Collective Memory

In the previous chapter we focused on the concept of collective action. In the same spirit, this chapter investigates another fundamental component of learning, i.e., memory, and attempts to reformulate this concept at the collective level. Do organizations remember? In which sense it is possible to talk about collective memory? What is the nature of such a memory? The chapter presents a model of organizational memory which can not be reduced to a metaphor, nor to a mere extension or generalization of individual memory.

Organizational Action

Do organizations act? How can we describe collective action? How does such an action come about? The aim of this chapter is to provide the reader with a review of the various perspectives and to propose a definition of collective action as an attempt by the organization to maintain stability and regularity, and create an externally recognizable identity.

Constructing Grey Knowledge

Through explanatory discourse people apply, construct and explain theories of action and attribute meaning to events and to their own actions and those of others. In this chapter we will conduct a detailed analysis of the structure of explanatory discourse and the character of its rationality. Through this analysis we will demonstrate (a) that the rationality of organizational actors is an argumentative rationality aimed at the construction of consensus and shared meanings; (b) that the knowledge contained in the explanations is both structured and opaque, (c) that this particular mix between opacity and structuring makes it possible to both accumulate past knowledge and construct new knowledge.

Organizational Learning and Social Computation

In Chapter XVIII we outlined the characteristics of a computational approach to support organizational analysis. Agent-based modeling, one of the several methodological tools presented in Chapter XVIII, is particularly suited for the modeling of learning processes in complex networks. In this appendix we want to provide the reader with an example of how it is possible to construct agent-based systems in order to simulate the collective behavior of social aggregates. We present a mathematical model aimed to represent and simulate adaptive organizational learning processes. With adaptive organizational learning processes we mean a learning process taking place in a social network in which individuals, by means of social interaction and subjective interpretative processes, contribute to the construction and the accumulation of shared experience. The proposed model implements a multiagent system aimed to represent a social network of interacting heterogeneous ‘virtual people’ operating in a virtual environment, here modeled as a network of resources. Learning for an agent means passing from an initial state to a target one through the identification of optimal paths within the environment by exploiting personal characteristics as well as interaction with other agents and the environment; such interaction allows agents to exchange information, to construct a collective memory on the basis of past individual experiences and to have access to resources.

Constructing Explanations

Organizations are systems designed to guarantee the regularity and continuity of collective actions through the standardization of patterns of action and the establishment of meaning. Artifacts direct theories of action and regulate the way in which the tasks are carried out. Organizations create stable and shared meanings through a process of social construction. But how concrete is such a process? In this chapter we will demonstrate how language, and in particular explanatory discourse, is a fundamental instrument both for the establishment of dominant systems, and for questioning and changing them.

The Paradox of Learning

In this chapter we show that the nature of organizational learning is intrinsically paradoxical. According to the model of organizational memory proposed in the previous chapter, organizational learning is produced, and at the same time, inhibited by existing artifacts and culture. How can organizations enhance learning, and at the same time, structure collective action in order to ensure regularity and predictability? In this chapter we argue that organizations can manage this tradeoff if they allow for a certain degree of “openness” when building their collective memory and, in particular, when constructing their artifacts.

Managing Knowledge in the Cognitive Organization

Digital technologies have played an important role in the diffusion of knowledge management (KM). The distinction between hardware and software, between platform and logical layer has revolutionized the concept of the machine. Machines become intelligent, while knowledge becomes an independent virtual object. An analogous revolution has occurred in organizations: the metaphor of the organization as a machine is replaced by that of the organization as a computer. In this type of organization there is a need to manage a critical new resource: knowledge. Organizations are different from machines and computers in one fundamental way: They are able to generate new knowledge through learning. After giving a brief history of the birth and evolution of KM, in this chapter, we will show how the main criticism of modern approaches to KM are due to the inadequacy of the metaphor of the computer. Finally, we show that in order to overcome such limits, KM needs to be framed within an organizational learning theory and the metaphor of computer organizations substituted with the paradigm of the learning organization.

Organizational Observers as Agents of Change

In this book we propose using verbal data such as discourses and speech as input for organizational analysis. One of the main differences between verbal data and traditional quantitative data is that the latter are objective whereas the former may give rise to multiple interpretations. In this chapter we deal with the issue of the reliability of discursive data and try to provide an answer to the following questions: How one can be sure the information contained in discourse has been correctly interpreted? Is there more than one admissible interpretation? When is an interpretation admissible? We show that in order to answer such questions the organizational analysts have to assume a mindset and research attitude that are rather different than the traditional objectivist point of view.

Modeling Rules

In Chapter XII we outlined the basic structure of a verbal model and its main components: Judgments, rules and qualifiers. This chapter illustrates several approaches in representing the relationships among linguistic variables contained in a verbal model (rules). The description of the examples will skip technical details and it is mainly aimed at illustrating possible applications, finalities and advantages of verbal models.

Eliciting Organizational Discourse

Beginning with this chapter we will describe a methodological approach to identify, represent and model explanatory discourses. In the first part of this chapter we will present the overall methodological framework while in the second part we will focus on the first step of the methodology, that is, the identification and acquisition of explanatory discourses. An interview technique is presented to elicit explanations followed by a detailed example and practical advice.