаженных систем глобальных поставок (таких как UPS). Международное информационное пространство (МИП) и автоматизация позволяют выстраивать глобальные цепочки поставок по модели «точно-в-срок», поддерживать баланс спроса и предложения в реальном времени, диверсифицировать продукт до миллионов нишевых рынков [1 , 1 5]. С другой стороны, инертность крупных компаний при усилении конкуренции и огромных скоростях распространения информации стимулирует поиск новых форм сотрудничества с динамичными и инновационными субъектами малого бизнеса (техника абордажных крючьев, экономические кластеры и др.).

В-третьих, новой возможностью ВЭД является всеобщая связанность людей, двигателями которой являются мобильная связь и социальные сети (веб 2.0). Все сферы жизни, начиная с дипломатических отношений и заканчивая ВЭД отдельных фирм, меняются, оказываясь помещенными в цифровое пространство и глобальные социальные сети. Например, новым курсом администрации президента США Б.Обамы стала «цифровая дипломатия», основанная на использовании социальных сетей [1 4]. При этом наблюдается широкая социальная и культурная дифференциация, ведущая к формированию специфических виртуальных сообществ. В этих сетях общения и отдыха, основанных на феномене «граудсвелл»[1 ], много «информационного шума», информация слабо структурирована и многократно дублируется. Однако развиваются инициативы, позволяющие привлечь экспертное сообщество для выработки конструктивных предложений модернизации экономики и стимулирования инноваций. Например, в российском проекте «Открытое Правительство» включены механизмы социальных сетей для общественного обсуждения законопроектов, сбора предложений совершенствования системы управления (большоеправительство.рф, россиябездураков.рф). Другим примером является недавно запущенный Агентством стратегических инициатив (АСИ) и Witology проект по улучшению инвестиционного климата (сайт проекта http://asi1, включивший коллективную проработку двух стратегических инициатив: поддержка доступа на рынки зарубежных стран и поддержка экспорта и совершенствование таможенного администрирования (см. Лучшие предложения планируется включить в итоговые документы и представить на рассмотрение Наблюдательному совету АСИ при Правительстве РФ. Профессиональная сеть Госбук, Регионалочка, многие аналогичные региональные и муниципальные проекты также основаны на использовании социальных сетей. Широко распространены государственные социальные сети с использованием геоинформационных си-

стем для обратной связи с правительствами. Например, в Европейских странах люди могут указать местоположение на карте, написать о правонарушении, проблеме и проголосовать за нее. Подобный проект был запущен в 2011 году Правительством Москвы для оценки состояния и качества уборки дорог, работы и расположения светофоров, дорожных знаков (портал В бизнес-среде использование социальных сетей также получило широкое распространение для решения задач по всему маркетинговому миксу 5P во многих отраслях от текстильной промышленности, ИТ, банковского сектора, сферы общепита до автомобильной и даже военной отрасли [7]. Эти и многие другие примеры показывают, что с помощью сетевого общения можно эффективно решать задачи любой сложности. Для обозначения нового типа предприятия, действующего в экономике знаний и всеобщей связанности, исследователями введена категория «эпистемического предприятия», «сетевого предприятия по производству знаний» или в других источниках - «облачное» (Witology), «социальное» (Gartner) предприятие [6, 7, 8]. Основной характеристикой нового типа предприятий является внедрение технологий краудсорсинга, образующих своеобразные надструктурные образования без четких внешних и внутренних границ и иерархий. Термин краудсорсинг (от англ. сrowd — толпа и sourcing — источник, использование ресурсов) означает передачу отдельных производственных функций неопределённому кругу лиц на основании публичной оферты, не подразумевающей заключение трудового договора. При этом используется коллективный интеллект и синергия взаимодействия большого количества людей. Краудсорсинг позволяет агрегировать информацию, опыт, мнения, прогнозы, предпочтения и оценки. В зависимости от

используемой технологии сетевых действий можно выделить краудголосование, которое предполагает простое голосование за различные варианты предлагаемых решений, без объяснения своего выбора и предложения других вариантов. Краудсторминг предполагает комментирование решений, генерацию идей. Возможно их сочетание, например, когда для краудсторминга используется определенный пул экспертов, а для краудвоутинга – неограниченный круг. Как правило, для формирования «инновационного туннеля» краудсторминг сопровождается голосованием и комментированием пользователей, что позволяет обеспечить рейтингование инновационных предложений. Краудслаппинг заключается в выплескивании негативных эмоций, возникающих по поводу неправильных действий компании или определенного лица («выпустить пар»), но при этом без предложения чего-либо конкретного. Краудпроизводство направлено на создание продукта, имеющего некий сетевой вес, например, производство рекламного видеоролика, программных продуктов, распределенный сбор информации, на основе которых принимаются управленческие решения. Возможен ограниченный краудсорсинг, который применяется для создания определенного круга людей, имеющих специальные навыки по данному вопросу, т.е. создается некий фильтр, для увеличения КПД предложений людей. Неограниченный краудсорсинг, напротив, является более незащищенным от непрофессиональных действий некоторых пользователей, и поэтому менее эффективен для оптимизации бизнеспроцессов. Однако при этом можно привлечь оригинальные идеи в результате открытого мозгового штурма и нестандартного, междисциплинарного подхода [7, 11 , 1 2]. Мотивацией участия в проектах краудсорсинга, краудсторминга и краудпрозводственных инициативах является самореализация, лидерство, признание со стороны коллег, желание улучшить качество товаров, работ, услуг. Материальное вознаграждение при этом или вообще не предполагается или

принимает форму участия в доходах, фиксированного приза за лучшие идеи или разовые премии в соответствии со значимостью предложений членов сообщества. Возможна также реальная и/или виртуальная монетизация активности участников на специализированных биржах идей с покупкой/продажей акций идеи, с помощью которой оценивается ее потенциал. Также учитывается мотивационный фактор использования этого механизма для формирования кадрового резерва, социального лифта для талантливых и креативных авторов. Выработанные с помощью краудсорсинга инновационные решения компаний более привлекательны для потребителей, если при их создании были задействованы непосредственно сами потребители и они могут свободно обмениваться своими идеями о продукте (мотивационный фактор участия в управлении). В результате постепенно стираются грани между устоявшимися векторами информационных потоков в сторону открытых консультаций, разделенного знания, взаимного признания опыта, поиска путей взаимодействия между компанией, сотрудниками, потребителями и поставщиками.

Рис. 1 . Методика создания комплексной инфраструктуры поддержки инновационного механизма развития на основе краудсорсинга

Использование краудсорсинга основано на законе Джоя, по которому в любой области деятельности большая часть знания находится за пределами любой действующей в этой области организации [2, 7]. Закон Джоя подтверждается исследованиями Ф.Хайека и Е.Хиппеля, утверждавшими, что знание о продукте распределено среди его пользователей в обществе и принимает форму цены на него в процессе коммуникации продавца и покупателя [3, 4, 7]. Основная задача краудсорсинга, таким образом, состоит в том, чтобы найти доступ к этому знанию. Новые возможности ВЭД, конвергенции социальных и бизнес-сетей, требуют дополнительного исследования новых управленческих подходов и формирования моделей использования этих технологий для решения актуальных проблем построения инновационного механизма развития российской экономики, кластерных зон, их интеграции в международное информационное пространство. Авторами для формирования комплексной инфраструктуры системы поддержки инновационного развития территории, массового внедрения инноваций, конкурентоспособной ВЭД разработана специальная методика (Рис.1 ). Выделенные в ней четыре блока соответствуют необходимым задачам формирования кооперационных связей в кластере, устойчивого кадрового обеспечения, инфраструктурного обеспечения в режиме «одного окна», интеграции в международное разделение труда и глобальные инновационные процессы. В результате реализации методики формируется сбалансированная для развития наукоемкого бизнеса инфраструктура, обеспечивающая полный цикл сопровождения инновации от глобальной креативности до международного маркетинга и решения производственных задач. Подробно c общими составляющими методики можно ознакомиться в нашей книге «Государственное и муниципальное управление с использованием информационных технологий» [1 ].

Рассмотрим конкретные аспекты применения краудсорсинга для формирования инфраструктурных блоков поддержки инновационных механизмов. На первом этапе методики проводится поиск емких российских и международных рынков сбыта, диагностика региональных условий, в том числе инвентаризация имеющегося задела. Для этого расставленные на федеральном уровне приоритеты, программы и проекты, международные тренды, анализируются с точки зрения их реализации на данной территории. На этом этапе следует использовать международные рынки предсказаний и форсайта[1 ], как для просмотра вероятности тех или иных событий, так и собственной проверки научно-технических предположений. Число компаний, которые используют рынки предсказаний для внутрикорпоративных целей, стремительно растет. Среди них крупнейшие технологические вендоры – IBM, HP, Siemens, Intel, Microsoft, Google и др. [7]. Благодаря такому подходу объединяются международные прогнозы с усилиями федеральных, региональных и местных органов власти, науки и бизнеса в сфере предвидения тенденций в инновационных областях. На этом этапе также осуществляется оценка перспектив образования кластера, т.е. горизонтальных и вертикальных кооперационных связей, которые позволят осуществлять взаимодействие участников в долгосрочной перспективе. Для этого могут использоваться все виды краудсорсинга для генерации и оценки направлений развития, имеющихся наработок, мониторинг социальных сетей с целью приглашения якорных инвесторов, кластеризации участников. При этом, в качестве ключевой цели, способной обеспечить консолидацию пользователей в социальные платформы, высокую емкость рынка сбыта, массовость инноваций, выделим ком-

фортность проживания как комплексную ситуационную характеристику социально-экономических и экологических условий проживания населения. В условиях глобальных интеграционных процессов следует учитывать международную дифференциацию комфортности, так как это влияет на размещение представительств, производственных и исследовательских подразделений транснациональных компаний – вендоров, владеющих самыми современными технологиями. Поэтому страны с развивающейся экономикой стремятся создать максимально комфортные условия для привлечения таких компаний на свою территорию. Краудсорсинг должен получить распространение для широкой генерации идей и инноваций, внедрения новых технологий для обеспечения комфортности проживания и ведения международного бизнеса. Например, в ЖКХ, здравоохранении, жилищном строительстве находят широкое применение информационно-коммуникационные технологии, нанотехнологии и наноматериалы [1 , c.261 ]. Для интеграции науки, образования и производства, подготовки кадров для инновационной сферы, развития креативного потенциала, формирования универсальных инновационных навыков необходимо устойчивое кадровое обеспечение (второй этап). Краудсорсинг способствует не только поиску идей, но и глобальному поиску специалистов, активных и авторитетных лидеров и инноваторов [7]. Посредством социальной сети целесообразно организовывать подготовку кадров, в том числе формирование и обсуждение программ дистанционного обучения менеджменту идей, инновационному менеджменту, ведению ВЭД с тренингами и вебинарами, с участием международных экспертов. Примером лекций-телемостов из ведущих лабораторий и университетов мира, представляющих последние технологические инновации и научные достижения, имеющие практическое применение в бизнесе, является дистанционный образовательный проект Knowledge Stream, организованный

Центром новых технологий и технологического предпринимательства Digital October (прямые трансляции и видеозаписи телемостов доступны на В такую международную социальную сеть постоянного обучения и тренинга должны быть включены все участники инновационной деятельности — руководители малых и инновационных предприятий, руководители организаций инфраструктуры поддержки, преподаватели по предпринимательству и развитию новых бизнесов, руководители и специалисты государственных и муниципальных органов власти, менторы — наставники молодых менеджеров. Формирование инфраструктурного обеспечения в режиме «одного окна» в свою очередь декомпозируется на три блока (этапы 3-5). В блоке инженерно-коммунальной инфраструктуры (этап 3) социальная сеть в сообществе ЖЖ ( или иной популярной сети будет способствовать коммуникативности предприятий между собой, обеспечению комфортной среды для инноваторов. Необходимый для инфраструктурного обеспечения режим «одного окна» обеспечивается в этом блоке как «единая точка сборки» таможенных, налоговых и других государственных услуг на основе социальных взаимодействий и сигналов обратной связи. В составе инфраструктуры также выделяется информационнотелекоммуникационная составляющая (дата-центр, центр обработки данных). Мощная инфраструктура может обслуживать краудсорсинговые платформы резидентов зоны, в том числе на основе современных моделей «облачных вычислений», виртуализации, моделей «система как сервис». Другой сценарий применения краудсорсинга в этом блоке основывается на распределенных вычислениях (добровольных или грид-вычислениях[2]). Например, распределенные вычисления использу-

ются для моделирования свертывания молекул белка, что необходимо для изучения болезней (сайт проекта, в проекте World Community Grid (WCG) и многих других (см. список проектов наДобровольные_вычисления). Блок финансовой инфраструктуры (этап 4) необходим для непрерывности финансирования бизнес-проектов на всех стадиях инновационного цикла от возникновения идеи до производства и ВЭД. При этом привлечение международного краудфандинга[3] в этом блоке будет способствовать не просто сбору средств, информированию о программах финансирования, но и открытой научной экспертизе, адресному консультированию пользователей друг друга на основе личного опыта, методическому содействию для качественной подготовки заявки для участия в конкурсах. Это характерно для тематических площадок, где собирают небольшие гранты на научные исследования, а также на поддержку стартапов молодых учёных, К открытой экспертизе проектов владельцы этих ресурсов привлекают профессиональных учёных. Данная схема предлагает инвесторам обратную связь со специалистами в формате ведения блога, в которых дается соответствующий отчет [11 ]. Целесообразно также создание виртуальных площадок, интегрированных в мировую экономику и помогающих инвесторам найти предпринимателей, выйти на международные рынки привлечения капитала (такие как реальные или виртуальные венчурные форумы, виртуальные IPO[4], деловые витрины продукции и инвестиционных предложений, сети социальных сообществ и синдикатов бизнес-ангелов и др.). Такие сообщества и виртуальные площадки общения могут быть выращены на базе популярных социальных сетей, таких как,, и др. Другая задача краудсорсинговой площадки — преодолеть «информационную асимметрию», присущую ранним стадиям развития инновационных предприятий, разрешить ситуацию неопределенности

и снизить риски венчурных вложений. Помощником в разрешении этой проблемы являются биржи предсказаний и форсайта, краудголосование для рейтингования инвестиционных проектов. Финансовая инфраструктура – один из элементов поддержки развития инноваций (рис.1 ). С помощью краудсорсинговых инструментов блока инновационной инфраструктуры (этап 5) обеспечивается консолидация пространства инновационных исследований, налаживание коллективных дистанционных форм обсуждения, в том числе новейшего научного оборудования мирового уровня. Краудсорсинговые технологии через публикацию и рассмотрение проектов могут способствовать образованию виртуальных центров трансфера технологий. Центры трансфера технологий являются необходимым элементом формирования кластера, поддерживая малые инновационные предприятия на начальном этапе их деятельности, обеспечивая помощь в разработке инновационных продуктов на «допосевной» стадии, создание базы идей компании и превращения разработки в реальный продукт, интересный для инвесторов. Для технологического аудита краудсорсинг позволит сформировать матричный пул сетевых экспертов, которые могут оценить научно-коммерческий потенциал и новизну разработки с точки зрения ее глобальной конкурентоспособности. Для определения контактных точек международного научно-технического сотрудничества, обеспечения наукоемкого экспорта и расширения международной технологической интеграции выделен блок интеграции в международное разделение труда (МРТ) (этап 6). В этом блоке должны быть подключены международные рынки идей, инноваций, исполняемых НИОКР. Список таких площадок, являющихся как самостоятельными платформами краудсорсинга

для открытых инноваций, так и продвигаемых владельцами брендов, в том числе созданных по их инициативе, достаточно велик [7]. Учитывая налоговые и таможенные льготы, снижение административных барьеров для ведения ВЭД в режиме «одного окна», инновационная зона имеет высокую инвестиционную привлекательность для осуществления высокотехнологического предпринимательства и развития наукоемкого аутсорсинга транснациональных компаний, которые могут быть привлечены через площадки открытых инноваций. Кроме того, неформальное общение на независимых площадках должно способствовать последующему образованию международных бизнес-связей и интеграции в МРТ. Для применения краудсорсинговых технологий для каждого блока предлагается использовать 6-ти этапный алгоритм. На первом этапе ставится цель проекта краудсорсинга. Целями краудсорсинга могут быть проведение «мозгового штурма» и экспертизы для решения какой-либо проблемы по перечисленным блокам, обеспечение глобальной и локальной коммуникации резидентов, участия в выработке стратегии и распределении ресурсов развития, формирование «обратной связи» с системой управления инфраструктурой. На следующем этапе происходит выбор площадки и инструментов краудсорсинга. Площадка может быть внутренняя, доступная только для идей и знаний сотрудников компании резидента, их клиентов и поставщиков и/или внешняя, международная площадка для открытых инноваций. Например, на площадке публикуются проблемы, которые не удаётся решить в рамках корпораций, лучшие решения получают денежные вознаграждения. Клиентами проекта являются NASA, корпорация Procter & Gamble и многие другие. Критериями выбора площадки должен являться ее функционал (отраслевая направленность, возможности аналитики сообщества, визуализации результатов, наличие мобильных

коммуникаций и т.п.) и доступ к широкому пулу участников. На 3-м этапе формируется сообщество путем информирования, мотивации, фильтрации. Важно также не только привлечь массу участников со всего мира, но и обучить генерации идей и инноваций. При этом, в качестве условия краудсорсинга следует выделить массовость и независимость участников, их децентрализацию в пространстве, разнородность участников (по опыту, возрасту, социальному статусу), прозрачность действий пользователей, истории изменений и всего процесса получения результата, наличие мотивации. Акцент также делается на максимальное простое привлечение в сообщество и взаимодействие с ним, поэтому эффективно использовать уже существующие международные площадки для мозговых штурмов и открытых инноваций. На этом этапе необходимо выработать различные правила работы площадки (определение лучших участников для схемы мотивации, правила модерации, фильтрации участников и т.д.). На четвертом этапе, ограниченном по времени, по выработанным ранее правилам, происходит запуск платформы краудсорсинга и управление ее работой (формирование тем, направление экспертов, модерирование предложений и комментариев). На пятом этапе происходит анализ и выбор представленных сообществом предложений, анализ участников, награждение победителей, принимается решения о закрытии или продолжении работы площадки. Для её последующей работы, а также для функционирования социального и профессионального лифта, поиска и привлечения креативных лиц, рейтингования предложений важными инструментами являются: — визуализация активности пользова-

телей, — кластеризация пользователей — генераторов, соавторов, экспертов, соединителей и мотиваторов [5], — выделение наиболее эффективных генераторов идей, — расчеты репутационного уровня и взвешивание голосов пользователей на основе их прошлых заслуг.

связь между блоками, оценку социальноэкономической эффективности по критериям соответствия результатов поставленным целям глобальной конкурентоспособности и сформулировать дальнейшие цели стратегического развития инновационной структуры. К показателям эффективности краудсорсинга можно отнести количество новых идей, сэкономленные компаниями средства в результате работы краудсорсера,

Для этого нового инструментария активно разрабатываются и совершенствуются специальные аналитические модели и технологии. Например, замечено, что авторами лучших идей становятся участники, испытывающие меньшее сетевое давление, то есть участники, занимающие брокерские позиции между кластерами сети и обеспечивающие ком- Рис.2. Типовая структура активности граждан по результатам муникации с различ- европейских исследований [1 3] ными группами измеренной добавленной стоимости специалистов[8]. Учитывая специфику, сложбренда или компании, снижение количеность и новизну инструментария анализа соства времени, потраченного для сбора общества сети, целесообразно при выборе данных в рамках формальной фокусплощадок учитывать встроенные возможногруппы или направления исследований, сти такой аналитики. объем привлеченных ресурсов (человеческих, инвестиционных и др.), в разрезе Важно не только сгенерировать идею, но и географии источников. Например, эксреализовать ее, превратить в инновацию и траполяция результатов европейских исготовый продукт/услугу комфортного прожиследований краудсорсинговых процессов вания. На этапе 6 формируются алгоритмы и (рис.2) на Зеленоградский администрапроекты внедрения новых технологий для тивный округ с численностью населения развития социально-экономической инфрав 211 тыс.человек, дает следующие поструктуры, повышение комфортности прожиказатели. Из 40% жителей пользующихся вания и ведения международного бизнеса. Интернет, 3% (2,54 т.чел) — очень активные. Если 2 часа в месяц они будут траВ процессе реализации алгоритма необходитить на решение проблем города в мо обеспечить постоянную итерационную

окружной социальной сети, то в результате органы исполнительной власти Зеленограда дополнительно получают 60 960 часов в год или 7620 человеко-дней, что соответствует работе 30 государственных служащих на регулярной основе. Результатами поиска и применения инструментов краудсорсинга из международного информационного пространства по предложенной авторской методике (рис.1 ), может являться достижение сочетания интересов различных бюджетных уровней, консолидация всех бенефициаров, привлечение необходимых человеческих, инновационных и инвестиционных ресурсов для повышения комфортности проживания на территории и интеграции в МИП. Не менее важна поддержка совместных исследований, интеграция малых, средних и крупных инновационных фирм в цепочки формирования стоимости в МРТ, развитие кооперационной сети «наука и образование – инновационный малый и средний бизнес – крупный бизнес», распространение знаний из сектора исследований и разработок и их капитализация, стимулирование развития научно-исследовательских комплексов и наукоемкого аутсорсинга. Развитие устойчивых международных научно-производственных кооперационных связей, внедрение инноваций в деятельность широкого круга предприятий, кластерных инновационных зон позволит обеспечить их обновление, стратегически важную диверсификацию экономики, снижение нагрузки сырьевой составляющей российской экономики и необходимое замещение импорта инновационных технологий и товаров. Литература:

1 . Иванов В.В., Коробова А.Н. Государственное и муниципальное управление с использованием информационных технологий. — М.: ИНФРА-М, 201 2. — 383 с. — (Национальные проекты). 2. Lakhani K., Panetta J. The principles of Distributed Innovation // Innovations: Technology, Governance, Globalization Summer

2007, Vol. 2, No. 3: 97–11 2. 3. Haek F. The use of knowledge in Society // The American economic review. — 1 945. — nw1 .html 4. Hippel Е. Democratizing Innovation. – CambridgeMA: MIT Press, 2005. — .ht m 5. Dabke P. Incentives DO work, monetary rewards may not! — 05.06.201 0 0/06/incentivesdo-work-monetary-rewards-may.html. 6. Bradley A., McDonald M. The Social Organisation: How to Use Social Media to Tap the Collective Genius of Your Customers and Employee. – Harvard Business Review Press, 2011 . 7. Witopedia. Энциклопедия Witology. — 8. Менеджмент идей. — Witology — http://crowd21 /book/node/com plex/1 72. 9. Naisbitt J. Global paradox: the bigger the world economy the more powerful its smallest players. - N.Y., Morrow, 1 994. 1 0. Håkanson L.The firm as an epistemic community: the knowledge based view revisited. — r.php?id=500937&cf=43. 11 . Гутарук Е. Скинемся на прогресс? – 201 0. — 1 2. Howe J. The Rise of Crowdsourcing. – WIRED, с2006. — 4.06/c rowds.html. 1 3. Поте Л. Е-Government 2.0 — Учебная программа, созданная группой российских и иностранных экспертов, объединенных проектом Европейского Союза «Поддержка электронного правительства в Российской Федерации». — 1 4. Черненко Е. Интернет-протокольная служба Госдепа // Коммерсант. - 2011 . — №1 72 (471 3). — 773567.

Ссылки: [1 ] Граудсвелл (от англ. Groundswell — массовый энтузиазм) — социальный тренд, при котором люди для удовлетворения потребностей в чём-либо обращаются посредством сетевых технологий к себе подобным в обход традиционных институтов. Термин был введен Дж.Берноффом и Ш.Ли в книге «Groundswell: Winning in a World Transformed by Social Technologies» (Harvard Business Press, 2008) [11 ]. [2] Форсайт (от англ. foresight – предвидение, взгляд в будущее) — эффективный инструмент формирования приоритетов и мобилизации большого количества участников для достижения качественно новых результатов в сфере науки и технологий, экономики, государства и общества (источник: Википедия) [3] Грид-вычисления (от англ. grid — решётка, сеть) — форма распределённых вычислений, в которой «виртуальный суперкомпьютер» представлен в виде кластеров слабосвязанных, гетерогенных компьютеров, соединённых с помощью сети (источник: Википедия). [4] Краудфандинг — сбор средств через сеть на реализацию проектов (см. список на [5] Виртуальное IPO или виртуальное «дорожное шоу» (англ. Road Show) – маркетинговая стадия IPO, заключающаяся в проведении презентаций через Интернет перед потенциальными инвесторами для выявления их интереса к акциям компании-эмитента, предлагаемым на публичную продажу.

Источник: Статья

опубликована в журнале "Российский внешнеэкономический вестник", № № 2, 3 - 201 2 год.

Jennings is an organisational development specialist, working in Organisational Effectiveness and Management Development at Qantas Airways Limited. She has a Masters in Management, from The University of Western Sydney, Master of Arts (Adult Education) and Bachelor of Science with a Diploma of Education from Macquarie University. She specialises in leadership and management development, organisational development and change management within large organisations. Prior to working with Qantas, Sue has also worked in the pharmaceutical and financial services industries. Sue

Benjamin R. Palmer

Ltd, Melbourne.

is Director of Research and Development at Genos Pty


Менеджеры по продажам и торговые представители прошли через обучение и программу развития эмоционального интеллекта (ЭИ), разработанную с целью увеличения прибыли от продаж. Уровень ЭИ участников экспериментальной группы (ЭГ) и выручки от продаж были измерены до и после программы и сопоставлены с аналогичными результатами в контрольной группе (КГ), диагностика по которой проводилась только до и после участия в программе. Уровень эмоционального интеллекта участников ЭГ увеличился на 1 8%, в то время как в КГ уменьшился на 4%. Кроме того, доход от общего объема продаж участников ЭГ увеличился в среднем на 1 2% по сравнению с показателями в КГ. В то время, как в некоторых исследованиях показана положительная взаимосвязь между уровнем ЭИ и уровнем продаж, наше исследование одним из первых в мире демонстрирует, что развитие ЭИ увеличивает уровень дохода от продаж. В статье описана методика программы и способ ее применения для улучшения других личностных характеристик, таких как лидерство и вовлеченность персонала.


Front line sales managers and sales representatives were put through a learning and development programme on emotional intelligence designed to enhance their sales performance. The emotional intelligence and sales revenue of participants was measured before and after the programme and compared

to that of a Control Group who were only assessed before and after the programme (i.e., given no development). The emotional intelligence of the participants was found to improve by a mean of 1 8% while the Control Group decreased by 4%. In addition, the total sales revenue of the participants was found to increase by an average of 1 2% in comparison with the

Control Group. While several studies have reported positive relationships between emotional intelligence and sales performance, this study is one of the first in the world to report improvements in sales revenue resulting from emotional intelligence development. The methodology of the programme is outlined and the way in which it could be adapted to improve other human captial variables, such as leadership and employee engagement, are discussed. Introduction

Sales professionals who achieve mediocre perform- ance typically suffer from poor interpersonal skills. Most of us at one stage or another have experienced the sales person who: • Does not listen or communicate effectively. • Focuses on the sales process rather than the client responses. • Focuses on the merits of the product rather than listening to and addressing clients’ concerns. • Does not pick-up on the way their sales behaviour and approach is being perceived by clients. These characteristics and behaviours, and others like them, often result from poorly developed interpersonal skills and are common amongst sales staff. A large number of sales development programmes focus on improving sales professionals’ interpersonal skills. In recent times, the concept of emotional intelligence (EI) has become popular as a medium to develop sales professionals’ interpersonal skills and performance. This rise in popularity can be attributed to a number of factors not least of which include a growing body of research stud-ies on the relationship between emotional intelligence and sales performance. For example, the Consortium for Research on Emotional Intelligence ( reports that: Partners (in a multi-national consulting firm) with high EI produced $1 .2 million more profit from their accounts than their less emotionally intelligent peers.

At L’Oreal selecting a cohort of sales professionals on the basis of high EI led to a net revenue increase of $2,559,360.00. New sales profesionals at Metlife high in EI sold 37% more life insurance in their first two years than their less emotionally intelligent peers. This and other research of its type has led leading authors in the area to postulate that EI may be a strong determinant of sales performance (Goleman, 1 995). Indeed EI may be a better predictor of sales success than how much experience people have in sales; how bright sales people are (IQ); the behavioural preferences and styles they have (personality); and other popular measure used in the recruitment of sales people such as the SPQ Gold, a measure of sales call reluctance (Bernstein, Dudley & Goodson, 2001 ). Despite this notion, few studies have examined whether EI can be developed and whether the development of sales professionals’ EI results in enhanced sales performance. We set out to answer these two questions with a large pharmaceutical company, namely, SanofiAventis. To ensure the programme got a fair trial we took a best prac- tice approach to implementation that included: • Using pre-and-post programme assessements (of EI, sales revenue and sales performance indicators). • Selecting a large cohort of sales people to participate who were similar only in terms of their pre-programme sales performance and experience. • Ensuring key executive buy-in, support and involvement in the programme. • Linking the initiative to corporate strategy. • Removing any competing learning initiatives. • Properly branding the iniative with appropriate logos and brand materials including note pads, drink bottles, pens and stress balls. • Sourcing external expertise to assist in the design and delivery of the programme.

Following an extensive vendor selection process the company selected Genos as their partners in the project. Genos were chosen to assist in the design and delivery of the programme over other vendors for three salient reasons: 1 . The Genos model and measure of EI best aligned with purpose of the initiative. 2. Genos was able to provide examples of where they had designed and delivered similar programmes and the return on investment achieved. 3. Genos proposed a programme that involved a blend of their expertise in the assessement and development of EI with the company’s expertise in pharamceutical sales. The emotional programme


ensure accountability for, and sustainability of, the learning and development programme.


The programme was collaboratively implemented with Sanofi-Aventis’s Learning and Development team, drawing on their expertise in learning mediums and approaches that were known to work well with company staff. Two versions of the programme were designed: 1 ) a Sales Manager Version; and 2) a Representative Version. Twenty Sales Managers each had two Sales Representatives from their team participate in the Representative Version (for a total of 40 Sales Representatives). Both programmes comprised a series of workshops, one-on-one and small group coaching sessions. In the Manager Version, Genos coached Sales Managers on how to develop their own emotional intelli- gence. Sales Managers were presented with theory on emotional intelligence and behavioural reherasal activities to practice. In these sessions Sales Managers were also coached on how the theory and activities could be utilised to improve sales performance with their Sales Representatives in the Representative programme. Coaching Sales Managers on how to develop emotionally intelligent sales skills in their Sales Representatives was a core component of the programme. This component was designed to

In the Representative Version, Genos also coached Sales Representatives on how to develop their own emotional intelligence. In addition, their Sales Manager helped them construct and practice the implementation of emotionally intelligent selling techniques (as conceputalised by their Sales Manager in the Manager sessions). Both programmes were conducted over a period of six months with the flow and content of both programmes following the seven skills of the Genos model as diagrammed in Figure 1. Figure 1

The Genos model of emotional intelligence comprises a set of seven skills that define how effectively people perceive, understand, reason with and manage both their own and others’ emotions. This is shown in Figure 1 . These seven skills of emotional intelligence were identifed from a large factor analytic study of other models and measures of emotional intelligence and represent a taxonomy of the construct (Palmer, Gignac, Ekermans & Stough, in Press). Drawing from Salovey and Mayer’s (1 990) original conceptualisation of emotional intelligence, the model also shows that in the workplace, as with other

areas of life, moods, feelings and emotions influence people’s decisions, behaviour, and performance. The Genos model purports that the skills of emotional intelligence help individuals deal effectively with their own and others’ emotions and can be used to enhance decisions, behaviour and performance at work. According to this model the Managers programme involved (in order), the following activities: 1 . Time 1 : 360-degree Genos Emotional Intelligence Assessment of their emotional intelligence; Managers self-assessed and were also rated by their manager, several peers and all of their direct reports. These results were used to benchmark Sales Manager’s EI at the start of the programme. 2. One-on-one feedback regarding their assessment results. Action plans were developed using these results. 3. A one-day programme launch (large group workshop). 4. Small group coaching sessions (5 x 2 hours conducted 2-3 weeks apart) covering the development of: 5. a) Emotional self-awareness—the skill of perceiving and understanding one’s own emotions. b) Emotional expression—the skill of effectively expressing how you feel. c) Emotional awareness of others—the skill of perceiv- ing and understanding the emotions of others’. d) Emotional reasoning—the skill of utilising emotion- al information from one’s self and others in decision-making. e) Emotional Management and Control (self & others)—the skill of effectively managing and controlling one’s own emotions and positively influencing the emotions of others. 1 . Time 2: Re-assessment of their emotional intelligence using the Genos 360 Emotional Intelligence Assessment and the same raters as Time 1 . 2. One-on-one feedback regarding their Time 2 assess- ment results. Action plans for sustaining developments and self-coaching were developed in this session.

3. One-day debrief workshop (large group workshop). The Representatives programme was similar to this approach. It involved: 1 . Time 1 : 360-degree Genos Emotional Intelligence Assessment of their emotional intelligence; Sales Representatives selfassessed and were also rated by their managers and several peers. These results were used to benchmark Sales Representative’s emotional intelligence at the start of the programme. 2. One-on-one feedback regarding their assessment results. Action plans were developed using these results. 3. A one-day programme launch (large group workshop) facilitated by Genos. 4. Emotionally intelligent sales coaching sessions with their Sales Managers (five sessions conducted 2-3 weeks apart) covering the same five skills as in the Manager programme but with a selling focus that included: • How to be emotionally self-aware in the sales envi- ronment. • How to effectively express yourself with clients to build rapport and trust. • How to effectively gauge your clients reactions using the skill of emotional awareness of others. • How to use emotional reasoning to make effective sales decisions and problem solve with clients. • How to use emotional management to manage your own emotions and positively influence the emotions of clients. 5. Time 2 (or re-assessment) of their emotional intelli- gence using the Genos 360 Emotional Intelligence Assessment and the same raters as Time 1 . 6. One-on-one feedback regarding their Time 2 assess- ment results. Action plans for sustaining developments and selfcoaching were developed in this session. Results—Correlations

First we examined the relationship between emotional intelligence and Sales Representatives performance. The

As shown in Table 1 Sales Representatives’ total emotional intelligence as rated by others, was positively correlated with their Performance to Budget results (r =.43, p<.05). Consistent with previous research, this finding suggests sales people high in emotional intelligence are likely to achieve greater sales revenue than their less emotionally intelligent peers. Interestingly, emotional intelligence was not found to correlate with any other performance variable used to determine Sales Representatives performance. Furthermore, the only other sales performance variable found to correlate more strongly than emotional intelligence with sales revenue (performance to budget results), was Long Call Average. In other words, emotional intelligence was found to be a stronger determinant of Sales Representatives sales revenue than other measures of sales performance. The relationship between emotional intelligence and sales revenue found in the current study is greater than that typically observed between IQ and sales performance (e.g., r=.04, Vinchur, Schippmann, Switzer & Roth, 1 998); personality and sales performance (e.g., r = .41 , with Achievement a component of Conscientiousness Vinchur et al.); and other popular measures used in the recruitment of sales representatives such as the SPQ Gold (r =.1 5, Bernstein et al., 2001 ). However, this finding requires replication in other studies before this notion can be more definitely claimed. Yet, it does lend weight to the assertion that emotional intelligence may be a better predictor of sales success than how much experience people have in sales; how bright sales people are; the behavioural

preferences and styles they have; and their tendency to be reluctant in making sales calls. Results—Emotional Development


To evaluate the efficacy of the learning and development programme (in terms of whether it improves individuals’ emotional intelligence), pre- and post-programme 360 Assessment results were compared. Sales Representatives’ results on the Genos Emotional Intelligence 360 Assessment Scale were compared to those from a ‘Control Group’ of Sales Representatives who undertook assessment only. The Control Group comprised 30 Sales Representatives matched to the participant group in terms of their sales performance and experience. Chart 1 below, presents the results for the participants in the Development Group and the Control Group respectively (average of ratings from others shown). Also presented is the industry benchmark and percentage of change in emotional intelligence assessment result assessments were completed in March 2006, three months after the completion of the programme. Percentile scores range from 1 to 99, with a population mean of 50.

Both the Development and Control groups were found to have higher mean levels of emotional intelligence than that found in the industry (i.e., Australian sales professionals’ benchmark). The mean emotional intelligence of the Sales Representatives in the Development Group was found to improve by 1 8% as a result of the interven- tion programme. In contrast, the mean emotional intelligence of the Sales Representatives in the Control Group was found to decrease by 4% over the seven-month period of the programme. It can be confidently concluded that the emotional intelligence of sales professionals can be improved through the emotional intelligence training and development programme. Chart 2 below, presents the pre- and postprogramme 360 degree emotional intelligence assessment scores for the Sales Managers who participated in the development programme. Also shown is the industry benchmark and percentage of change in emotional intelligence assessment results.

As shown in Chart 2, the pre-programme mean emotional intelligence of Sales Managers who participated in the development programme was found to be above the industry benchmark. Sales Managers’ emotional intelligence was found to improve as a result of the development intervention, however, not to the same degree as the Sales Representatives. Sales Managers’ emotional intelligence was high at the outset of the programme and higher than that of Sales Representatives. This finding is consistent with previous research showing that EI is a key underlying determinant of success and typically high in individuals that make it to management positions (Palmer, Gardner & Stough, 2003). Results—Enhanced sales performance

To evaluate whether improved emotional intelligence results in enhanced sales

performance, the average performance to budget sales figures of the Development and Control groups were compared. At the outset of the programme the sales performance of these two groups was approximately equal. The Control Group participants were chosen to match the Development Group in terms of their pre-programme performance to budget results and experi- ence with the company. Chart 3 below shows a performance to budget comparison of the Development and Control Groups post the commencement of the programme.

of the financial year). The increase in sales revenue meant that the programme returned approximately five dollars for every dollar invested in the programme within a six-month period. Furthermore, qualitative analyses of the programme made by the Development Group suggested that the programme was beneficial in improving: 1 ) Sales Managers leadership capa- bility; 2) relationships between Sales Managers and Sales Representatives, and 3) and participants job satisfaction.

As shown in Chart 3, the Development Group was found to be out performing the Control Group on average by approximately 1 2%. Following the November results major restructures of Sales Representatives occurred within the business and most participants changed territories, products and managers. As a result further comparisons were not possible. The finding would have been more conclusive if the trend in enhanced sales revenue continued for a greater period of time (e.g., during December and the fourth quarter


This programme is one of the first in the world to show that improvements in emotional intelligence can lead to increased sales revenue. In summary, the study found that: â&#x20AC;˘ Emotional intelligence was significantly correlated with Sales Representatives sales revenue (r = .43, p<.05) explaining more than 1 8% of the variance in this objective measure of sales performance.

• Mean levels of emotional intelligence were found to improve by 1 8% in the Development Group as a result of intervention and decrease in the Control Group by 4%. • The development programme was of significant benefit to those who participated in terms of performance and job satisfaction. Conclusion

This study makes a significant contribution to our knowledge about the construct of emotional intelligence and its utility as a learning and development medium. A number of academic research studies have found emotional intelligence to be positively correlated with sales performance. However, to-date there are no known studies or learning and development initiatives that conclusively show whether sales performance can be improved through emotional intelligence development. There are a number of unique attributes regarding the design of the learning and development methodology that we feel led to the success of the programme and could be utilised to improve other human capital variables. Firstly, the programme utilised pre- and post-programme assessments of both the development medium (i.e., emotional

intelligence) and the desired outcome (i.e., enhanced sales performance) to determine the return on investment. The use of assessment results like these provide: • Participants with insight into their pre- and post-pro- gramme level of skill and the amount of change achieved as a result of their efforts. • Accountability and responsibility on the part of participants and facilitators to achieve enhanced assessment results. • Facilitators and coaches with insight into individual’s strengths and weakness allowing them to focus more time and attention on specific areas of need. • Insight into the relationship between the development medium and the outcome helping participants understand the intrinsic value of focusing time and effort in the programme. We recommend the use of pre- and postprogramme assessment results in learning and development programmes for these aforementioned reasons. The use of a Control Group also allowed us to more directly measure the impact the learning and development programme had on the desired outcome. Using a Control Group provided more conclusive evidence that the

enhanced sales results were not simply a result of market influences. A second factor that greatly contributed to the success of this programme was the use of Sales Managers in the development of Sales Representatives sales performance. We feel this component contributed to the success of the programme because: • It helped hold the Sales Managers accountable for their own development (i.e., “having to walk the talk” of what they were coaching their Sales Representatives on). • Sales Managers constructed meaningful and applicable • development activities for their Sales Representatives. This so-called “blend of expertise” may be one of the factors that contributed not only to enhanced sales but indeed greater development in mean levels of EI amongst the Sales Representatives. • It contributed to enhanced working relationships • between Sales Managers and Sales Representatives. • It provided Sales Managers with tools and techniques they could apply in the continued professional development of their Sales Representatives (e.g., presentation skills). There is an old-age-adage that nothing teaches you something like having to teach it to others and we drew upon this knowledge in the design of our programme. The use of internal and external expertise could be similarly used in the development of other human capital variables such as leadership, customer service, and teamwork. Although not tested we also feel this approach may contribute to the sustainability of the development. This research study requires replication with larger sample sizes and sales professionals from different industries, before it can be concluded that emotional intelligence development can contribute to improvement in sales revenue. We used an assessment of emotional intelligence that measures how often individuals display emotionally intelligent workplace behaviour rather than emotional

intelligence as such. Although we showed improvements in emotional intelligence scores, that is, participants in our study more frequently demonstrated emotionally intelligent workplace behaviour, the question remains as to whether we developed our participants’ actual emotional intelligence. A multi-trait-multimethod study comprising different types of emotional intelligence measures (e.g., such as the MSCEIT, Mayer, Salovey & Caruso, 2000), would help answer this more academic and intriguing question. Acknowledgments

The authors wish to acknowledge the contribution of the following people in the design, implementation, analysis and overall success of the programme: Richard Harmer, from Genos, who was instrumental in the design of the programme, Sue McCarten, Colin Minness, Luke Fitzgerald, the Learning and Development team and all those who participated from Sanofi-Aventis. References

Bernstein, I.H., Dudly, G.W., & Goodson, S.L., (2001 ). “Sales man- agers: Sales call reluctance among Americans, Australians and New Zealanders”. Paper presented at the Annual Convention of the Southwestern Psychological Association, Houston, Texas, April 1 2-1 4. Goleman, D. (1 995). Emotional intelligence: why it can matter more than IQ. New York; Bantam Books. Palmer, B., Gardner, L., & Stough, C. (2003). “The relationship between emotional intelligence, personality and effective leadership”, Australian Journal of Psychology, vol. 55, pp:1 40-1 40. Palmer, B.R., Gignac, G., Ekermans, G., & Stough, C. “A comprehensive framework for emotional intelligence”. In Robert Emmerling, Manas K.Mandal & Vinod K. Shanwal (Eds). Emotional Intelligence: Theoretical & Cultural Perspectives. (in

press). Mayer, J.D., Salovey, P., & Caruso, D.R. (2000). The Mayer, Salovey, & Caruso Emotional Intelligence Test: Technical manual. Toronto: Multi-Health Systems. Salovey, P. & Mayer, J.D. (1 990). “Emotional intelligence”. Imagination, Cognition and Personality, 9, 1 85211 . Vinchur, A. J., Schippmann, J.S., Switzer, F.S., & Roth, P.L. (1 998). “A meta-analytic review of predictors of job performance for sales people”. Journal of Applied Psychology, 83, 586-597.

Source: Jennings,

S. & Palmer, B.R. (2007). Enhancing sales performance through emotional intelligence development. Organisations and People, 1 4, 55-61 .

Вице-президент Проектного департамента Witology. В Witology занимается разработкой методологии реализации краудсорсинг-проектов, типологизации ключевых операций, а так же программы обучения фасилитаторов и менеджеров.

Ассессмент (от англ. Assessment) — стандартизированная многоаспектная оценка персонала, включающая в себя множество оценочных процедур: интервью, психологические тесты, деловые игры и т.д. Данный инструмент используется при приеме на работу, при обучении и развитии персонала, при назначении сотрудников на руководящие должности, для принятия решения о соответствии сотрудника занимаемой должности, для определения возможности выполнять новые функции, при планировании дальнейшего обучения сотрудников внутри компании и определения их потенциала развития и т.д. Многие компании уже давно на регулярной основе проводят ассессмент, что позволяет не только понимать профессиональный уровень персонала, но так же формировать кадровый резерв, а также принимать управленческие решения разного рода, начиная от продвижения по карьерной лестнице и заканчивая открытием новых подразделений и направлений бизнеса. С появлением интернета многие компании стали проводить web-assessment, что позволяет, с одной стороны, сделать его более

быстрым, а с другой – охватить регионы (и даже другие страны), не посылая целые команды ассессеров в длительные командировки. В 2006 году появилась технология «краудсорсинг», которая, с точки зрения, бизнеса становится прорывным инструментом с широкой географией его применения в компаниях, начиная от PR и заканчивая созданием сложных, многоуровневых проектов (и даже созданием новых бизнесов). Начиная с 201 2 года, компания Witology использует инструмент краудсорсинга для целей ассессмента. В 201 2 году, мы впервые попробовали применить краудсорсинг при отборе молодых специалистов для компании РосАтом, интегрируя его в уже существующий турнир молодых профессионалов (ТеМП). И, как оказалось, очень успешно применили: в итоге проект ТеМП был признан лучшим HR-проектом в России 201 2 года.

Теперь мы разрабатываем методологию формирования оценки компетенций сотрудников компании на основании их работы на специализированной краудсорсинговой платформе. Что это дает бизнесу? Как бы это претенциозно не звучало – практически все. 1 . У каждой компании есть определенные задачи, решение которых требует привлечение внешних консультантов. С помощью краудсорсинга и специализированной площадки эти решения можно находить силами собственных сотрудников на постоянной основе; 2. Возможность формировать кадровый резерв за счет поиска и отбора специалистов внутри компании, которые проявляют активную позицию, креатив и желание всестороннего развития; 3. Формировать новые компетенции, за счет сложных интеллектуальных проектов или дистанционного обучения; 4. Производить оценку сотрудников компании. Изучая текущие подходы к проведению ассессмента, я выяснил, что для оценки 1 000 сотрудников необходимо не менее 3 месяцев работы. Использование краудсорсинга позволяет не только существенно сократить сроки, но и дает ряд дополнительных преимуществ, включая решения важных для бизнеса задач силами сотрудников компании. Мне очень нравится выражение, которое характеризует этот тренд: «Успейте запрыгнуть в поезд, который только набирает скорость».

Специалист по связям с общественностью, аспирантка кафедры «Педагогика» ВГСПУ, старший преподаватель кафедры «Психология и социальная работа» ВГСПУ, партнер проекта Crowdintell.

«Для успешного ведения бизнеса одних только товаров уже недостаточно. Потребители, уставшие от стандартизированной продукции, стремятся получить товар, созданный специально для них, да еще соответствующий их внутреннему миру. Поэтому, в настоящее время зарождается новая экономика – экономика впечатлений, ориентированная на ощущения потребителя» — так написано в аннотации к замечательной книге Джозефа Б. Пайна и Джеймса Х. Гилмора – «Экономика впечатлений. Работа – это театр, а каждый бизнес – сцена». И это очень меткое наблюдение. Поэтому, если мы хотим адекватно описать какое-либо событие и действовать эффективно в современном мире, нам следует конструктивно сочетать два понятия «Поведенческая экономика» и «Экономика впечатлений». Говоря об использовании новых подходов к оценке PR-деятельности, то с нашей точки зрения, интересен и перспективен вопрос систематизации и описания информационных потоков через crowd-технологии. Представим всех людей, мнения которых нам важны и на поведение которых мы, как субъекты рынка, хотим повлиять, в виде дискретного множества. Представим экран

огромного монитора, изображения на котором будут формироваться не комбинацией интенсивности всего трех цветовых точек, а комбинацией индивидуальных эмоциональных профилей, каждый из которых в свою очередь является совокупностью реакций объекта на различные раздражители. Изображение на таком мониторе и будет отображать всю гамму мнений всех объектов, составляющих такое сообщество. Если же посмотреть на такой экран через разные фильтры, то мы увидим совершенно разные картинки, иногда похожие, когда отображаются схожие реакции на разные раздражители, а иногда совершенно не совпадающие, будто речь идет о различных сообществах. Произойдет это потому, что люди совсем по-разному могут реагировать на, казалось бы, схожие раздражители. Мы научились считывать информацию, которую нам посылает активная часть сообщества, заинтересованная в донесении своего мнения или в ответ на конкретно заданный вопрос. Этот процесс получил название краудсорсинга. Но ведь не менее интересен вопрос, как воспринимается обществом информа-

ция, которая исходит от субъекта в форме рекламы или любого другого процесса, пропагандирующего некий товар, услугу или идею, который принято относить к категории связи с общественностью – PR. Как правило, мы можем оценить результат PR— или рекламной кампании только позднее, по росту или падению выручки. Но желательно мониторить процесс двустороннего обмена информацией между обществом (crowd– произвольной, никак не структурированной толпой) и заинтересованным ресурсом более-менее в реальном времени. А для этого надо вначале определить, за какими процессами мы хотим наблюдать. Краудсорсинг мы уже упомянули. Это процесс, который описывает поступление информации от источника, в качестве которого выступает сообщество (crowd) к некоему заинтересованному субъекту, инициировавшему запрос. Назовем краудсендингом (Crowsending) процесс, которым информация, транслируемая объектом, воспринимается сообществом, которому она адресована (crowd). Тогда интенсивность процесса краудсендинга может быть использована в качестве инструмента оценки качества PR— или рекламной кампании. На экране виртуального монитора, о котором мы писали выше, это могло бы отображаться в форме нарастания интенсивности изображения. Или наоборот, – статичного изображения или даже изображения с падающей интенсивностью в случае неудачной PR-акции или рекламной кампании. Особую важность изучение процесса краудсендинга приобретает в случае, когда необходимо донести объективно важную информацию, например, разъяснение идей гармоничного развития (sustainabledevelopment). Но и в случае обычных коммерческих проектов (не только инновационных, но просто нацеленных на конкуренцию) их восприятие целевой аудиторией обусловит и экономические результаты. В свете этого, изучение процесса краудсендинга может стать очень важным в инструментарии PR-технологий.

Представим себе, что потоки ментальной информации, идущие от каждого человека и, обратно, к каждому человеку в процессе краудсорсинга или краудсендинга могут быть интерпретированы как некое векторное поле, описывающее направления движения мнений во множестве людей, вовлеченных в решение задачи, стоящей в форме аккумулирования мнений (краудсорсинг) или их распространения (краудсендинг). Тогда это векторное поле может быть описано скалярными функциями, характеризующими степени распространения мнений среди людей. Подобную скалярную функцию принято именовать дивергенцией, и характеризует она суммарный поток некоего векторного параметра (в нашем случае – интенсивность восприятия-впечатления), проходящего через виртуальную границу исследуемого кластера (сообщества) и обозначается в математике и физике div x. Если div x <0, то речь идет о краудсорсинге, если же div x >0, то – о краудсендинге. В любом случае можно будет использовать известные математические инструменты для описания таких процессов. Хотя тут надо быть очень осторожным, так как классические математические уравнения, по которым определяются скалярные характеристики векторного поля, исходят из непрерывности этого поля, а изучаемые нами явления и процессы происходят в заведомо дискретных пространствах, да к тому же нечетко определенных в своих свойствах. Но основные подходы, как мы предполагаем, будут справедливы и могут быть использованы для решения конкретных задач. Выводы. 1 . Экономическое и социальное поведение человека сегодня вышло на новый уровень представлений и решений. Изучение поведения потребителя, в рамках поведенческой экономики, без учета его

эмоционального интеллекта и эмоционального фактора сообщества (кластера-реципиента), в который он входит и которому адресован товар или идея, непродуктивно и может привести к серьезными ошибкам в планировании и управлении бизнесом. 2. Представляется важным интерпретировать совокупность всех эмоциональных профилей членов сообщества (кластера-реципиента) как дискретное ментальное поле, описываемое классической моделью поля. 3. Наряду с термином краудсорсинг (div<0), описывающим процесс передачи информации от кластера-реципиента к адресанту, предлагается использовать термин краудсендинг (div>0), описывающим, соответственно, передачу информации от адресанта к кластеру-реципиенту.

is the Ralph and Dorothy Keller Distinguished Service Professor of Economics and Behavioral Science at the University of Chicago’s Graduate School of Business, where he is director of the Center for Decision Research. He is also a research associate at the National Bureau of Economic Research (NBER), where he codirects the behavioral economics project. Richard H. Thaler

is a professor of economics at Harvard University and a research associate with the NBER. In 2002, he was awarded a grant from the MacArthur Fellows Program. Sendhil Mullainathan

All of economics is meant to be about people’s behavior. So, what is behavioral economics, and how does it differ from the rest of economics? Economics traditionally conceptualizes a world populated by calculating, unemotional maximizers that have been dubbedHomo economicus. The standard economic framework ignores or rules out virtually all the behavior studied by cognitive and social psychologists. This “unbehavioral” economic agent was once defended on numerous grounds: some claimed that the model was “right”; most others simply argued that the standard model was easier to formalize and practically more relevant. Behavioral economics blossomed from the realization that neither point of view was correct. The standard economic model of human behavior includes three unrealistic traits—unbounded rationality, unbounded willpower, and unbounded selfishness—all of which behavioral economics modifies. Nobel Memorial Prize recipient Herbert Simon (1 955) was an early critic of the idea that people have unlimited informationprocessing capabilities. He suggested the term

“bounded rationality” to describe a more realistic conception of human problemsolving ability. The failure to incorporate bounded rationality into economic models is just bad economics—the equivalent to presuming the existence of a free lunch. Since we have only so much brainpower and only so much time, we cannot be expected to solve difficult problems optimally. It is eminently rational for people to adopt rules of thumb as a way to economize on cognitive faculties. Yet the standard model ignores these bounds. Departures from rationality emerge both in judgments (beliefs) and in choices. The ways in which judgment diverges from rationality are extensive (see Kahneman et al. 1 982). Some illustrative examples include overconfidence, optimism, and extrapolation. An example of suboptimal behavior involving two important behavioral concepts, loss aversion and mental accounting, is a mid-1 990s study of New York City taxicab drivers (Camerer et al. 1 997). These drivers pay a fixed fee to rent

their cabs for twelve hours and then keep all their revenues. They must decide how long to drive each day. The profitmaximizing strategy is to work longer hours on good days—rainy days or days with a big convention in town—and to quit early on bad days. Suppose, however, that cabbies set a target earnings level for each day and treat shortfalls relative to that target as a loss. Then they will end up quitting early on good days and working longer on bad days. The authors of the study found that this is precisely what they do. Consider the second vulnerable tenet of standard economics, the assumption of complete self-control. Humans, even when we know what is best, sometimes lack self-control. Most of us, at some point, have eaten, drunk, or spent too much, and exercised, saved, or worked too little. Though people have these self-control problems, they are at least somewhat aware of them: they join diet plans and buy cigarettes by the pack (because having an entire carton around is too tempting). They also pay more withholding taxes than they need to in order to assure themselves a refund; in 1 997, nearly ninety million tax returns paid an average refund of around $1 ,300. Finally, people are boundedly selfish. Although economic theory does not rule out altruism, as a practical matter economists stress selfinterest as people’s primary motive. For example, the freerider problems widely discussed in economics are predicted to occur because individuals cannot be expected to contribute to the public good unless their private welfare is thus improved. But people do, in fact, often act selflessly. In 1 998, for example, 70.1 percent of all households gave some money to charity, the average dollar amount being 2.1 percent of household income.1 Likewise, 55.5 percent of the population age eighteen or more did volunteer work in 1 998, with 3.5 hours per week being the average hours volunteered.2 Similar selfless behavior has been observed in controlled laboratory experiments. People often cooperate in prisoners’ dilemma games and turn down unfair offers in “ultimatum” games. (In an ultimatum game, the experimenter gives one player, the proposer, some money, say ten dollars. The proposer then makes an offer ofx,

equal or less than ten dollars, to the other player, the responder. If the responder accepts the offer, he gets x and the proposer gets 1 0 − x. If the responder rejects the offer, then both players get nothing. Standard economic theory predicts that proposers will offer a token amount (say twenty-five cents) and responders will accept, because twenty-five cents is better than nothing. But experiments have found that responders typically reject offers of less than 20 percent (two dollars in this example). Behavioral Finance

If economists had been asked in the mid1 980s to name a discipline within economics to which bounded rationality was least likely to apply, finance would probably have been the one most often named. One leading economist called the efficient markets hypothesis (see definition below), which follows from traditional economic thinking, the best-established fact in economics. Yet finance is perhaps the branch of economics where behavioral economics has made the greatest contributions. How has this happened? Two factors contributed to the surprising success of behavioral finance. First, financial economics in general, and the efficient market hypothesis (see efficient capital markets) in particular, generated sharp, testable predictions about observable phenomena. Second, highquality data are readily available to test these sharp predictions. The rational efficient markets hypothesis states that stock prices are “correct” in the sense that asset prices reflect the true or rational value of the security. In many cases, this tenet of the efficient market hypothesis is untestable because intrinsic values are not observable. In some special cases, however, the hypothesis can be tested by comparing two assets whose relative intrinsic values are known. Consider closed-end mutual funds (Lee et al. 1 991 ). These funds are much like typical (open-

end) mutual funds, except that to cash out of the fund, investors must sell their shares on the open market. This means that the market prices of closed-end funds are determined by supply and demand rather than set equal to the value of their assets by the fund managers, as in open-end funds. Because closed-end funds’ holdings are public, market efficiency would mean that the price of the fund should match the price of the underlying securities they hold (the net asset value, or NAV). Instead, closed-end funds typically trade at substantial discounts relative to their NAV, and occasionally at substantial premia. Most interesting from a behavioral perspective is that closed-end fund discounts are correlated with one another and appear to reflect individual investor sentiment. (Individual investors rather than institutions are the primary owners of closed-end funds.) Lee and his colleagues found that discounts shrank in months when shares of small companies (also owned primarily by individuals) did well and in months when there was a lot of initial public offering (IPO) activity, indicating a “hot” market. Since these findings were predicted by behavioral finance theory, they move the research beyond the demonstration of an embarrassing fact (price not equal to NAV) toward a constructive understanding of how markets work. The second principle of the efficient market hypothesis is unpredictability. In an efficient market, it is not possible to predict future stock price movements based on publicly available information. Many early violations of this principle had no explicit link to behavior. Thus it was reported that small firms and “value firms” (firms with low price-toearnings ratios) earned higher returns than other stocks with the same risk. Also, stocks in general, but especially stocks of small companies, have done well in January and on Fridays (but poorly on Mondays). An early study by Werner De Bondt and Richard Thaler (1 985) was explicitly motivated by the psychological finding that individuals tend to overreact to new information. For example, experimental evidence suggested that people tended to underweight base rate data (or prior information) in incorporating new data. De Bondt and Thaler hypothesized that if investors

behave this way, then stocks that perform quite well over a period of years will eventually have prices that are too high because people overreacting to the good news will drive up their prices. Similarly, poor performers will eventually have prices that are too low. This yields a prediction about future returns: past “winners” ought to underperform, while past “losers” ought to outperform the market. Using data for stocks traded on the New York Stock Exchange, De Bondt and Thaler found that the thirty-five stocks that had performed the worst over the past five years (the losers) outperformed the market over the next five years, while the thirty-five biggest winners over the past five years subsequently underperformed. Follow-up studies showed that these early results cannot be attributed to risk; by some measures the portfolio of losers was actually less risky than the portfolio of winners. More recent studies have found other violations of unpredictability that have the opposite pattern from that found by De Bondt and Thaler, namely underreaction rather than overreaction. Over short periods—for example, six months to one year—stocks display momentum: the stocks that go up the fastest for the first six months of the year tend to keep going up. Also, after many corporate announcements such as large earnings changes, dividend initiations and omissions, share repurchases, and splits, the price jumps initially on the day of the announcement and then drifts slowly upward for a year or longer (see Shleifer 2000 for a nice introduction to the field). Behavioral economists have also hypothesized that investors are reluctant to realize capital losses because doing so would mean that they would have to “declare” the loss to themselves. Hersh Shefrin and Meir Statman (1 985) dubbed this hypothesis the “disposition effect.” Interestingly, the tax law encourages just the opposite behavior. Yet Terrance Odean (1 998) found that in a sample of customers of a discount brokerage firm, investors were more likely to sell a stock that had increased in value than one that had

decreased. While around 1 5 percent of all gains were realized, only 1 0 percent of all losses were realized. Odean showed, moreover, that the loser stocks that were held underperformed the gainer stocks that were sold. Saving

If finance was held to be the field in which a behavioral approach was least likely, a priori, to succeed, saving had to be one of the most promising. Although the standard life-cycle model of savings abstracts from both bounded rationality and bounded willpower, saving for retirement is both a difficult cognitive problem and a difficult self-control problem. It is thus perhaps less surprising that a behavioral approach has been fruitful here. As in finance, progress has been helped by the combination of a refined standard theory with testable predictions and abundant data sources on household saving behavior. Suppose that Tom is a basketball player and therefore earns most of his income early in his life, while Ray is a manager who earns most of his income late in life. The life-cycle model predicts that Tom would save his early income to increase consumption later in life, while Ray would borrow against future income to increase consumption earlier in life. The data do not support this prediction. Instead, they show that consumption tracks income over individuals’ life cycles much more closely than the standard life-cycle model predicts. Furthermore, the departures from predicted behavior cannot be explained merely by people’s inability to borrow. James Banks, Richard Blundell, and Sarah Tanner (1 998) showed, for example, that consumption drops sharply as individuals retire and their incomes drop because they have not saved enough for retirement. Indeed, many low- to middle-income families have essentially no savings. The primary cause of this lack of saving appears to be lack of self-control. One bit of evidence supporting this conclusion is that virtually all of Americans’ saving takes place in forms that are often called “forced savings”—for example, accumulating home equity by paying the mortgage and participating

in pension plans. Coming full circle, individuals may impose another type of “forced” savings on themselves—high tax withholding—so that when the refund comes, they can buy something they might not have had the willpower to save up for. One of the most interesting research areas has been devoted to measuring the effectiveness of tax-advantaged savings programs such as individual retirement accounts (IRAs) and 401 (k) plans. Consider the original IRA program of the early 1 980s. This program provided tax subsidies for savings up to a threshold, often two thousand dollars per year. Because there was no tax incentive to save more than two thousand dollars per year, those saving more than the threshold should not have increased their total saving, but instead should have merely switched some money from a taxable account to the IRA. Yet, by some accounts, these programs appear to have generated substantial new savings. Some researchers argue that almost every dollar of savings in IRAs appears to represent new savings. In other words, people are not simply shifting their savings into IRAs and leaving their total behavior unchanged. Similar results are found for 401 (k) plans. The behavioral explanation for these findings is that IRAs and 401 (k) plans help solve self-control problems by setting up special mental accounts that are devoted to retirement savings. Households tend to respect the designated use of these accounts, and the tax penalty that must be paid if funds are removed prematurely bolsters people’s selfcontrol.3 An interesting flip side to IRA and 401 (k) programs is that these programs have generated far less than the full participation expected. Many eligible people do not participate, forgoing, in effect, a cash transfer from the government (and in some cases from their employer). TedO’Donoghue and Matthew Rabin (1 999) presented an explanation based on procrastination and hyperbolic discounting. Individuals typically show very sharp impatience for short-horizon decisions, but

much more patience at long horizons. This behavior is often referred to as hyperbolic discounting, in contrast to the standard assumption of exponential discounting, in which patience is independent of horizon. In exponential models, people are equally patient at long and short horizons. O’Donoghue and Rabin argued that hyperbolic individuals will show exactly the low IRA participation that we observe. Though hyperbolic people will eventually want to participate in IRAs (because they are patient in the long run), something always comes up in the short run (where they are very impatient) that provides greater immediate reward. Consequently, they may indefinitely delay starting an IRA. If people procrastinate about joining the savings plan, then it should be possible to increase participation rates simply by lowering the psychic costs of joining. One simple way of accomplishing this is to switch the default option for new workers. In most companies, employees who become eligible for the 401 (k) plan receive a form inviting them to join; to join, they have to send the form back and make some choices. The default option, therefore, is not to join. Several firms have made the seemingly inconsequential change of switching the default: employees are enrolled into the plan unless they explicitly opt out. This change often produces dramatic increases in savings rates. For example, in one company studied by Brigitte C. Madrian and Dennis F. Shea (2000), the employees who joined after the default option was switched were 50 percent more likely to participate than the workers in the year prior to the change. The authors also found that the default asset allocation—that is, the allocation the firm made among stocks, bonds, and so on if the employee made no explicit choice—had a strong effect on workers’ choices. The firm had made the default asset allocation 1 00 percent in a money market account, and the proportion of workers “selecting” this allocation soared. It is possible to go further and design institutions that help people make better choices, as defined by the people who choose. One successful effort along these lines is Richard Thaler and Shlomo Benartzi’s (2004) “Save More Tomorrrow” program (SMarT). Under the

SMarT plan, employers invite their employees to join a plan in which employees’ contribution rates to their 401 (k) plan increase automatically every year (say, by two percentage points). The increases are timed to coincide with annual raises, so the employee never sees a reduction in take-home pay, thus avoiding loss aversion (at least in nominal terms). In the first company that adopted the SMarT plan, the participants who joined the plan increased their savings rates from 3.5 percent to 1 3.6 percent after four pay raises (Thaler and Benartzi 2004). Further Reading

Banks, James, Richard Blundell, and Sarah Tanner. “Is There a Retirement-Savings Puzzle?” American Economic Review 88, no. 4 (1 998): 769–788. Camerer, Colin, Linda Babcock, George Loewenstein, and Richard H. Thaler. “Labor Supply of New York City Cabdrivers: One Day at a Time.” Quarterly Journal of Economics 11 2, no. 2 (1 997): 407–441 . Conlisk, John. “Why Bounded Rationality?” Journal of Economic Literature 34, no. 2 (1 996): 669–700. De Bondt, Werner F. M., and Richard H. Thaler. “Does the Stock Market Overreact?” Journal of Finance 40, no. 3 (1 985): 793–805. DeLong, Brad, Andrei Shleifer, Lawrence Summers, and Robert Waldman. “Noise Trader Risk in Financial Markets.” Journal of Political Economy 98, no. 4 (1 990): 703–738. Kahneman, Daniel, and Amos Tversky. “Judgement Under Uncertainty: Heuristics and Biases.” Science 1 85 (1 974): 11 24–11 31 . Kahneman, Daniel, and Amos Tversky. “Prospect Theory: An Analysis of Decision Under Risk.” Econometrica 47, no. 2 (1 979): 263–291 . Kahneman, Daniel, Paul Slovic, and Amos Tversky. Judgement Under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press, 1 982.

Laibson, David. “Golden Eggs and Hyperbolic Discounting.” Quarterly Journal of Economics 11 2, no. 2 (1 997): 443–477. Lee, Charles M. C., Andrei Shleifer, and Richard H. Thaler. “Investor Sentiment and the Closed-End Fund Puzzle.” Journal of Finance 46, no. 1 (1 991 ): 75–1 09. Madrian, Brigitte C., and Dennis F. Shea. “The Power of Suggestion: Inertia in 401 (k) Participation and Savings Behavior.” Quarterly Journal of Economics 11 6, no. 4 (2000): 11 49–11 87. Odean, Terrance. “Are Investors Reluctant to Realize Their Losses?”Journal of Finance 53, no. 5 (1 998): 1 775–1 798. O’Donoghue, Ted, and Matthew Rabin. “Procrastination in Preparing for Retirement.” In Henry Aaron, ed., Behavioral Dimensions of Retirement Economics. Washington, D.C.: Brookings Institution, 1 999. Shefrin, Hersh, and Meir Statman. “The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence.” Journal of Finance 40, no. 3 (1 985): 777–790. Shleifer, Andrei. Inefficient Markets: An Introduction to Behavioral Finance. Clarendon Lectures. Oxford: Oxford University Press, 2000. Shleifer, Andrei, and Robert Vishny. “The Limits of Arbitrage.” Journal of Finance 52, no. 1 (1 997): 35–55. Simon, Herbert A. “A Behavioral Model of Rational Choice.” Quarterly Journal of Economics 69 (February 1 955): 99–11 8. Thaler, Richard H. “Mental Accounting and Consumer Choice.”Marketing Science 4, no. 3 (1 985): 1 99–21 4. Thaler, Richard H., and Shlomo Benartzi. “Save More Tomorrow: Using Behavioral Economics to Increase Employee Saving.” Journal of Political Economy 11 2 (February 2004): S1 64–S1 87.

1 . Data are from the Chronicle of Philanthropy (1 999), available online at: 2/i 01 /1 201 whodonated.htm. 2. Data are from Independent Sector (2004), available online at: ms/research/volunteer_time.html. 3. Some issues remain controversial. See the debate in the fall 1 996 issue of the Journal of Economic Perspectives.


* This article is a revision of a manuscript originally written as an entry in the International Encyclopedia of the Social and Behavioral Sciences.

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Emotional Intelligence and Crowd Technologies Researches  

Behavioral Economics. Collection of Articles of Internet Conference

Emotional Intelligence and Crowd Technologies Researches  

Behavioral Economics. Collection of Articles of Internet Conference