Impact of microexpression recognition on sales performance

By Kasia Wezowski and Dominika Maison

Facial expressions play a crucial role in interpersonal interactions because they reflect the emotions being experienced by a person in a particular situation. Microexpressions are distinct from facial expressions in general in that they are universal, spontaneous, tough to fake, and last for only a fraction of second. As a consequence, they represent a very good way to recognize human feelings and emotions. Because customers often base their buying choices on emotions, the ability to read the emotions of customers can enhance sales. The main goal of this study in collaboration with Nocom was to assess how much impact microexpresions have on sales of products from two distinct vendors: Karnak and BMW dealers. Each salesperson’s ability to recognize microexpressions was assessed with the Micro Expressions Training Videos (METV) test and the sales performance for each participant was recorded for quarter. Statistical analyses were conducted to determine ther relationships between METV test scores and real-world sales performance.

Humans express their emotions verbally and nonverbally. In the realm of nonverbal communication, is the quite obvious array of human facial expressions. Spontaneous facial expressions are produced by the brain as a result of a combination of various determinants and emotional activities. However, people can, to some degree, fake and hide their emotions by subduing their spontaneous facial expressions. Nevertheless, in such cases, a very brief facial expression usually occurs that lasts for less than half a second and cannot be hidden (Wezowski and Wezowski, 2012). These brief involuntary expressions, known as microexpressions (Ekman and Friesen, 1969; Ekman, 2003a), can convey a great deal of moment by moment emotional information (Porter and ten Brinke, 2008).
The notion of microexpressions developed out of Haggard and Isaacs (1966) early work wherein what we know as microexpressions today were originally called “micromomentary” expressions. Importantly, microexpressions are universal in that they are common across cultures (Ekman, Sorenson, & Friesen, 1969; Ekman & Friesen, 1971; Ekman and Friesen, 1986; Matsumoto, 2001), and are even exhibited by people who have been blind since birth (Cole, Jenkins & Shott, 1989), indicating that they are not learned, but rather innate motor responses that display emotional impulses (Ekman et al., 1992). These characteristics of microexpressions are consistent with Charles Darwin’s (1872) early prediction that involuntary muscle actions should be universally human and Tomkins’ (1962, 1963) suggestions that emotion is the basis of human motivation, and that the seat of emotion is in the face. Facial expressions are coordinated motor behavioral responses that are signs of a complex neurological system, which all humans show in the same way (Matsumoto, Keltner, O’Sullivan, and Frank, 2007).
Because microexpressions are universal, they represent a very useful tool for universal recognition of human emotion. Indeed, the robustness of involuntary microexpressions makes them interesting for lie detection and criminal study, although their brief duration makes them difficult to capture. In the context of consumer behavior, microexpression studies are of interest for marketing. Indeed, a discipline called neuromarketing has been developed for this purpose. A new area in this field is the application of recognition of customer emotions to benefit sales outcomes.
Purchasing decisions can be highly emotionally motivated, and evidence of those emotions is reflected on customers’ faces as microexpressions. The better a sales person can read the emotions and associated thought processes of a customer, the more effective a motivator that salesperson has the potential to be. In this regard, we predict that the ability to read customers’ microexpressions should be beneficial to the success of sales people. The aim of the present work was to examine whether sales outcomes are related to the microexpression recognition abilities of particular sales persons. We conducted two experiments in which we compared quarterly sales outcomes to microexpression recognition test scores. We examined the sales and microexpression recognition performance of stationary sales personnel (experiment 1) and of automobile sales representatives (experiment 2). Microexpression recognition performance was assessed with the Micro Expressions Training Videos (METV) instrument. For each experiment, we tested the following three hypotheses:
H1: METV test scores will correlate with sales performance.
H2: Persons with good sales performance will obtain higher METV scores, on average, than persons not showing good sales performance.
H3: Persons with above-threshold METV scores (≥25% correct) will obtain significantly more quarterly sales, as a group, than persons with below-threshold METV scores.


This study included two experiments, each of which was conducted with a homogenous cohort. For the first experiment, a group of 83 sales staff (56 men, 27 women; mean age = 30 ± SD years) were recruited from the Karnak stationary company from throughout Italy. For the second experiment, a group of 18 sales representatives (12 men, 6 women; mean age = 31 ± SD) working in BMW automobile dealership showrooms located within Rome, Italy were recruited. The participants had no prior experience with METV and no prior microexpression-related training whatsoever.

The METV instrument is essentially a more elaborate video version of Paul Ekman’s earlier Micro Expression Training Test instrument, which used photos. In the METV program, users are shown short videos of a person’s face exhibiting one of seven basic microexpressions: anger, fear, disgust, contempt, happiness, sadness, and surprise. The emotional videos are interspersed with emotionally neutral situations and the group of videos includes models of a variety of cultures (i.e. European, Asian, Latin American, North American, African), ages (children, adolescents, young adults, mature adults), and situations (natural reaction on stimuli, in conversation). Neutral videos are included so that subjects do not become sensitized to expecting every video to be highly emotional. The video collection includes recordings of people simply listening as well as recordings of people speaking and carrying on with conversations. The main advantages of the METV program, relative to prior instruments that used still photos, are that (1) the videos show the full evolution of each microexpression and (2) it is possible to pause them or watch in slow motion or double-speed. We employed the Italian language version of the METV program. A more detailed description of the METV program, including the various languages for which it is available, can be found at:
Data collection and analysis
Each subject completed a METV test online. Sales performance data for each participant, in terms of quantity of items sold during the first quarter of 2013 (January through March), were collected from 83 persons at Karnak company (experiment 1) and from 18 persons at BMW (experiment 2). The data was shared with us by the central manager of each company based on collected for sales persons information about the quantity of units sold. Mean values are reported with standard deviations. For each experiment, the overall quantitative sales data were correlated to individual METV scores using Pearson’s correlational statistics. The mean METV scores of sales performance subgroups were compared using T-tests for independent samples and the sales data of METV score subgroups were compared with Spearman’s correlational rank test. P values less than .05 were considered statistically significant in all cases. All data analyses were conducted in SPSS software.


Experiment 1: METV performance and stationary sales
The METV test scores and quarterly sales performance data of Karnak stationary sales staff are reported in Table 1 together with the statistical results. Briefly, Pearson’s correlational analysis revealed that performance on the METV test correlated positively with sales results in a cohort of 83 Karnak sales staff (Table 1). This result supports our first hypothesis that microexpression detection ability, as measured by the METV test, would correlate with sales outcomes.
The 20 best sales performers scored better on the METV than did the 20 lowest sales performers (Table 1). This result confirms our second hypothesis that high-performing salespersons would obtain higher METV scores, on average, than low-performing salespersons.
In the examined quarter, salespersons who scored above the good recognition-ability threshold of ≥25% on the MTEV test sold, on average, 57% more stationery products than did salespersons who did not reach this threshold of microexpression recognition ability. The quantity of sales differed significantly between the above versus below METV good recognition-ability threshold subgroups (Table 1). Therefore, our third hypothesis—which predicted that persons with above good-performance threshold METV scores would obtain significantly more quarterly sales, as a group, than persons with below-threshold METV scores—was confirmed.

Experiment 2: METV performance and automobile sales
The METV tests scores and quarterly sales performance data of BMW automobile sales representatives are reported in Table 2 together with the statistical results. In contrast to experiment 1, in experiment 2, Pearson’s correlational analysis did not reveal a significant correlation between METV test scores and sales performance among a cohort of 18 BMW sales staff (Table 2). This result does not support our first hypothesis.
The surveyed individuals had been divided a priori into two subgroups based on their prior sales performance: a high-sales subgroup in which each person had sold at least 60 automobiles in the last quarter, and a low-sales subgroup in which the persons had not sold at least 60 automobiles in the prior quarter. Interestingly, despite the lack of an overall linear correlation, the high-sales subgroup achieved significantly better scores on the METV test than did the low-sales group (Table 2). Therefore, our second hypothesis—which predicted that good sales performers would achieve a higher METV score than their counterparts—was confirmed. Breaking down the METV test score subgroups in relation to the prior quarters sales performance subgroups, we found that 11 persons in the low-sales subgroup included 9 people who did not score above 20 points on the METV, and only 2 people who scored above 20; conversely, among the 7 persons in the high-sales subgroup, there were 3 people who did not score above 20 points on the METV, and 4 people who did score above 20.
Finally, when the sales of BMW salespersons who obtained low scores on the METV (

In our first experiment examining the relationship between METV test scores and quarterly stationary sales, all three of our hypotheses were confirmed. That is, we observed a positive correlation between METV scores and stationary sales performance for the experiment 1 cohort. Furthermore, the 20 best salespeople had significantly higher METV scores than the 20 poorest performing salespeople. Additionally, those who answered less than 25% of the trials correctly had significantly lower sales results than those who scored above the threshold of 25%.
Meanwhile, in our second experiment examining the relationship between METV test scores and quarterly automobile sales, only one of our three hypotheses was supported. That is, historically top salespersons achieved significantly better METV scores, as a group, than did historically lower performing salespersons. It is possible that there was not sufficient power in the second experiment to obtain significant results for our first and third statistical comparisons. If so, then a larger study involving more automobile salespersons may reveal significant effects that were not detectable in the current results.

Microexpressions in sales
Microexpressions appear even when people try to avoid displaying their emotions and do not speak (Ekman, 2003). Because they are not controlled easily, they can be highly useful in sales. In the case of high-stakes situations, in particular, the real intentions of a customer can be assessed through the recognition of microexpressions. Humans’ innate ability to read microexpressions allows us to develop accurate interpretations of how other people are feeling, and thus to adjust our actions, placing the microexpression reader in an advantageous position (Porter & ten Brinke, 2010). Hence, inferences and predictions made by reading such expressions can enable sales people to modify their own behavior such that they may be optimally responsive to the customer during interpersonal exchanges.
Sales of a product relies, in a sense, on the salesperson’s ability to sell an emotional construct. Sales representatives who are capable of awakening certain feelings in their customers, such as credibility/trust and the sense of a personal touch, have an advantage (Goleman, 1995). In this sense, purchase decisions may rely as much or more upon buyers’ emotions than on logic. Thus, the essence of effective sales is to discover what particular customers value and to stir the customer’s imagination and engender positive feelings toward the product. To be successful, the seller needs to be have a personalized approach and to remain alert and sensitive to the client, paying close attention to their facial expressions to understand how the customer is processing the interaction.
Examining the role of emotions in consumer choices, Rick and Loewenstein (2008) found that when respondents were exposed to various products that they intended to purchase, their nervous systems were activated quite differently depending on whether the price was shown soon thereafter, providing evidence of the emotional component of purchasing.

The emotionality of sales
Fundamentally, sales work deals with the performance of a manipulative behavior. When sales personnel meet the emotional needs of their clients, they end up making the desired deals. Emotions are stimulated by context, individuals’ own thoughts, and the negotiator’s actions (Fromm, 2008; Hoffman, 2008). They affect our ability to reach negotiation goals. In most negotiations, the parties involved have goals that include achieving instrumental and affective satisfaction. When sales people learn to deal effectively with their own and their clients’ emotions, they are more likely to attain their goals (Hurley, 2010). Accurate identification of emotional needs and appropriate responses to those needs will yield positive results in sales. When the emotional needs of customers are addressed, the sales person is able to tap into customers’ feelings of confidence and trust (Ekman, 1972; Shiota et al., 2004; Wiggins, 2005).
When sales people understand how to manipulate their own emotions, rather than just suppress them, their cognitive processing can be enhanced (Bar-on, 2000). The ability to recognize their own emotions and the emotions of others, and to modulate both, can enable sales people to be more creative and adaptive during the course of an interpersonal sales conversation (Pinci & Glosserman, 2008). Fromm’s (2008) theory suggests that negotiators can take control of their unwanted emotions, and to some extent the emotions of their customers, by being emotionally aware, using emotions in a strategic way, and dealing with negative emotions efficiently (Druckman et al., 2007).

Role of emotional intelligence (EI) in sales
The concept of EI represents a mixture of behaviors, abilities, and behavioral tendencies that are related to emotional constructs, such as optimism. EI describes the ability to perceive, understand, manage, and understand one’s own emotions and the emotions of others (Porter & Ten Brink, 2010). The ability to appraise emotion accurately, to access emotionality by thought processes, and to understand emotional responses promotes intellectual growth and can improve sales performance (Waal, 2003). Dimensions of EI are divided into two major categories: (1) interpersonal, meaning the perception of others’ emotions and empathy; and (2) intrapersonal, meaning self-regulation, -motivation, and -awareness. These dimensions are related directly to skills required by sales people for relationship building, and maintenance of customer relationships (Schulte, 2004).
EI is an antecedent variable in that it influences the sales persons’ aptitude, motivation, and role perception, factors which are all linked with their performance (Van et al, 2004). Positive relationships exist among extrinsic and intrinsic motivation, EI, work performance, and sales success (Bard, 2003). Emotional and social skills are important for sales personal since they deal with interpersonal issues and interact extensively with clients on a daily basis. They are prone to failures and negative feedback. Individuals with a high level of EI are better able to handle failure and its consequences (Jordan et al, 2002; Kolb et al, 2003). When sales personnel know how to manage their emotions, they enjoy their work more, which helps turn prospects into clients (Association for Psychological Science, 2008; Boyatis et al., 2000). Indeed theoretical researchers have described EI as being essential for success in sales careers. Firms are keen to employ sales people with good EI in the modern dynamic sales environment (Dixon, 2001). The EI of sales persons can be enhanced via a three-step process that begins with assessment of the current level of EI, proceeds with training, and concludes with posttraining evaluation of sales performance.

Microexpression recognition training tools
Microexpression training tools like the Subtle Expression Training Tool (SETT), which teaches users how to study minor expressions, and Facial Expression Awareness Compassion Emotions (FACE), which teaches users to recognize the first signs of emotions, are useful for sales and marketing personal (Clark et al., 2008; Burrows et al., 2006). The Facial Action Coding System (FACS) was developed for human facial expression assessment. In FACS, each observable emotion or movement in the face is coded as an action unit, and the series of action units is then decoded. Based on its still-image predecessor, the Micro Expression Training Tool developed by Ekman (2003), the recently developed METV tool used in the present study provides instruction on identifying microexpressions in video. Visual and verbal displays improve the ability of the observer to identify microexpressions (George & Zhou, 2011; Hoffman, 2008).

Duration of microexpressions
There is some debate regarding the delineation between microexpressions and the more standard sustained facial expressions known as macroexpressions (Ekman and Friesen, 1969; Ekman, 2001). For example, Ekman (2001) defined the microexpression as “…flashes on and off the face in less than one-quarter of a second”, whereas Ekman and Rosenberg, (2005) described them as lasting one-third of second or less, and Frank et al. (2009) and Matsumoto and Hwang (2011) considered them to have a duration of less than half a second. Porter and ten Brinke (2008) support Ekman and Friesen’s (1975) early suggestion that they may last from about 1/5 to 1/25 of a second, and Polikovsky et al. (2010)_suggest a similar duration of 1/3 to 1/25 of a second. These very brief durations are supported experimentally by the work of Clark et al. (2008) in which subjects were able to detect microexpressions in the range of 15–30 ms. Clark suggests that this experimentally supported duration range can inform us about the upper limit for the duration of microexpressions. Conversely, regarding a lower limit, it seems that it may be too brief to determine with current recording equipment. Given the technical limitations of the field, for our work, we have accepted the traditional range of within a half-second duration (Wezowski & Wezowski, 2012).

Two similar studies were carried out with employees from two very different companies (Karnak stationary and BMW automobiles) in Italy. Therefore, the present result should be of interest for sales across a wide diversity of sales-oriented companies. Moreover, although we examined the sales of goods, the results may be applicable for the service sector as well.
All three of our hypotheses regarding the association between microexpression detection ability and sales performance were confirmed in our first experiment involving 83 Karnak stationary salespersons. However, only the second of the three hypotheses were supported in our second experiment involving 18 BMW sales representatives. The second experiment had the limitation of a relatively small cohort size, as opposed to the first larger experiment. Thus, it is possible that two or even all three of the hypotheses would also have been supported in the second experiment if we had a larger cohort to analyze. Future studies involving large groups of sales personnel will be needed to confirm the relationship between microexpression recognition and sales performance. Overall, the present findings suggest a set of conclusions that warrants further discussion, replication, and investigation.

1. Association for Psychological Science. (2008). “Deal or No Deal? The Role of Emotions in Negotiating Offers.” Science Daily, 16 October, <>.
2. Bard, K. A. (2003). Development of emotional expressions in chimpanzees (Pan Troglodytes). Annals of the New York Academy of Sciences, 1000, 88-90.
3. Bar-On, R. (2000). Emotional and social intelligence: Insights from the Emotional Quotient Inventory. In R. Bar-On & J. D. A. Parker (Eds.), The handbook of emotional intelligence (pp. 363-388). San Francisco, CA: Jossey-Bass.
4. Bond C.F., Jr & DePaulo B.M. (2008) Individual differences in judging deception: accuracy and bias. Psychological Bulletin, 134(4), 477–492.
5. Boyatzis, R. E., Goleman, D., & Rhee, K. S. (2000). Clustering competence in emotional intelligence. Insights from the Emotional Competence Inventory. In R. Bar-On & J. D. A. Parker (Eds.), The handbook of emotional intelligence (pp.343-362). San Francisco, CA: Jossey-Bass.
6. Björklund, C. (2001). Work motivation: Studies of its determinants and outcomes. Stockholm: EFI.
7. Blanck, P. D., Rosenthal, R., Snodgrass, S. E., DePaulo, B. M., & Zuckerman, M. (1981). Sex differences in eavesdropping on nonverbal cues: Developmental changes. Journal of Personality and Social Psychology, 41(2), 391-396.
8. Burrows, A. M., Waller, B. M., Parr, L. A., & Bonar, C. J. (2006). Muscles of facial expression in the chimpanzee (Pan Troglodytes): Descriptive, comparative, and phylogenetic contexts. Journal of Anatomy, 208, 153-167.
9. Clark T., Winkielman P., & McIntosh D. (2008) Autism and the extraction of emotion from briefly presented facial expressions: stumbling at the first step of empathy. Emotion, 8, 803-809.
10. Cole, P. M., Jenkins, P. A., & Shott, C. T. (1989). Spontaneous expressive control in blind and sighted children. Child Development, 60(3), 683-688.
11. Darwin, C. (1872/1998). The expression of emotion in man and animals. New York: Oxford University Press.
12. Druckman, D. & Olekalns, M. (2007). Emotions in negotiation. Group Decision and Negotiation, 17, 1-11.
13. Dixon, A. L., Spiro, R. L., & Jamil, M. (2001). Successful and unsuccessful sales calls: Measuring salesperson attributions and behavioral intentions. Journal of Marketing, 65, 64-78.
14. Ekman, P. (1971). Universal and cultural differences in facial expression of emotion. In J. R. Cole (Ed.), Nebraska Symposium on Motivation, 1 (Vol. 19, pp. 207-283). Lincoln, NE: Nebraska University Press.
15. Ekman P. & Rosenberg E.L. (2005) What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS). New York, USA: Oxford University Press.
16. Ekman, P. (1985/2001). Telling lies: Clues to deceit in the marketplace, politics, and marriage. New York: Norton.
17. Ekman, P. (2003). Emotions revealed: Recognizing faces and feelings to improve communication and emotional life. New York, NY, US: Times Books/Henry Holt and Co.
18. Ekman, P. & Friesen, W. V. (1971). Constants across culture in the face and emotion. Journal of Personality and Social Psychology, 17, 124-129.
19. Ekman, P. & Friesen, W. V. (1975). Unmasking the face: A guide to recognizing emotions from facial clues. Oxford, England: Prentice-Hall.
20. Ekman, P. & Friesen, W. V. (1986). A new pan-cultural facial expression of emotion. Motivation & Emotion, 10(2), 159-168.
21. Ekman, P., Sorenson, E. R., & Friesen, W. V. (1969). Pancultural elements in facial displays of emotion. Science, 164(3875), 86-88.
22. Ekman, P., O’Sullivan, M., Friesen, W. V., & Scherer, K. R. (1991). Invited article: Face, voice, and body in detecting deceit. Journal of Nonverbal Behavior, 15(2), 125–135.
23. Engelberg, E. & Sjöberg, L. (2002). Emotional intelligence and attitude to money. Unpublished manuscript, Stockholm.
24. Fisher, R. & Shapiro, D. (2005). Beyond reason: Using emotions as you negotiate. New York: Penguin Group.
25. Frank M.G., Maccario C.J., & Govindaraju V. (2009) Behavior and Security. In: P. Seidenstat FXS, (Ed.) Protecting airline passengers in the age of terrorism. Santa Barbara: Greenwood Pub Group,. pp. 86–106.
26. Fromm, D. (2008) Emotion in negotiation. In C.M. Hanycz, T. Farrow, , & F. Zemans (Eds.), The theory and practice of representative negotiation. Toronto: Emond Montgomery Publications Ltd.
27. George, J. M. & Zhou, J. (2001). When openness to experience and conscientiousness are related to creative behavior: An interactional approach. Journal of Applied Psychology, 86(3), 513-524.
28. Haggard, E. A., & Isaacs, K. S. (1966). Micromomentary facial expressions as indicators of ego mechanisms in psychotherapy. In L. A. Gottschalk & A. H. Auerbach (Eds.), Methods of research in psychotherapy (pp. 154–165). New York: Appleton Century Crofts.
29. Hoffman J. (2008) Exhibition: How faces share feelings. Nature, 452(7186), 413.
30. Hurley C.M. (2010) The Effects of Motivation and Training Format on the Ability to Detect Hidden Emotions (Doctoral disseration). New York: State University of New York at Buffalo.
31. Jordan, P. J., Ashkanasy, N. M., & Hartel, C. E. J. (2002). Emotional intelligence as a moderator of emotional and behavioral reactions to job insecurity. Academy of Management Review, 27(3), 361-372.
32. Kolb, D. & Williams, J. (2003) Everyday negotiation: Navigating the hidden agendas of bargaining. San Francisco: Jossey-Bass.
33. Lewig, K. A., & Dollard, M. F. (2003). Emotional dissonance, emotional exhaustion and job satisfaction in call center workers. European Journal of Work and Organizational Psychology, 12(4), 366-392.
34. Ma, Q., Wang, X., Shu, L., & Dai, S. P300 and Categorization In Brand Extension, Neuroscience Letters, 431(1), 57-61.
35. Matsumoto, D. (2001). Culture and Emotion. In D. Matsumoto (Ed.), The Handbook of Culture and Psychology (pp. 171-194). New York: Oxford University Press.
36. Matsumoto D. & Hwang H. (2011) Evidence for training the ability to read microexpressions of emotion. Motivation and Emotion.
37. Matsumoto, D., Keltner, D., O’Sullivan, M., & Frank, M. G. (2008). What’s in a face? Facial expressions as signals of discrete emotions. In M. Lewis, J. M. Haviland & L. Feldman Barrett (Eds.), Handbook of emotions (pp. 211-234). New York: Guilford Press.
38. Mayer, J. D., Salovey, P., & Caruso, D. R. (2004). Emotional intelligence: Theory, findings, and implications. Psychological Inquiry, 15(3), 197-215.
39. O’Sullivan, M., Frank, M. G., Hurley, C. M., & Tiwana, J. (2009). Police lie detection accuracy: The effect of lie scenario. Law and Human Behavior, 33(6), 530–538.
40. Pinci, L., & Glosserman, P. (2008). Emotions are the key to sales success. NJ: Pearson Education.
41. Polikovsky S., Kameda Y., & Ohta Y. (2009). Facial Micro-expressions recognition using high speed camera and 3D-gradient descriptor. 3rd International Conference on Crime Detection and Prevention (ICDP 2009), London, pp. 1–6.
42. Porter, S. & ten Brinke, L. (2008). Reading between the lies: Identifying concealed and falsified emotions in universal facial expressions. Psychological Science, 19(5), 508–514.
43. Porter S. & ten Brinke L. (2010) . The truth about lies: What works in detecting high stakes deception? Legal and Criminological Psychology. 15(1), 57-75.
44. Porter, S., ten Brinke, L. & Wilson, K. (2009). Crime profiles and conditional release performance of psychopathic and non-psychopathic sexual offenders. Legal and Criminological Psychology, 14(1), 109-118.
45. Rick, S., Loewenstein, G. (2008). Intangibility in Intertemporal Choice”, Philosophical Transaction of the Royal Society, Biological Science, Nr 1511, s. 3813–3824.
46. Roberts, R. D., & Matthews, G. (2001). Does emotional intelligence meet traditional standards for an intelligence? Some new data and conclusions. Emotion, 1, 196-231.
47. Sauter, D., Eisner F., Ekman P., Scott S. K. (2010). Cross-cultural recognition of basic emotions through nonverbal emotional vocalizations, Proceedings of the National Academy of Sciences of the United States of America 107(6), 2408-2412.
48. Schulte, M. J., Ree, M. J., & Carretta, T. R. (2004). Emotional intelligence: Not much more than g and personality. Personality and Individual Differences, 37(5), 1059-1068.
49. Shiota, M. N., Campos, B., & Keltner, D. (2004). Positive emotion and the regulation of interpersonal relationships in P. Philippot & R. S. Feldman (Eds.), The regulation of emotion. Mahwah: Lawrence Erlbaum Associates.
50. Sjöberg, L. & Engelberg, E. (2002). Measuring and validating emotional intelligence as performance or self-report. . SSE/EFI Working Paper Series in Business Administration No 2004:3
51. Tomkins, S. S. (1962). Affect, imagery, and consciousness (Vol. 1: The positive affects). New York: Springer.
52. Tomkins, S. S. (1963). Affect, imagery, and consciousness (Vol. 2: The negative affects). New York: Springer.
53. Van Kleef, G. A., De Dreu, C. K. W., & Manstead, A. S. R. (2004) Interpersonal effects of anger and happiness in negotiations. Journal of Personality and Social Psychology 86(1), 57-76.
54. Waal, F. B. M. (2003). Darwin’s legacy and the study of primate visual communication. In P. Ekman, J. Campos, R. J. Davidson, & F. B. M. De Waal (Eds.), Emotions inside out: 130 years after Darwin’s The Expression of Emotion in Man and Animals (pp. 7-31). New York: New York Academy of Sciences.
55. Wezowski, K. & Wezowski, P. (2012). The micro expressions book for business. Antwerp: New Vision.
56. Wiggins, C. & Lowry, R. (2005). Negotiation and settlement advocacy: A book of Readings, 2nd ed. St. Paul: Thompson/West.
57. Wu Q, Shen X, Fu X. (2010). Micro-expression and its applications. Adv Psychol Sci., 18(9):1359-1368

Table 1. METV test scores predict quantity of stationary products sold per salesperson by linear correlation, sales performance subgroup comparison, and METV score rank correlation.

Table 2. Sales performance subgroups are predictive of METV test results.


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