The Influence of Firms’ Entrepreneurial Orientation

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    The Influence of Firms Entrepreneurial Orientation,

    Age and Size on Information systems use and Competitive Advantage:

    Empirical Evidence from Developing Countries

    1.Introduction

    Entrepreneurial orientation (EO) is gaining popularity among firms operating in a dynamic environment, particularly SMEs. With

    increased competition, business firms are changing from the early stable roles, processes and structures which were useful in

    planning, production and service delivery. Earlier on, with such a stable environment, it was possible to follow business andproduct life cycles, but with the current changes, dynamic competition pays more. Thus, entrepreneurial inclination has thepotential to replace the obsolete business models or change the firms position on the entrepreneurial orientation continuum from

    conservative to entrepreneurial firms (Covin et al., 2006).

    Firms entrepreneurial orientation is related to the first mover advantage whereby firms take advantage of emerging opportunities,which normally results in a positive influence on performance (Wiklund, 1999; Covin & Slevin, 1991). The emerging

    opportunities could include, among others, the use of state-of-the art technologies, in particular ICTs. This kind of initiativesinvolve risk-taking and resource commitment that have a potential downside (Rauch et al., 2009). With a number of ICT platforms

    that prevail, differences in performance among firm is most likely a function of their orientation and willingness to take risk,

    innovate and leverage on the information systems (IS) potential. This suggests that exploiting the value offered by information

    systems is contingent on among others, the enterprising tendency of the firm. This bet on the fact that availability of IS resourcesin a particular firm is worthless unless exploited intelligibility.

    Previous studies operationalized entrepreneurial orientation using three dimensions, which are; risk-taking propensity,innovativeness and proactiveness (see Yusof et al., 2007; Gurol & Atsan, 2006; Miller, 1983). According to Rauch et al. (2009),

    risk taking involves taking daring actions by venturing into unknown, borrowing and/or committing significant resources toventure in uncertain environment, while, innovativeness involves being creative and experimenting by introducing new

    products/services and technological leadership. Proactiveness is seen as an opportunity-seeking and a forward-looking propensity,characterized by the introduction of new products/services ahead of competitors.

    There has been a lot of discussion on the dimensions of EO which usually show high inter-correlation (Rauch et al., 2009,Lumpkin & Dess, 1996). As a result, most of the studies combine these dimensions into a single factor. However, in this study,

    entrepreneurial orientation is measured by using two dimensions, which are innovativeness and risk-taking propensity at firmlevel. Lumpkin and Dess (1996) argue that, even though factors mostly associated with entrepreneurial orientation (autonomy,

    innovativeness, proactiveness and competitive aggressiveness) are important, a firms success that is based on its entrepreneurialorientation may be achieved when only some of these factors are operating.

    Elly Tumsifu

    PhD, Business Administration

    University of Dar es Salaam Business School, Tanzania

    Abstract:

    The study investigates the influence of firms entrepreneurial orientation, age and size on information system resources,

    firms capabilities and competitive advantage. A confirmatory factor analysis is used to validate a model and test the

    hypothesized relationships using 217 cases from small and medium tourism enterprises in Tanzania. Findings show that,

    conservative firms rely on the state of the art ICT platforms in order to compete. Ceteris peribus entrepreneurial firms

    industriously compete using basic IS platforms. Findings demonstrate further that, regardless of their size, extant and

    entrant firms could compete on an equal footing using IS. Entrepreneurship has the potential to overcome tendency of

    acquiring and accumulating ICT resources among small firms with diminutive performance improvement in developing

    countries.

    ISSN 2277-5846

    THE INTERNATIONAL JOURNAL OF MANAGEMENT

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    Firms under study and the nature of the business in question imply a dynamic and rapid changing business environment. In orderto benefit from entrepreneurial orientation, firms operating in such dynamic environment need to seek out new opportunities

    (Rauch et al., 2009) and innovate frequently while taking risks in their product-market strategies (Miller & Friesen, 1982). Thus,

    risk taking propensity and innovativeness are deemed relevant in measuring firm performance in relation to entrepreneurialorientation in general and information systems studies in particular. The choice of the two dimensions reflects the perspective

    from which the study is undertaken as suggested by Lumpkin and Dess (1996). The use of each of the dimensions is contingent on

    external and internal factors, and the industry or business environment. As such, the relationship between entrepreneurialorientation and performance is context specific and their dimensions may vary independently in a given context. The metrics forinnovativeness and risk-taking propensity from a firm perspective are feasible, but proactiveness could require sophisticatedmetrics that include customers assessments of new products and services, thus limiting the choice to the two former variables.

    Similarly, since most of the time new and unfolding information technologies leave behind (are ahead of) business innovations,proactiveness is left to have little influence in such a scenario.

    In most of the previous studies, the use of information systems (IS) has been associated with firms entrepreneurial characteristics,size and age. When contrasting small and large firms for instance, Vanyushyn (2006) argues that the former can be entrepreneurial

    and outrun the latter when it comes to innovating with technologies. Moreover, Lumpkin and Dess (1996) propound that, for both

    start-up and existing firms, entrepreneurship carried on in pursuit of business opportunities spurs business expansion,

    technological progress and wealth creation. On the downside, small firms when compared with large firms may lack the financial

    resources and management capabilities necessary for innovative action. With respect to IS use, SMEs have been associated withconstraints such as lack of financial resources, lack of in-house expertise, and short range of management perspective (Delone,

    1998; Lees, 1987). These insights suggest that, entrepreneurial orientation, size, and age of the firm have an influence on thedevelopment, and deployments of IS. Eventually, these factors have an impact on the competitive advantage enjoyed by a

    particular firm. Moreover, variations in terms of IS adoption and use between small firms of varying sizes, and along firms

    entrepreneurial orientation continuum, are evident (Alpar & Reeves, 1990; Dholakia & Kshetri, 2004). The preceding discussionsuggest that entrepreneurial inclination could be vital especially when a need exist of replacing static business models with more

    dynamic ones. The age and size of the firm could have far-reaching effect on the IS resources, the IS-human resources and thecapabilities of the firm. Small firm characteristics such as size, age and entrepreneurial tendencies could shape IS resources

    acquisition and use as well as firms capabilities and consequently their competitive position. Therefore, the need exist to studythese factors in a more integrated and coherent manner in order to ascertain their effects.

    In the study context, where generally Africans portray strong patriarchy structures, power distance and traditionalism (Krauss et

    al., 2005), entrepreneurial orientation could be taken to imply a positive way of thinking about new ideas pertaining to products,services, administration or technological processes. According to West and Farr (1990), new ideas are not necessarily absolutenovelties but could be new to the relevant group, market and environment and thus context. This implies that firm performance is

    not only subject to the technological advances, but also to how these technologies are implemented and used in a given context

    The purpose of this study was to investigate the relationship between information systems resources (IS resources and human IS

    resources), firms capabilities and competitive advantage considering the firms entrepreneurial orientation, age and size.

    2.Methodology

    Survey techniques were used in this study to collect data from 217 small and medium tourism enterprises (SMTEs) in Tanzania

    (Babbie, 1992). As suggested by Ostgaard and Birley (1996) assessing the performance of a firm with less than two years inbusiness is illogical. Similarly, studying firm performance is seen as a relative concept which connotes assessment of continuous

    firm performance (Fahy, 2000), and thus only SMETs with three years or more in business were considered. The population under

    study considered SMTEs from Dar es Salaam, Moshi, Arusha, and Unguja, which account for more than 85% of all tourism

    enterprises in Tanzania (URT, 2007). The choice of the regions was purely on the density of tourism activities and enterprises perse (Olomi, 2001; URT, 2007). Since the aim of the study was to draw statistical inferences from the data, thus probabilitysampling was used in obtaining the firms (Saunders et al., 2007). Lumpkin and Dess (1996) posit that, firm level approach is

    consistent with classical economics in which an individual entrepreneur is regarded as a firm. The unit of analysis for this study

    was thus a firm in which owner/managers were involved in the study

    2.1.The Study Constructs

    The model of the study hypothesize the relationship between IS use and performance through a subset of constructs as shown inTable 1.

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    Latent /factor/construct

    Variables

    Manifest /indicator/ observed

    Variables

    Source/References

    based on

    Firm Competitive advantage

    (COMPETITIVEADV )

    Sales growth(SaleGr) Rivard et al, (2006);

    Ravichandran andLertwongsatien,

    (2005); Bhatt and

    Grover,(2005); Covinet al.,(2006)

    Market growth (MarkGr)

    Firm growth (FirmGr)

    Financial performance(FinLiq)Market share

    Firm Capabilities

    (CAPABILITIES)

    Operational efficiency(Opertn) Rivard et al, 2006;

    Ravichandran andLertwongsatien, 2005

    Market efficiency (Mrkt)

    Integrity efficiency (Intgeff)

    Innovation efficiency (Inoeff)

    Firm IS Resources (ISRES) Computing platform meeting current

    business needs, and link suppliers,customers, and firm(ISfcs), IS flexibility

    (ISflex), Service provider(Internet andweb hosting services)(ITServs)

    Bhatt and Grover,

    2005; Ravichandranand Lertwongsatien,

    2005Thong, 2001, Attewell,

    1992

    Developed based on

    pilot studyFirm Human IS resources

    competences (ISHR)

    Managerial IS skills(ISMgt) Ravichandran and

    Lertwongsatien, 2005;

    Bhatt and Grover, 2005Thong, 2001, Attewell,

    1992

    IS Business skills and experience(ISbus)

    Industry knowledge andexperience(ISind)

    Reliable IS knowledge source hired

    (ISexp)

    Entrepreneurial Orientation Innovativeness(Inno) Al-Qirim , 2005;

    Thong and Yap,1995,Lumpkin and Dess,

    (1996);Miller, (1983),Covin et al.,(2006);

    Miller and Friesen,(1982)

    Risk Propensity (Risk)

    Firm Factors (Not Latent) Firm SizeRavichandran and

    Lertwongsatien,(2005); Covin et al.

    (2006)

    Age of the firm

    Table 1: The Constructs Of The Model

    The relationship between variables is through five latent variables ( Figure 1), namely firm ISHR, ISRES, firm capabilities,

    entrepreneurial orientation and firm competitive advantage, measured by a number of manifest variables. The model has also ageand size being measured directly. The bold and faded arrows represent different assumptions on what influences IS use and firm

    performance. The bold arrows represent the basic study assumption that firm competitive advantage is a function of its ISHR and

    ISRES through various capabilities. On the other hand the faded arrows represent the assumptions that firm competitive advantage

    is not only contingent on its ISHR and ISRES but also that these variables could be shaped by the entrepreneurial orientation ofthe firm and its age and size.

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    Figure 1: Latent Variables Of The Study Model; Their Relationships

    2.2.Reliability And Validity

    The internal consistency of the variables was assessed using Cronbachs alpha (Cooper & Schindler, 2003). Table 2 summarisesthe Cronbachs alpha coefficients obtained. The findings shows that that the items used measure what they are purported to

    measure (Tabachnick & Fidell, 2007; Hair et al,. 2003).

    Construct Manifest Variable1 No. ofItems

    Cronbachs AlphaCoefficient

    ISRES ISfcs, ISflex, ITserv 3 0.891

    ISHR ISind, ISexp, ISmgt, 3 0.767

    ENTREPRENEURIAL Inno, Risk 2 0.760

    CAPABILITIES Mrkt, Opertn 2 0.637

    COMPETITIVEADV Finliq, SaleGr, MarkGr,

    FirmGr

    4 0.715

    Table 2: Cronbachs Alpha Values

    Convergent validity was assessed throughout the analysis of the competing models as presented in the ensuing section. Themeasurement models show that factor loading were greater than 0.55, except one which is acceptable2 for research use (Falk and

    Miller 1992).

    3.Competing Model Analysis

    A five-factor model was analysed using AMOS 15. The analysis followed the competing models analysis strategy (Hair, 2007;Ullman, 2007; Nielsen et al., 2007; Joreskog & Sorbom, 1993 Nielsen & Host 2007) which allowed for comparison between

    models at each run by looking at the fit indices. As shown in the ensuing section series of six models were generated andanalysed. The values of the goodness of fit indices are presented in each model and a summary table that compare the models.

    3.1.The Effect Of Age Of The Firm

    Findings as shown in Figure 2 indicate that the model with Age has (five) indices that meet the criterion for data fit. These are

    RMSEA, Chi-square, CMIN/DF, the GFI, AGFI, IFI and RMSEA.

    1 Variables presented are those with significant coefficient values in the model.

    2The item loading poorly is retained in the model to allow for alternative analysis. As suggested by MacCallum & Austin (2000)

    and Straub et al.(2004) a single item in a measurement model which load weakly does not present a problem in the validity of the

    model

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    3.6.The Effect Of Age, Entrepreneurial Orientation, And Size

    When size was added to the preceding model, the goodness-of fit indices obtained could not meet the proposed values of a good

    model fit. Only the RMSEA and normed2

    values have the recommended values see Figure 8 below.

    Figure 7: Basic Model _Age, Entrepreneurial Orientation And Size

    Table 3 below provide comparison for the models in terms of the fit indices. The basic model_age was the best one in terms of thenumber of indices (6) that graduated the minimum cut-off points. This was followed by the Basic Model_Entrepreneurial _Age.

    The latter has the best indices in terms of the magnitude of the values compared with the Basic Model_Entrepreneurial though all

    have four indices. So our discussion will be mainly on these two models.

    S/N Model name 2,DF

    probability

    CMIN/DF

    AGFI

    CFI

    GFI

    IFL

    RMSEA

    Noindices

    passed

    1 Basic model_Age 2

    118.739(99df) p= .086

    1.199 0.906 0.931 0.900 0.030 6

    2 Basic

    Model_Size

    2

    144.015(100df) p = .003

    1.440 0.917 0.045 3

    3 Basic Model_Size& Age

    2

    170.204(114df) p=.001

    1.493 0.907 0.048 3

    4 BasicModel_Entrepren

    eurial

    2

    234.983(114df) p=.000

    2.061 0.909 0.911 0.070 4

    5 Basic

    Model_Entrepreneurial & Age

    2

    245.228(129df) p=.000

    1.901 0.913 0.915 0.065 4

    6 Basic Model_Entrepreneurial,

    Age & Size

    2

    321.787(146df) p=.000

    2.204 0.075 2

    Table 3: Models Fit Comparison Summarized

    3.7.Assessment Of The Measurement Models

    The discussion of the measurement model considers only two models with the best-fit indices. A summary of the validity andreliability for both the measurement (indicator variables) and path coefficients has been provided in Table 4 below. The validity

    and reliability of the best models are thus compared with the de facto standards to ascertain the validity and reliability of the

    models (Straub et al., 2004; Tabachnick & Fidell, 2007; Nelson & Ghods, 1998).

    IS RES.87

    ITserv

    e7

    IS HR

    .55

    CAPABILITIES

    .55

    ISexp

    e3

    .54

    ISmgt

    e2

    res1

    Chi_square= 321.787(146 df)

    p=.000

    RMSEA= .075CMIN/DF = 2.204

    .01

    ISbus

    e1

    .12

    .93

    COMPETITIVE ADV

    .39

    MarkGr

    e12

    .37

    Mrkt

    e16

    .34

    Opertn

    e15

    .44

    FirmGr

    e13

    .27

    SaleGr

    e10

    res2

    .74

    .48

    ISflex

    e6

    .28

    FinLiq

    e9

    .92

    ISfcs

    e5

    .61

    .58

    .52 .63

    .67

    .01

    Intgef

    e14

    .48

    ISind

    e4

    .69

    .53

    .08

    .69

    .93

    .35

    .61

    .14

    .73

    .96.97

    .03

    Inoeff

    e17

    .18Size

    .00

    Age .03

    .30

    ENTREPRENEURIAL

    .75

    Risk

    e19

    .86

    .66

    Inno

    e18

    .81

    .31

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    Validity

    component

    Technique

    used

    Heuristic/de

    facto

    standards

    Study model validation

    Basic model_Age Basic Model

    Entrepreneurial &

    Age

    Discriminantvalidity CFA as used

    in SEM

    GFI>.90,NFI>.90,

    AGFI>80,insignificant2

    and

    significant t-values for item

    loadings

    CMIN/DF=1.901,2

    118.739 (99df) p=.086,

    AGFI=0.906,

    GFI=0.931,IFL=0.900,RMSEA=0.030,

    t-values aresignificant3

    CMIN/DF=1.199,CFI= .931, IFI=.915,

    RMSEA=.065t-values for factor

    loading are

    significant,

    Convergent

    validity

    CFA as used

    in SEM

    GFI>.90,

    NFI>.90,AGFI> .80,

    CMIN/DF=1.901,2

    118.739 (99df) p=.086,

    AGFI=0.906,GFI=0.931,IFL=0.900,

    RMSEA=0.030,t-values are significant

    CMIN/DF=1.199,

    CFI= .931, IFI=.915,RMSEA=.065

    t-values for factorloading are

    significant,

    Reliability(InternalConsistency)

    Cronbachs Cronbachs should be

    above .60 or

    .70

    Cronbachs valuesare above .60

    Cronbachs valuesare above .60

    Content validity LiteratureReview,

    Expert panels

    Higher degreeof consensus

    Study instrumentreviewed and consider

    experts opinion, pilottested

    Study instrumentreviewed and consider

    experts opinion, pilottested

    Nomologicalvalidity

    SEM Standardizepath

    coefficients

    All standardizedcoefficients have

    significant values

    greater than 0.2

    Paths coefficients forISHR and

    ENTREPRENEURIA

    L to CAPABILITIESand

    COMPETITIVEADV

    have significantvalues greater than0.2 ,

    Predictive validity SEM Explained

    variances in the

    .40 range orabove are

    desired

    Variance explained for

    capabilities is .56 and

    for the competitiveadvantage is .88

    Variance explained

    for capabilities is .55

    and for thecompetitive advantage

    is. 93

    Table 4: Study Models Validation4

    3.8.The Measurement Model For The Model With Age

    Standardized path coefficients between measured variables and factors (Table 5), show that all path coefficients betweenmeasured (manifest) variables and latent (unobserved) variables in the model are significant p < 0.05, except for integrity

    efficiency Intgeff and innovation efficiency inoeff. Second, the observed variables with significant probabilities have positive

    standardized regression weights greater than 0.56, only the ISbus had a value of 0.284 (Straub et al., 2004). Most of the factorloadings explaining the measurement model are adequate and thus show that the research constructs are reliable (Rivard et al.,

    2006).

    3Significant t-values are associated with C.R > 1.96, in our study models all factors loadings have significant t- values. Note that

    age is not considered here since it is a direct measure and not a factor.4 See (Straub et al., 2004; Tabachnick & Fidell, 2007; Nelson and Ghods, 1998; Hoe, 2008) for the heuristic/de factostandards in

    relation to SEM.

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    Estimate S.E C.R P Standardized

    Regression Weights

    CAPABILITIES

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    Estimate S.E. C.R. P Estimate

    CAPABILITIES

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    expected performance improvement. Better performance resulting from IS use occurs when firms consider innovating beyond

    technological diffusion to include process, products and services as well as the users of the technology (Lumpkin & Dess, 1996).Risk-taking dimension of firms using IS could be associated with the transformation undertaken as firms attempt to migrate to a

    more IS business environment. This may involve the application of systems which integrate customer information, IS platforms

    that provide support to customers, and end-to-end processing. This is a deal of both IT and human resource requirements, andchanging existing configurations. While these are important aspects in competing through technology, the managers risk-taking

    propensity play a major role in shaping these frontiers. Thus, SMEs that are ready to take risks towards this end are more likely to

    perform better than those that are risk averse. This may require firms to effectively scope the inherent risks (Bharadwaj, 2000)associated with infrastructure investments and technology trends.Firms which take chances in uncertain decision-making contexts are ready to invest their resources in opportunities with the

    possibility of costly failure. This is an important aspect which could be a source of difference in the successful development and

    deployment of IS, even for small firms with meager resources. Although small firms are known to be flexible towards innovationand technology, they might have difficulties in taking risks concerning new technologies. As both low and very high risk taking

    propensity are not favourable in respect of calculated risk taking, owner-managers in the position of taking such a calculated riskare more likely to have an impact on IS use and firm performance.

    4.2.The Effect Of The Firms Age

    The study investigated whether the firms age influenced its competitive advantage through its capabilities and resources. From

    the findings of this study, age contributed to the best model, where it has a significant correlation with ISHR. These findingssuggest that various ISHR capabilities are positively correlated to firm age. These competences of the firm have an element of

    time, i.e. they take time to develop and mainly through learning by doing (Barney, 2001). Thus, age of the firm influences varioushuman IS resources rather than directly influencing market and operational efficiencies. Ravichandran and Lertwongsatien (2005)claim that, on average, it takes four to six years to develop a mature systems delivery process, when firms systematically

    implement software.This could be explained by the fact that the presence of customized computer applications and increasinglythe open source and standard solutions makes it possible for even newly founded firms to acquire basic computing platform.

    However, by their nature the SMEs in this study require a basic computing platform in an onset of the business to be able to runtheir activities. This suggests that IS use among extant and new entrant firms is not restricted. Rao et al. (2003) acknowledge that,

    it is not necessary for a firm to always start with simple applications but can always assume any position in the developmentrange. This gives leeway to even young firms to enjoy the benefits of IS given the experience and knowledge they bring in or start

    with the business.

    4.3.Size Of The FirmsThe size of the firm as measured by the number of employees could not be accounted for by any of the two models.

    Balasubramanian and Lee (2008) acknowledged a contradiction with regard to the size of the firm and innovation, as Schumpeter

    (1934) had two apparently contradictory views. While entrepreneurs seem most likely to innovate, contradicts the assertion that

    large firms with some degree of monopoly power are also most likely to innovate. These findings seem to suggest that the

    frequently unfolding innovations related to IS are undifferentiated between firms of varying sizes. Technological improvementsincremental as they are do not necessarily prohibit small or large firms from acquiring and making use of them. Neither do theyfavour large and established firms at the expense of the small firms.

    5.Conclusion

    In both scenarios, the findings demonstrate that ISHR significantly influence the performance of a firm in which age of the firm

    favours the development of ISHR. However, the size of the firm has no influence whatsoever on competitive advantage. What

    differs between the two is the quality of ISRES. Whereas for conservative SMTEs, ISRES significantly influence firm capabilitiesand competitive advantage, this extent is less pronounced when SMTEs are entrepreneurial. Entrepreneurial SMTEs couldcompete using basic IS resources and in that case do not necessarily depend on the state-of-the-art technologies or an

    accumulation of IS resources. They only need ICT infrastructure as a source of value which provide the necessary IS platform to

    compete through. The tendency for majority of the users of ICT in Africa tend to automate and upgrade or change their facilitieseach time a new release is made available. This attitude does not necessarily translate into better performance. We argue that been

    industrious with ICT use pays more than just acquiring every released new version.

    These calls for policies that create a conducive operating environment, encouraging small firms to use IS and embraces anentrepreneurial culture that leads to greater business competency, improved performance and sustained competitive advantage.Policy priorities should be on entrepreneurial tendencies as they pave the way for better use of IS resources.

    The findings have far-reaching implications for owner managers entrepreneurial inclination. Whereas entrepreneurial tendenciescould lead to better performance through basic IS resources, the opposite is true for conservative managers. There is a need formanagers to inspire innovativeness and risk-taking propensity within the firm. Such aspects have the potential to result in above-

    normal performance. Entrepreneurial orientation (EO), particularly being more innovative in deploying basic ISRES incombination with the right ISHR, has the potential to overcome IS infrastructural over-dependence and thus provide firms with

    the required capabilities and competitive advantage, which is most desirable for SMEs in developing economies.

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