Applications of uncertainty-based mental models in organizational learning: A case study in the Indian automobile industry

Accting., Mgmt. & Info. Tech., Vol. 7. No. 2, pp. 87-112, 1997

Pergamon

0 1997 Elsevter Science Ltd. All riehts reserved Printed tn Great Britain O959-8022/97 $17.00 + 0.00

PII: SO959-8022(96)00019-7

APPLICATIONS OF UNCERTAINTY-BASED MENTAL MODELS IN ORGANIZATIONAL LEARNING: A CASE STUDY IN THE INDIAN AUTOMOBILE INDUSTRY V. Srinivas

B. Shekar Indian Institute of Management

Bangalore

Abstract-In this paper, we discuss the applicability of qualitative and quantitative reasoning techniques to study the process of Organizational Learning. We have used cognitive maps of a company (for the past five years) taken from the Indian automobile industry to understand the Organizational Learning process. We have conducted stochastic simulation experiment on an uncertainty-based cognitive map (the latest year). We generated scenarios for the future and analysed each scenario with respect to data obtained from the past fiveyears cognitive maps, in light of the theory on Organizational Learning. Ke~n~ords: Cognitive maps, Mental models. Probabilistic Bayesian belief networks, Organizational learning.

networks,

Stochastic

simulation,

Belief revision,

1. INTRODUCTION Organizational Learning has been studied in the context of various issues like: understanding the strategic behavior of organizations (Barr, Stimpert & Huff, 1992) assessing the role of Information Technology in various types of organizational forms (Lambert & Peppard, 1993) in establishing the link between individual and organizational learning (Kim, 1993) and analysing the relationship between learning and innovation at the strategic management level (Dodgson, 1993). Considering its relevance in studying various organizational issues, it has become increasingly important among academicians and senior managers (Stata, 1989). There are a variety of definitions for the term “organizational learning” (see Dodgson (1993) for a complete overview of the literature on organizational learning). For all practical purposes, in this paper we adopt the following definition of organizational learning (Garvin, 1993): A learning transferring insights.

organization is an organization skilled at creating, acquiring, knowledge, and at modifying its behavior to reflect new knowledge

Some of the prominent views on Organizational (for details refer to Dodgson, 1993): 1.1. Representation

Learning

theory are summarized

and and

in Table 1

of learning

A critical issue in Organizational Learning is its measurement (Garvin, 1993). Prior to measurement, one needs to “represent” learning. Barr et al. (1992) cite the role of networkbased formalisms (Cognitive maps, Axelrod, 1972) in the representation of mental models. 87

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and

B. SHEKAR

Table 1 Chn’s Argyris (1977): Argyris defined Organizational Learning into two categories: Single-Loop learning and Double-Loop learning. Organizations adopting single-loop learning, execute the present policies to achieve its objectives. Organizations adopting double-loop learning, try to question the underlying policies and objectives. March and Olsen (1984): March and Olsen proposed a model of organizational learning in which they make a distinction between individual and organizational actions. They argue that individuals actions are guided by their beliefs. These individual beliefs are called Individual Mental Models. These mental models act as perceptible filters which constrain individuals actions, hence organization’s actions. Senge (1990): Senge identified two types of learning that can occur in organizations from his research: Adaptive learning and Generative learning. Organizations undergoing adaptive learning (similar to single-loop learning) are more focused on the knowledge they have, e.g. policies. This is termed to form the know-how part in knowledge acquisition. In the generative type of organizational learning, organizations are bothered about the knocc/-n~h~ part of knowledge acquisition. Most often, organizations which are in dynamic and volatile environments need to have “double-loop” learning of the processes for its long-term survivability. In other words, according to Senge, organizations need to adapt new ways of looking at the world, often challenging assumptions, organizational norms and objectives. Kim (1993): Kim’s work focuses on the relationship between individual’s mental models and Organizational learning. Kim summarizes this relationship as follows: ‘., the mental models in individuals’ heads are where a vast majority of an organization’s knowledge (both know-/zoo, and knob-why) lies”. Thus, these mental models shape facilitate Organizational Learning.

Cognitive maps are cause-effect networks, with nodes representing concepts articulated by individuals, and directional linkages capturing causal dependencies. Cognitive maps provide an excellent representation scheme for studying organizational learning. In their case study on the U.S. rail road industry, Barr et ul. were able to find support for their hypothesis on organizational renewal by observing changes in the cognitive maps. Their conclusions were based on observations of patterns of change in the concepts and linkages in the cognitive maps. These changes in mental models can be used for measuring learning. Eden (1990) has used software systems like COPE to study strategic analysis of organizations. Another form of representing Organizational Learning is using Corporate Planning models. Corporate Planning models used in various organizations, normally applied to generate scenarios, are based on quantitative measures of structural parameters (Godet, 1987). They do not have provision to capture beliefs of individuals, i.e. mental models. These mental models are primarily qualitative, and have started to occupy an important role in Organizational Learning theory (Table 1). Thus, the scenario analysis done by existing Corporate Planning models lacks the important ingredient: Cognitive perceptions of the environment. As De Gues (1988) points out, for effective planning one needs to incorporate mental models into Corporate Planning models as these models fail to capture the uncertainties that surround an organization. This necessity arises from the fact that organizations are increasingly facing tough situations where predictability of the environment has become complex. Another factor to be considered in the existing Corporate Planning models is the representation of environment-uncertainty. To make Corporate Planning models more realistic in scenario generation, they need to be augmented with data on individual mental models, along with information pertaining to uncertainties of the organization’s environment. However, the networkbased formalisms (cognitive maps) used by researchers to represent mental models of top management, do not have to have provision for the representation of uncertain information.

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Traditionally, representation of uncertain information has been a great challenge to computer scientists in the field of Artificial Intelligence. Probabilistic models, fuzzy-logic based systems are some of the important representation schemes proposed. Probabilistic models are built over the well-grounded theory of Bayesian conditional dependency. Network-based representation is one of the most popular forms used in various applications. This is largely due to: (1) The inherent representation of Bayesian conditional independence in the topology itself, and (2) The pictorial representation of the decision problem that elicits the parameters explicitly. For a complete overview of probabilistic network models, refer to Pearl (1988). 1.2. Organization

of the paper

In this paper we explore the possibility of examining the different types of Organizational Learning with the help of uncertainty-based mental models (i.e. network representation of mental models which can capture subjective estimates of uncertainty). The objective of this paper is two-fold: (1) To show how network-based representation formalisms help in capturing the cognitive processes of top management thereby helping in knowing an organization is undergoing any learning process or not; and (2) To demonstrate the effectiveness of belief propagation algorithms built on uncertainty-based network formalisms in generating hypothetical scenarios (like “WHAT-IF” analysis), which can give additional insights into Organizational Learning theory. In the following section, we briefly examine various network-based representative schemes. We then bring out the advantages of representing mental models as belief networks. We also look at algorithms that have been developed on Bayesian belief networks which facilitate belief propagation. Following that, we present a case study taken from the Indian Automobile industry, which is the major contribution of this paper. The case study presents cognitive maps (mental models represented as networks) for five consecutive years of a key organization in this industry, and the latest of the cognitive maps is represented as a Bayesian belief network. We then present results of the computer simulation performed on the latest cognitive map along with hypothetical scenarios generated by the simulation. We look at the theory of Organizational Learning in light of the simulation results. We then discuss various issues and bring in Organizational Learning theory to study the patterns of belief revision obtained. Finally, we conclude by showing how such representation schemes, implemented in a computer model. augment Information Technology’s usefulness in studying Organizational Learning.

2. NETWORK-BASED

REPRESENTATION

SCHEMES AND RELATED

ISSUES

As we have noted in the previous section, the existing Corporate Planning models fail to capture subjective estimates of managers’ cognition of uncertain. In this section. we look at various network-based representation schemes, that help to capture a manager’s cognitive processes. We highlight the salient features in each representation and briefly discuss how they can be used to study some issues in Organizational Learning. Network-based representations of beliefs occupy an important place among the various schemes of knowledge (or human belief) representation. Some of the popular network-based formalisms that have occupied key roles in representing beliefs and capturing causality are: Belief Networks (Pearl, 1988) Qualitative Probabilistic Networks (Wellman, 1990) and Cognitive Maps (Axelrod, 1976). A network-based model is a graph-like structure, with nodes representing variables and hyper-edges connecting these nodes describing relationships among them (Wellman, 1990).

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Broadly, network-based structures can be classified into two categories: deterministic networks, and uncertainty-based networks. The distinguishing characteristic between these two categories is based on the nature of the nodes in the network: In uncertainty-based networks, each node has provision to represent a subjective view of the uncertainty in the environment. Deterministic networks do not have such a provision. Among the uncertainty-based network formalisms, our focus will be on probabilistic networks. Probabilistic network models have attracted attention in the field of decision sciences (Schachter, 1988), and Artificial Intelligence (Wellman, 1990). Pearl’s (1988) Bayesian Belief Networks, Schachter’s (1986; 1988) Influence Diagram’s, and Wellman’s (1990) Qualitative Probabilistic Networks (QPNs) are some of the prominent representation formalisms developed for various applications. Wellman (1990) describes the influencing ability of QPNs in the context of planning and decision making. Pearl (1988) uses a belief-network-based approach for reasoning under uncertainty. However, there have been few practical applications other than in the field of medical diagnosis (Andreassen, Woldbye, Falck & Anderson, 1987). Figure 1 gives a taxonomy of network-based representation techniques. On the other hand, deterministic networks have been used to represent human beliefs in many application domains (Axelrod, 1976). Cognitive map is one such popular form of deterministic representation of beliefs. It has found wide application in the field of social sciences: representing beliefs of top management of a company (Barr et cd., 1992); understanding beliefs of politicians (Axelrod, 1976); and capturing beliefs of consumers in the field of marketing research (Bettman, 1970). Till now these two fields have taken shape independently. Recently

Network-based

Deterministic

Representation

Schemes

Networks

Uncertainty-based

Networks

I

Non-Bayesian

Qualitative

Belief

Propagation

Qualitative

Bayesian

Belief

Belief

Networks

Propagation

(BBN).

Quantitative

I I

Belief

Propagation

I I

I

r Qualitative Belief Propagation (Henrion & Druzdzel,

Algorithm 1990)

Fig. I. Taxonomy of network-based

representation

schemes

Formalisms

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Wellman (1993) has demonstrated the inferencing ability of cognitive maps when they are represented as QPNs. The advent of graphical dependency models for probabilistic reasoning (Pearl, 1987; Wellman, 1990) has formalized the technical underpinnings of deterministic networks like cognitive maps. See Figure 1 for a taxonomy of the network-based representations. 2.1. Cognitive maps Cognitive maps provide graphical descriptions of unique ways in which individuals view a particular domain (Axelrod, 1976; Eden, 1990). The term “cognitive map” has been used to describe several forms of diagrammatic representation of an individual’s cognitions. Causal map is one such form. These are essentially network representation of beliefs of individuals. The networks have nodes representing concepts, and arcs representing directional relationships between these nodes. The intention of drawing a cognitive map is to describe an individual’s conscious perception of the environment. However, the aim is not to map an individual’s entire set of beliefs, or to present a model that simulates actual and complete cognition, but to focus on his role in the problem domain. Typically, in practice, the map is restricted to a particular domain. This is done by filtering out details that relate to specific situations or detailed instances of an individual’s experience, from a general set of observations. Broadly speaking, cognitive mapping is a form of content analysis. However, the notion of “cognitive mapping” is different from some forms of “content mapping”. Content analyses are conducted in two different ways: manifest content and latent content (Erdner & Dunn, 1990). “Manifest content” focuses on such features as word frequency counts and key words in context. This can be measured in terms of: a raw score, percentage of total words, or a ratio in comparison with the same set of words in context. The critical assumption here is that these key words relate to underlying concepts or constructs that are germane to the research question in hand. However, validity of this measure is always questionable as there is rarely a perfect match (Erdner & Dunn, 1990). For example, content analysis of annual reports related to “new product development” will not capture textual material that identifies and discusses new products by brand name without mentioning key words (like “product development” or “new product”, etc.). “Latent content” tries to capture the underlying meaning embodied in a text, and addresses the researcher’s concern for validity in a better way. While content analysing an annual report for, say, the term “new product development”, the text is examined for synonymous definitions of “new product development”. “Latent content” depends to a great extent on subjective interpretation and judgment, during the qualifying process for relevance. Thus, latent content analysis introduces problems of reliability. This problem is usually addressed by means of additional precautionary measures to cross-check the coder’s subjective interpretation. Cognitive mapping techniques can be placed on a continuum (Huff, 1990). At one end are mapping methods that deal with “manifest content”. At the other end of the continuum are methods commonly such as used in the fields of anthropology and Artificial Intelligence for knowledge representation. These methods involve considerable interpretation on the part of the researcher, and they draw on more complex models of cognition. Thus, a cognitive map can be drawn with information obtained by using “manifest content” or by “latent content”, or adopting both. In this paper cognitive maps that are developed fall mid-way. According to Huff (1990) the central benefit of cognitive map representation is that it encourages wholistic synthesis rather than reductive analysis. The main concern in this paper is about causal relationships among concepts, rather than frequency counts. For example, in the process of cognitive mapping an annual report, the focus is on how a concept, say, “new product

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development” is linked to other concepts like “manufacturing” or “competition”, and how it changes over time. Here the coder is allowed to semantically look for concepts in case they are not stated explicitly (i.e. look for hidden meaning). Refer to Figure 2, which is a part of the cognitive map constructed from the annual report of an important player in the Indian automobile industry (the name is not revealed, based on a request made by the company). Concepts criticalfbreign_exchangegosition and tough fiscal_measures are the causes for concept economic_prowth. The negative qualitative and influence (represented by a sign ‘_‘) between criticalforeign_e.xchangegosition economicJrowth captures the belief that both of them are negatively related. In a macroeconomic sense, these relations imply the following: In any economy, when the foreign exchange position becomes critical (i.e. becomes adverse), the probability of the economy having a higher growth rate becomes low. This is because imports and exports of the economy are not favorable, in turn affecting the economy’s productivity. Similarly, if any economy maintains tough fiscal measures (like a drive towards proper taxation), then these will affect the growth rate of the economy in a positive manner. Since the network in Figure 2 is abstracted from the Chairman’s statement to the shareholders, it need not be construed to be universally applicable. They are to be construed as the expert’s (here the CEO of the company) interpretation of the domain. 2.2. Uncertainty-based

network representation formalisms

Uncertainty-based (non-deterministic) reasoning may be classified into Bayesian formalisms and non-Bayesian formalisms (see JFigure 1 for the depiction of the taxonomy). This classification is based on Bayesian conditional dependency theory. Bayesian Belief Networks (Pearl, 1988) and Qualitative Probabilistic Networks (Wellman, 1990) are classified under Bayesian formalisms. Formalisms based on theories like Dempster-Shafer, fuzzy logic, etc. are classified under non-Bayesian formalisms. In this paper the focus is on Bayesian formalisms primarily because of its proven adequacy in belief representation and belief-propagation (Henrion & Druzdzel, 1991; Pearl, 1988). 2.2.1. Bqesian formalisms. Two related graph-based formalisms that have been advocated for computer representation of probabilistic knowledge are Pearl’s Belief Networks (1988) and Qualitative Belief Networks (Wellman, 1990). Graph representations are computationally attractive and have conceptual advantages as their focus is on dependencies among probabilistic variables. Both the representations encode probabilistic data as directed graphs, with nodes representing uncertain variables and links denoting probabilistic dependence. Each node has a table containing the distribution of the node’s values for every combination of values of its direct predecessor nodes. Distributions of interest pertaining to various scenarios (or decisions) may be computed through propagation or graph reduction techniques.

Critical_foreign_exchange_poaition

Tough_fiscal_measures

Economic-growth

Fig. 2. An example of a cognitive map.

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2.2.1.1. Bayesian Belief Networks (BBNs). The nodes in a Bayesian Belief Network follow the probability calculus of Bayesian conditionalization. Bayesian methods provide a formalism for reasoning about partial beliefs under conditions of uncertainty. They are also called Causal probabilistic networks, Bayesian Belief Networks (Pearl, 1988) Probabilistic Influence Diagrams (Schachter, 1988). In this paper a Bayesian belief network is referred as a belief network. One of the advantages of belief networks is that they represent probabilistic relationships concisely. Any belief network consists of a graphical structure augmented by a set of probabilities. The graph structure is a directed, acyclic graph in which nodes represent domain variables. Marginal probabilities are assigned to source nodes, and conditional probabilities are associated with arcs. In particular, for each source node Xi (i.e. nodes without any incoming arcs), there is a marginal probability function Pr (xi), which is the marginal probability of node x,. For each node xi with one or more immediate predecessors, Sj, there is a conditional probability distribution Pr (xJ,). A general belief network can be represented as a triple (VA,P), where V is the set of variables (i.e. vertices or nodes). A is the set of arcs between variables and P the set of probability distributions. Belief networks are capable of representing probabilities over any discrete sample space, so that the probability of any sample point in that space can be computed using the network. A key feature of belief networks is the explicit representation of conditional independence among nodes. The assumption here is that all relevant “causal factors” and “influences” have been faithfully captured by arcs. Henrion (1989) used this scheme to represent knowledge with respect to apple-tree root disorders. 2.2.1.2. Qualitative Probabilistic Networks (QPNs). Formally, a qualitative probabilistic network can be represented as an ordered pair (KQ) where V is the set of variables (or vertices) of the graph, and Q is the qualitative relationships among the variables. There are basically two types of qualitative relationships that are represented in these networks. The first one is the qualitative influence. which is the relationship describing the sign that exists between a pair of variables. The second type is called qualitative synergy, which is the interaction among the qualitative influences (Wellman, 1990). Consider the network in Figure 2. Here the model and reason is in a qualitative sense based on the semantics dictated by the notation of BBNs (or QPNs). Nodes criticial_foreign_ exchangegosition. tough_fiscal_measurees and economic~rowth are probabilistic in nature. If the network is considered as a BBN, the nodes have a belief estimate (a probability measure) as an additional attribute. Further, the nodes are constrained by the joint probability distribution (in point estimate notation) represented as: Pr(criticalJoreign_exchangegosition.

tough_fiscal_measures,

economic_growth).

If we observe economicJrowth, the probability of critical_foreign_exchangegosition being true gets lowered. In a similar way, if Pr(criticalforeign_exchangegosition) is known (i.e. the expert’s subjective estimate of the “concept” being true), then the conditional probability, Pr(economicqrowth_criticaIforeign_exchangegosition) is calculated as: Pr(economic_growth,

criticalJoreign_exchange_position)

Pr(criticalforeign_exchangegosition) Thus, as evidence gets accumulated belief updation takes place. There are other inferencing schemes that can be applied on probabilistic networks. Evidential reasoning in causal models is

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one of them. Here, we try to diagnose the causes, based on the information at effect nodes. In other words, we try to work against the causal direction of the model. Once the problem is represented as a causal model, determination of relationships among effects for the same cause, or causes for the same effect is intuitively straightforward (Henrion & Druzdzel, 1991). Reasoning in a multi-cause single effect network is called inter-causal reasoning. Inter-causal reasoning is an important component of diagnosis and explanation (Wellman & Henrion, 1993). 2.2.2. Non-Bayesian formalisms. There are a number of formalisms in this category of uncertainty representation. The most important among them are Dempster-Shafer theory (Pearl, 1988) Fuzzy Logic (Kosko, 1992) Truth Maintenance Systems, TMS (Pearl, 1988) etc. The present discussion is limited to Dempster-Shafer theory only, as this has similarities with Bayesian Networks (discussed earlier), in the sense that both use “probability” as an estimate to reason about uncertainty. Pure Bayesian theory requires the specification of a complete probabilistic model before reasoning can commence. An alternate method of handling uncertainty, particularly partially specified models, is provided by Dempster-Shafer (D-S, henceforth) theory (Gordon & Shortliffe, 1984). Here, a partially specified model is used for representing qualitative relationships of compatibility among the propositions involved. These qualitative relationships are then used as a logic for assembling proofs that lead from evidence to conclusion. The essential differences between Bayesian approach and D-S theory is that D-S theory accepts incomplete probabilistic model when some parameters are missing. However, D-S theory does not give full answers to queries when evidence propagation and belief revision has to take place. Considering this deficiency, Bayesian formalisms are made use of in this paper. 2.3. Be&f propagation

in Bayesian networks

One of the central issues addressed by a knowledge representation scheme is its inferencing ability. How do beliefs change as new evidences are added to the existing knowledge base? In uncertainty-based knowledge-based systems like MYCIN, various customized belief updating schemes have been used. In expert system shells like PROSPECTOR, normative theories like Bayesian conditional dependency have been used to calculate the posterior probabilities. This sub-section deals with issues pertaining to belief propagation in network-based representation schemes like QPNs. Belief propagation is the process by which impact of a new evidence (belief) is propagated throughout the knowledge base. In an uncertainty-based representation scheme like Bayesian networks (including QPNs), impact of new evidence is transmitted throughout the network. For this, the existing beliefs at each node are updated by simple Bayesian posterior probability calculations. This process, Evidential Reasoning, is more commonly referred to as “belief revision” (Pearl, 1988). In singly connected networks, evidence flows from observed variables towards all the remaining nodes of the network, and never in the opposite direction. In the case of multiply connected networks (such as Figure 3), this process is not so simple, as the evidence flowing into a node can come from different directions. For any link that is part of a clique of nodes, it becomes impossible to determine in which direction the evidence has flown. Quantitative belief propagation in multiply connected graphs has the problem of possibly infinite sequence of local belief propagation. This may result in unstable equilibrium that does not necessarily correspond to a new probabilistic state of the network (Pearl, 1988, pp. 195-223). Algorithms adopting this belief propagation paradigm for multiply connected belief networks treat loops in the underlying graph separately and reduce the graph to a singly connected one. In other words, these algorithms alter the topology of the graph.

CASE STUDY IN THE INDIAN AUTOMOBILE INDUSTRY

Fig. 3. Cognitive map abstracted from the annual report of company X. a: New_Industrial_Polic~v_ &-Trade-Reforms; b: Competitive_In_True_Market_Environment; c: Economic_Growth; d: Introduction_ Of_New_Products; e: Upgradation_of_Quality_and_Manufacturing_Standards; f: Programme_of_Plant_ Modernisation_and_Value_Engineering; g: reduction_of_manufacturing_costs; h: achieving_technological_autonomy,_quali~_consciousness; i: increase_the_crash_survivabili~_of_vehicles; j: improvefuel_ efJiciency_reduce_em&~n_levels; a’: governments_thrustJor_development; c’: critical_foreign_ exchangeposition; c”: touglz~iscal_measures; d’: increase_in_customers’greference; e’: meeting_ international_quality_standards; f’: automating_criticalprocesses; g’: proce_of_products_get_reduced; i’: long_term_competitive_advantage.

In recent years various forms of belief revision methods have been formulated for networkbased representations of knowledge. The various types of algorithms can be classified based on the following: (1) updation of beliefs (qualitatively or quantitatively), (2) alteration of topology of the graph. Till recently, the prominent algorithms for qualitative and quantitative propagation schemes focused on altering the topology of the graph (Schachter, 1986; Wellman, 1990). Efficient belief updation algorithms have been developed which do not alter the topology of the network (Druzdel & Henrion, 1993; Chin & Cooper, 1989). Belief propagation in probabilistic networks can be classified as follows: Qualitative Belief Propagation and Quantitative Belief Propagation. 2.3.1. Qualitative belief propagation. Qualitative reasoning primarily involves usage of ordinal properties such as greater and smaller, warmer and cooler, increase and decrease, and the like. These ordinal properties have been used to study dynamic behavior of systems (like chemical process plants, modeling human intuition, etc.), when there is a lack of information regarding exact functional relationships among variables. For example, one need not know the exact quantum of increase in inflation to infer whether the average prices of consumer goods have increased or not. Since the emphasis in such situations is to know gross level impact (increase or decrease), cardinal relationships (like twice, thrice, etc.) between the variable are not particularly needed. Qualitative belief propagation methods focus on propagating such signs that correspond to qualitative reasoning (or qualitative beliefs) in a network. QPNs allow qualitative belief propagation. Normally, qualitative belief revision is performed when precise numerical estimates are not available. Further, even where such a quantitative belief network is

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available, it may often be useful to reduce it to a qualitative form, and base explanations purely on qualitative reasoning (Druzdel & Henrion, 1993). Qualitative belief revision algorithms discussed by Wellman (1988) follow the graphreduction approach of Schachter (1988). This approach reduces nodes and changes the direction of influence in the causal network, thereby altering the network structure. Druzdzel and Henrion (1993) have proposed a computationally efficient algorithm (termed the Qualitative Belief Propagation algorithm). This approach traces the effect of observing a node on to other network variables, by propagating the sign of any change from that particular node to the rest of the network. The essential pre-requisite for belief propagation in Bayesian probabilistic networks is the exclusion of directed cyclicity. [Refer to Pearl (1988) for excellent treatment on this topic.] However, directed cyclicity has an important role to play in cognitive maps (Huff, 1990). Since the objective of this paper is to link cognitive map representation scheme and probability-based representation schemes, only cognitive maps with acyclic loops are considered. We elucidate the algorithm of Druzdzel and Henrion (1993) to demonstrate its efficacy in qualitative belief propagation. The qualitative belief propagation algorithm of Druzdzel and Henrion (1993), traces the effect of an observation k on other network nodes by propagating the sign of change from k through the entire network. We brief the algorithm here for the sake of completeness. Refer to Druzdzel and Henrion (1993) for more details. This algorithm is based on local message passing: The goal is to determine the sign for each node, representing the direction of change in belief for that node based on new evidence for an observed node. Initially, each node is set to “O”, except the observed node which is set to the specified sign. A message is sent to each of its neighboring nodes. The sign of each message becomes the sign product (Table 2) of the following: Sign of the node which sends the message, and the sign of the link the message traverses. Each message keeps a list of the nodes it has visited along with its origin, so that it can avoid visting any node more than once. Effectively, each message relates to only one possible evidential trial. On receiving a message, each node updates its own sign with the sign sum (Table 2) of its own sign and the sign of the message. Then the node passes a copy of the message to all un-visited neighbors that need to update their signs. As an example for the above, consider Figure 3. We start by clamping the observed variables to the set value. If, say, we observed up~rudation_of_quali~_and_manufacturin~_standard.~ to be true (Boolean logic), then we make the node value “+“. The rest of the node values are made “0”. Suppose we want to see the impact of New_indu.ustrial~olicy_&_trade_r~forms on the rest of the variables (this node is termed as evidence node), then set set the node value for this concept to be “+“. By the algorithm described above, and using the qualitative sign combination matrices in Table 2, we infer the impact by tracing the qualitative sign propagation iteratively. Node, New_industrial_policy_&_trade_rrlfi,rms has as its parent Competition_inJree_market_ environment. The sign on the parent-node is updated by the message sent by the child node. The sign of the message is “+” (as the sign product of “child-node sign” and the “sign of the link”

Table 2. Sign multiplication X, and sign addition + operations X

+

_

+

+

_

_

_

;

0 ?

0 7

?

0

‘I

+

+

_

0 0 0 0

?

+

+

‘1

7

_

‘1

0

+

$7

?

0 ?

0 +

‘I ‘I

_

_

7

_

0

3

7

‘I

?

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through which the message traverses is “+“). Node Competition_in_free_market_environment has four children that need to be updated. These are economicJrowth, introduction_of_new_ range_ofgroducts, upgradation_oj~quality_and_manufacturing_standards (whose value has been clamped “+” earlier because this node plays the role of evidence variable) and achieving_ technological_autonom~_and_quality_consciousness. Since all these nodes have “0” value (except node upgradation_of_qualit~_and_manufacturing_standards), and the sign on the links is “+“, the updated value becomes “+“. Node upgradation_of_quality_and_manufacturing_ standards updates its child progress_of~lant_modernization_and_value_engineering to “+” as the sign of the link is “+“, and the sign of the node prior to updation is “0”. This node updates the belief of its child reduction_of_manufacturing_co.sts to “+“. Similarly, node achieving_ updates the values of its two nodes increase_ technological_autonomy,_quali~_consciousness the_crash_survivability_of_vehicle and improve_fuel_efficiency,_reduce_emission_levels to “+“. At the end of this propagation run, all nodes are updated from their original set values to updates values. In certain situations, as depicted in the sign combination table (Table 2) the qualitative belief propagation algorithm can result in ambiguity (“?’ sign), which would lead to dead-lock in the propagation. As observed in Section 1, we know that an organization is undergoing double-loop learning when it tries to change its mental models (or more bothered about the “know-why” of technological adaptation). Here, in this case, node achieving_technological_autonomy, quality_ cortsciousness captures the effort this organization is putting in to indigenize the technology know-how. Thus, we may conclude that the top management prefers to go for indigenization through technological autonomy rather than going in for borrowed technology in light of the new industrial and trade policy. The top management prefers to institutionalize the learning process than to allow ad hoc implementations Nonaka (1991) describes the assimilation of knowledge as the right approach to build sustainable competitive advantage. In this example, we observe how new_industrial~~olicy_&_trade_reforms has a positive influence on the company’s operations. The company would try for reduction_of_manufacturing_costs, and improve~uel_~~icficienc~,_reduce_emis.sion_levels, if there is an upgradation_of_qualiQ_and_ manufacturing_standards. In other words, by clamping nodes appropriately, we can test their impact on the rest of the network in a probabilistic sense. 2.3.2. Quantitutive belief propagation. Dealing with ordinal relationships alone does have disadvantages in some situations (as demonstrated in Section 2.3. I)-leads to ambiguities while propagating beliefs (Wellman, 1990; Srinivas, 1995). In such situations one resorts to cardinal data for making stronger conclusions. Probability estimates, which are based on ordinal properties (human beliefs), are treated as cardinal numbers assigned to these properties (beliefs). This type of assignment has been popular in physical sciences (Mohr’s scale for hardness, measurement of intensity of light, etc.) and in social sciences (rating scales of market research) [Nunnally, 19791. Probability belief estimates assigned by humans in quantifying uncertainties are to be treated in the same spirit. Quantitative belief propagation schemes are a step towards eliminating ambiguity. Quantitative belief propagation schemes employ formalisms like Bayesian belief revision methods (Pearl, 1988) to update beliefs represented as nodes in a network. Some issues and methods for propagating quantitative beliefs are discussed in greater detail in Pearl (1988). Methods for quantitative belief revision can be broadly classified as (Henrion, 1990): Exact methods and approximate methods. Exact methods deal with studying the impact of evidence by explicitly computing the joint distribution overall variables as product of all a priori probabilities and conditional distributions. This method gets complicated in the case of multiply-

98

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connected poly trees (like cognitive maps). The clique-triangulation method, and Schachter’s (1986; 1988) graph reduction method address belief revision in such graphs. However, these methods reduce the number of nodes during belief revision and alter the causal topological structure of the graph, consequently leading to loss of information. A completely different form of belief revision is done by employing simulation (Monte Carlo techniques), termed as approximate methodology. Pearl (1988), and Chin and Cooper (1989) have proposed Stochastic Simulation as one such alternative under this broad category. The key advantage of these methods over exact methods is the reduced complexity of the algorithm taking the size of the entire graph into consideration. However, the complexity of algorithm is exponential with respect to the number of observed (or evidence) nodes. In addition, this method preserves the topology of the graph. The main emphasis of our case study is to demonstrate the impact of new evidence on all other nodes in a graph without altering the topology. This is done by adopting Pearl’s (1988) stochastic simulation approach.

3. AN ILLUSTRATIVE In this Industry) numeric learning

CASE STUDY

section we discuss a case study (based on a company taken from the lndian Automobile that uses cognitive maps abstracted from a larger map, to generate scenarios by belief revision. We then relate the scenarios generated to the theory of organizational in conjunction with the observations made from the raw cognitive maps.

3.1. The Indian automobile

industry (commercial

vehicle segment only)

The Indian automobile industry has undergone major structural changes because of the liberalization process initiated by the Government of India. This process is seen as a step towards integrating the Indian economy with the global economy. The sustained growth achieved till 1989 has been affected by factors like steep rise in the price of fuel, increase in interest charges forcing squeeze in credit capital (thereby affecting fresh purchases by transport operators) and steady rise in excise duties. All these increased the input costs (as the import content is still high) which got transferred to the customer by escalated price of the vehicle. These factors led to steady recession and slump in the demand in the early 1990s. In addition to these changes in the market, greater environment consciousness (initiating the search for alternate fuels), inadequate growth of industry infrastructure facilities (like road networks) and labor unrests also affected industry growth. The Indian automobile industry is categorized primarily into four segments: the Heavy Commercial Vehicle segment (HCV), the Light Commercial Vehicle segment (LCV), the passenger car segment, and the two and three wheeler segment. In this paper, we consider HCV and LCV segments only. This is primarily because, the company under study (X, to maintain anonymity) has been a key player and has restricted its operations in these segments only. The HCV segment is geared up for massive investments in capacity building and modernization plans. In the LCV segment, competition became much stiffer with the entry of Japanese collaborators in the early 1980s. Company X has been a manufacturer of trucks and buses (which constitute the HCV segment) since the early 1960s. Its entry into LCV segment (in the mid 1980s) has been very successful. It captured a sizeable market share within a short span of time. Over the years, company X has achieved considerable expertize in developing its own process technology. Its large scale production has enabled it to opt for high volume processes. It has also achieved a high degree of vertical integration, which enabled it to produce better quality products. This aspect has made the company’s entry into LCV segment a successful one.

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The company had considered large scale changes (primarily due to the liberalization process) in the environment as permanent. It had also embarked on a programme of self-renewal through various structural changes in developing sustainable competitive advantage. 3.1.1. Research methodology. To recapitulate, the main objective of this paper is to demonstrate usefulness of uncertainty-based mental models in the representation of organizational learning process. The central assumption is that mental models direct action. An essential precursor to organizational learning is Organizational Action (Barr et al., 1992). As detailed in Section 1, we look at the changes in mental models (cognitive maps) and examine whether company X is learning or not. We also demonstrate the usefulness of techniques such as stochastic simulation in generating scenarios of mental models. The methodology for stochastic simulation exercise is described in Pearl (1987). 3.1.2. Data sources and time frame. Learning in organizations is characterized by changes in the linkages in a mental model. The primary source of data in these types of studies (understanding mind-sets) are causal articulations of managers. From these articulations, researchers identified causal-texts, and then represented them as cognitive maps. The primary data-sources to construct the cognitive maps for this study are the annual reports published by the company (X) over the period 1988-1993. The Chairman’s and the Director’s statements in the annual reports are of particular interest. Researchers have used such material from annual reports to: identify corporate strategies, assess causal reasoning within firms, explain differences in joint venture activity (Fiol, 1990), and to give cognitive explanations to strategic behavior of firms (Barr et al., 1992). As regards the performance of industry, published data by various authorized agencies have been consulted. These include Government of India publications on economy. Typically, annual reports are known to be biased, because most often they are prepared by the Public Relations Department. Since there are few rival data sources which can provide insight into the changing mental models of top managers over time, annual reports were considered as the primary source of data. Also, since the focus here is on understanding gross level strategic orientation of the firms, i.e. an organizational level understanding, it is believed that manual reports can provide data to make such an interpretation. For cognitive mapping purposes, one year is considered as a temporal unit for stratification of data. In other words, all the causal articulations of managers in a particular year are considered to generate a cognitive map for that year. We captured the subjective probabilistic estimates for all concepts of the cognitive map (uncertainty-based mental model) developed for the year 1992-1993 from the executives of company X (see Table 3, for the subjective probability estimates of the cognitive map given in Figure 3). The scenarios generated using the uncertainty-based mental model helped us to look at the firm’s behavior in conjunction with observations made from the cognitive maps for the years 1988-1989 to 1992-1993. 3.1.3. Conceptuul schemefor the analysis of cognitive maps. The conceptual scheme for this study is built on the scheme reported by Narayanan and Fahey (1990). Inferring from raw

Table 3. Pr(a) Pr(cib) Pr(hlb) Pr(f/e) Pr(i/h)

= 0.8; = 0.85; = 0.75; = 0.8: = 0.85;

Pr(b/a) = 0.9; Pr(c/-b) = 0.6; Pr(h/-b) = 0.4; Pr(f/-e) = 0.45; Pr(i/-h) = 0.5;

Pr(b/-a) = 0.3; Pr(d/b) Pr(e/b) Pr(g/f) Pr(j/h)

= 0.9; = 0.9; = 0.6; = 0.9;

Pr(d/-b) Pr(e/-b) Pr(g/-f) PrCj/-h)

= 0.6 = 0.45 = 0.3 = 0.1

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cognitive maps is quite complex as they primarily map causal assertions into a “map” structure comprising nodes connected by arrows. To understand the changes in patterns over a number of years in a better way, we transformed these causal relationships into a framework of Narayanan and Fahey (1990). A concept here is interpreted as an abstraction of properties. The conceptual scheme here is classified as follows: Functional (organization related functions like Marketing, Finance, etc.), Industry (concepts related to automobile industry, like growth, etc.), Macro-Environment (economy, Law&Order, etc.), Objectives (concepts like Growth, Sales, Profits, etc.) and finally Key-Concepts. Concepts categorized as key-concepts need some elaboration. Key-concepts are factors which any firm identifies, for achieving sustainable competitive advantage and thus effecting the mind-sets of the management. Sudden importance to concepts like: large scale indigenization programmes and quality consciousness, represents a change in the top management’s mental model. This also indicates that the particular organization is adapting “double-loop learning”, as it is more interested in the know-why of the knowledge acquisition. A change in mental models also refers to the top management’s reaction to changes in the environment. Similarly, sudden inclusion of key-concepts like “Globalization” means that the management has started looking more outward, which essentially means that it is trying to change from the existing localized mind-set. The key indicator of a change in the behavior of an organization is based on how relationships pertaining to key-concepts change over time. The relationships involving these key-concepts are arrived at after looking at the raw cognitive maps. The pattern of inclusion and deletion of these key-concepts underscore the critical issue of organizational learning. The raw cognitive map for each year (say, 1988-1989) is transformed into the conceptual scheme. It is this transformed raw-cognitive map that is being dealt with to determine this company’s organizational learning capabilities. It is to be noted here that the concepts which are not present in the raw cognitive map are to be construed as concepts that are

Benign or benevelont

1 environment

Competitive environment

1 :

1

4-T Interpretation

:

of

change in organizational environments

/---_-_-_-_----_-_---------Fig. 4. A cognitive model of organizational

renewal (modified)

(Barr et al., 1992).

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not present in the annual report of the company for that year. It is not to be construed that these concepts are not at all relevant for that year in the organization. We use the frame work of Barr et al. (1992) to study changes in the mental models with respect to environmental changes. In other words, we study the changes in the conceptual cognitive across the years with the help of this framework. We reproduce the framework here (Figure 4) with modifications. We include the feedback loop from “CHANGE IN TOP MANAGERS’ MENTAL MODELS” to “MENTAL MODELS OF TOP MANAGERS”, which is not present in the original framework of Barr et al. (1992). This is primarily to indicate that the process of organizational learning is continuous, and not a one time event because new mental models replace existing ones. Managers interpret changes in organizational environment with these updated mental models. mental model (which 3.1.4. Subjective probabilities. In constructing the uncertainty-based primarily is a belief network), we adapt the techniques discussed in Henrion (1989). Henrion’s experimental study focuses on the diagnosis and treatment selection for disorders in apple trees. It makes use of the decision analytic/influence diagram model. The initial network, in this case, was constructed from elicitations made by an expert on apple tree root disorders. Here in this paper, subjective estimates of nodes in cognitive maps are obtained from the expert as cognitive maps were developed based on the articulations of top management of the company. Each variable (node) is modeled using binary logic, i.e. a variable, say economic growth, takes two values: 0 and 1. For example Pr(economic_growth = 0) captures the probability of not observing variable economic growth. All influences are captured in a conditional probability matrix. Initially, the executives were asked to make qualitative judgments about the strength of the influences. Generally, each conditional probability was first expressed as a phrase, such as “likely possible”, “unlikely possible” etc. Then each phase was quantified by a numerical estimate. The captured subjective estimates are depicted in Table 3. We use the notation stated earlier.

3.2. Analysis oj’cognitive maps Here we look at the cognitive maps of company X for each year and briefly discuss the changes in linkages among concepts. The abstract forms of cognitive maps for all years are given in Figure 5(a)-(e). Following this, we discuss simulation study results using subjective estimates given in Table 3. 3.2.1. Observations

on the cognitive maps Year: 1988-I 989,

The central thrust in this year’s map is on employees’ persistent strike at their main plant, almost paralyzing the company (and the HCV segment). Also, there is a mention of unfriendly government policies towards indigenization, as firm X had already embarked on massive indigenization programmes without accruing any governmental benefits. To achieve greater scales-of-economy, the process of indigenization was seen as very critical for developing sustainable competitive advantage. This indigenization programme made the company’s entry into the LCV segment one-year earlier (launching of its pickup truck) a very successful one. Year: 1989-l 990. The general upward trend in the economy caused industry demand to increase over the previous years. The performance of the company was better than the previous year. The company’s first reference to globalization to give impetus to its exports programme with the collaborators was a notable change. There is a mention of good industrial relations being the cause for its profits. This

V. SRINIVAS and B. SHEKAR

102

(a> FUNCTIONS:

INDUSTRY:

Management Industry I

KEY +

\/r+

Xc

CONCEPTS:

Business

defn.

Profits

MACRO-ECONOMY: Market

leader

I

dev.

4

Alternate

Economy Law & order Govt.

Modernisation

+

I

1

+

(

Indicenisation

I

3

Globilisation

i

Infrastructure

Demand

Sales

Exports Product

growth

policies

3 1

]

Environment

I

1

fuels

Populan.

growth

)

Liberalisation

+

Collaborators New challenges Founder

Y Fig. S(a)-caption

on p. 106.

contradicts the actual happenings in the previous year at their main plant, where employees gone on a massive strike for the first time in its history.

had

Year: 1990-1991.

From the cognitive map we see that the top management was concerned with the decline in the industry growth rate which, according to them, was due to the inadequate government policies, riots, and large scale disorder and recession in the economy. This caused its sales to decrease during the previous year which subsequently caused profits to go down. The top management was even forced to change its business definition to conform to the economic changes. There was an increased amount of environment consciousness prompting them to look for fuel efficient engines and start research on alternate fuels. To have a firm foothold as market leader, the competition prompted it to look for product development. Company X responded to these changes by

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103

(b) FUNCTIONS:

INDUSTRY:

Management Employees Finance

I

I

Marketing

I

c

R&D

I

Suppliers Customers

3

+ )

Competition

OBJECTIVES:

/

Manufacturing Demand

KEY

CONCEPTS:

Business

+

Profits

defn.

I

I

i ~,,ACRO-;.;OM;:

Market

leader

Fuel efficiency Law & order Govt.

I

Modernisation

]

Environment

Indigenisation Alternate

policies

Populan.

fuels

growth

Liberalisation

Collaborators

I I

New challenges Founder

] 1 Fig. S(b)--caption

on

p. 106.

increasing its capacity through modernization plans. The indigenization its successful entry into the passenger vehicles segment.

programme had helped in

Year: 1991-1992.

Here, the central thrust was on the company’s objectives. The general recession in the economy causing a slump in the demand for CVs is evident from the maps. The top management attributes external factors like economy, government policies and population growth to the decrease in sales volume which caused reduced profits in the previous year. The presence of new concepts like “New Challenges” (it is not explained in the shareholders reports what exactly they are) promoted it to look at issues like globalization, modernization and customer focus (this is the first time any mention is made about this aspect). These attributions make one conclude that firm X has started looking at new issues in this state of recession.

V. SRINIVAS and B. SHEKAR

(cl FUNCTIONS:

1

Management Employees

INDUSTRY: ]

1 f

1 r

\

Ic

R&D

;\

_ OBJECTIVES:

Manufacturing

c

KEY

Suppliers Customers

1 I 1

Competition

Growth

\r

Infrastructure

r

Demand

1

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CONCEPTS:

‘> MACRO-ECONOMY (

Economy Law & order

Modernisation

I

Environment Populan.

Alternate

Y

fuels

growth

Liberalisation

Collaborators

I

Founder

I Fig.

S(c)-caption

on p. 106.

Year: 1992-1993. From the cognitive maps we infer that firm X attributed its growth to external factors like industry demand and economy. The other important factors it visualized for its future growth is fuel efficient vehicles. To achieve fuel efficient vehicles, it felt indigenization was the only way. Another important inter-linkage that figures prominently is establishment of competition in the causal maps, and that too is linked to product development. From this we infer that competition is driving the company to the development of new products. Also, competition is forcing it to increase its capacity (manufacturing facilities). 3.2.2. Analysis of the cognitive maps. From the company’s reports we can find evidence corroborating change in mental models with respect to changes in the environment. The impact of liberalization on the economy has changed the company’s outlook on the environment. This is evident from the increase over the years in the number of concepts like product development, indigenization, quality consciousness (this is not shown in the abstract maps) and modernization

CASE STUDY IN THE INDIAN AUTOMOBILE

105

INDUSTRY

(d) FUNCTIONS:

INDUSTRY:

Management

I

Employees

1

I

Finance

J

I

Marketing

1

I

R&D

(

Suppliers

3

-===&

J

Manufacturing

_ /’

OBJECTIVES:

‘-\

/

Growth _ ?7==-

KEY

CONCEPTS:

(Busincas

I

+

MACRO-ECONOMY: Market

leader

Exports Product

dev. )

f

\

Globilisation

Law & order Govt.

+

policies J

I

Environment Populan.

I

p

J

growth

Liberalisation

1

+

New challenges Founder

1

1

Fig. S(d)--c&on

on p. 106.

of the existing plants. The company has started looking at collaboration with a foreign partner as a way to assimilate knowledge (not just acquiring the know-how of technology, but also in the know-why part of it). Increase in linking of Key-Concepts such as business-definition and searchfor alternate-fuels to other concepts reinforces the observation that the top management’s mental model is undergoing “double-loop” learning. The act of re-looking at the businessdefinition essentially means that the company has started questioning its long term priorities. As steps in integrating the economy to global markets are concretized, the number of references to the external environment has also increased. Garvin (1993) identifies that learning organizations are skilled with respect to five main activities: Systemic problem solving, experimentation with new approaches, learning from experience and past history, learning from experiences and best practices of others, and transferring knowledge quickly and efficiently throughout the organization. The first activity, systemic problem solving, is mainly about the philosophy and methods of quality movement. To

106

V. SRINIVAS and B. SHEKAR

INDUSTRY:

FUNCTIONS: Management Employees

\ Finance

1

( c

Marketing

Suooliers I

1

Customers

R&D

I t

I

OBJECTIVES: Manufacturing

Growth

JI 1

Competition

•t

Demand

Sales

KEY CONCEPTS: Profits Business

defn.

MACRO-ECONOMY: /

Alternate

fuels

Liberalisation

Collaborators New challenges

I

Founder

Fig. 5. Conceptual

cognitive

> map

for: (a) 1988-1989; (b) 1989-1990; (c) 1990-1991; (d) 1991-1992; (e) 1992-1993.

testify whether company X is emphasizing on this activity, in addition to annual reports we need much more data to build the cognitive maps. Hence, we do not look al this aspect here. We find evidence for other activities practiced by the top management from the mental models. The company’s entry into the LCV segment supports the fact that this company is learning from its past experience and history. The company has embarked on programmes like: Renewal of collaboration with a leading European automobile manufacturer, increased emphasis on making a world-class CV, heavy investments for automating most of the processes in their main plant. These confirm that the company is learning from their experiences and best practices of others. Regarding transferring knowledge quickly and efficiently throughout the organization, the company has increased its emphasis on conducting training programmes on Total Quality Management, and the like. In fact, the patterns of inclusion and deletion in mental models form the “unfreezing-change-refreezing” cycle of learning process. Increased emphasis on issues

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Table 4. Clamped nodes

:

A = I, D = I, J = 1

Posterior

No. of runs

Probabilities Pr (B/A,D,J) Pr (C/A,D,J) Pr (E/A,D,J) Pr (F/A,D.J) Pr (G/A,D,J) Pr (H/A,D,J) Pr (I/A,D.J)

10

50

0.9 0.0 0.0 0.3 0.6 0.0 0.7

0.9 0.14 0.02 0.42 0.58 0.28 0.44

100 0.91 0.15 0.02 0.44 0.59 0.31 0.39

250 0.91 0.15 0.02 0.44 0.59 0.31 0.39

500 0.91 0.16 0.02 0.44 0.59 0.32 0.39

related to macroenvironment, objectives and Key-Concepts confirm this. Redefining business also means that un-learning and re-learning is taking place in the minds of the top management. We summarize our findings as follows. The cognitive maps show that the company has created systems and processes that support organizational learning for long term competitive advantage. This involves, as we have seen in Section 1 and according to Figure 4, a change in mind sets of the people. Change in mind sets of the people is what facilitates organizational learning. From the above discussion, we observe the existence in mental models and this can be traced back to the top management’s interpretation of the environment. We also observe patterns of inclusion and deletion in the mental models of top managers, and these patterns are well corroborated by the theory of organizational learning. 3.3. Stochastic

simulation

experiment

(quantitative

belief propagation)

Stochastic simulation is a method of computing probabilities by calculating the fraction of time for which events occur in a series of simulation runs. If a causal model of a domain is available, the model can be used to generate random samples of hypothetical scenarios that are likely to develop in the domain. The probability of any event or a combination of events can then be computed by recording the fraction of time it registers true in the samples generated (Pearl,

Table 5. Nodes clamped:

Nodes

C=l

F=l

Posterior probabilities 0.4 0.9 0.12 0.11 0.35 0.27 0.33 0.69

V. SRINIVAS

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and B. SHEKAR

1987). We conduct stochastic simulation on the BBN (uncertainty-based mental models), and generate possible scenarios. As discussed earlier, the scenarios generated on these mental models represent top management’s revised beliefs in that particular hypothetical situation. Given the subjective estimates for the nodes present in the cognitive map (Table 3), the main task is to compute posterior probability estimate for every node in the map. We clamp certain observed nodes to one of the two values: 0 or 1. The unobserved nodes are instantiated to some arbitrary initial state (0 or 1). The observed nodes that are clamped do not change their assigned values throughout the simulation run. All unobserved nodes change their state in accordance to the conditional probability dictated on it and the current state of the nodes that are in the Markov blanket. In a simulation run, the number of times each unobserved node takes a value of “1” is counted. This gives the conditional probability (which is the revised belief estimate) for that node for the given set of “clamped” nodes (observed nodes). Thus, we get revised belief estimates for the nodes of the causal model in a hypothetical situation. For greater details and examples, refer to Pearl (1987). The simulation exercise was done on a IBM-PC clone using C++ language. The average number of runs for convergence was around 150. Random numbers were generated using a randomized seed value, whose initial value was given by the user. For each scenario, we conducted five-six runs with different seed values for each run. Then we averaged the belief estimates. Table 4 gives the final revised belief estimates for hypothetical scenarios with respect to the cognitive map shown in Figure 3. While concerned with scenario generation, we primarily looked at “change” in the probability value in each scenario. If a node changes its belief value from 0.3 to 0.5, then the change in belief for this particular node is termed as 0.2 probability value. Hence, the actual probability value a node takes (say 0.3 or 0.7) is irrelevant here. An important factor in belief revision exercises is when to consider a change in the probability estimate as significant enough. In this paper, we assume a change of about 0.02 probability value in a node as significant. Refer to Schachter and Peot (1990) for greater details on this aspect. From the above table we can see that the optimal number for the simulation model to converge is around 100. We conducted the experiment for different hypothetical scenarios. In all cases we found that the model converged in about 100 runs. Consider a situation where the company’s executives observe the following: there is economic growth (i.e. node “c” in Figure 3 is clamped to “l”), they have embarked on a programme towards plant modernization and value engineering (i.e. node “f” = “1”). The simulation run yielded the following results (Table 5).

Table 6. Nodes clamped:

C=l,F=l,B=l

Nodes

Posterior probabilities

A D E G H I J

0.38 0.38 0.45 0.35 0.48 0.33 0.52

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Table 7. Nodes Nodes clamped:

Posterior probabilities A=l,B=l

0.2 0.09 0.1

0.42 0.6 0.3 0.36 0.68

A=l,B=l,C=O

_ 0.11

0.02 0.44 0.65 0.25 0.38 0.67

Further, we assume that the top management feels the presence of competition in free market environment. In addition to nodes C and F clamped to “I”, we clamp node B to “l”, also indicating this fact. The new belief estimates are given in Table 6. We notice a significant increase in the probability estimates of nodes D and H. From Figure and node H represents_ 3. node D represents Introduction_oj’_new_range_ofJroducts achieving_technological_autc~nomy,_quality_corzsciousness. This aspect follows from the observations we made on the cognitive maps. Also, company X, which had increased its capacity primarily due to macro-environmental factors like liberalization, would try to achieve technological autonomy. This is clearly reflected in the belief revision of the network. Also. we have seen that competition was driving company X towards development of new products. Once the top managers knew that there was competition in free market environment (node B of Figure 3), the company attempted introducing new products. This aspect also gets reflected in the revised belief estimates in the scenario. The theory on organizational learning discussed in Section 1 corroborates these changing beliefs in mental models. Managers interpret changes in the environment with the help of mental models they possess. This example demonstrates that mental models act as “filters” and interpretations are greatly influenced by these perceptible filters. The following example examines whether company X is undergoing “double-loop” learning. From the earlier discussions on cognitive maps, we found company X was undergoing “doubleloop” learning. We clamped nodes A and B of the map given in Figure 3 to “1”. The revised belief estimates are given in Table 7. We then represent the information that there is no economic growth (i.e. C = 0). We observe change in belief revision of nodes H and E, as they are indicators of “double-loop” learning. This is because, node H (achieving_technological_autonomy_and_ quality_consciousness) and node E (upgradation_qf_quali~_and_manufacturing_standards) capture processes that need change in the mind-sets of individuals. From the discussion in Section 1, we know that these processes capture the know-why of knowledge acquisition. The estimates shown in Table 7 reveal that information about the absence of “economic growth” reduces the beliefs of nodes E and H. This contradicts the “double-loop” process of organizational learning theory. In other words, with the present mental models, the company does not have a long term commitment towards knowledge conceptualization. We can also observe that there is an increase in the belief value of node G (i.e. reduction_of_ manufucturing_costs). To reduce manufacturing costs, the company needs to change its

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operational procedures. In other words, the company needs to be interested in the know-how of knowledge acquisition, which essentially means single-loop learning. Double-loop learning involves changing the firm’s knowledge base and firm-specific competencies. Dodgson (1993) discusses the reductionist tendency of behaviorism in double-loop learning. He says that the mind-sets of individuals in coping with different situations can alter the learning process. Argyris and Schon (1978) identify the third type of learning process (deutro-learning) which organizations resort to when they face complex uncertain environment. In deutrolearning, organizations resort to both single-loop and double-loop learning. In the above example, we can conclude that the company’s managers are resorting to this process of deutro-learning in the following way: From the analysis of cognitive maps, we find evidence for double-loop learning, and in this hypothetical scenario, we find that the organization resorts to single-loop learning. Thus, in actuality, this organization resorts to a higher form of learning, i.e. deutro-learning. To summarize, we have seen the applicability of techniques like stochastic simulation to understanding organizational learning. The discussion in Section 1 clearly substantiates that corporate planning models do not facilitate representation of mental models. Bayesian belief network representation of mental models captures the uncertainties in the environment as perceived by individuals. The above case study and simulation results demonstrate the usefulness of methods like stochastic simulation in understanding the importance of cognitive processes in Organizational Learning.

4. CONCLUSIONS The main contributions of this paper are: (1) Demonstration of how various network-based formalisms (deterministic and uncertainty-based) can facilitate in capturing the mind-sets of top management, which in turn help in understanding the type of Organization Learning taking place; (2) Demonstration of the usefulness of belief propagation algorithms developed on these network-based formalisms in generating WHAT-IF analysis, and in augmenting the power of Information Technology in studying Organisational Learning. First, we have addressed the issue of knowledge representation with respect to Organizational Learning. Organizational learning hinges on an individual’s mental models that selectively filter the environmental issues. To capture the learning process and the subjective estimates of environmental-uncertainty, we resorted to network-based representation formalisms that capture subjective beliefs of environment uncertainty. These representations build upon the theory of Bayesian Belief Network representation. The advantage of representing a mental model as a Bayesian Network is that both qualitative and quantitative belief revisions can be carried out. We looked at the applicability of Druzdel and Henrion’s (1993) Qualitative Belief Propagation algorithm in understanding the organizational learning process with respect to a cognitive map abstracted from a key player in the Indian automobile industry. We used the cognitive maps of a key player in the Indian automobile industry to demonstrate the usefulness of this quantitative belief propagation procedure. This forms the central contribution of our study. We looked at the simulation results in light of the discussion on organ;zational learning theory and in conjunction with the study of the company. The main thesis of this paper is that the following schemes like BBNs which can be used to represent mental models, augment Information Technology’s role in understanding the process of organizational learning.

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Traditional support systems like Executive Systems (EIS) and other such Decision Support Systems, do not have provisions to capture uncertainty and subjective beliefs. A more comprehensive system involving mental models of different companies can help in understanding the dynamics of the industry and help in competition analysis (Srinivas, 1995). As seen in our case study, BBN network representation not only has provisions to represent subjective beliefs, but also helps in generating scenarios (that are useful in understanding cognitive process underlying organizational learning). Such representation schemes can also be used in Group Decision Support Systems (GDSS). Understanding the behavior of different segments of thought processes in a group of individuals in various hypothetical scenarios can help in predicting group dynamics. Acknowledgements-The first author acknowledges Professor J. Ramachandran, Corporate Policy & Strategy Area, Indian Institute of Management Bangalore for his insightful comments on the case study. The authors would like to acknowledge the helpful comments of the two anonymous reviewers and those of the Editor-in-Chief which helped in structuring the presentation of the paper.

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