- Email: [email protected]
Learning from strategic success and failure
.I BUSN RES 1987:15:503-517
503
Learning from Strategic Success and Failure Theresa K. Lant
David B. Montgomery
New
Stanford
York
University
University
Organizations facing complex, ambiguous, and dynamic environments find adaptive learning a key to survival and success. This study proposes three models of organization response in such environments: 1) A model of how aspiration levels or goals adapt over time, 2) a model of the riskiness of strategic choices made, and 3) a model of the innovativeness of search activities (R & D). In each model, the difference between performance and aspiration level is posited to be an important explanatory variable. Using the Markstrat game as a research environment, the data are consistent with all three models. Introduction Learning to mediate and adapt to the environment are important determinants of organization health and even survival. Such learning occurs largely through organizational interaction with and observation of its environments. However, adaptive learning is difficult in the usual case where the relationship between an organization and its environment is complex, ambiguous, and dynamic. Thus, it is important for organizational theories of adaptation to address the question of how organizations learn in such situations. The literature on organizational adaptation has typically addressed either of two questions. First, what type of decisions and behavior lead to adaptation? Second, how are such decisions made? The former question has been addressed, for example, by structural-contingencies theory [4,18] and resource-dependence theory [31]. The latter has been addressed in the administrative theories of Barnard [l], Simon [35], and the organizational theories of March and Simon [25] and Cyert and March [6], and more recently by decision-process research, such as Weick [42], Cohen, March, and Olsen [5], and Dutton, Fahey, and Narayanan [8]. The present research addresses the latter issue by investigating the learning process that leads to decisions regarding strategic organizational adaptation. Specifically, we are concerned with how organizational goals and success in achieving these goals affect
Address correspondence Stanford, CA 94305-5015.
to Theresa
Journal of Business Research 15, 503-517 (1987) 0 1987 Elsevier Science Publishing Co., Inc. 1987 52 Vanderbilt Ave., New York, NY 10017
K. Lant,
Graduate
School
of Business,
Stanford
University,
014%2963/871$3.50
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the setting of future goals, the riskiness of choices, and innovativeness of search activities. The literatures that have delved into these issues include models of individual and organizational learning and adaptation, models of strategic decision making, cognitive and behavioral theories of individual decision making, and the work on human inference and attribution. The thread that ties these literatures together is evident in the typical situation facing a group of strategic decision makers. These decision makers take certain actions, monitor the activities of their organization and their environment, and then make future decisions concerning future organizational activities. It can generally be agreed that these individuals have the capability of learning from experience. Further, they enlist this ability when trying to adapt to their environments [6]. However, the relationships between individual, organizational, and environmental actions are often complex, ambiguous, and change rapidly [22]. Further, decision makers are subject to cognitive limitations, such as bounded rationality. This combination of factors creates barriers to effective, adaptive learning. How, then, do decision makers manage under these circumstances? This issue is investigated by studying the decisions of several groups of players of the Markstrat marketing strategy game. Mark&-at provides a complex and dynamic decision-making setting in which to study decision makers. The R ole of Aspirations
in Organizational
Learning
March and Simon [25] and Cyert and March [6] have suggested several mechanisms by which organizations can make decisions despite the complex situations that face them. March and Simon [25] have suggested that organizations set multiple operational goals rather than a general, nonoperational goal. Setting specific goals enables the organization to make concrete performance evaluations, which provide a basis for future action. Cyert and March [6] model how organizational decision makers make basic operating and strategic decisions. Organizations face substantial complexity generated by the demands of multiple coalitions and interest groups that make up the organization and its environment. Response to this complexity involves attending to multiple goals and attempting to satisfice on each. The present study explores whether goals, or aspiration levels, are attended to by decision makers, and if so, how performance relative to these aspirations affects the type of actions taken in trying to manage the organization-environment relationship. The aspiration levels of concern here are goals held by the dominant coalition regarding certain organizational outcomes, such as sales and market share. While early work on aspirations focused on individual behavior [20,34], later work has applied models of aspirations to the organizational domain [25,6]. Before discussing the possible effects of aspiration levels on behavior, it is important to understand the significance of aspiration levels alone to decision making. Specific attention to modeling organizational aspirations has been taken by Levinthal and March [19]. They developed a model of organizational learning and adaptation in which organizations set performance aspirations, compare actual performance to these aspirations, and modify future aspirations based on this comparison. Herriot, Levinthal, and March [lo] extended this model to include organizational responses to other organizations in the environment. They added factors such as the diffusion
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of experience and the diffusion of aspirations across organizations. Thus, they modeled aspiration level as a function of the past performance of other organizations as well as the past performance of the focal organization. The difference between the aspiration level for a period and the actual performance level is called attainment discrepancy [20]. Attainment discrepancy is calculated by subtracting aspiration level from actual performance achieved. The evaluation of new performance in light of one’s attainment discrepancy generates a feeling of success or failure. Attainment discrepancy is used as a cue that determines the next aspiration level and affects other behavior. Thus, attainment discrepancy serves as a crucial piece of information that decision makers look for in assessing the organization-environment relationship, and that colors their perceptions of possibilities for action. One key to the importance of attainment discrepancy is the degree of complexity and ambiguity that faces most decision makers. Concrete operational goals are set in organizations because they provide a concrete link to specific actions. A vague, nonoperational goal, such as maximizing long-term profit, provides little information for taking specific actions. The relationship between possible actions and this desired, but vague, outcome is ambiguous. A more concrete goal, about which one can receive concrete feedback, provides more information about which actions will lead to this goal, thus enhancing learning in this situation. Levinthal and March [19] found that organizations generally learned adaptively when there were strong, unambiguous signals. At the individual level, expectancy theory links environmental conditions, individual beliefs, and individual behavior. This research found that beneficial behavior was more likely when the links between components were clear [30,17]. Thompson and Tuden [38] classified organizational decision processes according to the certainty of cause-effect relations and the clarity of preferences. When there are clear preferences for goals, and the cause-effect relationship between means and goals is known, decisions can be made by computation. By setting concrete, agreed-upon goals, and gathering performance information, decision makers attempt to mold their ambiguous situation into one that approximates clear goal preferences and clear cause and effect relationships. There is substantial evidence that decision makers seek to simplify the decision situation facing them. For example, Payne [28] and Olshavsky [27] f ound that while simple tasks were evaluated holistically, more complex tasks were simplified by the use of decision rules that quickly eliminated large numbers of alternatives. Russo and Dosher [32] found that, even for simple tasks, subjects dimensionalized the task rather than examining it holistically. Thus, in setting aspiration levels and comparing them with actual performance, decision makers are seeking clear signals about how they are doing. The value of attainment discrepancy is hypothesized to determine the level of aspiration set in the future, the level of risk taken in taking certain actions, and the extent of innovation in search activity. Attainment
Discrepancy
and Riskiness
of Choice
Several organization and cognitive psychology models have used aspiration levels as a key element. However, there has been little explicit use of the concept of attainment discrepancy, and little empirical work on setting aspirations and the
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effect of attainment discrepancy on behavior. In the cognitive psychology literature relating performance and risk taking, Payne, Laughunn, and Crum [29], Fishburn [9], and Tversky and Kahneman [40] all suggest that aspiration levels, often conceptualized as a reference point or target return, serve as a cognitive frame of reference for decision makers, and thus should be incorporated as a concept in models of choice behavior. These researchers have found that individuals, rather than being uniformly risk averse or risk seeking, exhibit varying risk-taking behavior depending on their perceived performance position relative to some aspiration level. Kahneman and Tversky’s [12] prospect theory makes predictions about individual valuations of risky prospects. In simple terms, their theory states that, in contrast to the assumptions of expected utility theory, the carriers of value or utility are changes in wealth rather than final asset positions. The decision maker first simplifies the choice situation by coding the situation as either above or below the reference point. Then the decision maker determines how much he or she stands to lose or gain, relative to the reference point. One of prospect theory’s predictions is that decision makers will exhibit risk-averse choices when faced with outcomes above their reference point, and risk-seeking choices when faced with outcomes below the reference point. Several studies in the organization literature have also found that aspiration levels mediate the relationship between performance and risk. Consistent with a general prediction from prospect theory, Bowman [2] found that high average profit firms had lower risk over time. In a separate study [3], he found that individuals in a loss situation made more risk-seeking choices, and that firm performance was negatively related to the riskiness of decisions. In an early study of illegal business activity, Lane [15] found that poor performing firms were more likely to violate government regulations than other firms. Staw and Szwajkowski [36] found that firms cited for trade violations had significantly worse performance records than firms that were not cited. To the extent that illegal acts can be classified as highrisk activities, these findings support the predictions of organizational theory regarding performance and risk taking. A common limitation, however, of the empirical work on the relationships between performance, aspirations, and risk taking is that aspiration levels have been assumed rather than measured directly. Also, in the organizational studies, risk tends to be measured post hoc. Research is needed in this area that measures aspirations and perceptions of risk as reported directly by decision makers. This study seeks to fill this gap. The effect of context on risk taking in organizations is further explicated by March [21) in a model of variable risk preferences where aspirations adapt over time, and these changing aspirations form the context against which organizational decision makers make choices. March suggests that risk behavior varies depending on this contextual evaluation. An intelligent response to this contextual evaluation is to vary one’s risk taking in the following way. An organization performing well wants to maintain its position, and thus will shun risky choices. However, an organization performing poorly will only have an opportunity to improve its position by taking some risks. Thus, the tendency to prefer risky alternatives when performance is poor and nonrisky alternatives when performance is good is shown to result in a lower probability of ruin than when risk preference remains stable across
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situations. This model also shows that when aspirations adapt over time, an organization with variable risk behavior will yield a higher average return than one with fixed risk behavior. Attainment
Discrepancy
and Innovativeness
of Search
Just as the desire for change, and thus the willingness to take risks, is evoked when performance is below a satisfactory level, search for substantial changes in current activities is likely to be evoked when performance associated with the status quo is unsatisfactory. Simon [3.5] introduced the idea that outcomes are coded by decision makers as either satisfactory or unsatisfactory, and that search for additional alternatives is a response to unsatisfactory outcomes. The original theory of satisficing implied that organizations would innovate when in trouble [23]. March and Simon [25] suggest that one implication of satisficing behavior is that no action or change is required as long as a satisfactory level of performance is met, where “no action” is defined as continuity of current activities, not a lack of activity. Cyert and March [6] described two types of search that go on in organizations. Problemistic search was defined as search for small refinements in current activities and more efficient activities. Alternatives are searched for that are similar to what the organization is already doing. This type of activity can be seen as consistent with March and Simon’s notion of continuity. Innovative search, on the other hand, was defined as search for new technologies such as new products, new processes, new goals, and so forth. It is activity directed at finding alternatives that are different from what the organization is already doing. Thus, this type of search is consistent with change. Given that refinement search is consistent with continuity, and innovative search is consistent with change, what would we expect to be the relationships between refinement, innovation, and attainment discrepancy? From the arguments of the satisficing literature, we would predict a positive relationship between attainment discrepancy and refinement, and a negative relationship between attainment discrepancy and innovation. Research
Hypotheses
While the concept of aspirations has played an important role in the organization literature, it still remains to be seen empirically if it is actually an important variable to organizational decision makers. That is, do they routinely set aspiration levels? Do they remember the targets they have set? Do they compare actual performance to these targets? Do they adjust their aspirations based on experience? Do they adjust their behavior based on the comparison of performance to aspiration level? The following three models will investigate these issues empirically. The first model addresses the question of whether decision makers behave as though they remember their aspirations and compare them to actual performance, and if they adjust their aspirations on the basis of this comparison. The second model addresses the question of whether the amount of risk taken in certain strategic decisions is affected by the comparison of performance to aspiration. The third model addresses the question of whether the extent of innovative and problemistic search is affected by this comparison.
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Table 1. Research
Hypotheses Model l-The
Adaptation
of Aspiration
Levels
H1.l Aspiration level in the previous time period will have a positive effect on aspiration level in the current period. (q > 0) H1.2 Attainment discrepancy will have a positive effect on aspiration level (e,* > 0) Model 2-Riskiness
of Choice
H2.1 Attainment discrepancy will have a negative effect on risk taking. (PI < 0) H2.2 Prior risk taking will have a positive effect on current risk taking. (& > 0) Model 3-Innovativeness
of Search
H3.1 Attainment discrepancy will have a negative effect on innovativeness of search (7, < 0) H3.2 Proportion of successfully completed R & D projects will have a negative effect on the current innovativeness of search. (rz < 0) H3.3 Amount of resources available will have a positive effect on innovativeness of search. (y? > 0) H3.4 Innovativeness of search in the previous period will be positively associated with innovative search in the current period. (y4 > 0)
Model 2. This model tests the prediction that decision makers will adapt their aspiration levels on the basis of their past aspiration level and their past performance relative to this aspiration level. That is, Y;, = a 0 + cx,Yir-, + cQXir-, + Eir = (Y()+ a,Y,,-, + %[Zit - 1 - yir-11 + c-1 (1) where Y, = aspiration level for goal i in period t. Xi,-, = Zi,-I - Yi,-, = attainment discrepancy for goal i in period t - 1. Zi,- , = actual performance on goal i in period t - 1. l i, = residual error. This is a more general model than the Levinthal and March [19] model that views aspiration level as an exponentially-weighted moving average of past performance. Model 1 is equivalent to the Levinthal and March model when CL”= 0 and (Y, = 1.’ The hypotheses associated with Model 1 (as well as Models 2 and 3) are summarized in Table 1. Cyert and March [6] and Levinthal and March [19] suggest that aspirations adjust to changes in performance, but at a slower rate than changes in performance. Thus, there is some inertia in the adjustment of aspiration levels. Decision makers anchor on their prior target as a starting point from which to make adjustments, and are unlikely to make a full adjustment in a single time period to the level of performance achieved. The phenomenon of inadequate adjustment, or anchoring, to a past behavior or judgement has been well documented by the work of Tversky and Kahneman [39] and Nisbett and Ross [26]. A positive relationship between prior aspiration level and current aspiration level would indicate that decision makers do remember their past aspiration levels, and that these aspirations constrain the amount of adjustment that will be made in subsequent aspirations. Thus, Hypothesis H1.l states that (Y, > 0.
‘The Levinthal
and March
model is Y,, = [I - CQ]Y,,-,
+ u2Z,,-,
that is equivalent
to (1) under the stated conditions.
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The literature on aspirations also suggests that decision makers adapt their aspirations to performance. This is modeled by suggesting that decision makers make a mental calculation of the discrepancy between their performance and the aspiration level they had set. They will adapt their aspirations in the direction of this discrepancy, and proportional to the size of the discrepancy. That is, if performance is above their aspiration level, they will adjust their aspiration upward. If performance is below their aspiration level, they will adjust their aspiration downward. The larger the discrepancy, the greater the extent of adjustment. Consequently, Hypotheses H1.2 holds that (Y* > 0. Model 2. This model predicts that decision makers will change their risk-taking behavior based on prior experience. Risk in the current period is modeled as a function of risk in the prior period, attainment discrepancy, and profit in the prior period. The specific equation is:
R;r= Po + WC-,
+ W;r--I
+ P&r-t
+ sir
(2)
where R, is the amount of risk taken in the current period, R,,+! is the amount of risk taken in the previous period, X,-, is the attainment discrepancy from the previous period, and Pi,-, is the profit achieved in the previous period.2 The crucial piece of information from a decision maker’s experience is their attainment discrepancy. Since prior theory suggests that performance below a certain target will lead decision makers to take more risks than performance above a target, negative attainment discrepancy should cause more risk-taking behavior to take place, and positive attainment discrepancy will result in less risk taking. Hence, Hypothesis H2.1 specifies p, < 0. In addition to the predicted relationship between attainment discrepancy and risk, an anchoring effect similar to the one suggested in the aspiration level model is predicted. That is, the amount of risk taken in the previous period will have a constraining effect on risk in the current period. Thus, Hypothesis H2.2 states that P3 > 0. While the major argument has been that performance relative to an aspiration level is the causal mechanism at work, it is reasonable to allow for effects of performance that are not measured relative to an aspiration level. That is, absolute performance may serve as a cue that affects future behavior. Thus, a measure of profit has been included to control for this effect. Model 3. This model proposes to test in part the relationship between the innovativeness of search and attainment discrepancy, past success, and amount of resources. Specifically,
zit = YO + Ylxit-l
+
YZDir--l
+
Y&if-
I
+
YJir-l
+
Cr
(3)
where 1, is the innovativeness of search in the current period, Iit+, is the innovativeness of search in the prior period, Xi,+, is the attainment discrepancy in the
‘This
model posits a linear relationship, but, as has been suggested by March and Shapira [24], the relationship may not be constant over all levels of performance, and may exhibit nonlinearities, with a possible reversal of effect in the extreme positive and negative regions. Possible nonlinearities in the relationship will be explored in future research.
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prior period, D,,-, is the proportion of R & D projects successfully completed in the prior period, and M,,-, is the amount of resources available in the prior period. This model assumes that problemistic search is a form of continuity and innovative search is a form of change. Following the logic of satisficing theory, attainment discrepancy is predicted to be negatively related to the innovativeness of search. That is, change is more likely when performance is below expectations than when it is above. Hypothesis H3.1 specifies y1 < 0. Attainment discrepancy is based on a measure of performance that is not directly related to search activity. It is based on a measure of performance for which decision makers are likely to set specific targets. The search activity in this study is research and development projects. It is likely that the degree of success of these projects, in terms of being successfully completed, will have an effect on the type of R & D conducted in the future. A similar satisficing effect is predicted. The greater the proportion of R & D projects successfully completed in the previous period, the less likely it is that more substantial changes in technology will be sought after immediately. Hypothesis H3.2 specifies yZ < 0. Because different types of research projects may require more resources to pursue than others, it is important to control for the amount of resources available to the organization. Since it is most likely that innovative projects cost more than problemistically oriented projects, we expect there to be a positive relationship between innovativeness and resources. Thus, H3.3 specifies y3 > 0. A lagged value of the innovativeness of search is also included as a control variable. Since it may take several periods for R & D projects to be completed, it is likely that the same projects will be pursued for multiple periods. Thus, the innovativeness of the projects pursued in the current period will be positively associated with the innovativeness of projects in the prior period. Consequently, H3.4 specifies y4 > 0. The Empirical
Setting
In order to test model predictions, it is necessary to find a decision-making setting in which similar decisions are made over multiple time periods, since the models are dynamic. It is also important to be able to track the specific decisions, actions, and performance of the decision makers. Further, the setting should be complex enough so that decisions that are made are similar to those that must be made in a real organization. The Murkstrut marketing-strategy game [16] provides such a setting. Markstrut was written as a comprehensive model of marketing dynamics that incorporated knowledge from prior marketing research and real-world experience. Its realism has led to its being adopted as a pedagogical and research tool. Discussions with managers from a variety of large, successful corporations who use Markstrut for their in-house management training revealed that they feel the game has a great deal of external validity. Murkstrut has also been used for research purposes [14, 41, 371, and specifically for research on decision making [ll]. A typical play of Murkstrut consists of five teams, each representing the marketing-profit center of an organization, who compete with each other in a single industry for up to 10 periods. The five competitors can produce and sell two types of consumer products-Sonites and Vodites. The teams are responsible for making strategic and resource-allocation decisions. Decisions are made regarding the types
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of products to market, product characteristics, advertising expenses, research and development projects, distribution channels, and size of sales force. Thus, this game offers the opportunity to observe teams of decision makers setting objectives, making strategic and resource-allocation decisions, and receiving feedback, over several periods of time. The teams are also making decisions in a complex environment. The Markstrat game is controlled by complicated algorithms that simulate a competitive market. It is a multidimensional, interdependent world that is difficult to understand by observing organizational actions and environmental responses. Operational
Definitions
Data were Management sources were the computer periods, and
gathered from a Markstrut industry comprised of five teams of Sloan Fellows at the Stanford Graduate School of Business. Three data used: the actual decision forms filled out by the Markstrat players, generated results, and questionnaires3 The teams played for seven data were collected for each period.
Model I. The variables for Model 1 were measured as follows. Aspiration levels were measured using self-reports of unit sales goals from each team regarding each brand they were marketing in each period. These goals are reported by the teams in the questionnaire they fill out each week. In the course of planning their strategies, most Markstrut teams will set performance objectives. The questionnaires were used to insure that a systematic record of these objectives was kept. The actual performance achieved was measured as the corresponding unit sales associated with each brand, and was collected from the computer-generated results each week. Attainment discrepancy was then calculated simply as performance minus aspiration. The unit of analysis for Model 1 is at the brand level because the causal mechanisms are posited to work primarily at this level. The teams make their decisions specific to each individual brand they are marketing. They are trying to position each brand in a certain market segment, and are making brand-specific decisions in their attempt to do so. They set their performance targets at the brand level, and watch performance at the brand level. Thus, it is important to tap the decision-making process at the brand level. Model 2. For Model 2, the dependent variable, riskiness of choice, was calculated from information gathered by the weekly questionnaire and from the decision forms. This model is concerned specifically with choices regarding brand marketing. A number of strategic actions (i) can be taken toward each brand ci>being marketed by each team in each period. The actions of interest are: 1) do nothing to change the brand from last period, 2) reposition the brand through advertising, 3) change the physical characteristics of an existing brand, 4) change the retail price of a brand, 5) introduce a new brand of Sonites, 6) introduce a new brand of Vodites,
‘All data are at the team level. Decisions filled out by the team.
are made as a team, results are given for the team, and questionnaires
are
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7) withdraw a brand of Sonites, 8) withdraw a brand of Vodites. Each of these variables are coded 1 if the action was taken, 0 if it was not. During each period when these decisions were made, each team reported the degree of risk they felt was associated with each of these actions. For each of the eight actions of interest, risk was subjectively scaled on a 1 to 10 scale anchored by very low and very high variance or uncertainty. These subjective perceptions of risk are combined with actual actions taken to determine the degree of risk taken in brand management. An index is created, whereby the value of each action variable (0,l) is multiplied by the corresponding risk rating. These values are then summed for each brand to yield a measure of the riskiness of activity associated with each brand. In summary: Risk, = CSi= l[[SubjRisk,][Actionjj]]. For each brand marketed, a performance measure called gross marketing contribution is computed, which is the revenues generated less the costs associated with the brand. Thus, it is a measure of gross profit. It is used conceptually in the model as an “objective” measure of success for each brand, as opposed to the subjective measure of attainment discrepancy. The attainment discrepancy measure is the same as that used in model one. Model 3. The search process being investigated is research and development. The innovativeness of R & D projects is measured by a self-report by each team in each period. They categorized their R & D projects as either pursuing cost reduction, brand modification, or new-product research. New-product research is defined as more innovative than brand modification, which, in turn, is more innovative than cost reduction.4 Cost reduction is coded as 0, modification as 1, and introduction as 2. The average level of innovation across R & D projects (i) pursued is computed for each team in each period as Avglnnov = [C4i=, LevelZnnov,]l Number of Projects. This calculation comprises the measure of innovativeness of search. The past success of research and development is computed as the proportion of R & D projects successfully completed by a team in a given period. The proportion is simply the number of successful projects divided by the total number of projects attempted in that period. This information is collected from the computer-generated results. The amount of available resources is computed as the budget granted each team plus any exceptional budget items. It represents the amount of resources available to a team that they can use in resource-allocation decisions. Attainment discrepancy is basically the same measure used in the first two models, except that in this model it is the average attainment discrepancy across all brands being marketed by a team in a given period.
Results The results for the three models are presented in Table 2. Use of the residual plots suggested by Draper and Smith [7] indicated that heteroskedacticity was not an apparent problem. However, both Model 1 and Model 2 indicated positively au-
?his
categorization
corresponds
well to team reports
of what constitutes
innovative
R & D.
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Table 2. Results Model l-Aspiration Explanatory
Variable
Standard Error
T Statistic
Beta Weight
31140.6 1.0987 1.2571
13282.1 .0502 ,093s
2.345 21.881 13.439
.8653 5288
o.,,Constant (Y, Aspiration level,-, CQAttainment discrepancy,- I Estimate of serial correlation N=105 Adjusted R’ = .8389 F statistic (2,102) = 271.73 Generalized
= ,300 p < .OOl
Least Squares Model 2-Riskiness
Explanatory p,, Constant f3, Attainment BZ Profit,- I
Levels
Estimated Coefficient
Variable
of Choice
Estimated Coefficient
Standard Error
T Statistic
Beta Weight
7.3745 -.000012 .0000087 .232218
1.3504 .000005 .000026 .090952
5.4609 -2.3436 .3398 2.5532
-. 1984 .0289 .2524
discrepancy,-,
Pi Risk,- I Estimate of serial correlation = ,384 N= 101 Adjusted R’ = .0822 F statistic (3,97) = 3.986 p < .Ol Generalized Least Squares
Model 3-Innovativeness Explanatory
Variable
y,, Constant y, Attainment discrepancy,+ r y2 R & D success,+, yi Resources,- I y4 Innovativeness,- I N=30 Durbin-Watson = 1.99 Adjusted R2 = .4266 F statistic (4,25) = 6.394 Ordinary
of Search
Estimated Coefficient
Standard Error
T Statistic
Beta Weight
1.10082 -.0000036 .7.54792 .000000006 .323343
.270742 .0000018 .235521 .000000008 .138220
4.0659 -2.0310 -3.2048 -.7475 2.3393
-.2956 -.4546 -.1081 .3325
p < ,001
Least Squares
tocorrelated residual$ consequently, these models were estimated using CochraneOrcutt Iterative Generalized Least Squares. Model 3 was estimated by OLS. In all cases, the differences in the parameter estimates were very small between OLS and GLS, and the conclusions from the hypothesis tests were identical.
‘The OLS estimates of Models 1 and 2 were tested for autocorrelation by comparison to d,, which Kenkel [13] has shown to bc quite accurate for models with lagged endogenous variables. Significance levels were determined from the extended
tables of Savin and White
[33].
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Model 1. This model provides an excellent fit to the data with the coefficients
of both variables highly significant in the predicted direction. Consequently, the results are consistent with Model 1, and hypotheses 1.1 and 1.2. Further, the simpler exponential smoothing model of Levinthal and March [19] is not consistent with the data since (Y()# 0 and (Y, # 1 at about the .05 level. While this model fits the data well, it is important to consider that there may exist alternative models that better describe the true relationships at work. In order to test this idea, the model was compared to three other models that could be considered given past research on aspirations. The first alternative suggests that aspiration level is simply a function of past aspiration levels. As could have been guessed from the results of Model 1, past aspiration levels do go a long way in explaining current aspiration levels. However, the results of testing the first alternative suggest, not surprisingly, that a model that contains attainment discrepancy explains the behavior of aspiration levels much better.‘j The second alternative model suggests that the relationship between attainment discrepancy and aspirations is not linear. However, attainment discrepancy squared is not significant when added to the model. Finally, it is plausible that the causal relationship between aspiration level and attainment discrepancy is not a one-period lag. Again, attainment discrepancy lagged for two periods is not significant when added to the model. Model 2. This model provides a significant fit to the data, albeit a much weaker one than for Model 1. The coefficients for attainment discrepancy (p,) and lagged risk (p3) are both significant in the directions predicted by the hypothesis in Table 1. Thus, hyptheses 2.1 and 2.2 are found to be consistent with the data. The profit covariate does not have a significant effect on the riskiness of choice. As in Model 1, attainment discrepancy adds significant explanatory power to a model containing lagged risk only. Further, the linear form of attainment discrepancy seems to be preferable to a quadratic term in this variable. Model 3. This model fits the data well. The coefficients of attainment discrepancy and R & D success are both significant in the predicted negative direction. The lagged coefficient of innovativeness of search is also significant in the predicted positive direction. Resources available did not turn out to be significantly related to innovativeness of search, and had the wrong sign. Consequently, hypothesis 3.1, 3.2, and 3.4 were confirmed, while hypothesis 3.3 did not receive support. Again, specification checks indicate that attainment discrepancy adds significant explanatory power to an autoregressive model in innovativeness of search, and a quadratic term in attainment discrepancy does not enhance the model.
Conclusions The results of Model 1 strongly support the prediction that aspiration levels are a linear function of past aspiration levels and the discrepancy between prior aspiration
%sing GLS, the adjusted A389 when attainment
R2 for the model using lagged aspirations only was S799 versus an overall discrepancy is added to the model. The result is highly significant.
adjusted
R* of
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and actual performance. Further, they suggest that the effect is summarized in a single-period lag. This study is perhaps the first to empirically test this model of the behavior of aspirations, using aspiration levels directly reported by the decision makers using them. Not only are aspirations shown to track the movement of actual performance, but the discrepancy between aspiration and performance is shown to be a distinct construct that can be used to predict the movement of aspiration levels. Model 2 offers substantial support for the prediction that risk taking is a function of past risk and attainment discrepancy. Decision makers do seem to anchor on their past position of risk taking as would be predicted by the cognitive social psychology literature. They also exhibit an effect consistent with some of the predictions of Kahneman and Tversky [12] and March [21]. That is, they take more risks when performance is below aspiration level than when performance is above aspiration level. A major contribution of the second model, beside the fact that attainment discrepancy is computed relative to self-perceived aspirations, is that the amount of risk taken is based on risk as perceived by the decision makers. The causal mechanism that is posited in this model is based on the idea that decision makers are cognizant of the discrepancy between their aspirations and performance, associate different levels of risk with different actions, will take fewer perceived risks when they are satisfied with their performance, and will take more perceived risks when they are dissatisfied with performance. Thus, it is important to tap both aspirations and risk from the decision maker’s point of view. The testing of Model 3 offers support for the satisficing predictions of the model. The greater the extent of previous success in search activities, the less innovative is the next set of search activities. The more negative one’s attainment discrepancy, the more innovative one will be in subsequent search activity. These results are consistent with the notion that innovativeness of search represents a change in current activities, and will not be pursued unless current activities are unsatisfactory. The testing of the three models in this study has made significant steps toward the goal of contributing to our knowledge of decision making and learning in a complex and ambiguous setting. It has provided evidence that decision makers pay attention to their aspirations, adjust their aspirations in response to experience, and use performance relative to their aspiration level as a cue that affects the nature of their future decisions. Understanding these processes will help us in studying the question of when the learning process is adaptive and when it is not. Better knowledge of these issues will help both researchers and managers improve their understanding of the managing of organization-environment relations. References 1. Barnard, Chester I., Functions of the Executive. Harvard University Press, Cambridge, Mass., 1938. 2. Bowman, E.H., A Risk-Return Paradox for Strategic Management, Sloan Management Review 21 (1980): 17-31. 3. Bowman, Edward H., Risk Seeking by Troubled Firms, Sloan Management Review (Summer 1982):33-42. 4. Burns, Tom, and Stalker, G.M., The Management of Innovation. Tavistock, London, 1961.
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5. Cohen, Michael D., March, James G., and Olsen, Johan P., A Garbage Can Model of Organizational Choice, Administrative Science Quarterly 17 (March 1972):1-25. 6. Cyert, Richard M., and March, James G., A Behavioral Theory of the Firm. PrenticeHall, Englewood Cliffs, N.J., 1963. 7. Draper, N.R., and Smith, H., Applied Regression Analysis. Wiley, New York, 1966. 8. Dutton, Jane E., Fahey, Liam, and Narayanan, V.K., Toward Understanding Strategic Issue Diagnosis, Strategic Management Journal 4 (1983):307-323. 9. Fishburn, P.C., Mean Risk Analysis and Risk Associated with Below Target Returns, American Economic Review 67 (1977):116-126. 10. Herriott, Scott R., Levinthal, Daniel, and March, James G., Learning from Experience in Organizations, Proceedings of the American Economic Association (May 1985). 11. Hogarth, Robin M., and Makridakis, Spyros, The Value of Decision Making in a Complex Environment: An Experimental Approach, Management Science 27 (1981):93107.
12. Kahneman, Daniel, and Tversky, Amos, Prospect Theory: An Analysis of Decision Under Risk, Econometrica 47 (March 1979):263-292. 13. Kenkel, James L., Some Small Sample Properties of Durbin’s Tests for Serial Correlation in Regression Models Containing Lagged Dependent Variables, Econometrica 42 (July 1974):763-769. 14. Kinnear, Thomas C., Problems and Opportunities in Using MARKSTRAT for Experimental Research in Marketing Management Decisions. Paper presented at Summer Marketing Educator’s Conference, 1986. 15. Lane, R.E., Why Businessmen Violate the Law, Journal of Criminal Law, Criminology and Political Science 19 (July-August 1953):151-165. 16. Larreche, Jean-Claude, and Gatignon, Hubert, Markstrat: A Marketing Strategy Game, The Scientific Press, Palo Alto, Calif., 1977. 17. Lawler, Edward E., Secrecy about Management Compensation: Are There Hidden Costs?, Organizational Behavior and Human Performance 2 (1967):182-189. 18. Lawrence, Paul R., and Lorsh, Jay W., Organization and Environment. Graduate School of Business Administration, Harvard University, Boston, 1967. 19. Levinthal, Daniel, and March, James G., A Model of Adaptive Organizational Search, Journal of Economic Behavior and Organization 2 (May 1981):307-333. 20. Lewin, Kurt, Dembo, Tamara, Festinger, Leon, and Sears, Pauline Snedden, Level of Aspiration, Personality and the Behavior Disorders, J. McV. Hunt, ed., The Ronald Press Company, New York, 1944. 21. March, James G., Variable Risk Preferences, Succeeding, and Surviving. Unpublished, Stanford University, September 1985. 22. March, James G., and Olsen, Johan P., Ambiguity and Choice in Organizations. Universitetsforlaget, Bergen, 1976. 23. March, James G., and Shapira, Zur, Behavioral Decision Theory and Organization Decision Theory, in Decision Making: An Interdisciplinary Inquiry. Gerard0 R. Ungson and Daniel N. Braunstein, eds., Kent Publishing, Boston, 1982. 24. March, James G., and Shapira, Zur, Managerial Perspectives on Risk and Risk Taking. Unpublished, Stanford University and Hebrew University, October 1986. 25. March, James G., and Simon, Herbert, Organizations. Wiley, New York, 1958. 26. Nisbett, T., Richard, and Ross, Lee, Human Inference: Strategies and Shortcomings of Social Judgment. Prentice-Hall, Englewood Cliffs, N. J., 1980. 27. Olshavsky, Richard W., Task Complexity and Contingent Processing in Decision Making: A Replication and Extension, Organizational Behavior and Human Performance 24 (1979):300-316. 28. Payne, John W., Task Complexity and Contingent Processing in Decision Making: An
Learning from Strategic Successand Failure Information
30. 31. 32. 33.
34. 35. 36.
37. 38. 39. 40. 41. 42.
517
Search and Protocol Analysis, Organizational Behavior and Human 16 (1976):366-387. Payne, John W., Laughhunn, Dan J., and Crum, Roy, Translation of Gambles and Aspiration Level Effects on Risky Choice Behavior, Management Science 26 (1980): 1039-1060. Peters, Lawrence H., Cognitive Models of Motivation, Expectancy Theory and Effort: An Analysis and Empirical Test, Organizational Behavior and Human Performance 20 (1977):~~. 129-148. Pfeffer, Jeffrey, and Salancik, Gerald R., The External Control of Organizations: A Resource Dependency Perspective. Harper & Row, New York, 1978. Russo, Edward J., and Dosher, Barbara Anne, Strategies for Multiattribute Binary Choice, Journal of Experimental Psychology: Learning, Memory, and Cognition 9 (1983):676-696. Savin, N.E., and White, K.J., The Durbin-Watson Test for Serial Correlation with Extreme Sample Sizes or Many Regressors, Econometrica 45 (November 1977):19891996. Siegel, S., Level of Aspiration and Decision Making, Psychology Review 64 (1957):253262. Simon, Herbert A., A Behavioral Model of Rational Choice, in Models of Man. Herbert A. Simon, ed., Wiley, New York, 1957. Staw, Barry M., and Szwajkowski, Eugene, The Scarcity-Munificence Component of Organizational Environments and the Commission of Illegal Acts, Administrative Science Quarterly 20 (September 1975):345-354. Strong, Edward C., and Nolan, Johannah J., Any Number Can Play: A Cross-Sectional View of Marketing Strategies. Paper presented at Summer Marketing Educator’s Conference, 1986. Thompson, James D., and Tuden, Arthur, Strategies, Structures, and Processes of Organizational Decision, in Comparative Studies in Administration. James D. Thompson, ed., University of Pittsburgh Press, 1959. Tversky, Amos, and Kahneman, Daniel, Judgment Under Uncertainty: Heuristics and Biases, Science 185 (1974):1124-1131. Tversky, Amos, and Kahneman, Daniel, The Framing of Decisions and the Psychology of Choice, Science 211 (1981). Utsey, Marjorie F., Is Marketing A Martingale Process? Paper presented at Summer Marketing Educator’s Conference, 1986. Weick, Karl E., The Social Psychology of Organizing. Addison-Wesley, Reading, 1979.
Performance
29.
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Learning from Strategic Success and Failure Theresa K. Lant
David B. Montgomery
New
Stanford
York
University
University
Organizations facing complex, ambiguous, and dynamic environments find adaptive learning a key to survival and success. This study proposes three models of organization response in such environments: 1) A model of how aspiration levels or goals adapt over time, 2) a model of the riskiness of strategic choices made, and 3) a model of the innovativeness of search activities (R & D). In each model, the difference between performance and aspiration level is posited to be an important explanatory variable. Using the Markstrat game as a research environment, the data are consistent with all three models. Introduction Learning to mediate and adapt to the environment are important determinants of organization health and even survival. Such learning occurs largely through organizational interaction with and observation of its environments. However, adaptive learning is difficult in the usual case where the relationship between an organization and its environment is complex, ambiguous, and dynamic. Thus, it is important for organizational theories of adaptation to address the question of how organizations learn in such situations. The literature on organizational adaptation has typically addressed either of two questions. First, what type of decisions and behavior lead to adaptation? Second, how are such decisions made? The former question has been addressed, for example, by structural-contingencies theory [4,18] and resource-dependence theory [31]. The latter has been addressed in the administrative theories of Barnard [l], Simon [35], and the organizational theories of March and Simon [25] and Cyert and March [6], and more recently by decision-process research, such as Weick [42], Cohen, March, and Olsen [5], and Dutton, Fahey, and Narayanan [8]. The present research addresses the latter issue by investigating the learning process that leads to decisions regarding strategic organizational adaptation. Specifically, we are concerned with how organizational goals and success in achieving these goals affect
Address correspondence Stanford, CA 94305-5015.
to Theresa
Journal of Business Research 15, 503-517 (1987) 0 1987 Elsevier Science Publishing Co., Inc. 1987 52 Vanderbilt Ave., New York, NY 10017
K. Lant,
Graduate
School
of Business,
Stanford
University,
014%2963/871$3.50
504
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the setting of future goals, the riskiness of choices, and innovativeness of search activities. The literatures that have delved into these issues include models of individual and organizational learning and adaptation, models of strategic decision making, cognitive and behavioral theories of individual decision making, and the work on human inference and attribution. The thread that ties these literatures together is evident in the typical situation facing a group of strategic decision makers. These decision makers take certain actions, monitor the activities of their organization and their environment, and then make future decisions concerning future organizational activities. It can generally be agreed that these individuals have the capability of learning from experience. Further, they enlist this ability when trying to adapt to their environments [6]. However, the relationships between individual, organizational, and environmental actions are often complex, ambiguous, and change rapidly [22]. Further, decision makers are subject to cognitive limitations, such as bounded rationality. This combination of factors creates barriers to effective, adaptive learning. How, then, do decision makers manage under these circumstances? This issue is investigated by studying the decisions of several groups of players of the Markstrat marketing strategy game. Mark&-at provides a complex and dynamic decision-making setting in which to study decision makers. The R ole of Aspirations
in Organizational
Learning
March and Simon [25] and Cyert and March [6] have suggested several mechanisms by which organizations can make decisions despite the complex situations that face them. March and Simon [25] have suggested that organizations set multiple operational goals rather than a general, nonoperational goal. Setting specific goals enables the organization to make concrete performance evaluations, which provide a basis for future action. Cyert and March [6] model how organizational decision makers make basic operating and strategic decisions. Organizations face substantial complexity generated by the demands of multiple coalitions and interest groups that make up the organization and its environment. Response to this complexity involves attending to multiple goals and attempting to satisfice on each. The present study explores whether goals, or aspiration levels, are attended to by decision makers, and if so, how performance relative to these aspirations affects the type of actions taken in trying to manage the organization-environment relationship. The aspiration levels of concern here are goals held by the dominant coalition regarding certain organizational outcomes, such as sales and market share. While early work on aspirations focused on individual behavior [20,34], later work has applied models of aspirations to the organizational domain [25,6]. Before discussing the possible effects of aspiration levels on behavior, it is important to understand the significance of aspiration levels alone to decision making. Specific attention to modeling organizational aspirations has been taken by Levinthal and March [19]. They developed a model of organizational learning and adaptation in which organizations set performance aspirations, compare actual performance to these aspirations, and modify future aspirations based on this comparison. Herriot, Levinthal, and March [lo] extended this model to include organizational responses to other organizations in the environment. They added factors such as the diffusion
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of experience and the diffusion of aspirations across organizations. Thus, they modeled aspiration level as a function of the past performance of other organizations as well as the past performance of the focal organization. The difference between the aspiration level for a period and the actual performance level is called attainment discrepancy [20]. Attainment discrepancy is calculated by subtracting aspiration level from actual performance achieved. The evaluation of new performance in light of one’s attainment discrepancy generates a feeling of success or failure. Attainment discrepancy is used as a cue that determines the next aspiration level and affects other behavior. Thus, attainment discrepancy serves as a crucial piece of information that decision makers look for in assessing the organization-environment relationship, and that colors their perceptions of possibilities for action. One key to the importance of attainment discrepancy is the degree of complexity and ambiguity that faces most decision makers. Concrete operational goals are set in organizations because they provide a concrete link to specific actions. A vague, nonoperational goal, such as maximizing long-term profit, provides little information for taking specific actions. The relationship between possible actions and this desired, but vague, outcome is ambiguous. A more concrete goal, about which one can receive concrete feedback, provides more information about which actions will lead to this goal, thus enhancing learning in this situation. Levinthal and March [19] found that organizations generally learned adaptively when there were strong, unambiguous signals. At the individual level, expectancy theory links environmental conditions, individual beliefs, and individual behavior. This research found that beneficial behavior was more likely when the links between components were clear [30,17]. Thompson and Tuden [38] classified organizational decision processes according to the certainty of cause-effect relations and the clarity of preferences. When there are clear preferences for goals, and the cause-effect relationship between means and goals is known, decisions can be made by computation. By setting concrete, agreed-upon goals, and gathering performance information, decision makers attempt to mold their ambiguous situation into one that approximates clear goal preferences and clear cause and effect relationships. There is substantial evidence that decision makers seek to simplify the decision situation facing them. For example, Payne [28] and Olshavsky [27] f ound that while simple tasks were evaluated holistically, more complex tasks were simplified by the use of decision rules that quickly eliminated large numbers of alternatives. Russo and Dosher [32] found that, even for simple tasks, subjects dimensionalized the task rather than examining it holistically. Thus, in setting aspiration levels and comparing them with actual performance, decision makers are seeking clear signals about how they are doing. The value of attainment discrepancy is hypothesized to determine the level of aspiration set in the future, the level of risk taken in taking certain actions, and the extent of innovation in search activity. Attainment
Discrepancy
and Riskiness
of Choice
Several organization and cognitive psychology models have used aspiration levels as a key element. However, there has been little explicit use of the concept of attainment discrepancy, and little empirical work on setting aspirations and the
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effect of attainment discrepancy on behavior. In the cognitive psychology literature relating performance and risk taking, Payne, Laughunn, and Crum [29], Fishburn [9], and Tversky and Kahneman [40] all suggest that aspiration levels, often conceptualized as a reference point or target return, serve as a cognitive frame of reference for decision makers, and thus should be incorporated as a concept in models of choice behavior. These researchers have found that individuals, rather than being uniformly risk averse or risk seeking, exhibit varying risk-taking behavior depending on their perceived performance position relative to some aspiration level. Kahneman and Tversky’s [12] prospect theory makes predictions about individual valuations of risky prospects. In simple terms, their theory states that, in contrast to the assumptions of expected utility theory, the carriers of value or utility are changes in wealth rather than final asset positions. The decision maker first simplifies the choice situation by coding the situation as either above or below the reference point. Then the decision maker determines how much he or she stands to lose or gain, relative to the reference point. One of prospect theory’s predictions is that decision makers will exhibit risk-averse choices when faced with outcomes above their reference point, and risk-seeking choices when faced with outcomes below the reference point. Several studies in the organization literature have also found that aspiration levels mediate the relationship between performance and risk. Consistent with a general prediction from prospect theory, Bowman [2] found that high average profit firms had lower risk over time. In a separate study [3], he found that individuals in a loss situation made more risk-seeking choices, and that firm performance was negatively related to the riskiness of decisions. In an early study of illegal business activity, Lane [15] found that poor performing firms were more likely to violate government regulations than other firms. Staw and Szwajkowski [36] found that firms cited for trade violations had significantly worse performance records than firms that were not cited. To the extent that illegal acts can be classified as highrisk activities, these findings support the predictions of organizational theory regarding performance and risk taking. A common limitation, however, of the empirical work on the relationships between performance, aspirations, and risk taking is that aspiration levels have been assumed rather than measured directly. Also, in the organizational studies, risk tends to be measured post hoc. Research is needed in this area that measures aspirations and perceptions of risk as reported directly by decision makers. This study seeks to fill this gap. The effect of context on risk taking in organizations is further explicated by March [21) in a model of variable risk preferences where aspirations adapt over time, and these changing aspirations form the context against which organizational decision makers make choices. March suggests that risk behavior varies depending on this contextual evaluation. An intelligent response to this contextual evaluation is to vary one’s risk taking in the following way. An organization performing well wants to maintain its position, and thus will shun risky choices. However, an organization performing poorly will only have an opportunity to improve its position by taking some risks. Thus, the tendency to prefer risky alternatives when performance is poor and nonrisky alternatives when performance is good is shown to result in a lower probability of ruin than when risk preference remains stable across
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situations. This model also shows that when aspirations adapt over time, an organization with variable risk behavior will yield a higher average return than one with fixed risk behavior. Attainment
Discrepancy
and Innovativeness
of Search
Just as the desire for change, and thus the willingness to take risks, is evoked when performance is below a satisfactory level, search for substantial changes in current activities is likely to be evoked when performance associated with the status quo is unsatisfactory. Simon [3.5] introduced the idea that outcomes are coded by decision makers as either satisfactory or unsatisfactory, and that search for additional alternatives is a response to unsatisfactory outcomes. The original theory of satisficing implied that organizations would innovate when in trouble [23]. March and Simon [25] suggest that one implication of satisficing behavior is that no action or change is required as long as a satisfactory level of performance is met, where “no action” is defined as continuity of current activities, not a lack of activity. Cyert and March [6] described two types of search that go on in organizations. Problemistic search was defined as search for small refinements in current activities and more efficient activities. Alternatives are searched for that are similar to what the organization is already doing. This type of activity can be seen as consistent with March and Simon’s notion of continuity. Innovative search, on the other hand, was defined as search for new technologies such as new products, new processes, new goals, and so forth. It is activity directed at finding alternatives that are different from what the organization is already doing. Thus, this type of search is consistent with change. Given that refinement search is consistent with continuity, and innovative search is consistent with change, what would we expect to be the relationships between refinement, innovation, and attainment discrepancy? From the arguments of the satisficing literature, we would predict a positive relationship between attainment discrepancy and refinement, and a negative relationship between attainment discrepancy and innovation. Research
Hypotheses
While the concept of aspirations has played an important role in the organization literature, it still remains to be seen empirically if it is actually an important variable to organizational decision makers. That is, do they routinely set aspiration levels? Do they remember the targets they have set? Do they compare actual performance to these targets? Do they adjust their aspirations based on experience? Do they adjust their behavior based on the comparison of performance to aspiration level? The following three models will investigate these issues empirically. The first model addresses the question of whether decision makers behave as though they remember their aspirations and compare them to actual performance, and if they adjust their aspirations on the basis of this comparison. The second model addresses the question of whether the amount of risk taken in certain strategic decisions is affected by the comparison of performance to aspiration. The third model addresses the question of whether the extent of innovative and problemistic search is affected by this comparison.
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Table 1. Research
Hypotheses Model l-The
Adaptation
of Aspiration
Levels
H1.l Aspiration level in the previous time period will have a positive effect on aspiration level in the current period. (q > 0) H1.2 Attainment discrepancy will have a positive effect on aspiration level (e,* > 0) Model 2-Riskiness
of Choice
H2.1 Attainment discrepancy will have a negative effect on risk taking. (PI < 0) H2.2 Prior risk taking will have a positive effect on current risk taking. (& > 0) Model 3-Innovativeness
of Search
H3.1 Attainment discrepancy will have a negative effect on innovativeness of search (7, < 0) H3.2 Proportion of successfully completed R & D projects will have a negative effect on the current innovativeness of search. (rz < 0) H3.3 Amount of resources available will have a positive effect on innovativeness of search. (y? > 0) H3.4 Innovativeness of search in the previous period will be positively associated with innovative search in the current period. (y4 > 0)
Model 2. This model tests the prediction that decision makers will adapt their aspiration levels on the basis of their past aspiration level and their past performance relative to this aspiration level. That is, Y;, = a 0 + cx,Yir-, + cQXir-, + Eir = (Y()+ a,Y,,-, + %[Zit - 1 - yir-11 + c-1 (1) where Y, = aspiration level for goal i in period t. Xi,-, = Zi,-I - Yi,-, = attainment discrepancy for goal i in period t - 1. Zi,- , = actual performance on goal i in period t - 1. l i, = residual error. This is a more general model than the Levinthal and March [19] model that views aspiration level as an exponentially-weighted moving average of past performance. Model 1 is equivalent to the Levinthal and March model when CL”= 0 and (Y, = 1.’ The hypotheses associated with Model 1 (as well as Models 2 and 3) are summarized in Table 1. Cyert and March [6] and Levinthal and March [19] suggest that aspirations adjust to changes in performance, but at a slower rate than changes in performance. Thus, there is some inertia in the adjustment of aspiration levels. Decision makers anchor on their prior target as a starting point from which to make adjustments, and are unlikely to make a full adjustment in a single time period to the level of performance achieved. The phenomenon of inadequate adjustment, or anchoring, to a past behavior or judgement has been well documented by the work of Tversky and Kahneman [39] and Nisbett and Ross [26]. A positive relationship between prior aspiration level and current aspiration level would indicate that decision makers do remember their past aspiration levels, and that these aspirations constrain the amount of adjustment that will be made in subsequent aspirations. Thus, Hypothesis H1.l states that (Y, > 0.
‘The Levinthal
and March
model is Y,, = [I - CQ]Y,,-,
+ u2Z,,-,
that is equivalent
to (1) under the stated conditions.
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The literature on aspirations also suggests that decision makers adapt their aspirations to performance. This is modeled by suggesting that decision makers make a mental calculation of the discrepancy between their performance and the aspiration level they had set. They will adapt their aspirations in the direction of this discrepancy, and proportional to the size of the discrepancy. That is, if performance is above their aspiration level, they will adjust their aspiration upward. If performance is below their aspiration level, they will adjust their aspiration downward. The larger the discrepancy, the greater the extent of adjustment. Consequently, Hypotheses H1.2 holds that (Y* > 0. Model 2. This model predicts that decision makers will change their risk-taking behavior based on prior experience. Risk in the current period is modeled as a function of risk in the prior period, attainment discrepancy, and profit in the prior period. The specific equation is:
R;r= Po + WC-,
+ W;r--I
+ P&r-t
+ sir
(2)
where R, is the amount of risk taken in the current period, R,,+! is the amount of risk taken in the previous period, X,-, is the attainment discrepancy from the previous period, and Pi,-, is the profit achieved in the previous period.2 The crucial piece of information from a decision maker’s experience is their attainment discrepancy. Since prior theory suggests that performance below a certain target will lead decision makers to take more risks than performance above a target, negative attainment discrepancy should cause more risk-taking behavior to take place, and positive attainment discrepancy will result in less risk taking. Hence, Hypothesis H2.1 specifies p, < 0. In addition to the predicted relationship between attainment discrepancy and risk, an anchoring effect similar to the one suggested in the aspiration level model is predicted. That is, the amount of risk taken in the previous period will have a constraining effect on risk in the current period. Thus, Hypothesis H2.2 states that P3 > 0. While the major argument has been that performance relative to an aspiration level is the causal mechanism at work, it is reasonable to allow for effects of performance that are not measured relative to an aspiration level. That is, absolute performance may serve as a cue that affects future behavior. Thus, a measure of profit has been included to control for this effect. Model 3. This model proposes to test in part the relationship between the innovativeness of search and attainment discrepancy, past success, and amount of resources. Specifically,
zit = YO + Ylxit-l
+
YZDir--l
+
Y&if-
I
+
YJir-l
+
Cr
(3)
where 1, is the innovativeness of search in the current period, Iit+, is the innovativeness of search in the prior period, Xi,+, is the attainment discrepancy in the
‘This
model posits a linear relationship, but, as has been suggested by March and Shapira [24], the relationship may not be constant over all levels of performance, and may exhibit nonlinearities, with a possible reversal of effect in the extreme positive and negative regions. Possible nonlinearities in the relationship will be explored in future research.
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prior period, D,,-, is the proportion of R & D projects successfully completed in the prior period, and M,,-, is the amount of resources available in the prior period. This model assumes that problemistic search is a form of continuity and innovative search is a form of change. Following the logic of satisficing theory, attainment discrepancy is predicted to be negatively related to the innovativeness of search. That is, change is more likely when performance is below expectations than when it is above. Hypothesis H3.1 specifies y1 < 0. Attainment discrepancy is based on a measure of performance that is not directly related to search activity. It is based on a measure of performance for which decision makers are likely to set specific targets. The search activity in this study is research and development projects. It is likely that the degree of success of these projects, in terms of being successfully completed, will have an effect on the type of R & D conducted in the future. A similar satisficing effect is predicted. The greater the proportion of R & D projects successfully completed in the previous period, the less likely it is that more substantial changes in technology will be sought after immediately. Hypothesis H3.2 specifies yZ < 0. Because different types of research projects may require more resources to pursue than others, it is important to control for the amount of resources available to the organization. Since it is most likely that innovative projects cost more than problemistically oriented projects, we expect there to be a positive relationship between innovativeness and resources. Thus, H3.3 specifies y3 > 0. A lagged value of the innovativeness of search is also included as a control variable. Since it may take several periods for R & D projects to be completed, it is likely that the same projects will be pursued for multiple periods. Thus, the innovativeness of the projects pursued in the current period will be positively associated with the innovativeness of projects in the prior period. Consequently, H3.4 specifies y4 > 0. The Empirical
Setting
In order to test model predictions, it is necessary to find a decision-making setting in which similar decisions are made over multiple time periods, since the models are dynamic. It is also important to be able to track the specific decisions, actions, and performance of the decision makers. Further, the setting should be complex enough so that decisions that are made are similar to those that must be made in a real organization. The Murkstrut marketing-strategy game [16] provides such a setting. Markstrut was written as a comprehensive model of marketing dynamics that incorporated knowledge from prior marketing research and real-world experience. Its realism has led to its being adopted as a pedagogical and research tool. Discussions with managers from a variety of large, successful corporations who use Markstrut for their in-house management training revealed that they feel the game has a great deal of external validity. Murkstrut has also been used for research purposes [14, 41, 371, and specifically for research on decision making [ll]. A typical play of Murkstrut consists of five teams, each representing the marketing-profit center of an organization, who compete with each other in a single industry for up to 10 periods. The five competitors can produce and sell two types of consumer products-Sonites and Vodites. The teams are responsible for making strategic and resource-allocation decisions. Decisions are made regarding the types
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of products to market, product characteristics, advertising expenses, research and development projects, distribution channels, and size of sales force. Thus, this game offers the opportunity to observe teams of decision makers setting objectives, making strategic and resource-allocation decisions, and receiving feedback, over several periods of time. The teams are also making decisions in a complex environment. The Markstrat game is controlled by complicated algorithms that simulate a competitive market. It is a multidimensional, interdependent world that is difficult to understand by observing organizational actions and environmental responses. Operational
Definitions
Data were Management sources were the computer periods, and
gathered from a Markstrut industry comprised of five teams of Sloan Fellows at the Stanford Graduate School of Business. Three data used: the actual decision forms filled out by the Markstrat players, generated results, and questionnaires3 The teams played for seven data were collected for each period.
Model I. The variables for Model 1 were measured as follows. Aspiration levels were measured using self-reports of unit sales goals from each team regarding each brand they were marketing in each period. These goals are reported by the teams in the questionnaire they fill out each week. In the course of planning their strategies, most Markstrut teams will set performance objectives. The questionnaires were used to insure that a systematic record of these objectives was kept. The actual performance achieved was measured as the corresponding unit sales associated with each brand, and was collected from the computer-generated results each week. Attainment discrepancy was then calculated simply as performance minus aspiration. The unit of analysis for Model 1 is at the brand level because the causal mechanisms are posited to work primarily at this level. The teams make their decisions specific to each individual brand they are marketing. They are trying to position each brand in a certain market segment, and are making brand-specific decisions in their attempt to do so. They set their performance targets at the brand level, and watch performance at the brand level. Thus, it is important to tap the decision-making process at the brand level. Model 2. For Model 2, the dependent variable, riskiness of choice, was calculated from information gathered by the weekly questionnaire and from the decision forms. This model is concerned specifically with choices regarding brand marketing. A number of strategic actions (i) can be taken toward each brand ci>being marketed by each team in each period. The actions of interest are: 1) do nothing to change the brand from last period, 2) reposition the brand through advertising, 3) change the physical characteristics of an existing brand, 4) change the retail price of a brand, 5) introduce a new brand of Sonites, 6) introduce a new brand of Vodites,
‘All data are at the team level. Decisions filled out by the team.
are made as a team, results are given for the team, and questionnaires
are
512
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T. K. Lant and D. B. Montgomery
7) withdraw a brand of Sonites, 8) withdraw a brand of Vodites. Each of these variables are coded 1 if the action was taken, 0 if it was not. During each period when these decisions were made, each team reported the degree of risk they felt was associated with each of these actions. For each of the eight actions of interest, risk was subjectively scaled on a 1 to 10 scale anchored by very low and very high variance or uncertainty. These subjective perceptions of risk are combined with actual actions taken to determine the degree of risk taken in brand management. An index is created, whereby the value of each action variable (0,l) is multiplied by the corresponding risk rating. These values are then summed for each brand to yield a measure of the riskiness of activity associated with each brand. In summary: Risk, = CSi= l[[SubjRisk,][Actionjj]]. For each brand marketed, a performance measure called gross marketing contribution is computed, which is the revenues generated less the costs associated with the brand. Thus, it is a measure of gross profit. It is used conceptually in the model as an “objective” measure of success for each brand, as opposed to the subjective measure of attainment discrepancy. The attainment discrepancy measure is the same as that used in model one. Model 3. The search process being investigated is research and development. The innovativeness of R & D projects is measured by a self-report by each team in each period. They categorized their R & D projects as either pursuing cost reduction, brand modification, or new-product research. New-product research is defined as more innovative than brand modification, which, in turn, is more innovative than cost reduction.4 Cost reduction is coded as 0, modification as 1, and introduction as 2. The average level of innovation across R & D projects (i) pursued is computed for each team in each period as Avglnnov = [C4i=, LevelZnnov,]l Number of Projects. This calculation comprises the measure of innovativeness of search. The past success of research and development is computed as the proportion of R & D projects successfully completed by a team in a given period. The proportion is simply the number of successful projects divided by the total number of projects attempted in that period. This information is collected from the computer-generated results. The amount of available resources is computed as the budget granted each team plus any exceptional budget items. It represents the amount of resources available to a team that they can use in resource-allocation decisions. Attainment discrepancy is basically the same measure used in the first two models, except that in this model it is the average attainment discrepancy across all brands being marketed by a team in a given period.
Results The results for the three models are presented in Table 2. Use of the residual plots suggested by Draper and Smith [7] indicated that heteroskedacticity was not an apparent problem. However, both Model 1 and Model 2 indicated positively au-
?his
categorization
corresponds
well to team reports
of what constitutes
innovative
R & D.
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Table 2. Results Model l-Aspiration Explanatory
Variable
Standard Error
T Statistic
Beta Weight
31140.6 1.0987 1.2571
13282.1 .0502 ,093s
2.345 21.881 13.439
.8653 5288
o.,,Constant (Y, Aspiration level,-, CQAttainment discrepancy,- I Estimate of serial correlation N=105 Adjusted R’ = .8389 F statistic (2,102) = 271.73 Generalized
= ,300 p < .OOl
Least Squares Model 2-Riskiness
Explanatory p,, Constant f3, Attainment BZ Profit,- I
Levels
Estimated Coefficient
Variable
of Choice
Estimated Coefficient
Standard Error
T Statistic
Beta Weight
7.3745 -.000012 .0000087 .232218
1.3504 .000005 .000026 .090952
5.4609 -2.3436 .3398 2.5532
-. 1984 .0289 .2524
discrepancy,-,
Pi Risk,- I Estimate of serial correlation = ,384 N= 101 Adjusted R’ = .0822 F statistic (3,97) = 3.986 p < .Ol Generalized Least Squares
Model 3-Innovativeness Explanatory
Variable
y,, Constant y, Attainment discrepancy,+ r y2 R & D success,+, yi Resources,- I y4 Innovativeness,- I N=30 Durbin-Watson = 1.99 Adjusted R2 = .4266 F statistic (4,25) = 6.394 Ordinary
of Search
Estimated Coefficient
Standard Error
T Statistic
Beta Weight
1.10082 -.0000036 .7.54792 .000000006 .323343
.270742 .0000018 .235521 .000000008 .138220
4.0659 -2.0310 -3.2048 -.7475 2.3393
-.2956 -.4546 -.1081 .3325
p < ,001
Least Squares
tocorrelated residual$ consequently, these models were estimated using CochraneOrcutt Iterative Generalized Least Squares. Model 3 was estimated by OLS. In all cases, the differences in the parameter estimates were very small between OLS and GLS, and the conclusions from the hypothesis tests were identical.
‘The OLS estimates of Models 1 and 2 were tested for autocorrelation by comparison to d,, which Kenkel [13] has shown to bc quite accurate for models with lagged endogenous variables. Significance levels were determined from the extended
tables of Savin and White
[33].
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Model 1. This model provides an excellent fit to the data with the coefficients
of both variables highly significant in the predicted direction. Consequently, the results are consistent with Model 1, and hypotheses 1.1 and 1.2. Further, the simpler exponential smoothing model of Levinthal and March [19] is not consistent with the data since (Y()# 0 and (Y, # 1 at about the .05 level. While this model fits the data well, it is important to consider that there may exist alternative models that better describe the true relationships at work. In order to test this idea, the model was compared to three other models that could be considered given past research on aspirations. The first alternative suggests that aspiration level is simply a function of past aspiration levels. As could have been guessed from the results of Model 1, past aspiration levels do go a long way in explaining current aspiration levels. However, the results of testing the first alternative suggest, not surprisingly, that a model that contains attainment discrepancy explains the behavior of aspiration levels much better.‘j The second alternative model suggests that the relationship between attainment discrepancy and aspirations is not linear. However, attainment discrepancy squared is not significant when added to the model. Finally, it is plausible that the causal relationship between aspiration level and attainment discrepancy is not a one-period lag. Again, attainment discrepancy lagged for two periods is not significant when added to the model. Model 2. This model provides a significant fit to the data, albeit a much weaker one than for Model 1. The coefficients for attainment discrepancy (p,) and lagged risk (p3) are both significant in the directions predicted by the hypothesis in Table 1. Thus, hyptheses 2.1 and 2.2 are found to be consistent with the data. The profit covariate does not have a significant effect on the riskiness of choice. As in Model 1, attainment discrepancy adds significant explanatory power to a model containing lagged risk only. Further, the linear form of attainment discrepancy seems to be preferable to a quadratic term in this variable. Model 3. This model fits the data well. The coefficients of attainment discrepancy and R & D success are both significant in the predicted negative direction. The lagged coefficient of innovativeness of search is also significant in the predicted positive direction. Resources available did not turn out to be significantly related to innovativeness of search, and had the wrong sign. Consequently, hypothesis 3.1, 3.2, and 3.4 were confirmed, while hypothesis 3.3 did not receive support. Again, specification checks indicate that attainment discrepancy adds significant explanatory power to an autoregressive model in innovativeness of search, and a quadratic term in attainment discrepancy does not enhance the model.
Conclusions The results of Model 1 strongly support the prediction that aspiration levels are a linear function of past aspiration levels and the discrepancy between prior aspiration
%sing GLS, the adjusted A389 when attainment
R2 for the model using lagged aspirations only was S799 versus an overall discrepancy is added to the model. The result is highly significant.
adjusted
R* of
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51.5
and actual performance. Further, they suggest that the effect is summarized in a single-period lag. This study is perhaps the first to empirically test this model of the behavior of aspirations, using aspiration levels directly reported by the decision makers using them. Not only are aspirations shown to track the movement of actual performance, but the discrepancy between aspiration and performance is shown to be a distinct construct that can be used to predict the movement of aspiration levels. Model 2 offers substantial support for the prediction that risk taking is a function of past risk and attainment discrepancy. Decision makers do seem to anchor on their past position of risk taking as would be predicted by the cognitive social psychology literature. They also exhibit an effect consistent with some of the predictions of Kahneman and Tversky [12] and March [21]. That is, they take more risks when performance is below aspiration level than when performance is above aspiration level. A major contribution of the second model, beside the fact that attainment discrepancy is computed relative to self-perceived aspirations, is that the amount of risk taken is based on risk as perceived by the decision makers. The causal mechanism that is posited in this model is based on the idea that decision makers are cognizant of the discrepancy between their aspirations and performance, associate different levels of risk with different actions, will take fewer perceived risks when they are satisfied with their performance, and will take more perceived risks when they are dissatisfied with performance. Thus, it is important to tap both aspirations and risk from the decision maker’s point of view. The testing of Model 3 offers support for the satisficing predictions of the model. The greater the extent of previous success in search activities, the less innovative is the next set of search activities. The more negative one’s attainment discrepancy, the more innovative one will be in subsequent search activity. These results are consistent with the notion that innovativeness of search represents a change in current activities, and will not be pursued unless current activities are unsatisfactory. The testing of the three models in this study has made significant steps toward the goal of contributing to our knowledge of decision making and learning in a complex and ambiguous setting. It has provided evidence that decision makers pay attention to their aspirations, adjust their aspirations in response to experience, and use performance relative to their aspiration level as a cue that affects the nature of their future decisions. Understanding these processes will help us in studying the question of when the learning process is adaptive and when it is not. Better knowledge of these issues will help both researchers and managers improve their understanding of the managing of organization-environment relations. References 1. Barnard, Chester I., Functions of the Executive. Harvard University Press, Cambridge, Mass., 1938. 2. Bowman, E.H., A Risk-Return Paradox for Strategic Management, Sloan Management Review 21 (1980): 17-31. 3. Bowman, Edward H., Risk Seeking by Troubled Firms, Sloan Management Review (Summer 1982):33-42. 4. Burns, Tom, and Stalker, G.M., The Management of Innovation. Tavistock, London, 1961.
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5. Cohen, Michael D., March, James G., and Olsen, Johan P., A Garbage Can Model of Organizational Choice, Administrative Science Quarterly 17 (March 1972):1-25. 6. Cyert, Richard M., and March, James G., A Behavioral Theory of the Firm. PrenticeHall, Englewood Cliffs, N.J., 1963. 7. Draper, N.R., and Smith, H., Applied Regression Analysis. Wiley, New York, 1966. 8. Dutton, Jane E., Fahey, Liam, and Narayanan, V.K., Toward Understanding Strategic Issue Diagnosis, Strategic Management Journal 4 (1983):307-323. 9. Fishburn, P.C., Mean Risk Analysis and Risk Associated with Below Target Returns, American Economic Review 67 (1977):116-126. 10. Herriott, Scott R., Levinthal, Daniel, and March, James G., Learning from Experience in Organizations, Proceedings of the American Economic Association (May 1985). 11. Hogarth, Robin M., and Makridakis, Spyros, The Value of Decision Making in a Complex Environment: An Experimental Approach, Management Science 27 (1981):93107.
12. Kahneman, Daniel, and Tversky, Amos, Prospect Theory: An Analysis of Decision Under Risk, Econometrica 47 (March 1979):263-292. 13. Kenkel, James L., Some Small Sample Properties of Durbin’s Tests for Serial Correlation in Regression Models Containing Lagged Dependent Variables, Econometrica 42 (July 1974):763-769. 14. Kinnear, Thomas C., Problems and Opportunities in Using MARKSTRAT for Experimental Research in Marketing Management Decisions. Paper presented at Summer Marketing Educator’s Conference, 1986. 15. Lane, R.E., Why Businessmen Violate the Law, Journal of Criminal Law, Criminology and Political Science 19 (July-August 1953):151-165. 16. Larreche, Jean-Claude, and Gatignon, Hubert, Markstrat: A Marketing Strategy Game, The Scientific Press, Palo Alto, Calif., 1977. 17. Lawler, Edward E., Secrecy about Management Compensation: Are There Hidden Costs?, Organizational Behavior and Human Performance 2 (1967):182-189. 18. Lawrence, Paul R., and Lorsh, Jay W., Organization and Environment. Graduate School of Business Administration, Harvard University, Boston, 1967. 19. Levinthal, Daniel, and March, James G., A Model of Adaptive Organizational Search, Journal of Economic Behavior and Organization 2 (May 1981):307-333. 20. Lewin, Kurt, Dembo, Tamara, Festinger, Leon, and Sears, Pauline Snedden, Level of Aspiration, Personality and the Behavior Disorders, J. McV. Hunt, ed., The Ronald Press Company, New York, 1944. 21. March, James G., Variable Risk Preferences, Succeeding, and Surviving. Unpublished, Stanford University, September 1985. 22. March, James G., and Olsen, Johan P., Ambiguity and Choice in Organizations. Universitetsforlaget, Bergen, 1976. 23. March, James G., and Shapira, Zur, Behavioral Decision Theory and Organization Decision Theory, in Decision Making: An Interdisciplinary Inquiry. Gerard0 R. Ungson and Daniel N. Braunstein, eds., Kent Publishing, Boston, 1982. 24. March, James G., and Shapira, Zur, Managerial Perspectives on Risk and Risk Taking. Unpublished, Stanford University and Hebrew University, October 1986. 25. March, James G., and Simon, Herbert, Organizations. Wiley, New York, 1958. 26. Nisbett, T., Richard, and Ross, Lee, Human Inference: Strategies and Shortcomings of Social Judgment. Prentice-Hall, Englewood Cliffs, N. J., 1980. 27. Olshavsky, Richard W., Task Complexity and Contingent Processing in Decision Making: A Replication and Extension, Organizational Behavior and Human Performance 24 (1979):300-316. 28. Payne, John W., Task Complexity and Contingent Processing in Decision Making: An
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Performance
29.
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