Firm Performance in the Face of Fear: How CEO Moods Affect Firm Performance

Akansu, Ali N.; Cicon, James; Ferris, Stephen P.; Sun, Yanjia

doi:10.1080/15427560.2017.1338704

Cited by 30 publications

(14 citation statements)

References 24 publications

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“…Such fear may cause top managers to consider particular programs or initiatives aimed at improving operating results, which may eventually influence the share price level. Nevertheless, fear has a temporary character and disappears quickly (Akansu et al , 2017). On the basis of the conducted literature review, it can be shown that the most often used methods of fear reduction include proper communication, ensuring a friendly work environment, allocating new tasks, giving up rankings and time based standards, as well as assessing managerial/leadership competences and personality traits.…”

Section: The Possibilities and Methods Of Fear Reductionmentioning

confidence: 99%

The problem of fear in TQM – causes, consequences and reduction methods – a literature review

Bugdol

2020

TQM

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PurposeThe primary objective of the paper is to present the role and meaning of fear in organizations implementing TQM. The author's intention is to identify the causes of fear in the TQM components as well as the main types of fear, their causes, consequences and methods of reduction.Design/methodology/approachThe main research method applied is a systematic review of the literature on the subject. The applied systematic review procedure takes into consideration the indicated research objective, the selection of the basic literature and publications, the presentation of the publication database and content analysis. The author also makes use of his own observations ensuing from his acting in the capacity of a TQM consultant and a juror of the Polish Quality Award.FindingsThe paper presents the causes of fear in the TQM components, the main types of fear and its consequences. The limited possibilities of eliminating fear are also indicated. Fear appears when, for various reasons, TQM is improperly implemented and maintained, but also when resources are allocated incorrectly (e.g. a wrong selection of quality team members), and it is included in social processes (e.g. in the quality team forming process). It appears when only structural empowerment rather than mental empowerment is applied, when there exist contradictory expectations with regard to empowerment, in case of excessive and/or “have to” commitment in particular employees, and when too much emphasis is placed on commitment.Research limitations/implicationsThe reflections included in the paper may become useful for quality management practitioners, as such knowledge allows them to avoid mistakes which are the cause of fear, that is an emotion making quality improvement difficult. The paper does not present the manifestations and sources of fear in all TQM components, and they are certainly included in a broadly understood quality culture. Nevertheless, the gathered and arranged knowledge can be the source of further research.Originality/valueBeing the result of the studies of the literature on the subject, this paper is one of few publications discussing in a detailed manner one of the principles of effective quality management formulated by E. Deming, namely “driving fear out”.

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Section: The Possibilities and Methods Of Fear Reductionmentioning

confidence: 99%

The problem of fear in TQM – causes, consequences and reduction methods – a literature review

Bugdol

2020

TQM

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“…The system not only outperforms the state-of-the-art based on the posed expressions but also provides satisfactory performance on spontaneous facial emotions. The system has been used to read Chief Executive Officers' (CEO) facial expressions to forecast firm performance by only using recorded video signal [59]. It utilizes Regional Hidden Markov Model (RHMM) as its classifier to train the states of three face regions: the eyebrows, eyes, and mouth, as tabulated in Table 1.…”

Section: Automatic Facial Emotion Recognition Systemmentioning

confidence: 99%

“…During this 5 s calibration period, we computed the mean measures of all emotion states. We then subtracted these mean measures from the remainder of facial expressions for baseline adjustment [59]. The emotion type of a facial video clip v was recognized by calculating the largest probability among Anger, Disgust, Fear, Happiness, Neutral, Sadness, and Surprise expressed as…”

Section: Data Pre-processing For Brain Activity and Facial Expressionmentioning

confidence: 99%

Multimodal Affective State Assessment Using fNIRS + EEG and Spontaneous Facial Expression

2020

Self Cite

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Human facial expressions are regarded as a vital indicator of one’s emotion and intention, and even reveal the state of health and wellbeing. Emotional states have been associated with information processing within and between subcortical and cortical areas of the brain, including the amygdala and prefrontal cortex. In this study, we evaluated the relationship between spontaneous human facial affective expressions and multi-modal brain activity measured via non-invasive and wearable sensors: functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. The affective states of twelve male participants detected via fNIRS, EEG, and spontaneous facial expressions were investigated in response to both image-content stimuli and video-content stimuli. We propose a method to jointly evaluate fNIRS and EEG signals for affective state detection (emotional valence as positive or negative). Experimental results reveal a strong correlation between spontaneous facial affective expressions and the perceived emotional valence. Moreover, the affective states were estimated by the fNIRS, EEG, and fNIRS + EEG brain activity measurements. We show that the proposed EEG + fNIRS hybrid method outperforms fNIRS-only and EEG-only approaches. Our findings indicate that the dynamic (video-content based) stimuli triggers a larger affective response than the static (image-content based) stimuli. These findings also suggest joint utilization of facial expression and wearable neuroimaging, fNIRS, and EEG, for improved emotional analysis and affective brain–computer interface applications.

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“…An assessment of these factors, to a large extent, influences investors' choice of stock (or portfolio) and has the potential to alter the stance of classical market theories (Shefrin and Statman, 2000). However, although many studies in the literature have documented both linear (Edmans et al, 2007; Akansu et al, 2017) and non‐linear relationships (Bollen et al, 2011; Essaddam and Karagianis, 2014; Chu et al, 2016; Salhin et al, 2016; Bekiros et al, 2016; Yang et al, 2017) between stock market behaviour and sentiments, the controversy still lingers due to multifarious and imperfect proxies for market sentiments 4…”

Section: Contextualization Of the Studymentioning

confidence: 99%

Sentiment‐Apt investors and UK sector returns

et al. 2020

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This paper examines the relationship between sentiment‐apt investors and UK stock returns at industry level over the period January 1988 to December 2017. Using two new sentiment proxies (laggards to leaders and growth opportunity index) for 10 discrete sector groupings, we provide novel evidence on how returns in the UK stock market react to the activities of sentiment‐disposed investors. First, using threshold nonlinear regression, we document a significant relationship between the laggards to leaders sentiment proxy and sectoral returns. Our findings reveal that aggregate returns in the sector are affected by activities of investors who embark on profit‐taking when there is an increase in the proportion of lagging to leading stocks beyond the threshold value. Secondly, when using the growth opportunity sentiment proxy, we report that the increase in growth above the growth threshold value has a significant impact on sector returns. This study further confirms significant impact of non‐threshold variables on sector groupings. Our findings are robust, having been subjected to a range of robustness checks.

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Firm Performance in the Face of Fear: How CEO Moods Affect Firm Performance

Cited by 30 publications

References 24 publications

The problem of fear in TQM – causes, consequences and reduction methods – a literature review

The problem of fear in TQM – causes, consequences and reduction methods – a literature review

Multimodal Affective State Assessment Using fNIRS + EEG and Spontaneous Facial Expression

Sentiment‐Apt investors and UK sector returns

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