國 立 交 通 大 學
企業管理碩士學位學程
碩 士 論 文
關於動態能力的測量
On the Operationalization of Dynamic Capabilities
研 究 生：莫尚
指導教授：姜真秀
National Chiao Tung University
Master Degree Program
of Global Business Administration
Thesis
關於動態能力的測量
On the Operationalization of Dynamic Capabilities
Student: Jaun-Paul Mouton
Advisor: Prof. Jin-Su Kang
關於動態能力的測量
On the Operationalization of Dynamic Capabilities
研 究 生：莫尚
Student: Jaun-Paul Mouton
指導教授：姜真秀
Advisor: Dr. Jin-Su Kang
國 立 交 通 大 學
管理學院
企業管理碩士學位學程
碩 士 論 文
A Thesis
Submitted to Master Degree Program of Global Business Administration College of Management
National Chiao Tung University
In partial Fulfillment of the Requirements For the Degree of Master in Business Administration
June 2012
Hsinchu, Taiwan, Republic of China
i
Student: Jaun-Paul Mouton Advisors: Dr. Jin-Su Kang
Master Degree Program of Global Business Administration College of Management
National Chiao Tung University
ABSTRACT
In this paper, we construct a universally applicable measure by which the dynamic capability of any one firm, for a given period, may be expressed and analytically compared to that of others in the system, based on readily available, critically unambiguous, financial statement data. In order to express the capacity for dynamic capability mathematically we employ the beta coefficient of a simple linear regression equation that examines the covariance of a target firm’s pre-tax operating margin change against the aggregate mean change of pre-tax operating margin for all companies within the system. Utilizing the net change in pre-tax operating margin for the system, we may then calculate the change in pre-tax operating for a target firm that may be ascribable to the function of this capacity for dynamic capability. We contend this last measure constitutes a measurement of the effectiveness of dynamic capability on firm
performance for a given period. We validated the proposed measurement with about 2000 public companies in the US during 2002-2011.
ii
Acknowledgement:
For my mom, Rae Catherine Mouton. 1935-01-01 ~ 2010-11-17
I love you. Thank you for my life. I miss you.
I’d also like to thank the following people:
Denise Ritchie, thank you for being my rock. Everything each of us achieves is ours together. I love you always.
Johannes Mouton, Dad thank you for everything. I still think you’re amazing everyday. I love you.
JinSu Kang, thanks for making this thesis possible. I would never have written it without you.
Julie Ritchie, thanks for all the support and keeping us close while we’ve been half way round the world.
iii
Contents:
Acknowledgement: ... ii Contents: ... iii List of Tables ... iv List of Figures ... v Introduction: ... 1From Definition to Operationalization ... 4
Shortfalls in Current Operationalization ... 16
Operationalization ... 25
Methodology: ... 31
Results and Recommendations: ... 34
Bibliography: ... 60
iv
List of Tables
Table 1: Dataset (Firm Performance and Capacity for Dynamic Capability)…………...45 Table 2: Dataset (Firm Performance and Potential Dynamic Capability)……….52 Table 3: Top 25 Most Potentially Dynamically Capable firms……….54 Table 4: Top 25 Most Dynamically Capable firms (Intercept > 0)………...60 Table 5: Dataset (Firm Performance and Dynamic Capability, Intercept > 0)……….….62 Table 6: Dataset (Firm Performance and Dynamic Capability, Intercept < 0)…………..64
v
List of Figures
Figure 1: Conceptual Construction of Dynamic Capability Indicator………...……38
Figure 2: System Operational Capability Performance ………43
Figure 3: Firm Performance and Capacity for Dynamic Capability………..46
Figure 4: Supposed Ideal Scenario Management of Dynamic Capability……….49
Figure 5: Firm Performance and Potential Dynamic Capability………...53
Figure 6: Firm Performance and Dynamic Capability (Intercept > 0)………..63
1
Introduction:
In the years ensuing the formative papers of Teece and Pisano (1994) and Teece, Pisano and Shuen (1997), an ever-increasing body of literature has grown in the field of dynamic capability. However, while there has been some measure of consensus and synchronous development surrounding the definition and characteristics of the subject, attempts to construct a universally applicable and objective measure by which the dynamic capability of any one firm may be expressed and analytically compared to that of others has met far more limited success. We would address this issue by the development of a new
operationalization that is based on readily available, critically unambiguous, financial statement data. To do so, we first employ a linear regression to calculate the portion of a target firm’s pre-tax operating margin change that may be attributed to environmental dynamism or systemic1 influence. Thereafter, by solving for the target firm’s change in pre-tax operating margin in relation to the entire system over the period, we can show conclusively the level of dynamic capability for the target firm over the said period.
1
While our choice of which firms comprise the system for our study is discussed in more detail later, we feel it important, even at this early stage, to note that systemic influence may be defined as the network level interplay of all firms grouped by an applicable secondary variable (the effect of the firm on the system and the system both in aggregate and in parts on the firm). In this regard, any number of secondary
variables including geographical area, economy, political economy, industry, technology and many others may prove apt for study. In our case we have chosen all firms publicly traded in the USA between 2002 and 2011.
2 Building from the basis of existing theory, we contend the operational capability of any firm is accurately expressed, through financial statement data, as its pre-tax operating margin. Furthermore, since dynamic capabilities are those that both modify or change operational capabilities, and do so in response to fluctuations in the system, we argue that the portion of a firm’s growth or decline in operating margin that may be accounted for by the level of systemic exposure that firm has is an accurate reflection of its dynamic capability. In order to express this mathematically we employ a simple linear regression equation that examines the covariance of a target firm’s pre-tax operating margin change against the aggregate mean change of pre-tax operating margin for all companies within the system. The beta coefficient of this equation represents the portion of change in operating margin that occurs as a result of the firm’s level of concomitant exposure to and integration with the aggregate interplay between firms occurring at the systemic level.
Through the course of this paper we will show that the development of our indicator to measure for dynamic capability is not only necessary but also appropriate. In order to do so, we begin with an examination of the literature to date on the notion of dynamic capability and show that our conceptualization remains true to the ever-growing body of work already developed in definition of the term. We find that despite increasing
complexity within the field on the nature, role, context, creation, and development of dynamic capabilities, such particularities do not detract from the fact that dynamic
3 in as much, the effects of changes in dynamic capability may be seen in changes in pre-tax operating margin.
Furthermore, by examining empirical work already undertaken in the field we show that while several obstacles have stood in the way of development of a universal index from existing research, our model not only addresses these but also offers great benefit for future research, chiefly through its through widespread applicability. The most pressing of the issues arresting existing research is the subjective nature of indicators employed by researchers in the field, along with the limited samples and data utilized. The latter factor is symptomatic, in itself, of the want for a practical, objective indicator for dynamic capability that may be readily constructed from widely available data.
This paper will also illustrate the methodology we have followed in the development of our indicator. In many ways the strength of our operationalization lies in the fact that it is not only mathematically rather unassuming but also in that it may be derived from readily available SEC filings (in the case of listed companies) or regular financial data (for private firms). We finally discuss the results of our study and find proof that dynamic capabilities, when working to modify sound operational capabilities, indeed enhance firm performance. Moreover, we are able to show that the capacity for dynamic capability alone is not sufficient to result in guaranteed increases in firm performance.
4
From Definition to Operationalization
Our conceptualization of dynamic capability is born out of extensive review of the existing scholarly research on the subject. We have viewed these investigations from a different perspective in that we are more focused on the financial statement effects of dynamic capability, than on the antecedents or nature of these capabilities themselves. We find that from the very first definitions of dynamic capability as an extension of the Resource-Based View (RBV), our conceptualization finds material resonance in the existing literature however. To build our theory, we will first deconstruct the notion of operational capability. Thereafter, we illustrate the relationship between dynamic capabilities and operational ones and also highlight the importance of the connection between dynamic capabilities and environmental dynamism. We conclude this section with a brief examination of the relationship between dynamic capabilities and financial statement data.
Operational Capability:
Much like Teece & Pisano’s seminal 1997 paper on Dynamic capability that first created scholarly interest in the notion (Teece, Pisano, & Shuen, 1997), our operationalization also finds its origins in an extension of RBV of the firm (Amit & Schoemaker, 1993; Barney, 1986; Makadok, 2001). The RBV explains the conditions under which a firm may achieve sustained competitive advantage as a result of that firm’s resources and
5 capabilities. Resources in this case refers to “stocks of available factors that are owned or controlled by the firm”, while capabilities refers to the firm’s “capacity to deploy
resources, usually in combination, using organizational processes, to affect a desired end” (Amit & Schoemaker, 1993). We assert that the authors’ concept of “resources”
counterparts the snapshot notion of a balance sheet very accurately, since the latter too reflects through a statement of assets, liabilities and equity, the summation of the “factors that are owned and controlled by the firm”. In much the same way, the concept of
“capabilities” proposed by Amit and Schoemaker, is at its core a review of the Income Statement. This conception of “capability” indicates the efficacy with which a firm may utilize the factors under its control to achieve the outcome of selling products and services for profit. The exercising of any of the firm’s “capacity” through “deploy[ing] resources” is reflected in its income statement as either an increase or decrease in revenues and expenses, with resultant effect on the “desired end“ – pre-tax operating margin. A firm’s operational capability is thus accurately reflected in its pre-tax operating margin2 performance, relative to its competitors and the nature and phase of the business. This concept of equivalence between the models of “resources” and “capabilities” with those of the Balance Sheet and Pre-Tax Operating Margin respectively, is our point of departure for explaining and measuring dynamic capability and it is critical to our
2
It warrants brief mention that the non-operating section of the income statement is not subject to our understanding of dynamic capability. Since dynamic capability is the ability to change or modify operational capabilities, it follows that only income from operations be accounted for in our
conceptualization. Consider too that non-operating activities are not sources of competitive advantage and hence not antecedents nor outcomes of dynamic capability
6 understanding of the term. Specifically we assert that the operating margin of the firm is equivalent to the concept of its operational capability.
However, while the RBV can be likened to the firm picking resources to best employ within a specific competitive environment, this view is too static to accurately capture the turbulent environment within which modern firms operate. Teece et al recognized this shortcoming and developed the very first definition of dynamic capability as “the firm’s ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments” (Teece et al., 1997). These two fundamental theoretical principles are crucial for our consideration:
1) Dynamic capabilities modify operational ones.
2) Dynamic capabilities arise as a product of environmental dynamism.
Dynamic Capabilities vs. Operational Capabilities:
Winter, writing in 2003, pays particular attention to this distinction between operational capabilities (he calls these “zero-sum” capabilities) and dynamic ones (which he refers to as “first-order” capabilities). He notes that dynamic capabilities are “those that operate to extend modify or create ordinary capabilities” and considers operational capabilities to be those employed to “earn a living by producing and selling the same product on the same scale and [to] the same customer population”. Dynamic capabilities then are concerned
7 with changes in the product (or its production process), the scales of the operation or the market segments served (Winter, 2003).
Zahra et al corroborate this view when they refer to “substantive capabilities” as those used to solve a problem and dynamic capabilities as the “higher-level” capabilities that function to bring about a change in the former (Zahra, Sapienza, & Davidsson, 2006). To be sure, the exploratory nature of dynamic capabilities equates somewhat to March’s exploration-exploitation view, in that dynamic capabilities are employed in the
exploration of new opportunities, while operational capabilities are concerned with the effective exploitation of the existing resource mix (March, 1991).
For our conceptualization, recognizing that operational capabilities are those modified by dynamic capabilities, it follows that by measuring changes in pre-tax operating margin we are able to measure dynamic capability. Furthermore, by capturing the volatility of change (rather than simply change year on year) in pre-tax operating margins our
indicator accurately takes the nature of these “higher-level”, “first-order” or “explorative” capabilities into account. Changes in product or the production process are made in an effort to increase revenues, while they also incur costs in doing so; similarly increases in scale, or the exploration of new markets have the same effect. The manner and nature of these changes of “substantive”, “zero-sum” or “exploitive” capabilities, in relation to the trends within the system as a whole, shows their effectiveness. (March, 1991; Winter, 2003; Zahra, et al., 2006). We contend that firms with a higher level of dynamic
8 capability then are those who are able to modify their operational capabilities so as to pursue maximum growth in pre-tax operating margin for a given period.
Dynamic capabilities and financial statement data:
There is a great focus in current literature on the various types of dynamic capability, so much so that we feel it necessary to include this section to show that irrespective of the nature of dynamic capabilities they are reflected accurately in the notion of changes to pre-tax operating margin (in response to systemic influences). Teece et al along with many others, expressly define dynamic capability as an ability or capacity, with a specific desired end (addressing rapidly changing environments), thus underlining the importance of strategic management in the exercise of dynamic capability (Barreto, 2009). For our own indicator, the importance of strategic management is correspondingly fundamental. The idea of “integrating, building, and reconfiguring internal and external competencies” is an expression of management’s role in modifying a firm’s objectives, changing its operational, financial and investment policies, developing programs and projects to achieve these objectives, and the allocation of resources to the above, all in interests of increasing returns (Teece et al., 1997).
Winter argues that dynamic capabilities are “a learned and stable pattern of collective activity through which the organization systematically generates and modifies its
operating routines in pursuit of improved effectiveness” (Zollo & Winter, 2002). They go on to define “routines” as “behavior that is learned, highly patterned, repetitious, or
9 quasi-repetitious, founded in part in tacit knowledge – and the specificity of objectives” (Winter, 2003). The authors’ insistence on “operating routines” and “improved
effectiveness” are acute demarcations. Since operating routines are in practice the summation of processes that draw in revenues and have concomitant costs (i.e. the components of operating margin); quite clearly the effects of change in these processes to bring about increased effectiveness must be reflected in changes to the operating margin of the firm. These changes in operating margin then result in increases or decreases in the performance of the firm as a whole. For our conceptualization this notion serves as reaffirmation of the locus of dynamic capability, firmly seated within the operational realm of firm activity. However, it bears mentioning, even at this early juncture, that effectiveness as used here, does not necessarily equate to increased operating income in currency terms alone. To be sure, a firm may even choose to have lower operating
income in some cases (such as taxation benefits). Rather, effectiveness refers to how well the firm is able to increase its operating margin over a given period.
Echoing Teece et al, Eisenhardt & Martin find dynamic capabilities to be “the firm’s processes that use resources – specifically the processes to integrate, reconfigure, gain, and release resources – to match and even create market change. Dynamic capabilities [they argue] are thus the organizational and strategic routines by which firms achieve new resource configurations as markets emerge, collide, split, evolve, and die”
(Eisenhardt & Martin, 2000). As we have already noted, resources are analogous to the summation of the balance sheet while the income statement in turn, shows how these resources are reconfigured over time. Dynamic capabilities then, paraphrasing Eisenhardt
10 & Martin (2000), are those processes that the firm undertakes (and which are recorded on the income statement) that realign its resources (i.e. the balance sheet) to shape new market opportunities, face changes, challenge new emergences and deal with sudden endings. For illustration, a company may face a significant challenge to its market share (revenues) by the development of a disruptively innovative new product by a competitor. The company in response must defend its top line by developing new products or services (through greater expenses perhaps in R&D or in the acquisition of patents from another firm). Conversely, the firm itself may drive growth by developing innovations of its own. In another scenario, a firm might deal with new legislation that drastically increases raw material costs (expenses). Once again, to protect against erosion of operating income, the firm must act to realign it resources (assets and liabilities) to offset the expense increase and maintain profitability by perhaps vertically integrating its procurement process (capital expenditure) or by changing its product entirely (implying R&D or other costs). There are a myriad of different circumstances that can illuminate this point: A firm must protect its operations (operating income) by engaging in processes (that either increase or decrease revenues and expenses) and ultimately realign assets, liabilities and equity as reflected on the balance sheet. This realignment is reflected in turn in the way the firm is able to protect and grow its operating margin over time.
In summation, consider the excellent operationalization of dynamic capability, built by Pavlou and El Sawy, that captures much of the research to date into dynamic capabilities (Pavlou & El Sawy, 2011). They claim that (measurable) dynamic capabilities fall into categories of “sensing, learning, integrating and coordinating”. While the capability
11 nomenclature employed may imply a “soft skill” perspective, the hard effects of the various capabilities on the income statement are nascent and implied if not taxonomically expressed. Certainly, the “sensing abilities” involved in generating, disseminating and responding to market intelligence all imply incurring expense in the pursuit of future revenues (Pavlou & El Sawy, 2011). Similarly, “acquiring, assimilating, transforming and exploiting knowledge” as expressed in the notion of “learning capabilities” incurs its own expenses. The idea of an “integrating capability” denotes how these costs may be lower for one firm than another by virtue of its increased capabilities in this regard. Finally, resources are allocated to tasks and activities orchestrated (both further expenses) in an effort to ultimately bring about a shift in operational capabilities that results in new revenues (or reduced costs) to the firm and a sustained competitive advantage. It is this explorative exercising of dynamic capability, leading to a shift in operational capability, that constitute the mitigation or exploitation of trends within the system the firm operates in, for the aims of increased returns. Periods of decline in operating margin for the system as a whole are unimpressive upon the dynamically capable firm as it pursues its
protective operating margin strategy with minimal impediment. Conversely, during periods of expansion, the firm is able to employ its dynamic capability to rapidly change its operational capabilities and expansively increase the rate of change in its pre-tax operating margin. Indeed, irrespective of the nature, role, purpose or specifics
surrounding the creation and development of dynamic capability, or indeed the various kinds of dynamic capabilities promulgated, our summative indicator, the capacity of exposure to systemic changes in pre-tax operating margin combined with the
12 effectiveness with which firms are able to manage this capacity, can be said to
encapsulate the core of all many of the definitions put forth in the literature, thus far.
Environmental dynamism and dynamic capability.
Scholars are divided among those who argue that the concept is exclusively applicable to highly dynamic environments, others who accept a spectrum of varying degrees of dynamism, those who believe the concept is applicable to both stable and dynamic environments and finally those who exclude the characteristics of the environment completely (Barreto, 2009).
Teece et al (1997) embedded their theory in the awareness of rapidly changing environments in recognition of the constantly shifting competitive horizons that most firms face today. They accurately noted that the RBV fails to capture this notion of constant redevelopment and redefining of competitive advantage, since it is concerned primarily with the notion of “resource picking” while dynamic capability is focused squarely on the concepts of “resource renewal” and reconfiguration (Pavlou & El Sawy, 2011). Moreover, for this reason, any attempt to measure dynamic capability would not be satisfied by simply measuring changes in the operating margin of a firm in isolation of the context in which it operates. In 2007 Teece reiterates the point when he refines this appositeness further to mean those environments characterized by international
13 technological and managerial knowledge markets, systematic technical change and susceptibility to institutional and regulatory shocks (Teece, 2007).
However, other authors like Eisenhardt and Martin argue that dynamic capabilities are valuable not only in highly dynamic environments but also in “moderately dynamic” ones where “change occurs frequently, but along predictable and linear paths” (Eisenhardt & Martin, 2000). Conversely, Zahra et al promulgate that “a volatile or changing
environment is not a necessary component of a dynamic capability” (Zahra et al., 2006). Zollo and Winter confirm this view and argue that dynamic capabilities arise and are employed in less dynamic environments (Zollo & Winter, 2002). Lastly, some scholars such as Makadok ignore the issue of environmental dynamism as being extraneous to their conceptualizations (Makadok, 2001).
In our operationalization, the level of environmental dynamism is similarly peripheral. While dynamic capability may be more or less important in environments of varying levels of turbulence, this has no bearing on the relevance of our indicator or on its calculation. We contend that comparison of our indicator for a particular firm with that of its competitors (defined by those sharing a similarly turbulent environment) will be sufficient to comment on the importance of its dynamic capability in that milieu. Some firms, for example, may enjoy advantage because of their comparative high levels of dynamic capability within a static environment, while not being a particularly
dynamically capable overall. Others may find themselves unable to sustain a competitive advantage (i.e. have a comparatively low dynamic capability) within a particularly
14 turbulent environment, despite having high dynamic capability when compared to the entire system. We consider dynamic capability to remain the ability to modify a firm’s operational capabilities in light of changes to the greater context in which the firm operates. If a firm were to operate within an environment of low fluctuation, the firm would still possess some measure of dynamic capability. The importance or application of this capability while being perhaps greatly reduced is not annulled.
Dynamic capability outcomes:
Zollo and Winter argue that the very viability of an organization will prove transitory should it have no dynamic capabilities (Zollo & Winter, 2002). We do not subscribe to this sweeping view of dynamic capability outcomes. Instead we recognize that dynamic capabilities have varying levels of significance in different environments. However, since we also contend that these capabilities are nevertheless expressed as changes in
operational capabilities (operating margin), it follows that we argue for some measure of causal (though not linear) relationship between a firm’s level of dynamic capabilities and its performance. Teece et al comprehensively state that a firm’s competitive advantage and, hence, capacity for wealth creation and ultimately success or failure rest with its dynamic capability (Teece et al., 1997). While we may contend that every firm’s success or failure need not rest with solely with its dynamic capabilities, we do recognize the link between these capabilities, profit, and firm value.
15 In a later paper, Teece notes that the “ambition of the dynamic capabilities framework is nothing less than to explain the sources of enterprise level competitive advantage over time” (Teece, 2007). Eisenhardt and Martin seem to moderate this view somewhat in an earlier paper when they advance that “dynamic capabilities are necessary, but not sufficient, conditions for competitive advantage” (Eisenhardt & Martin, 2000). We concede, like Eisenhardt and Martin, that competitive advantage (particularly in
industries with low environmental dynamism) may be ascribable, at least in part, to more static type operational capabilities (like scale for example, or even to external sources like import tariffs and other artificial mechanisms). We also recognize that since competitive advantage equates to a firms ability to sell more products or services at a cheaper cost than its competitors, over time the corporation's competitive advantage, and hence profitability and returns on both assets or equity, must be enhanced and by its dynamic capabilities.
16
Shortfalls in Current Operationalization
The lack of a universally agreed upon measurement index has certainly hampered research in the field of dynamic capability. While several important empirical studies have been undertaken into the characteristics, outcomes and antecedents of Dynamic capability (for an excellent taxonomy see Barreto, 2010), without exclusion these studies have had to stop short of extrapolation toward the ideal of a universal index for
measuring dynamic capability. Clearly development of precisely such an indicator is of great value to the field since it would allow for a wealth of comparative research into the relevance of dynamic capabilities and its effects across industries, geographic locations, length of supply chain, marketing spend, innovation, capital expenditure and a myriad of other secondary variables.
Pressing among the problems associated with such a development has been the fact that past studies, either through subjective data sources or limited sample sizes are not suitable for universal application. That is not to say that these studies are not without merit or flawed in their own right. The point is that they are not suitable for development into an operationalization of dynamic capability that may be applied across all industries and over time. The purpose of this section of the paper then is merely to illustrate the limitations of existing studies for the quantification of dynamic capability; and
furthermore, to inform how our new construct will thus fulfill an important void in the literature and allow for comparative research in the future.
17
Specificity:
In 1997, Helfat undertook one of the first “empirical investigation[s] of dynamic R&D capabilities” that dealt with the “role of complementary know-how and other assets in the context of changing conditions in the U.S. petroleum industry during the 1970’s and early 1980’s” (Helfat, 1997). While Helfat was able to show that companies with either larger physical assets and technical knowledge responded to the rising oil prices through greater R&D spend on coal conversion technologies, the fact that the study focused on only the largest energy firms in the U.S.A. and only on R&D expenses, precludes expansion of the model to embrace a universal cross-industry understanding of dynamic capability. While R&D expenses and scale might very well be antecedent indicators of dynamic capability in certain cases, a more holistic approach is needed to account for the different sources of sustained competitive advantage. In other sectors, for example, price pressures like those in the petrochemical industry during the period under study might have been addressed through increased marketing expenditure, raw material sourcing or a myriad of other options not available or not employed by energy firms.
This kind of industry or even firm specific research into dynamic capability has continued within the literature, without any real possibility of a move toward more collective or comparative application. In 2000, Rosenbloom examined the role of managers as a central element of dynamic capability (Rosenbloom, 2000). The study focused on just one firm – NCR Corporation – a provider of “self-service solutions for
18 ATM machines and software, POS and Retail systems and airline check-in systems” (NCR Corporation, 2012). As with the previous Helfat study, while management might have a great effect on dynamic capability for NCR, the question of how NCR
management’s influence on dynamic capability might be compared to other firms or sectors remains unanswered.
Similarly, in 2000, when Galunic and Eisenhardt undertook “an intensive and inductive study of a single Fortune 100 corporation, [that] describes how dynamic capabilities … reconfigure division resources”; their theories explaining the characteristics of dynamic capability within multi-business firms, while important, lacks comparability with other kinds of businesses with different structures (Galunic & Eisenhardt, 2001). Moreover, the fact that “data were collected through interviews, questionnaires, observations, and company archives” makes replication of the study across many firms impossible (Galunic & Eisenhardt, 2001).
Also in 2001, Yahoo! And Excite were the subject of a study into how the “form,
function, and competitive advantage of these firms dynamically coevolved”, a process the authors labeled “continuous morphing” (Rindova & Kotha, 2001). Again, this study, while insightful, cannot elucidate how the dynamic capabilities of these two companies compare to other firms not in the midst of the dawning of the Internet age. Consider too, Gilbert’s 2006 study of a newspaper organization (Gilbert, 2006). Once again,
instrumental comprehension of dynamic capability in its relation to the coming of the digital epoch to newspaper printing is gained from the study, however, we are limited in
19 our application of the theoretical constructs purported by this study to firms who
experience some similar kind of discontinuous change. So too in Lampel and Shamsie’s study of the “evolution of capabilities in the Hollywood movie industry in the aftermath of the transition from a studio era dominated by integrated hierarchies to a post-studio era dominated by flexible hub organizations supplied by networks of resource providers” (Lampel & Shamsie, 2003).
To be sure, these and a multitude of other studies that also focus on firm or industry specific dynamic capabilities, often in relation to very specific periods of economic development or change, do not lend themselves to our aims of developing a universal index to be used across multiple contexts. It is very difficult to deduce comprehensive understandings of dynamic capability from studies that are so (necessarily for their purpose) narrow in their application.
Empirical restrictions:
Other studies, meanwhile, have in a positive development included greater numbers of firms or industries in their dataset, however, the total lack of an operationalization for dynamic capability based on concrete financial statement or similar inarguable numbers, has forced these researchers to rely on the use of inappropriate data for extrapolation to a larger scale. Clearly while larger datasets would generally speak to a more widespread applicability for research findings, these particular studies are at once inherently flawed (for our aims of indicator operationalization) by the subjective nature of surveys, and the
20 difficulty of replicating such studies on a very large scale. In 2005, for example Song et al examined responses from 466 joint ventures operational between 1990 and 1997 (Song, Droge, Hanvanich, & Calantone, 2005). Their study examined the performance outcomes of dynamic capability through survey responses from “79 presidents; 214 vice-presidents of marketing or directors for marketing operations; 187 vice-presidents of R&D or manufacturing; and 61 others”. The insights gained from this data, while critical for the research the authors undertook, lack the extra gravity that impartiality would lend to our proposed single indicator based on indisputable financial data.
Similarly, Slater et al in an express effort to increase the generalizability of their work built a study that included responses from 380 marketing executives from “manufacturing and service businesses operating in 20 different 2-digit SIC code industries” (Slater, Olson, & Hult, 2006). The authors effectively explored the links between strategic orientation (as exemplified through the “Miles and Snow (1978) and Porter (1980) typologies” and dynamic capability (Slater et al., 2006). However, despite the rigorous testing of the data and the barrage of statistical analyses to which it may have been
exposed; the numbers still carry less precision than that which may have been afforded by the use of an indicator based not on “multi-item scales” but on collected financial data (Slater et al., 2006).
Comparably, consider Marcus and Anderson, who utilized 1997 survey data from 108 U.S. grocery chains in their 2006 study aimed at a better understanding of the
21 supply chain and environmental management (Marcus & Anderson, 2006). Or, for that matter, Kale & Singh who investigated 175 large U.S. firms involved in alliances (Kale & Singh, 2007), or Danneels who studied 77 U.S. public manufacturing firms (Danneels, 2008), or Døving and Gooderham who investigated 254 Norwegian small accountancy practices (Døving & Gooderham, 2008). All exhibit use of survey data that, for our purposes, negates their use in construction of our operationalization. In as much as these studies are excellent in their own right, they validate the position that a universal
indicator is both desirable and necessary within the community of research into the notion of dynamic capability.
And, to be sure, such a necessity has already been recognized. Resultantly, more encompassing quantifications of dynamic capability have been built, chiefly relying on archival sample data. The problem with these studies, in juxtaposition with their counterparts discussed above, is that rather than having a problem of “too little” data, they have “too much”. Indeed, in an effort to remain true to the definitions of dynamic capability, empirical researchers recourse to the addition of more and more variables to their operationalization in order to capture the multitude characteristics proposed in the ever-growing body of theory.
King and Tucci, for example, in building their extraordinarily detailed, if complex measure, employed 13 different variables to operationalize the manner in which a firm’s “experience influenced both the value and probability of market entry” (King & Tucci, 2002). This model illustrates how difficult building composite indexes for even a single
22 component or characteristic (in this case market entry) of dynamic capability may be. Kor and Mahoney’s 2005 study is another excellent example of this phenomenon. In this case the researchers found, inter alia, that “in a sample of technology-based entrepreneurial firms … a history of increased investments in marketing is an enduring source of
competitive advantage” (Kor & Mahoney, 2005). Their methodology included the use of a 4-part model “regression analysis of effects of resource deployments on Tobin’s q”. The latter being the economic firm level performance indicator chosen, while the effecting variables were measured in an array of forms including “… firm-specific experience of top managers … institutional investor ownership … management ownership, R&D deployment intensity and marketing deployment intensity” (Kor & Mahoney, 2005). While the methodology is sound, employing such a multitude of variables, both difficult to ascertain and replicate, diminishes the exportability and
extrapolative power of the study. We purport that a single indicator of dynamic capability, rather than such a complicated index, would be ideal.
Similarly consider a study of Spanish Banks between 1983 and 1997, by Zuniga-Vicente and Vicente-Lorente. Here the authors sought to contrast the “adaptation view (classic strategic management and dynamic capabilities) and the ecological approach” in terms of strategic change. They found a “positive and significant effect of strategic moves (or strategic change) on the likelihood of organizational survival” (Zuniga-Vicente & Vicente-Lorente, 2006). In order to do so their paper relied on “two methodological innovations: (a) the definition and measurement of ‘strategic moves’ (or strategic change) by using a … cluster algorithm, the MCLUST; and (b) the control of the non-observable
23 heterogeneity using panel data models for ‘probit’ regression”. Again, the study had to rely on very complex tools to operationalize just a single component of dynamic capability, and resultantly we again contend that our proposed operationalization, that holistically captures the volatility of income and expenditure changes brought about by all operating activities related to the exercise of dynamic capability is merited in the current literature.
Even in cases where a indicator construction itself is not very complex, for example Karim in 2006, who purported that “acquired and internally developed units serve different roles in the process of change” in her study of 250 medical firms between 1978 and 1997 (Karim, 2006), the tracking of each unit in question, over the entire study period as it structurally evolves, is not the kind of process that is easily repeatable for thousands of firms. Conversely, the regression coefficient we propose is able to do precisely that by relying exclusively on readily available financial statement data.
In sum, while there is a definite want and need for a universal dynamic capability indicator, not least of all because such an indicator would allow true comparative study across all industries, timeframes and economies, there are also considerable issues with existing research that precludes the use of current methodology for such an endeavor. In many instances, data or methodology used is either highly subjective or greatly specific to a particular firm or industry. In other cases, operationalization of dynamic capability has despite exceeding complexity, lacked replicability or in some cases applicability across milieus. A great advantage to further research in the field of dynamic capability
24 would hence be an operationalization that upholds the definitions of the construct while at the same time being easily replicable and based on readily available, objective data. In short, we contend that our proposed indicator fulfills that promise.
25
Operationalization
Hypothesis:
The central hypothesis we present in this paper is that the effectiveness with which a firm is able to manage the opportunities for change to its operational capability that arise as a result of the environment in which it operates fulfills the requirements for a universal indicator for the dynamic capability of that firm. Our indicator is quantified by the resulting net change in pre-tax operating margin for a target firm that may be ascribed to its systemic integratedness during the same period. To that end, we have shown already that our conceptualization holds true to most of the definitions of dynamic capability, as it originated in the RBV of the firm, strategic management theory, and evolutionary economics and throughout the growing research into the subject, regardless of the variations of definition encountered.
Operationalization:
To reiterate, we assert that corporations attain a specific level of operational capability. In financial terms, the corporation is able to sell a certain number of its products or services, i.e. generate revenues of a certain dollar value, while at the same time incurring a
particular set of associated costs and expenses for doing so. The move from here to operating margin is merely the division of the resulting profit from operating activities
26 into top line revenues to give the pre-tax operating margin of the firm, expressed as a percentage. Importantly, a firm’s operating margin is also an analysis of its competitive advantage. The firm is defends, expands or retreats from a set market share while incurring a cost in for example, research and development, marketing, development of additional facilities or productive capacity for doing so. (The pre-tax operating margin that a firm achieves therefore represents how it has selected and applied the valuable bundle of resources at its disposal to bring about an advantage over its competitors and thereby win market share and increase revenues, decrease expenses and ultimately increase returns over time.
Since dynamic capabilities are those that modify operational capabilities in response to changing environments, we assert that changes to operating margin that are occur as a result of the firms integratedness with the system itself constitutes the dynamic capability of that firm. In practice each firm within the system similarly exercises its own
capabilities resulting in a market that is not static and predictable but subject to
fluctuation and change. For example, competitors may increase their expenses in terms of marketing or research and development in an attempt to steal market share from the firm and in so doing leave the firm with less revenues than expected. Prices for raw materials and goods may affect the firm’s own expenses as well as those for all companies in the market. A competitor may even, through some disruptive innovation, drastically shrink the market for one of the firm’s own products. Internally, critical staff may be lost to competitors, plants become obsolete and conflicts between management occur. Indeed, all the complexities of economic evolution are captured in this notion of pressure and
27 opportunities within the system, that create the capacity for a firm to exercise dynamic capability by modifying its operational capability.
In seeking to mathematically define dynamic capability, we have employed a simple linear regression to investigate the covariance of a target firm’s operating margin and the overall trend in operating margin growth or decline experienced by the system in which it operates. The known x’s and y’s in this model are thus given by yearly change of the aggregate mean of pre-tax operating margin for all companies in the system and the yearly change of pre-tax operating margin for a target company, respectively. It is this slope (or the beta coefficient of the regression equation) that describes the volatility with which the pre-tax operating margin of a target company moves in relation to the
performance of all firms within the system.
Assumptions:
Three critical assumptions bare mention here. Firstly, we hold that all the companies in our system (publically traded USA companies operational between 2002 and 2011), rather than just competitors within a specific sector, are the appropriate basis for our regression model. This notion is born out of the importance of the interconnected nature of contemporary economic systems. For illustrative purposes, consider that the increase in selling price of a particular product or service, while increasing the revenues of the manufacturer (and hence it’s operating margin), also has the concomitant effect of increasing the cost of goods for all downstream companies, irrespective of the sector in
28 which they operate, (and thereby challenging their own margins). In this way, an increase in the oil price is likely to have a negative effect on operating margins within the auto sector as fewer consumers purchase new vehicles and hence revenues decline. The same holds for routines and capabilities; as upstream suppliers become more efficient or innovative, these effects are also translated into very real changes in revenues and expenses for their clients. Legislative effects are similarly translated across sectorial boundaries. A stringent tax on carbon emissions for example has a ripple effect throughout the system as those players whose processes are emission intensive face drastically increased operating expenses that are passed along the value chains of their clients. In some merely examining the operating margins for just a particular sector negates the influences that more widespread connections may have on any one firm.
Furthermore, consider the very similar evolutionary economics perspective employed by Teece et al (1997). They explain the complexity of interdependence, competition, growth, structural change, and resource constraints that a firm faces, in terms of the routines, path dependencies and organizational learning that it undergoes in order to adapt, evolve and survive (Nelson & Winter, 1982; Schumpeter, 1934). While the nature of mechanisms employed by dynamic capabilities are not central to our study, the effects of their
engagement are. The often intangible mechanisms of knowledge creation and transfer, for example, have very real cost and revenue implications; Consider for illustration that as organizations learn, the results thereof may be seen in decreased costs associated with production or increased revenues from new product development, and reflected as such in financial statement data. This fact speaks again to our assertion that irrespective of the
29 nature of and character of dynamic capabilities, without fail the effects of their
employment are visible in changes to the pre-tax operating margin of a firm.
Secondly, we accept that firms in differing stages will have different operating margin growth expectations. We contend that this notion is superfluous to our model however. In our discussion above we have illustrated a stable and mature company hoping to achieve growth in operating margin over time, while other firms may for example have very high margins on their existing products and services but are likely to see these returns slip rapidly as competition enter the business. At the same time, while firms with newer innovations typically have increased initial expenses, they also become cheaper over time as learning effects and network externalities come into play. While in still other cases, one may encounter a firm that is consistently losing money and has a negative operating margin. In all permutations however, volatility, rather than growth, relative to the other firms in the system is the central estimation we are concerned with.
Thirdly, whether or not firms have a high or low operating margin (in dollar terms) is irrelevant to the study since we are concerned merely with the change in operating margin (expressed as a percentage) year on year. To be sure, while firms may have reasons to pursue, for example, a low operating margin (tax benefits for one), the fact remains that the in practice most every firm desires a consistent if not increasing operating margin. Moreover, we are concerned only with the portion of movement in operating margin growth that can be credited to exposure to systemic forces, rather than in any way offering commentary on the overall trend in operating margin itself. Thus a
30 firm with a negative operating margin for a period may still have a positive dynamic capability movement for the same period. Furthermore, a pre-tax figure is used in our methodology to negate the effects of varying tax levels on reflected company
performance.
31
Methodology:
The original scope of our study comprised all firms that were traded publicly in the United States between 2002 and 2011. After initial data was gathered utilizing figures prepared by Value Line Inc. (Value Line, 2012) and collated by Dr. Aswath Damodaran, Professor of Finance at the Stern School of Business at New York University
(Damodaran, 2012), applicable figures for over 7000 companies were obtained. By eliminating companies that ceased to exist between 2002 and 2011 as well as those with missing, abnormal or outlier data we arrived at a smaller dataset of 1967 companies.
From this dataset, Pre-Tax Operating Margin, expressed as a percentage, from 2002 to 2011was tabulated. Thereafter, the yearly change in in this pre-tax operating margin was calculated for each firm by simply subtracting the pre-tax operating margin for each year of the study from the same figure for the previous year. The change of aggregate mean of pre-tax operating margin for all firms was also calculated. In this case by adding all the margins together and then dividing by the number of firms in the dataset. These two arrays thus become the inputs for a simple linear regression, in which the model is given by:
(1)
32 x: change of the aggregate mean of pre-tax operating margin for all firms in the system per year
y: change in pre-tax operating margin for a target firm per year
The slope
, a regression model parameter, stands for the capacity for dynamiccapability as it is proposed in this study. It is the influence of systemic forces upon the firm in its pursuit of increased operating margin and the firm’s effect on the system at the same time (in other words, the firm’s integration with the aggregate interplay between firms occurring at the systemic level, at the indicated level of significance). The
intercept of the regression equation shows the change in pre-tax operating margin thatmay be theoretically ascribed to the firm itself, in isolation of the system.
The product of
and the x value (mean change in operating margin for all firms in the period) gives the size of increased operating margin for a target firm that may be ascribed to this systemic influence or its dynamic capability. The regression models (Equation 1) were built for the1967companies mentioned. From this juncture we eliminated those firms whose significance for the dynamic capability capacity indicator was 0.1. Through this process our data set was reduced to 857 companies.Finally, we computed the dynamic capability indicator of the firm by multiplication of
33 formula given below thus constitutes the dynamic capability indicator (d) for a specific firm in a given period relative to the system in which it operates:
∆ (2)
The period and system applicable for study is left open to interpretation. In future studies it may be of great value to limit these variables according to specific periods of volatility (stock market crashes), geographical areas (countries or regions), political economies (socialist and capitalist), environmental dynamism, industries, technologies and many others. Indeed, most every financial ratio really only becomes meaningful relative to a specific context. In our study we have expressly examined the whole system of public companies that were publicly traded on stock exchanges (but not necessarily physically operating) in the United States between 2002 and 2011, to build the broadest possible base of applicability upon which to argue our case.
34
Results and Recommendations:
Appendix A compounds the results of our study and includes the following data for each firm: Company Name, Ticker Symbol, Industry Name, SIC Code, Intercept and
Significance, Beta Coefficient and Significance of Regression Model, Net Change in Operating Margin and Dynamic Capability Indicator (i.e. the change in operating margin directly related to systemic influence). The firms are arranged firstly according to
dynamic capability and secondly (for reasons that will become apparent) by
greaterthan zero. Selected pertinent findings are presented below for more detailed discussion.
System Aggregate Performance:
In the period under study, we find an initial sharp incline in mean aggregate pre-tax operating margin for publicly traded companies in the United States; followed by a longer decline from 2005 to 2010 before recovering somewhat again. The net effect in the period is a marginal growth of 1.87% (from 7.92% in 2002 to 9.79% in 2011). This net change over the period, along with the systemic fluctuations between is illustrated graphically below:
35
Figure 2: System Operational Capability Performance (mean aggregate pre-tax operating margin growth 2002-2011)
The Beta Coefficient (Capacity for Dynamic Capability):
After running regression models for the 857 firms in our finalized dataset we find that beta coefficient varies widely from a high (i.e. greater capacity for dynamic capability as a result of increased exposure to systemic influence) of 11.96 for Vaalco Energy, to a low of -9.16 Ciena Corporation. Returning to the notion proposed in our methodology, recall
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00% 2002 2004 2006 2008 2010 M ea n A g g re g at e P re -T ax O p er at in g M ar g in Year
36 that it is this coefficient (
) that stands for the capacity for dynamic capability. It is thelevel of systemic influence on operational capability changes. Furthermore, we contend it is this capacity when multiplied with the actual net change in operating margin of the system that accounts for the effects of dynamic capability on the operating margin of the target firm (i.e. the theoretical size of changes in operational capability, or pre-tax operating margin, arising as a response to environmental change).
Some scholars, however, have argued that dynamic capability is limited only to the capacity for change, or the coefficient (
), and not the product of this influence. Helfatet al raise the issue when they assert that dynamic capabilities denote “the capacity of an organization to purposefully create, extend, or modify its resource base” (Helfat et al., 2007). While we wholly concur with the aim of the firm being to reflect a situation on its balance sheet that reiterates its strategic intent, we however differ from Helfat et al in that we contend that the capacity for dynamic capability is not the full measure of the concept. All firms have a capacity for dynamic capability but through mismanagement of this capacity or through financial over extension firms are sometimes not able to translate this capacity into more effective operational capabilities and hence increasing returns. In order to do so, the firm must carry out its operations (exercise its total operational
capability including the modifications by dynamic capabilities) in such a manner so as to increase its operating margin and thereby returns.
37 We can illustrate this point graphically (both the dataset and the curve itself are shown below). We arrive at the curve by first ranking firms, according to their net change in operating margin between 2002 and 2011, and then grouping them in 10-percentile lots. By plotting each 10-percentile group of firm’s average net change in operating margin, against their average capacity for dynamic capability (as expressed as
in theregression equation) we find, very clearly, no evidence of a linear relationship
(suggesting that increased capacity results in increased operating margin performance) between the two:
Table 1: Dataset showing relationship between Pre-Tax Operating Margin Firm Performance and Capacity for Dynamic Capability
Capacity (
)Firm Performance (Net Change in Op. Margin)
Terms Top 10% of firms 3.26 37.97% 86 Next 10% 1.66 10.98% 85 Next 10% 1.22 6.53% 85 Next 10% 1.10 3.79% 86 Next 10% 1.03 2.16% 86 Next 10% 0.71 0.74% 85 Next 10% 0.95 -0.79% 86 Next 10% 1.08 -2.38% 86 Next 10% 1.36 -5.07% 85 Bottom 10% 1.87 -16.43% 86
38
Figure 3: Graphic Relationship between Pre-Tax Operating Margin Firm Performance and Capacity for Dynamic Capability
-20.00% -10.00% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 A v er a g e F ir m P er fo rm a n ce (m ea n c h a n g e in P re -T a x O p er a ti n g M a rg in )
Average Capacity for Dynamic Capability (mean Beta Coefficient)
39 Instead, we find that capacity without effective application is shown to be as good, or even better in some cases, as no capacity at all. For around 90% of the companies under study, a capacity for dynamic capability of between 0.71 and 1.66 might be considered hypothetically equal in chance for advantageous or disadvantageous changes in operating margin as a result. As one would expect, increased systemic exposure goes together with risk of either great benefit or peril.
In general, however, it appears that past a certain threshold (1.87 beta coefficient in our study), any greater capacity for dynamic capability tends to coincide with always
positively improved effectiveness of dynamic capabilities in their effect on net operating margin. This does not mean that firms past a certain threshold of systemic exposure can always expect better firm performance as a result. We propose instead that at a high enough coefficient (somewhere between a beta coefficient of 1.66 and 1.87 in our dataset here), firms have such great systemic exposure that they must either adapt and ensure the effective use of this capacity, or face death. Their operational routines and resources are unsuitable for any modification by their dynamic capabilities that can effect positive firm performance, as a result they must alternately develop either sufficient wholly new operational capabilities for them to profitably enact their dynamic capabilities upon or indeed allow their dynamic capabilities to supersede their operational ones and become in themselves a primary source of operational capability or competitive advantage.
Further contending against the capacity as summative measure of dynamic capability argument, consider that firms with low beta coefficients (i.e. lower capacity for dynamic
40 capabilities) under conditions prevalent in 2005/2006 when average pre-tax operating margins fell almost 5.5% would have minimized exposure to the decrease. A firm with a coefficient of 0.8 would be expected to only have experienced a -4.38% drop in growth of its pre-tax operating margin. In this scenario it is easy to argue that capacity alone accounts for dynamic capability and that this particular firm may be considered to have better used its minimized systemic influence to offset challenges to its operational capabilities and hence operating margin.
However, when the situation is reversed and the average pre-tax operating margins within the system are rising, such as the case in 2011, those same firms would not see increases in operating margin growth equal to the average across the system, if they maintained their operational capabilities as they were before. The same firm in this case would be expected to see increases of only 2.36% while the average for the market increased by 2.67%. A large loss following a large increase is obviously as good as no increase at all.
It may further be argued that a firm would thus ideally pursue a strategy that would allow it to minimize exposure to systemic influence during periods of decline in operating margin growth, while following a more expansive and engaging strategy that allowed it to fully exploit and drive periods of increasing pre-tax operating margin growth. In other words the firm would like to have a low beta coefficient of the regression equation during periods of declining growth in operating margin, while also instantaneously being able to switch to a more immersive strategy and hence higher beta coefficient during periods of high growth in operating margin. Dynamic capability in this case would merely be the
41 firms’ ability to change its level of exposure to systemic changes in pre-tax operating margin, in such a way that negative effects on pre-tax operating margin are minimized while periods of growth are maximized. Such a supposed ideal scenario is presented in Figure 1 below. The shaded areas indicate the range of acceptable capacity for dynamic capability proposed by this theory.
42 In practice, however, such an eventuality is patently implausible. The gamut of “sensing, learning, integrating and coordinating” capabilities comprising dynamic capabilities are not given to incredibly short time frames of yearly or monthly change (Pavlou & El Sawy, 2011). Similarly, financial leverage instruments (as may be required for a firm to employ a more expansive strategy) do not become immediately available at a moments notice, nor are the terms of such instruments usually as short as a year or two. As such, the strategic management decisions associated with dynamic capability ultimately reduce, in part, to a question of risk tolerance: how much risk is a firm willing to incur in order to maximize future profits? Dynamic capabilities that stress risky, over-extending behavior from the firm are not ones that translate to a healthy operational capability or sustained competitive advantage.
Furthermore, we contend that the firm must also choose the most applicable bundle of resources to build upon and modify given its operational context. Having a high capacity but then exercising this on the incorrect bundle of resources is not tantamount to dynamic capability. Thus in order to accurately measure a firms dynamic capability we must also consider the net change in operating margin the firm is able to realize from modifying its resource bundle. Dynamic capability implies that a firm is able to follow the longer-term path of steadily increasing pre-tax operating margins with measured and effectively utilized exposure to volatility along the way. Thus while there are periods of expansion and contraction of pre-tax operating margin within the system, the dynamically capable firm is able to manage these both with reserve. Firms that are unable to do so find themselves in severe danger of financial difficulty.
43
Dynamic Capability and Firm Performance:
At this juncture we are now able to calculate the potential dynamic capability of each of the firms in our study (in terms of the theoretical net changes to each respective firm’s pre-tax operating margin that are ascribable to systemic influence). It is important to note that this calculation produces only the potential effect that dynamic capability may have. In many cases not all, and in some cases very little, of the potential benefit to operating margin is actually realized by the firm. We explore this notion in more detailed fashion in a later section. Nonetheless, we can graphically show the relationship between potential dynamic capability and firm performance by plotting of the hypothetical effects of fully realized dynamic capabilities (quantified as the value of net change in pre-tax operating margin a firm may hope to achieve as a result of systemic influence) against the actual net change in pre-tax operating margin firm the firm does achieve (in the same manner as we did with only the capacity of dynamic capability or beta coefficient, earlier).
This time the curve that is produced (shown below) suggests a far more linear
relationship between dynamic capability and firm performance. In other words, firms with higher potential dynamic capabilities are more likely to have higher net increases in operating margin than those who don’t. We take this as part proof of our assertion that potential higher dynamic capability (i.e. higher theoretical contributions to firm
performance arising from exposure to systemic influence) increases the performance of any given firm and, moreover, that capacity alone (i.e. merely the exposure to systemic
44 influence) is absolutely not a reliable indicator of increased performance in itself. Once again, both the simplified datasets and the graph itself are reproduced below:
Table 2: Dataset showing relationship between Pre-Tax Operating Margin Firm Performance and Potential Dynamic Capability
Dynamic Capability x mean change in operating margin (all firms)
Firm Performance (Net Change in Op.
Margin) Terms Top 10% of firms 8.20% 21.50% 86 Next 10% 4.47% 3.75% 85 Next 10% 3.40% 2.22% 85 Next 10% 2.75% 3.17% 86 Next 10% 2.25% 1.01% 86 Next 10% 1.88% 1.36% 85 Next 10% 1.55% 0.30% 86 Next 10% 1.27% 1.18% 86 Next 10% 0.99% 1.46% 85 Bottom 10% 0.20% 0.68% 86
45
Figure 5: Graphic relationship between Pre-Tax Operating Margin Firm Performance and Potential Dynamic Capability
-5.00% 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% A v er ag e N et c h an g e in P re -T ax O p er at in g M ar g in
46
Potential vs. Actual Dynamic Capability:
Using the measure of potential dynamic capability just described we can rank the top 25 companies according to the theoretical increases in operating margin they could have achieved by fully realizing their dynamic capabilities. This will become instrumental as we later expand on the complex interrelationship between operational capabilities and dynamic ones. At this stage, note that between 9.22% (Gilead Sciences) and the high of 22.37% (achieved by Vaalco Energy), there appears at first inspection to be great benefit to firms (in the form of pre-tax operating margin gain) from the employ of dynamic capability.
Table 3: Top 25 Most Potentially Dynamically Capable firms.
Company Name Industry Name Capacity Dynamic
Capability
Performance
VAALCO Energy Inc Petroleum (Producing) 11.96 22.37% 138.13% Pope Resources L.P. Paper/Forest Products 9.67 18.08% 78.55%
Sohu.com Inc. Internet 7.93 14.83% 120.40%
Aetrium Inc Electronics 6.91 12.92% 26.92%
Consol. Tomoka Land Property Management 6.89 12.88% 21.53%
