Organizational learning capability: a proposal of measurement

Journal of Business Research 58 (2005) 715 – 725

Organizational learning capability: a proposal of measurement Pilar Jerez-Go´meza,*, Jose´ Ce´spedes-Lorentea, Ramo´n Valle-Cabrerab a

University of Almeria, La Can˜ada de San Urbano, s/n, 04120 Almeria, Spain b University Pablo de Olavide, Seville, Spain

Received 1 November 2002; received in revised form 1 October 2003; accepted 1 November 2003

Abstract This paper develops a measurement scale for organizational learning capability, supported by the results of a validation study covering a sample of 111 Spanish firms from the chemical industry. From a strategic viewpoint, the measurement scale identifies the elements that form learning capability, highlighting its complex and multidimensional nature. The evidence that the results provide regarding the scale’s validity suggests that we may use this tool in future research work requiring a measurement of learning capability. Likewise, the scale provides information that could be of use to those managers wishing to improve learning capability in their firms. D 2004 Published by Elsevier Inc. Keywords: Organizational learning; Strategic capability; Measurement

1. Organizational learning capability: a proposal of measurement The analysis of organizational learning has become an increasingly important study area over recent years. Various works have dealt with the analysis of this construct from differing viewpoints. There are studies that focus on this construct using a psychological approach (Cyert and March, 1963; Daft and Weick, 1984), a sociological approach (Nelson and Winter, 1982; Levitt and March, 1988), or from the point of view of Organizational Theory (Cangelosi and Dill, 1965; Senge, 1990; Huber, 1991). More recently, learning has been considered, from a strategic perspective, as a source of heterogeneity among organizations, as well as a basis for a possible competitive advantage (Grant, 1996; Lei et al., 1996, 1999). From this latter approach arises the concept of learning organization, which implies a change in the traditional way of dealing with business management. Although research into organizational learning has provided some relevant insights, there are still certain aspects that have not been sufficiently analyzed. On one hand, the widely accepted idea that organizational learning is an essential element to successfully compete in a global mar-

* Corresponding author. Tel.: +34-950015183; fax: +34-950015178. E-mail address: [email protected] (P. Jerez-Go´mez). 0148-2963/$ – see front matter D 2004 Published by Elsevier Inc. doi:10.1016/j.jbusres.2003.11.002

ketplace (Prahalad and Hamel, 1990) comes up against the lack of empirical research that has been carried out to this respect (Garvin, 1993). Although various case studies have taken an in-depth look at the inherent complexity of the organizational learning construct (e.g., Leonard-Barton, 1992), measuring and empirically testing an organizational learning scale may contribute towards the field of study, making generalizable conclusions more easily drawn. Thus, we need to take into account the multidimensional nature of the construct, recognized in various studies (e.g., Senge, 1990; Lei et al., 1999). Our objective is to contribute towards the level of knowledge regarding organizational learning, developing a measurement tool that is adapted to its multidimensional nature. We test its validity and reliability in a sample of 111 Spanish manufacturing firms from the chemical industry. Designing this measurement scale may be relevant for various reasons. First, to facilitate work that allows the antecedents and the learning effects on organizations to be evaluated. Second, to identify the different dimensions from which it is formed and hence the underlying relationships. This would allow the evaluation of tools that are adequate for the provision of organizational learning. We first establish the concept of organizational learning, concentrating on its complex nature. We then develop a measurement scale according to this complex nature, paying particular attention to checking its validity. Finally, we set out the main conclusions and implications of the study.

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2. Organizational learning: establishing the concept In the literature on organizational learning, we come across a constant evocation of the psychological concept of individual learning. Its influence, direct or indirect, on the way in which organizations learn justifies the fact that many theories on organizational learning are based on observations of individual learning and of the organization –individual analogy (Kim, 1993). Although organizational learning has its roots in individual learning (Shrivastava, 1983; Senge, 1990), the process that leads to its development is not as simple as just adding together the individual learning of the organization’s different members (Argyris and Scho¨n, 1978; Hedberg, 1981). Organizational learning is seen as a dynamic process based on knowledge, which implies moving among the different levels of action, going from the individual to the group level, and then to the organizational level and back again (Huber, 1991; Crossan et al., 1999). This process stems from the knowledge acquisition of the individuals and progresses with the exchange and integration of this knowledge until a corpus of collective knowledge is created (Hedberg, 1981), embedded in the organizational processes and culture. This collective knowledge, which is stored in the so-called organizational memory (Walsh and Ungson, 1991), has an impact on the type of knowledge acquired and the way in which it is interpreted and shared. What an individual learns in an organization greatly depends on what is already known by the other members of the organization—in other words, on the common knowledge base (Simon, 1991). Fig. 1 reflects the continuity and dynamism of the learning process. Analyzing learning as a process highlights three main aspects. First, knowledge and, more specifically, its acquisition or creation, along with its dissemination and integration within the organization, become a key strategic resource (Grant, 1996; Zander and Kogut, 1995; Teece et al., 1997). This gives rise to the idea that organizational learning has a collective nature that goes beyond the individual learning of persons (Shrivastava, 1983). Second, this creation and dissemination of new knowledge imply the existence of constant internal changes that can occur at a cognitive or behavioral

level (Fiol and Lyles, 1985). Third, these internal changes lead to a process of constant improvement that allows the firm’s actions to be maintained or bettered (Fiol and Lyles, 1985; Garvin, 1993; Slocum et al., 1994), or even to achieve a competitive advantage based on firms’ different learning capabilities (Mahoney, 1995; Brenneman et al., 1998). The aforementioned aspects enable us to conceptualize organizational learning as the capability of an organization to process knowledge—in other words, to create, acquire, transfer, and integrate knowledge, and to modify its behavior to reflect the new cognitive situation, with a view to improving its performance. The effective development of organizational learning capability requires four conditions. First, company management must provide decisive backing to organizational learning (Stata, 1989; Garvin, 1993). Management should spearhead the process, making clear its support and involving all the personnel (Williams, 2001). Second, it requires the existence of a collective conscience that allows the firm to be seen as a system in which each element must make its own contribution so as to obtain a satisfactory result (DeGeus, 1988; Senge, 1990). If a shared vision is lacking, the individual actions do not contribute towards organizational learning (Kim, 1993). Third, it needs the development of organizational knowledge, based on the transfer and integration of knowledge acquired individually (Nonaka and Takeuchi, 1995). Creating a corpus of organizational knowledge, steeped in the routines and processes of the work itself, is essential for guaranteeing the organization’s continuous learning, irrespective of the individuals that form part of it (Daft and Weick, 1984). Lastly, simply adapting to the changes within the established framework does not suffice for learning capability to be a source of heterogeneity among firms inasmuch as adaptation is an inadequate response in the current competitive environment (Hedberg, 1981; McGill and Slocum, 1993). The firm must go beyond an adaptive learning and concentrate on the learning level needed to question the organizational system in force and, if necessary, make changes in search of more innovative and flexible alternatives—generative learning (Senge, 1990; McGill et al., 1992)—a learning that requires an open mentality towards new ideas and a great deal of experimentation (Leonard-Barton, 1992).

3. The multidimensionality of organizational learning capability

Fig. 1. Organizational learning process.

Authors who point out the necessity of describing organizational learning fully and precisely maintain that it is essential to develop reliable, valid methods of measurement (Easterby-Smith et al., 2000). One of the traditional ways of measuring learning has been to use so-called learning curves (Yelle, 1979; Lieberman, 1987) and experience curves (Boston Consulting Group, 1968). However,

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these curves are ‘‘incomplete measuring tools’’ (Garvin, 1993, p. 89) because they concentrate exclusively on learning by doing and measure learning in terms of the results obtained, in search of short-term efficiency. Besides studying experience curves, learning has also been measured by taking into account other variables, such as number of patents (Decarolis and Deeds, 1999) or R & D expenditure (Bierly and Chakrabarti, 1996). The common characteristic shared by all these techniques is that they focused on process outcomes, rather than the actual learning processes, but ‘‘organizational learning is a complex multidimensional construct . . . encompassing multiple subprocesses’’ (Slater and Narver, 1994, p. 2). We consider organizational learning to be a latent multidimensional construct inasmuch as its full significance lies beneath the various dimensions that go towards its makeup. Thus, an organization should show a high degree of learning in each and every one of the dimensions defined to be able to state that its learning capability is high. These dimensions, called managerial commitment, systems perspective, openness and experimentation, and knowledge transfer and integration, sum up the aspects mentioned previously as the basic elements needed for an organization to learn, and constitute our organizational learning structure model. 3.1. Organizational learning capability dimensions 3.1.1. Managerial commitment Management should recognize the relevance of learning, thus developing a culture that promotes the acquisition, creation, and transfer of knowledge as fundamental values (Stata, 1989; McGill et al., 1992; Garvin, 1993; Nonaka and Takeuchi, 1995). Management should articulate a strategic view of learning, making it a central visible element and a valuable tool with an influence on the obtaining of longterm results (Ulrich et al., 1993; Slocum et al., 1994; Nevis et al., 1995; Hult and Ferrell, 1997). Likewise, management should ensure that the firm’s employees understand the importance of learning and become involved in its achievement, considering it an active part in the firm’s success (Senge, 1990; Slater and Narver, 1995; Spender, 1996; Williams, 2001). Finally, management should drive the process of change, taking the responsibility for creating an organization that is able to regenerate itself and face up to new challenges (Lei et al., 1999). Management should eliminate old beliefs and mental models that may have helped to interpret reality in the past but may now be seen as obstacles inasmuch as they help to perpetuate assumptions that do not correspond to the current situation (DeGeus, 1988; McGill and Slocum, 1993; Nonaka, 1994; Nonaka and Takeuchi, 1995). 3.1.2. Systems perspective Systems perspective entails bringing the organization’s members together around a common identity (Senge, 1990; Sinkula, 1994). The various individuals, departments, and

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areas of the firm should have a clear view of the organization’s objectives and understand how they can help in their development (Hult and Ferrell, 1997; Lei et al., 1999). The organization should be considered as a system that is made up of different parts, each with its own function but act in a coordinated manner (Stata, 1989; Leonard-Barton, 1992; Kofman and Senge, 1993; Nevis et al., 1995). Viewing the firm as a system implicitly involves recognizing the importance of relationships based on the exchange of information and services (Ulrich et al., 1993), and infers the development of shared mental models (Senge, 1990; Kim, 1993; Miller, 1996). Inasmuch as organizational learning implies shared knowledge, perceptions, and beliefs, it will be enhanced by the existence of a common language and joint action by all the individuals involved in the process. Thus, the presence of a common language favors knowledge integration—a crucial aspect in the development of organizational learning (Grant, 1996). In this way, organizational learning goes beyond the employees’ individual learning and takes on a collective nature (McGill et al., 1992). 3.1.3. Openness and experimentation Our unit of analysis is generative or double-loop learning, which requires a climate of openness that welcomes the arrival of new ideas and points of view, both internal and external, allowing individual knowledge to be constantly renewed, widened, and improved (Senge, 1990; LeonardBarton, 1992; Slocum et al., 1994; Sinkula, 1994). To create a climate of openness, there needs to be a previous commitment to cultural and functional diversity, as well as a readiness to accept all types of opinions and experiences and to learn from them, avoiding the egocentric attitude of considering one’s own values, beliefs, and experiences to be better than the rest (McGill et al., 1992; McGill and Slocum, 1993; Nevis et al., 1995). Openness to new ideas, coming from within the organization or from outside it, favors experimentation—an essential aspect for generative learning—inasmuch as it implies the search for innovative flexible solutions to current and future problems, based on the possible use of different methods and procedures (Leonard-Barton, 1992; Garvin, 1993). Experimentation requires a culture that promotes creativity, an enterprising ability, and the readiness to take controlled risks, supporting the idea that one can learn from one’s mistakes (Slocum et al., 1994; Slater and Narver, 1995; Naman and Slevin, 1993). 3.1.4. Knowledge transfer and integration This fourth dimension refers to two closely linked processes, which occur simultaneously rather than successively: internal transfer and integration of knowledge. The efficacy of these two processes rests on the previous existence of absorptive capacity (Cohen and Levinthal, 1990), implying the lack of internal barriers that impede the transfer of best practices within the firm (Szulanski, 1996). Transfer implies the internal spreading of knowledge acquired at an individual level, mainly through conversa-

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tions and interaction among individuals (Brown and Duguid, 1991; Kofman and Senge, 1993; Nicolini and Meznar, 1995)—in other words, through fluid communication, dialogue, and debate. Fluid communication relies mainly on the existence of agile information systems that guarantee the accuracy and availability of the information (McGill and Slocum, 1993). With regard to dialogue and debate, work teams and personnel meetings can be ideal forums in which to openly share ideas (Stata, 1989; Garvin, 1993; Nonaka, 1994; Slater and Narver, 1995; Lei et al., 1999). The main role of work teams in developing organizational learning is frequently underlined in the literature (DiBella et al., 1996; Snell et al., 1996), with particular emphasis placed on multidiscipline and multifunction teams (Garvin, 1993; Ulrich et al., 1993; Nonaka, 1994; Nonaka and Takeuchi, 1995). Team learning places the group above the individual, allowing the transfer, interpretation, and integration of the knowledge acquired individually (Senge, 1990; Hult and Ferrell, 1997). This integration leads to the creation of a collective corpus of knowledge rooted in organizational culture, work processes, and the remaining elements that form the ‘‘organizational memory’’ (Huber, 1991; Walsh and Ungson, 1991). Thus, the knowledge can be subsequently recovered and applied to different situations, guaranteeing the firm’s constant learning in spite of

the natural rotation of its members (Levitt and March, 1988; Simon, 1991). Table 1 sums up the theoretical support for our model, grouping together the different components identified by other authors in terms of their relation to each dimension. 3.2. Relations among the dimensions of organizational learning capability Although the four dimensions identified are different, they are related. Thus, the aforementioned consideration of organizational learning as a dynamic process reveals the interaction between openness and experimentation and knowledge transfer and integration. To ensure the effective development of organizational learning, the knowledge acquired and created on an individual level has to be transferred and integrated into the organization (Huber, 1991). Furthermore, the success of this integration, as Grant (1996) points out, depends on the presence of a common language and a shared vision by all the organization’s members (systems perspective). Organizational culture plays an important role in this, and its development will depend on management support (managerial commitment). Common factors underlie the four dimensions, explaining why they are so closely linked. For example, the four

Table 1 Dimensions of organizational learning capability: an overview of the literature Dimension

Related factors according to authors reviewed

Managerial commitment

n Managerial backing (Stata, 1989) n Shared vision and mental models (Senge, 1990) n Personal efficacy (McGill et al., 1992) n Leadership commitment (Garvin, 1993; McGill and Slocum, 1993; Goh and Richards, 1997) n Strategic intent (Slocum et al., 1994) n Leadership and intention (Nonaka, 1994; Nonaka and Takeuchi, 1995) n Involved leadership (Nevis et al., 1995) n Facilitative leadership (Slater and Narver, 1995) n Learning orientation (Hult and Ferrell, 1997) n Shared vision (Senge, 1990) n Systems thinking (Stata, 1989; Senge, 1990; Leonard-Barton, 1992) n Systemic thinking (McGill et al., 1992) n Systems perspective (Nevis et al., 1995) n Clarity of purpose and mission (Goh and Richards, 1997) n Systems orientation (Hult and Ferrell, 1997) n Openness to new ideas (Stata, 1989) n Independent problem solving, continuous innovation and experimentation and integration of external knowledge (Leonard-Barton, 1992) n Openness and creativity (McGill et al., 1992) n Continuous learning and experimentation culture (McGill and Slocum, 1993) n Experimentation and learning from past experience and from others (Garvin, 1993) n Continuous experimentation and learning from past situations (Slocum et al., 1994) n Entrepreneurship (Slater and Narver, 1995) n Operational variety, multiple advocates, climate of openness and experimental mind-set (Nevis et al., 1995) n Experimentation (Goh and Richards, 1997) n Team work (Stata, 1989; Nonaka, 1994; Nonaka and Takeuchi, 1995) n Team learning (Senge, 1990) n Integration of internal knowledge (Leonard-Barton, 1992) n Knowledge transfer (Garvin, 1993) n Transfer of knowledge and teamwork and group problem solving (Goh and Richards, 1997) n Team orientation and memory orientation (Hult and Ferrell, 1997)

Systems perspective

Openness and experimentation

Knowledge transfer and integration

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dimensions analyzed emphasize the relevance of employee participation, teamwork, cooperation, and involvement. These elements can be related to competitive advantage if there is trust in an organization (Mayer et al., 1995; McAllister, 1995). According to Jones and George (1998), trust can promote the free exchange of knowledge and information, high involvement, or subjugation of personal needs and ego to pursue a common goal. All these effects are included in the four dimensions we have identified. Therefore, the relationship between trust and the dimensions of the organizational learning capability construct is complex and may be reciprocal, with growth and changes in one supporting and reinforcing growth in the other.

4. Methods 4.1. Sample and procedures The population is made up of Spanish chemical product manufacturing firms (Code CNAE 24). The firms were selected using the Duns database. We chose companies with 50 or more employees, which gave us an objective population of 415 firms. The questionnaire was delivered by means of postal survey. It was addressed to the general manager and to the HR manager so as to obtain two responses per firm. The first mailing was carried out on October 30, 2000. To increase the response rate, the firms were first contacted by phone to confirm the identity of the contacts and explain the objectives of the study. A second mailing was carried out at the beginning of 2001, sending the questionnaire to those firms that had not responded the first time round. As in the first mailing, the firms were first contacted by phone to reiterate the importance of their response. One hundred and fifty questionnaires were returned, of which 140 were considered valid. Both copies of the questionnaire were received from 29 firms, giving a final sample of 111 firms, which represents a response rate of 26.75%. For those firms that returned both copies of the questionnaire, we opted for taking the mean of the two responses as a representation of the firm’s position, given the strong correlation we found between the responses from each source (correlations ranged from .7432 to .9174). In addition, the results of an ANOVA indicate that there are no significant differences between one source and the other, in any of the items included in the questionnaire. These results are an indication that a single respondent may be used as a proxy for our organizational learning construct. In any case, other studies have deemed it appropriate to measure this construct using a single respondent holding a position of maximum responsibility (e.g., Templeton et al., 2002). With respect to the companies’ employees, assets, profits, and equity, no significant differences were observed between the respondent and nonrespondent groups. An ANOVA was also carried out to identify significant differ-

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ences between the firms that returned the questionnaire after the first mailing and those that did so the second time around. No statistically significant differences were found in any of the questionnaire’s items. 4.2. Measuring organizational learning capability Learning capability is a complex multidimensional construct. Although various studies have identified different dimensions or components (e.g., Senge, 1990; Slater and Narver, 1995; Lei et al., 1999), most do so from a theoretical point of view, there being very few that actually design a measurement scale based on the dimensions identified. Goh’s and Richards’ (1997) study identifies five dimensions (clarity of purpose and mission, leadership commitment and empowerment, experimentation and rewards, transfer of knowledge, teamwork, and group problem solving) and establishes a learning scale made up of 21 items. The questionnaire was sent to all the employees in four organizations, two public and two private. The results enable the authors to establish differences among the firms with regard to their learning ability, concluding that the private companies, operating in less-regulated environments, score highest in the different dimensions. This study offers only preliminary evidence on the scale’s reliability, convergent validity, and discriminant validity. Hult’s and Ferrell’s (1997) study is more exhaustive with regard to the validation of the scale they design, formed by 23 items that attempt to measure the four dimensions they consider part of organizational learning capability (team orientation, systems orientation, learning orientation, and memory orientation). As opposed to Goh’s and Richards’ (1997) work, this study uses a large sample of firms and pays particular attention to verifying the reliability, the content validity, and the convergent and discriminant validity of the scale, describing the whole process in detail. However, the limitation of this work, in terms of the scale’s subsequent utility, is its domain inasmuch as it concentrates on learning capability with regard to the activities and relationships between the strategic business units and the purchasing center of a multinational corporation. The items in our scale were generated by using some of the items included in the two aforementioned scales, and other items from the scale proposed by Oswald et al. (1994) for measuring shared vision (systems perspective), adapting them as necessary. After reviewing the relevant literature, new items were also generated. This process was facilitated by a preliminary test, which was administered by personally interviewing different general managers and HR managers from firms in the same industry. These interviews served a double purpose. First, to ensure that the different items were comprehensible and adequately reflected the aspect to be measured. Second, we were able to closely analyze the coherence in the items’ interpretation by managers from the same firm.

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Table 2 Descriptive statistics and correlation matrix Variables Mean S.D. 1

2

3

4

5

6

7

8

9

10

11

12

MC1 MC2a MC3 MC4 MC5 SP1 SP2 SP3 EX1 EX2 EX3 EX4 TR1 TR2 TR3a TR4

.54*** .52*** .52*** .20** .28*** .25** .32*** .28*** .23** .28*** .15 .34*** .23** .20**

.47*** .41*** .25** .16 * .28*** .36*** .27*** .26*** .16 .20** .35*** .25*** .23**

.48*** .36*** .34*** .35*** .23** .22** .33*** .32*** .34*** .40*** .22** .30***

.22** .24** .22** .17 * .11 .23** .24** .19** .29*** .20** .32***

.55*** .52*** .17 * .16 .33*** .17 * .32*** .33*** .27*** .23**

.59*** .33*** .17 * .36*** .34*** .45*** .35*** .30*** .42***

.28*** .10 .28*** .31*** .42*** .26*** .18 * .29***

.45*** .33*** .37*** .31*** .27*** .27*** .31***

.42*** .45*** .28*** .22** .06 .17 *

.40*** .42*** .37*** .23** .38***

.49*** .25** .53*** .18 * .50*** .57*** .35*** .55*** .51*** .42***

4.99 5.76 5.79 5.24 5.00 5.14 4.89 4.89 5.39 5.22 5.59 5.78 5.21 5.39 5.65 5.03

1.26 1.25 1.11 1.54 1.36 1.40 1.36 1.38 0.99 1.13 1.12 0.97 1.39 1.26 1.26 1.62

.53*** .42*** .49*** .48*** .26*** .28*** .34*** .12 .13 .20** .33*** .20** .25*** .22** .17 *

13

14

15

a

Item was reverse-coded. * P < .10. ** P < .05. *** P < .01.

It was therefore easier to identify those items that proved to be inadequate and to reword those that were difficult to understand. From an initial total of 23 items, 16 were finally included (see Appendix). Each item is measured using a Likert-type scale, 1 representing ‘‘totally disagree’’ and 7 ‘‘totally agree’’. The items are grouped together into four subscales that represent the four dimensions identified (management commitment—MC; systems perspective—SP; openness and experimentation—EX; and knowledge transfer and integration—TR). Table 2 shows the descriptive statistics and the correlation matrix for these 16 variables. To check the scale’s validity, we reviewed four critical components: content validity, reliability or internal consistency, convergent validity, and discriminant validity. 4.2.1. Content validity Content validity refers to how adequately the magnitude analyzed has been described in the form of items (Nunnally, 1978). Unlike other types of validity, there is no definitive quantitative criterion by which to evaluate content validity (Hoskisson et al., 1993), with the said evaluation being based on qualitative aspects. However, in our case, there are two possible indicators which support content validity, namely, (1) the exhaustive overview of the literature and (2) the preliminary test using personal interviews with five general managers and six human resource managers. 4.2.2. Reliability or internal consistency The reliability indicates to what extent the different items are coherent with each other and whether they can be used to measure a specific magnitude. To check whether the dimensions proposed are backed up by the results obtained from the study, we first made a factor analysis, using

principal components extraction with oblique rotation, on the set of 16 items of the scale (see Table 3). Four factors emerged from the analysis, each with an eigenvalue above one, which provide an explanation for 63% of the total variance. These results reveal that the items corresponding to the same dimension load on a single factor. This is an indication of the questionnaire’s validity as regards measuring the four learning capability dimensions proposed. Table 3 shows that there are items with loads above .4 on more than one factor. This highlights that there are relevant relations among the dimensions. Thus, the systems perspective dimension appears to be related to items whose main Table 3 Factor analysis of organizational learning capability scale a Item

Factor loadings Factor 1

Factor 2

Factor 3

Factor 4

MC1 MC2b MC3 MC4 MC5 SP1 SP2 SP3 EX1 EX2 EX3 EX4 TR1 TR2 TR3b TR4

.20 .25 .30 .35 .31 .32 .43 .28 .34 .14 .42 .32 .75 .82 .82 .76

.76 .82 .74 .73 .75 .30 .28 .33 .29 .23 .26 .28 .19 .40 .27 .25

.17 .34 .34 .32 .17 .19 .35 .28 .71 .84 .65 .72 .49 .31 .15 .38

.38 .22 .18 .44 .24 .79 .83 .84 .23 .11 .41 .37 .52 .32 .24 .37

a b

Oblique rotation. Item was reverse-coded.

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load is on another factor. The strong correlation with item TR1 indicates that the vision of the firm as a system implicitly entails the recognition of the importance of information-transfer-based relations (Ulrich et al., 1993). Likewise, the correlation it shows with item MC4 is justified inasmuch as systems perspective implies that the various individuals and areas of the firm should have a clear view of the firm’s objectives (learning, in this case) and understand how they can help in their development (Hult and Ferrell, 1997; Lei et al., 1999). As regards the knowledge transfer and integration dimension, the relation with item SP2 reveals the need for there to be coordination among the different parts of the firm to successfully integrate individual knowledge. This, in turn, requires a clear understanding of how each part can contribute towards the common objective (Kofman and Senge, 1993; Nevis et al., 1995). The correlation existing with item EX3 indicates that knowledge exchange, a fundamental aspect in the correct development of the organizational learning process, requires an environment that is willing to accept all types of opinions and experiences, both internal and external, and to learn from them (McGill et al., 1992; McGill and Slocum, 1993). Lastly, with regard to the openness and experimentation dimension, the correlation existing with item TR1 can be justified in that experimentation requires a culture that promotes creativity and the readiness to take controlled risks, supporting the idea that one can learn from one’s mistakes (Slocum et al., 1994; Slater and Narver, 1995; Naman and Slevin, 1993). To obtain a sounder judgement of the structural relationships between them and the wider construct over which they lie, a second-order confirmatory factor analysis was made (Hair et al., 1999). Prior to carrying out this analysis, the 16 indicators on the learning scale were subjected to the multivariate normality test. The results obtained show that the set of variables observed is significantly different with regard to a normal distribution, both in terms of asymmetry and of kurtosis (v2 = 1257.32; P=.000). These results, in

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principle, advise against using the Maximum Likelihood (ML) method to estimate the model. In this case, the Weighted Least Squares (WLS) method would appear to be more appropriate, but the sample size prevents us from calculating the asymptotic covariance matrix, so applying this method is not viable. Another possible solution to the problem is to use the Generalized Least Squares (GLS) method on the robust covariance matrix (Urbany et al., 1996). This solution is particularly appropriate when the proposed model is not excessively complex. Thus, we carried out the second-order confirmatory factor analysis using LISREL 8 (Jo¨reskog and So¨rbom, 1993) on the robust covariance matrix, using the GLS method. Nonetheless, given that the software packages currently used to estimate structural equation models produce robust results (Hair et al., 1999), the covariance matrix was also subjected to the confirmatory analysis using the default method (ML), to check whether similar results were obtained in both cases. Following a competing models strategy, 15 models or structural equation systems were analyzed. In the first 14, a first-order confirmatory analysis was applied (Model 1: single dimension; Models 2 to 7: two dimensions; Models 8 to 13: three dimensions; Model 14: four dimensions). Model 15, on the other hand, would be our second-order model proposed. Analysis of the results of the fit using both GLS and ML reveals that they are similar in terms of selecting the best model out of the 15 compared. Likewise, the standardized loads of the models estimated using both procedures are very similar. Table 4 provides a summary of the results obtained from the confirmatory analysis by applying ML to the covariance matrix of the items included in the organizational learning scale. The fit for each of the models has been evaluated using several indicators. Table 4 shows that only Models 14 and 15 include acceptable values in all the indicators, with Model 15—the model proposed—being slightly better in terms of the RMSEA and the RFI values. The previous models’ fit could be improved by allowing the error terms of some of the

Table 4 Summary results of confirmatory factor analysis: competing models Models

df

v2

v2/df

RMSEA

GFI

NFI

CFI

IFI

RFI

M1: 1 dimension M2: 2 dimensions (MC – TR) (SP – EX) M3: 2 dimensions (MC – SP – TR) (EX) M4: 2 dimensions (MC – EX – TR) (SP) M5: 2 dimensions (MC – SP – EX) (TR) M6: 2 dimensions (MC – SP) (EX – TR) M7: 2 dimensions (SP – EX – TR) (MC) M8: 3 dimensions (MC – TR) (SP) (EX) M9: 3 dimensions (MC – SP) (EX) (TR) M10: 3 dimensions (MC – EX) (SP) (TR) M11: 3 dimensions (SP – TR) (MC) (EX) M12: 3 dimensions (SP – EX) (MC) (TR) M13: 3 dimensions (EX – TR) (MC) (SP) M14: 4 dimensions M15: second-order confirmatory

104 103 103 103 103 103 103 101 101 101 101 101 101 98 100

264.84 239.87 239.70 224.54 217.71 208.72 182.95 198.03 178.62 164.74 156.97 152.90 140.71 110.97 111.95

2.55 2.33 2.33 2.18 2.11 2.03 1.78 1.96 1.77 1.63 1.55 1.51 1.39 1.13 1.12

0.12 0.11 0.11 0.10 0.10 0.097 0.085 0.094 0.084 0.076 0.072 0.069 0.060 0.035 0.033

0.73 0.75 0.75 0.76 0.77 0.78 0.82 0.78 0.81 0.82 0.84 0.84 0.86 0.89 0.89

0.62 0.65 0.65 0.68 0.69 0.70 0.74 0.71 0.74 0.76 0.77 0.78 0.80 0.84 0.84

0.72 0.76 0.76 0.79 0.80 0.82 0.86 0.83 0.86 0.89 0.90 0.91 0.93 0.98 0.98

0.73 0.77 0.77 0.79 0.81 0.82 0.86 0.84 0.87 0.89 0.91 0.91 0.93 0.98 0.98

0.56 0.60 0.60 0.62 0.63 0.65 0.69 0.66 0.69 0.72 0.73 0.74 0.76 0.80 0.81

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observed variables, such as EX4 and TR1, to correlate. The literature, however, recommends that no changes be made to the model when solely based on modification indices but only when there is a solid theoretical backing to them (Hair et al., 1999); therefore, we have not considered this possibility. Fig. 2 provides a graph of the second-order model proposed, indicating the individual standardized loads of the different variables. As we use self-report organizational data, we have tested for problems associated with common method variance. Podsakoff and Organ (1986) revised various techniques to analyze the common method variance, including a procedure that denominated the single factor approach. The logic behind this approximation is that if the method variance explains the relation between two or more variables, a factorial analysis should lead to a single overall factor. McFarlin and Sweeny (1992) indicate that this approach has its weaknesses, although it provides useful information. To complete the procedure, these authors suggest combining it with a confirmatory factorial analysis, comparing models that increase its complexity. We carried out a confirmatory factorial analysis considering one, two, three, and four factors. The least complex model examined—the single-factor model—did not fit the data so well as the more complex models (see Table 4). Thus, according to this criterion, the common method variance does not pose a problem in this study.

The Cronbach’s alpha coefficient has also been independently calculated for each of the four dimensions, so as to check the degree of internal consistency. The values of this coefficient for MC, SP, EX, and TR dimensions are .82, .78, .73, and .80, respectively. The Cronbach’s alpha for the whole scale is .88. All the values are above the limit of .70 (Nunnally, 1978). Lastly, we calculated Fornell and Larker (1981) composite reliability index, which gives values of .83, .79, .72, and .81 for each of the four learning dimensions, respectively, and of .94 for the whole learning scale. All these results, along with those obtained with regard to the quality of the fit, provide enough evidence to confirm the second-order model proposed. 4.2.3. Convergent and discriminant validity Convergent validity refers to how great the coincidence is among multiple measurements of the same magnitude using different methods (Hoskisson et al., 1993). It implies the corroboration, by means of independent procedures, of the concept being studied. In operative terms, convergent validity exists when a significant correlation is obtained among the variables that supposedly formed part of the construct being studied (Liden and Maslyn, 1998). In our case, there should be significant correlations among the four subscales. Table 5 shows that the correlations among the

Fig. 2. Second-order confirmatory model. aNoncalculated t value. Parameter set at 1. bItem was reverse-coded. * * * P < .01.

P. Jerez-Go´mez et al. / Journal of Business Research 58 (2005) 715–725 Table 5 Correlations among organizational learning capability subscales

MC SP EX TR

MC

SP

EX

TR

1.00 .42*** .42*** .42***

1.00 .40*** .48***

1.00 .48***

1.00

*** P < .01.

different subscales are significant (.01), which corroborates the existence of convergent validity. The discriminant validity of a scale implies that this scale is measuring a single fundamental construct, as opposed to multiple constructs. In line with McGrath (2001), the presence of discriminant validity was checked by comparing the correlation between the items within each learning subscale with the correlation between the items of one subscale and the items of each of the remaining subscales. This comparison should reveal that the correlations in the first case are greater than in the second. As shown in Table 2, the correlation among the items within each subscale is, in all cases, greater than the correlations among items belonging to different subscales, except for one, the correlation between EX4 and TR1, where the correlation is slightly higher (.49) than the highest value for the openness and experimentation subscale (.45). Discriminant validity can also be tested by measuring the extent to which the construct we are dealing with differs from other concepts that are different but, at the same time, related to said construct. A firm’s level of innovation is a concept related to but different from that of organizational learning (Leonard-Barton, 1992; Hurley and Hult, 1998; Lei et al., 1999; McGrath, 2001). Therefore, the comparison between the different learning subscales and the level of innovation could provide evidence regarding discriminant validity. The questionnaire included five items to measure the firm’s level of innovation, using statements such as ‘‘this firm is one of the industry leaders in terms of product innovation’’ or ‘‘this firm is one of the quickest in the industry with regard to including innovations in its products and work processes’’. We carried out a principal components factor analysis, including the 16 items from the organizational learning scale and the 5 for the level of innovation. The results show that all the items had a greater loading in the factor to which they should be assigned. We can say, therefore, that organizational learning capability and level of innovation are clearly differentiated, which in turn confirms the discriminant validity.

5. Discussion and conclusions This paper represents an initial testing and validation of the organizational learning capability scale, within one industry and nation. The creation of this scale responds to the need expressed in the literature to progress in the field of

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learning measurement by developing an instrument that allows an organization’s level of learning to be evaluated (Garvin, 1993). In the validation process, both the principal components and the confirmatory factor analyses clearly corroborate the existence of the four dimensions mentioned in the theoretical work. The second-order factor analysis provides an empirical backing to the proposed organizational learning structure model, in which learning is considered to be a latent construct that lies under four dimensions, which are also latent and which are measured using different observable variables. The scale has behaved well in the statistical analyses carried out to check for the presence of internal consistency and convergent and discriminant validity. The empirical analysis carried out, however, has revealed certain limitations to this study. First, our questionnaire investigates the degree to which certain learning processes are developed within the firm. Inasmuch as responses to these questions are subject to the respondent’s perception, there is a potential danger that the answers are not in line with what is actually done. During the scale development process, we attempted to minimize this potential danger in various ways. Thus, during the pretest phase, in-depth interviews were carried out with different managers so as to check that the different items would actually reveal the firm’s actions and not what the manager believed ought to be done. In addition to this, the aim of the reliability and validity analyses is to check that the scale measures just what it is designed for. This potential problem is common to all those studies that use perception-based variables. Second, although centering the study on a single industry allows the context to be examined in greater detail and minimizes possible external influences on the performance, it can also limit its external validity (Rajagopolan, 1996). Due to the fact that most of the relationships evaluated have not been analyzed or considered previously, it would be worthwhile employing the organizational learning capability scale for testing in other national and industrial contexts, to establish its ultimate reliability and validity. Third, the size of the sample used for the empirical analysis could suppose certain limitations when extending the conclusions reached to the whole of the industry that was studied. Lastly, the exploratory nature of the organizational learning measurement scale proposed has its limitations in that this scale has not been used before. Although some of the items originally belonged to one of the two scales mentioned previously in this study—those of Goh and Richards (1997), and Hult and Ferrell (1997)—duly modified and adapted to our case in particular, most of them have been developed here for the first time, based on the review of the literature. This means that we could not benefit from the experience of alreadyvalidated scales. This study considers trust to be an antecedent that lies beneath the different dimensions of organizational learning capability. It could be argued that, in reality, trust is a further learning dimension related to but different from

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the other four. This is an empirical question that should be examined in future studies. The study also highlights the important role played by organizational learning in the current context of competitiveness, in which knowledge is considered a key resource. An important implication for business can be established from this. In spite of the general consensus in the literature with regard to the efficient management of constant learning and knowledge as powerful instruments for the maintenance and improvement of a firm’s competitiveness (Pettigrew and Whipp, 1991; Hamel and Prahalad, 1994), there is not such a wide consensus in terms of how managers can contribute towards a more efficient development of a superior learning capability. Establishing a measurement scale helps reveal the different areas of organizational learning in which managers can act to develop this capability. A relevant implication, therefore, is the approach taken in the activities and relations that need to be present for a firm to be considered a learning organization (Hult and Ferrell, 1997). The scale designed is useful for evaluating more complex models in which the effect of different antecedents on organizational learning can be analyzed. For example, human resource practices can be a fundamental tool in developing the organization’s learning capability (McGill et al., 1992; Mohrman and Mohrman, 1993; Snell et al., 1996), which means that analyzing their possible influence opens a new field of study that has rarely been dealt with. The scale may also be useful for analyzing the influence of learning on the firm’s performance, in a search for empirical evidence that supports learning potential as a source of a sustainable competitive advantage (Lei et al., 1999).

Acknowledgements This research was partially funded by Spanish MCYT and FEDER aid SEC 2001-1578-C02-02, and Fundacio´n CENTRA.

Appendix A. Items in the organizational learning capability scale

Managerial commitment (MC) MC1. The managers frequently involve their staff in important decisionmaking processes. MC2. Employee learning is considered more of an expense than an investment. MC3. The firm’s management looks favorably on carrying out changes in any area to adapt to and/or keep ahead of new environmental situations. MC4. Employee learning capability is considered a key factor in this firm. MC5. In this firm, innovative ideas that work are rewarded. Systems perspective (SP) SP1. All employees have generalized knowledge regarding this firm’s objectives.

SP2. All parts that make up this firm (departments, sections, work teams, and individuals) are well aware of how they contribute to achieving the overall objectives. SP3. All parts that make up this firm are interconnected, working together in a coordinated fashion. Openness and experimentation (EX) EX1. This firm promotes experimentation and innovation as a way of improving the work processes. EX2. This firm follows up what other firms in the sector are doing, adopting those practices and techniques it believes to be useful and interesting. EX3. Experiences and ideas provided by external sources (advisors, customers, training firms, etc.) are considered a useful instrument for this firm’s learning. EX4. Part of this firm’s culture is that employees can express their opinions and make suggestions regarding the procedures and methods in place for carrying out tasks. Knowledge transfer and integration (TR) TR1. Errors and failures are always discussed and analyzed in this firm, on all levels. TR2. Employees have the chance to talk among themselves about new ideas, programs, and activities that might be of use to the firm. TR3. In this firm, teamwork is not the usual way to work. TR4. The firm has instruments (manuals, databases, files, organizational routines, etc.) that allow what has been learnt in past situations to remain valid, although the employees are no longer the same.

References Argyris C, Scho¨n DA. Organizational learning: a theory of action perspective. Reading (MA): Addison-Wesley; 1978. Bierly P, Chakrabarti A. Generic knowledge strategies in the US pharmaceutical industry. Strateg Manag J 1996;17:23 – 135. Boston Consulting Group. Perspectives on experience. Boston: Boston Consulting Group; 1968. Brenneman WB, Keys JB, Fulmer RM. Learning across a living company: the shell companies’ experiences. Organ Dyn 1998;27(2):61 – 70. Brown JS, Duguid P. Organizational learning and communities-of-practice: toward a unified view of working, learning and innovation. In: Cohen M, Sproull LS, editors. Organizational learning. London: Sage; 1991. p. 40 – 57. Cangelosi VE, Dill WR. Organizational learning: observations toward a theory. Adm Sci Q 1965;10:175 – 203. Cohen WM, Levinthal DA. Absorptive capacity: a new perspective on learning and innovation. Adm Sci Q 1990;35:128 – 52. Crossan M, Lane HW, White RE. An organizational learning framework: from intuition to institution. Acad Manage Rev 1999;24(3):522 – 37. Cyert R, March J. A behavioural theory of the firm. Englewood Cliffs (NJ): Prentice-Hall; 1963. Daft RL, Weick KE. Toward a model of organizations as interpretation systems. Acad Manage Rev 1984;9(2):284 – 95. Decarolis DM, Deeds DL. The impact of stocks and flows of organizational knowledge on firm performance: an empirical investigation of the biotechnology industry. Strateg Manag J 1999;20:953 – 68. DeGeus AP. Planning as learning. Harvard Bus Rev 1988 (March – April); 70 – 4. DiBella AJ, Nevis EC, Gould JM. Understanding organizational learning capability. J Manag Stud 1996;33(3):361 – 79. Easterby-Smith M, Crossan M, Nicolini D. Organizational learning: debates past, present and future. J Manag Stud 2000;37(6):783 – 96. Fiol CM, Lyles MA. Organizational learning. Acad Manage Rev 1985; 10(4):803 – 13. Fornell C, Larcker DF. Evaluating structural equations models with unobservable variables and measurement error. J Mark Res 1981;18:39 – 50.

P. Jerez-Go´mez et al. / Journal of Business Research 58 (2005) 715–725 Garvin DA. Building a learning organization. Harvard Bus Rev 1993 (July – August);78 – 91. Goh S, Richards G. Benchmarking the learning capacity of organizations. Eur Manag J 1997;15(5):575 – 83. Grant RM. Prospering in dynamically-competitive environments: organizational capability as knowledge integration. Organ Sci 1996;7(4):375 – 87. Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate data analysis. 5th ed. New York: Prentice-Hall; 1999. Hamel G, Prahalad CK. Competing for the future. Boston: Harvard Business School Press; 1994. Hedberg B. How organizations learn and unlearn. In: Nystrom PC, Starbuck WH, editors. Handbook of organizational design, 1. New York: Oxford Univ. Press; 1981. p. 3 – 27. Hoskisson RE, Hitt MA, Johnson RA, Moesel DD. Construct validity of an objective (entropy) categorical measure of diversification strategy. Strateg Manag J 1993;14:215 – 35. Huber GP. Organizational learning: the contributing processes and the literatures. Organ Sci 1991;2(1):88 – 115. Hult GT, Ferrell OC. Global organizational learning capacity in purchasing: construct and measurement. J Bus Res 1997;40:97 – 111. Hurley RF, Hult GT. Innovation, market orientation and organizational learning: an integration and empirical examination. J Mark 1998;62:42 – 54. Jones GR, George JM. The experience and evolution of trust: implications for cooperation and teamwork. Acad Manage Rev 1998;23(3):531 – 46. Jo¨reskog KG, So¨rbom D. LISREL 8 User’s reference guide. Chicago: SSI; 1993. Kim DH. The link between individual and organizational learning. Sloan Manage Rev 1993 (Fall);37 – 50. Kofman F, Senge PM. Communities of commitment: the heart of learning organizations. Organ Dyn 1993;22(2):4 – 24. Lei D, Hitt MA, Bettis R. Dynamic core competencies through meta-learning and strategic context. J Manage 1996;22(4):549 – 69. Lei D, Slocum JW, Pitts RA. Designing organizations for competitive advantage: the power of unlearning and learning. Organ Dyn 1999; 24 – 38. Leonard-Barton D. The factory as a learning laboratory. Sloan Manage Rev 1992;23 – 38. Levitt B, March JG. Organizational learning. Annu Rev Sociology 1988;14: 319 – 40. Liden RC, Maslyn JM. Multidimensionality of leader – member exchange: an empirical assessment through scale development. J Manage 1998; 24(1):43 – 72. Lieberman MB. The learning curve, diffusion and competitive strategy. Strateg Manag J 1987;8(5):441 – 52. Mahoney JT. The management of the resources and the resource of management. J Bus Res 1995;33:91 – 101. Mayer RC, Davis JH, Schoorman FD. An integrative model of organizational trust. Acad Manage Rev 1995;20:709 – 34. McAllister DJ. Affect- and cognition-based trust as foundations for interpersonal cooperation in organizations. Acad Manage J 1995;38:24 – 59. McFarlin DB, Sweeney PD. Distributive and procedural justice as predictors of satisfaction with personal and organizational outcomes. Acad Manage J 1992;35(3):626 – 37. McGill ME, Slocum JW. Unlearning the organization. Organ Dyn 1993;67 – 79. McGill ME, Slocum JW, Lei D. Management practices in learning organizations. Organ Dyn 1992;21(1):5 – 17. McGrath R. Exploratory learning, innovative capacity and managerial oversight. Acad Manage J 2001;44(1):118 – 31. Miller D. A preliminary typology of organizational learning: synthesizing the literature. J Manage 1996;22(3):485 – 505. Mohrman SA, Mohrman AM. Organizational change and learning. In: Galbraith JR, Lawler EE, editors. Organizing for the future: the new logic for managing complex organizations. San Francisco: Jossey-Bass; 1993. p. 87 – 108. Naman JL, Slevin DP. Entrepreneurship and the concept of fit: a model and empirical test. Strateg Manag J 1993;14(2):135 – 52.

725

Nelson RR, Winter SG. An evolutionary theory of economic change. Cambridge: Belknap Press; 1982. Nevis EC, DiBella AJ, Gould JM. Understanding organizations as learning systems. Sloan Manage Rev 1995 (Winter);73 – 85. Nicolini D, Meznar M. The social construction of organizational learning: conceptual and practical issues in the field. Hum Relat 1995;48(7): 727 – 46. Nonaka I. Dynamic theory of organizational knowledge creation. Organ Sci 1994;5(1):14 – 37. Nonaka I, Takeuchi H. The knowledge-creating company. New York: Oxford Univ. Press; 1995. Nunnally JC. Psychometric theory. New York: McGraw-Hill; 1978. Oswald SL, Mossholder KW, Harris SG. Vision salience and strategic involvement: implications for psychological attachment to organization and job. Strateg Manag J 1994;15:477 – 89. Pettigrew A, Whipp R. Managing change for competitive success. Oxford: Blackwell; 1991. Podsakoff PM, Organ DW. Self-reports in organizational research: problems and prospects. J Manage 1986;12(4):531 – 44. Prahalad CK, Hamel G. The core competence of the corporation. Harvard Bus Rev 1990 (May – June);79 – 91. Rajagopolan N. Strategic orientations, incentive plan adoptions and firm performance: evidence from electric utility firms. Strateg Manag J 1996;18:761 – 85. Senge PM. The fifth discipline: art and practice of the learning organization. New York: Doubleday; 1990. Shrivastava P. A typology of organizational learning systems. J Manag Stud 1983;20(1):8 – 28. Simon HA. Bounded rationality and organizational learning. Organ Sci 1991;2(1):125 – 34. Sinkula JM. Market information processing and organizational learning. J Mark 1994;58:35 – 45. Slater SF, Narver J. Market oriented isn’t enough: build a learning organization. Marketing science institute report, number 94-103, Cambridge (MA); 1994. Slater SF, Narver FJ. Market orientation and the learning organization. J Mark 1995;59:63 – 74. Slocum JW, McGill M, Lei DT. The new learning strategy: anytime, anything, anywhere. Organ Dyn 1994;33 – 47. Snell SA, Youndt MA, Wright PM. Establishing a framework for research in SHRM: merging resource theory and organizational learning. Res Pers Hum Resour Manage 1996;14:61 – 90. Spender JC. Making knowledge the basis of a dynamic theory of the firm. Strateg Manag J 1996;17:45 – 62. Stata R. Organizational learning: the key to management innovation. Sloan Manage Rev 1989 (Spring);63 – 74. Szulanski G. Exploring internal stickiness impediments to the transfer of best practice within the firm. Strateg Manag J 1996;17:27 – 44. Teece DJ, Pisano G, Shuen A. Dynamic capabilities and strategic management. Strateg Manag J 1997;18(7):509 – 33. Templeton GF, Lewis BR, Snyder CA. Development of a measure for the organizational learning construct. J Manage Inf Syst 2002;19(2): 175 – 218. Ulrich D, Jick T, Von Glinow MA. High-impact learning: building and diffusing learning capability. Organ Dyn 1993;52 – 66. Urbany JE, Dixon PR, Kalapurakal R. Price search in the retail grocery market. J Mark 1996;60(2):91 – 105. Walsh JP, Ungson GR. Organizational memory. Acad Manage Rev 1991;16(1):57 – 91. Williams AP. A belief-focused process model of organizational learning. J Manag Stud 2001;38(1):67 – 85. Yelle LE. The learning curve: historical review and comprehensive survey. Decis Sci 1979;10(2):302 – 78. Zander U, Kogut B. Knowledge and the speed of the transfer and imitation of organizational capabilities: an empirical test. Organ Sci 1995;6(1): 76 – 92.