Bringing ethics back: A humanistic approach to organizational survival

Many theories about organizations completely ignore two crucial factors: learning and ethics in decision making.

While there are many theories about organizations, they are often incompatible with each other, explain the same phenomena to a similar extent, and completely ignore two crucial factors: learning and ethics.

In his research paper, "Beyond Economic Criteria: A Humanistic Approach to Organizational Survival," published in the Journal of Business Ethics, IESE Prof. Josep Maria Rosanas proposes basic principles for an ethics-based theory of decision making in organizations and offers companies three criteria to follow in order to achieve this.

But before he can bring ethics back, Rosanas first looks at several theories of organizations to pinpoint their shortcomings. The author mainly focuses on agency theory, which emphasizes Pareto efficiency, ignoring issues such as lying, cheating and abuse of power, or merely taking them for granted; and institutional theory, which ignores ethical considerations because it is based on non-rational behavior, plain imitation of what others are doing.

To integrate ethics and management theory, Rosanas suggests we need a different approach, one that provides intentional explanations and is realistic, promotes rationality and includes ethical concepts.

The learning of the two agents

Drawing from the work of Juan Antonio Pérez López, the author presents a theory based on interrelationships between people.

First, he illustrates the importance of learning in any relationship between two people or economic agents: the "Active Agent" (AA), who wishes to obtain explicit results through the cooperation of another person, and the "Reactive Agent" (RR). Importantly, instead of only looking at external, explicit results, there is a key internal consequence as a result of the action/reaction dyad: the learning of the two agents.

"The AA has to worry about the learning of RA, if only because what can be expected in the future depends on the learning that takes place now," Rosanas states. Likewise, AA's own learning is also important, as it conditions the future for both AA and RA.

In order for this relationship to work over time, the effectiveness of the decision, the learning (both operational and evaluative) of the RA, and the AA's own learning must be taken into account, stresses the author.

Three criteria on which to base decisions

Rosanas then applies this analysis to the organizational context as a basis for organizational decision making. In this grander scheme, AA can represent the firm, the CEO or any manager, and RA can represent employees or customers. The important aspect of this interactive learning is to build trust within an organization to ensure its long-term survival.

Using this two-way learning as a basis, the professor offers three criteria that companies can base decisions on.

The first is short-run effectiveness - the impact on immediate, explicit, measurable variables, of which the economic ones are particularly important.

"It would be absurd for a business firm to forget about such variables. This is related to the satisfaction of the extrinsic motives of producers and consumers as well, and is the only aspect considered in reductionist models of the firm based strictly on economics as a discipline and as a theory," he states.

The second is development of distinctive competence, or the contribution of the chosen course of action to the development of a company's external mission. This goes beyond the immediate economic variables by defining the type of real needs of customers that the firm intends to satisfy.

The third is unity of the organization: identification with the organizational objectives and with the other employees.

"This three-criteria decision-making process obviously adds some complexity to the usual analysis based only on effectiveness, but is much better." Again, the author stresses the importance of learning dimensions, stating that "the importance of learning is often greater than the quantitative analysis."