Using Physiological Methods to Study Emotions in

New Ways of Studying Emotions in Organizations Using Physiological Methods to Study Emotions in Organizations Suzanne J. Peterson Christopher S. Reina David A. Waldman William J. Becker

Downloaded by Doctor William Becker At 09:23 17 July 2015 (PT)

Article information: To cite this document: Suzanne J. Peterson Christopher S. Reina David A. Waldman William J. Becker . "Using Physiological Methods to Study Emotions in Organizations" In New Ways of Studying Emotions in Organizations. Published online: 08 Jul 2015; 1-27. Permanent link to this document: http://dx.doi.org/10.1108/S1746-979120150000011002 Downloaded on: 17 July 2015, At: 09:23 (PT) References: this document contains references to 0 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 8 times since NaN* Access to this document was granted through an Emerald subscription provided by Token:BookSeriesAuthor:A5BB107E-88B0-46EC-8308-C4A9F81E464B:

For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.

About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. *Related content and download information correct at time of download.


SECTION I NOVEL METHODOLOGICAL APPROACHES TO STUDYING EMOTIONS IN ORGANIZATIONS


CHAPTER 1


USING PHYSIOLOGICAL METHODS TO STUDY EMOTIONS IN ORGANIZATIONS Suzanne J. Peterson, Christopher S. Reina, David A. Waldman and William J. Becker ABSTRACT The application of physiological methods to the study of psychological phenomena has garnered considerable interest in recent years. These methods have proved especially useful to the study of emotions, since evidence suggests that validly measuring a person’s emotional state using traditional, psychometric methods such as surveys or observation is considerably more difficult than once thought. The present chapter reviews the challenges associated with measuring emotions from a purely psychological perspective, and suggests that the study of emotions in organizations can benefit from the use of physiological measurement to complement traditional assessment methods. We review more established approaches to physiological measurement, including those related to hormone secretion, cardiovascular activity, and skin conductance. We then highlight somewhat more recent attempts to use neurological scanning. A theme of this chapter is that both psychological and physiological measures are relevant to understanding and assessing emotions in

New Ways of Studying Emotions in Organizations Research on Emotion in Organizations, Volume 11, 3 27 Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1746-9791/doi:10.1108/S1746-979120150000011002

3

4

SUZANNE J. PETERSON ET AL.

organizations. Accordingly, we propose a multi-method approach involving both types of assessment. Finally, we discuss the practical and ethical implications of employing various forms of physiological measurement in the study of emotions, specifically in the context of organizations. Keywords: Emotions; physiology; measurement; neuroscience


INTRODUCTION From a layperson perspective, emotions should be fairly easy to measure. Emotions are often seen as synonymous for feelings so simply asking a person how he or she feels should be adequate. Another common way to measure the emotions of others is to watch and infer based on how they behave or act. This includes facial expression, vocal expression and body language. Increasingly, however, researchers are recognizing that selfreport and observational methods do not capture the full picture of emotions (Davidson, 2003; Mauss & Robinson, 2009). For instance, an individual might answer that he or she feels calm, but that person’s heart is beating rapidly. Someone else might smile when another person walks into a room, but report not feeling anything. Yet another individual might report feeling disgust toward another, yet will hug him or her when they meet, perhaps for appearances sake. In short, traditional psychological methods are important, yet limited. They are best suited to evaluate an individual’s subjective experience of emotion or others’ subjective interpretation of behaviors that they believe indicate emotion. What they cannot ensure is accuracy that is whether individuals are really feeling what they are reporting or displaying. The use of physiological methods in the study of emotion at work can address this limitation by providing a more ecologically valid way of capturing emotions that employees are experiencing. For instance, self-report measures of emotion focus on how someone reports to feel. Although this is important, we also know that certain physiological changes occur in our bodies when we experience emotions (e.g., when we feel anxious our heart rate increases and sweat glands activate). Furthermore, we may believe that we are accurately capturing excitement by asking people whether they feel excited or by asking others if they believe someone is excited based on their behavior. However, can we be sure that what we see is excitement, rather than nervousness? Individual differences in physiological activity can allow


Using Physiological Methods to Study Emotions in Organizations

5

us to draw more valid conclusions regarding the differences between these two related, yet different, emotions. Quite simply, physiological measurement can increase our confidence that someone is truly feeling what he/she is reporting to feel or what others believe to be observing that one feels. Second, and perhaps more importantly, physiological methods allow researchers to answer more complex questions regarding emotions that should facilitate the development of stronger theories. For instance, are emotions really all in the head? Psychological theories (e.g., Lazarus, 1991; Scherer, 1984) suggest that they are, while neurophysiologists suggest that there is a strong visceral component to emotion (Adolphs, Tranel, & Damasio, 2003). Another question is whether emotions are always conscious feeling states? Neurophysiological measures of brain activity such as EEG and fMRI allow researchers to record rapid, immediate changes in emotional responses that would otherwise be impossible to assess without interrupting an individual’s participation in an experiment, or simply impractical through self-report because these processes are not available in the conscious mind. In summary, a complete assessment of emotions should take into account all levels of analysis, ranging from the feelings and behaviors associated with emotion to how they are measured at the physiological and neural level. Therefore, the purpose of this chapter is to inform researchers in the applied psychology and organizational behavior fields on how physiological methods can complement and advance the study of emotions at work. We are not suggesting a “throw the baby out with the bathwater approach” when it comes to psychological methods. Rather, we argue that psychological measurement needs a supporting cast to fully capture or complement (Becker & Cropanzano, 2010) the complexity associated with emotion measurement. We also seek to better inform those who may not be fully aware of the increasing accessibility of physiological methods. In our experience, many researchers in the organizational sciences assume these methods to be too complex namely, they require deep-level expertise to understand and operate, invasive to participants, and expensive to employ. We seek to dispel those largely faulty perceptions to some degree by highlighting the advent of new methods. Our hope is that this chapter encourages more widespread use of these methods in organizational research. In the section “Psychological Approach to the Study and Measurement of Emotions,” we describe how we are defining emotion for the purposes of this chapter. We then provide an overview of the most commonly used psychology-based measures of emotion and highlight why emotion should


6


not be studied from a purely psychological perspective. In the section “Connecting the Physiological and Neurological Measures,” we describe the most readily available or practical physiological (also known as biological) as well as neurological methods, and emphasize how they might help to overcome some of the problems found in traditional, psychological methods. In the section “Ethical and Practical Considerations,” we point out which conceptual areas related to emotions at work can most benefit from physiological methods. Finally, we conclude with a discussion of the ethical and practical considerations surrounding the use of these methods in organizations.

PSYCHOLOGICAL APPROACH TO THE STUDY AND MEASUREMENT OF EMOTIONS A common challenge facing psychologists engaged in emotion research relates to the blurred definition between emotion and affect. Although some researchers have suggested that emotion refers to the unconscious experience of emotion and affect refers to the conscious experience of emotion (Damasio, 1999), others use the terms interchangeably (Davidson, 2003, Panksepp, 2000). For the purposes of clarity and consistency in this chapter, we use emotion as an umbrella term for all the behavioral expressive, cognitive, and physiological changes that occur in individuals (Panksepp, 2000). This includes moods, discrete emotions, and general affectivity. Our goal is not to make a statement regarding construct definitions in the realm of emotion and affect, but rather to provide an overview of physiological methods that can be used to study a variety of emotional and/or affective phenomena in organizations. In organizational research, self-report and observer methods are by far the most commonly used methods to capture emotional phenomena at work. Although they offer researchers important insight into people’s perceptions of emotion and are easy to administer, important limitations should be recognized. We detail these methods and their associated limitations below. Self-Report Self-report methods can take various forms, but generally consist of asking people to describe the nature of their emotional experience. Participants



7

may be given a list of emotion terms (e.g., angry, alert, enthusiastic, and so forth) and are asked to choose the term to best describe their emotional experiences, rate the intensity of emotion, or state how long that emotion has been experienced. Alternatively, participants may be provided with traditional Likert-style questionnaires regarding their emotional states. Asking participants to evaluate their level of agreement regarding felt emotion (e.g., strongly agree) exemplifies this approach. Self-report methods of emotions are beneficial and popular primarily because they are efficient and easy to administer. Yet, several challenges remain. Self-report methods assume that people are consciously aware of their emotions and willing to report them. Similarly, self-report methods are subject to response bias such that several alternatives may bias the individual to choose them. Finally, people not accurately report what they feel due to social desirability concerns (Lopatovska & Arapakis, 2011; Mauss & Robinson, 2009). In addition, self-report measures are vulnerable to time effects (Robinson & Clore, 2002). Specifically, reports concerning one’s past, future, or trait-related experiences (i.e., how one feels “in general”) have been found to be less valid than reports that assess how someone feels “right now,” that day, or immediately following an emotional event (Mitchell, Thompson, Peterson, & Cronk, 1997).

Observer/Behavior Observer or behavior measures of emotions involve inferring a person’s emotional state from their vocal characteristics, facial expressions, and body language (Mauss & Robinson, 2009). For example, when people cringe, we infer disgust, if they raise their voice and yell we infer anger, and so forth. These methods are qualitative in nature. They involve directly watching and coding the facial, vocal, and gesture reactions of people when they are exposed to emotional stimuli. Like self-report methods, observer methods are popular because they are relatively unobtrusive. However, they are limited because how people react behaviorally is highly dependent on environmental conditions (e.g., light, noise, temperature). For example, if a person is folding her arms across her chest, is this a sign of boredom, anger, disgust, or defensive posture, or is she just cold? Moreover, some responses can be faked (e.g., the quintessential fake smile) and importantly, the presence of emotional expression cannot be equated with emotions. Just because one expresses happiness for a friend’s success does not mean that he/she is not feeling jealous, for example.


8


In summary, if a study is mainly interested in people’s perceptions and explanations of felt emotions, self-report may suffice. However, for more complex questions such as whether someone is really feeling what they claim to feel or to predict emotional responding, different methods may be necessary. Moreover, both self-report and observational methods may be adequate if research questions are focused on identifying specific emotions (happy vs. sad), capturing the valence of emotions (positive vs. negative), or interpreting arousal (anger implies high arousal and quiet implies low arousal). However, for more in-depth questions, more sophisticated methodology that assesses changes in the activity of the body, including the brain, are required to ensure emotions are being adequately captured.

Physiological Measures of Emotion Physiological measures relate to automatic responses triggered by the brain but manifested in the body. Even subtle, implicit emotions send ripples through the nervous system that prepare the body for action. Some of the methods described next will be at least passingly familiar to many researchers. In fact, it was not that long ago that many of these methods were more widely used in organizational research than they are today (Austin, Scherbaum, & Mahlman, 2002). However, physiological measures are on the cusp of resurgence due to improved technology and a renewed interest in investigating and understanding the underpinnings of emotion and how emotions influence behavior (Becker & Menges, 2013). Here, we will discuss a few of the most promising methods, including: (1) endocrinology, (2) cardiovascular, and (3) electrodermal measures. Each has been used recently to investigate emotions in organizationally relevant contexts. Endocrinology The levels of neurotransmitters such as cortisol, testosterone, and oxytocin can now be measured by relatively unobtrusive means and these levels have been shown to be reliable indicators of emotional arousal and response (Akinola, 2010). Cortisol is widely recognized as an indicator of stress response and negative emotions. There is a growing understanding of exactly how cortisol affects emotional and cognitive brain functioning. Recently, this new understanding has been used to show that, for example, in demanding situations, elevated cortisol produces short-term benefits to decision-making (Akinola & Mendes, 2012). Elevated cortisol levels have also been shown to influence risk preferences of financial traders



9

(Kandasamy et al., 2014). At the same time, chronic elevation of cortisol levels has harmful effects on employee health and job performance (Lundberg, 2005; Melamed et al., 1999). In these studies, cortisol levels are typically measured before and after an emotion-eliciting event, and the difference in the levels is operationalized as a measure of the stress experienced during the event. More recently, researchers have begun to artificially elevate levels of hormones in attempts to moderate behavior in adaptive ways. In these studies, hormone levels are manipulated using a nasal mist. For example, research has shown that elevating oxytocin can lead to increased trust (Kosfeld, Heinrichs, Zak, Fischbacher, & Fehr, 2005) and conformity to group norms (Stallen, De Dreu, Shalvi, Smidts, & Sanfey, 2012). Other studies have shown that elevated oxytocin can have potentially harmful effects, particularly for out-group members (De Dreu, 2011; Radke & De Bruijn, 2012). Thus far, oxytocin levels have not been directly investigated in organizational settings. However, even this brief overview suggests a number of important implications that might be investigated. Cardiovascular Cardiovascular measures represent even older and seemingly pedestrian methods for examining human physiology that have experienced a theoretical and practical renaissance. Equipment is now available that allows researchers to measure heart rate, blood pressure, and even cardiac activity (EKG) in naturalistic settings. Heart rate measures provide a direct assessment of rate and volume of heart activity, which has been shown to be an indicator of arousal. A variety of blood pressure measures, including arterial, systolic, and diastolic, can also be produced that can be used alone or in combination as a measure of autonomic nervous system response. EKG measures are generally transformed to provide a more readily usable measure of cardiac reactivity (Akinola, 2010). New analysis methods allow the combination of measures to identify emotional states and differentiate between hindrance and threat stress appraisal and response (Tomaka, Blascovich, Kibler, & Ernst, 1997). Combining a variety of cardiovascular measures also allows a reasonable amount of differentiation between discrete emotions (Herrald & Tomaka, 2002; Kreibig, 2010). These measures have been used in the laboratory to link discrete emotions with cognitive appraisals during ongoing real-life emotional episodes (Herrald & Tomaka, 2002). Cardiovascular measures have been paired with cognitive appraisals to differentiate between challenge and threat stress that is induced by the

10



presence of unknown others during the performance of familiar and unfamiliar tasks (Blascovich, Mendes, Hunter, & Salomon, 1999). More recently, cardiovascular measures were used to evaluate mentee emotional and cognitive responses to two different methods of coaching in a simulated organizational setting (Passarelli, 2014). The continued development of cardiovascular methods is particularly promising because they are relatively simple to administer. Electrodermal Another type of physiological methods includes those that measure electrodermal response. Despite arousing memories of lie detector machines, modern technology has produced electrodermal equipment that is portable and highly sensitive. This new equipment has proven extremely adaptable to the investigation of emotional responses in a variety of settings. Most emotions produce increased electrodermal activity to support the action tendencies associated with the particular emotion (Kreibig, 2010; Sequeira, Hot, Silvert, & Delplanque, 2009). Modern electrodermal equipment provides a measure of change in skin conductance that is extremely sensitive in terms of level and time, yet readily incorporated into data analyses. Skin response has been used to investigate cognitive and emotional responses when experiencing and reflecting on negative experiences (Nikula, 1991; Pennebaker, Hughes, & O’Heeron, 1987). Even more promising, electrodermal methods have been used to investigate implicit and anticipatory emotional responses that do not enter conscious awareness (Denburg, Recknor, Bechara, & Tranel, 2006). One drawback of these measures is their lack of discrete emotion specificity (Kreibig, 2010). Nonetheless, their relative ease of use and adaptability to field applications make them a valuable tool for investigating general levels of emotional arousal at the conscious and nonconscious level (Akinola, 2010).

Neurological Measures of Emotion Neurologically based methods represent a related, yet distinct, family of methods that has also seen an increase in popularity in the organizational sciences in recent years (e.g., Becker & Cropanzano, 2010; Hannah, Balthazard, Waldman, Jennings, & Thatcher, 2013; Reynolds, 2006; Reynolds, Leavitt, & DeCelles, 2010; Waldman, Balthazard, & Peterson, 2011a), including considerations of how neuroscience could potentially be applied to the study of emotions in organizations (see Waldman,



11

Balthazard, & Peterson, 2011b). Nevertheless, the widespread use of neuromethods have been relatively limited. We believe that this lack of actual application is due in part to a lack of awareness regarding the rapidly evolving availability and accessibility of neurologically based methods, as well as limited understanding of how best to apply these methods to questions relevant to emotions in organizational life. A complete consideration of neuroscience applications to the study of emotions in organizations would need to include theoretical issues pertaining to the neurological location of brain activity relevant to specific emotions, and how to operationalize that activity in terms of specific variables. Considerations of these issues can indeed be found in recent work (e.g., Waldman et al., 2011b; Lindquist, Wager, Kober, Bliss-Moreau, & Barrett, 2012). Our goal here is to outline two key issues that are relevant to the neurological assessment of emotions in organizational research: (1) state versus trait assessment and (2) choice of a scanning technology. State versus Trait The stimulation of emotional states represent a common way of considering how emotions are evoked in organizational life. For example, negative feedback from a supervisor could evoke fear and anger from an employee. This framing of emotions also parallels a common view of the brain as reflective of dynamic environmental stimuli (Raichle & Snyder, 2007). In a series of studies by Greene and colleagues (e.g., Greene et al., 2009), researchers demonstrated how portions of the brain associated with the ventromedial prefrontal cortex were commonly stimulated when individuals were shown pictures depicting moral dilemmas. Interestingly, this area of the brain is also associated with emotional processing, leading Greene et al. to consider connections between moral reasoning and emotions. The term mirroring has been used when the stimulation of the brain is specifically based on an imitation or reflection of another person’s emotions (Iacoboni, 2009). Thus, groups of neurons in Person A may become activated on the part of Person B when the latter observes the emotional experience of the former (e.g., pain, excitement, and so forth). It follows that neurological mirroring may be largely responsible for empathy (Decety & Meyer, 2008), as well as such processes as emotional contagion (Pugh, 2001). Neuroscience researchers have increasingly recognized that the brain is not simply in a reactive state based on stimuli that are presented to it. Rather, it can also be characterized in terms of a resting state that is relatively stable over time (Raichle, 2010; Raichle & Snyder, 2007). Such a


12


resting state is indicative of what has been termed the intrinsic brain, which has not been activated through information processing or stimuli (Waldman et al., 2011b). With that said, this resting state does not imply an inactive brain. Cacioppo et al. (2003) suggested that the brain in a resting state is indeed engaged in measurable activities. Further, intrinsic brain functioning differs across individuals, making the intrinsic brain analogous to the concept of individual trait differences. Increasingly, organizational research is showing the relevance of the intrinsic brain in understanding organizational phenomena and outcomes (Balthazard, Waldman, Thatcher, & Hannah, 2012; Hannah et al., 2013; Waldman et al., 2011a). In short, it is possible to conceive of neural assessment of emotional phenomena in terms of dynamic or changing states, as well as more trait-like aspects of emotions. For example, using the intrinsic brain approach, a researcher could attempt to identify areas of the brain associated with emotion-based traits (often referred to as affect) that are relatively stable over time, thus reflecting the individual’s enduring personality or temperament (Izard, 2009). Alternatively, and depending on the research question, certain stimuli that are designed to elicit discreet emotional reaction could be linked to particular areas of the brain. Together, these neurologically based approaches could help answer questions regarding the role of emotions in organizational behavior. Scanning Technologies But with all of that said, researchers may be unsure about the second key issue mentioned above, specifically the choice of a scanning technology. Simply stated, brain scanning forms the foundation of neuroscience assessment methods, much like tools such as surveys form the foundation of psychometrics. A number of scanning techniques are available. However, we consider here the two most popular to date in the social sciences: (1) functional magnetic resonance imaging or fMRI and (2) quantitative electroencephalogram or qEEG. “fMRI relies on the paramagnetic properties of oxygenated and deoxygenated hemoglobin to see images of changing blood flow in the brain associated with neural activity” (Waldman et al., 2011b, p. 1096). The most common metric produced by fMRI is known as blood oxygen level dependence (BOLD), which can be readily subjected to statistical analyses and matched with other types of data, such as those produced by psychometrics. In addition, colorful images reflecting BOLD activity, especially when an individual’s brain is stimulated, are commonly associated with fMRI. For example, an individual could be presented with different images or sounds, and potentially engage in actions with limited

13



physical movement (e.g., press a button). The neural result (i.e., BOLD) can be used to better understand brain structures and processes associated with perception of stimuli, or action that is taken based on stimuli. qEEG represents a second neuroimaging technique that has grown in popularity in recent years (e.g., Hannah et al., 2013; Waldman et al., 2011a). It uses advanced signal processing techniques to reveal electrical energy data about the brain through the scalp and skull (Niedermeyer & Silva, 1995). Two general categories of qEEG variables are commonly examined across delta (1 4 Hz), theta (4 8 Hz), alpha (8 12 Hz), beta (12 30 Hz), and gamma (30 100 Hz) frequency bands: (1) power or amplitude measures and (2) network connection measures such as coherence. As with fMRI, variables formed through qEEG can be used in statistical analysis programs. fMRI and qEEG are both conducive to the reactive/reflexive and intrinsic approaches discussed above, although fMRI is probably more known for reactive/reflexive assessment, while qEEG has been applied more to intrinsic assessment (Waldman et al., 2011b). Table 1 shows a comparison between fMRI and qEEG techniques in terms of other issues. A potential advantage of fMRI is its spacial resolution, or precision. However, as discussed by Balthazard et al. (2012), the precision of fMRI (especially MRI) may be more important for things such as medical purposes (e.g., location and nature of tumors), rather than understanding the neural origins of aspects of organizational behavior. Perhaps more importantly, qEEG is superior in terms of temporal resolution in that electrical data are recorded immediately in relation to the concomitant brain activity. In contrast, BOLD has been shown to represent a delayed response to actual neuronal activity, often taking several seconds to materialize after that behavior. Accordingly, it may be challenging to distinguish distinct BOLD data with regard to events that occur within a short timeframe. Other advantages are evident for qEEG. As compared to fMRI, qEEG is much more practical, perhaps especially for studies involving Table 1. A Comparison of fMRI and qEEG Technologies. fMRI Spatial resolution Temporal resolution Practicality Cost-effectiveness

High degree of resolution Lower degree of resolution Restricted use to laboratory settings Highly expensive

qEEG Lower degree of resolution High degree of resolution Highly portable; can be used in naturalistic settings Relatively low costs


14


organizational participants. fMRI is not portable, thus requiring participants to go where the equipment is located (i.e., a university-based laboratory). Moreover, fMRI requires participants to remain highly immobilized in a confined space (i.e., within a confined tube). In contrast, qEEG is portable and can be completed while people are comfortably seated and even engaged in various tasks, such as decision-making and group-based conversations (e.g., see Waldman et al., 2013). Finally, and perhaps most importantly, the high cost and lack of accessibility of fMRI scanners simply makes fMRI a cost-prohibitive technique for most organizational researchers. Scanners represent multimillion dollar devices, staffed by highly trained technicians, and are largely located at medical facilities or clinical research laboratories. Due in part to high cost and low accessibility, the number of participants in fMRI studies is characteristically small (i.e., less than 20; see Lieberman, Berkman, & Wager, 2009). As noted by Lindebaum and Jordan (2014), such N-sizes are too small for the types of statistical analyses in which organizational researchers typically engage. In contrast, cost, accessibility, and capability to process large numbers of research participants make qEEG much more feasible for organizational researchers. Accordingly, we suggest that, as compared to fMRI, qEEG applications are better suited for immediate adoption across a wide range of questions (e.g., those pertaining to emotions) and settings. While fMRI remains a powerful tool, its growth in organizational research is likely to progress more slowly.

CONNECTING THE PHYSIOLOGICAL AND NEUROLOGICAL MEASURES We believe that a systematic application of multiple methods, including physiological and brain imaging technologies, will be necessary to more fully understand emotional phenomena in organizations. When considering the above methodologies, two key issues come to mind. First, most of the methods surveyed here are best suited for the assessment of emotional reactions or relatively brief states. Examples include cardiovascular and fMRIbased assessments to stimuli. But with that said, we have also summarized how qEEG could be used to assess what has been referred to as the “intrinsic” brain (Waldman et al., 2011b), which may be able to tap the physiological basis of more trait-like or enduring emotional characteristics.



15

Second, some promising technology is emerging that is capable of assessing neurological and other physiological measures simultaneously. For example, Waldman et al. (2013) used equipment developed by Advanced Brain Monitoring, Inc. (advancedbrainmonitoring.com) that has simultaneous qEEG and qECG (i.e., electrocardiogram) capabilities. In other words, the brain and heart can be assessed simultaneously in real time. Waldman et al. (2013) demonstrated how the wireless nature of this technology could be used to provide physiologically assessment in a natural setting on a millisecond-by-millisecond basis. For example, with this technology, physiologically based assessment of individual- and team-level emotions could be made throughout a team problem-solving process.

Specific Applications of Physiological Measurement The use of physiological and neurological measures have important implications for advancing the study of three commonly discussed areas within the emotional realm of management research. Specifically, we discuss emotional regulation, emotional expression, and emotional transfer or contagion as three broad areas that are especially ripe to utilize the alternative techniques considered above in order to facilitate a deeper level of understanding beyond that which psychological measurement can provide. Emotional Regulation Emotional regulation refers broadly to individuals’ ability to manage their expressions and feelings (Grandey, 2000). Emotional labor, defined as the management of feeling and emotion in order to produce a certain public display (Hochschild, 1983), represents an often-studied aspect of emotional regulation. Employees in a broad range of industries ranging from call centers, to restaurants, to sales organizations are required to interact with customers in a certain way which has been termed “service with a smile” (Pugh, 2001). The overt act of smiling to customers, no matter what may be going on within an employee’s life, is an example of surface acting defined as regulating an outward display of emotion in order to meet the expectations of the work environment (Hochschild, 1983). A second type of emotional labor called deep acting involves employees actually inducing certain emotions so that they can genuinely experience these emotions rather than only outwardly simulating the experience of such emotions, as is the case with surface acting (Hochschild, 1983).


16


Recent meta-analytic evidence suggests that surface acting, but not deep acting, exhibited positives relationships with emotional exhaustion, psychological strain, psychosomatic complaints and negative relationships with job satisfaction and organizational attachment (Hu¨lsheger & Schewe, 2011). Given the accumulation of research suggesting the negative implications of employee surface acting, researchers recommend that employees utilize deep acting instead. However, assessing the extent to which an individual is utilizing surface or deep acting emotional labor is difficult to ascertain via psychological measurement techniques given that individuals may not be fully aware of which strategy they are using, may exhibit biases related to social desirability, or may not be able to separate how they feel internally regarding their emotions from what they are attempting to feel via emotional regulation. In other words, for any number of reasons, employees may not be aware of which method of emotional regulation they are using, and subsequently may be utilizing surface acting, rather than deep acting. This clearly has important implications for the health and well-being of employees and highlights the need to have an ecologically valid way of assessing the underlying emotions that employees are experiencing. Better measurement, including physiological measures, may ultimately help researchers design more effective training in order to help employees avoid the negative implications associated with emotional labor. Physiological measures could be utilized to help employees better regulate their emotions by providing real-time feedback regarding the emotions that they are physiologically exhibiting, versus the emotions that they are outwardly trying to exhibit. In short, self-report measures of emotional regulation may give us a general sense of how successful employees are in regulating their emotions to match the needs of the situation, but more ecologically valid methods will allow researchers to identify when there are gaps between what employees say and actually feel internally. This will facilitate an understanding of the trust “cost” of the emotional labor process. A second area under the umbrella of emotional regulation is emotional intelligence. Despite a level of confusion associated with the construct and the various ways to conceptualize and measure emotional intelligence, Chernis (2010) suggests that researchers commonly agree on the common core of emotional intelligence which is “the ability to perceive and express emotion, assimilate emotion in thought, understand and reason with emotion, and regulate emotion in the self and others” (Mayer, Caruso, & Salovey, 2000, p. 396). Of the various conceptualizations of emotional



17

intelligence, the Mayer and Salovey (1997) approach has generally garnered the most attention due to its conceptualization of emotional intelligence as an ability and its close alignment with the common core of the construct shared by emotional intelligence researchers (Ashkanasy & Daus, 2005; Walter, Cole, & Humphrey, 2011). This approach has also provided researchers with two methods to measure emotional intelligence: (1) an ability-based test that captures individual’s performance in solving emotional problems, and (2) self and/or other-report, perception-based measures that ask individuals to reflect on their own or others’ levels of emotional intelligence. While researchers disagree as to the ultimate utility of emotional intelligence (see Antonakis, Ashkanasy, & Dasborough, 2009), the basic idea that individuals differ in their ability to process emotional information is simple (Ashkanasy & Humphrey, 2011). Researchers are already beginning to utilize neuroscience to understand how the brains of individuals who exhibit high emotional intelligence differ from those individuals who exhibit lower emotional intelligence (see Timoshanko, Desmond, Camfield, Downey, & Stough, 2014). We suggest that diverse measures in the realm of physiology or neurology will allow researchers to ask and answer research questions to further unlock our understanding of emotional intelligence. For example, neurological methods may help to shed light on the debate between researchers, on one side advocating that general intelligence is the only form of intelligence that matters, versus other researchers who suggest that there are multiple types of intelligence (see Antonakis et al., 2009 for a debate). Studying emotional intelligence with neuroscience methodologies will further clarify which of the current models most closely represents how emotional intelligence functions in relation to other personality variables and which models most accurately account for differences in performance on emotional tasks. The stream of emotional intelligence research that is commonly referred to as trait emotional intelligence (Petrides & Furnham, 2003) has been criticized for encompassing everything except cognitive ability (Ferris, Perrewe´, & Douglas, 2002). A neuroscientific approach to studying emotional intelligence could help reveal whether this is the case by parsing out the variance in outcomes accounted for by various individual differences such as personality. In sum, we suggest that alternative approaches to studying emotional intelligence will advance research in the management field by more clearly defining the construct and determining the similarities and differences in the various conceptualizations of the construct so that we can more accurately understand how emotional intelligence functions.


18


Emotional Expression We suggest that physiological measurement would also be useful to further our understanding of the expression of emotions in the workplace. Emotional expression is considered a subcomponent of emotion regulation and refers to the observable verbal and nonverbal behaviors that communicate emotional states (Gross, 1999). Examples are smiling or crying. Emotional expression is especially important because of the social nature of work and given the common knowledge that employees leave managers/leaders rather than companies. Ashkanasy and Humphrey (2011) provide a useful framework to understand how the expression of emotional phenomena in the workplace affects others. They suggest that individual’s moods, emotions, attitudes, emotional traits and states such as neuroticism and emotional intelligence affect interpersonal relationships and trust via leadership, and ultimately affect the emotional climate and performance of an organization. We suggest that physiological methods would be especially useful to understand the origins of these traits and states in order to help leaders be more in tune with the emotions that they are feeling inside and how these are linked to the behaviors they exhibit, which ultimately impact how they are perceived by others. For example, leaders who may come off cold or not empathetic to followers might lead a team that reports low engagement or motivation. Alternatively, leaders who overreact in fits of rage might be able to gain insight about how they process information through “deeper” emotion-based measures that go beyond self-report. In short, using physiological methods, researchers will be better able to understand how the brain and body respond to various situations at work and better understand the link between what goes on inside an individual and how they outwardly behave. Researchers can use physiological data such as heart rate, hormone secretion, blood pressure, skin conductance, and qEEG brain data to triangulate the causes of leader behavior and in turn, train leaders to be more aware of these physiological changes and how they relate to their behaviors. Recent work suggests the strong link between our neuroanatomy, our behaviors, and our interpersonal relationships (see Cozolino, 2014). We suggest that a broad range of physiological methods can similarly help researchers understand the linkages between what goes on inside us with what goes on between us in the workplace. Management topics such as aggressive and abusive leader behaviors toward employees, are ideal areas to seek a more complete understanding of emotional expression, given the harmful effects these negative behaviors have in the workplace (see Tepper, 2007 for a review).



19

Emotional Contagion Emotional contagion, or the idea that individuals “catch” the emotions and moods from those with whom they interact (Hatfield, Cacioppo, & Rapson, 1994), represents a third and highly interrelated area that has already begun to benefit from the use of physiological methods (Decety & Meyer, 2008; Iacoboni, 2009). Organizational scholars have long recognized that emotions and moods seem to spread between individuals, almost as if one individual’s emotional system copies that of another individual via social learning (Bandura, 1971). More recently, neuroscientists have studied the neurological foundations of emotional contagion, suggesting that this copying behavior actually begins in the brain when an individual’s neurons copy or mirror the same neurological activity patterns of another individual who is observed or with whom the individual comes into contact (Gallese, Fadiga, Fogassi, & Rizzolatti, 1996). These neurons fire in such a way that even though an individual is not engaging in the same behavior pattern as the person who is observed, the individual’s mirror neurons are in essence “playing along.” This explains how followers may ultimately end up feeling the same emotion or mood as their leader despite not being consciously aware of this occurring. Using fMRI technology, researchers have supported this process of emotional transfer through the replication of others’ facial expressions and subsequent activation of the limbic system to induce an emotion that is consistent with those facial expressions (Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003). Although mirror neurons reproduce similar patterns of neuronal firing in the brain of one individual in the presence of another, it is important and interesting to note that the patterns are not identical. Researchers have demonstrated that mirror neurons fire in accordance with the intention of the action rather than simply the action by itself, which suggests that mirror neuron firing is a complex process that takes a lot of information into account (Fogassi et al., 2005). This is important because it provides an explanation for how emotions and moods spread between individuals, not just based on what is observed between two individuals, but also based on the interpretations and attributions individuals make regarding the intention or purpose of another’s actions. Thus, observing high levels of positive emotion will serve as one input into the emotional contagion process at the neurological level, but so will the assumed intention of why this high level of positive emotion exists. Organizational research on topics such as leader and follower relationship quality, team composition, vision articulation and vision sharing, organizational culture, helping behaviors, and perceptions of justice and


20


trust only begin to scratch the surface of the many topics that could benefit from a more in-depth understanding of how emotions and moods become shared between individuals and how this complex process unfolds. Physiological methods hold much promise in allowing researchers to further understand how the various levels of emotions articulated by Ashkanasy and Humphrey (2011) reinforce each other and cascade from one level to another, and in which directions. While physiological and neurological measures have important implications for furthering our understanding of many different topics within the management literature, we have discussed three particular areas in which these measurement techniques may especially hold promise in allowing us to better understanding emotional phenomena in the workplace. Emotional regulation, emotional expression, and emotional transfer via contagion are important and interrelated topics that provide an important emotional base from which other management topics build, and we suggest that these three areas can greatly benefit from increased attention via physiological/ neurological measurement perspectives.

ETHICAL AND PRACTICAL CONSIDERATIONS Despite the positives to be garnered from the use of physiological methods to advance the study of emotions at work, researchers must account for certain ethical and practical concerns before considering their use. Similar to psychological methods, researchers employing physiological methods in organizational research must address concerns related to privacy and confidentiality. However, because of the biological and even medical nature of the data, these concerns may be exacerbated. Employees will naturally be concerned with how their data are handled, stored, and used. For example, although researchers may be interested in measuring physiology only to assess employees’ emotional states, what if employees’ physiology (e.g., heart rate) is symptomatic of a medical issue (e.g., serious cardiovascular issue)? How should researchers handle this information with employees as well as employers? Although researchers can certainly guarantee confidentiality of the data, what moral or legal obligations do they have to report health problems and what implications might these problems have on employment decisions or healthcare claims? Another potential limitation relates to the invasiveness of the methods. In order to gather physiological or neurological data, employees need to



21

wear or be attached to specialized equipment. Moreover, they may be required to come to a laboratory or undergo experiments. Whether organizations and their employees will be willing to accommodate and endorse these methods remains to be seen. There are practical concerns related to the disruption of work. For example, will employees be willing to wear heart rate monitors during meetings or electrodes while operating machinery? Employers would have to weigh any requests that could interfere with employee productivity or motivation before advocating participation to their employees. Moreover, human resource and legal departments may be naturally more wary to approve projects that they believe are either invasive to employee privacy or that have medical implications. Such concerns can surely be overcome, but will take patience, candor, and trust between researchers and organizations to ensure employee safety and confidentiality, as well as to convince organizations of the benefit of physiologicalbased research. The good news is that the methods described in this chapter are increasingly becoming less invasive, thereby allowing employees to wear equipment that is virtually undetectable. For example, wireless heart rate monitors are now available that can clip to the earlobe or finger, and wearable monitors that measure cardiovascular resistance can be worn under the participants’ normal clothing (Pantelopoulos & Bourbakis, 2010). From the perspective of researchers, there are other hurdles to consider. First, attempts to link physiology and emotions are inherently interdisciplinary in nature. It may simply not be practical for researchers who are specialized in the organizational sciences to feel confident that they have the necessary technical expertise or knowledge of the associated literature to effectively utilize these methods (Waldman et al., 2011b). Applying a physiological or neurological lens to the study of emotions in organizations will require a diverse research team. Such collaborations can be cumbersome given the diversity of training and academic backgrounds. Furthermore, even when interdisciplinary teams are successfully formed, the product is not always rewarded or encouraged. Second, as noted by Waldman et al. (2011b), academic departments with stringent tenure requirements are often resistant to rewarding publications in journals (even premier ones) if they are not in their specific discipline. Similarly, journals encourage interdisciplinary work on the one hand yet do not often publish it on the other. This is due in part to the difficulty of finding reviewers qualified to evaluate the work. For example, it is difficult for editors of a management journal to have a neurophysiologist at their disposal. Without such an expert, editors cannot feel confident that the


22


work is of the caliber to warrant publication. Although these hurdles are somewhat understandable, the bottom line is that such behavior discourages faculty (especially untenured faculty) from engaging in interdisciplinary research topics and methods. The hope is that over time, journals in the organizational sciences will continue to build a diverse reviewer base and will publish more of this type of work. Special issues of journals are a common way to begin this journey. Another researcher concern relates to the resource requirements of this type of research. Researchers in the organizational sciences are used to the relatively minimal expenses involved in survey research (Waldman et al., 2011b). However, as discussed earlier, technologies representative of the physiological or neurological methods typically carry a higher financial burden. Although such methods are less costly than they once were, they still require substantially more investment than traditional psychometric methods. Moreover, much of this work requires the presence of a lab, which carries another financial cost. Research that carries a larger cost is often dependent on external grant money. However, researchers in the organizational sciences are not as accustomed to writing and submitting proposals and winning the grant, at least when compared to those in the hard sciences. Therefore, the development of strong research networks are particularly important in this area of research so that labs and other such resources can be shared across research teams. The result is that publications of this type may have large author groups ranging from five to ten authors. This is similar to what we see in the medical fields. Despite these obstacles, they are not insurmountable by any means. Researchers with the interest and expertise are able to use physiological and neurological methods to study emotions with greater ease than ever before. As more researchers join this arena, surely the path will become easier for others.

CONCLUSION The goal of this chapter was to provide researchers with an introduction to the most commonly used physiological methods that can be used to study emotion in organizations. We argue that the study of emotion can best be moved forward by the use of diverse and multiple methods. As such, we advocated for the use of physiological methods as a complement (rather



23

than replacement) to traditional, psychological methods. All the reviewed methods have pros and cons, and should be chosen based on the study goals. For example, studies that are interested in employee perceptions and explanations of felt emotions should utilize self-report methods. Studies that are interested in how individuals react to emotional stimuli from a systems perspective should consider physiological methods. Moreover, physiological and neurological measures could be used in conjunction with self-report or observation measures to ensure the accuracy and reliability, and create a more complete picture of employees’ emotional states and experiences.

REFERENCES Adolphs, R., Tranel, D., & Damasio, A. R. (2003). Dissociable neural systems for recognizing emotions. Brain and Cognition, 52, 61 69. Akinola, M. (2010). Measuring the pulse of an organization: Integrating physiological measures into the organizational scholar’s toolbox. Research in Organizational Behavior, 30, 203 223. Akinola, M., & Mendes, W. B. (2012). Stress-induced cortisol facilitates threat-related decision making among police officers. Behavioral Neuroscience, 126(1), 167 174. Antonakis, J., Ashkanasy, N. M., & Dasborough, M. T. (2009). Does leadership need emotional intelligence? The Leadership Quarterly, 20, 247 261. Ashkanasy, N. M., & Daus, C. S. (2005). Rumors of the death of emotional intelligence in organizational behavior are vastly exaggerated. Journal of Organizational Behavior, 26, 441 452. Ashkanasy, N. M., & Humphrey, R. H. (2011). Current emotion research in organizational behavior. Emotion Review, 3, 214 224. Austin, J. T., Scherbaum, C. A., & Mahlman, R. A. (2002). History of research methods in industrial and organizational psychology: Measurement, design, analysis. In S. G. Rogelberg (Ed.), Handbook of research methods in industrial and organizational psychology (pp. 3 33). Oxford: Blackwell. Balthazard, P., Waldman, D. A., Thatcher, R. W., & Hannah, S. T. (2012). Differentiating transformational and non-transformational leaders on the basis of neurological imaging. The Leadership Quarterly, 23, 244 258. Bandura, A. (1971). Social learning theory. New York, NY: General Learning Press. Becker, W. J., & Cropanzano, R. (2010). Organizational neuroscience: The promise and prospects of an emerging discipline. Journal of Organizational Behavior, 31, 1055 1059. Becker, W. J., & Menges, J. I. (2013). Biological implicit measures in HRM and OB: A question of how not if. Human Resource Management Review, 23, 219 228. Blascovich, J., Mendes, W. B., Hunter, S. B., & Salomon, K. (1999). Social “facilitation” as challenge and threat. Journal of Personality and Social Psychology, 77(1), 68 77. Cacioppo, J. T., Berntson, G. G., Lorig, T. S., Norris, C. J., Rickett, E., & Nusbaum, H. (2003). Just because you’re imaging the brain doesn’t mean you can stop using your


24


head: A primer and set of first principles. Journal of Personality and Social Psychology, 85, 650 661. Carr, L., Iacoboni, M., Dubeau, M. C., Mazziotta, J. C., & Lenzi, G. L. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas. Proceedings of the National Academy of Sciences, 100, 5497 5502. Chernis, C. (2010). Emotional intelligence: Toward clarification of a concept. Industrial and Organizational Psychology, 3, 110 126. Cozolino, L. (2014). The neuroscience of human relationships: Attachment and the developing social brain (2nd ed.). New York, NY: W. W. Norton and Company. Damasio, A. (1999). The feeling of what happens: Body emotion and the making of consciousness. London: Vintage. Davidson, R. J. (2003). Seven sins in the study of emotion: Correctives from affective neuroscience. Brain and Cognition, 52, 129 132. De Dreu, C. K. W. (2011). Oxytocin modulates cooperation within and competition between groups: An integrative review and research agenda. Hormones and Behavior, 61(3), 419 428. Decety, J., & Meyer, M. (2008). From emotion resonance to empathic understanding: A social developmental neuroscience account. Development and Psychopathology, 20, 1053 1080. Denburg, N. L., Recknor, E. C., Bechara, A., & Tranel, D. (2006). Psychophysiological anticipation of positive outcomes promotes advantageous decision-making in older persons. International Journal of Psychophysiology, 61(1), 19 25. Ferris, G. R., Perrewe´, P. L., & Douglas, C. (2002). Social effectiveness in organizations: Construct validity and research directions. Journal of Leadership & Organizational Studies, 9, 49 63. Fogassi, L., Ferrari, P. F., Gesierich, B., Rozzi, S., Chersi, F., & Rizzolatti, G. (2005). Parietal lobe: From action organization to intention understanding. Science, 308, 662 667. Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119(Pt. 2), 593 609. Grandey, A. A. (2000). Emotional regulation in the workplace: A new way to conceptualize emotional labor. Journal of Occupational Health Psychology, 5, 95 110. Greene, J. D., Cushman, F. A., Stewart, L. E., Lowenberg, K., Nystrom, L. E., & Cohen, J. D. (2009). Pushing moral buttons: The interaction between personal force and intention in moral judgment. Cognition, 111, 364 371. Gross, J. J. (1999). Emotion regulation: Past, present, future. Cognition and Emotion, 13, 551 573. doi:10.1080/026999399379186 Hannah, S. T., Balthazard, P. A., Waldman, D. A., Jennings, P., & Thatcher, R. (2013). The psychological and neurological bases of leader self-complexity and effects on adaptive decision-making. Journal of Applied Psychology, 98, 393 411. Hatfield, E., Cacioppo, J., & Rapson, R. L. (1994). Emotional contagion. Paris: Cambridge University Press. Herrald, M. M., & Tomaka, J. (2002). Patterns of emotion-specific appraisal, coping, and cardiovascular reactivity during an ongoing emotional episode. Journal of Personality and Social Psychology, 83(2), 434. Hochschild, A. R. (1983). The managed heart: Commercialization of human feeling. Berkeley, CA: University of California Press.



25

Hu¨lsheger, U. R., & Schewe, A. F. (2011). On the costs and benefits of emotional labor: A meta-analysis of three decades of research. Journal of Occupational Health Psychology, 16(3), 361 389. Iacoboni, M. (2009). Imitation, empathy, and mirror neurons. Annual Review of Psychology, 60, 653 670. Izard, C. E. (2009). Emotion theory and research: Highlights, unanswered questions, and emerging issues. Annual Review of Psychology, 60, 1 25. Kandasamy, N., Hardy, B., Page, L., Schaffner, M., Graggaber, J., Powlson, A. S., & Coates, J. (2014). Cortisol shifts financial risk preferences. Proceedings of the National Academy of Sciences, 111(9), 3608 3613. Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., & Fehr, E. (2005). Oxytocin increases trust in humans. Nature, 435(7042), 673 676. Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: A review. Biological Psychology, 84(3), 394 421. Lazarus, R. S. (1991). Emotion and adaptation. New York, NY: Oxford University Press. Lieberman, M. D., Berkman, E. T., & Wager, T. D. (2009). Correlations in social neuroscience aren’t voodoo. Perspectives in Psychological Science, 4, 300 310. Lindebaum, D., & Jordan, P. J. (2014). A critique on neuroscientific methodologies in organizational behavior and management studies. Journal of Organizational Behavior, 35, 898 908. Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012). The brain basis of emotion: A meta-analytic review. Behavioral and Brain Sciences, 35, 121 202. Lopatovska, I., & Arapakis, I. (2011). Theories, methods and current research on emotions in library and information science, information retrieval and human-computer Interaction. Information Processing and Management. doi:10.1016/j.ipm.2010.09.001 Lundberg, U. (2005). Stress hormones in health and illness: The roles of work and gender. Psychoneuroendocrinology, 30(10), 1017 1021. Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. Cognition and Emotion, 23, 209 237. Mayer, J. D., Caruso, D. R., & Salovey, P. (2000). Selecting a measure of emotional intelligence: The case for ability scales. In R. Bar-On & J. D. A. Parker (Eds.), The handbook of emotional intelligence (pp. 320 342). New York, NY: Jossey-Bass. Mayer, J. D., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey & D. J. Sluyter (Eds.), Emotional development and emotional intelligence (pp. 267 298). New York, NY: Basic Books. Melamed, S., Ugarten, U., Shirom, A., Kahana, L., Lerman, Y., & Froom, P. (1999). Chronic burnout, somatic arousal and elevated salivary cortisol levels. Journal of Psychosomatic Research, 46(6), 591 598. Mitchell, T. R., Thompson, L., Peterson, E., & Cronk, R. (1997). Temporal adjustments in the evaluation of events: The “rosy view”. Journal of Experimental Social Psychology, 33(4), 421 448. Niedermeyer, E., & Silva, F. L. (1995). Electroencephalography: Basic principles, clinical applications and related fields. Baltimore: Williams & Wilkin. Nikula, R. (1991). Psychological correlates of nonspecific skin conductance responses. Psychophysiology, 28(1), 86 90.


26


Panksepp, J. (2000). Affective consciousness and the instinctual motor system: The neural sources of sadness and joy. The caldron of consciousness: Motivation, affect and self-organization, advances in consciousness research. Amsterdam: John Benjamins Pub. Co. Pantelopoulos, A., & Bourbakis, N. G. (2010). A survey on wearable sensor-based systems for health monitoring and prognosis. Systems, Man, and Cybernetics, Part C: IEEE Transactions on Applications and Reviews, 40, 1 12. Passarelli, A. M. (2014). The heart of helping: Psychological and physiological effects of contrasting coaching interactions. Doctoral dissertation, Case Western Reserve University, Cleveland, OH. Pennebaker, J. W., Hughes, C. F., & O’Heeron, R. C. (1987). The psychophysiology of confession: Linking inhibitory and psychosomatic processes. Journal of Personality and Social Psychology, 52(4), 781 793. Petrides, K. V., & Furnham, A. (2003). Trait emotional intelligence: Behavioural validation in two studies of emotion recognition and reactivity to mood induction. European Journal of Personality, 17, 39 57. Pugh, D. S. (2001). Service with a smile: Emotional contagion in the service encounter. Academy of Management Journal, 44, 1018 1027. Radke, S., & De Bruijn, E. R. A. (2012). The other side of the coin: Oxytocin decreases the adherence to fairness norms. Frontiers in Human Neuroscience, 6, 193. Raichle, M. E. (2010). Two views of brain function. Trends in Cognitive Sciences, 14, 180 190. Raichle, M. E., & Snyder, A. Z. (2007). A default mode of brain function: A brief history of an evolving idea. Neuroimage, 37, 1083 1090. Reynolds, S. J. (2006). A neurocognitive model of the ethical decision-making process: Implications for study and practice. Journal of Applied Psychology, 91, 737 748. Reynolds, S. J., Leavitt, K., & DeCelles, K. (2010). Automatic ethics: The effects of implicit assumptions and contextual cues on moral behavior. Journal of Applied Psychology, 95, 752 760. Robinson, M. D., & Clore, G. L. (2002). Episodic and semantic knowledge in emotional selfreport: Evidence for two judgment processes. Journal of Personality and Social Psychology, 83(1), 198 215. Scherer, K. R. (1984). Emotion as a multicomponent process: A model and some cross cultural data. Review of Personality and Social Psychology, 5, 37 63. Sequeira, H., Hot, P., Silvert, L., & Delplanque, S. (2009). Electrical autonomic correlates of emotion. International Journal of Psychophysiology, 71(1), 50 56. Stallen, M., de Dreu, C. K. W., Shalvi, S., Smidts, A., & Sanfey, A. G. (2012). The herding hormone oxytocin stimulates in-group conformity. Psychological Science, 23(11), 1288 1292. Tepper, B. J. (2007). Abusive supervision in work organizations: Review, synthesis, and research agenda. Journal of Management, 33(3), 261 289. Timoshanko, A., Desmond, P., Camfield, D. A., Downey, L. A., & Stough, C. (2014). A magnetic resonance spectroscopy (1H MRS) investigation into brain metabolite correlates of ability emotional intelligence. Personality and Individual Differences, 65, 69 74. Tomaka, J., Blascovich, J., Kibler, J., & Ernst, J. M. (1997). Cognitive and physiological antecedents of threat and challenge appraisal. Journal of Personality and Social Psychology, 73(1), 63 72.


27


Waldman, D. A., Balthazard, P. A., & Peterson, S. J. (2011a). The neuroscience of leadership: Can we revolutionize the way that leaders are identified and developed? Academy of Management Perspectives, 25, 60 74. Waldman, D. A., Balthazard, P., & Peterson, S. J. (2011b). Social cognitive neuroscience and leadership. The Leadership Quarterly, 22, 1092 1106. Waldman, D. A., Wang, D., Stikic, M., Berka, C., Balthazard, P. A., Richardson, T., … Maak, T. (2013). Emergent leadership and team engagement: An application of neuroscience technology and methods. Academy of Management Best Paper Proceedings. Walter, F., Cole, M. S., & Humphrey, R. H. (2011). Emotional intelligence: Sine qua non of leadership or folderol? Academy of Management Perspectives, 25, 45 59.