The value and dangers of remembrance in changing worlds: a model of cognitive and operational memory of organizations

Download 82.14 Kb.
Date conversion03.05.2016
Size82.14 Kb.
The value and dangers of remembrance in changing worlds: a model of cognitive and operational memory of organizations
Giovanni Dosi1, Luigi Marengo2, Evita Paraskevopoulou3, and Marco Valente4
1 LEM, Scuola Superiore Sant’ Anna, Pisa and visiting professor, Friedrich-Schiller-Universität, Jena

2 LEM, Scuola Superiore Sant’ Anna, Pisa,

3 Departamento de Economia de la Empresa, Universidad Carlos III de Madrid,

4 School of Economics, Universit`a de L’Aquila,
1. Introduction
The notion of organizational memory stands for an elusive albeit crucial feature of the organizational reproduction of knowledge as distinct from the memory of individuals, namely the ability of organizations to elicit stored information from an organization’s history that can be retrieved to bear on present decisions (Walsh and Ungson, 1991). The property of memory of being “organizational” means that, first, it may well be distributed within the organization in ways such that no individual or individual subunit embodies the full representation or the full behavioural repertoires contained in the memory itself. Second, the organizational character of the memory also implies that it is resilient - to degrees to be established, which we shall indeed discuss in this work - to the turnover of individuals within the organization itself.
In many respects “memory”, is a crucial corollary of organizations being path-dependently reproducing institutions – or whatever name is chosen for collective behavioural entities as distinct from agency-theoretic constructs (no matter whether of the ‘incomplete contract’ kind). Organizations “remember” because they entail explicit norms and, together, more tacit practices addressed to collectively solve practical and cognitive problems, ranging from the production of a car, all the way to e.g. the identification of a malaria-curing molecule. This is another way of saying that organizations learn, store, elicit and modify over time routines and other “quasi genetic action patterns “ as Winter (in Cohen et al., 1996) puts it. Together, organizational routines bear the memory (and possibly the continuing threat) of conflict of power and income distribution which contributed to generate them and continues (or not) to sustain them.
Granted all that, organizational memory concerns, first, the structure of beliefs, interpretative frameworks, codes, cultures by which the organization interprets the state of the environment and its own “internal states” (Levitt and March, 1988): in brief, call all this the cognitive memory of the organization. Second, organizational memory includes routines - comprising standard operating procedures, rules and other patterned actions- : call that operational memory of the organization.
Both, cognitive models and operational repertoires are the outcomes of learning processes and thus evolve over time in response to experimentation and feedbacks from the environment. However, they might often entail quite high degrees of inertia and path-dependent reproduction, as in fact, by its nature, organizational memory reproduces over time what an organization has learned throughout its history. In short, the two types of memory concern the organizational capabilities to “understand” the characteristics of the environment, on one hand, and to coordinate particular sequences of actions across different decision units and individuals, on the other. In turn, a major question we shall address below concerns indeed the role of memory in changing environments. “Competence traps” clearly belong to this domain of analysis.
Cognitive and operational memories entail an “if….then” structure. Signals from the environments, as well as from other parts of the organization, elicit particular cognitive responses, conditional upon the “collective mental models” that the organization holds, which are in turn conditional upon the structure of its cognitive memory. Cognitive memory maps signals from an otherwise unknown world into “cognitive states” (“…this year the conditions of the market are such that it will be profitable to produce X…”). Conversely, the operational memory elicits operating routines in response to cognitive states (“…this year we should produce X…”), internal states of the organisation (“…prepare the machine M to start producing piece P…”) and also environmental feedbacks (“…after all X is not selling too well…”).
A promising candidate to model both types of memory finds its roots into the formalism of classifier systems (cf. Holland, 1975 and 1986). The model that we shall propose below finds its ascendancy there, and in their application in Marengo (1992), albeit with significant modifications. On purpose in the following we do not commit to any specific “if …then” structure, which in an organizational setting implies also specific commitments to rules of selection and evolution (so, technically, we also depart from the “bucket brigade” formalization of rule reinforcement).
Moreover, in the model we explore below, we allow the possibility of having on the “if” part both “cognitive organizational states” associated with explicit organizational rules but also more idiosyncratic present or past “inner states” and cognitive frames. Moreover, we implicitly take on board the coupled dynamics between e.g. standard organizing procedures (SOP) belonging to the organizational domain of explicit rules –whose likely memory entails devices such as operational manuals and straightforward commands –, on the one hand, and partly uncodified responses to whatever “if” in the “then” behavioural response – ranging from tacit repertoires all the way to conflictual responses or sheer mistakes.

On the ground of such formal apparatus we shall first analyze the structure of what is remembered conditional on the characteristics of the environment. Second, we shall explore the conjecture –well grounded in several empirical studies- that a memory structure well “fit” for a particular environment may turn out to be pernicious under different technological or market conditions (cf. among others Tripsas and Gavetti, 2000 and Bresnahan, Greenstein and Henderson, 2010). And conversely, we shall try to identify the circumstances under which “learning” implies primarily “intentional unlearning”. Third, we shall address the possibility of sort of “organizational cognitive dissonance” characterized by the mismatching between the mental models of the organization –with the ensuing operational strategies- and its operational repertoires (meaning also the possibility of being “successful” - against whatever evaluation criterion –with a “wrong” model of the world, or conversely, failing despite having the “right” one). In fact, in many respects, the proposed work is an exploration of the outcomes of the mappings between cognitive and operational memory, on one hand, and patterns of environmental change, on the other.

We shall proceed as follows. In Section 2 we shall attempt a broad even if necessarily concise assessment of the state of the art of the incumbent knowledge concerning organizational memory. First, straightforwardly we shall address the meaning of such notion, its structure and the process of its storing and retrieving. Second, we discuss the inevitable path dependent and inertial nature of organizational memory. Third, we shall survey the evidence on the role (and dangers) of organizational memory especially in changing environments. In that, crucial issues regard, as mentioned, (cognitive and operational) “traps” as well as the importance of organizational forgetting. Fourth, we explore the (even more scattered) evidence on mismatches between “collective mental models”, behavioural patterns and payoffs thereof; that is the evidence on organizational cognitive dissonance.
Section 3 will present the structure of the model which, in a simulation environment addresses the interpretative questions stemming from the foregoing pieces of evidence and explores the dynamics of collective cognition , behaviour and ensuing (most often opaque) environmental payoff feedbacks.

Section 4 will discuss the major simulation results.

2. Organizational Memory: Characteristics, determinants and dynamics
2.1. Organizational Memory and Organizational Routines

The existence and importance of organizational memory is associated with the very ability of organizations to interpret their environment, learn how to solve operational problems and, by doing that, built constructs of knowledge that can be stored and reused. One side of the story is in a broad sense cognitive.

The view of organizations as fragmented and multidimensional interpretation systems is grounded on the importance of collective information processing mechanisms that yield shared understandings (Daft and Weick, 1984), or “cognitive theories” (Argyris and Schon, 1978), of the environment in which they operate , and assist organizations to bear uncertainty, and, as we shall see, environmental and problem-solving complexity . If one subscribe to the notion that organizational learning is a process of refinement of shared cognitive frames involving action-outcome relationships (Duncan and Weiss, 1979) and that this knowledge is retained –at least for some time- and can be recalled upon, this is like saying that organizational learning is in fact the process of building an organizational memory. This cognitive part of the memory is made of “mental artefacts” embodying shared beliefs, interpretative frameworks, codes and cultures by which the organization interprets the state of the environment and its own “internal states” (Levitt and March, 1988).
Together, there is an operational side to the organizational memory involving the coupling between stimuli (events, signals -both external and internal ones-) with responses (actions), making up a set of rules that remain available to guide the orientation of the organization and execute its operations. In this domain the memory largely relates to the ensemble of organizational routines - patterned actions that are employed as responses to environmental or internal stimuli, possibly filtered and elaborated via the elements of cognitive memory (much more on routines in Nelson and Winter, 1982; Cohen et al, 1996; Becker et al., 2005; Becker, 2005 and the literature reviewed here ). As Cohen and Bacdayan (1994) put it, this procedural side is the “memory of how things are done”, bearing a close resemblance with individual skills and habits, often with relatively automatic and inarticulated features (p.554)
In fact, the characteristics and evolution of organizational memory mirrors the characteristics and evolution of organizational routines. In the case of routines, the memory elicits a “relatively complex pattern of behaviour triggered by a relatively small number of initiating signals or choices” (Winter, 1996). How small or big is the initiating set of signals in itself is an important interpretative question, which has to do with the ways the organization categorizes environmental and intra-organizational information. And likewise the behavioural patterns are likely to display different degrees of conditionality upon particular sets of signals. So, at one extreme the action pattern might be totally unconditional and “robust”: “perform the vector of actions X* irrespectively of the perceived state of the world”. At the opposite extreme actions might be very contingent on the fine structure of their “if” part.
As we shall explore below, it might well be that the coarsesness of the “ifs” and the “robustness” of the “then” parts might well depend on the nature of the environment and its dynamics. A conjecture in this respect is that the more complex and unpredictably changing is the environment, the less contingent is the behaviour (Heiner, 1983; Dosi et al., 1999). After all, routines can be seen as an uncertainty reducing device (Becker and Knudsen, 2005; Dosi and Egidi, 1991): robust and largely uncontingent routines might be those memorised under highly complex and changing environments.

The importance of memory is not confined to its value as a stock of information referring to past activities and decisions of organizations; most importantly, its significance resides in its knowledge contribution for current problem solving and possibly to learning (Huber, 1991), to organizational legitimacy and consolidation power (i.e. “this is how we have always been doing things”) that can be used to overcome intra-organizational conflicts (Feldman and March, 1981) and influences the establishment of future strategies and their implementing actions. The other side of the coin is the ensuing danger of hindering organizational change. Indeed, depending on environmental dynamics, organizational memory can become an asset for success or, on the contrary, an obstacle to change (see Section 2.4)

With regards to its structure, organizational memory represents a stock of knowledge whose elements are characterized by different degrees of latency and “tacitness” and are unevenly distributed manner across the organization. As the elements of the memory are distinguishable –although often interrelated- in terms of the problem they address (or addressed), the actors that embody them etc., it follows that there exists a variety of storage locations within the organization. The organizational location of different elements of the memory is determined by their content: a first distinction to make is between “storage bins” that carry elements containing information about the stimulus of a decision - the “if” part - and others that contain the elements with primarily the knowledge on the response to that stimulus –the “then” part- (Walsh and Ungson, 1991)1. These distributed “knowledge reservoirs” (McGrath and Argote, 2000) can nevertheless be retrieved and possibly recombined under the “appropriate” environmental and internal states. The frequency of use (or length of inactivity), the capacity of the storage location, the degree of interconnectedness and the cost of maintenance and retrieval of the elements of memory are all factors that determine the characteristics and (re)use of the constituents of organizational memory. As such memory structures tend to differ across organizations and over time time.
The retrieval of multiple memory elements entails the necessity of mechanisms that bring together, interpret and categorize environmental stimuli, supported by the inner social network of the organization, its sequence of routines and the physical technologies used (Argote and Ingram, 2000). Retrieval is collective in nature, which might also mean that it can be accompanied by tensions and conflicts whose resolution relies upon “horizontal” coordination and hierarchal authority.
2.2 Path dependence
Inertia and path dependence are an almost inevitable corollary of the very existence of organizational memory. The organization is able to recall specific cognitive frames and behavioural repertoires precisely because they are stored and inertially reproduced (possibly with slight modifications) over time. Organizations are inertial because their cognitive and behavioural performances are a far cry from this response “plasticity” normally postulated by any view of organizations as “bundles of optimal contracts” (Rumelt, 1995). Organizations path dependently carry with them their birthmarks and what they have subsequently learned throughout their history. It is true that firms typically live in selective environments which tend to “weed out” the most dysfunctional traits and behaviours. However, typically their overall “fitness” (say, their revealed competitiveness) depends upon multiple inter-related traits: in such cases, selection happens on a fitness landscape with multiple local maxima that are determined by (random) initial conditions (Levinthal, 1997; Castaldi and Dosi, 2006). Indeed, organizations typically compete on such complex landscapes and interrelated technological and behavioural traits are responsible for path dependent reproduction of organizational arrangements (Marengo, 1996; Levinthal, 2000). The link between what firms do and the way they are selectively rewarded in the market is utterly opaque for at least three reasons: (i) the complexity of the environments where they operate; (ii) the already mentioned multiple “epistatic correlations2” amongst behavioural and technological traits; and (iii) significant lags between organizational actions and performance-revealing feedbacks. In such circumstances, path dependence can also be fuelled by behavioural/procedural and “cognitive” forms of inertia (Tripsas and Gavetti, 2000), as well, not too paradoxically, by the reinforcement of traits, behaviours technologies which have been highly successful in the past but might turn out to be detrimental under changing environmental conditions (see also the next subsection): competence traps (Levitt and March, 1988) belong to this general heading. Widespread path-dependent properties emerge also from the “organizational ecology” literature investigating the long-lasting bearing of founding conditions of new enterprises, which become imprinted in the organization and mold their subsequent development in terms of organizational practices and broad strategic orientations (among several more see Carroll and Hannan, 2004).
In the model which follows we shall address below isomorphic issues by means of simulation exercises addressing, among other issues, the relationships between the “depth” and inertiality of memory and path-dependencies in organizational behaviours.
2.3 Organizational memory in changing environments
Organizational memory carries overtime what the organization has learned, directly through its past experiences, vicariously by observation of the experiences of other entities, or has been so to speak “brought in” by members of the organization – especially by the top management, executives and technicians- with their strategic orientations, cognitive views, heuristics and know-how. A crucial question regards the usefulness over time of the outcomes of such learning activities as carried by the organizational memory. The question, in turn, boils down to both the characteristics of learning and the depth of environmental changes the organization faces.
To a considerable extent, we have already mentioned, organizations learn from their experience, reproducing beliefs and actions that have been associated with good outcomes and avoiding actions associated with bad ones. If the world makes simple sense and is stable, the repeating the “good” routines is an effective organizational behaviour. However, the world is rarely simple enough to make experience an infallible teacher (March, 1981; March and Olsen, 1976).The interpretation of experience itself is ridden with cognitive frames and subject to cognitive biases, so that what is learned depends as much upon history as on the frames applied to that history (Levitt and March, 1988; Fishhoff, 1975; Pettigrew, 1985), while beliefs, stories and routines will be conserved notwithstanding disconfirmation.
Moreover, quite independently from any possible cognitive bias, the environment may well change in ways that decrease the “fitness” of cognitive and behavioural patterns which were well suited to the “old” environment or even make them detrimental. This is indeed what competence traps are essentially about (Levitt and March, 1988). Note that competence traps may refer primarily to the cognitive domain or alternatively to the operational one. In the former case the “trap” concerns primarily the reproduction beyond their times of usefulness of previously successful strategic orientations and heuristics. Call them cognitive traps; relatedly, the escape is likely to involve “strategic reorientation” possibly linked with the substitution of the top management. Conversely, the “trap” might concern the “way of doing things” – that is the ensemble of routines and other recurrent action patterns. In these circumstances the remedy is likely to involve also procedural and organizational changes. In actual fact, the cognitive and operational lock-ins are likely to come often together.
Bresnahan, Greenstein and Henderson (2011) present an excellent illustration of this point3 in two cases of “Schumpeterian transitions” across different technological trajectories and of the vicissitudes of the firms which were market leaders under the old ones - in their examples, IBM facing the emergence of personal computers and Microsoft vis-à-vis the arrival of the browser-. Take the IBM case. Strong technological capabilities match a commitment to incrementalism in product architectures, cumulative learning, vertical integration, proprietary standards, coordinated strategic governance and, on the market side, a reputation for post-sale service. This “IBM model”, Bresnahan et al. (2011) insightfully show, is well aligned to market requirements under the mainframe/mini computer trajectories, but becomes misaligned to the requirements of effective production and marketing of personal computers. It is not that the “raw” capabilities are not there. They are. And in fact IBM even proceeds to a rather successful exploration of a new combinatorics between elements of technological capabilities, organizational set-ups and market orientation well suited to the personal computer world. However, that very success accelerates the clash between the “PC organizational model” and the incumbent “IBM (mainframe) model”. This latter wins and by doing that IBM ultimately kills its PC line of business.
It is a story vividly illustrating the path-dependent reproduction of capabilities, shared strategic models, specific organizational arrangements and the ensuing traps. To repeat, it is not that IBM lacked any of the elements underlying successful “PC-fit” combinations. It is just that capabilities, “visions” and organizational set-ups and their specific combinations are better described at least in the short term as state variables rather than control variables, in Winter (1987) characterization. Of course, also state variables can and are indeed influenced by purposeful discretionary strategies, that is, by the explicit manipulation of control variables. However this takes time and is tainted by initial birthmarks and subsequent historical paths the organization has taken with respect to both operational repertories and higher level collective visions concerning the very identity of the organization.
In fact, technological and market discontinuities, –quite a few analyses suggest-, demand forgetting and unlearning (Hedberg, 1981; Huber, 1991; Nystrom and Starbuck, 1984; Walsh, 1995; Klein, 1989) involving also changes in the organizational structures and the purposeful erasing of at least parts of the cognitive and procedural memory of the organization. A revealing example regards the “unlearning” activities involved in the Merger and Acquisition processes. Kunisch, Wolf and Quodt (2010) distinguish three domains of possible “misfit” between the two merging organizations - at the level of artefacts, behaviours and corporate cultures. On the ground of a large database on M&A in Germany, they find that cultural misfits are particularly conducive to a lower subsequent performance, while - irrespectively of the sources of misfit – more unlearning is associated with easier absorption of new knowledge and better post-merger performances.
Clearly, unlearning comes at the cost of the loss of a good deal of the experiential wisdom of the organization itself (Gavetti and Levinthal, 2000): however, whether this is actually a cost, in terms of organizational “fitness” is likely to depend upon the depth of the changes in the appropriate technological capabilities and in the market environments. The general intuition stemming from the empirical literature is in fact that the value, or the cost, of cognitive changes and procedural forgetting is a function of the changes in the fitness landscape which the organization faces. Indeed, in the following, we shall explore more formally this conjecture by explicitly modelling shocks on such landscapes and studying the ensuing impacts upon organizational performances under different degrees of cognitive and procedural inertia, or conversely “forgetfulness” of organizations.
A related but distinct question concerns the type of cognitive structures and routines characterizing organizational memory, depending on different environmental features and dynamics. In particular , could it be that, as already mentioned, in highly turbulent , highly uncertain, environments it efficient to develop “robust”, relatively invariant, routines which explicitly neglect pieces of available information.

2.4 Organizational cognitive dissonance
The patterns of change of organizational memory are determined by changes in both its cognitive and operational part and may or may not be parallel and synchronized. Changes in the cognitive and procedural memory are driven by different processes, with cognitive search often broad and loosely specified, while more narrow (local) experiential research often guide the latter (Gavetti and Levinthal, 2000).
In changing environments, the presence of different hierarchical forms and the possibility of competence traps (cognitive or procedural or both) increase the risk of mismatching between the mental models of the organization –with the ensuing strategic orientations - and its operational repertoires. Such an organizational cognitive dissonance may translate into being “successful” -against whatever evaluation criterion –with a “wrong” model of the world, or conversely, failing despite having the “right” one.
Changes in the environment trigger processes of adjustment and adaptation of organizations and ignite processes of interpretation of the new states of the environment. That very process tends to decouple the “if..then” rules. In such circumstances organizational memory is activated in two ways. First, it is called for in understanding the new state of the environment as the receivers of new signals will commence their interpretations relying on existing cognitive structures. Second, new interpretations are followed by a retrieval process of elements of procedural memory involving the evaluation and revision of existing operational routines, deciding whether the existing operational repertoire is useful under the new conditions or whether new rules need to be created. Re-coupling the cognitive and operational elements of the memory under new environmental conditions is a task that might not be symmetric either in terms of depth or speed: hence the mismatching between cognition and action.
Inertia and path dependence are ubiquitous attributes of both the interpretation process as well as the evaluation heuristics addressing the degree of fitness of incumbent routines, – with evaluation “meta- routines” too being themselves part of an organization´s memory . It is indeed the case that inertial evaluation heuristics can slow down organizational adaptation and change in so far as they are crucial in altering or reinforcing the cognitive states of organizations (Garund and Rappa, 1994).
The existence of distinct bundles of evaluation heuristics concerning cognitive and procedural memory can become the source of cognitive dissonance. In that, the distinction between automatic and controlled retrieval of memory elements (Walsh and Ungston, 1991) – especially with regards to the procedural part of the memory - assists in the explanation of failures to accord the cognitive state with the operational routines of an organization. Automatic retrieval entails the danger of using existing operational routines, when the “new reality” would actually call for the development of new ones.

Conversely, controlled retrieval (comforted by hierarchical power) may again induce mismatches between cognition and action as, for instance, top layer management may impose the development of new actions although the existing ones could well fit the new environmental conditions.

Section 3: A model of cognitive and operational memory of organizations
3.1 Scoping of existing model (Evita)
3.2 Formalizing the notion of organizational memory

  • The organizational problem is to develop a vector of interdependent actions in a complex environment characterized by a (large) set of interdependent features

  • The (large) of environmental configurations can be partitioned in equivalence classes, where each class requires a different action profile.

  • A payoff or fitness function which, for every environmental profile, gives the payoff of every action profile

Three notions of complexity of the problem: Three notions of complexity of the problem

  • Categorizability: how large are these equivalence classes? The larger, the more invariant the action. In some of the simulations only a few environmental features (“core features”) influence the relative fitness of actions, all the others are irrelevant.

  • Neutrality: are such classes made of similar environmental profiles? If we modify one bit of the environmental configuration, does the fittest action tend to be same or not?

  • Ruggedness: if we modify one bit of the environmental configuration, do the fitness value of the action profiles tend to change smoothly or abruptly?

More formally

  • Set of environmental features: E={e1, e2,…., en}, with ei={0,1}, thus 2n environmental profiles

  • An action profile is the choice of values for m interdependent actions: A={a1, a2,…., am}, with ai={0,1}, thus 2m action profiles

  • The fitness landscape: F: E  A  R attributes a real valued payoff to each of the 2n+m environment-action states. The shape of this landscape is defined by the three dimensions of complexity defined above

  • Action is chosen by means of a system of condition-action rules that prescribe a specific course of action when some environmental condition is met. Each rule takes the form:

c1, c2,…., ck  a1, a2,…., am with ci={0,1,#} where ci sets a condition on the i-th environmental feature, which is met if ci = ei or ci = #

  • Action is chosen by means of a system of condition-action rules that prescribe a specific course of action when some environmental condition is met. Each rule takes the form:

  • c1, c2,…., ck  a1, a2,…., am with ci={0,1,#} where ci sets a condition on the i-th environmental feature, which is met if ci = ei or ci = #

Rules and Memory

  • The number of rules an agent can store is the size of his memory

  • If a rule’s condition matches the current environmental profile, the rule is called active

  • Rules that remain inactive for δ periods are discarded; δ is a memory decay parameter


  • Learning takes place through rule selection and rule modification 

Rule selection

  • Only active rules can act. Among the active rules the one with highest fitness is chosen

Rule Modification

  • On the action part local search (one-bit mutations) is performed

  • On the condition part two algorithms for the generation of new rules:

specification: whenever a rule ci = # acts, it is compared with other rules which, under the current environmental state, trigger a different action mapping into a higher payoff.

generalization: if no rule is active, the one which better matches the current environmental profile generates a new one with enough #’s to be active.

Note: at the outset an agent is endowed with a rule whose condition is made entirely of # and a random action. Then rules are generated and modified with the above mentioned mechanisms
Section 4: Simulations and Results
4.1 Simple landscape and online learning

  • A first baseline bunch of simulations evaluate the learning properties of an agent in a “simple” landscape with three core bits.

  • Our learning agent develops rules that correctly match environmental and action profiles.

  • Initially we have an exploration phase in which a large number of new rules are generated with very low degree of specificity.

  • At a later exploitation stage, actions become increasingly tailored to the correct environmental conditions. This process is generated by the cumulation of evidence that a given rule is systematically better when the set of core bits are in a given configuration. Notice that average specificity does not reach its maximum because a default hierarchy persists

Exploration in a simple environment

If we keep the landscape simple, but increase the number of core bits memory requirements increase sharply.

Complexity of the environment and number of rules

4.2 Complex environment and online learning

A second baseline bunch of simulations evaluate the learning properties of an agent in a “complex” landscape.

If the number of core bits is low (i.e. the landscape is locally rugged but with a lot of neutrality), learning is much slower that in a simple landscape, gets stuck in many local optima.

If we increase the number of core bits instead the learning processes settles into a much lower number of more general rules (routines emerging)

4.3 Changing Environments

Punctuated Equilibria” with system-wide shocks: Rule specificity

The Marks of path-dependency: Even in unchanging environments, firm-specific cognitive frames and action repertoires…

Persistent cognitive and operational diversities across firms

When (path-dependent) memory becomes an obstacle to adaptation:

Environmental “punctuation”…
Punctuated Equilibria” with system-wide shocks: Rule age

Punctuated Equilibria” with system-wide shocks: Relative Fitness

Alonso, R., Dessein, W., and Matouschek, N., (2008), “When Does Coordination Require Centralization?” American Economic Review, 98:1, 145–179

Aoki, M., (1986), “Horizontal vs. Vertical Information Structure of the Firm”, The American Economic Review, 76: 5, 971-983

Aoki, M., (1990) Toward an Economic Model of the Japanese Firm, Journal of Economic Literature, 28: 1, 1-27

Aoki, M., (2001), Toward a Comparative Institutional Analysis, MIT Press, Cambridge, MA

Aoki, M., and Dosi, G., (2000), 'Corporate Organization, Finance and Innovation', in Finance and the Enterprise, Vera Zamagni (ed.), Academic Press, 1992, 37-61, reprinted in G. Dosi, Innovation, organization and economic dynamics: selected essays, 2000, Edward Elgar

Argote, L. and Ingram, P. (2000), Knowledge Transfer: A Basis for Competitive Advantage in Firms, Organizational Behavior and Human Decision Processes, Vol. 82:1, p.150-169

Argyris, C., and Schon D., (1978), Organizational Learning: A Theory of Action Perspective, Reading, MA: Addison-Wesley Publishing Co.,

Becker, M. and Knudsen, T., (2005),” The role of routines in reducing uncertainty”, Journal of Business Research, 58:6, pp.746-757

Becker, M., (2005), A Framework for applying organization routines in empirical research: Linking antecedents, characteristics and performance outcomes of recurrent interaction patterns, Industrial and Corporate Change,14:5 , pp. 817-846

Becker, M., Lazaric, N., Nelson, R. R. and Winter, S. G., (2005), “Applying organizational routines in understanding organizational change”, Industrial and Corporate Change, 14:5, pp. 775-791.

Bresnahan, T., S. Greenstein and R. Henderson, (2011), “Schumpeterian competition and diseconomies of scope: illustrations from the histories of Microsoft and IBM”, NBER Working Paper

Carroll, G.R. and M.T Hannan (2004), The demography of corporations and industries, Princeton, Princeton University Press

Castaldi, C., and Dosi, D., (2006), “The grip of history and the scope for novelty:some results and open questions on path dependency in economic processes”, in A.Wimmer and R. Koessler (eds), Understanding Change Models, Methodologies and Metaphors, London , Palgrave

Cohen, M. and Bacdayan, P., (1994), Organizational Routines Are Stored As Procedural Memory: Evidence from a Laboratory Study, Organization Science, 5: 4, pp. 554-568

Cohen, M., Burkhart, R., Dosi, G., Egidi, M., Marengo, L., Warglien, M., and Winter, S., (1996), Routines and Other Recurring Action Patterns of Organizations: Contemporary Research Issues, Industrial and Corporate Change, 5:3, pp. 653-698

Cyert, R.M., and March, J., (1963), A behavioural theory of the firm, Blackwell: Oxford.

Daft, R., and Weick K.E., (1984), "Toward a model of organizations as interpretation systems," Academy of Management Review, 9, 284-295

Dosi, G. and Egidi, M., (1991) “Substantive and procedural uncertainty: An exploration of economic behaviours in changing environments”, Journal of Evolutionary Economics, 1, pp 145-168

Dosi G, Marengo, L., Bassanini, A., and Valente M., (1999), “Norms as emergent properties of adaptive learning: the case of economic routines”, Journal of Evolutionary Economics, 9, pp 5-26

Duncan, R., Weiss, A. (1979) “Organizational learning: implications for organizational design”, in Research in Organizational Behavior, ed. B. M. Staw, 1:75-123. Greenwich, CT: JAI Press

Feldman, M., and March, J., (1981), Information in Organizations as Signal and Symbol, Administrative Science Quarterly, 26:2, 171-186

Fischhoff, B., (1975), “Hindsight or foresight: The effect of outcome knowledge on judgment under uncertainty”, Journal of Experimental Psychology, 1:288-99

Garund, R., and Rappa, M., (1994), “A Socio-Cognitive Model of Technology Evolution: The Case of Cochlear Implants”, Organization Science, 5: 3, 344-362

Gavetti, G. and Levinthal, D., (2000), “Looking Forward and Looking Backward: Cognitive and Experimental Search”, Administrative Science Quarterly, 45, 113–137.

Hedberg, B., (1981) “How organizations learn and unlearn?” in P. C. Nystrom & W. H. Starbuck (Eds.), Handbook of organizational design (pp. 8-27). London: Oxford University Press.

Heiner R (1983), “On the origins of predictable behaviour”, American Economic Review, 73, 560-595

Heiner, R. (1988), “Imperfect decisions and routinized production: implications for evolutionary modelling and inertial technical change”, in Technical Change and Economic Theory (ed. G. Dosi et al.), Pinter, London.

Holland J. H., (1986), “Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule based systems”, in R.S. Michalski, J.G. Carbonell and T.M. Mitchell (eds.), Machine Learning II, Los Altos, CA, Morgan Kaufmann.

Holland, J.H, (1975), Adaptation in Natural and Artificial Systems, Ann Arbor, University of Michigan Press

Huber, G., (1991), Organizational Learning: The Contributing Processes and the Literatures, Organization Science, 2:1, 88-115

Klein, J. I. (1989), "Parenthetic Learning in Organizations: Toward the Unlearning of the Unlearning Model," Journal of Management Studies, 26, 291-308.

Kogut, B., and Zander, U, (1996), What Firms Do? Coordination, Identity, and Learning, Organization Science, 7:5, pp. 502-518

Kunisch S., C.Wolf and J. Quodt (2010), “When forgetting is the key: the value of unlearning activities during post-acquisition integration” , Performance , 3 , 5-12, Ernst & Young

Levinthal, D. (2000), Organizational Capabilities in Complex Worlds” in The Nature and Dynamics of Organizational Capabilities, edited by G. Dosi, R. Nelson, and S. Winter. Oxford University Press

Levinthal, D., (1997), “Adaptation on Rugged Landscapes”, Management Science, 43, 934-950

Levinthal, D., and March, J., (1981), “A model of adaptive organizational search”, in J. March (ed), Decisions and Organizations, pp.187-218, Basil Blackwell: Oxford

Levitt, B., and J.G. March, (1988), “Organizational Learning”, Annual Review of Sociology, Vol. 14 pg. 319-340

March J., and Olsen, J.P., (1976), Ambiguity and Choice in Organizations, (2nd Ed.) Bergen: Universitetsforlaget

March, J., (1981), “Footnotes to Organizational Change”, Administrative Science Quarterly, 26: 4, 563-577

March, J., and Simon, H., (1958), Organizations, Blackwell: Oxford

Marengo, L. (1996), “Structure, Competence and Learning in an Adaptive Model of the Firm”, in G. Dosi and F. Malerba (eds.), Organization and Strategy in the Evolution of the Enterprise. London: MacMillan

Marengo, L. and G. Dosi (2005), “Division of Labor, Organizational Coordination and Market Mechanisms in Collective Problem-solving", Journal of Economic Behavior and Organization, 58, 303-326

Marengo, L., (1992), “Coordination and organizational learning in the firm”, Journal of Evolutionary Economics, Vol. 2, pp. 213-226

McGrath, J. E, &, Argote, L. (2000), Group processes in organizational contexts, in M. A. Hogg & R. S. Tindale, (Eds.), Blackwell handbook of social psychology: Group processes (Vol. 3), Oxford, UK, Blackwell

Nelson R., and Winter, S., (1982), An evolutionary theory of economic change, Belknap Press/Harvard University Press: Cambridge MA

Nystrom, P.C. and Starbuck, W.H. (1984), “To Avoid Organizational Crises, Unlearn”, Organizational dynamics, Vol. 12, pp. 53-65

Pentlant, B., and Rueter, H., (1994), “Organizational routines as grammars of action”, Administrative Science Quarterly, 39, pp.484-510

Pettigrew, A. M, (1985), The Awakening Giant: Continuity and Change in Imperial Chemical Industries, Oxford: Blackwell

Rantakari, H., (2008), “Governing Adaptation”, Review of Economic Studies, 75, 1257–1285

Rumelt, R., (1995), Inertia and Transformation, in Montgomery (1995) Montgomery, C.A. (ed.), (1995), Resource-based and Evolutionary Theories of the Firm, Boston, Kluwer

Simon, H. A (1962), “The Architecture of Complexity”, Proceedings of the American Philosophical Society, 106, 467-482.

Simon, H. A. (1981), The Science of Artificial, 2nd Ed.. Cambridge, MA: MIT Press

Tripsas, M., and G. Gavetti, (2000), “Capabilities, cognition, and inertia: Evidence from digital imaging”, Strategic Management Journal, Vol. 1: 10/11, pp. 1147-1161

Tushman, M. L., Romanelli, E., (1985), “Organizational evolution: a metamorphosis model of convergence and reorientation”, in Research in Organizational Behavior, ed. L. L. Cummings, B. M. Staw, 7:171-222. Greenwich, CT: JAI Press

Tushman, M.L., and Rosenkopf, L., (1996), “Executive Succession, Strategic Reorientation and Performance Growth: A Longitudinal Study in the U.S. Cement Industry”, Management Science, 42: 7, 939-953

Walsh, J.P. and G.R. Ungson, (1991), “Organizational Memory”, The Academy of Management Review, Vol. 16: 1, pp. 57-91

Walsh, J.P., (1995), “Managerial and Organizational Cognition: Notes from a Trip Down Memory Lane”, Organization Science, 6:3, 280-321

Winter, S. (1987), “Knowledge and competence as strategic assets”, in D. Teece (Ed.), The competitive challenge: 159-184. Cambridge, MA: Ballinger.

Winter, S., (1986), “The research program of the behavioural theory of the firm: orthodox critique and evolutionary perspective”, in B. Gilad and S., Kaish (eds), Handbook of Behavioural Economics, vol.A, pp. 151-188, JAI Press: Greenwich, CT

Winter, S., (1996) in Cohen, M. D., Burkhart, R., Dosi, G., Egidi, M., Marengo, L., Lassimo, W., & Winter, S (1996), “Routines and other recurring action patterns of organizations: Contemporary research issues”, Industrial and Corporate Change, 5:3, 653-698

1 See Walsh and Ungson (1991) also for a detailed discussion of the different “storage bins” of organizational memory.

2 More on the application of this notion to the economic domain in Levinthal (1997) and Marengo and Dosi (2005); see also below

3 Even if, admittedly, the authors are inclined to offer a somewhat different interpretation of the evidence in terms of economies and diseconomies of scope in presence of jointly shared assets

The database is protected by copyright © 2016
send message

    Main page