Occupational A Practical Guide For Managers_14

104 Organizational Learning from Performance Feedback they hold are the source of a good portion of the production efficiency of modern firms but also a risky investment for the individual firm and for society, we should be interested in how firms acquire resources. We should also be interested because resources play an important role in current theory of strategy management. First,letusdefinearesourceasfollows(BarneyandArikan2001:138): “Resources are the tangible and intangible assets firms use to conceive of and implement their strategies.” Organizations acquire resources to operate and make profits, and use some of their profits to acquire addi-tional resources. A central task of managers is to make decisions on the acquisition and use of resources that are useful in the long term, that is, to acquire organizational assets. Strategic management researchers treat assets in two different ways. One is to view assets as commitments that shape interactions between firms by giving competitors of firms with as-sets committed to a given market incentives to avoid competitive battles (Caves and Porter 1977; Ghemawat 1991). Firms engage in confronta-tions such as price wars for the sake of gaining market share that gives futureprofits,andmayavoidconfrontationswhentheopponenthascom-mitted so many assets that it is unlikely to back down. The other is to view assets as giving the firm capabilities that make it a better supplier of its goods than other firms, increasing the likelihood that competitors will lose confrontations they engage in (Wernerfeldt 1984). Both views predict that a good strategy for acquiring assets can lead to high perfor-mance over the long run by making other firms reluctant to compete with the focal firm. Theory stating that resources held by the firm give competitive ad-vantage has led to the resource-based view of the firm (Barney 1991; Lieberman and Montgomery 1998; Wernerfeldt 1984), which is an ac-tive research tradition currently (Barney 2001; Barney and Arikan 2001; Priem and Butler 2001). The resource-based view considers resources that are valuable and unique to the firm to be sources of competitive advantage, and studies the role of such resources in giving high perfor-mance (Brush and Artz 1999; Makadok 1998, 1999; Miller and Shamsie 2001) and shaping strategic decisions such as diversification strategies (Hitt, Hoskisson, and Kim 1997; Silverman 1999). Resources are inter-preted broadly to include nonmaterial assets such as knowledge, which has given the resource-based view of the firm an affiliation with learn-ing theory (Barnett, Greve, and Park 1994; Collis 1991; Hamel 1991; McGrath, MacMillan, and Venkataraman 1995; Noda and Bower 1996). Given the interest in strategic resources spawned by this theory, one might think that the acquisition of assets (physical or otherwise) would be an active area of research in strategic management. Remarkably, it Applications 105 is not (Barney and Arikan 2001). Empirical research from the resource-basedviewhasemphasizedtheconsequencesoffirmdifferencessostrongly that research on their origins has been lagging. Researchers examining resource acquisition have mainly worked from a learning-theory point of view, and have examined the acquisition of non-physical assets such as knowledge and routines (Barnett, Greve, and Park 1994; McGrath, MacMillan, and Venkataraman 1995). The problem seems to be that it is difficult to explain why some firms acquire scarce and valuable resources andothersdonot,asitseemsobviousthatallfirmswouldbeinterestedin pursuing such resources. The key to solving this problem is to realize that acquiring resources is a risky organizational change that many managers hesitate to make. We can study the acquisition of assets by pursuing the usual idea that performance below the aspiration level causes organizational change and managerial risk taking. Investment in production facilities is an impor-tant strategic decision in its own right, and may be regarded as a test case of how firms approach the more general problem of obtaining scarce and valuable resources. Large or modern assets can give the firm a com-parative advantage in the competition, but also give greater fixed costs. For industries with highly variable demand and rigid supply, the scale of production facilities directly determines the effect of fluctuations in the economic macro-environment on the organizational profits. Large facili-ties allow the organization to take on more work on good times, but give greater losses in bad times. It is thus a type of organizational change with high potential for solving problems of low performance, but also with great risks. If we view asset acquisition as a risky problem-solving behavior, the-ory of performance feedback predicts that firms add fewer resources to their production facilities when their performance is above the aspiration level.Theyaddmoreresourceswhentheperformanceisbelowtheaspira-tion level, but organizational inertia makes the link between performance feedback and resource acquisition weaker below the aspiration level than above it. The result is the kinked-curve relation from performance to change predicted in chapter 3. If the theory is correct, then asset buildup works a lot like bicycle races. The leader is slowed by the headwinds of complacency, while those following are pulled along by the leader. Over time, such performance feedback processes act as an equalizing force in resource-based competition. Some well-known cases of firms adding to their production assets sug-gestthatlowperformanceindeedspursinvestments.Upgradingthefacto-ries was one of the strategies pursued by GM after the entry of Japanese firms depressed its performance, as discussed in chapter 1. The same 106 Organizational Learning from Performance Feedback strategy is well known from other industries where physical assets are im-portant for competitiveness. For example, Intel’s first reaction to harsh competitionintheRAM(randomaccessmemory)marketwastoupgrade its factories; only later did it change its market niche to processing chips (Burgelman 1991, 1994). Although Intel reversed its strategy of invest-ments in factories for producing memory chips, the strategy of investing more in times of trouble is still followed by makers of semi conducting devices. For example, the Taiwanese chip foundry TSMC embarked on an ambitious and controversial upgrade of its factories shortly after the demand for semiconductors tanked, giving it a capacity utilization below 50 percent (Einhorn 2001). To see whether there might be a systematic relation from performance feedback to asset acquisition, I turn to evi-dence from a focused study of an industry where production assets are crucial for competitive strength. AsinthesectionsonR&Dandinnovations,IusedatafromtheJapanese shipbuilding industry. Industries producing industrial investment goods, such as production machinery and non-consumer vehicles, experience greatly fluctuating demand and competition partly based on production assets. This makes them good contexts for testing how asset growth is af-fected by performance feedback. The decision is especially consequential and risky in such industries, fitting our emphasis on decisions of great strategic import and uncertain consequences. The scale and quality of shipyards are very important in the competition for ship construction contracts, so investments in production facilities are strategic moves for these firms. Table 4.5 shows the results of analyzing the growth of total production assets in each shipyard. This measure might be relatively unresponsive to performance feedback since it includes both strategically important assets such as docks and machinery and less important assets with a high degree of routine maintenance (buildings are a good example). Never-theless, the table shows clear and strong effects of performance feedback on the growth rate. As before, model 1 only contains control variables describing current economic conditions and leading indicators of ship-building activity. The next three models add performance relative to his-torical and social aspiration levels and slack, respectively, and the final model includes all variables. Performancerelativetothehistoricalaspirationlevelhasastrongeffect on asset growth above the aspiration level, and higher performance re-duces the asset growth as predicted. Model 2 shows that performance relative to the historical aspiration level is negatively related to asset growth,butonlyabovetheaspirationlevel.Belowtheaspirationlevel,the performance does not have a statistically significant effect on the growth rate, and the estimated coefficient is very close to zero. Success reduces Applications 107 Table 4.5 Models of shipyard asset growth in response to performance feedbacka Performance – Historical Aspiration (if <0) Performance – Historical Aspiration (if >0) t test of difference of <0 and >0 Performance – Social Aspiration (if <0) Performance – Social Aspiration (if >0) t test for difference of <0 and >0 Absorbed slack Unabsorbed slack Potential slack R-squared (unadjusted) R-squared (adjusted) Model 1 0.93807 0.93734 Model 2 0.498 (0.414) −1.940∗∗ (0.494) [3.299]∗∗ 0.93866 0.93786 Model 3 0.113 (0.478) −0.103† (0.056) [0.435] 0.93820 0.93740 Model 4 −0.784 (0.699) 0.014 (0.048) 0.0009 (0.0017) 0.93814 0.93729 Model 5 0.447 (0.450) −2.028∗∗ (0.495) [3.252]∗∗ 0.008 (0.522) −0.123∗ (0.058) [0.244] −1.124 (0.706) 0.047 (0.049) 0.0010 (0.0017) 0.93894 0.93796 †p<.10; ∗p<.05; ∗∗p<.01; two-sided significance tests. aGrowth models with fixed effects for thirteen firms. Control variables for the growth parameter, oil shock, order reserve, annual production, oil freight rate, and shipping income are not shown. asset growth, but failure does not increase asset growth. If we compare thisfindingwiththepredictioninfigure3.3,itsuggeststhatinertialforces are so strong that the effect of problem-based search below the aspira-tion level is canceled out. Performance relative to the historical aspiration level seems to be the only variable that strongly affects the asset growth. Models 3 and 4 show that performance relative to social aspiration lev-els weakly affects the growth of assets, and organizational slack does not affect the growth at all. Model 5 has all variables included, and confirms the results of the preceding models. Table 4.6 shows the estimates of growth models of shipyard machinery value.Thisvariableomitsslow-adjustingassetslikebuildings,andshould be more responsive to managerial decisions. The results are very similar to the analyses of total production asset value in table 4.5. Model 2 shows adeclineininvestmentasperformancerelativetothehistoricalaspiration level increases, but only above the aspiration level. Performance relative to the historical aspiration level is the only significant feedback variable in 108 Organizational Learning from Performance Feedback Table 4.6 Models of machinery growth in response to performance feedbacka Performance – Historical Aspiration (if <0) Performance – Historical Aspiration (if >0) t test for difference of <0 and >0 Performance – Social Aspiration (if <0) Performance – Social Aspiration (if >0) t test for difference of <0 and >0 Absorbed slack Unabsorbed slack Potential slack R-squared (unadjusted) R-squared (adjusted) Model 1 0.95259 0.95211 Model 2 0.039 (0.532) −1.401∗∗ (0.514) [1.653]† 0.95286 0.95232 Model 3 −0.732 (0.507) −0.036 (0.057) [1.325] 0.95269 0.95215 Model 4 −0.558 (0.688) −0.020 (0.045) 0.0005 (0.0017) 0.95268 0.95211 Model 5 0.203 (0.551)∗ −1.403∗∗ (0.514) [1.822]† −0.614 (0.528) −0.042 (0.057) [1.046] −0.704 (0.687) −0.017 (0.044) 0.0005 (0.0016) 0.95303 0.95240 †p<.10; ∗p< 05; ∗∗p<.01; two-sided significance tests. aGrowthmodelswithfixedeffectsfortenfirms.Controlvariablesforthegrowthparameter, oil shock, order reserve, annual production, oil freight rate, and shipping income are not shown. Standard errors of coefficient estimates are shown in round brackets; tests of difference of coefficients are shown in square brackets. these models. For machinery growth the social aspiration level is insignif-icant, and the slack variables are insignificant as before. The models of machinery value show slightly higher explanatory power than the models of shipyard assets. The higher explanatory power suggests that machin-ery size is adjusted more readily to the economic conditions and the firm performance than total assets are, as one would expect. A graph helps understand the results better. Figure 4.2 displays the predicted growth rates of assets based on the estimates of model 5 of table 4.5. The curve is made by normalizing the growth rate to one at the origin and computing how the growth rate varies as each dependent variable varies from 2.5 standard deviations below to 2.5 standard devi-ations above the mean. The actual growth rates will differ depending on the values of other covariates. The growth rate of assets peaks when the performanceequalstheaspirationlevel,butsincetheupwardslopebelow theaspirationlevelisnotsignificantlydifferentfromzero,therelationship ... - tailieumienphi.vn