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Influence of transformationalleadership on organizationalinnovation and performance
depending on the levelof organizational learning
in the pharmaceutical sectorVıctor J. Garcıa-Morales
School of Economics, University of Granada, Granada, Spain, and
Fernando Matıas-Reche and Nuria Hurtado-TorresUniversity of Granada, Granada, Spain
Abstract
Purpose – This empirical study aims to examine the influence of transformational leadership (TL) onorganizational innovation (OI) and performance (OP) depending on the level of organizational learningin technological firms.
Design/methodology/approach – The research examined a sample of 164 pharmaceutical firms.A global model is formulated and the hypotheses are tested using structural equations.
Findings – First, the study shows a positive relation between TL and OI, between TL and OP andbetween OI and OP. Second, the study verifies that these relationships are more strongly reinforced inorganizations with high-organizational learning than in organizations with low levels of organizationallearning. Third, the study supports the theoretical arguments made but not demonstrated empirically inthe prior literature.
Practical implications – Organizational learning takes places in a technological community ofinteraction in which knowledge is created and expands in a constant dynamic between the tacitand the explicit with cognitive and behavioral change. Organizations with greater organizationallearning generate a network of learning that will make it easier for them to learn what they need toknow and to innovate, enabling the organization to maintain its competitive position as atechnological center. This shows that organizational learning improves relations substantiallybetween TL, OI and OP.
Originality/value – This study serves as a reference for fostering organizational learning intechnological firms. Organizational learning improves relations among TL, OI and OP. Previousstudies, although contributing to the understanding of the direct and indirect relations amongleadership, innovation and performance, have not addressed the different effects depending on thelevel of organizational learning in these technological firms.
Keywords Transformational leadership, Organizational innovation, Organizational performance,Pharmaceuticals industry, Innovation
Paper type Research paper
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0953-4814.htm
This work is being financed by the investigation project of Spanish Ministry of Technology andScience and excellence project of Junta de Andalucıa.
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Journal of Organizational ChangeManagementVol. 21 No. 2, 2008pp. 188-212q Emerald Group Publishing Limited0953-4814DOI 10.1108/09534810810856435
IntroductionThe concept of innovation is always current. All technological groups and organizationsare interested in knowing what influences the results they achieve, how and why theysucceed or fail. Belief that their results are related to organizational innovation (OI) hascontinued to inspire questions and research on the subject by professionals andacademics. Although innovation is widely recognized as essential for the survival andgrowth of organizations (Hurley and Hult, 1998), different definitions of innovation havebeen proposed. Here, we use the definition of innovation formulated by the ProductDevelopment and Management Association (PDMA, 2004): a new idea, method, ordevice. The act of creating a new product or process. The act includes invention as wellas the work required to bring an idea or concept into final form.
Although firm innovation is widely prescribed as a means of improvingorganizational performance (OP), many firms do not or cannot develop it properly.Researchers have urged attention to what enables firms to innovate, the search foranswers “beyond semiautomatic stimulus-response processes” (Zollo and Winter, 2002,p. 341). Leadership style has been emphasized as one of the most important influenceson firm innovation, because leaders can decide directly to introduce new ideas into atechnological organization, set specific goals, and encourage innovation initiativesfrom subordinates (Kanter, 1983; Senge et al., 1994).
Transformational leadership (TL), which has been contrasted with “traditional” or“transactional” leadership, stimulates OI and generates greater advantages for OP. TL canbe defined as the style of leadership that heightens consciousness by the organization’smembers of a collective interest and helps them to achieve it. In contrast, transactionalleadership focuses on promoting the individual interests of the leaders and their followersand attaining the satisfaction of contractual obligations on the part of both by establishingobjectives and monitoring and controlling the results (Bass and Avolio, 2000). Leadersshould thus commit themselves to TL, undertaking self-evaluation of their way of acting(Bass, 1999). Managers’ perceptions of the organizational style of leadership stronglyinfluence the capability of promoting this kind of leadership.
Theories of TL emphasize emotions, values, and the importance of leadership orientedto encouraging creativity in employees. Employees should be considered the firm’s mostvaluable resource, a resource for which the firm must take responsibility and whoseprofessional development it must promote. Such leadership creates emotional links with itsfollowers and acts to inspire higher values. It is leadership that transmits the importance ofhaving a shared mission and infusing a sense of purpose, direction and meaning intothe followers’ labor (Bass, 1999). TL becomes the motor and transmitter of innovativeculture and of the dissemination of technological knowledge oriented to seeking the bestpossible OP. The example of TL committed to the organization’s goals, which stimulatestheir internalization in its followers, will encourage commitment to results on the part of theorganization’s members (Bass, 1999; Bass and Avolio, 2000; Atwater and Spangler, 2004).
Although evidence shows that this kind of transformational leader exercises asubstantial influence on innovation, understanding of the processes through which theleader exerts this influence on innovation is still limited and largely speculative (Bass,1999; Conger, 1999). Further, few studies have been designed to trace systematicallythe causal path of the effects of TL on performance by examining the intermediateinfluence of OI (Bass, 1999; Conger, 1999) or the effects of organizational learning(Argyris and Schon, 1996; Senge et al., 1994).
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Transformational leaders have charisma, inspiration and intellectual stimulation(Bass, 1999; Conger, 1999; Bass and Avolio, 2000). Charisma generates the pride, faithand respect that leaders encourage their workers to have in themselves, their leadersand their technological organizations. Inspiration is the ability to motivate followers,largely through communication of high-technological expectations. Intellectualstimulation refers to behavior by the leader that promotes employees’ intelligence,knowledge and learning so that they can be innovative in their problem-solving andsolutions.
One factor to be considered in the relation between TL and OI is organizational learning(Argyris and Schon, 1996; Senge et al., 1994). Organizational learning involves theacquisition of explicit and tacit knowledge (development or creation of skills, insights,relationships), knowledge sharing, and knowledge utilization (DiBella et al., 1996). Thisprocess takes place within a community of interaction in which knowledge is created andexpands in a constant dynamic between the tacit and the explicit (Nonaka and Takeuchi,1995). It involves cognitive and behavioral change (Garcıa-Morales, 2004).
Previous studies, although contributing significantly to the understanding of therelationships – between TL and OI, between TL and OP, and between OI and OP –have not addressed these influences as they depend on the level of organizationallearning in technological firms. In the technological context, it is especially importantto gain a better understanding of factors influencing the successful development ofinnovations (van de Ven, 1993).
We should also underline the fundamental role of CEOs. They play a major role ininforming and molding these variables by determining the types of behavior that areexpected and supported (Baer and Frese, 2003). Further, although numerous actorsmay be involved in the management process, the CEO is ultimately responsible forplotting the organization’s direction and plans, as well as for guiding the actionscarried out to achieve them. Therefore, the CEO’s perception is fundamental to the firm.To make sense of the complex environment surrounding them, managers tend to formsimplified internal cognitive representations (mental models). Managers use thesemental models, managers to focus on certain variables that they judge to be critical.They make decisions and measure their organizational learning, OI, OP, etc. based onthese variables (Porac and Thomas, 1990).
To achieve our goals, we have structured the paper as follows. Based on previousresearch, second section suggests a series of hypotheses on the influence of TL on OIand OP and the influence of OI on OP. The section analyzes how TL affects OI and OPdifferently depending on the level of learning in an organization. Third section presentsthe data and the method used to analyze empirically the hypotheses developed insecond section in pharmaceutical organizations. Fourth section presents the resultsobtained, and fifth section discusses the results and implications for managers. Finally,fourth section outlines the main conclusions and points out some of the limitations ofthis study.
Background and hypothesesThe effects of TL on OI and OP in technological firms depend on the presence ofprevious capabilities by which firms synthesize and acquire technological knowledgeresources and generate new applications from those resources (Calantone et al., 2002).In this section, we present a model consisting of different hypotheses about how TL
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affects OI and OP depending on the level of organizational learning. We analyze theseantecedents and integrate them to achieve systemic thinking in a global model. Figure 1shows the proposed model. The results, discussion and conclusions sections will helpus to understand whether the hypothesized relationships are empirically verified andwhat their implications may be.
The influence of TL on OI depending on the level of organizational learningThe strategic literature highlights leadership style as an especially important influenceon innovation (Kanter, 1983; McDonough, 2000). There is currently broad consensusthat a collaborative and participative leadership style (transformational) is more likelyto encourage innovation in the technological organization (Kanter, 1983) thantransactional styles of leadership (Manz et al., 1989). It is important to highlight thatmanagers’ perceptions of their own role in their organizations strongly influences thecapability to promote this kind of leadership in a technological organization.
Several features of TL are relevant for firm innovation. Transformational leadershave an interactive vision and pay maximum attention to effective communication andvalue sharing (Adair, 1990) and to encouraging an appropriate environment forinnovative teams (Tushman and Nadler, 1986). They support collective processes oforganizational learning (Manz et al., 1989), reciprocal trust between organizationmembers and leaders (Scott and Bruce, 1994), and favorable attitudes towardproactivity, risk (Lefebvre and Lefebvre, 1992) and creativity (Tierney et al., 1999). Allof these features together enable a better understanding of the strong relationshipsbetween TL and factors positively influencing OI (Kanter, 1983).
As indicated earlier, transformational leaders have charisma, inspiration andintellectual stimulation. These characteristics are more active if there are organizationallearning processes that enable organizations to be more innovative (Bass and Avolio,2000; Bass, 1999; Conger, 1999). The influence of TL on OI is encouraged by the processof organizational knowledge creation (Cohen and Levinthal, 1990; Senge et al., 1994;Nonaka and Takeuchi, 1995).
The extensive and diverse literature on OI has received important contributions fromresearch on organizational learning in the last decade. Many of these contributions have
Figure 1.Hypothesized model
x1Transformational
Leadership
h1Organizational
Innovation
h2OrganizationalPerformance
H1(+)
H2(+)
H3(+)
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observed a greater relationship between TL and OI encouraged by organizationallearning (de Weerd-Nederhof et al., 2002). Different types of organizational learning(adaptive/generative) mediate the relationship between TL and OI (incremental/radical).The deeper innovation reaches, the greater the change process rate and the degree oflearning required. Thus, the process of organizational knowledge creation by which newknowledge is drawn from existing knowledge (organizational learning) stimulates OI(Cohen and Levinthal, 1990; Nonaka and Takeuchi, 1995).
For innovation to come to the fore by means of TL in technology organizations, ahigh degree of effective organizational learning is required (Bessant and Buckingham,1993; Glynn, 1996). An increasing number of firms are thus analyzing innovation as anorganizational learning process (Maastricht Economic Research Institute onInnovation and Technology, 1992) or applying organizational learning models tospecific aspects of the innovation process (Cohen and Levinthal, 1990).
A technological organization committed to learning increases the effect of TL oninnovation capability, because the organization is not likely to miss the opportunitiescreated by emerging market demand. It has the ability and knowledge to anticipateand understand customer needs, possesses better state-of-the art technology, uses thattechnology in innovations and has a stronger capacity to understand the strengths andweaknesses of rivals. By learning from both its successes and its failures, theorganization can generate greater innovation capability than its competitors(Calantone et al., 2002). These arguments lead to the following hypothesis:
H1. TL will be more positively associated with OI in technological organizationswith high-organizational learning than in technological organizations withlow-organizational learning.
The influence of TL on OP depending on the level of organizational learningLeadership in organizations today should move toward becoming TL, which inspiresemployees to participate with enthusiasm in team efforts and to analyze beyond theirown interests, reorienting the training and construction of teams to improve OP (Bass,1991). Among the main internal causes of the organization’s failure is the leader’sinability to assume the transformational role needed to stimulate OP, to create synergybetween the transformational leader and OP. High performance in the organization isstimulated by creating an organizational climate. This climate is fostered by theexistence of TL, which enables the articulation of leadership as a process fullyintegrated in the organization.
Leaders invest time and resources to construct teams and organizations thathave the competencies required to execute strategic and anticipatory changes. Thisis a function more in accord with a transformational style of leadership (Nadlerand Tushman, 1990). Transformational leaders construct systems that providedirection, energy and enthusiasm to the organization, creating good conditions forlearning and innovation in the whole organization to improve OP (Tushman andNadler, 1986).
Previous studies have also asserted relationships between leadership andorganizational learning, demonstrating their influence on OP (Tushman and Nadler,1986; Senge, 1990; McGill et al., 1992; Senge et al., 1994; Bierly et al., 2000). Traditionalleadership is highly individualistic and asystematic, making team and organizationallearning less necessary. In contrast, TL builds teams and provides them with support
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for processes of change that improve OP through organizational learning (Bass,1999; McDonough, 2000). This style allows the organization to learn throughexperimentation, exploration, communication and dialogue (Tushman and Nadler,1986; Slater and Narver, 1995; Senge et al., 1994; Lei et al., 1999).
More specifically, TL is fostered by organizational learning by promotingintellectual stimulation, inspirational motivation, and self-confidence amongorganization members (Coad and Berry, 1998). Organizational learning encouragesshared mental models in technological organizations. These favor TL and facilitatetechnological learning and the use of new technologies that improve OP (Senge et al.,1994). Based on the foregoing, we propose that:
H2. TL will be more positively associated with OP in technological organizationswith high-organizational learning than in technological organizations withlow-organizational learning.
The influence of OI on OP depending on the level of organizational learningDifferent theories have revealed that innovation is essential for better performance.Marketing theories show that organizations that concentrate on speed of innovationgain a greater market share, which produces high income and high profitability.Research in strategic theory underscores that organizations that adopt an innovationfirst are able to create “isolation mechanisms”. Because knowledge of the innovation isnot available to competitors, these mechanisms protect profit margins, allowingimportant benefits to be gained. Likewise, the theory of resources and capabilitiesmaintains that the capabilities, resources and technologies needed to adopt theinnovation make external imitation more difficult and allow firms to sustain theircompetitive advantages and obtain greater OP (Lengnick-Hall, 1992; Lieberman andMontgomery, 1988).
Thus, there is a positive link between innovation and OP (Zaltman et al., 1973) orbetween different aspects of innovation (e.g. innovation design or speed, flexibility) andperformance (Calantone et al., 2002). The innovation literature also includes variousempirical studies supporting this relationship, as do various works that useeconometric methods to demonstrate it empirically (Loof and Heshmati, 2002).
The more valuable, imperfectly imitable and rare innovations (e.g. technological)are, the higher performance will be (Irwin et al., 1998). Technological organizationswith greater innovation will achieve a better response from the environment, obtainingmore easily the capabilities needed to increase OP and consolidate a sustainablecompetitive advantage (Zaltman et al., 1973; Bommer and Jalajas, 2004). Not promotinginnovative projects and activities will have a negative effect on productivity and OP(Loof and Heshmati, 2002). However, being the first to market by innovation is notalways optimal (Tellis and Golder, 1996).
Organizational learning’s influence on the relation between OI and OP should beanalyzed empirically, since little knowledge is available concerning its influence andthe mechanisms by which it encourages this relation (Snyder and Cummings, 1998).It is wrong to assert that an increase in organizational learning always improves thisrelation and growth in OP (Inkpen and Crossan, 1995), since learning may not alwaysimprove an organization’s results (Huber, 1991). Nonetheless, generally speaking,organizational learning has a positive influence on this relation and on improvementsin performance (Argyris and Schon, 1996; Fiol and Lyles, 1985; Senge, 1990).
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Firms that show a greater breadth, depth and speed of organizational learning havegreater performance levels (Hurley and Hult, 1998; Zahra and George, 2002). Thisnormally occurs in firms from all sectors, for both manufacturing firms (Schroederet al., 2002) and technological companies (Decarolis and Deeds, 1999). We should notforget that organizations that encourage the organizational learning spiritsacrifice some immediate performance to achieve future performance, sinceimmediate performance is due to the organizational learning drawn from yesterdaywhile future performance will be the product of today’s organizational learning process(Senge, 1990). We thus propose the following hypothesis:
H3. OI will be more positively associated with OP in technological organizationswith high-organizational learning than in technological organizations withlow-organizational learning.
MethodologyThis section presents the research methodology used in this study. We describe thesample used, discuss how each of the variables included in the study is operationalizedand finally present the statistical analysis.
Sample and procedureThe first step in an empirical study is selecting the population to be analyzed. Thepopulation for this study consists of the most important pharmaceutical organizationsin Europe and America. The sample was selected randomly from the Amadeus andHoovers database. Choosing a sample of firms located in the pharmaceutical sector andin a relatively homogeneous legal and political space minimizes the impact of thevariables that cannot be controlled in the empirical research (Adler, 1983).
Drawing on our knowledge about key dimensions of this investigation, previouscontacts with interested managers and scholars, and new interviews with managersand academics interested in the topic and familiar with technology and thepharmaceutical sector, we developed a structured questionnaire to investigate howorganizations face these issues. We omitted the responses of the interviewees in thisfirst stage from the subsequent analysis of the survey data. We decided to use CEOsas our key informants, since they receive information from a wide range ofdepartments and are therefore a very valuable source for evaluating the differentvariables of the organization (Baer and Frese, 2003). The same types of informant werechosen to ensure that the level of influence among the organizations is constant, whichincreases the validity of the variables’ measurements (Glick, 1985).
Surveys were mailed to the selected pharmaceutical organizations (high-technologyfirms) along with a cover letter. We used this method because it allowed us to reach agreater number of organizations at a lower cost, to exert less pressure for immediatereply, and to provide the interviewees with a greater sense of autonomy. Initially, weexplained the goal of the study and offered recipients the possibility of receivingthe results once the study was completed. To reduce possible desirability bias, wepromised to keep all individual responses completely confidential and confirmed thatour analysis would be restricted to an aggregate level that would prevent theidentification of any organization. We told interviewees that they would receive thequestionnaire soon and reiterated the necessity that the person chosen answer it, even
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at the cost of receiving fewer responses. If the survey was not returned, we sent anothercopy later. This enabled us to obtain 164 valid responses.
To assess non-response bias, the characteristics of responding businesses werecompared to those of the nonresponding businesses. This analysis indicated thatrespondents did not differ significantly from nonrespondents in respect to return onassets, return on equity, return on sales or number of employees. Nor did we findsignificant difference between early and late respondents (Armstrong and Overton,1977). Table I provides the details of this analysis. Likewise, a series of x 2 and t-testsrevealed no significant differences due to geographical location or size in the variablesstudied.
MeasuresBefore embarking on the construction of the causal relations model, we evaluated thepsychometric properties of the different constructs that we wish to use in the model.To do this, we used the program Lisrel 8.30 and confirmatory factor analysis on eachconstruct. For the indicator to be reliable, we confirmed that the factor loads shouldbe higher than 0.4 and significant (t . 1.96; p , 0.05), and individual reliability above50 percent. Should more than one indicator not fulfil the reliability requirements, theseindicators should are eliminated one by one. Once the individual reliability of each ofthe indicators was assured, we studied the composite reliability of the whole scale for
Variables
Non-respondingbusinesses
(total)
Respondingbusinesses
(total)t-value(sig.)
Respondingbusinesses
(first mailing)
Respondingbusinesses
(secondmailing)
t-value(sig.)
Return onassets 21.69 23.17
0.458(0.649) 22.67 23.34
2 0.09(0.9)
Return onequity 22.02 27.92 1.73 (0.1) 35.27 25.63
0.924(0.379)
Return onsale 18.68 20.67 0.791(0.429) 16.95 22.90
2 0.968(0.338)
Number ofemployees 466.11 453.53
2 0.137(0.891) 370.48 510.30
2 0.771(0.442)
Sector PharmaceuticalGeographicallocation
Europe,America
Methodology Structuredquestionnaire
Procedure Aleatorysampling
Sample(response)size
2,476 (164)companies
Sample error 7.6 percentConfidencelevel
95 percent,p-q ¼ 0.50;Z ¼ 1.96
Period ofdatacollection
From February2003 to June2004
Table I.Technical details
of the research
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each of the scales (Table II). To do this, we applied the a Cronbach, the compositereliability (.0.7) and the average variance extracted (.0.5).
Transformational leadership. The strategic literature includes research thatmeasures and evaluates TL (Podsakoff and Organ, 1986; Coad and Berry, 1998). Owingto its extremely close relationship to our work and to the fact that it reflected thedifferent prior trends well, we used the scale designed by Podsakoff et al. (Podsakoffet al., 1996) for diverse aspects of TL. We established a Likert-type five-point scale(1 – “total disagreement” 5 – “total agreement”) of four items (Appendix) to reflectCEOs’ perceptions of TL in the organization. (We are testing not their transformationalstyle but the CEOs’ perceptions of TL in the technological organization.) Using aconfirmatory factor analysis (x2
2 ¼ 1:07, root mean square error of approximation(RMSEA) ¼ 0.001, normed fit index (NFI) ¼ 0.99, non-normed fit index (NNFI) ¼ 0.99,comparative fit index (CFI) ¼ 0.99), we validated our scales and then verified eachscale’s unidimensionality and its high validity and reliability (a ¼ 0.761).
Organizational innovation. We defined innovation for respondents, noting that OI andnot industry or market innovation should be their focus. Using a confirmatory factoranalysis (x 2
2 ¼ 0:31, RMSEA ¼ 0.001, NFI ¼ 0.99, NNFI ¼ 0.99, CFI ¼ 0.99) wevalidated our scales and then verified each scale’s unidimensionality and its highvalidity and reliability (a ¼ 0.772). The strategic literature has used both subjectiveperceptions of managers and objective data (Hitt et al., 1997) to measure innovation. Weincluded questions that drew on both types of assessment in our interviews. We calculatedthe correlation between the objective and subjective data and found it to be high andsignificant (0.88, p , 0.01). We preferred to use objective data for the last year (Appendix).
Organizational performance. After reviewing how performance is measured indifferent studies of strategic research (Homburg et al., 1999), we drew up a scale thatincluded four items to measure OP. We developed a confirmatory factor analysis tovalidate our scales (x2
2 ¼ 33:927, RMSEA ¼ 0.071, NFI ¼ 0.96, NNFI ¼ 0.96,CFI ¼ 0.96) and showed that the scale of four items was unidimensional and hadhigh reliability (a ¼ 0.821). Many researchers have used the subjective perceptions ofmanagers to measure beneficial outcomes for firms, but others have preferred objectivedata, such as return on assets (Coombs and Gilley, 2005; Wan and Hoskinson, 2005).Scholars have widely established a high correlation and concurrent validity betweenobjective and subjective data on performance, which implies that both are valid whencalculating a firm’s performance (Homburg et al., 1999). We included questions thatdrew on both types of assessment in our interviews, calculated the correlation betweenthe objective and subjective data and found it to be high and significant (0.84,p , 0.05). We preferred to use objective data (Appendix).
Organizational learning. The capability of organizational learning has received muchmore theoretical than empirical attention. Further, there are wide differences among theassumptions, procedures, and objectives of previous measures. Owing to the fact thatthere is a closer link with our research, that they reflected the different prior trends welland that the scale’s validity was verified in detail, we used the first two items from thescale developed by Kale et al. (2000) and added two items based on Edmondson’s (1999)work to compose a multi-item scale of organizational learning. These items havebeen duly adapted to the present study. We used a Likert-type five-point scale(1-“total disagreement” 5-“total agreement”) with four items and asked to managers toexpress their level of agreement or disagreement with various questions. We developed
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0.67
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Table II.Validity, reliability
and internal consistencyof the scales
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197
a confirmatory factor analysis to validate our scales (x22 ¼ 5:07, RMSEA ¼ 0.087,
NFI ¼ 0.98, NNFI ¼ 0.96, CFI ¼ 0.99) and showed that the scale of four items wasunidimensional and had good reliability (a ¼ 0.779). We also requested information onspecific aspects of organizational learning (Appendix). A subsequent analysis enabledus to divide the organizations into organizations with a high degree of organizationallearning (96 organizations showed a mean value for organizational learning greater thanor equal to 4) or medium-low degree of the same (68 organizations showed an averagevalue for organizational learning lower than 4).
Model and analysisLisrel 8.30 program was used to test the theoretical model. Figure 1 shows the basis ofthe model proposed and the hypotheses to be contrasted. We used a recursivenon-saturated model, taking TL (j1) as exogenous latent variable; OI (h1) as thefirst-grade endogenous latent variable; and OP (h2) as the second-grade endogenouslatent variable. Through its flexible interplay between theory and data, this structuralequation model approach bridges theoretical and empirical knowledge for a betterunderstanding of the real world. Such analysis allows for modeling based on bothlatent and manifest variables, a property well suited to the hypothesized model, wheremost of the constructs represented are abstractions of unobservable phenomena.Furthermore, structural equation modeling takes into account errors in measurement,variables with multiple indicators and multiple-group comparisons.
ResultsThis section presents the main results of our research. Table III reports the means andstandard deviations for all of the measures, as well as the inter-factor correlationsmatrix for the study variables. Consistent with the two-step approach advocated byAnderson and Gerbing (1988), we estimated a measurement model before examiningstructural model relationships, differentiating between organizations withhigh-organizational learning and organizations with low-organizational learning.We used Lisrel 8.30 to estimate the model.
From Table IV, we can see that all of the indexes show very good fit with the model.The constructs display satisfactory levels of reliability, indicated by compositereliabilities ranging from 0.81 to 0.90 and average variance extracted coefficients from0.50 to 0.70. Convergent validity – the extent to which maximally different attempts tomeasure the same concept agree – can be judged by examining both the significance ofthe factor loadings and the average extracted variance. The amount of variance sharedor captured by a construct should be greater than the amount of measurement error(average variance extracted . 0.50). All of the multi-item constructs met this criterion,each loading (l) being significantly related to its underlying factor (t-values greater
Variables Means SD 1 2 3 4 5
1. Transformational leadership 3.837 0.717 1.0002. Organizational innovation 3.714 0.574 0.492 * * 1.0003. Organizational performance 3.670 0.714 0.365 * * 0.229 * 1.0004. Organizational learning 3.932 0.667 0.552 * * 0.426 * * 0.268 * * 1.000
Notes: *p , 0.01; * *p , 0.001 (two-tailed)
Table III.Means, standarddeviations andcorrelations
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0.80
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911
0.72
1T
L-O
I(0
.40-
0.57
)x
2¼
46.6
8(d
f¼
51),
GF
I¼
0.96
AG
FI¼
0.94
,N
FI¼
0.96
,N
NF
I¼
0.99
,IF
I¼
0.99
,R
FI¼
0.96
,C
FI¼
0.99
,R
MS
EA
¼0.
001
hig
h-o
rgan
izat
ion
alle
arn
ing
TR
AN
SL
EA
20.
85* (
10.2
7)0.
73T
RA
NS
LE
A3
0.86
* (11
.29)
0.74
TR
AN
SL
EA
40.
88* (
11.4
6)0.
78O
rgan
izat
ion
alin
nov
atio
n(O
I)O
RG
INN
O1
0.84
* (11
.75)
0.71
0.75
60.
880
0.65
1T
L-O
P(0
.85-
0.93
)O
RG
INN
O2
0.84
* (11
.80)
0.70
OR
GIN
NO
30.
84* (
12.0
0)0.
71O
RG
INN
O4
0.65
* (7.
66)
0.52
Org
aniz
atio
nal
per
form
ance
(OP
)P
ER
FO
R1
0.87
* (13
.20)
0.75
0.76
00.
898
0.68
9O
I-O
P(0
.63-
0.78
)P
ER
FO
R2
0.75
* (10
.59)
0.56
PE
RF
OR
30.
91* (
14.0
1)0.
83P
ER
FO
R4
0.78
* (11
.12)
0.62
Org
aniz
atio
ns
wit
hlo
w-o
rgan
izat
ion
alle
arn
ing
Tra
nsf
orm
atio
nal
lead
ersh
ip(T
L)
TR
AN
SL
EA
10.
64* (
9.20
)0.
510.
714
0.81
20.
501
TL
-OI
(0.3
4-0.
45)
x2¼
181.
16(d
f¼
51),
GF
I¼
0.95
,A
GF
I¼
0.92
,N
FI¼
0.90
,N
NF
I¼
0.90
,IF
I¼
0.95
,R
FI¼
0.92
,C
FI¼
0.95
,R
MS
EA
¼0.
093
Org
aniz
atio
nal
inn
ovat
ion
(OI)
TR
AN
SL
EA
20.
68* (
10.3
7)0.
56T
RA
NS
LE
A3
0.69
* (10
.42)
0.55
TR
AN
SL
EA
40.
77* (
10.9
8)0.
60O
rgan
izat
ion
alp
erfo
rman
ce(O
P)
OR
GIN
NO
10.
84* (
11.5
8)0.
710.
707
0.86
70.
622
TL
-OP
(0.7
3-0.
84)
OR
GIN
NO
20.
79* (
16.6
5)0.
62O
RG
INN
O3
0.69
* (14
.27)
0.67
OR
GIN
NO
40.
71* (
14.5
4)0.
60P
ER
FO
R1
0.84
* (11
.45)
0.70
0.85
10.
863
0.61
5O
I-O
P(0
.61-
0.72
)P
ER
FO
R2
0.68
* (12
.74)
0.56
PE
RF
OR
30.
67* (
11.5
1)0.
63P
ER
FO
R4
0.76
* (13
.12)
0.74
Notes:
* p,
0.00
1(tw
o-ta
iled
).M
easu
rem
ent;a¼
aC
ron
bac
h;
CR¼
com
pos
ite
reli
abil
ity
;A
VE¼
aver
age
var
ian
ceex
trac
ted
Table IV.Measurement model
results
Influence oftransformational
leadership
199
than 7.26) in support of convergent validity. A series of x 2 difference tests on the factorcorrelations showed that discriminant validity – the degree to which a constructdiffers from others – was achieved among all constructs (Anderson and Gerbing,1988). More specifically, discriminant validity was established between each pair oflatent variables by constraining the estimated correlation parameter between them to1.0 and then performing a x 2 difference test on the values obtained for the constrainedand unconstrained models (Anderson and Gerbing, 1988). The resulting significantdifferences in x 2 indicate that the constructs are not perfectly correlated and thatdiscriminate validity is achieved. We also confirmed that the confidence interval for thecorrelation between each pair of critical dimensions does not produce a value of 1,which shows the presence of discriminant validity (Anderson and Gerbing, 1988).
Table V presents the results for the structural model shown in Figures 2 and 3,differentiating between organizations with high-organizational learning andorganizations with low-organizational learning. Structural equation modelling(Bollen, 1989) was performed to estimate direct and indirect effects using Lisrel withthe correlation matrix and asymptotic covariance matrix as input. The overall fit of thestructural model was good, and the completely standardized path estimates indicatesignificant relationships among the constructs. If we examine the standardized parameterestimates (Table V), the findings show that TL is highly related to and affects OI. Further,this relation is greater in technological organizations with high-organizational learning(g11 ¼ 0.71, p , 0.001) than in technological organizations with low-organizationallearning (g11 ¼ 0.38, p , 0.001), as was predicted in H1. This is the case because TLencourages OI fostered by the construction of competencies focused on learning (Sengeet al., 1994; Lei et al., 1999). The current leader is faced with a combination of newchallenges in innovating, based on intellectual capital, mind power and know-how(Bennis, 1999). Such challenges are better overcome by learning organizations that have aclimate that promotes experimentation, capacity for systems thinking and shared vision(Coad and Berry, 1998; McGill et al., 1992; Senge et al., 1994), and qualities that fosterinterpersonal and collective shared spaces where knowledge for innovation is created andshared (Nonaka and Takeuchi, 1995; Slater and Narver, 1995).
As predicted in H2, OP appears to be influenced more strongly by TL intechnological organizations with high-organizational learning (g21 ¼ 0.54, p , 0.001)than in technological organizations with low-organizational learning (g21 ¼ 0.32,p , 0.001). The results also show an indirect effect (0.37, p , 0.001 and 0.23, p , 0.001in technological organizations with high-organizational learning andlow-organizational learning, respectively) of TL on OP due to OI (see, for instance,Bollen, 1989, for calculation rules). The global influence of TL on OP is thus 0.91( p , 0.001 in technological organizations with high-organizational learning) and 0.55( p , 0.001 in technological organizations with low-organizational learning). Thisinfluence may occurs because the leader designs the proposals, vision and centralvalues that guide the organization to improve its performance by means of policies,strategies and structures stimulated by organizational learning (Argyris and Schon,1996; Senge, 1990; Wick and Leon, 1995). The leader inspires OI and OP, helping themembers of the organization to discover their mental models, restructure their visionsof reality to see beyond surface conditions and develop their systemic understanding(Senge et al., 1994).
JOCM21,2
200
Eff
ect
from
To
Dir
ect
effe
ctsa
tIn
dir
ect
effe
ctsa
tT
otal
effe
ctsa
tG
ood
nes
sof
fit
stat
isti
cs
Org
aniz
atio
ns
wit
hh
igh
-org
aniz
atio
nal
lear
nin
gT
L!
OI
0.71
†7.
290.
71†
7.29
x2¼
46.6
8(d
f¼
51);
GF
I¼
0.96
;A
GF
I¼
0.94
;N
FI¼
0.96
;N
NF
I¼
0.99
;IF
I¼
0.99
;R
FI¼
0.96
;C
FI¼
0.99
;R
MS
EA
¼0.
001
TL
!O
P0.
54†
4.36
0.37
**
*3.
780.
91†
10.2
2O
I!
OP
0.46
†3.
810.
46†
3.81
Org
aniz
atio
ns
wit
hlo
w-o
rgan
izat
ion
alle
arn
ing
TL
!O
I0.
38†
4.69
0.38
†4.
69x
2¼
181.
16(d
f¼
51);
GF
I¼
0.95
;A
GF
I¼
0.92
;N
FI¼
0.90
;N
NF
I¼
0.09
0;IF
I¼
0.95
;R
FI¼
0.92
;C
FI¼
0.95
;R
MS
EA
¼0.
093
TL
!O
P0.
32†
4.10
0.23
**
*3.
440.
55†
6.28
OI
!O
P0.
30†
3.75
0.30
†3.
75
Notes:
* p,
0.10
;*
* p,
0.05
;*
** p
,0.
01;
†p,
0.00
1;ast
and
ard
ized
stru
ctu
ral
coef
fici
ents
Table V.Structural model results
(direct, indirect and totaleffects)
Influence oftransformational
leadership
201
We also verify the positive relationship between OI and OP. This relation is greater intechnological organizations with high-organizational learning (b21 ¼ 0.46, p , 0.001) thanin technological organizations with low-organizational learning (b21 ¼ 0.30, p , 0.001),supporting H3. Because organizations’ capability to innovate enables the improvement ofperformance and because this capability is fostered by organizational learning and OP,management should develop these dynamic capabilities (Calantone et al., 2002; Loof andHeshmati, 2002). Integrated learning systems permit sharing and transfer of technologicalknowledge between the different areas and people in the organization, improving OP(Senge et al., 1994). In contrast, poor learning between individuals and betweenorganizations will become an obstacle to transforming OI in performance (Senge et al.,1994). Organizational learning and OI are dynamic capabilities united in the achievement ofsustainable competitive advantages (Nonaka and Takeuchi, 1995; Calantone et al., 2002).
Finally, we performed a t-test for equality of means between organizations withhigh-organizational learning and organizations with low-organizational learning toanalyze whether there are significant differences with regard to TL, OI and OP. Theresults of these tests can be seen in Table VI. For each construct, the table provides themean score, the Levene’s test for equality of variances and the t-value. Significantdifferences can be observed between all constructs.
Figure 2.Results of structuralequation model
x1Transformational
Leadership
h1Organizational
Innovation
h2OrganizationalPerformance
TRANSLEA1d1
TRANSLEA2d2
TRANSLEA3d3
ORGINNO1
e1
ORGINNO2
e2
ORGINNO3
e3
ORGINNO4
e4
PERFOR1
e5
PERFOR2
e6
PERFOR3
e7
PERFOR4
e8
z1
z2
lx11 = 0.80
lx12 = 0.85
lx13 = 0.86
ly11 = 0.84 ly
12 = 0.84ly13 = 0.84 ly
14 = 0.65
ly25 = 0.87 ly
26 =0.75 ly27 = 0.91 ly
28 = 0.78
TRANSLEA3d4lx
14 = 0.88
g11 = 0.71***
b21= 0.46***
g21 = 0.54***
Note: Organizations with high-organizational learning
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202
DiscussionLeaders in technology firms are aware that their businesses are sustained by a strongscientific and technical base that is continually renewed due to the fact that newtechnology tends rapidly to make current technology obsolete. As new technologiesemerge, their implications create or revolutionize markets and demand (Shanklin andRyans, 1997). Successful confrontation of the high complexity of the productsavailable, the high number of competitors, consumer needs and rapid change requirestechnological transformational leaders. Such leaders design the main technologicalpurpose, vision and values that will guide the organization, creating policies, strategiesand structures that enable it to face technological challenges and improve OP (Senge,1990; Senge et al., 1994).
Technological transformation leaders drive the scientific and technologicalinnovation processes in high-technology industries to improve OP (Medcof, 1999).To achieve this end, they commit themselves seriously to innovation, making one oftheir goals the dissemination of innovation on all levels of the organization (Lei et al.,1999). These leaders create a shared culture that favors innovative value, generating anorganizational mental model that protects the organization against new challenges(McGill et al., 1992). Since innovation derives from the efforts of people who interactorganizationally, all of the organization’s members must be involved in the innovationprocess for it to succeed (Hartman et al., 1994).
Figure 3.Results of structural
equation model
h1Organizational
Innovation
h2Organizational
Innovation
TRANSLEA1
TRANSLEA2
TRANSLEA3
ORGINNO1 ORGINNO2 ORGINNO3 ORGINNO4
PERFOR1 PERFOR2 PERFOR3 PERFOR4
TRANSLEA3
d1
d2
d3
d4
e1 e2 e3 e4
e5 e6 e7 e8
ly11 = 0.84
ly25 = 0.84 ly
26 = 0.68 ly27 = 0.67 ly
28 = 0.76
ly11 = 0.64
ly12 = 0.68
ly13 = 0.69
ly13 = 0.77
ly12 = 0.79 ly
13 = 0.69 ly14 = 0.71
g11 = 0.38***
g21 = 0.32***
x1Transformational
Leadership
z2
z1
b21 = 0.30***
Note: Organizations with low-organizational learning
Influence oftransformational
leadership
203
Sustained innovation and performance also require that transformational leadersincorporate innovation as a significant component in the organization’s strategy(Dougherty and Hardy, 1996). TL stimulates innovation strategies that improve OPfostered by the presence of organizational learning. Learning practices stimulate theorganization’s abilities and those of its employees, the learning of new capacities andcontinuous experimentation (Argyris and Schon, 1996). This learning generatescompetencies, skills and abilities that encourage the growth and personal andprofessional improvement of all members of the organization. It also creates a sense ofcommunity in the organization, which encourages collective commitment, acting as astimulating motor that drives and unites all the organization’s members to work toachieve what is in the common interest.
Leaders work with cognitive maps to stimulate mental models and reasoningsystems. To observe the totality of and interrelations between the parts of theorganization, the leader encourages system thinking and generates reflection, not onlyon the individual but also on the team and organizational levels. All of these factorsrequire the existence of integrated organizational learning that stimulates anenvironment of dialogue (Isaacs, 1993; Senge et al., 1994).
In this way, TL achieves greater influence on innovation and OP through the correctmanagement of organizational learning. These innovations are valuable, imperfectlyimitable and rare, leading to better OP (Irwin et al., 1998). Technological organizationswith better innovations will achieve better OP and consolidate a sustainablecompetitive advantage.
Conclusions and future researchThis study serves as a reference for fostering innovation in technological firms througha style of leadership that improves OP. In the knowledge economy, effectiveorganizational learning generates the environment that technological organizationsmust have if TL is to enable innovation and maintain the competitive positions of thefirms as technological centers (Senge et al., 1994). Management of these variables givesrise to values within the organization that are difficult to copy and that generate profitsand competitive advantages.
Means
Levene’s test forequality ofvariances
t-Test for equalityof means
Variables
Organizations withhigh-organizational
learning
Organizations withlow-organizational
learning FSig.
(two-tailed) tSig.
(two-tailed)
Transformationalleadership 4.0667 3.5205 2.284 0.133 4.870† 0.000Organizationalinnovation 4.6774 4.2879 0.196 0.659 4.442† 0.000Organizationalperformance 4.6316 4.2132 2.883 0.091 3.724† 0.000
Notes: *p , 0.1; * *p , 0.05; * * *p , 0.01; †p , 0.001 (two-tailed)
Table VI.Independent samplest-test
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204
TL encourages a combination of beliefs, expectations and basic principles. Thesebeliefs and expectations encourage rules of behavior with the power to shape theconduct of the individuals and groups in the organization and thereby to differentiate itfrom other organizations (Schein, 1993). Because these beliefs also correspond to theperceptions of appropriate and inappropriate behavior in a social unit (Rousseau, 2000),it is important to analyze how organizational learning affects the relation between TLand OI, between TL and OP and between OI and OP in technological organizations.Because organizational learning is an essential element for converting individualmental models into shared ones (Senge et al., 1994), it will enable the entity to transformitself into an intelligent organization that innovates and that enables greaterperformance (Senge, 1990; Argyris and Schon, 1996).
Our research has shown that TL has different effects on innovation andperformance depending on the level of organizational learning in a technologicalorganization. Organizational learning is committed to TL and OI and propels thesecapabilities (Senge, 1990; Swieringa and Wierdsma, 1992), overcoming obstacles thatcan impede them (Wick and Leon, 1995). Technological organizations that encouragetechnologies of learning establish a path for professional development that enablesthem to acquire aptitudes or competencies that provide sustainable advantage throughinnovation (Senge et al., 1994).
Further, we verify that TL encourages OI and OP at a higher level if there arecompetencies focused on organizational learning that minimize the cost of internalchange (Slater and Narver, 1995; Lei et al., 1999; Zahay and Handfield, 2004).The innovative organization is an organization that requires learning and thatknows how to make and keep itself technologically competent. Learning willenable the organization to change its behavior and thus to renew and reinvent itselftechnologically, preventing it from falling into technological stagnation and allowing itto generate innovation. While organizations will find themselves in different states ofevolution in learning, the sooner they foster measures to prevent this stagnation and todisseminate learning, the less complicated the leap to innovation will be (Bessant andBuckingham, 1993; Glynn, 1996).
Our results also support the importance of encouraging TL in generatinginnovation. This result is especially appealing because it supports the characterizationof TL as more concerned with collective decisions, collective goals, and thegeneration of capabilities than is traditional leadership, which focuses more ontop-down decisions, standardized procedures, and the production of products andservices (McDonough, 2000).
Finally, technological organizations need innovation to improve their performancein changing real-life business environments. We verify empirically that moreinnovation generates better OP in technological firms and that this relationship ismore intense as the level of organizational learning increases. The empirical resultsshow that sources of innovating and achieving sustainable competitive advantagesand sustainable development in technological firms are sustained by the presence of acomplex of essential competencies or resources and technological capabilities thatorganizations possess (e.g. organizational learning). Each organization should analyzeand enable all of the resources and capabilities that permit it to obtain a bettercompetitive position on the market. It should also regenerate its essential competencies,so it can use innovation to face the technological changes in its environment.
Influence oftransformational
leadership
205
In this way, the organization acquires a dynamic and innovative vision that enables itto improve OP, generating resources and technological capabilities that are unique,valuable, hard to replace and difficult to imitate (Irwin et al., 1998).
This investigation has several limitations that may suggest further possibilities forempirical research. First, survey data based on self-reports may be subject to socialdesirability bias (Podsakoff and Organ, 1986). However, an assurance of anonymitycan reduce such bias even when responses are related to sensitive topics (Konrad andLinnehan, 1995). The low risk of social desirability bias in this study was indicated byseveral managers who commented that it made no sense at all for their companies to gobeyond regulatory compliance. Still, the responses are subject to interpretation byindividual managers.
Second, the absence of an objective measure of organizational learning is alimitation. However, the external validation of this variable from the archival dataof a subset of respondents and the use of objective measures for other variables(e.g. OP, OI) increased confidence in the self-reports and reduced the risk of commonmethod variance (Sharma, 2000). Further, the possibility of common method bias wastested using Harman’s one-factor test and other methods. We also used objective dataand randomized the order of presentation of the survey items across the subjects.Common method bias does not appear to be present (Podsakoff and Organ, 1986;Konrad and Linnehan, 1995).
Third, the cross-sectional nature of the research into a series of dynamic concepts(OI, organizational learning) allows us to analyze only a specific situation in time of theorganizations studied, not their overall conduct through time. Our approach hasreduced the magnitude of this problem, since dynamic characteristics and causalaffirmations can be made if the relationships are based on theoretical rationales(Hair et al., 1999). For this reason, we began with a theoretical analysis to enable us toidentify and confirm the formal existence of the different cause-effect relationships.Nonetheless, future research should focus on longitudinal study. Innovation is verynecessary and complex in technological firms, as it is influenced by organizational,personal, and environmental issues. Longitudinal research can approach innovationwith greater precision and study its determinants, processes, and resultssystematically, for this approach allows us to analyze the evolution of its variablesover time and to draw more reliable conclusions about this activity.
Fourth, the use of a single respondent may have influenced the accuracy of somemeasurements. However, difficulties in obtaining sponsorship for research based onmultiple views for each firm, the value of CEOs’ knowledge of their firms, and commonpractice in organizational research all supported the use of CEOs as respondents. Fifth,we have concentrated on the pharmaceutical sector. In firms from other sectors theresults may be different. Above all, it would be interesting to analyze theserelationships in non-technological sectors where the organizational characteristicscould differ more.
Finally, our model only analyzes the different effects of TL on OI and OP dependingon the level of organizational learning. Other factors could be analyzed, e.g. technologyshared vision and technology teamwork (Senge, 1990; Senge et al., 1994). Researchshould also examine other consequences of introducing an innovation process intechnological organizations (e.g. quality improvement, staff satisfaction,improvements in relational capacity). More attention to the influence of specific
JOCM21,2
206
technological strategic factors on innovation is necessary in the future. Empiricalpapers supporting (or rejecting) our results in different contexts would be welcomed(especially longitudinal studies). Future studies should be based on a larger sample andmight well explicitly integrate the influences of external factors. It would also beinteresting to study similar characteristics with information provided by lower levelsof management and employees in the organization.
CEOs and managers, clients, suppliers, public administration, investors, strategicallies, the financial system, accountants and the academic world would welcome amore in-depth investigation of the internal conditions that technological firms musthave in order to innovate and the consequences of these conditions for OP. Somecompanies with vast resources may not obtain competitive advantage without thesestrategic factors. Development of a collaborative scheme between academics andpractitioners would allow an organizational strategy to be generated and would permitfurther study of the processes, means and mechanisms by which to transform OI intosustainable competitive advantage.
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AppendixTransformational leadershipIn a technological context:
. The organization’s management has a clear common view of its final aims and is able totransmit them and achieve the commitment of the rest of the organization’s members.
. The organization has leaders who are capable of motivating and guiding their colleagueson the job (masters).
. The firm’s management is always on the lookout for new opportunities for theunit/department/organization.
. The firm’s management always acts as the organization’s leading force.
Organizational innovation. Number of new products, processes, methods or ideas developed and marketed by the
organization.. Number of new markets that the firm has entered.. Total amount that the company had spent on R&D.. Total number of employees dedicated to task of R&D.
Organizational performance. The firm’s performance measured by return on assets.. The firm’s performance measured by return on equity.. The firm’s performance measured by return on sales.. The firm’s market share in its main products and markets.
Organizational learning
(1) The organization has acquired and used much new and relevant knowledge that hasprovided competitive advantage.
(2) The organization’s members have acquired critical capacities and skills that haveprovided competitive advantage.
(3) Organizational improvements have been influenced fundamentally by new knowledgeentering the organization.
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(4) The organization is a learning organization:. What are the main and most relevant kinds of knowledge acquired and used by the
organization? What competitive advantage have they provided?. What are the main critical capacities and skills acquired by the organization’s
members? What competitive advantage have they provided?. What organizational improvements have been influenced fundamentally by new
knowledge entering the organization?. What shows that the organization is becoming a learning organization?
Corresponding authorVıctor J. Garcıa-Morales can be contacted at: [email protected]
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