Dynamic capabilities and operational capabilities: A knowledge management perspective

Journal of Business Research 60 (2007) 426 – 437

Dynamic capabilities and operational capabilities: A knowledge management perspective Gabriel Cepeda a,⁎, Dusya Vera b,1 a

University of Seville, Management and Marketing Department, Ramon y Cajal, 1, 41018 Seville, Spain b University of Houston, Department of Management, Houston, 77204-6021 TX, USA Received 1 December 2006; received in revised form 1 January 2007; accepted 1 January 2007

Abstract This paper contributes to the clarification of the link between operational (how you earn your living) capabilities and dynamic (how you change your operational routines) capabilities. In doing so, the article builds on a knowledge management (KM) perspective to capture KM processes behind the development and utilization of dynamic capabilities and to examine their impact on operational capabilities. Empirical evidence is provided by performing survey research with a sample of 107 firms in the information technology and communication industry in Spain. The article includes conclusions and practical steps for managers with an interest in KM practices supporting dynamic capabilities. © 2007 Elsevier Inc. All rights reserved. Keywords: Knowledge management; Dynamic capabilities; Operational capabilities; Organizational knowledge

1. Introduction In the last decade, the notion of dynamic capabilities as the ultimate source of competitive advantage (Teece et al., 1997) has catapulted these concepts to the forefront of strategy research. Nevertheless, in a recent review of the dynamic capabilities field, Zahra, Sapienza, and Davidsson conclude that the field is still in its infancy and that the literature is “riddled with inconsistencies, overlapping definitions, and outright contradictions” (2006, p. 917). The focus of interest has been diverse with different authors looking at the nature of dynamic capabilities, their antecedents, outcomes, or associated processes. Nevertheless, empirical work is still scarce and there has been little effort to consolidate findings in a unifying picture. Perhaps the largest source of confusion is the lack of agreement about a definition of dynamic capabilities and the interplay between dynamic and operational capabilities (Winter, 2003; Zahra et al., 2006). Early definitions tend to equate

⁎ Corresponding author. Tel.: +34 954 55 44 33; fax: +34 954 55 69 89. E-mail addresses: [email protected] (G. Cepeda), [email protected] (D. Vera). 1 Tel.: +1 713 743 4677; fax: +1 713 743 4652. 0148-2963/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2007.01.013

dynamic capabilities with competitive advantage and argue that in changing environments competitive advantage does not necessarily come from resources (tangible and intangible assets) or capabilities (organizational processes and routines), but from the firm’s capability to continually create new capabilities (Leonard-Barton, 1992; Teece et al., 1997). These early definitions of dynamic capabilities become tautological if competitive advantage is incorporated into them (Priem and Butler, 2001). Furthermore, if there is always a capability behind a capability, we face an infinite regress problem and it is impossible to identify the ultimate source of competitive advantage (Collis, 1994). More recent definitions differentiate between operational (“how you earn your living”) capabilities and dynamic (“how you change your operational routines”) capabilities (Helfat and Peteraf, 2003; Winter, 2003) and argue that competitive advantage comes from new configurations of resources and operational capabilities and not from dynamic capabilities per se (Eisenhardt and Martin, 2000; Makadok, 2001; Pavlou and El Sawy, 2004). The purpose of this paper is to contribute to the clarification of the link between dynamic capabilities and operational capabilities by building on a knowledge management (KM) perspective to unpack the concept of dynamic capabilities. In

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2. Theoretical background

logistics, and marketing campaigns), and a second category of capabilities, which deal with the dynamic improvement to the activities of the firm. Zollo and Winter (2002) and Winter (2003) also differentiate between operational (zero-order) and dynamic (first-order) capabilities. Operational capabilities are geared towards the operational functioning of the firm, including both staff and line activities; these are “how we earn a living now” capabilities. Dynamic capabilities are dedicated to the modification of operational capabilities and lead, for example, to changes in the firm's products or production processes. This classification has increasingly been adopted in recent models of dynamic capabilities (e.g., Helfat and Peteraf, 2003; Zahra and George, 2002; Zahra et al., 2006) and helps to eliminate the tautological flavor associated with dynamic capabilities. As Helfat and Peteraf explain, “Dynamic capabilities do not directly affect output for the firm in which they reside, but indirectly contribute to the output of the firm through an impact on operational capabilities” (2003, p. 999). To capture this thinking, we adopt in this paper Zahra et al.'s (2006) definition of dynamic capabilities as the processes to reconfigure a firm's resources and operational routines in the manner envisioned and deemed appropriate by its principal decision makers. Examples of dynamic capabilities are product development, strategic decision making, and alliance management (Eisenhardt and Martin, 2000). In summary, despite a lack of agreement about the nature of dynamic capabilities, consensus is emerging about the need for a hierarchy of capabilities, taking into consideration four critical aspects: (1) Capabilities are organizational processes and routines rooted in knowledge, (2) The input of dynamic capabilities is an initial configuration of resources and operational routines, (3) Dynamic capabilities involve a transformation process of the firm’s knowledge resources and routines, and (4) The output of dynamic capabilities is a new configuration of resources and operational routines.

2.1. Dynamic capabilities defined

2.2. Knowledge management and dynamic capabilities

Many definitions of dynamic capabilities are available. Some definitions are prescriptive; they assume that dynamic capabilities are always good and are a source of competitive advantage. For example, Griffith and Harvey (2001, p. 597) state, “A global dynamic capability is the creation of difficult-to-imitate combinations of resources, including effective coordination of inter-organizational relationships, on a global basis that can provide a firm competitive advantage” and Lee, Lee, and Rho (2002, p. 734) mention that “dynamic capabilities are conceived as a source of sustainable advantage in Shumpeterian regimes of rapid change.” The problem with these definitions is that they are tautological; if the firm has a dynamic capability, it must perform well, and if the firm is performing well, it should have a dynamic capability. In an effort to understand the true nature of dynamic capabilities, several authors propose the need to differentiate among the types of processes and routines available in firms. Collis (1994) distinguishes between a first category of capabilities, which reflect an ability to perform the basic functional activities of the firm (e.g., plant layout, distribution

Because dynamic capabilities are organizational routines, learning and KM processes guide their development, evolution, and use (Eisenhardt and Martin, 2000). For example, repeated practice is a learning process that helps individuals to understand a routine more fully and do it better (Argote, 1999). Knowledge codification into procedures and technologies also makes experience and routines easier to apply (Zander and Kogut, 1995). The depth and breadth of knowledge searches, for example, influence new product introduction routines (Katila and Ahuja, 2002). Improvisation and learning by doing also permit the creation and correction of routines (Crossan and Sorrenti, 1997). In fact, trial-and-error learning plays a key part in the development of technological innovation capabilities (Van De Ven and Polley, 1992), and improvisation is useful in product development capabilities (Eisenhardt and Tabrizi, 1995). In one of the most comprehensive efforts to link knowledge and dynamic capabilities explicitly, Zollo and Winter (2002) propose a “knowledge evolution cycle” to describe the development of dynamic capabilities and operational routines; this cycle enables firms to change the way they do things in

doing so, we describe the KM processes associated with dynamic capability development and utilization and their effect on operational capabilities. We define knowledge management as the formalized approach of managing the creation, transfer, retention, and utilization of an enterprise's explicit and tacit knowledge assets (Liebowitz and Wilcox, 1997; O'Leary, 1998). A KM perspective of capabilities has important managerial implications, considering the high levels of global corporate spending on KM services in the last years (IDC Group, 2002). KM processes and dynamic capabilities are closely intertwined because the creation and evolution of dynamic capabilities requires experience accumulation and knowledge articulation and codification (Zollo and Winter, 2002). Furthermore, a firm's operational routines form the foundation of its knowledge bases. Because dynamic capabilities are about how firms develop new skills and routines that allow them to compete, top managers' definition of what the firm needs to know versus what it actually knows is basic for the development of new routines. In our model we describe how managers’ decisions about the breadth and depth of organizational knowledge are at the core of the renewal of operational routines. This article makes three contributions to the literature. First, by making an explicit distinction between (1) KM processes and configurations of knowledge as part of dynamic capability development and utilization and (2) new operational capabilities as the output of dynamic capabilities, we help to clarify the nature of dynamic capabilities. Second, we test the link between dynamic and operational capabilities through survey research with a sample of 107 firms in the information technology and communication industry in Spain. Third, our sequential model provides practical steps for managers interested in KM practices supporting dynamic capabilities.

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pursuit of greater rents. The knowledge evolution cycle includes four phases: generative variation, internal selection, replication, and retention. In the variation phase, individuals and groups generate ideas on how to approach old problems in novel ways or how to tackle new challenges. The selection phase implies the evaluation of ideas for their potential for enhancing the firm’s effectiveness. Through knowledge articulation, analysis, and debate, ideas become explicit and the best are selected. The replication phase involves the codification of the selected change initiatives and their diffusion to relevant parties in the firm. The application of the changes in diverse contexts generates new information about the routines' performance and can initiate a new variation cycle. In the retention phase, changes turn into routines and knowledge becomes increasingly embedded in human behavior. Authors such as Pavlou and El Sawy (2004) describe similar processes when discussing how the deployment of dynamic capabilities leads to new configurations of functional competences that better match the environment. Similarly, Kogut and Zander (1992) describe “combinative capabilities” that allow the synthesis of existing resources into new applications. In the next section, we build on this previous work to propose a model of knowledge-enabled dynamic capabilities. 3. Conceptual model and hypotheses Fig. 1 shows the sequential model of the link between dynamic and operational capabilities. To prevent conceptual ambiguity, we discuss this model in the concrete context of strategic decisions-decisions important to a firm's future direction and success that are typically the responsibility of top managers (Eisenhardt and Zbaracki, 1992). This context is ideal for our study because it is at the top level of the firm that many decisions about capability building and resource transformation are made (Carpenter et al., 2001). Eisenhardt

and Martin (2000) have also highlighted strategic decision making as a critical dynamic capability. We start by focusing on the relationships depicted in the dotted box in Fig. 1, which represents KM processes behind the development and use of dynamic capabilities. KM processes transform a firm's knowledge configuration or knowledge base. Knowledge configurations can be defined in terms of breadth and depth. The breadth of a knowledge configuration denotes the multiple areas in which a firm has skills and expertise; the depth of a knowledge configuration refers to the firm's mastery of the knowledge it has (Zahra et al., 2000). However, because some areas of knowledge are more strategic than others, our definition of knowledge configuration focuses in particular on the depth of knowledge the firm has in specific areas we call “critical knowledge areas.” Critical knowledge areas are connected to the key success factors of an industry. For example, in the North American computer software industry, the key success factors are the establishment of efficient channels of distribution and the provision of after-sales support. Knowledge and expertise in these two areas are priorities for firms in this market. The question managers need to ask themselves to identify critical knowledge areas in their industry is: Given the economic structure of this industry and customers' needs, what knowledge do we need to be successful? Once managers identify the list of critical knowledge areas in their industry, these areas constitute the breadth of knowledge the firm desires to achieve. With the breadth established, the concept of “knowledge configuration” communicates the depth of knowledge the firm has achieved in each of those critical areas. The notion of critical knowledge areas is consistent with Zack's (1999) description of a knowledge-based SWOT analysis, in which firms can map their knowledge against their strategic opportunities and threats in the industry to better understand their points of advantage and weakness and identify

Fig. 1. Conceptual model.

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knowledge gaps that need to be closed. We differentiate in our model between the “desired” knowledge configuration the firm should have given its industry and strategy, and the “available” knowledge configuration the firm actually implements. As noted in Fig. 1, our model builds on the knowledge evolution processes described by Zollo and Winter (2002) in the sense that the “desired” knowledge configuration results from the generation of strategic alternatives and the selection of those that will be implemented, while the “available” knowledge configuration results from efforts to replicate the knowledge needed across the firm and to retain it in the firm's KM infrastructure. The first step to articulate an organization’s desired knowledge configuration is to let strategy guide decisions on new initiatives and knowledge needs. Zack (1999), in describing the link between strategy and knowledge, argues that every strategic position is linked to some set of knowledge resources; that is, given what the firm believes it must do to compete, there are some things it must know. Strategy provides a framework for KM efforts because the strategic choices firms makeregarding products and services, markets, and technologieshave a profound influence on the knowledge resources required for the company to compete and succeed in an industry (Makadok, 2001; Zack, 1999). We focus in this paper on two established strategy variables, which are also well-known management tools – a firm's mission and value proposition – and argue that their explicit examination facilitates decisions about the knowledge configuration the firm should pursue. Campbell and Yeung (1991) define a mission as “an organization's character, identity and reason for existence” and argue that it can be divided into four inter-relating parts: purpose, strategy, behavior standards, and values. This holistic view of missions brings together strategy, organization, and human resource issues. Campbell and Yeung (1991) differentiate mission from vision in terms of their time orientation. A vision refers to a future state; it “articulates a view of a realistic, credible, attractive future for the organization, a condition that is better in some important ways to what now exists” (Bennis and Nanus, 1985). In contrast, a mission refers to the present purpose, strategy, and culture of the firm (Bart and Baetz, 1998). Value proposition, one of the most widely used terms in business contexts in recent years, is an element of strategy (and of the mission) that communicates the fundamental benefits the firm has chosen to offer to customers, conveying a clear understanding of the customer's business priorities (Anderson et al., 2006). Having an official mission or value proposition statement to guide KM efforts is not enough. In many firms, missions are only empty statements divorced from strategy and performance (Bart and Baetz, 1998). However, a shared understanding and acceptance of a mission can provide a rationale for action for executives to generate alternative strategic initiatives. Each alternative is associated with certain knowledge requirements that need to be articulated in order to implement the initiative (Beckett et al., 2000). For example, in case research at a European innovation and technology center, Cepeda, Galan, and Leal (2004) observe that the exercise of examining the

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center's explicit mission helped the top management team to articulate and evaluate a set of strategic projects and the most desired knowledge configurations associated with them. Given that the center's mission was the promotion of innovation and research efforts to firms in the industry, executives identified the need to achieve greater depth of expertise in the critical knowledge areas of training and development, and industry social networks. In the case of the value proposition, in order to deliver the benefits promised to customers, executives need to generate alternative courses of action concerning the value-chain activities the firm intends to perform. Attention to the intended value proposition and assessment of performance gaps help executives to decide on the depth needed in the critical knowledge areas and to articulate the most desired knowledge configuration that would enable the firm to deliver its promises to customers. The managers of the European innovation and technology center mentioned above reported that the exercise of explicitly articulating or examining the value proposition was very helpful in order to come up with the desired knowledge configuration that would support its offerings to customers (Cepeda et al., 2004). Thus, Hypothesis 1. Executives' explicit articulation of the firm's (a) mission and (b) value proposition influences the identification of the desired configuration of critical knowledge areas. Once a desired knowledge configuration has been identified, the next step is to try to acquire or develop the knowledge resources considered important so that they become part of the available knowledge configuration. However, identifying certain knowledge assets as desired does not guarantee that the firm will be able to capture and disseminate them or make them available throughout the organization. Access to knowledge depends on factors such as awareness of its existence and potential, the presence of channels for communicating knowledge, and the absorptive capacity of the possible users (Buckley and Carter, 2002). A critical element in the systematic integration of new knowledge is the existence of a KM infrastructure, which encompasses the people, technology, and procedures the company dedicates to the management of knowledge (Gold et al., 2001). Implementing a desired knowledge configuration requires an understanding of the infrastructure needed to support the acquisition, transfer, and storage of tacit and explicit knowledge. It involves the coordination and integration of multiple processes, technological tools, and individuals with diverse roles in the organization. Once executives articulate and codify a desired knowledge configuration, the role of the KM infrastructure is to diffuse the newly approved change initiatives to the corresponding parties in the firm and to replicate the novel solutions in multiple organizational contexts (Gold et al., 2001). In Fig. 1, the available knowledge configuration represents the actual knowledge assets the firm captures and retains; these assets are available for the use of organizational members in implementing the strategic initiatives. Managers face a continuous challenge in modifying their knowledge configurations as needed. An existing KM infrastructure positively influences

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new learning because firms that have invested in such infrastructure have greater awareness about the value of knowledge (Cepeda et al., 2004). On the other hand, an existing KM infrastructure may also constrain the learning paths of an organization (Becker, 2001). For example, guided by previous knowledge investments, managers may think developing strategic initiatives that leverage the current KM infrastructure and the available knowledge configuration would be faster and easier than changing the KM infrastructure and pursuing the desired knowledge configuration. For a KM infrastructure to positively influence new learning, it must be flexible in order to adapt to the requirements of the desired knowledge configuration, and effective in order to capture and disseminate the actual knowledge configuration the firm achieves. We expect that firms that have invested in a KM infrastructure-KM personnel, technology, and procedures-will be aware of the path-dependent nature of knowledge resources and make proactive efforts to adapt their KM infrastructure and to update their depositories of knowledge. Hence, we predict a mediating role for KM infrastructure in the relationship between desired and available knowledge configurations. Hypothesis 2. he desired configuration of critical knowledge areas will influence the existing knowledge management infrastructure, which at the same time will influence the “available” configuration of critical knowledge areas the firm achieves. Finally, consider operational capabilities as the output of dynamic capabilities deployment. Eisenhardt and Martin (2000), Helfat and Peteraf (2003), and Zollo and Winter (2002) argue that the value of dynamic capabilities lies in the configuration of operational capabilities they create. For example, Collis (1994) is particularly explicit in making the point that dynamic capabilities govern the rate of change of ordinary capabilities—later called operational capabilities by Winter (2003). Going one step further, Zott (2003, p. 98) relates dynamic capabilities not only to operational capabilities but also to firm performance when stating that “dynamic capabilities are indirectly linked with firm performance by aiming at changing a firm's bundle of resources, operational routines, and competencies, which in turn affect economic performance.” Dynamic capabilities reconfigure the firm's knowledge resources and routines and, consequently, change how organizational members do things. The renewal of operational capabilities in order to create or respond to market change prevents the obsolescence of outdated knowledge configurations; in contrast, firms that are slow in shaping their operational capabilities end up with core rigidities (Leonard-Barton, 1992). Through knowledge transformation processes, executives generate, articulate, and codify new available knowledge configurations, which are the foundation for improvements in the way firms operate. In other words, what the firm gets to know changes what it can do. Hence, Hypothesis 3. The depth of the available configuration of critical knowledge areas impacts the firm's operational capabilities.

4. Method 4.1. Sample and procedures In order to test the model, the study uses the key informant method to collect survey data in the information and communication technology industry in Spain during 2002. Companies in this industry are characterized as knowledge intensive (Starbuck, 1992) and are more likely to articulate and codify their knowledge configurations in an explicit way. Our empirical study involves three phases. First, we undertook an in-depth literature review to increase our familiarity with industryspecific issues, such as the key success factors and terminology of the information and communication technology industry (Wilcox King and Zeithaml, 2003). As part of this phase, the present study includes an in-depth case study in one company in this industry. The authors met with individuals at different levels of management in an effort to identify the critical knowledge areas of the industry and to articulate and codify the firm's knowledge configuration and the elements of its mission and value proposition. The output of the first phase was a list of critical knowledge areas and a preliminary set of scales for knowledge configuration, mission, and value proposition. Second, we asked a panel of 23 industry and KM experts (12 in the information and communication technology industry and 11 in academia) to reflect on the critical knowledge areas that may provide competitive advantage to firms competing in the industry of interest and to provide suggestions on our scales. The output of the second phase was a refined set of scales. Third, the study includes making contact with ETICOM— the Andalusian Association of Information and Communication Technology. As of July 2002, 168 firms were ETICOM members, all of which were invited to participate in the study. Panel members selected this association as suitable to the study's goals. A survey was sent to the firms’ CEOs and asked that it be completed by the CEO or by another member of top management. We assured participants that responses would be confidential and that results would be reported in aggregate. We received 107 usable surveys (63.8% response rate). Of the firms in the sample, 77% have less than 50 employees, 15% have between 50 and 250 employees, and 8% have more than 250 employees. In addition, 43% of the firms have a sales volume under 600 million, 32% have a sales volume between 600 million and 3000 million, and 25% have a sales volume greater than 3000 million. We did not find a significant difference between the number of employees and sales volume of the firms in the sample and those that did not respond to the survey; thus, nonresponse bias did not seem to be a major concern. 4.2. Measures The Appendix shows the questions in our survey, which also provides participants with definitions of each construct. Our survey has a combination of reflective and formative measures (Fornell, 1982). Reflective indicators are determined by the construct and, hence, covary at the level of that construct (Hulland, 1999). In contrast, formative measures are expressed

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as a function of the items; that is, the observed items form or precede the construct. Because the latent variable is viewed as an effect rather than a cause of the formative indicators, these indicators are not necessarily correlated (Hulland, 1999). The study models operational capabilities as a formative second-order construct with five formative dimensions. The study uses Hall's scales (1992, 1993) to measure regulatory (nine items), positional (six items), functional (five items), and cultural (six items) capabilities. Hall's measures describe a firm's processes and resources that help it to do more and better than its competitors. To Hall's competitor-centered categories the study adds the notion of knowledge-based value-creation capabilities, which describe a firm's routines to extract customer value out of its knowledge assets (Hamel, 2000; Rastogi, 2003). By adding this fifth category we explicitly capture firms' efforts to leverage knowledge as the source of most improvements in customer value (Anderson and Narus, 1998). We developed a four-item scale for knowledge—based value-creation capabilities based on a literature review (Anand and Khanna, 2000; Hamel, 2000; Rastogi, 2003). Participants assessed the degree to which the operational capabilities were available in their firms. The study models KM infrastructure as a reflective secondorder construct with three reflective dimensions: processes (seven items), technology (four items), and people (six items). Scales were adapted from measures developed by Davenport and Prusak (1998), KPMG Consulting (2000), Consortium GCDIT (2001), and Cabrera and Cabrera (2002). Participants assessed the degree to which the various elements of a KM infrastructure were present in their firms. In the case of the scale for knowledge configuration, the study includes a literature review, a case study, and an expert panel to identify a relatively comprehensive inventory of 20 critical knowledge areas of firms competing in the information and communication technology industry in Spain (Wilcox King and Zeithaml, 2003). Participants were asked to rate the degree of importance their firm assigned to each critical knowledge area (“desired” knowledge configuration construct) and the degree to which each of the critical knowledge areas was available or implemented in their firm (“available” knowledge configuration construct). Following recommended guidelines for the development of formative constructs (e.g., Diamantopoulos and Winklhofer, 2001), the scale based on an assessment of multicollinearity was depurated and the study retained six items for each measure (see Appendix).

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The literature review, case study, and expert panel also helped in creating a list of 12 elements describing the missions and 11 elements describing the value propositions of companies in the selected industry. Participants reported the degree to which the items were incorporated as part of their firm's mission and value proposition. After the multicollinearity assessment of these formative scales, we retained seven items for each measure (see Appendix). Because the use of a single survey for data collection created the potential for common-method bias, a number of steps were taken to minimize bias (Podsakoff et al., 2003). Procedural remedies are recommended when including formative constructs. Procedural remedies were applied for protecting respondent anonymity and reducing evaluation apprehension by ensuring subjects that there were no right and wrong answers, improving scale items with the input of an expert panel and case study information, and counterbalancing question order. 5. Results 5.1. Reliability and validity of the scales Table 1 shows descriptive statistics and correlations. Composite reliabilities (ρc) of the reflective scales exceed the benchmark of 0.70 recommended for early stages of research (Nunnally, 1978). Table 2 shows construct-to-item loadings and cross-loadings of the reflective measures. All item loadings exceed 0.70 and load more highly on their own construct than on others. In addition, all constructs share more variance with their own measures than with others. The evaluation of formative measures is different from that of reflective ones. One examines the weights (Mathieson et al., 2001), which represent a canonical correlation analysis and provide information about how each indicator contributes to the respective construct (see Table 2). Weights do not need to exceed any particular benchmark because a census of indicators is required for a formative specification (Diamantopoulos and Winklhofer, 2001). The concern with formative scales is potential multicollinearity with overlapping items, which could produce unstable estimates (Mathieson et al., 2001). Results of a collinearity test of our depurated measures show the variance inflation factor (VIF) of all items far below the common cut-off of 10. In addition, we confirmed the validity of the

Table 1 Descriptive statistics and correlation matrix

1. Operational capabilities 2. Desired knowledge configuration 3. Available knowledge configuration 4. Mission 5. Value proposition 6. Knowledge management infrastructure ⁎ p < 0.05. ⁎⁎ p < 0.01.

Mean

S.D.

Composite reliability

1

2

3

4

5

6

3.3 3.6 3.1 2.9 3.3 3.2

1.27 1.10 1.04 1.31 1.40 1.27

– – – – – 0.86

(–) 0.47 ⁎⁎ 0.55 ⁎⁎ 0.24 ⁎ 0.41 ⁎⁎ 0.46 ⁎⁎

(–) 0.70 ⁎⁎ 0.41 ⁎⁎ 0.50 ⁎⁎ 0.57 ⁎⁎

(–) 0.31 ⁎⁎ 0.36 ⁎⁎ 0.60 ⁎⁎

(–) 0.40 ⁎⁎ 0.16

(–) 0.42 ⁎⁎

(0.83)

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Table 2 Assessment of reflective and formative constructs (A) Reflective constructs: loadings and cross-loadings

KP KP1 KP2 KP3 KP4 KP5 KP6 KP7 PP PP1 PP2 PP3 PP4 PP5 PP6 T T1 T2 T3 T4

Existing KM infrastructure

Processes

People

Technology

0.86 0.68 0.62 0.64 0.75 0.68 0.53 0.68 0.76 0.51 0.59 0.71 0.51 0.59 0.63 0.85 0.57 0.72 0.77 0.52

1.00 0.74 0.81 0.76 0.85 0.83 0.80 0.76 0.46 0.41 0.44 0.45 0.40 0.41 0.40 0.66 0.51 0.53 0.61 0.45

0.46 0.51 0.31 0.30 0.35 0.35 0.45 0.40 1.00 0.78 0.77 0.76 0.76 0.79 0.82 0.44 0.23 0.33 0.49 0.29

0.37 0.54 0.45 0.47 0.57 0.58 0.35 0.50 0.44 0.42 0.39 0.44 0.30 0.51 0.42 1.00 0.76 0.74 0.87 0.79

5.2. Hypothesis testing

(B) Formative constructs: weights Desired knowledge configuration

Mission

Regulatory capabilities

sample respondents by comparing each indicator variance with the correlation between the two constructs (Fornell and Larcker, 1981). In all cases the indicator variance is higher than the correlation, which confirms discriminant validity.

Functional capabilities

K6 K9 K10 K16 K18

0.38 0.21 0.24 0.31 0.37

MI3 MI5 MI6 MI7 MI8 MI9

0.19 0.06 0.58 0.05 0.04 0.31

RCD1 RCD2 RCD3 RCD4 RCD5 RCD6

0.17 0.15 0.15 0.07 0.10 0.11

FCD1 FCD2 FCD3 FCD4

0.22 0.39 0.09 0.30

K19

0.26

MI11

0.74

RCD7 RCD8

0.10 0.14

CCD1 CCD2 CCD3

0.17 0.16 0.15

Cultural capabilities

Available knowledge configuration

Value proposition VP1

0.05

Positional capabilities

CCD4

0.18

K4 K6 K10 K14

0.42 0.13 0.44 0.02

VP2 VP3 VP4 VP5

0.12 0.29 0.54 0.44

PCD1 PCD2 PCD3 PCD4

0.18 0.16 0.20 0.17

CCD5 CCD6

0.17 0.17

K18 K19

0.31 0.02

VP7 VP8

0.12 0.37

PCD5 PCD6

0.17 0.12

VCD1 VCD2 VCD3 VCD4

Knowledge-based value creation capabilities 0.17 0.40 0.37 0.36

formative measures using the procedures suggested by Fornell and Larcker (1981) and Mackenzie, Podsakoff and Jarvis (2005). To further validate the measures for “desired” and “available” knowledge configurations we obtained second responses from 20 firms and tested the correlations between these two scales using two sets of data (sample respondents and “second respondents”). The results indicate consistent correlation ratios. We also tested the discriminant validity of the “desired” and “available” knowledge configuration scales using data from

The analyses include testing the hypotheses simultaneously using partial least squares (PLS), a structural equation modeling technique employing a principal component-based estimation approach (Chin, 1998). PLS was selected due to the characteristics of our model and sample. The model uses formative indicators, the sample size is relatively small (107 cases), and the data are non-normal. Other techniques of structural equation models (e.g. covariance based model performed by LISREL or AMOS) make it impossible to run these models (e.g., Diamantopoulos and Winklhofer, 2001). For hypothesis testing, we use the bootstrapping procedure recommended by Chin (1998). Consistent with Hypothesis 1(a) and (b), the paths between mission and desired knowledge configuration (β = 0.25, p < 0.05) and between value proposition and desired knowledge configuration (β = 0.40, p < 0.001) indicate positive and significant relationships between these strategic variables and the knowledge areas that firms considered critical (see Fig. 2). The findings include differences between the two types of knowledge configurations (desired and available); these differences represent the knowledge gap between what firms perceive they need to know and what they actually know. Of our original list of 20 knowledge configuration elements, six elements are consistently available in firms in our sample, including legal knowledge and knowledge about the competition, internal policies, management styles, the public sector, and market segmentation. Both knowledge configurations have four elements in common, including legal knowledge and knowledge about internal policies, the public sector, and market segmentation. This comparison shows that while knowledge about suppliers and about knowledge documentation is identified as desirable, these areas are not consistently available in our sample. Similarly, knowledge about competitors and about management styles is available in our sample, but these areas are not consistently identified as desired. Adopting the approach used by Tippins and Sohi (2003), the presence of a mediating effect was checked by performing a competing model analysis, in which two substantive models are estimated and evaluated for significant differences. Fig. 2 shows the results of the competing model analysis. The first model (direct effect) examines the direct relationship between “desired” knowledge configuration (DKC) and “available” knowledge configuration (AKC), while the second model (partial mediation) examines the same relationship with KM infrastructure acting as a mediator. The results of the partial mediation model support Hypothesis 2. First, the partial mediation model explains more variance in AKC than does the direct effect model (0.55 vs. 0.47). Second, positive relationships exist between DKC and KM infrastructure (H2a: β = 0.57, p < 0.001) and between KM infrastructure and AKC

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Fig. 2. Results.

(H2b: β = 0.30, p < 0.001). Third, the significant relationship between DKC and AKC in the direct effect model (H2: β = 0.690, p < 0.001) diminishes in the partial mediation model (H3c: β = 0.53, p < 0.001). Together these three points provide evidence that there is a discernible mediating effect of KM infrastructure and that the partial mediation model represents a significant improvement over the direct effect model. The partial mediation model explains a good amount of the variance in operational capabilities (R2 = 0.30). In addition, as predicted by Hypothesis 3, a positive and significant relationship occurs between available knowledge

configuration and operational capabilities (β = 0.54, p < 0.001). Finally, we performed the Stone-Geisser test of predictive relevance to assess model fit in PLS analysis (Geisser, 1975; Stone, 1974). When q-square is greater than zero, the model has predictive relevance. In our model, q-square is 0.43. 6. Discussion The purpose of this study is to examine the relationship between dynamic capabilities and operational capabilities from a KM view. In doing so, we unpack the concept of dynamic

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capabilities by capturing the KM processes behind the development and use of dynamic capabilities and testing their impact on operational capabilities. We break up the KM processes behind dynamic capabilities by focusing on the interplay between the firm's strategic context (its mission and value proposition), the articulation and codification of a desired knowledge configuration, the use of a KM infrastructure to replicate and retain the new knowledge, and the articulation and codification of the actual knowledge configuration available in the firm. Our results support this disaggregation of KM processes. Managers' examination of their explicit mission and value proposition influences their articulation of the knowledge configuration that is more desirable. Although we focus here on only two elements of the firm's strategy–its mission and value proposition–other strategic factors that guide executives' generation of strategic initiatives are the firm's goals, product and market focus, and competitive intelligence about rivals, among others. Our results show that an understanding of the strategic context contributes to the articulation and codification of the knowledge configuration needed to thrive. As firms develop dynamic capabilities, the firm's KM infrastructure plays a key role in managing the organization's knowledge gap. Not all desired knowledge becomes available knowledge. As organizational members pursue new learning, resources such as knowledge search tools and knowledge sharing practices are instrumental in creating an internal environment in which individuals can openly speak about knowledge requirements and take steps towards the implementation of a knowledge repository that supports the firm's goals. The findings also show that the outcome of dynamic capabilities deployment – the available knowledge configuration – is the cornerstone of new operational capabilities. Firms in our sample have diverse profiles of operational routines. Nevertheless, the weights of the formative construct suggest that functional (weight = 0.63) and knowledge-based valuecreation (weight = 0.57) capabilities are most important, followed by regulatory (weight = 0.49) and cultural (weight = 0.42) capabilities. Positional capabilities have a very small importance (weight = 0.01); however, this finding should not be a surprise in the selected industry. These firms may consider the use of positional assets, such as databases and networks, the minimum requirements to be an industry player. The results highlight the micro-processes through which knowledge-enabled dynamic capabilities impact operational capabilities. Firms in our sample build primarily on six available knowledge areas (legal knowledge and knowledge about the competition, internal policies, management styles, the public sector, and market segmentation) to develop and improve functional and knowledge-based value-creation capabilities to a greater degree, and regulatory and cultural capabilities to a lesser degree. 7. Limitations, implications, and future research directions Consider some limitations of this study. First, this research takes place within one industry to control for industry effects across firms. Nonetheless, this design affects the external

validity of our results. Since the inventories of critical knowledge areas, missions, and value propositions are representative of the information and communication technology industry in Spain, the results are likely to be generalizable in markets with similar characteristics. Some relevant features of the industry we study need to be taken into consideration: (1) firms are relatively small in size and young in age, (2) most of the personnel hold a university degree, and (3) the technological environment is changing continuously. Future studies could compare our results with those in other industries and regions. Second, the cross-sectional design does not allow us to observe the short- and long-term impact of dynamic capabilities on operational capabilities. Although our model proposes sequenced relationships between knowledge processes and operational routines, we measure all these constructs at one point in time. Also, our measures do not directly capture dynamic change in KM processes and dynamic change in operational routines, but the positive association between KM processes and operational routines at one point. This positive association is suggestive of how a change in one variable is related to change in the other variable. Given the dynamic nature of the processes and constructs implied in our model, and the possibility of feedback looks and circular relationships characteristic of the dynamic capabilities perspective, our study will benefit from a more longitudinal approach in order to understand more fully the link between dynamic and operational capabilities. This article makes three contributions to the dynamic capabilities and knowledge management literature. First, by differentiating between KM processes and knowledge configurations that are part of dynamic capability development and use, and operational capabilities as the output of dynamic capabilities, we help to clarify the nature of dynamic capabilities and to bridge the dynamic capabilities and knowledge management fields. Once agreement is achieved in the field about the definition of dynamic capabilities as the processes that transform a firm's operational routines, the next step would be to examine the indirect impact of dynamic capabilities on competitive advantage through the creation of new operational routines. We invite researchers to consistently include operational capabilities in their models of the performance implications of dynamic capabilities; these efforts will help to eliminate the tautological feel of dynamic capabilities. Second, the study provides empirical evidence of the link between dynamic and operational capabilities using a sample of 107 firms in the information technology and communication industry in Spain. In doing so, this study helps to overcome the lack of empirical grounding of the dynamic capabilities field (Priem and Butler, 2001; Williamson, 1999). Furthermore, as part of our empirical study, we offer a methodology to create an inventory of critical knowledge areas in a given industry. The process we followed included an in-depth literature review, a case study of how one particular firm articulated and codified its knowledge configuration, and input from an expert panel. This method helps fill the gap in quantitative work in the dynamic capability and knowledge management fields, in which measures of organizational knowledge are scarce and often rely on crude proxies.

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Third, the results also shed light on tangible means for managers to enhance their firm's dynamic capabilities through KM initiatives. Winter (2003, p. 991) points out that there is “doubt that deliberate efforts to strengthen dynamic capabilities are a genuine option for managers.” This article's sequential model provides practical steps for managers interested in KM practices that support dynamic capabilities. Researchers often describe dynamic capabilities in abstract terms. In contrast, KM projects are part of the daily work of many organizations. By bridging the dynamic capabilities and knowledge management fields this article increases the tangibility of the dynamic capabilities concept. In fact, the method here develops the inventory of critical knowledge areas can be used within one single firm to make the mission and value proposition explicit and to create awareness at all organizational levels of how the firm's strategic context should guide what it learns. This connection is not trivial in firms; in many cases strategic tools such as the mission or value proposition are empty statements, disconnected from KM projects. To some extent, the hypotheses about the relationships between mission, value proposition, and the desired knowledge configuration provide an example of a normative theory; while missions and value propositions are meant to drive decisions about a firm's knowledge configuration, this fact does not mean they will. Managers need to actively manage their knowledge gap between the knowledge they need to have and the knowledge they actually have. From a conceptual point of view the distinction between desired and available knowledge configurations stresses the existence of knowledge gaps in organizations. From a practical point of view, this distinction is helpful as part of a methodology that guides managers to decide what knowledge they should have to support their strategy, compare that knowledge with the current knowledge they do have, and make decisions about how to develop or acquire the missing knowledge. In addition, managers need to be aware of the role of the KM infrastructure in closing the knowledge gap. Because past investments in KM can both foster and impede the adaptation of a knowledge configuration, top leaders need to create a culture of continuous learning, flexibility of the KM infrastructure, and critical assessment of the relevance of knowledge assets. As part of this assessment, the results emphasize the need for managers to have an explicit understanding of how their critical knowledge can be leveraged to renew their operational (e.g., regulatory, positional, functional, cultural, and knowledge-based valuecreation) capabilities when needed. More efforts to continue unpacking the black box of dynamic capabilities will greatly benefit management practice. Future research will need to continue developing tools to articulate, codify, and measure organizational knowledge. Because knowledge entails scope and context, and is enacted through the perspective of multiple knowers in a firm and captured through language, the choosing of the “knowers” who will identify what the firm knows is key (Von Krogh et al., 1994; Wilcox King and Zeithaml, 2003). The study relies on the CEO or a member of the top management team as a key knower of the company to learn the depth of knowledge achieved in

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each of the critical knowledge areas by companies in our sample. Future research sampling multiple knowers within a firm will be helpful to test for inter-rater reliability and to improve the internal validity of knowledge management studies. Furthermore, the emphasis here is on finding commonalities among firms in terms of the critical knowledge areas they considered important and had available. The study controls for knowledge breadth by creating a relatively broad list of critical knowledge areas; then, the study assesses knowledge depth by asking firms how much knowledge they have available in each area. While commonalities exist in the study, the depth and breadth of knowledge resources varies across firms competing in the same industry. Future efforts to measure knowledge resources in an industry and across industries will need to include novel ways to capture the diversity in firms' knowledge assets and the views of organizational knowers. Acknowledgment The authors thank Jean McGuire, Jose Luis Galán, Antonio Leal, Wynne Chin, and the anonymous reviewers for their very helpful comments and suggestions on earlier versions of this paper. Appendix A. Survey questions Mission: In answering the next questions, please think of the elements that compose your firm's mission. Please indicate the degree to which the following items are important elements of your mission (Likert scale: 1 = strongly disagree; 5 = strongly agree). MI1 Promotion of innovation initiatives MI2 Customer support services MI3 Outsourcing services for public sector a MI4 Training and development MI5 Link between firms a MI6 Services for small businesses a a

Information services a Industrial design services a Environmental servicesa

MI7 MI8 MI9

MI10 Organizational learning MI11 Development of informal networks a MI12 Value-added services

Final items after depuration of formative scales.

Value proposition: In answering the next questions, please think of the elements that compose your firm's value proposition. Please indicate the degree to which the following items are important elements of your value proposition (Likert scale: 1 = strongly disagree; 5 = strongly agree). VP1 Provision of training to firms a VP2 Provision of information to firms a VP3 Provision of quality certifications a VP4 Performance of projects and studies a VP5 Performance of IT diagnostics a

VP7 VP8 VP9

Social responsibility a Respect of legal norms a Long-term customer relations

VP10 Innovation development time VP11 Re-engineering and implementation

VP6 Provision of customer service a

Final items after depuration of formative scales.

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G. Cepeda, D. Vera / Journal of Business Research 60 (2007) 426–437 VCD3: Globalization of local knowledge VCD4: Conversion of knowledge into new strategic opportunities

Desired knowledge configuration: Listed below are areas of knowledge and expertise. Please indicate the degree to which these areas are important for your firm's success (Likert scale: 1 = strongly disagree; 5 = strongly agree). K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 a

Customers Business environment Products and services Competition Development of individual skills Legal knowledge a Internal procedures Technology Suppliers a Internal policies a

K11 K12 K13 K14 K15 K16 K17 K18 K19 K20

Employee development Teamwork Learning and innovation Management styles Management by objectives Knowledge documentation a Quality practices Public sector a Market segmentation a Organizational climate

Final items after depuration of formative scales.

Available knowledge configuration: Listed below are areas of knowledge and expertise. Please indicate the degree to which knowledge and expertise on these areas is available in your firm's culture, procedures, structures, or systems (Likert scale: 1 = strongly disagree; 5 = strongly agree). K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 a

Customers Business environment Products and services Competition a Development of individual skills Legal knowledge a Internal procedures Technology Suppliers Internal policies a

K11 K12 K13 K14 K15 K16 K17 K18 K19 K20

Employee development Teamwork Learning and innovation Management styles a Management by objectives Knowledge documentation Quality practices Public sector a Market segmentation a Organizational climate

Operational capabilities: In answering the next questions, please think of the processes and assets your firm uses to perform its business. Please indicate the degree to which the following items match your firm's operational processes and assets (Likert scale: 1 = strongly disagree; 5 = strongly agree).

Positional PCD1: Databases PCD2: Reputation of product PCD3: Reputation of company PCD4: Networks PCD5: Value chain configuration PCD6: Established distribution network

Functional FCD1: Know-how of FCD2: Know-how of FCD3: Know-how of FCD4: Know-how of

KP : Processes KP1: Existence of a system for the analysis and filtration of information KP2: Processes to inform organizational members of stored information and codification tools KP3: Existence of tools to access the stored knowledge KP4: Existence of individuals who update the stored knowledge

KP5: Fast and easy search tools

KP6: Effective systems for the dissemination of knowledge

PP: People PP1: Personal attitudes oriented to KM are evaluated. PP2: Internal training plan is oriented to generate and share knowledge. PP3: Employee selection considers the competences that encourage KM. PP4: The creation of an internal forum of reflection, debate, and practice encourages the enabling of idea exchange among experts with similar and complementary knowledge. PP5: Compensatory policies are adequate to enhance education levels and KM. PP6: Employees are encouraged to participate in external events that promote monitoring of environmental shifts, self-criticism, change, and organizational learning.

KP7: Quality control of the acquired knowledge

Final items after depuration of formative scales.

Regulatory RCD1: Trade secrets RCD2: Contracts RCD3: Licenses RCD4: Patents RCD5: Copyright RCD6: Trademarks RCD7: Registered designs RCD8: Utility models

Knowledge management infrastructure: In answering the next questions, please think of the formal mechanisms your firm has implemented to manage knowledge. Please indicate the degree to which the following items match your firm's knowledge management processes, technology, and human resource practices (Likert scale: 1 = strongly disagree; 5 = strongly agree).

suppliers customers distributors partners

T: Technology T1: Existence of efficient non-computerized knowledge support systems (e.g., library) T2: Existence of workflow charts T3: Existence of document management system T4: Existence of electronic search tools

References Cultural CCD1: Perception of quality CCD2: Perception of service CCD3: Ability to manage change CCD4: Ability to innovate CCD5: Team working ability CCD6: Participative management style Knowledge-based value-creation VCD1: Knowledge embedded into new products and services VCD2: Application of new knowledge to old products

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