DYNAMIC CAPABILITES OF THE FIRM AND STRATEGIC CHANGE

Paper to be presented at the Summer Conference 2009 on CBS - Copenhagen Business School Solbjerg Plads 3 DK2000 Frederiksberg DENMARK, June 17-19, 2009 DYNAMIC CAPABILITES OF THE FIRM AND STRATEGIC CHANGE Anuja Gupta Wharton School, University of Pennsylvania, USA anuja@wharton.upenn.edu Sidney Winter Wharton School, University of Pennsylvania, USA winter@wharton.upenn.edu Abstract: Some firms seem to possess the capability of performing well over extended periods of time in the context of change. These firms are able to make sweeping changes in their strategies in situations of environmental turbulence, shifting from one strategic context to the other (as if contextual knowledge of the firm does not matter). A good example is IBM that transformed from a hardware company into an integrated services company: or Monsanto that transformed from an agricultural chemicals company to a biotech company. The question is: what are these dynamic capabilities and how do managers develop them? We explore this issue of the nature and origin of dynamic capabilities of the firm in this paper. JEL - codes: A, F, F

Dynamic Capabilities of the Firm and Strategic Change Submission to the DRUID Summer Conference, 2009 1

Abstract Some firms seem to possess the capability of performing well over extended periods of time in the context of change. These firms are able to make sweeping changes in their strategies in situations of environmental turbulence, shifting from one strategic context to the other (as if contextual knowledge of the firm does not matter). A good example is IBM that transformed from a hardware company into an integrated services company; or Monsanto that transformed from an agricultural chemicals company to a biotech company. The question is: what are these dynamic capabilities and how do managers develop them? We explore this issue of the nature and origin of dynamic capabilities of the firm in this paper. Keywords: Dynamic Capabilities, Strategic Change 2

Introduction In recent years, many industries have been subject to turbulent environments. These environments are characterized by intense and rapid competitive moves, in which competitors must move quickly to build [new] advantages and erode the advantages of their rivals (D Aveni, 1994, pages 217-218). The fast pace of change characterizes both demand and supply conditions leading to structural changes in the industry and erosion of firm competitive advantage (Bettis and Hitt, 1995; D Aveni, 1994, 1995; Hamel, 2000; Wiggins and Ruefli, 2002). Some firms, however, seem to weather these storms of change and perform well over extended periods of time by making sweeping changes in their strategies, shifting from one context to the other (as if contextual knowledge of the firm does not matter). A good example is IBM that moved from being a hardware company to transform into an integrated services company, acquiring PWC in the process; or Monsanto that transformed from an agricultural chemicals company to being a new-age biotech company. The question that arises is: What are these dynamic capabilities that allow firms to change and maintain high performance in the context of change? In particular, are these skills of accomplishing change generic skills or do they represent high levels of accumulated expertise in the particular problem environment the firm finds itself in? We find conflicting views in the literature, and in this research, seek to extend and clarify the theory of dynamic capabilities under conditions of environmental change. 3

Theory and literature Dynamic capabilities of the firm play a role in allowing the firm to integrate, build and reconfigure internal and external competencies to achieve new forms of competitive advantage (Teece, Pisano and Shuen, 1997). In this section we briefly review the two views on dynamic capabilities in the literature in light of our research question. I First Dynamic Capabilities view: Based on the evolutionary theory, this view suggests that firms do possess capabilities to change, but these capabilities are domain specific, requiring consistent investment and learning on the part of the firm in the given domain. This view defines dynamic capabilities as those that operate to extend, modify or create ordinary capabilities (Winter, 2003). The main features of dynamic capabilities in this view are: 1. Dynamic capabilities are higher order routines i.e. regular patterns of activities allowing change in the current capabilities of the firm i.e. these are regular, structured and skill based activities 2. The building of dynamic capabilities requires consistent and costly investment on the part of the firm so that firms at various points in time, make long-term quasi-irreversible commitments to certain domains of competence. (e.g. Pisano (1996) finds that firms build process development capability by effectively capturing the process learning that comes out of any on-going project; Zollo and Winter (2002) identify learning processes including experience accumulation without deliberate investment in learning, knowledge articulation and knowledge codification that lead to building a capability) 3. The development of new capabilities is path dependent 4

4. Dynamic capabilities are domain specific (Winter, 2003; Teece, Pisano and Shuen, 1997) Thus, according to this view, dynamic capabilities are higher order routines (Winter, 1999) that consist of the the firm s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Dynamic capabilities thus reflect an organization s ability to achieve new and innovative forms of competitive advantage given path dependencies and market positions (Teece, Pisano and Shuen, 1997) in a repetitive, consistent manner. An example: Intel developing the next generation of chips while consistently producing the current generation of chips. This company has built a specific expertise/competence in this domain of activity i.e. producing ever faster microprocessors, and it applies this competence to this activity in a consistent, repetitive manner The building of dynamic capabilities requires consistent and costly investment on the part of the firm so that firms at various points in time, make long-term quasi-irreversible commitments to certain domains of competence. An identifiable process of learning in the firm leads to building of capabilities e.g. Pisano, in his study of manufacturing processes for drugs in the pharmaceutical sector, finds that firms build process development capability by effectively capturing the process learning that comes out of any on-going project. Zollo and Winter (2002) identify learning processes including experience accumulation without deliberate investment in learning, knowledge articulation and knowledge codification that lead to building a capability, and the conditions under which each learning process would be effective. Bryce finds that firms 5

develop new capabilities (and therefore change their existing capabilities) by entering distant markets gradually by creating intermediate stepping stones i.e. by entering markets sequentially. There is also experimental evidence that the context of the learning situation or problem solving is critical for the subjects performance. Subjects have been found to take much longer to carry out logical isomorphs of several puzzles (e.g. the Tower of Hannoi experiments by Simon and his colleagues) (Hayes and Simon, 1985). In this view, then, firms do possess the capability to change within their specific domain of expertise, but the set of organizational routines comprising the capability translates into economic success because stable characteristics of the environment are being exploited, not because of any distinctive competence that transcends the particular domain. If the environment changes and demand for the whole class of products diminishes, the company can do little with its competence. Thus, this view concludes that firms may not be able to transcend domains as a response to environmental turbulence, though they are able to make changes successfully within their domains. Hence, the evolutionary view on dynamic capabilities purports that the context in which the firm operates is extremely important in its performance outcomes, if the context changes, the firm s performance may deteriorate. II. Alternative Dynamic Capabilities view We discern a second view on dynamic capabilities, put forth by Eisenhardt and Martin (2000) which agrees that the evolutionary view of dynamic capabilities is relevant in moderately dynamic markets in which change occurs frequently, but in a predictable and linear manner, and 6

industry structure remains stable; therefore, the firm successfully relies on its existing knowledge to deal with the change. However, they contend that in high velocity markets in which change is non-linear and unpredictable, dynamic capabilities consist of simple rules and structural principles that o keep managers focused on the important issues without locking them into specific behaviors o provide enough structure for managers to focus their attention Managers respond to rapid change by using simple rules, not complex routines, on the basis of developing intuition and heuristics (Eisenhardt and Tabrizi, 1998) and firms manage to change successfully. Managers do not extensively use their existing tacit knowledge, rather they create new context-specific knowledge by engaging in experiential actions to learn quickly like prototyping, early testing; this gives new knowledge and rapid feedback. This allows the firm to change to respond to changing external conditions, in some sense overcoming the current positions and path dependencies (emphasized in the first view). These are almost in the form of ad-hoc responses to situations as they arise, but nevertheless have their own systematic logic. They are not long lasting (i.e. are not imprinted in the memory of the organization) and are fragile, have little structure and are unstable. Hence, according to this view, dynamic capabilities seem to be unstructured/loosely structured (flexible) ways of doing things. One of the main characteristics of this conception of dynamic capabilities seems to be flexibility that is almost context free i.e. applicable and transferable easily from one domain to the other. It suggests the existence of a capability to change that is virtually generic, allowing firms to make large scale strategic changes to adapt to 7

changing environmental conditions. Exercise of these dynamic capabilities may lead organizations to make wholesale change in their strategies An example: Eisenhardt (1989) in her study of decision making in microcomputer firms finds that firms that make fast decisions use real time information (that aids faster issue recognition, helps develop intuition); simultaneously evaluate multiple options; use experienced counselors; engage in experiential actions to learn quickly; dynamic capabilities proceed in an iterative fashion managers develop alternatives, then implement them In context of the evolutionary view on dynamic capabilities, the alternate view raises these questions: does such a generic capability to change exist? How do managers create this capability or the intuition, heuristics, structure that allows them to be flexible and make decisions in environments characterized by rapid change (that may in some cases lead to revolutionary jumps in terms of strategic position of the firm)? Is there a consistent learning and investment pattern as in the first view of capabilities? If yes, what is this learning process? Managerial Skills Another part of the literature that is relevant to our question is that of managerial skills. Managerial skills (which have also been called human capital, Becker, 1964) have been of interest to scholars in management who have studied managerial decision making and provided classifications of managerial skills (e.g. Katz, 1974; Mintzberg, 1973). Katz (1974) talked about technical, human and conceptual skills of managers; Mintzberg (1973) suggested eight skills 8

including peer relationships, leadership, conflict resolution, information processing. These categories, thus talk about generic skills of managers. However, managers in different industries require specialized skills relevant to that industry (Katz, 1974; Castanias and Helfat, 1991, 1992; Bailey and Helfat, 2003). Managers also acquire firm-specific skills, involving an in-depth understanding of factors such as a company s history, culture, and internal strengths and weaknesses (Bailey and Helfat, 2003; Castanias and Helfat, 1991, 1992; Puffer and Weintrop, 1991). In empirical studies testing which of these skills are most important, e.g. Harris and Helfat (1997) find that industry and firm specific skills play an important role in determining executive compensation during succession events. They find that successors without firm specific skills need to be compensated higher for the risk they undertake in moving to a new company; and that successors with no industry specific skills also need to be compensated higher. Bailey and Helfat (2003) find that industry specific skills play a significant role in reducing variance in subsequent firm performance than do generic skills after a succession event. This literature thus recognizes that managers have different kinds of skills, some of which carry across industries; others that do not. Typically the value of specific skills has been found to be higher than generic skills. These studies, thus largely support the evolutionary view of dynamic capabilities that context-specific investments and training are important for managers and for the firms. These studies, however, do not study the role of different skills in different kinds of environments. 9

Thus, in a nutshell, we start by asking the question: what explains the ability of some firms to make strategic changes and move into new domains of activity (as if contextual knowledge does not matter)? In other words: can managers make transitions from one domain to the other in the face of change, and maintain high performance? The evolutionary suggests that change within a specific domain is possible; while the alternate view suggests that change from one domain to other is possible. We study successful managers and examine if they spent their careers in the same domain or if they jump around domains and still do well. We use individual level arguments in this paper. While organizational routines are the building blocks of capabilities and dynamic capabilities, these involve human actors as they do equipment, facilities etc. With respect to CEOs of firms, some matching between CEO attributes and characteristics of firms approach to change is plausible. Therefore CEO characteristics/approach to change will to some extent define the firm s approach to change as well. Hence, we believe that individual level data has the potential to help us understand the mechanisms behind firm approaches to strategic change and dynamic capabilities. The two competing hypotheses can be stated as follows: H1 (a): Managers that move around in domains will be less successful than managers spending more time in the same domain under environments subject to change, because success requires accumulation of expertise in the same domain (according to the evolutionary view of dynamic capabilities) 10

H1 (b): Managers that move around in domains will be successful in environments of change, because change/success requires quick creation of contextual knowledge and thus, accumulation of expertise is less necessary (according to the alternative view of dynamic capabilities) Methods Data & Design We build a unique data set to test the hypotheses. Our interest lies in analyzing whether successful managers change domain frequently over their careers or not; and how this compares with careers of less successful managers, particularly in dynamic, high velocity environments. We thus collect CEO career data and code changes of jobs and domains of activity in these careers as explained below. We use two different strategies to data collection: Data Set I: We identify successful managers using public contests for best CEO awards in several business forums like Business Week magazine, Financial World magazine, and a few other prominent public sources. These contests involve evaluations of the performance and contributions of the contesting CEOs either by their peers, or by analysts following the industries, or academics. In this way, these contests come up with a list of stars for each year (Wade et al, 2005). These awards, by combining numerous individual judgments on uniform criteria, enable comparisons among the rated actors (Fombrun, 1996). We use these lists as representing successful managers in their respective industries. The next step is to categorize the industries in which the CEO was active when he or she got the award as a dynamic or placid industry. Our interest lies in studying dynamic 11

environments. For this purpose, we use a ranking of industries by level of dynamism developed by Thomas and D Aveni (2006). The authors use rate of variability in firm profitability (the temporary competitive advantage component of performance) as one of the key measures to rank all manufacturing 4-digit SIC codes according to level of dynamism. We use this ranking (thus limiting our analysis to manufacturing industries only) and code industries in the top quartile of the ranking as dynamic, and those in the bottom quartile as placid. We then selected star managers that fall in the top and bottom quartile for our analysis. This gives us a set of 47 Star CEOs in dynamic environments, and 33 Star CEOs in placid environments. We then collect the career histories of these managers over their entire working lives. We get the SIC code for each job held by the individual, and any movement across SIC codes is coded as a change in domain; and any change in organization as a change in jobs. This gives us a count of job and domain changes for each CEO over their working careers. To create a control group, we build a matched sample of non-leaders i.e. less successful managers in both placid, and dynamic industries. We match star CEOs with non-star CEOs on the basis of industry and firm size. We undertake this matching by choosing among the close competitors of the firm as listed in the Hoover s online database. This design enables us to control for any industry, and environmental factors affecting performance of the CEOs. Our final sample consists of a total of 131 CEOs. 80 of these CEOs are star CEOs, 51 are non-star CEOs; and 79 CEOs operated in dynamic environments the year they were identified as stars while 52 operated in placid environments. A brief summary of the sample appears in Table 1. --------------------------------------------------------------- Insert Table 1 about here --------------------------------------------------------------- 12

Data Set II: We create a second sample that uses a stock market based performance measure to identify stars and non-stars. We create a list of companies trading on the NASDAQ in the year 1990 & 1991 (this could be potentially incomplete due to non-availability of the population of companies listed, though we expended significant effort in getting as expansive a set as possible). We then categorize these companies into dynamic and placid industries using the ranking of industries by level of dynamism developed by Thomas and D Aveni (2006) as in Data Set I, described above. We generate the list of all CEOs of all these companies for the years 1990-2005. For each of these CEOs we compute a stock market based performance measure, for 3 or 5 years of their tenure, depending on the length of their tenure in the company. The final sample consists of 140 CEOs in dynamic industries only. We did not obtain a sufficient sample of firms, and therefore of CEOs in placid industries. This is not surprising given that more newage firms operating in the more dynamic industries favor listing on the NASDAQ. The performance measure we use is the Net Annualized Return (NAR) to the investor based on the Market Adjusted Shareholder Value Index. This involves calculating an index of returns to the investor in the company s stock over the desired time period, which is then adjusted by a factor equivalent to what a market index-based portfolio would have returned to this same investor over the same time period. This value therefore gives a measure of the return the investor obtains from this stock beyond what a market index based portfolio would have returned to her. We rank all CEOs based on the NAR during their tenures, assign CEOs that fall in the top quartile of this ranking to the leader (or star) category, and those that fall in the bottom quartile to the non-leader category. 13

Analysis & Results Analysis based on Data Set I Data Set I consists of the career histories of a matched sample of 131 CEOs divided between dynamic and placid environments, and between high and low/average performing CEO categories. The variables of interest are number of job changes, and number of changes in domain as indicated/measured by change in SIC code across jobs. I. Difference of Means Test: We undertake a simple Differences of Mean test as a first step to analyze this data, focusing on the Star CEO part of the sample. The results are presented in Table 2. --------------------------------------------------------------- Insert Table 2 about here --------------------------------------------------------------- A preliminary look at the table indicates that star managers have an average of 0.84 domain changes over their careers. They have an average of 1.11 job changes. Non star managers have an average of 0.45 domain changes, and an average of 0.55 job changes. The first striking thing about this data is that both high and average performing managers do not change domain frequently. The average number of domain and job moves for both types of managers are only 0.70 & 0.90, respectively. Thus managers tend to spend long durations of time within a few domains, and in a few organizations. The second observation is that star managers move around in terms of domain and jobs much more frequently than non star managers (an average of.84 domain changes compared to.45 domain changes; and an average of 1.11 job changes to.55 job changes, respectively). 14

The Difference of Means test confirms that star managers do change domain more frequently than non-stars, and that this difference is statistically significant at the 5% level. Stars move 85% more than non-stars in terms of domain, across different kinds of environments. This result lends some support to H1(b) that managers can move around domains and still be successful. Also, stars have a higher average number of job changes than non-stars, and this is significant at the 5% level. If we divide the sample according to the two different environments and analyze the difference between mean domain changes of stars and non stars, we find that there is a statistically significant difference between the average number of domain changes between stars and non stars in dynamic environments. Stars change domain more frequently than non stars in dynamic environments i.e. the mean number of domain changes is significantly higher than nonstars. Stars move across domains 135% more frequently than non stars. II. ANOVA : We also use ANOVA to analyze the difference between star CEOs and non-star CEOs in terms of domain changes in dynamic environments. The results are presented in Table 3. --------------------------------------------------------------- Insert Table 3 about here --------------------------------------------------------------- The results lead us to reject the null hypothesis of equal population means, and to conclude that there is a statistically significant difference between the population means. The average number of domain changes for stars is higher than the average of non-leaders, and this difference is statistically significant. 15

This first set of results based on the Difference of Means test and the ANOVA test leads us to conclude that, managers can successfully move across domains, and maintain high performance in dynamic environments. Thus, there do seem to exist some generic, loosely structured dynamic capabilities that allow managers to move across domains, and allow firms to change strategic direction in high velocity environments. We also find that there is a significant difference in the mean number of job changes for stars and non-stars in dynamic environments. Stars change jobs almost twice as frequently as non stars, which is not surprising given the difference in the average number of domain changes. A further look at the job and domain change data in dynamic industries suggests that in cases where managers have made at least one job change, 30% of them have changed jobs, but not domains. This finding lends some support to H1(a) that many successful managers do not change domain, and are hired in new organizations to inject a fresh outsider perspective into the organization. We also find that most managers that changed domains in dynamic environments moved across equally dynamic industries. Hence, our preliminary results suggest that managers can indeed move across domains and remain successful leading us to believe that there exist some flexible dynamic capabilities that allow firms and managers to successfully migrate across strategic domains. III. Bryce-Winter Index: We also use the Bryce-Winter Index to code domain changes (Bryce and Winter, 2006). This is a general inter-industry relatedness index that provides a percentile relatedness measure for all possible pairs of four digit manufacturing SIC codes. The index provides a more refined measure of relatedness between industries by incorporating information 16

on scope economies gained through diversification decisions of firms in the manufacturing universe of the US. We use the normalized percentile ranking of relatedness (called the z score); this score has a mean of 0, and a Standard deviation of 1. The z score, applied to our data would provide a continuous measure of the degree of difference between domains when managers change jobs (rather than generating a count variable of domain changes by coding all changes in SIC across job switches as 1) and would provide a more refined understanding of the nature of the domain change. Thus, for each change in SIC code for managers in our sample, we get the z score of the pair of SIC codes to be able. Unfortunately, we lose most of the domain change data by this procedure since the Bryce Winter index only codes pairs of manufacturing industry SIC codes. We are thus unable to code the manufacturing to service or service to manufacturing moves made by our sample CEOs. However, it is interesting to note that the indicative results (with no testing for statistical significance) suggest a higher mean relatedness score for stars in dynamic industries than in placid industries; and a lower variability of relatedness score for stars in dynamic industries. These indicative results suggest that high performing CEOs change to more related domains (and with low variability in range of domains entered) in dynamic industries. This provides some support for hypothesis 1(a), i.e. for the evolutionary view (with the above mentioned caveat/limitation) that context is important and managers may move across related domains, but moves across unrelated domains will diminish performance. 17

Analysis based on Data Set II We use a similar approach described above to analyze the NASDAQ based data set, and conduct Difference of Means test and ANOVA. I. Difference of Mean test: The results of the difference of mean test are presented in Table 4. --------------------------------------------------------------- Insert Table 4 about here --------------------------------------------------------------- The table presents the analysis of firms in dynamic environments only since the sample size for firms in placid industries is very small. The results indicates that there is significant difference between the average number of domain changes for leaders and non-leaders; leaders change domain less frequently than non-leaders. This result contradicts the one we obtained from Data Set I. II. ANOVA: The results of the ANOVA are presented in Table 5. --------------------------------------------------------------- Insert Table 5 about here --------------------------------------------------------------- The ANOVA also leads us to reject the null hypothesis of no difference in the population means; in fact the mean domain changes for stars is less than the mean domain changes for non-stars. Thus this set of results lead us to conclude that there is support for hypothesis 1(a) that managers will not be able to move across domains and remain successful since contextual knowledge is of critical importance to high performance. 18

Discussion & Conclusion This study explores the issue of strategic change by firms in dynamic environments by examining the tension between two views on dynamic capabilities of the firm. The evolutionary theory based view suggests that firms may possess dynamic capabilities that allow them to change within their specific domain of expertise. Firms may not be able to transcend domains as a response to environmental turbulence and maintain high performance, since high performance is based on accumulation of skills and knowledge in the firm s particular domain of activity. The alternative view, on the other hand, suggests that dynamic capabilities in high velocity environments confer on the firm a flexibility that is almost context free allowing it to make changes in domain without the attendant negative performance consequences. Thus, these two points of view lead to different conclusions about the ability of firms to make successful strategic changes i.e. changes in their domains of activity. We explored this issue by studying CEO careers in dynamic and placid environments. The data we are collecting provides a unique way of studying this issue. Our analyses provide us with contradictory findings. The first set of results provides support the argument that in environments subject to rapid change, managers can indeed move around domains successfully. There is a significant difference between the mean number of domain changes of stars and non-stars, with star CEOs making 135% more domain changes than non-star CEOs. Most of these changes in domain, we find, are among equally dynamic industries. This supports the second hypothesis, derived from the alternate view on dynamic capabilities. There seem to exist some flexible dynamic capabilities that allow managers and firms to move from one domain of activity to another, while maintaining high performance. 19

There is some cognitive mechanism at play which allows managers to develop heuristics and intuition about these dynamic situations across domains, which then translate into loosely structured, flexible and simple rules allowing the firm to change in response to changing external conditions, and to overcome its current positions and path dependencies The second set of results provides support to the evolutionary theory based view on dynamic capabilities and suggest that managers that move around in domains will be less successful than managers spending more time in the same domain under environments subject to change, since success is based on the accumulation of expertise in the same domain. Hence, the analyses presented in this paper leave us with more of a question than an answer to the issue we seek to explore. We are currently putting together a much larger sample based on all firms listed in US stock exchanges. This will enable us to perform more sophisticated analyses and lead us to more conclusive findings. We believe that the larger issue we raise in this paper, namely firm adaptability under conditions of environmental turbulence is a very important research area, and that the results we find here are very intriguing and warrant further examination. How do managers function under highly dynamic environments? How do firms accumulate skills to maintain high performance under such circumstances? What are dynamic capabilities, and how do firms and managers develop these? These are important questions both from a theoretical and practical point of view, and our research program starts to shed some light on these issues. 20

TABLE 1 Sample Description Stars Non-Stars Dynamic Environment 47 star CEOs 32 non-star CEOs 79 in dynamic Placid Environment 33 star CEOs 19 non-star CEOs 52 in placid 80 stars 51 non-stars 21

TABLE 2 Results : Difference of Mean Analysis Star CEO sample Stars Non-Stars t ratio p value Conclusion Domain Changes Total (Across both environment states ) (Mean) (Mean) (Difference of means test) 0.84 0.45 2.34** 0.0276 Significant Difference Dynamic Environment Environment 0.96 0.41 2.60*** 0.0110 Significant Difference Job Changes *** p <.05 p <.01 Total (Across both environment states ) Dynamic Environment 1.11 0.55 2.70*** 0.0078 Significant Difference onment 1.28 0.56 2.69*** 0.0087 Significant Difference 22

TABLE 3 Results : ANOVA Dynamic Environments; ; Star CEOs versus Non-Star CEOs Source Sum of Squares (SS) Degrees of Freedom (df) Mean Square (MS) F p-value Treatment 5.784 1 5.784 6.785.01 Error 65.634 77 0.852 23

TABLE 4 Results : Difference of Means NASDAQ based sample Stars Non-Stars t ratio p value Conclusion Domain Changes Dynamic Environment (Mean) (Mean) (Difference of means test) ronment 1.06 1.59 1.7321 0.08 Significant Difference TABLE 5 Results : ANOVA NASDAQ based sample Source SS DF MS F p-value Treatment 4.757 1 4.757 2.802.05 Error 115.429 68 1.697 24

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