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Aggressive or conservative, general or specific? A study of organizations adopting different learning strategies in an artificial world
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Expert Systems with Applications Expert Systems with Applications 34 (2008) 1018–1027 www.elsevier.com/locate/eswa
Aggressive or conservative, general or specific? A study of organizations adopting different learning strategies in an artificial world Te-Tsai Lu b
a,b,*
, Jong-Chen Chen a, Guo-Xun Liao
a
a Department of Information Management, National Yunlin University of Science and Technology, Taiwan, ROC Department of Business Administration, Kun Shan University, No. 949, Da Wan Road, Yung-Kang, Tainan Hsien 710, Taiwan, ROC
Abstract An increasing growth of keen competitions among global industries greatly challenges present organizations (companies). The purpose of this study is to construct a computer simulation system for investigating the effects of organizations adopting different learning strategies on the change in consumer market share. The system constructed is an abstract model that captures some general features of consumers and organizations as well as their interactions in the real world, which constitutes an artificial world. It has four major modules: consumers, organizations, fitness evaluation, and organizational learning. These modules, including their interactions, are linked through the discrete-event simulation techniques. Experimental results show that different organizational learning strategies are better suited for different environments or the same environment at different times. Organizational learning provides organizations a chance of undergoing transformation to better meet consumers’ needs. However, it is risky to blindly adopt an aggressive learning strategy when the environment (market) is not stable. Instead, it may be better to adopt a conservative learning strategy. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Artificial World; Discrete-Event Simulation; Market Orientation; Organizational Learning; Consumer Behavior
1. Introduction The astonishing progress of modern technology allows people to access and share information in a fantastically fast and easy manner. The consequence is that people are fickle and tend to change their behaviors easily, rapidly, or even unexpectedly. To cope with these changes, an organization must make appropriate adjustments so as to sustain and enhance its competitive advantages in the market. The issue of ‘‘organizational learning’’ (or ‘‘reengineering’’) is thus proposed. Its significance is noted by a number of researchers. Cyert and March (1963) are the earliest two scholars *
Corresponding author. Address: Department of Information Management, National Yunlin University of Science and Technology, Taiwan, ROC. Tel.: +886 62050542x11; mobile: +886 919786343; fax: +886 62050543. E-mail address: [email protected] (T.-T. Lu). 0957-4174/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2006.10.036
who propose organizational learning. They claim that organizational learning is an adaptive process for an organization to cope with environmental changes. Fiol and Lyles (1985) point out that different environments should have different learning strategies. This is particularly important when the environment (market) is dynamic (Daft, Sormunem, & Parks, 1988; Fulmer, 1994). Garratt (1987) points out that without organizational learning an organization will lose its competitiveness and superiority in a market. Fulmer (1994) further says that organizational learning is extremely important that it might affect whether an organization can survive or not. Thus, O’Keeffe (2005) reminds that an organizational leader must stay alert to any market changes and make appropriate organizational adjustments in responding to these changes. From the viewpoint of marketing, Narver and Slater (1990) suggest that an organization must learn from consumers as well as other organizations. More specifically,
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an organization must maintain a close relationship with its consumers, and, in the meantime, keep a close eye on other organizations (or imitate them if necessary). In the following, we first introduce two important issues related to consumer behaviors. Then, we describe organizational learning. Finally, we explain the motivation and assumption of our model. Montgomery and Urban (1969) propose that consumer feature analysis is an important issue to understand consumer behaviors. However, so far, there is no single set of features that can specify all consumer behaviors (Nicosia, 1966). One of the feature sets commonly accepted by most people is the one proposed by Reynolds and Wells (1977). They suggest nine important consumer features, including census variables, socioeconomic variables, personality characteristics, life style, consciousness, preference, willingness, purchase, and expenditure. The other issue related to consumer behaviors is the degree (or extent) of consumer satisfaction with commodities, called consumer satisfaction. Cardozo (1965) is among the first researchers who point out that consumer satisfaction is an effective index for evaluating the performance of an organization. Muller (1991) further stresses that consumer satisfaction is one of the key factors that determines the degree of success of an organization. Reichheld (1966) points out that high consumer satisfaction will yield more organizational profits. As a consequence, a number of researchers (Berry, 1995; Berry & Parauraman, 1991) advocate that each organization should make its best effort in increasing its consumer satisfaction and enhancing a long-term relationship with its present and potential customers. There are many definitions of the term ‘‘consumer satisfaction’’. Roughly speaking, there are two definitions generally accepted. One is the subjective judgment of an individual that consumer satisfaction is strictly determined by one’s personal experience or feeling with a specific commodity (Beardden & Teel, 1983; Churchill & Surprenant, 1982; Oliver, 1980; Woodruff, 1993; Woodside, Frey, & Daly, 1989). The other definition is the gap between the expectation and actual feeling of an individual with a specific commodity (Churchill & Surprenant, 1982; Kotler, 1994; Oliver, 1981; Oliver & DeSarbo, 1988; Westbrooks, 1980; Woodruff, 1993). The smaller the gap is, the higher the satisfaction one has. Argyris (1977) divides organizational learning strategies into two types: single-loop learning and double-loop learning. The former emphasizes the strategies of how to improve (enhance) an organization within current organizational norms while the latter concentrates on the strategies that include not only the improvement of current norms but also the generation of new norms. Later, classification of organizational learning takes the view of how to set up organizational goals to meet environmental changes. Senge (1990) proposes the concepts of adaptive learning and generative learning; the former is similar to single-loop learning and the latter, double-loop learning. March (1991) calls the former exploitative learning and the latter, explorative learning. He also suggests that exploitation learning strategy is
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suitable for a stable, constant environment whereas explorative learning strategy suits better a dynamic, variable environment. To verify this, Adler and Cole (1993) conducted a survey of consumer preference in the car industry. Their conclusion is that it is better to choose exploitative learning strategy when consumer preference is homogeneous (constant) and to choose exploitative learning strategy when consumer preference is heterogeneous (variable). Hawley (1968) points out that organizations in the same environment tend to imitate the behaviors or strategies of other organizations in a tangible or intangible way. Theoretically, this may result in the so-called ‘‘isomorphism’’ phenomenon. That is, all organizations become identical. However, practically this will not happen, as no organizations are able to completely imitate other organizations (Hannan & Freeman, 1977). There are at least two possible reasons. One is that an organization cannot find an appropriate role model. The other reason is that it is difficult to undergo proper organizational transformation within current organizational norms such as culture, leadership style, financial resources and so on (DiMaggio & Powell, 1983; Mayer & Rowan, 1997; Zucker, 1977). DiMaggio and Powell (1983) call this the phenomenon of mimetic isomorphism. Statistical approaches, including qualitative and quantitative analyses, are common approaches used to address the issues related to organizational learning. These approaches provide us some general information regarding the interactions between consumers and organizations. However, the process of such interactions has been totally neglected. In the real world, these interactions are extremely complex. We all know that sometimes an individual might respond inconsistently to the same things at different times, places, or with different people. Moreover, the interactions are dynamic such that people are responding to an environment that consists of other people responding to their environment, which consists of people responding to an environment of other people’s responses. The sophistication makes it difficult to predict what behavior will emerge from the intensive interactions among consumers and organizations, let alone to study the effects of altering organizational strategies on consumer behaviors. The purpose of this study is to establish a computer system to study the effects of organizations adopting different learning strategies on consumer market share. The system is an artificial word model that captures some important features of consumers and organizations in the real world. This is because it is difficult, or even impossible, to fully imitate the real world with a small computer model. Simplifying assumptions and compromises are inevitable. As noted by Fogel, Chellapilla, and Angeline (1999), most systems are incomplete. That is, a model can fit some particular assumptions, but not all. Our focus is not on how to extract every element from the real world in detail, but to extract their important features and establish an interactive relation between different elements, and observe its possible results when the system is self-functioning. Without doubts, all of
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the experimental results performed with this model are the consequences of assumptions. As a result, it is not appropriate to draw general conclusions out of the experiments performed with this abstract model, given that the issue of organization/consumer interactions is so complicated. Nevertheless, we can use it as an exploratory tool to generate a new phenomenon that is unknown, then either something new has been discovered which could come into effect under appropriate circumstances, or there is some universal constraint that is operative that quenches this effect. 2. Artificial world model This section describes the system framework and its components, including the related literature that supports the construction of this system. There are four main modules in this system: consumers, organizations, fitness evaluation, and organizational learning. In the following, we first describe how to represent consumers and organizations in digital systems. Then, we explain how to determine the fitness of organizations and how to implement organizational learning with evolutionary learning algorithms. Finally, we show the implementation of the system with the discrete-event simulation techniques. 2.1. Consumers Up to the present day, there is still no consensus about which set of consumer traits plays a vital role in affecting consumer behaviors. In this artificial world model, we assume that there does exist such a general set of parameters that can be used to describe the characteristics of an individual. Motivated from the set of consumer traits1 proposed by Reynolds and Wells (1977), we assume that there are 12 traits in the present implementation. To avoid falling into the dilemma of controversial argument in management science, we prefer not to explicitly pin down the terms of these traits. Undoubtedly, the mechanism that we choose to represent an individual may be too simple to fully describe a consumer in the real word. However, we will be trapped in a situation that has to justify and defend every detail with the researchers in social science if we attempt to mimic the real world faithfully. This is for sure not the intention of our study, as our purpose is to construct a test bed for us to perform various experiments. Through the modification of system parameters, we can look into the dynamics of the interactions between consumers and organizations. 2.2. Organizations
p1
p2
pi
pm
Phenotypic traits
wij Genotypic traits
g1 g2
gj
gn
Fig. 1. Genotype–phenotype structure.
to describe the characteristics of an organization. We note that the sets of parameters for describing consumers and organizations are independent. That is, there is no interaction between these two sets of parameters. The following is to establish their interaction that the changes of an organizational parameter will affect its fitness on the consumers. A two-level structure (Fig. 1) is employed to convert the parameters (to be referred to as genotypic traits) of an organization into the parameters (to be referred to as phenotypic traits) that determine the fitness of an organization on the consumers. The aforementioned two-level framework is motivated from a number of studies, including NK-landscapes (Kauffman, 1990), RNA-evolution (Eigen, 1985), EVOLVE III (Rizki & Conrad, 1985, 1986) genetic algorithms (Holland, 1975), genetic programming (Koza, 1992), CBM–BRAIN (Garis de, 1994, 1999), and Quo Vadis EHW (Sipper et al., 1997). It had been previously employed to simulate human resource management (Chen, Lin, & Kuo, 2002). A number of researchers (Asselmeyer, Ebeling, & Rose´, 1996; Shipman, Shackleton, & Harvey, 2000) further point out that the transformation is important for facilitating evolutionary learning. Our somewhat arbitrary summary of the literature review on organizational learning includes 12 genotypic traits2. Likewise, we prefer not to explicitly pin down the terms of these organizational traits. We also assume that there are 12 phenotypic traits, corresponding to the 12 consumer traits. The weights between these two sets of parameters are set up in a random manner, but within in the range of 0 and 1. Each phenotypic value is the sum of each genotypic value multiplied by its corresponding weight (Eq. (1)). In the above setup, the two-level framework seems to be superfluous as one set of parameters should be enough, given the initial parameter values and their weights assigned randomly. However, we prefer to keep this two-level architecture for future study, as we can link it to a specific problem domain whenever we have sufficient information about the genotypic and phenotypic traits as well as their weights. n 1 X wij gj k i j¼1
ki ¼
n X
Similar to the consumer module, we also assume that there exists a general set of parameters that can be used
pi ¼
1 Census variables, socioeconomic variables, personality characteristics, life style, consciousness, preference, willingness, purchase, expenditure.
2 Organizational structure, technology capability, leadership style, quality, packing and servicing, brand image, and so on.
wij
ð1Þ
j¼1
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2.3. Fitness evaluation The following explains how to evaluate the fitness of an organization by consumers. However, it should be noted that this is not an easy job in the physical world, as people tend to view things in their own idiosyncratic way. Our approach is motivated from the discrepancy theory (Locke, 1969; Porter, 1961; Wanous & Lawler, 1977) that the degree of satisfaction for an individual is determined by the difference between one’s expectation and actual return. Steenkamp and Baumgartner (1992) further propose the inverted ‘‘U’’-shaped curve that one receives great satisfaction when there is no difference between his (her) expectation and actual return, and that the degree of satisfaction gradually decreases when the difference increases. The main drawback of this curve is that the degree of satisfaction drops significantly (i.e., to zero) when the difference exceeds a specific value. Some researchers (Baumgartner & Steenkamp, 1996; Meier, Nieuwland, Zbinden, & Hacisalihzade, 1992; Shiv & Fedorikhin, 1999) take this into account and propose the normal distribution curve. In this study, we use the normal distribution curve (Fig. 2) to measure the fitness of a consumer’s expectation on a specific phenotypic trait for a specific commodity offered by an organization (the actual return). An individual will receive the perfect fitness, 1, if the return value offered by an organization is the same as one’s expected value. The fitness of an individual decreases when the return value is greater or smaller than one’s expected value. The equation for calculating consumer-organization fitness on a specific trait is sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ðpi ciÞ r2
ð2Þ
The individual adequacy of a commodity to a consumer is defined as the sum of the fitness of each phenotypic trait multiplied by its corresponding weight (see Eq. (3)). This is motivated from the multi-attribute attitude model, proposed by Fishbein (1963), that the satisfaction of a consumer is derived from his (her) overall evaluation of each commodity attribute. In the present implementation, we assume that each phenotypic trait is equally important to a consumer (i.e., the weights are the same). Fj ¼
m X
ðfi wi Þ
i¼1
Fig. 2. Fitness curve for a consumer.
ð3Þ
Fig. 3. Degrees of satisfaction for two different consumers. c1 and c2 represent the values of a specific trait expected by two different consumers whereas p is the actual value provided by a commodity. The fitness values for c1 and c2 are f1 and f2, respectively.
As noted above, each consumer tends to have a subjective view of commodities. We assume that each consumer has his (her) own expected phenotypic values. Fig. 3 shows an example of two different fitness values, f1 and f2, for a specific phenotypic trait of a commodity evaluated by two different consumers, respectively. 2.4. Organizational learning We assume that there is only one type of commodities offered by each organization in this study. The fitness of an organization is the sum of the fitness of its commodities evaluated by each of the consumers in the market. The interaction between consumers and organizations in our model is motivated from Nicosia (1966) that involves the activity of buying and selling goods in the market. Note that the activity in the real world is extremely complex. In this paper, we simply assume that each consumer will buy a constant amount of commodities sold by the organizations over a certain amount of time. The sales figures implicitly indicate how well consumers react to the organizations. A best-performing organization is defined as the one providing the greatest fitness to consumers. To increase its performance, a lesser-performing organization tends to imitate best-performing organizations in some ways. Organizational learning thus occurs. In this study, evolutionary learning algorithms are employed to simulate organizational learning. Evolution is one of the important mechanisms commonly used by biological systems to cope with environmental changes (Conrad, Kampfner, & Kirby, 1988). However, there are very few studies regarding the application of evolutionary procedure to organizational learning. Richard and Steinmueller (2000) suggest three evolutionary steps (evaluation/selection, mutation, recombination) for improving organizational performance. They point out that evaluation/selection allows an organization to make good use of its previous experience, derive new ideas through mutation, and incorporate past experience and new ideas into a new management strategy or policy. We had previously employed evolutionary learning algorithms
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to study which kinds of learning strategies are better when one is confronted with a specific leadership type and what occurs if the leadership type changes (Chen et al., 2002). The following two steps describe how organizational learning is implemented with evolutionary learning in our system. i. Evaluation/Selection: We first evaluate the fitness of each organization and then select the organizations with the highest fitness values as best-performing organizations. The number of organizations selected is dependent on the types of experiments performed (to be described in the next section). ii. Copy/Variation (learning): In the present implementation, imitation involves the lesser-performing organizations copying the genotypic values of bestperforming organizations (Eq. (4)). The average genotypic values are used if more than one bestperforming organization are selected. Variable g represents the rate of learning. The higher the value is, the closer the two organizations are. When its value is set to be 1, two organizations become identical. 8i ; gi ¼ gi þ g
n 1X g gi n k¼1 k
! ð4Þ
where gi and gk are the genotypic values of lesser- and best-performing organizations, respectively; g is a proportionality constant that determines the rate of learning; and n is the number of best-performing organizations selected. 2.5. Discrete-event simulation The system is realized with the discrete event simulation techniques. In this model, a number of variables are used to describe the state of the systems. All the interactions in the model are represented with specific events. Each event has a time of occurrence and an associated event routine. All events are placed in a time-ordered list. A system clock keeps track of the time. When the clock time is the same as the occurrence time of the first event on the list, that event is removed and the associated routine is activated. When an event is processed, it will change system state. In addition, the processing of an event might cause the cancellation of some other scheduled events, the activation of some new events, or the rescheduling of the events list. This process continues until the event list is empty. 3. Experimental results The following experiments were motivated from the concepts of ‘‘explorative’’ and ‘‘exploitative’’ learning proposed by March (1991). The problem addressed can be stated as follows. Given a number of different organizations in
aggressive moderate conservative
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100
200
300
400
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cycle Fig. 4. Number of consumers held by organizations with different learning strategies.
a dynamic market composed of various consumers, which kinds of learning strategies are better? And what occurs during the course of interactions among organizations adopting different learning strategies? The parameter that we used to evaluate the fitness of an organization was the number of consumers that it held (see Fig. 4). The more consumers an organization held, the higher fitness it had. We assumed that each consumer had complete and perfect information of all the commodities offered in the market, from which one could decide which commodity matched best his (her) need. We also assumed that each organization had complete and perfect information of the fitness (performance) of other organizations. This allowed a lesser-performing organization to be able to choose other better-performing organizations as its role model, from which it could imitate. Then an interesting phenomenon might take place: all organizations became homogeneous. This was so-called the phenomenon of mimetic isomorphism. However, in a dynamic environment, these clusters were not very stable and tended to be broken when another better-performing organization popped up. In such a case, another run of organizational learning would start. What kinds of learning strategy (general or specific, conservative or aggressive) should an organization adopt in a dynamic environment was the issues to be addressed in the following experiments. 3.1. Conservative or aggressive Given a dynamic environment comprising organizations and consumers whose features were randomly assigned, should an organization adopt an aggressive, a moderate, or conservative learning strategy? Organizations adopting an aggressive learning strategy involved making a comparatively high degree of genotypic changes at one time during the course of evolutionary learning (organizational learning). By contrast, organizations adopting a conservative learning strategy involved making a comparatively small degree of genotypic changes at one time. Organizations adopting a moderate learning strategy were in the middle between the first two groups. In this study, it was imple-
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number of consumers
12000
specific medium general
9000 6000 3000 0 0
50
100
150
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cycle Fig. 5. Effects of different role model strategies on consumer market share.
mented by setting the learning rate g (see Eq. (4)) at different values (0.3, 0.5, and 0.7) to represent organizations adopting different (conservative, moderate, and aggressive) learning strategies. For each learning cycle, 10 bestperforming organizations were selected as role models for lesser-performing organizations. This experiment was performed 10 times. For each experiment, a different random seed was used. The following discussion was made according to the average results of 10 runs. Twelve parameters were used to specify the features of a consumer. In this experiment, there were 12,000 consumers whose trait values were randomly decided. Likewise, we used 12 parameters to describe the features of an organization. Initially, there were 120 organizations whose genotypic values were generated at random, resulting in organizations with distinct features. These organizations were equally divided into three groups. One of these groups included organizations adopting the conservative learning strategy, another the moderate learning strategy, and the last the aggressive learning strategy. The randomization of organizational features and individual genotypic values provided a fair start for organizations with different learning strategies to compete for consumers. All parameters related to consumers were held constant during the course of learning. Experimental results showed that the number of consumers held by the organizations adopting the conservative learning strategy increased significantly in the early stage of learning (before cycle 50), then stayed the same for a while (between cycles 50 and 250), and finally decreased gradually as learning proceeded (between cycles 250 and 400) before it reached an equilibrium state (after cycle 400). The result of the organizations adopting the aggressive learning strategy was contrary to that of the ones adopting the conservative strategy. (Note that there were no significant changes in the number of consumers held by the organizations adopting the moderate strategy.) The above result was somewhat out of our earlier expectation. Our intuition before conducting the experiment was that the organizations adopting the aggressive strategy should be able to get the most consumers, as their strategy was to adjust themselves quickly to resemble best-performing organizations. Detailed analysis of the experimental
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data showed that in the early stage of learning, nearly every organization did possess a number of consumers since all the initial values of organizational and consumers’ traits were uniformly distributed (i.e., they were set up at random). The numbers of consumers held by the different organizations were roughly the same. The performance of best-performing organizations selected as role models for other organizations was only slightly better than the others in the early stage of learning. That is, the organizations that were selected as role models were only temporary and subject to change as learning proceeded. It was thus not suitable for the organizations to adopt the aggressive learning strategy. For the period when the result appeared to be the same (between cycles 50 and 250), closer examination of the data shows that the learning of the system did not stagnate, but only that a slight change of consumers occurred for each learning cycle. That is, the numbers of consumers held by the organizations continued to change slightly for a while before they reached an equilibrium state. In the later stage of learning, the organizations selected as role models became more stable. This allowed the organizations to adopt the aggressive learning strategy to adjust themselves to resemble best-performing organizations. In a nutshell, the organizations with the conservative learning strategy were better suited for variable environments whereas those with the aggressive learning strategy were better suited for constant environments. 3.2. General or specific The goal of this experiment was to investigate the effects of different strategies of choosing role model on consumer market share. Organizations were divided into three groups, each adopting a particular role model strategy. The first strategy was called the specific role model strategy that only one best-performing organization was chosen as the role models. The second strategy was called the general role model strategy that 10 best-performing organizations were chosen as the role models. The third strategy was called the medium role model strategy that five bestperforming organizations were chosen as the role models. In this experiment, we assumed that all organizations adopted the conservative learning strategy. The setup of consumers and organizations were the same as the previous experiment. This experiment was performed 10 times. For each experiment, a different random seed was used. The following discussion was also made according to the average results of 10 runs. The experimental results showed that in the early stage of learning (before cycle 50) the number of consumers held by the organizations adopting the specific role model strategy gradually decreased whereas that of the organizations adopting the general strategy increased. Among the three types of organizations, those adopting the medium role model strategy showed no significant change in the number of consumers. It was because an organization was respond-
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ing to the environment that consisted of other organizational responses to their environment, which consisted of organizations responding to an environment of yet other organizational responses. Thus, the change of genotypic values was more dramatic when an organization adopted the specific strategy than when an organization adopted the general strategy, as the change of the former was dependent on a single organization whereas the latter was dependent on a group of organizations. The result was opposite from cycle 50 to cycle 150. The organizations adopting the specific role model strategy gradually attracted the consumers from the organizations adopting the general role model strategy. Interestingly, the former even had more consumers than the latter after cycle 150. Finally, the system reached an equilibrium state. Our conclusion was that the organizations with the general role model strategy were better suited for variable environments whereas the ones with the specific strategy were better suited for constant environments. The above discussion was made according to the average results of 10 runs. In the following, we looked into each individual experiment. For each of these role model strate-
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gies, we put the learning curve of each individual run together and tried to understand more about the patterns of interactions between organizations and consumers during the course of learning. It should be noted that there was no ‘‘single best way’’ of interpreting these curves. That is, there was no specific truth regarding the interpretation of these figures, as different people possessed different viewpoints. One of the interesting results was that the curves (Fig. 6) appeared like a tug of war between the organizations adopting the specific and general strategy from cycle 40 to cycle 90. Closer examination of the data shows that quite a number of consumers were switching between these two types of organizations. We should note that only one of the 10 experiments demonstrated such a result. The above case was rare. This implies that the system possessed sufficient dynamics for exploring some known or unknown phenomena. Fig. 7 shows the result of the remaining nine runs of the experiment. The number of consumers held by organizations adopting the specific role model strategy fluctuated during the course of learning, but basically constituted a
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‘‘U’’-shaped curve. The result of organizations adopting the general role model strategy was contrary to that of organizations adopting the specific role model strategy, an inverted ‘‘U’’-shaped curve. The above result was consistent with that shown in Fig. 5. Finally, we note that the above conclusion is derived from our very subjective observation of the data. Different readers may come up with different interpretations. Unfortunately, we are not able to justify our conclusions. To verify or validate it, we have to examine each simulation step closely. It will turn the model into a purely mathematic model or an analytic model. This is for sure not the purpose of the study, as our objective is to construct a rich dynamic model for investigating some problems related to organizational learning. 4. Discussion and conclusions In the physical world, it is difficult, even impossible, to perform empirical studies of the dynamic interactions between organizations and their consumers. In this paper, we have presented a computer simulation model that provides a feasible test bed for addressing a variety of questions pertinent to organizational learning, in which individual behavior is taken into account.
The problem addressed in this study can be stated as follows. In a highly competitive customer market, what would be the effects of different organizational learning strategies on consumer distribution (assuming that organizational features and consumers’ characteristics are initially randomly determined)? The experimental results show that different organizational learning strategies are better suited for different environments (or the same environment at different times). Adopting an aggressive or specific learning strategy allows organizations to adjust themselves to provide a better match for consumers’ needs, which in turn increases their fitness. However, this may not necessarily be true when organizations and consumers are not in a stable state. Instead, it may be better to adopt a conservative or general organizational learning strategy. In the real world, the implication is that it may be too risky to blindly follow some leading or highly profitable organizations, especially when the environment is still not very stable. Occasionally, we are informed that there do exist some new products (or services) that pop up and flourish for a moment. This might in turn attract a number of other organizations to join the market. However, it should be reminded that imitating the behaviors of these leading organizations too soon or too closely might be in
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vain, as the flourishing trend could be the so-called ‘‘blindness’’ phenomenon of consumers. Note that people choose the one that matches best their needs from all the products available in a market. The choice of consumers is possibly momentary (or temporary), as people tend to change when another better choices pop up. This usually happens when a product is still at a premature stage. By contrast, the uncertainty of consumers’ choice decreases when the product reaches a mature stage. Then, imitating other organizations in an aggressive manner may become more appropriate. An interesting result emerges from our computer simulation. At a specific critical point, a great number of consumers switch between the organizations with two extreme types of learning strategies (specific and general) in an alternate manner. This suggests that small modifications in some parameters might produce a large variation in the system. It should be noted that none of these results could be foreseen earlier. The experimental results in this study have implications for computational intelligence through evolutionary learning. An important feature of this model is the introduction of evolutionary computation dynamics into the study of organizational learning, which is too complicated to be investigated either analytically or empirically. Our model serves as a test bed for performing a large suite of experiments that cannot be conducted in the real word with a high degree of flexibility. This model is undoubtedly a highly abstracted computer simulation program, as part of the present implementation is still very simple in some senses when we compare it to the real world. Future work with this model is to make it a more complete model by incorporating more detailed components. References Adler, P. S., & Cole, R. E. (1993). Designed for learning: a tale of two auto plants. Sloan Management Review, 34(3), 85–94. Argyris, C. (1977). Double loop learning in organizations. Harvard Business Review, 55(5), 115–125. Asselmeyer, T., Ebeling, W., & Rose´, H. (1996). Smoothing representation of fitness landscapes – the genotype–phenotype map of evolution. BioSystems, 39(1), 63–79. Baumgartner, H., & Steenkamp, J.-B. E. M. (1996). Exploratory consumer buying behavior: conceptualization and measurement. International Journal of Research in Marketing, 13, 121–137. Beardden, W. O., & Teel, J. E. (1983). Selected determinants of consumer satisfaction and complaint reports. Journal of Marketing Research, 20(1), 21–28. Berry, L. L. (1995). Relationship marketing of services – growing interest, emerging perspectives. Journal of the Academy of Marketing Science, 23(4), 236–245. Berry, L. L., & Parauraman, L. A. (1991). Marketing service – competing through quality. New York: Free Press. Cardozo, R. N. (1965). An experimental study of customer effort, expectation and satisfaction. Journal of Marketing Research, 2, 244–249. Chen, J. C., Lin, T. L., & Kuo, M. H. (2002). Artificial worlds modeling of human resource management systems. IEEE Transactions on Evolutionary Computation, 6(6), 542–556.
Churchill, G. A., & Surprenant, C. (1982). An investment into determinants of customer satisfaction. Journal of Marketing Research, 19(4), 491–504. Conrad, M., Kampfner, R. R., & Kirby, K. G. (1988). Neuronal dynamics and evolutionary learning. In M. Kochen & H. Hastings (Eds.). Advances in cognitive science: steps toward convergence (Vol. 104, pp. 169–189). Boulder, Co: Westview Press. Cyert, R., & March, J. G. (1963). A behavioral theory of the firm. New Jersey: Prentice-Hall. Daft, R. L., Sormunem, J., & Parks, D. (1988). Chief executive scanning, environmental characteristics, and company performance: an empirical study. Strategic Management Journal, 9, 284–295. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48, 147–160. Eigen, M. (1985). Macromolecular evolution: dynamical ordering in sequence space. In D. Peins (Ed.), Emerging synthesis in science (pp. 25–69). Reading, MA: Addison Wesley. Fiol, C. M., & Lyles, M. A. (1985). Organizational learning. Academy of Management Review, 10(4), 803–813. Fishbein, M. (1963). An investigation of the relationships between beliefs about an object and the attitude toward that object. Human Relations, 16, 233–240. Fogel, D. B., Chellapilla, K., & Angeline, P. J. (1999). Inductive reasoning and bounded rationality reconsidered. IEEE Transactions on Evolutionary Computation, 3(2), 142–146. Fulmer, R. M. (1994). A model for changing the way organizations learn. Planning Review, 22(3), 20–24. Garis, H. de, (1994). An artificial brain: ATR’s cam-brain project aims to build/evolve an artificial brain with a million neural net modules inside a trillion cell cellular automata machine. New Generation Computing Journal, 12(2), 215–221. Garis, H. de, (1999). Review of proceedings of the first NASA/Dod workshop on evolvable hardware. IEEE Transactions on Evolutionary Computation, 3(4), 304–306. Garratt, R. (1987). The learning organization. London: Fontana/Collins. Hannan, M. T., & Freeman, J. (1977). The population ecology of organizations. American Journal of Sociology, 60, 509–528. Hawley, A. (1968). Human ecology. In David L. Sills (Ed.), International encyclopedia of the social sciences (pp. 328–337). New York: Macmillan. Holland, J. H. (1975). Adaption in natural and artificial systems. Ann Arbor, MI: University of Michigan Press. Kauffman, S. A. (1990). Origins of order: self-organization and selection in evolution. Oxford: Oxford University Press. Kotler, P. (1994). Marketing management: analysis, planning, implementation and control (8th ed.). Prentice-Hall Inc. Koza, J. R. (1992). genetic programming: on the programming of computers by means of Natural Selection. Cambridge: MIT Press. Locke, E. A. (1969). What is job satisfaction? Organization Behavior and Human Performance, 309–336. March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. Mayer, J., & Rowan, B. (1997). Institutionalized organizations: formal structure as myth and ceremony. American Journal of Sociology, 83(2), 340–363. Meier, R., Nieuwland, J., Zbinden, A., & Hacisalihzade, S. (1992). Fuzzy logic control of blood pressure during anesthesia. IEEE Control System, 12–17. Montgomery, D. B., & Urban, G. L. (1969). Management science in marketing. Prentice-Hall Inc. Muller, W. (1991). Gaining competitive advantage through customer satisfaction. European Management Journal, 9(2), 201–221. Narver, J. C., & Slater, S. F. (1990). The effect of a market orientation on business profitability. Journal of Marketing, 54(10), 20–35. Nicosia, F. M. (1966). Consumer decision process – marketing and advertising implications. New Jersey: Prentice-Hall.
T.-T. Lu et al. / Expert Systems with Applications 34 (2008) 1018–1027 O’Keeffe, T. (2005). Towards zero management learning organizations: a honey-bee perspective. Journal of European Industrial Training, 29(8), 764–779. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. Oliver, R. L. (1981). What is customer satisfaction? Wharton Magazine, 5(3), 36–41. Oliver, R. L., & DeSarbo, W. S. (1988). Response determinates in satisfaction Judgments. Journal of Customer Research, 14, 495–507. Porter, L. W. (1961). A study of perceived need satisfaction in bottom and middle management Jobs. Journal of Applied Psychology, 1–10. Reichheld, F. F. (1966). The loyalty effect: the hidden force behind growth, profit and lasting value. Boston: Harvard Business School Press. Reynolds, F. D., & Wells, W. D. (1977). Consumer behavior. McGrawHill. Richard, N. L., & Steinmueller, W. E. (2000). Strategy and circumstance: the response of american firms to japanese competition in semiconductors, 1980–1995. Strategic Management Journal, 21, 1163–1173. Rizki, M., & Conrad, M. (1985). EVOLVE III: a discrete events model of an evolutionary ecosystem. BioSystems, 18, 121–133. Rizki, M., & Conrad, M. (1986). Computing the theory of evolution. Physica D, 22, 83–89. Senge, P. M. (1990). The fifth discipline. New York: Doubleday.
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Expert Systems with Applications Expert Systems with Applications 34 (2008) 1018–1027 www.elsevier.com/locate/eswa
Aggressive or conservative, general or specific? A study of organizations adopting different learning strategies in an artificial world Te-Tsai Lu b
a,b,*
, Jong-Chen Chen a, Guo-Xun Liao
a
a Department of Information Management, National Yunlin University of Science and Technology, Taiwan, ROC Department of Business Administration, Kun Shan University, No. 949, Da Wan Road, Yung-Kang, Tainan Hsien 710, Taiwan, ROC
Abstract An increasing growth of keen competitions among global industries greatly challenges present organizations (companies). The purpose of this study is to construct a computer simulation system for investigating the effects of organizations adopting different learning strategies on the change in consumer market share. The system constructed is an abstract model that captures some general features of consumers and organizations as well as their interactions in the real world, which constitutes an artificial world. It has four major modules: consumers, organizations, fitness evaluation, and organizational learning. These modules, including their interactions, are linked through the discrete-event simulation techniques. Experimental results show that different organizational learning strategies are better suited for different environments or the same environment at different times. Organizational learning provides organizations a chance of undergoing transformation to better meet consumers’ needs. However, it is risky to blindly adopt an aggressive learning strategy when the environment (market) is not stable. Instead, it may be better to adopt a conservative learning strategy. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Artificial World; Discrete-Event Simulation; Market Orientation; Organizational Learning; Consumer Behavior
1. Introduction The astonishing progress of modern technology allows people to access and share information in a fantastically fast and easy manner. The consequence is that people are fickle and tend to change their behaviors easily, rapidly, or even unexpectedly. To cope with these changes, an organization must make appropriate adjustments so as to sustain and enhance its competitive advantages in the market. The issue of ‘‘organizational learning’’ (or ‘‘reengineering’’) is thus proposed. Its significance is noted by a number of researchers. Cyert and March (1963) are the earliest two scholars *
Corresponding author. Address: Department of Information Management, National Yunlin University of Science and Technology, Taiwan, ROC. Tel.: +886 62050542x11; mobile: +886 919786343; fax: +886 62050543. E-mail address: [email protected] (T.-T. Lu). 0957-4174/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2006.10.036
who propose organizational learning. They claim that organizational learning is an adaptive process for an organization to cope with environmental changes. Fiol and Lyles (1985) point out that different environments should have different learning strategies. This is particularly important when the environment (market) is dynamic (Daft, Sormunem, & Parks, 1988; Fulmer, 1994). Garratt (1987) points out that without organizational learning an organization will lose its competitiveness and superiority in a market. Fulmer (1994) further says that organizational learning is extremely important that it might affect whether an organization can survive or not. Thus, O’Keeffe (2005) reminds that an organizational leader must stay alert to any market changes and make appropriate organizational adjustments in responding to these changes. From the viewpoint of marketing, Narver and Slater (1990) suggest that an organization must learn from consumers as well as other organizations. More specifically,
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an organization must maintain a close relationship with its consumers, and, in the meantime, keep a close eye on other organizations (or imitate them if necessary). In the following, we first introduce two important issues related to consumer behaviors. Then, we describe organizational learning. Finally, we explain the motivation and assumption of our model. Montgomery and Urban (1969) propose that consumer feature analysis is an important issue to understand consumer behaviors. However, so far, there is no single set of features that can specify all consumer behaviors (Nicosia, 1966). One of the feature sets commonly accepted by most people is the one proposed by Reynolds and Wells (1977). They suggest nine important consumer features, including census variables, socioeconomic variables, personality characteristics, life style, consciousness, preference, willingness, purchase, and expenditure. The other issue related to consumer behaviors is the degree (or extent) of consumer satisfaction with commodities, called consumer satisfaction. Cardozo (1965) is among the first researchers who point out that consumer satisfaction is an effective index for evaluating the performance of an organization. Muller (1991) further stresses that consumer satisfaction is one of the key factors that determines the degree of success of an organization. Reichheld (1966) points out that high consumer satisfaction will yield more organizational profits. As a consequence, a number of researchers (Berry, 1995; Berry & Parauraman, 1991) advocate that each organization should make its best effort in increasing its consumer satisfaction and enhancing a long-term relationship with its present and potential customers. There are many definitions of the term ‘‘consumer satisfaction’’. Roughly speaking, there are two definitions generally accepted. One is the subjective judgment of an individual that consumer satisfaction is strictly determined by one’s personal experience or feeling with a specific commodity (Beardden & Teel, 1983; Churchill & Surprenant, 1982; Oliver, 1980; Woodruff, 1993; Woodside, Frey, & Daly, 1989). The other definition is the gap between the expectation and actual feeling of an individual with a specific commodity (Churchill & Surprenant, 1982; Kotler, 1994; Oliver, 1981; Oliver & DeSarbo, 1988; Westbrooks, 1980; Woodruff, 1993). The smaller the gap is, the higher the satisfaction one has. Argyris (1977) divides organizational learning strategies into two types: single-loop learning and double-loop learning. The former emphasizes the strategies of how to improve (enhance) an organization within current organizational norms while the latter concentrates on the strategies that include not only the improvement of current norms but also the generation of new norms. Later, classification of organizational learning takes the view of how to set up organizational goals to meet environmental changes. Senge (1990) proposes the concepts of adaptive learning and generative learning; the former is similar to single-loop learning and the latter, double-loop learning. March (1991) calls the former exploitative learning and the latter, explorative learning. He also suggests that exploitation learning strategy is
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suitable for a stable, constant environment whereas explorative learning strategy suits better a dynamic, variable environment. To verify this, Adler and Cole (1993) conducted a survey of consumer preference in the car industry. Their conclusion is that it is better to choose exploitative learning strategy when consumer preference is homogeneous (constant) and to choose exploitative learning strategy when consumer preference is heterogeneous (variable). Hawley (1968) points out that organizations in the same environment tend to imitate the behaviors or strategies of other organizations in a tangible or intangible way. Theoretically, this may result in the so-called ‘‘isomorphism’’ phenomenon. That is, all organizations become identical. However, practically this will not happen, as no organizations are able to completely imitate other organizations (Hannan & Freeman, 1977). There are at least two possible reasons. One is that an organization cannot find an appropriate role model. The other reason is that it is difficult to undergo proper organizational transformation within current organizational norms such as culture, leadership style, financial resources and so on (DiMaggio & Powell, 1983; Mayer & Rowan, 1997; Zucker, 1977). DiMaggio and Powell (1983) call this the phenomenon of mimetic isomorphism. Statistical approaches, including qualitative and quantitative analyses, are common approaches used to address the issues related to organizational learning. These approaches provide us some general information regarding the interactions between consumers and organizations. However, the process of such interactions has been totally neglected. In the real world, these interactions are extremely complex. We all know that sometimes an individual might respond inconsistently to the same things at different times, places, or with different people. Moreover, the interactions are dynamic such that people are responding to an environment that consists of other people responding to their environment, which consists of people responding to an environment of other people’s responses. The sophistication makes it difficult to predict what behavior will emerge from the intensive interactions among consumers and organizations, let alone to study the effects of altering organizational strategies on consumer behaviors. The purpose of this study is to establish a computer system to study the effects of organizations adopting different learning strategies on consumer market share. The system is an artificial word model that captures some important features of consumers and organizations in the real world. This is because it is difficult, or even impossible, to fully imitate the real world with a small computer model. Simplifying assumptions and compromises are inevitable. As noted by Fogel, Chellapilla, and Angeline (1999), most systems are incomplete. That is, a model can fit some particular assumptions, but not all. Our focus is not on how to extract every element from the real world in detail, but to extract their important features and establish an interactive relation between different elements, and observe its possible results when the system is self-functioning. Without doubts, all of
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the experimental results performed with this model are the consequences of assumptions. As a result, it is not appropriate to draw general conclusions out of the experiments performed with this abstract model, given that the issue of organization/consumer interactions is so complicated. Nevertheless, we can use it as an exploratory tool to generate a new phenomenon that is unknown, then either something new has been discovered which could come into effect under appropriate circumstances, or there is some universal constraint that is operative that quenches this effect. 2. Artificial world model This section describes the system framework and its components, including the related literature that supports the construction of this system. There are four main modules in this system: consumers, organizations, fitness evaluation, and organizational learning. In the following, we first describe how to represent consumers and organizations in digital systems. Then, we explain how to determine the fitness of organizations and how to implement organizational learning with evolutionary learning algorithms. Finally, we show the implementation of the system with the discrete-event simulation techniques. 2.1. Consumers Up to the present day, there is still no consensus about which set of consumer traits plays a vital role in affecting consumer behaviors. In this artificial world model, we assume that there does exist such a general set of parameters that can be used to describe the characteristics of an individual. Motivated from the set of consumer traits1 proposed by Reynolds and Wells (1977), we assume that there are 12 traits in the present implementation. To avoid falling into the dilemma of controversial argument in management science, we prefer not to explicitly pin down the terms of these traits. Undoubtedly, the mechanism that we choose to represent an individual may be too simple to fully describe a consumer in the real word. However, we will be trapped in a situation that has to justify and defend every detail with the researchers in social science if we attempt to mimic the real world faithfully. This is for sure not the intention of our study, as our purpose is to construct a test bed for us to perform various experiments. Through the modification of system parameters, we can look into the dynamics of the interactions between consumers and organizations. 2.2. Organizations
p1
p2
pi
pm
Phenotypic traits
wij Genotypic traits
g1 g2
gj
gn
Fig. 1. Genotype–phenotype structure.
to describe the characteristics of an organization. We note that the sets of parameters for describing consumers and organizations are independent. That is, there is no interaction between these two sets of parameters. The following is to establish their interaction that the changes of an organizational parameter will affect its fitness on the consumers. A two-level structure (Fig. 1) is employed to convert the parameters (to be referred to as genotypic traits) of an organization into the parameters (to be referred to as phenotypic traits) that determine the fitness of an organization on the consumers. The aforementioned two-level framework is motivated from a number of studies, including NK-landscapes (Kauffman, 1990), RNA-evolution (Eigen, 1985), EVOLVE III (Rizki & Conrad, 1985, 1986) genetic algorithms (Holland, 1975), genetic programming (Koza, 1992), CBM–BRAIN (Garis de, 1994, 1999), and Quo Vadis EHW (Sipper et al., 1997). It had been previously employed to simulate human resource management (Chen, Lin, & Kuo, 2002). A number of researchers (Asselmeyer, Ebeling, & Rose´, 1996; Shipman, Shackleton, & Harvey, 2000) further point out that the transformation is important for facilitating evolutionary learning. Our somewhat arbitrary summary of the literature review on organizational learning includes 12 genotypic traits2. Likewise, we prefer not to explicitly pin down the terms of these organizational traits. We also assume that there are 12 phenotypic traits, corresponding to the 12 consumer traits. The weights between these two sets of parameters are set up in a random manner, but within in the range of 0 and 1. Each phenotypic value is the sum of each genotypic value multiplied by its corresponding weight (Eq. (1)). In the above setup, the two-level framework seems to be superfluous as one set of parameters should be enough, given the initial parameter values and their weights assigned randomly. However, we prefer to keep this two-level architecture for future study, as we can link it to a specific problem domain whenever we have sufficient information about the genotypic and phenotypic traits as well as their weights. n 1 X wij gj k i j¼1
ki ¼
n X
Similar to the consumer module, we also assume that there exists a general set of parameters that can be used
pi ¼
1 Census variables, socioeconomic variables, personality characteristics, life style, consciousness, preference, willingness, purchase, expenditure.
2 Organizational structure, technology capability, leadership style, quality, packing and servicing, brand image, and so on.
wij
ð1Þ
j¼1
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2.3. Fitness evaluation The following explains how to evaluate the fitness of an organization by consumers. However, it should be noted that this is not an easy job in the physical world, as people tend to view things in their own idiosyncratic way. Our approach is motivated from the discrepancy theory (Locke, 1969; Porter, 1961; Wanous & Lawler, 1977) that the degree of satisfaction for an individual is determined by the difference between one’s expectation and actual return. Steenkamp and Baumgartner (1992) further propose the inverted ‘‘U’’-shaped curve that one receives great satisfaction when there is no difference between his (her) expectation and actual return, and that the degree of satisfaction gradually decreases when the difference increases. The main drawback of this curve is that the degree of satisfaction drops significantly (i.e., to zero) when the difference exceeds a specific value. Some researchers (Baumgartner & Steenkamp, 1996; Meier, Nieuwland, Zbinden, & Hacisalihzade, 1992; Shiv & Fedorikhin, 1999) take this into account and propose the normal distribution curve. In this study, we use the normal distribution curve (Fig. 2) to measure the fitness of a consumer’s expectation on a specific phenotypic trait for a specific commodity offered by an organization (the actual return). An individual will receive the perfect fitness, 1, if the return value offered by an organization is the same as one’s expected value. The fitness of an individual decreases when the return value is greater or smaller than one’s expected value. The equation for calculating consumer-organization fitness on a specific trait is sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ðpi ciÞ r2
ð2Þ
The individual adequacy of a commodity to a consumer is defined as the sum of the fitness of each phenotypic trait multiplied by its corresponding weight (see Eq. (3)). This is motivated from the multi-attribute attitude model, proposed by Fishbein (1963), that the satisfaction of a consumer is derived from his (her) overall evaluation of each commodity attribute. In the present implementation, we assume that each phenotypic trait is equally important to a consumer (i.e., the weights are the same). Fj ¼
m X
ðfi wi Þ
i¼1
Fig. 2. Fitness curve for a consumer.
ð3Þ
Fig. 3. Degrees of satisfaction for two different consumers. c1 and c2 represent the values of a specific trait expected by two different consumers whereas p is the actual value provided by a commodity. The fitness values for c1 and c2 are f1 and f2, respectively.
As noted above, each consumer tends to have a subjective view of commodities. We assume that each consumer has his (her) own expected phenotypic values. Fig. 3 shows an example of two different fitness values, f1 and f2, for a specific phenotypic trait of a commodity evaluated by two different consumers, respectively. 2.4. Organizational learning We assume that there is only one type of commodities offered by each organization in this study. The fitness of an organization is the sum of the fitness of its commodities evaluated by each of the consumers in the market. The interaction between consumers and organizations in our model is motivated from Nicosia (1966) that involves the activity of buying and selling goods in the market. Note that the activity in the real world is extremely complex. In this paper, we simply assume that each consumer will buy a constant amount of commodities sold by the organizations over a certain amount of time. The sales figures implicitly indicate how well consumers react to the organizations. A best-performing organization is defined as the one providing the greatest fitness to consumers. To increase its performance, a lesser-performing organization tends to imitate best-performing organizations in some ways. Organizational learning thus occurs. In this study, evolutionary learning algorithms are employed to simulate organizational learning. Evolution is one of the important mechanisms commonly used by biological systems to cope with environmental changes (Conrad, Kampfner, & Kirby, 1988). However, there are very few studies regarding the application of evolutionary procedure to organizational learning. Richard and Steinmueller (2000) suggest three evolutionary steps (evaluation/selection, mutation, recombination) for improving organizational performance. They point out that evaluation/selection allows an organization to make good use of its previous experience, derive new ideas through mutation, and incorporate past experience and new ideas into a new management strategy or policy. We had previously employed evolutionary learning algorithms
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to study which kinds of learning strategies are better when one is confronted with a specific leadership type and what occurs if the leadership type changes (Chen et al., 2002). The following two steps describe how organizational learning is implemented with evolutionary learning in our system. i. Evaluation/Selection: We first evaluate the fitness of each organization and then select the organizations with the highest fitness values as best-performing organizations. The number of organizations selected is dependent on the types of experiments performed (to be described in the next section). ii. Copy/Variation (learning): In the present implementation, imitation involves the lesser-performing organizations copying the genotypic values of bestperforming organizations (Eq. (4)). The average genotypic values are used if more than one bestperforming organization are selected. Variable g represents the rate of learning. The higher the value is, the closer the two organizations are. When its value is set to be 1, two organizations become identical. 8i ; gi ¼ gi þ g
n 1X g gi n k¼1 k
! ð4Þ
where gi and gk are the genotypic values of lesser- and best-performing organizations, respectively; g is a proportionality constant that determines the rate of learning; and n is the number of best-performing organizations selected. 2.5. Discrete-event simulation The system is realized with the discrete event simulation techniques. In this model, a number of variables are used to describe the state of the systems. All the interactions in the model are represented with specific events. Each event has a time of occurrence and an associated event routine. All events are placed in a time-ordered list. A system clock keeps track of the time. When the clock time is the same as the occurrence time of the first event on the list, that event is removed and the associated routine is activated. When an event is processed, it will change system state. In addition, the processing of an event might cause the cancellation of some other scheduled events, the activation of some new events, or the rescheduling of the events list. This process continues until the event list is empty. 3. Experimental results The following experiments were motivated from the concepts of ‘‘explorative’’ and ‘‘exploitative’’ learning proposed by March (1991). The problem addressed can be stated as follows. Given a number of different organizations in
aggressive moderate conservative
15000
number of consumers
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12000 9000 6000 3000 0 0
100
200
300
400
500
cycle Fig. 4. Number of consumers held by organizations with different learning strategies.
a dynamic market composed of various consumers, which kinds of learning strategies are better? And what occurs during the course of interactions among organizations adopting different learning strategies? The parameter that we used to evaluate the fitness of an organization was the number of consumers that it held (see Fig. 4). The more consumers an organization held, the higher fitness it had. We assumed that each consumer had complete and perfect information of all the commodities offered in the market, from which one could decide which commodity matched best his (her) need. We also assumed that each organization had complete and perfect information of the fitness (performance) of other organizations. This allowed a lesser-performing organization to be able to choose other better-performing organizations as its role model, from which it could imitate. Then an interesting phenomenon might take place: all organizations became homogeneous. This was so-called the phenomenon of mimetic isomorphism. However, in a dynamic environment, these clusters were not very stable and tended to be broken when another better-performing organization popped up. In such a case, another run of organizational learning would start. What kinds of learning strategy (general or specific, conservative or aggressive) should an organization adopt in a dynamic environment was the issues to be addressed in the following experiments. 3.1. Conservative or aggressive Given a dynamic environment comprising organizations and consumers whose features were randomly assigned, should an organization adopt an aggressive, a moderate, or conservative learning strategy? Organizations adopting an aggressive learning strategy involved making a comparatively high degree of genotypic changes at one time during the course of evolutionary learning (organizational learning). By contrast, organizations adopting a conservative learning strategy involved making a comparatively small degree of genotypic changes at one time. Organizations adopting a moderate learning strategy were in the middle between the first two groups. In this study, it was imple-
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number of consumers
12000
specific medium general
9000 6000 3000 0 0
50
100
150
200
cycle Fig. 5. Effects of different role model strategies on consumer market share.
mented by setting the learning rate g (see Eq. (4)) at different values (0.3, 0.5, and 0.7) to represent organizations adopting different (conservative, moderate, and aggressive) learning strategies. For each learning cycle, 10 bestperforming organizations were selected as role models for lesser-performing organizations. This experiment was performed 10 times. For each experiment, a different random seed was used. The following discussion was made according to the average results of 10 runs. Twelve parameters were used to specify the features of a consumer. In this experiment, there were 12,000 consumers whose trait values were randomly decided. Likewise, we used 12 parameters to describe the features of an organization. Initially, there were 120 organizations whose genotypic values were generated at random, resulting in organizations with distinct features. These organizations were equally divided into three groups. One of these groups included organizations adopting the conservative learning strategy, another the moderate learning strategy, and the last the aggressive learning strategy. The randomization of organizational features and individual genotypic values provided a fair start for organizations with different learning strategies to compete for consumers. All parameters related to consumers were held constant during the course of learning. Experimental results showed that the number of consumers held by the organizations adopting the conservative learning strategy increased significantly in the early stage of learning (before cycle 50), then stayed the same for a while (between cycles 50 and 250), and finally decreased gradually as learning proceeded (between cycles 250 and 400) before it reached an equilibrium state (after cycle 400). The result of the organizations adopting the aggressive learning strategy was contrary to that of the ones adopting the conservative strategy. (Note that there were no significant changes in the number of consumers held by the organizations adopting the moderate strategy.) The above result was somewhat out of our earlier expectation. Our intuition before conducting the experiment was that the organizations adopting the aggressive strategy should be able to get the most consumers, as their strategy was to adjust themselves quickly to resemble best-performing organizations. Detailed analysis of the experimental
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data showed that in the early stage of learning, nearly every organization did possess a number of consumers since all the initial values of organizational and consumers’ traits were uniformly distributed (i.e., they were set up at random). The numbers of consumers held by the different organizations were roughly the same. The performance of best-performing organizations selected as role models for other organizations was only slightly better than the others in the early stage of learning. That is, the organizations that were selected as role models were only temporary and subject to change as learning proceeded. It was thus not suitable for the organizations to adopt the aggressive learning strategy. For the period when the result appeared to be the same (between cycles 50 and 250), closer examination of the data shows that the learning of the system did not stagnate, but only that a slight change of consumers occurred for each learning cycle. That is, the numbers of consumers held by the organizations continued to change slightly for a while before they reached an equilibrium state. In the later stage of learning, the organizations selected as role models became more stable. This allowed the organizations to adopt the aggressive learning strategy to adjust themselves to resemble best-performing organizations. In a nutshell, the organizations with the conservative learning strategy were better suited for variable environments whereas those with the aggressive learning strategy were better suited for constant environments. 3.2. General or specific The goal of this experiment was to investigate the effects of different strategies of choosing role model on consumer market share. Organizations were divided into three groups, each adopting a particular role model strategy. The first strategy was called the specific role model strategy that only one best-performing organization was chosen as the role models. The second strategy was called the general role model strategy that 10 best-performing organizations were chosen as the role models. The third strategy was called the medium role model strategy that five bestperforming organizations were chosen as the role models. In this experiment, we assumed that all organizations adopted the conservative learning strategy. The setup of consumers and organizations were the same as the previous experiment. This experiment was performed 10 times. For each experiment, a different random seed was used. The following discussion was also made according to the average results of 10 runs. The experimental results showed that in the early stage of learning (before cycle 50) the number of consumers held by the organizations adopting the specific role model strategy gradually decreased whereas that of the organizations adopting the general strategy increased. Among the three types of organizations, those adopting the medium role model strategy showed no significant change in the number of consumers. It was because an organization was respond-
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ing to the environment that consisted of other organizational responses to their environment, which consisted of organizations responding to an environment of yet other organizational responses. Thus, the change of genotypic values was more dramatic when an organization adopted the specific strategy than when an organization adopted the general strategy, as the change of the former was dependent on a single organization whereas the latter was dependent on a group of organizations. The result was opposite from cycle 50 to cycle 150. The organizations adopting the specific role model strategy gradually attracted the consumers from the organizations adopting the general role model strategy. Interestingly, the former even had more consumers than the latter after cycle 150. Finally, the system reached an equilibrium state. Our conclusion was that the organizations with the general role model strategy were better suited for variable environments whereas the ones with the specific strategy were better suited for constant environments. The above discussion was made according to the average results of 10 runs. In the following, we looked into each individual experiment. For each of these role model strate-
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gies, we put the learning curve of each individual run together and tried to understand more about the patterns of interactions between organizations and consumers during the course of learning. It should be noted that there was no ‘‘single best way’’ of interpreting these curves. That is, there was no specific truth regarding the interpretation of these figures, as different people possessed different viewpoints. One of the interesting results was that the curves (Fig. 6) appeared like a tug of war between the organizations adopting the specific and general strategy from cycle 40 to cycle 90. Closer examination of the data shows that quite a number of consumers were switching between these two types of organizations. We should note that only one of the 10 experiments demonstrated such a result. The above case was rare. This implies that the system possessed sufficient dynamics for exploring some known or unknown phenomena. Fig. 7 shows the result of the remaining nine runs of the experiment. The number of consumers held by organizations adopting the specific role model strategy fluctuated during the course of learning, but basically constituted a
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‘‘U’’-shaped curve. The result of organizations adopting the general role model strategy was contrary to that of organizations adopting the specific role model strategy, an inverted ‘‘U’’-shaped curve. The above result was consistent with that shown in Fig. 5. Finally, we note that the above conclusion is derived from our very subjective observation of the data. Different readers may come up with different interpretations. Unfortunately, we are not able to justify our conclusions. To verify or validate it, we have to examine each simulation step closely. It will turn the model into a purely mathematic model or an analytic model. This is for sure not the purpose of the study, as our objective is to construct a rich dynamic model for investigating some problems related to organizational learning. 4. Discussion and conclusions In the physical world, it is difficult, even impossible, to perform empirical studies of the dynamic interactions between organizations and their consumers. In this paper, we have presented a computer simulation model that provides a feasible test bed for addressing a variety of questions pertinent to organizational learning, in which individual behavior is taken into account.
The problem addressed in this study can be stated as follows. In a highly competitive customer market, what would be the effects of different organizational learning strategies on consumer distribution (assuming that organizational features and consumers’ characteristics are initially randomly determined)? The experimental results show that different organizational learning strategies are better suited for different environments (or the same environment at different times). Adopting an aggressive or specific learning strategy allows organizations to adjust themselves to provide a better match for consumers’ needs, which in turn increases their fitness. However, this may not necessarily be true when organizations and consumers are not in a stable state. Instead, it may be better to adopt a conservative or general organizational learning strategy. In the real world, the implication is that it may be too risky to blindly follow some leading or highly profitable organizations, especially when the environment is still not very stable. Occasionally, we are informed that there do exist some new products (or services) that pop up and flourish for a moment. This might in turn attract a number of other organizations to join the market. However, it should be reminded that imitating the behaviors of these leading organizations too soon or too closely might be in
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vain, as the flourishing trend could be the so-called ‘‘blindness’’ phenomenon of consumers. Note that people choose the one that matches best their needs from all the products available in a market. The choice of consumers is possibly momentary (or temporary), as people tend to change when another better choices pop up. This usually happens when a product is still at a premature stage. By contrast, the uncertainty of consumers’ choice decreases when the product reaches a mature stage. Then, imitating other organizations in an aggressive manner may become more appropriate. An interesting result emerges from our computer simulation. At a specific critical point, a great number of consumers switch between the organizations with two extreme types of learning strategies (specific and general) in an alternate manner. This suggests that small modifications in some parameters might produce a large variation in the system. It should be noted that none of these results could be foreseen earlier. The experimental results in this study have implications for computational intelligence through evolutionary learning. An important feature of this model is the introduction of evolutionary computation dynamics into the study of organizational learning, which is too complicated to be investigated either analytically or empirically. Our model serves as a test bed for performing a large suite of experiments that cannot be conducted in the real word with a high degree of flexibility. This model is undoubtedly a highly abstracted computer simulation program, as part of the present implementation is still very simple in some senses when we compare it to the real world. Future work with this model is to make it a more complete model by incorporating more detailed components. References Adler, P. S., & Cole, R. E. (1993). Designed for learning: a tale of two auto plants. Sloan Management Review, 34(3), 85–94. Argyris, C. (1977). Double loop learning in organizations. Harvard Business Review, 55(5), 115–125. Asselmeyer, T., Ebeling, W., & Rose´, H. (1996). Smoothing representation of fitness landscapes – the genotype–phenotype map of evolution. BioSystems, 39(1), 63–79. Baumgartner, H., & Steenkamp, J.-B. E. M. (1996). Exploratory consumer buying behavior: conceptualization and measurement. International Journal of Research in Marketing, 13, 121–137. Beardden, W. O., & Teel, J. E. (1983). Selected determinants of consumer satisfaction and complaint reports. Journal of Marketing Research, 20(1), 21–28. Berry, L. L. (1995). Relationship marketing of services – growing interest, emerging perspectives. Journal of the Academy of Marketing Science, 23(4), 236–245. Berry, L. L., & Parauraman, L. A. (1991). Marketing service – competing through quality. New York: Free Press. Cardozo, R. N. (1965). An experimental study of customer effort, expectation and satisfaction. Journal of Marketing Research, 2, 244–249. Chen, J. C., Lin, T. L., & Kuo, M. H. (2002). Artificial worlds modeling of human resource management systems. IEEE Transactions on Evolutionary Computation, 6(6), 542–556.
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