The articles under analysis discuss the question of emotional intelligence and its impact on leadership skills and strategies. The regulation of emotions in work situations is a complex process that uses an ancient neurochemical technology tuned by modern socialization and learning processes. These capacities are then applied in rapidly changing environments that, except for face-to-face encounters, rely on communications media ( that are extremely new from a historical perspective.
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Emotional regulation generally occurs in the context of multiple work demands and may involve interactions with relatively unfamiliar individuals (customers). A further complication arises because emotions are rapidly occurring processes, and self-regulatory processes often rely on slower, deliberative cognitive activity.
J. Antonakis (2003) “Why “Emotional Intelligence” does not Predict Leadership Effectiveness”, discusses the research study by Prati, Douglass, Ferarris Ammeter, and Barckley. The researcher finds that leadership behavior can explain variances ineffectiveness and possible changes in a leader’s behavior and organizational settings influenced by emotions. A final complication is that a key component of emotional communication, facial expression, may be designed by evolution to encode and decode several emotions automatically. Consequently, emotional regulation, particularly under trying situations, is a challenge for human architecture.
Social and organizational processes must therefore create many explicit norms and rules for feeling and displaying emotions, and individuals must effectively learn these multiple requirements for emotions at work to be regulated effectively.
The article Jr. Martin (2004) “Salience of Emotional Intelligence as a Core Characteristic of Being a Counselor”, finds that emotional regulation in situations has similar requirements, which are especially challenging when emotions are negative and extreme. The article develops a basis for understanding emotions and emotional regulation at multiple levels involving intraindividual information processing, personality, and dyadic processes, and group and organizational factors. We believe this multilevel perspective is needed to understand both problems and opportunities for effective emotional regulation at work.
The article states that both positive and negative emotions can be socially communicated with automatic, unintentional connectionist-based processes, creating a genuine and adaptive aspect of social interactions. Yet genuine, immediate reactions may not always serve higher-level business or societal needs or even an individual’s current goals. Thus, workers must often alter or suppress their more immediate emotional reactions using symbolic-level processes.
Consequently, issues like emotional labor, which involve work with a strong emotional regulation component, have become popular applied topics as we move to a more service-based economy. A strength of the Lord and Harvey framework is that by translating emotional labor into the interplay between various architectures, we not only gain a greater understanding of work-related emotional labor, we can also broaden our perspective to see emotional regulation as part of all intra- and interindividual self-regulatory activities.
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The main similarity between the two articles is that they underline the importance of emotional intelligence and its impact on employee relations. The researchers state that response output also involves both automatic and controlled processes. Expression of emotions is subject to display rules that may involve the automatic use of well-learned social norms when in the presence of others, or when in organizational contexts, more specific organizational display rules may be used.
The use of organizational display rules generally requires the conscious suppression of response tendencies, because organizational norms are not as well practiced as social norms. Pugh also reviews evidence indicating that conscious suppression has greater costs in terms of consuming cognitive resources and creating greater stress on an individual. Emotions occur in context, and the relevant context establishes the intensity of emotions and the time parameters in which events and processes must occur to have meaning.
The main difference is that Antonakis (2003) rejects the opinion of a possibility to predict emotional intelligence in leaders while Martin et al (2004) state that the intensity and time parameters thereby encourage or limit how emotions can be perceived and regulated. For example, consider the well-discussed phenomenon of road rage. When driving, we are primed to respond quickly to internal and external signals, often before we can consciously evaluate such signals.
Emotions, being an internal signal, help us react quickly to potential danger without having to think consciously about our actions: emotions come to the surface, and we respond. For example, someone cuts in front of us when driving, slam on our brakes, and label the person with a name or respond with a gesture, all before we have had time to think carefully about the situation.
Both articles demonstrate that the positive aspect of emotions in that they cue the appropriate response and prepare the body for that response—in the example, attention to the situation and strength to slam on the brake. Martin et al (2004) state that cognitive and computer scientists who have struggled with such issues have recognized that understanding information processing and intelligent behavior often requires more than just an explication of specific processes, choices, sequences, or acquired knowledge. In addition, it often requires an assessment of the nature of the technology or architecture that produces these cognitive activities.
Many theories of emotion or emotion regulation include cognitive processing constructs; however, a comprehensive information processing approach analyzes cognitive processing at a deeper level. Specifically, it begins with an assessment of the human information processing technology or the constraints and affordances provided by the human hardware that implements processing operations. In this section, we develop such an approach to understanding emotions. In connectionist networks, the amount of activation (inhibition) transmitted from one unit to another depends on connection weights among units, which have been determined through learning.
The weights reflect the strength of positive and negative constraints between units, and meaning is created when the network simultaneously satisfies such constraints. The satisfaction of multiple constraints depends on feedback processes that allow units to interact and update activation (inhibition). Antonakis (2003) underlines that this occurs dynamically over many cycles and should result in all units’ reaching an asymptote or steady-state. This dynamic, constraint satisfaction process is referred to as settling in or relaxing. When the network has settled in, generally, the most possible constraints have been satisfied, and a coherent interpretation can be made.
This process can contextualize the meaning of stimuli or the production of specific responses to stimulus patterns. The combination of such factors may be responsible for important individual differences in sensitivity to positive versus negative stimuli (such as positive versus negative affectivity). Thus, connectionist architectures may have a critical role in predisposing individuals to notice positive or negative stimuli, which then produce reactions largely through emotional architectures. This is but one example of a very general process in which connectionist architectures can help develop individual differences (such as personality dimensions) that then affect sensitivity to and perception of emotional stimuli.
In sum, the role of emotions in influencing cognition is equally important. Indeed, many specific types of negative emotions are thought to occur because they facilitate appropriate cognitive and behavioral responses to specific types of threats. Although negative emotions have often been seen as influencing cognitions, less attention has been given to the effects of positive emotions on cognition. Positive emotional responses are not as distinctive as negative emotional responses, and their associated role on cognition is less clear. The interaction of emotions and cognitions can also be understood from a more physiological perspective.
Constraints on such networks help produce differentiated patterns (meanings) from otherwise undifferentiated connections. Constraints may be relatively fixed, as by genes, or when learning modifies synaptic efficiency. In contrast, they also can be “tuned” (made more efficient) by the release of classical transmitters at low frequencies or neuropeptides at high frequencies. Thus, the type of neurotransmitter released into the synapse constrains the interaction of neurons without leaving any trace, providing a flexible, fluid-based overlay to more fixed constraints. They note that “global attunements” can be produced by motivational states such as hunger, which may predispose one to think about food or eating. Similarly, emotional states can be thought of as global attunements rather than behavioral dispositions.
The nature of this interaction is highly dependent on contextual factors, particularly the time pace of activities, the strength of emotions, and the intensity of other inputs. Such means also provide a system by which a variety of emotions can have differential effects on cognition and behavior, with emotional intensity increasing the importance of emotional relative to other internal or external constraints. In sum, in most circumstances, emotional, connectionist, and symbolic architectures interact in producing affective and behavioral responses to situations.
- Antonakis, J. (2003). Why “Emotional Intelligence” does not Predict Leadership Effectiveness. International Journal of Organizational Analysis; 11 (4), 355-361.
- Martin Jr., W.E. Easton, C. Takemoto, M., Sullivan, S. (2004). Salience of Emotional Intelligence as a Core Characteristic of Being a Counselor. Counselor Education and Supervision, 44 (2). 17-27.