Since Simon’s (1947) administrative man – featured by bounded rationality – and the conceptualization of a firm’s performance as the result of the decision makers’ collective choice (Cyert and March, 1963) – a series of studies in management studies advanced the debate of how organizational actors (individually or collectively) make decisions (Kahneman, 2011; Lovallo and Sibony, 2018; Cristofaro, 2021a). Over the years, managerial decision making – concerning the decisional activities made at the low-, middle-, and top-management levels (Koontz et al., 1980) – attracted the interest of scholars in various areas, mainly due to its cross-disciplinary nature (Cristofaro, 2017; Adinolfi, 2021). A great advancement has been made by the Behavioral Decision Theory (BDT), that originated in the ‘60s for the study of the real behavior of people when making decisions (Edwards, 1961), and then increasingly adopted/developed in managerial decision-making research stimulating reflections on bounded rationality (e.g., Edwards, 1961; March, 1978; Kahneman, 2011; Powell et al., 2011; Sibony et al., 2017; Abatecola et al., 2021; Cristofaro and Giannetti, 2021).

From the progress made, the role of affective states has continuously and increasingly gained momentum within managerial decision-making research (e.g., Cristofaro, 2017, 2019, 2021a,b). This happened mainly because affective states are considered the first biological reaction to stimuli in a decisional environment, condensing all other irrational impulses (Weick, 1979). Emotional response, however, may not only directly influence the initiation and/or the output of a decision path, but they can also influence the content and depth of thought within decision-making processes at the individual and collective levels (e.g., Damasio, 1994; Lerner et al., 2013; Cristofaro, 2019).

However, as reported by the Call for Papers on the research topic “Affect and Cognition in Upper Echelons’ Strategic Decision Making: Empirical and Theoretical Studies for Advancing Corporate Governance” to which this article contributes, what remains largely unknown in managerial decision-making research is “the interplay of affective states and cognition; considered by some scholars to be two parallel competitive systems of the human mind”. From that, we want to answer the following research question: how do affect and cognition interact in managerial decision making? Concerning the definition of affect, we can recall the contribution brought by Forgas (1995), who asserted that this word depicts a broad array of various affective states (the main term we will use hereafter), among which the preeminent ones are “moods” and “emotions”. Specifically, moods are related to low intensive and lasting affective circumstances (e.g., feeling down) that cannot be identified as a reaction to a precedent situation, while emotions, on the contrary, group all those affective reactions into a specific event. With regard to the definition of cognition, Neisser (1967) has defined it as the mental procedure through which inputs, such as information, are transformed, reduced, elaborated, and gathered, and then put into practice when needed.

To answer this study’s research question in a solid and “new” way, we offer a first systematization of contributions on managerial decision making that implement neuroscience techniques/points of view; thus allowing this review to robustly inform the affect-cognition debate. In this regard, a series of position papers/reviews/commentaries have been published about the impact of neuroscience approach and techniques in management studies (e.g., Becker et al., 2011; Powell, 2011; Ashkanasy et al., 2014; Ward et al., 2015; Jack et al., 2019; Massaro et al., 2020; Cucino et al., 2021), but only a few looked at the decision-making processes of managers within organizations. The sole contribution that tried to look at the influence of neuroscience studies in managerial decision making was Butler et al. (2016); however, for their results categorized under the “organizational behavior cluster” heading, some of the 12 contributions in that sample did not deal with managerial decision-making processes (e.g., Peterson et al., 2008), as well as it was not focused on shedding light on the affect-cognitive debate.

The collected 23 papers have been thematically analyzed by considering the type of managerial decisions, and under the reflexive (X-) and reflective (C-) systems of our brain, which seem to be devoted differently in describing consciousness, awareness, and mental processes (Lieberman et al., 2002; Lieberman, 2007; Lieberman-Aiden et al., 2009). From the analysis, affect and cognition work in a meaningful interplay that directs decision-making processes of managers, with affective states having an initial (but not exclusively) driving role. This result shed light on the possibility of a “unified” mind processing theory (Sadler-Smith, 2016; Cristofaro, 2020a; Cristofaro and Giannetti, 2021) for which the two Systems of our mind – System 1, devoted to operating mental processes that are fast and automatic, and System 2, devoted to operating mental processes that are consciously monitored (Kahneman, 2003) – are not in conflict, but they operate dialectically. These are the main theoretical implications of this paper. Yet, the main implications for research coming from this paper are related to the need for: (i) continuing the deconstruction of the distinction between irrational/automatic vs. rational/deliberate; (ii) articulating the ecological dimension of decision-making; and (iii) trying distinguishing the brain default activity by the task-evoked one, occurring while performing decision tasks.

In order to answer the research question: “How do affect and cognition interact in managerial decision making?”, we implemented a Systematic Literature Review (SLR) of contributions dealing with the role of affect and cognition in managerial decisions that adopted neuroscience techniques/points of view. In this regard, we identified the SLR methodology as the suitable research design to consolidate and synthesize academic research. In particular, this method differs from the traditional narrative reviews in: (a) assisting in linking future research to the questions and concerns that have been posed by past research, and (b) being more explicit in the selection process by employing rigorous and reproducible evaluation methods. In this work, the established SLR procedure by Tranfield et al. (2003) has been followed; see also Figure 1.

(1) The databases for the identification of the studies were: (a) Business Source Premier (EBSCO); (b) ProQuest’s ABI/Inform; (c) ISI Web of Science; (d) Scopus; (e) PsycINFO; and (f) PubMed (including MedLine);

(2) Only peer-reviewed journal articles published in English have been included to enhance quality control. Furthermore, the research was not restricted to a given starting period (end period December, 31th 2021) and type of paper (qualitative or quantitative). These two criteria enhanced the value-added of this work compared to the one by Butler et al. (2016), who just considered empirical papers published between 2007 and 2014.

(3) Only articles adopting a neuroscientific methodology/point of view have been considered by using the keywords: “neuro*” or “brain*” or “Functional Magnetic Resonance Imag*” or “FMRI” or “Electroencephalograph*” or “EEG” or “Magneto Encephalograph*” or “MEG” or “Transcranial Magnetic Stimulation*” or “TMS” or “Transcranial Electric Stimulation” or “tES” or “Positron Emission Tomograph*” or “Functional Near Infrared Spectroscop*” or “fNIRS” or “skin conductance” or “Galvanic Skin Response” or “GSR” or “GRV” or “skin conduct*” or “electrodermal activ*” or “eye track*” or “gaze track*” or “pupillometr*” or “pupil diamet*” or “heart rate*” or “HRV” or “facial expression recognition” or “emotion recognition” or “non-invasive brain stimulation” or “NIBS” or “cortisol” or “testosterone.” These keywords have been derived – and enlarged – following Butler et al. (2016) and Ascher et al. (2018) and aim to identify works related to cognitive neuroscience. 28,705 results were produced.

(4) The substantive relevance of contributions to the managerial decision-making theme has been ensured by requiring that the selected abstracts contained at least one of the following words: “decision*” or “choic*” or “preference*” or “judg*.” These keywords have been derived from the SLR by Cristofaro (2019) on affecting management decisions. 3,070 results were produced.

(5) Only articles regarding business issues have been considered by using the keyword “organization*” and its synonyms: “compan*” or “manag*” or “corporat*,” or “firm*” or “business*” or “enterprise*” or “venture*” or “start-up*” (these keywords have been derived following De Vita et al., 2013). 633 results were produced.

(6) Duplicates from databases were eliminated at this stage thanks to the integration operated through reference-manager software. 401 hits were produced;

(7) The resulting articles were scanned by reading all the abstracts to ensure their substantive context, mainly according to their coherence with the review’s aim. When there was doubt about the content regarding the inclusion/exclusion of an article, the full text was examined. 77 results were produced;

(8) The remaining papers were fully read to ensure their alignment with the research objective. Thus, we included in the sample only scientific contributions dealing with managerial decision-making processes that implement neuroscience techniques/points of view; 20 results were produced. In this regard, we implemented the same exclusion criteria as Butler et al. (2016) – see their online S-2 Appendix. Initially, the authors individually read the articles and then compared their evaluations; when disagreeing, the authors assessed the papers together and decided whether or not to include those papers within the sample. Cronbach’s alpha for inter-rater reliability was 0.95.

(9) Snowballing techniques have been applied to the reference lists of the resultant 20 articles. This ensured that important works in the field were included that might have been missed. Three were added arriving a final sample of 23 articles (almost double that of Butler et al., 2016; N = 12; yet, these two samples are very different too in terms of selected papers; see Table 1).

The selection at points 7 and 8 followed criteria used by Sandberg and Tsoukas (2015); in particular, studies have been included that explicitly: (i) aim to contribute to the development of managerial decision making, and (ii) apply neuroscience in their research. So, similarly to Butler et al. (2016), we excluded articles “that only briefly highlighted cognitive neuroscience in a cursory way, for example, in a one-line reference to the topic” (p. 546) and that did not use the terms at point (5) in relation to organizational contexts (e.g., waste-water management).

Following Butler et al. (2016), the 23 contributions related to the role of affect and cognition in managerial decisions that implemented neuroscience techniques/points of view have been structured into three clusters. The literature itself (Braun and Clarke, 2006) defined these inductive emergent clusters and reflected the type of decision made by managers. Sample articles categorized into these three clusters have been read by looking at the assumed relationship of decisions with the reflexive (X-) and reflective (C-) systems (Lieberman et al., 2002;

Lieberman-Aiden et al., 2009). This course of action helps build a solid neuroscientific basis for the assumed connections between neuroscience and the affect and cognition of managers proposed in the discussion section below.

Among the 23 papers in the sample, only three contributions (i.e., Krueger et al., 2009; Boyatzis et al., 2012; Hannah et al., 2013) are present also in the “organizational behavior cluster” of Butler et al. (2016), substantiating originality and novelty in the systematization we propose. The majority of the 23 papers in our sample are conceptual (12; 52%), then 9 empirical articles (39%) and 2 review works (9%) complete the sample. Among these, authors who have published empirical contributions have used many techniques to test their assumptions; the main ones used have been fMRI (40%), EEG or qEEG (20%), and electrodermal activity (13%). Other techniques used for the data analysis have been: Computed Tomography scans (5%), Facial Expression Recognition (5%), Eye-tracking (5%), fNIRS (5%), and Testosterone (5%). With regard to authors, Gerard P. Hodgkinson is the only one present with two contributions (both conceptual).

The 23 selected papers cover a period of 18 years, with the distribution shown in Figure 2. There is an average of two publications per year. Furthermore, the selected contributions were published in many different journals; among them, 4 (15%) appeared in The Leadership Quarterly.

An inductive thematic analysis of the 23 selected papers’ manuscripts has been implemented, aimed at identifying the type of decisions treated. In particular, thematic analysis is principally employed to acquire a nuanced comprehension of spontaneous and sophisticated processes (Mills et al., 2009), such as decision making. Within inductive thematic analysis, there is no presence of an initial codebook and themes are free to emerge. In line with Strauss and Corbin (1998), the coding has relied on the research question to determine themes associated with the main aspects of the analysis based on the theoretical background. The resulting themes/decision types emerging from the manuscripts dealing with managerial decision making are: (a) ethical decisions, (b) innovation decisions, and (c) data-enabled decisions.

Generally speaking, the distinction between the X- and C-systems, building upon the lateralization of the brain, although valid, is a coarse classification of many distinct human faculties that pertain to one of two broad domains and their interaction. This general distinction oversimplifies brain lateralization and assumes absolute functional differences in management studies. Our literature review consistently shows that the distinction between reflexive and reflective systems dominates the debate with marginal articulations within each of the systems (Laureiro-Martínez et al., 2015a,b; Boone et al., 2020). In this regard, we recognize from our SLR that three different schools of thought emerged: (i) the C-system has a predominant role over the X-system; (ii) the X-system has a predominant role over the C-system; (iii) the C- and X-systems interact, and neither of the two has a primacy.

For the first cluster, Reynolds (2006) study is among the few important works. Indeed, he was the first to report the interaction of the C- and X-systems in the ethical decision-making process. Ethical judgment would be highly positively correlated to ethical behavior if performed by the C-system rather than by the reflexive one. Hence, according to Reynolds (2006), the C-system exerts control over the X-system for the management of organizations. Recently, Boone et al. (2020) conducted a study to assess the pivotal importance of the controlled C-system over the automatic X-system for pushing Top Management Teams (TMTs) to invest in corporate social responsibility.

With reference to the second cluster, many other scholars, instead, have devoted their efforts to analyzing the influence that the X-system has on the C-system. Among them, it is noteworthy to recall the contribution brought by Leger et al. (2014), who, using electrodermal activity to measure psychophysiological responses elicited by arousal, have confirmed what was previously proposed by Krueger et al. (2009), thus that the more a person becomes confident with a task, the more intuitive his/her behavior will be and, therefore the X-system will prevail over the C-system. This latter idea also has found fertile support from Antonakis and Dietz (2010), who have asserted that the decision makers’ emotional intelligence, combined with other elements such as the working memory and the intelligence quotient, plays a pivotal role in shaping leaders’ answers in critical situations, such as when they face constraints in their own cognitive resources. Particularly, leaders’ ability to handle emotional intelligence allows them to be engaged in higher quality decision-making processes since, due to the aforementioned skill, they are more competent in the creation of proactive environments where a contamination of ideas is widely supported and positively perceived (Antonakis et al., 2009).

A third cluster supports the idea that there is a mutual influence between the C- and X-systems. Among them, we recall the important works by Hodgkinson et al. (2009) and Hodgkinson and Healey (2014), proposing a theoretical framework in which the two systems of human mind have close interaction and mutual influence. According to this third cluster, lateralization of the brain appears to be more complex than how it is popularly investigated (Toga and Thompson, 2003). As suggested by Hodgkinson et al. (2009): “old models based on a simplistic left brain/right brain dichotomy are giving way to more sophisticated conceptions, in which intuitive and analytical approaches to decision making are underpinned by complex neuropsychological systems” (p. 277).

From the above, we should be aware that the traditional distinction between reflexive (X-) and reflective (C-) systems, considered alone, cannot be framed as a complete theoretical framework as the nature of the interplay defines specific paradigms. Indeed, many theories assume dual processing of information, but they radically differ in their articulation. A fundamental distinction, mainly discussed in literature with reference to System 1 and System 2, is between a parallel-competitive and a default-interventionist approach (Evans, 2021). In this last regard, from the analysis of the sample contributions described above, it can be derived that the managerial decisions result as the product of an emotional-driven dialectic of affect and cognition (e.g., Damasio, 1994; Sadler-Smith, 2016; Abatecola et al., 2018; Cristofaro, 2020a,b, 2021a,b), redirecting the discussion on information processing from dual-mind processing theories (e.g., Stanovich and West, 2000; Hodgkinson and Sadler-Smith, 2018) to a “unified” mind processing theory (Sadler-Smith, 2016) for which the two systems of our mind are not in conflict and for which affective states have an initial (but not exclusive) primary driving role. As a consequence, the recent affect-cognitive interplay emerges, under a neuroscientific point of view, as supported, and may be considered as the fertile ground from which a renewed understanding of managerial decision making can move forward – also because its explanations are intertwined with other relevant streams of research such as the Upper Echelons Theory (Hambrick and Mason, 1984; Abatecola and Cristofaro, 2020) and Behavioral Strategy (Powell et al., 2011; Sibony et al., 2017; Abatecola et al., 2021; Cristofaro and Giannetti, 2021). In particular, it seems to be that the provided understanding supports the recent Affect-Cognitive Theory of management decisions by Cristofaro (2021a); in fact, assumptions of this theory clearly identifies an interplay of affect and cognition, with affective states having an initial (but not exclusive) primary driving role, for the formation of choices supporting the cited “unified” mind processing theory.

Neuroscience can help to deconstruct and reformulate from scratch some traditional problems – i.e., the roots of behavioral strategy (Powell et al., 2011) – that connote the agenda of management studies. Notwithstanding such premises, the contributions present in our literature review seem not to follow this trend. Indeed, they are characterized more by a mere integration of neuroscientific methods than a radical reformulation of research questions based on neuroscientific evidence.

The main limits of the papers in our sample are that: (a) neuroscientific studies are often conducted on non-representative populations, because studies on practitioners are limited, and nothing can grant that the evidence found on non-representative populations (e.g., students) can be generalized; (b) when practitioners are present, samples are often limited or biased, as there is difficulty in balancing the different profiles of participating organizations and teams; and (c) studies are often based on laboratory experiments.

The above limitations are commonly connected with the intrinsic difficulty of implementing several neuroscientific tools in ecological conditions (e.g., fMRI outside a medical hospital center) and ethical problems in adopting them. In this regard, as expressed by other scholars (e.g., Tivadar and Murray, 2019; Zwaan et al., 2019), since managerial decision-making processes are filled with strong interplay between affective and cognitive contents, the use of either laboratory experiments or non-representative populations will unquestionably lead to results affected by a lower ecological validity and, in turn, to a lesser practical utility of these results to help scholars in the study and explanation of peculiar situations (e.g., by controlling, in real-time, the neurobehavioral mechanisms affecting executives’ decisions while they have to counteract sudden organizational/financial crises).

Furthermore, the neuroscientific contributions examined in this study often present limitations concerning the statistical analysis techniques. More specifically, the number of significance tests carried out in neuroimaging analyses (e.g., fMRI, EEG, and qEEG) are extremely likely to inflate the risk of Type I error (false positiveness) – in line with Jack et al. (2019). A clear example of limitations regarding the statistical methods has been explicitly declared by Balthazard et al. (2012) who have not correctly applied multiple comparisons in their analyses, because this “would impose an overly conservative and impractical limit for exploratory studies like our own” (p. 255). To overcome this limit, Balthazard et al. (2012) have wisely improved the likelihood of any “spurious results” by the means of a second population to replicate their results. Therefore, despite having cross-validated their findings to overcome statistical limitations, this represents a common and evident barrier that many other contributions have had to face.

How do affect and cognition interact in managerial decision making? This is the research question that we tried to answer through the SLR of contributions produced on managerial decision making, which consider neuroscience techniques/points of view. In terms of originality, this is the first contribution filling this gap, stemming from the fact that the only other SLR produced (Butler et al., 2016) did not deeply focus on managerial decision making.

Results of the sample papers show alternative views about the X- and C-systems that seem differently devoted to non-conscious and complicated reasoning. Selected works are not unanimous, but, from their systematic analysis, it can be advanced that the relationship between affective states and cognition is dialectical, with affective states having a driving role toward cognition: the X-system initially drives the C-system. This is aligned with brain studies that point toward a driving role of affective states, since they come from the biochemical response of individuals to their context. In this regard, seeing the relationship between System 1 and System 2 in managerial decision making as parallel, reorients the discussion on information processing from the tradition of sequential dual-mind processing to a “unified” mind processing theory for which the two Systems are not in contrast and for which affective states have an initial (but not exclusive) primary driving role (e.g., Damasio, 1994; Cristofaro, 2020a,b).

From this work, the relationship between BDT – including its developments – and neuroscience emerges as stronger, because one modifies/reinforces the other in a virtuous scientific debate. However, as advanced by Powell (2011), management scholars must not forget that neuroscience can add significant value to the current state of the art in management research only if the former is “at the service” of the latter. Otherwise, neuroscience results could be not perceived as relevant for practitioners and for management scholars themselves, reducing the communication power of the neuroscience-management duality in decision-making research. To avoid that, traditional managerial problems must be re-articulated through a neuroscience lens. Brain imaging techniques can reveal the specific brain area involved in specific decision-making domains. But, there is more. The investigation of intrinsic brain activity could represent a future domain of organizational neuroscience research in which the default-mode of brain functioning can be considered as a “boundary condition” for decision making. This trend is also favored by the increasing use of dedicated devices (such as a stress bracelets, EEG, etc.) in organizational settings. Measuring the default-mode parameters of brain activity can inform a next-generation of practitioners on how to improve decision making.

Our study also shows that real novelty in hypothesis generation, informed by cognitive neuroscience, is somehow missing. While neuroscience allows deconstructing consolidated categories – to reformulate old management problems in a fresh manner – this is not what our literature review shows; instead of generating radically novel hypotheses, it seems that management scholars are more prone to use novel neuroscientific tools to investigate old management problems. For example, with reference to innovation decisions, the dynamics that lead to innovation are framed within the traditional paradigms such as organizational myopia (Laureiro-Martínez et al., 2015b) or managers’ adaptability (Hannah et al., 2013). Interestingly several neuroscientific accounts of innovation-related problems – such as technical reasoning and technological culture (e.g., Osiurak and Reynaud, 2020) – are not explored. This type of consideration also applies to the data-enabled decision, where neuroscientific tools are used in an ecological setting that involves data manipulation (e.g., Leger et al., 2014 measured electrodermal activity during the use of an enterprise resource planning ERP system). Interestingly, despite their neuroscientific claims, such contributions seem to ignore a relevant tradition of neuroscientific evidence dealing with related problems (such as manipulating numerical magnitudes and formats, e.g., Kadosh and Walsh, 2009). Generally speaking, management scholars are more and more prone to neuroscientific investigations. Still, their hypotheses do not seem to be well-informed by the art of cognitive neuroscience and its related debates.

However, if we think that moving from consolidated neuroscientific evidence and debates can be the solution to a solid generation of hypotheses in management studies, we are wrong. Cognitive neuroscience is a dynamic domain characterized by different theories and views of the brain that are often incommensurable. This is particularly true for affect and cognition that, far from being distinct domains, have been shown entangled as cognition is affectively modulated (Damasio, 1994; Adolphs and Damasio, 2001). What should guide future research is the awareness, informed by neuroscientific evidence, that the interplay between affect and cognition could be radically different from what a folk approach could suggest.

In terms of practical implications, managers should take into consideration that their decisions are the concurring product of affective and cognitive influences, with the former having an initial (but not exclusive) role. In this regard, decision makers’ course of action can be regulated acting on the perceived affective state; e.g., decision makers interested in enhancing accurate analyses for a choice should consider planning them after recognizing the dramatic impact that a wrong decision may have – this can be done by referring to the “pre-mortem” technique of Klein (2007) aimed at discovering why a project may fail – so as to insert a negative mood. From what has been said, it also suggested investigating the emotional side (by using, for example, the Positive and Negative Affect Schedule questionnaire) of potential collaborators – at all levels – for a complete evaluation of their decision-making processes. Yet, as advanced many times in this study, one of the biggest limitations of the empirical studies in this field lays in the scarce possibility to accomplish ecological testing of the neurobehavioral processes that shape managers’ decision-making choices. Consequently, this has prompted scholars to rely mainly on laboratory experiments or non-representative populations reducing the validity – given the absence of important stressors (e.g., the awareness that their choices, being taken in the laboratory, will not affect the safety or stability of their organization) – and, in turn, the practical utility of the resulting insights. Therefore, in addition to having a higher confidence toward neuroscience, as well as an improved availability of the instruments belonging to this scientific field, it is desirable to reach greater synergy between scholars and practitioners in order to produce more complete, trustworthy, and meaningful understandings of the real neurobehavioral processes that affect managerial decision-making outcomes, thus resulting in a mutual win. Doing that ensures following the recommendations by Powell (2011) in using neuroscience as the mean to explore behavioral assumptions of managerial decision making, reinforcing, in turn, behavioral strategy research (Powell et al., 2011).

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

MC and APM equally contributed to section introduction. APM contributed to the sections: neuroscience in management and organization studies, and ethical decisions. MC contributed to the sections: affect and cognition in decision making, methodology, data-enabled decisions, and discussion. PG contributed to sections: affect and cognition in neuroscience, results, innovation decisions, and limitations of the research field and future research. AM contributed to the section: an agenda for future research. MC and AM equally contributed to the section implications and conclusion. All authors approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.