Part 2: New Approaches
The manipulations observed in the social accounts and the sustainability reports of the companies are generally explained by models derived from the “Cressey triangle” or by the psychological biases analyzed by Kahneman and Sversky. But new approaches borrowed from phenomenology and psychoanalysis now allow to explain the development of managerial practices in “gray areas”.
Traditional approaches
Fraud covers intentional behavior contrary to laws, regulations, and financial, social, and environmental standards. It has been the subject of much research since the founding work of Sutherland, author of the famous “white collar crime” formula. The reference model applied to its various practices is that of the “fraud triangle”, proposed by Cressey (1967), according to which the fraud process develops along three axes:
– “The opportunity” to commit an illegal act and/or contrary to the interests of an organization, which is often offered by privileged access to sensitive resources (data, systems, bank accounts …) insufficiently protected.
– The “motivation” of the fraudster, which covers different types of psychological bias and psychic affects: the need for money, the quest for recognition, ambition, the taste for risk, mimicry, addiction to fraud.
– The “rationalization” of the fraudster’s behavior, which corresponds to the practices of adverse selection intended to mask the fraudulent acts and to thwart the confidence of third parties, in particular by accounting concealments, which reflect the “excuses” that the fraudster gives to himself: “he only borrows the money”; “He falsifies the accounts to save the company”.
Cressey’s model has been adapted to new forms of management. Albecht (1982) distinguished three factors favorable to fraud: environmental pressure, opportunism, and the psychological profile of the fraudster. Rezaee (2002) designed the “3C model” (Choice, Conditions and Corporate Framework). Bealey (2000) observed the internal contingencies (the history of the company) and external (its institutional framework) of the fraud process. The Statement of Auditing Standards classified 25 different factors of corporate fraud risk, according to 3 axes: the personalities of the leaders, the economic environment of the company and its organization. The Cressey triangle was reinterpreted by Dominey et al. (2012), who propose a model no longer focusing on the fraudster but on his practices. Called the “fraudulent act triangle model”, it also has three facets: a more or less sophisticated methodology (a misappropriation of assets, a transfer of liabilities…), a concealment of fraud (a false accounting entry, a file destruction…), a conversion of the proceeds of fraud into exploitable assets (money laundering). According to Smith and Lewis (2011), corporate accounting manipulations are generated by managerial drifts based on four types of paradoxes:
– organizing paradoxes, which occur when groups of actors oppose methods (accounting-real, fraudulent-non-fraudulent);
– belonging paradoxes, which arise when a goal can be achieved by different means (accounting-real), or when there are conflicting goals (short or long term);
– performing paradoxes, which arise from more or less conflicting interests between stakeholders;
– learning paradoxes between tradition and innovation, which result in a “phygital” treatment (combining experience and algorithms) of accounting manipulations.
According to Boudon (1990), fraud or manipulation has become a social phenomenon marked by “mimicry effects” and “composition effects”, by which interactions between the types of actors (intentional and unintentional manipulators, fraudsters and non-fraudsters) lead to perverse effects contrary to the intentions of each.
According to Tversky and Kahneman (1974), the behaviors of corporate actors are subject to four classes of bias that have been reinforced by the development of Artificial Intelligence and that particularly affect communication to meet ESG principles. The first class covers cognitive biases that distort the processed data, their processing models, and the interpretation of the results, including familiarity and confirmation biases. Faced with an urgent decision or a complex problem, managers choose the option they think they can best control or the solution that mobilizes immediately available resources and/or involves easily controllable issues. They are also subject to biases of “conservatism” which reflect the tendency to overestimate information in line with their convictions (Festinger, 1957), or anchoring biases, which consist in discarding discordant or confusing information and to seek only those confirming their own choices (Goetzman and Pelès, 1997). The second class of heuristics transposable to AI deals with excesses of optimism and confidence. Managers tend to interpret the “solutions” provided by the applications as “self-fulfilling discourses” or “performative presentations”, which give them the illusion of controlling the situation. They are victims of overconfidence, usually accompanied by self-justification in the event of a bad decision. The decision-maker has the illusion that they “manage in compliance”, that they “master the ESG criteria”, that they “inspire the confidence of their stakeholders” … They believe they do not need advice; they rationalize past events a posteriori (retrospective bias); they attribute all the merits of a success (self-attribution bias), according to Roll (1986) … The third form of bias relates to the effects of imitation or conformity, which affect, according to Hong, Kubik and Stein (1994), designers influenced by socio-professional norms, or by the follow-up of pioneers, charismatic leaders or events. The fourth form of drift caused by generative AI covers perceptual and/or emotional biases, which can blur the mental representation of a phenomenon (Higgs, Dulewicz, 2002). Certain ambiguous or counter-intuitive solutions revealed by AI can induce different behaviors from one actor to another in the face of identical situations. These biases can distort individual decisions in business. Loewenstein et al. (2001) have shown that the fear of an uncertain event is often motivated by the possibility – and not the probability – of its negative consequences; because the more “the latter are perceived as important, the more the affective prevails over the cognitive”.
The respondents’ answers (presented in the section make it possible to distinguish three new approaches to gray areas within organizations, which have not yet been proposed – or which have only been mentioned – by researchers and experts on the issue of gray areas in management. This exploratory survey makes it possible to go beyond the traditional approaches, according to which (non-fraudulent) manipulations in gray areas result from three main types of bias:
– Cognitive: The decision is made in an environment of limited rationality, without knowledge of its technical and legal feasibility, mainly based on its acceptability (satisfacing) by the company’s stakeholders; it results from intuitions and experiments with new behaviors;
– perceptual: the decision or information is ambiguous or ambivalent – intentionally or unintentionally – to circumvent regulations and codes;
– affective: the decision aims to preserve the resilience of the company and the jobs of its employees.
This approach does not really explain why accounting and statistical manipulations are multiplying under the combined effects of a hardening, instability and increasing complexity of the regulatory and normative frameworks applicable to companies, but also of increased investor pressure (including “activist” shareholders) on managers, in search of short-term financial value creation, as well as increased information requirements (more accurate and faster) from all stakeholders of the company (shareholders, partners, staff, customers, suppliers, savers, interest groups…).
New approaches
The new approaches are philosophical, sociological, and psychoanalytic.
The situationist or phenomenological approach of the gray areas
According to this view, behaviors in gray areas respond to a “situation law” (Follett, 1924): the decision is part of a “plan of action” with indeterminate effects, bringing together several actors (stakeholders) with intentions, values and interests that are more or less divergent. This analysis is in line with that of Girin (1990) according to which decision-makers are confronted with “management situations” where “the actors involved must perform a collective action in a determined time leading to an external judgment”. The decision-maker feels compelled to transgress the ethics of his profession, the ethics of his company (without evading the law) and/or his own values, in order to seek a “common sense” compromise between the representations of the situation by the actors involved, who are considered to be in a “cognitive proximity”. The decision-maker may then be led to favor the values or interests of a stakeholder (shareholders, employees, customers, etc.), deemed dominant according to the stakes of the situation faced. He then aims – consciously or not – to make a decision and take action that should be “satisfactory” for the other parties according to the theories of satisficing (Simon, 1953) and “situated and finalized action” (Habermas, 1981), without overestimating the risks (legal, financial and/or relational) incurred. The meaning given by the decision-maker to his decision is based on a representation of the situation which, in a phenomenological approach, makes it possible to become a reality to be shared by the stakeholders of the company. He is not content to analyze the situation, he believes he is rebuilding it in an environment of limited rationality, without trying to align with the standards.
The Socio-Dynamic Approach to Gray Areas
Some of the auditors’ responses also assume that the actors of the company constitute a social group (Mendel, 1968) that acts in a gray area unintentionally and/or unconsciously, as observed by Anzieu (1984). The group engages in manipulation, misinformation, or non-information, through “lack of organizational dynamics”: it does not track the data; it believes it does not have the time to align its accounts and reports with an overly demanding framework; it is content with non-encrypted and plagiarized accounts that “embellish” projects in favor of ESG; it aligns with the objectives of competitors… Conversely, it can act in a gray area through “excessive organizational dynamics” by being subject, according to the concepts defined in particular by Enriquez (1977), to “a driving imagination” (it strives through accounting or statistical manipulations to avoid bankruptcy, job losses, or customers); it can circumvent the law or the norm in order to better innovate or serve the common good. It may be the victim of a “luring imagination” or “false beliefs”: the group mistakenly considers itself to be a leader or pioneer in its market; it believes or makes believe that it is invested with a mission in the service of the planet or the common good…
This approach goes beyond the current analysis of gray area behaviors through cognitive, affective, or perceptual biases (or AI “hallucinations”), because it perceives the gray area as a complex, evolving, and living entity. Some decisions “on the edge” of law and ethics, unconsciously aim to contribute to the survival or transformation of the company, or to the achievement of a common good superior to the law. This approach deepens the reflections of March and Olsen (1976) on the “organizational madness” (in fact, the organizational non-alignment) of companies. It reveals the importance of sociological and psychological factors in explaining gray area manipulations.
The psycho-analytical approach to gray areas
Some of the auditors’ responses lead to another hypothesis about the factors of development of gray areas: the manipulators are subjected to a form of “emotional contagion” (Redl, 1942), defined as “the influence of the style of leadership on the actors of the organization”, who are in symbiosis with the leader and enslaved to his fantasies (Stern, 2000). They are victims of the “psychopathology” of a leader (Levinson, 1984), a founder or his ghost (Bazin and Leclair, 2019). They are influenced by the ego of the power holder in the company, whose compulsive or neurotic character they fear (Kets de Vries, 1975). The manipulator can be influenced by the ideal type represented by a “charismatic leader”, a “rectifier” (cost-killer), an “oracle”, etc., which inspires him to make decisions or adopt behaviors so that the company is considered to be efficient, pioneering, resilient, etc. They form a “group psychic apparatus” (Kaes, 1993) around an instance dominated by a common ideal ego under control, which includes topical, fantastical, dynamic, economic, and genetic dimensions. (Anzieu, 1971).
The auditors’ responses therefore suggest that a gray area can be generated by the style, personality and psychopathology of a company manager, leader or founder, who leads the company’s staff in a regressive process that can have positive effects in the short term but negative in the long term. The behavior of manipulators is influenced by “anxious” leaders imposing caution (and non-information), “paranoid” leaders advocating resistance (to over-regulation), “obsessive” leaders in search of extraordinary performance or excellence (at the cost of misinformation).
The gray area can be a vector of transformation of the company, but also a symptom of the “neurotic company” (Kets de Vries and Miller (1985), exposed to malfunctions, hidden costs and/or uncontrollable risks.
Towards new representations of the gray areas of companies
The exploration of the gray areas of management restored in this research, underlines the interest of their situationist, socio-dynamic, and psychoanalytic approaches. It shows the importance of accounting audits, compliance checks and auditing of digital protection systems. It is an invitation for business leaders to empower staff and raise awareness of ethics, as well as an incentive for the professions of numbers, law and consulting, to train them to secure procedures, systems and data, but it shows above all that the still imprecise or uncertain notions of gray area or sensitive subject cannot be precisely defined and applied without resorting to concepts, heuristics and cures pertaining to the psychoanalysis of organizations.
To go further
W.S. Albrecht, How to detect and prevent business fraud, Prentice Hal, 1982.
D.Anzieu , L’illusion groupale, Nouvelle Revue de Psychanalyse , 1971, n° 4, pp. 73-93. Texte repris in Le groupe et l’inconscient, Dunod, 1975, Nouvelle édition refondue, 1981
I. Barth, L’interstitiel, un nouvel espace de jeu entre psychanalyse et management., Revue internationale de psychosociologie (2011/43 Vol. XVII).
Y. Bazin, M. Leclair, I see dead people. À la rencontre des fantômes organisationnels qui hantent les entreprises. Revue Française de Gestion, 6 (283), 2019, pp.11-29.
R. Boudon, L’art de se persuader, Fayard, 1990.
M.S. Bealey, Fraudulent financial reporting, Accounting Horizons, vol.14, n°4, 2000, p.441-455.
E.L Black. & al, The value relevance of multiple occurences of non recurring items, Review of Quantitative Finance and Accounting, vol 15,2000, .391-411.
E.Chiapello, Transformation des conventions comptables, in M.Capron, Les normes comptables internationales, instruments du capitalisme financier, La découverte, 2005.
D. Cressey, Methodological problems in the study of organized crime as a social problem, Annals of the American Academy of Political and Social Science, 374, 1967.
M. Crozier, E. Friedberg, L’acteur et le système, Le Seuil, 1977, rééd. coll. Point Seuil, 1990.
P.M. Dechow, I.D. Dichev, The quality of accruals and earnings : the rôle of estimation errors, The accounting review, vol 77, 2002.
J. Dorminey, AS Fleming, M-J Kranacher, R-A Riley, The evolution of fraud theory , Issues in accounting education, 2012, vol 27, n°2, p.555-579.
E. Enriquez , Les jeux du pouvoir et du désir dans l’entreprise, Desclee de Brower, 1977.
l. Festinger, A Theory of Cognitive Dissonance. California: Stanford University Press, 1957.
J.Francis, D. Nanda,P. Olson, Disclosure incentives, earnings quality and cost of capital, Journal of Accounting Research, vol 46(1), 2008, p.53-99. M.P. Follett, Creative experience, Lonman Harlow.
E.Freeman, Strategic Management: a Stakeholder Approach, Pittman Publishing Inc, 1984.
J.Girin , L’analyse empirique des situations de gestion : éléments de théorie et de méthode , in Martinet Alain-Charles [ed] Epistémologies et sciences de gestion, Paris, Economica, 1990, 141-182W. Goetzman, & Peles, N. , Cognitive Dissonance and Mutual Fund Investors. Journal of Financial Research, 20, 1997, 145-158.
J. Habermas, Théorie de l’agir communicationnel, Fayard, 1981 (trad. 1987)
Higgs, M. and Dulewicz, V. (2002), Making Sense of Emotional Intelligence, 2nd ed., NFER Nelson, Windsor.
R. Kaes, le groupe et le sujet du groupe. Éléments pour une théorie psychanalytique , Dunod, 1993 .
M.F. Kets de Vries, & Miller, D. , Narcissism and leadership: An object relations perspective. Human Relations, 38(6), 1985, 583–601.
O. Lascar, Deepfake. L’IA au service du faux, Eyrolles.
J.G. March, and Olsen, J.P., Ambiguity and Choice in Organizations. American Journal of Sociology, 84, 1976, 765-767.
Y. Mard, S.Marsat ( ), Gestion des résultats comptables et structure de l’actionnariat : le cas français», Comptabilité, Contrôle, Audit, vol 10(2), 73-98.
G.Mendel, La révolte contre le père, Grasset, 1968.
B. Raffournier, Théorie de Comptabilité Financière, Economica, 2018.
F. Redl, F. Group emotion and leadership. Psychiatry: Journal for the Study of Interpersonal Processes, 5, 1942, 573–596.
Z. Rezaee , Financial statement fraud, John Wiley and sons, 2005.
| W.K. Smith, & Lewis M. W., (2011), Toward a theory of paradox : A dynamic equilibrium | |
| model of organizing, Academy of Management Review, 36 (2), 2015, 381-403. | |
P.C.Stern, New Environmental Theories: Toward a Coherent Theory of Environmentally Significant Behavior, Journal of social issues, 2002.
H. Stolowy, G.Breton, “la gestion des données comptables: une revue de la litterature”, Comptabilité, Contrôle, Audit, vol 9(1), 2003, 175-208.
E. Sutherland, White collar crime, Yale University Press, 1949.
A. Tversky and D.l Kahneman, Judgment under Uncertainty: Heuristics and Biases, Science, New Series, Vol. 185, No. 4157 (Sep. 27, 1974), 1124-1131 .
J-J. PLUCHART