Strategic orientation and new venture performance in the hospitality industry

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266 Int. J. Business Environment, Vol. 6, No. 3, 2014

Copyright © 2014 Inderscience Enterprises Ltd.

Strategic orientation and new venture performance in the hospitality industry

María-del-Carmen Alarcón-del-Amo* Business Economics Department, Universitat Autònoma de Barcelona, Facultat d’Economia i Empresa, 08193 Bellaterra, Barcelona, Spain E-mail: [email protected] *Corresponding author

Jorge Eduardo Gómez Administration and Marketing Department, Tecnológico de Monterrey, Av. Gral. Ramón Corona 2514, Col. Nuevo México, 45201 Zapopan, Jalisco, México E-mail: [email protected]

Joan Llonch and Josep Rialp Business Economics Department, Universitat Autònoma de Barcelona, Facultat d’Economia i Empresa, 08193 Bellaterra, Barcelona, Spain E-mail: [email protected] E-mail: [email protected]

Abstract: Despite the important role of new ventures in any developed market, and the economic significance of the hospitality industry, the influence of key strategic orientation on new venture performance has not been previously researched. This study develops and tests a model where market orientation (MO) is an antecedent of entrepreneurial orientation (EO), EO is critical to innovation success (IS), and MO and IS are important drivers of new venture performance. Moreover, our results suggest that with respect to new ventures, learning orientation does not moderate the relationship between strategic orientation and performance outcomes.

Keywords: market orientation; entrepreneurial orientation; innovation success; learning orientation; business performance; new ventures; hospitality industry.

Reference to this paper should be made as follows: Alarcón-del-Amo, M-d-C., Gómez, J.E., Llonch, J. and Rialp, J. (2014) ‘Strategic orientation and new venture performance in the hospitality industry’, Int. J. Business Environment, Vol. 6, No. 3, pp.266–283.

Biographical notes: María-del-Carmen Alarcón-del-Amo is a Visiting Professor at the Autonomous University of Barcelona. She is author and/or co-author of different journals, such as Cyberpsychology, Behavior and

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Strategic orientation and new venture performance in the hospitality industry 267

Social Networking, Social Behavior and Personality, Information Systems and e-Business Management, Journal of Research in Interactive Marketing and International Journal of Internet Marketing and Advertising, and several book chapters about social media and management in Springer and Emerald editorial. Her main research line is focused on the analysis of online consumer behaviour in the context of social networking sites, electronic commerce and Web 2.0. She is member of the editorial board of the Internet Research.

Jorge Eduardo Gómez is the Department Director of Administration and Marketing at the Business School, Tecnológico de Monterrey, Campus Guadalajara, México. He has published in International Small and Medium Enterprise Review, Academy of Strategic Management Journal (forthcoming), and Cuadernos de Gestión. His main research interests are strategic orientations, family business and entrepreneurship.

Joan Llonch is an Associate Professor of Marketing in the Business Economics Department of Autonomous University of Barcelona, Spain. He is author and/or co-author of different books, chapter books or papers in both national and international academic journals such as European Management Journal, International Journal of Contemporary Hospitality Management, Journal of Fashion Marketing and Management, Transformations in Business & Economics, among others. His research interests are: strategic marketing, service marketing and marketing in internet. He also serves as an ad-hoc reviewer for different academic journals.

Josep Rialp is an Associate Professor in Marketing and Market Research in the Business Economics Department of Autonomous University of Barcelona, Spain. He is author and/or co-author of different chapter books, books or papers in both national and international academic journals such as International Business Review, Advances in International Marketing, Journal of World Business, Regional Studies, Journal of Advertising, Journal of Small Business Management and European Management Journal, among others. His research interests are: SME management and marketing, marketing metrics, interorganisational collaboration, and research techniques. He also serves as an Associate Editor in Cuadernos de Economía y Dirección de la Empresa as well as ad-hoc reviewer for different academic journals.

1 Introduction

New ventures contribute fundamentally to economic development (Carter et al., 1994). However, insufficient market reputation, limited knowledge of the environment, supply problems, smaller product or service range, and limited resources can cause new ventures to be at an initial disadvantage compared to established firms (Gruber, 2007). Moreover, small size and lack of market experience can cause new ventures to experience difficulty (Watson et al., 1998) in the early years of operation, and most of these ventures fail (Laitinen, 1992). Complex challenges highlight the importance of studying the strategic drivers of new venture performance.

Resource-based view (RBV) theory posits that the strategic orientation of a firm has been considered an important indicator of organisational capability and a key driver of performance (Zhou et al., 2005). Market orientation (MO), entrepreneurial orientation (EO) and learning orientation (LO) are considered to be three key strategic orientations

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because they have been revealed as important drivers of organisational performance (Calatone et al., 2002; Wiklund and Shepherd, 2005). However, although it has been proposed that the relationship between a firm’s strategic orientation and performance depends on the firm’s age (Brettel et al., 2009; Kakati, 2003), the effects of these three orientations on performance have been previously analysed only among mature organisations (Hult et al., 2004) or high tech new ventures (Rhee et al., 2010). To the best of our knowledge, new ventures in the hospitality industry have not been studied. In summary, this study analyses the relationships among three key strategic orientations (MO, EO and LO) in new ventures and the effects of these orientations on new venture performance in the hospitality industry.

This paper is composed of seven sections. The theoretical framework is presented first and discusses strategic orientation and RBV theory, which helps to formulate the hypotheses in the following section. Next, the methodology is presented followed by hypotheses testing and analysis of the results. Finally, conclusions and managerial implications are outlined.

2 Theoretical framework: strategic orientation and the RBV

Strategic orientation can be defined as strategic direction that is implemented by a firm to create the proper behaviours for sustainable superior business performance (BP) (Gatignon and Xurueb, 1997). The strategic orientation of a firm influences organisational perception of the environment (Kohli and Jaworski, 1990; Lumpkin and Dess, 1996), goal setting, resource allocation, structuring of the value creation process (Andrews, 2000; Siguaw et al., 2006), and the building of organisational as well as dynamic capability (Eisenhardt and Martin, 2000; Rosenbusch et al., 2011).

Strategic orientation has been discussed in both marketing and strategic management literature (Grinstein, 2008) that has suggested that strategic orientation reflects the beliefs and mental models of senior executives (Hitt et al., 1997). Such literature has suggested that a variety of strategic orientations can positively affect a firm’s competitive advantage and performance; the key strategic orientations discussed are MO, LO and EO (Baker and Sinkula, 1999a; Hult and Ketchen, 2001; Liu et al., 2002).

A firm’s strategic orientation consists of fundamental underlying factors that determine the scope and nature of a firm’s activities (Peterson, 1989). Firm orientation influences the way in which organisation members process information and reach decisions, and it creates an internal environment that encourages and rewards desirable behaviour and actions (Li et al., 2006).

RBV theory offers that a firm’s strategic orientation is considered to be a critical organisational capability (Hult and Ketchen, 2001; Zhou et al., 2005). The RBV of a firm highlights idiosyncratic resources (Barney, 1991; Wernerfelt, 1984); assets or capabilities (Day, 1994; Hunt and Morgan, 1995; Wernerfelt, 1984), especially resources, that exist within an organisation (Lee et al., 2001). RBV theory is concerned with the influence that firm resources and capabilities have with respect to firm differentiation and its ability to achieve and sustain competitive advantage (Barney, 1991). RBV theory considers a firm to be a collection of resources and suggests that these resources significantly affect a firm’s competitive advantage and performance (Barney, 1991; Peteraf, 1993). For a resource to become a factor of competitive advantage, it should be valuable, scarce, imperfectly tradable and difficult to imitate (Barney, 1991, 2001; Reed and DeFilippi,

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1990). Thus, because strategic orientation is a valuable, scarce, imperfectly tradable and difficult to imitate resource, it can be described as a significant source of competitive advantage (Bhuian et al., 2005; Hult and Ketchen, 2001).

The literature states that MO, EO and LO are three strategic orientations with significant implications for BP (Calatone et al., 2002; Kirca et al., 2005; Wiklund and Sheperd, 2005).

3 Research hypotheses

MO refers to a firms’ adoption of the marketing concept (Kohli and Jaworski, 1990). Accordingly, MO is considered to be “the very heart of modern marketing management and strategy” [Narver and Slater, (1990), p.20]. MO measures a firm’s ability to interact with customers and to react to competitor activity (Narver and Slater, 1990). According to Monferrer et al. (2013), MO not only provides the information and knowledge that a business needs to be competitive in turbulent environments, but it also encourages the integration of information into firm strategy, thereby influencing the firm’s actions.

Lumpkin and Dess (1996, p.136) define EO as “… the methods, practices and decision making styles managers use to achieve entrepreneurial behavior”. EO is generally associated with three dimensions: innovativeness, proactiveness and risk taking (Covin and Slevin, 1986; Zahra, 1991).

LO is an organisational feature that affects a firm’s proclivity to value learning that encourages change in basic organisational norms and values; LO is a consequence of proactive behaviour towards organisational learning (Baker and Sinkula, 1999a; Hult et al., 2004). LO influences the development of knowledge within the organisation.

Empirical research conducted over the last twenty years suggests a direct and positive link between MO and performance (Baker and Sinkula, 2009; Kirca et al., 2005; Narver and Slater, 1990). Considering that new ventures are usually characterised by a lack of formal planning and processes as well as uncertain environments (Pelham, 2000), an organisation-wide MO can provide consistent guidelines for objectives, decisions and activities and should therefore enhance organisational performance (Brettel et al., 2009). This study offers the following hypothesis:

Hypothesis 1 In the context of new ventures, MO is directly related to BP.

Businesses with a high EO can target premium market segments, charge high prices and ‘skim’ the market ahead of competitors, which should provide them with larger profits and allow them to expand faster (Rauch et al., 2009). Thus, conceptual arguments suggest that EO leads to higher performance. However, considerable variation exists in the level of influence of the reported relationship between EO and BP (Jantunen et al., 2008; Rauch et al., 2009; Wiklund and Shepherd, 2005). Nevertheless, previous studies indicate that EO is a key resource for new ventures in obtaining sustained competitive advantage (Lee et al., 2001; Li et al., 2009; Zahra et al., 1999). Thus, we offer Hypothesis 2:

Hypothesis 2 In the context of new ventures, EO is directly related to BP.

High market-oriented organisations report a higher level of entrepreneurship (Liu et al., 2003). Moreover, knowledge and information acquired through marketing are generally important for the entrepreneurial process and affect entrepreneurial behaviour (Bhuian et al., 2005; Liu et al., 2002; Luo et al., 2005). Research has shown that firms that score

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high on MO often tend to be more entrepreneurially oriented (Bhuian et al., 2005; Luo et al., 2005). In the case of new ventures, MO is even more important because it enables young firms to learn and adapt to their environment thereby facilitating quicker reaction to opportunities and threats (Luo et al., 2005). We therefore offer Hypothesis 3:

Hypothesis 3 In the context of new ventures, MO is positively related to EO.

MO likely enhances innovation because it involves new or different responses to market conditions (Hult et al., 2004; Kirca et al., 2005). Empirical research corroborates this perspective. A clear and consistent link exists between MO and new product success (Gatignon and Xurueb, 1997). MO is related to all aspects of innovation and is a stronger predictor of innovation than LO (Mavondo et al., 2005). Thus, we offer Hypothesis 4:

Hypothesis 4 In the context of new ventures, MO is positively related to innovation success (IS).

The existence of a strong interrelationship between innovation and entrepreneurship has been suggested; Drucker (1985), for example, offers that innovation is the primary activity of entrepreneurship, and Lumpkin and Dess (1996) argue that innovation is a key component of EO. Many studies of new companies have demonstrated that entrepreneurial efforts to adopt creative attitudes, without fear of the risks involved with those attitudes, promote organisational innovation (Kakati, 2003; Rhee et al., 2010). Therefore, we offer Hypothesis 5:

Hypothesis 5 In the context of new ventures, EO is directly related to IS.

IS is a primary means by which firms maintain and expand consumer and product markets (Baker and Sinkula, 2009). The greater the use of innovative products, the greater the competitive advantage of the firm, and the more difficulty competitors will have in developing effective responses (Sandvik and Sandvik, 2003). Because new firms are often more flexible and agile than their established counterparts, pioneering innovation provides a prolonged period of operation under conditions of limited competition (Rosenbusch et al., 2011), that improves organisational performance (Li and Atuahene-Gima, 2001; Rhee et al., 2010). We therefore offer Hypothesis 6:

Hypothesis 6 In the context of new ventures, IS is directly linked to BP.

Most research on the relationship between LO and other key strategic orientations, or between LO and performance, has considered only direct relationships; however, empirical results on these direct relationships are highly controversial (Calatone et al., 2002; Farrell, 2000; Farrell and Oczkowski, 2002; Grinstein, 2008; Keskin, 2006; Rhee et al., 2010; Santos et al., 2005).

New ventures that are more learning-oriented can more effectively leverage the effect of MO, EO and IS on BP compared to those ventures that are less focused on learning (Monferrer et al., 2012). Due to the empirical context of the sample in this study that is based on new ventures, we suggest that LO can enhance the relationship between MO, EO, IS and performance because a learning-oriented firm is not likely to miss opportunities created by emerging market demand. This phenomenon occurs because a learning-oriented firm possesses the attitude, knowledge and ability to understand and anticipate customer needs. Therefore, a firm’s LO improves the quality of its market-oriented behaviour (Baker and Sinkula, 1999a). Likewise, an organisation that is committed to learning is likely to be more effective in its EO, is likely to have better

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innovation capability than competitors and will therefore be committed to innovation, have state-of-the-art technology, and use that technology in innovations that improve company performance. Therefore, in this context, instead of testing a direct relationship as has been the tradition, we propose a moderating role of LO on such relationships. Thus, we propose Hypothesis 7:

Hypothesis 7 In the context of new ventures, LO moderates the relationship between MO, EO, IS and performance.

4 Methodology

We use a sample of firms from the hospitality industry that consists mainly of hotels and restaurants. This sector was selected because of its significance from the perspective of job creation in the Spanish context, as evidenced by the large number of new businesses established in the last five years. We selected those firms that were established between 2001 and 2008 and that had more than ten employees as reported in the 2009 SABI database for Spanish companies. The final total number of companies in the study was 916. 203 valid questionnaires were received from 22.2% of the total number of companies.

Likert scales with seven range values were used to measure the different strategic orientations (1 strongly disagree and 7 strongly agree). MO was assessed using the MKTOR scale (Narver and Slater, 1990). LO was measured using the scale suggested by Baker and Sinkula (1999a). EO was captured with an adaptation of a scale from Naman and Slevin (1993). IS was measured using an adaptation of the scale from Baker and Sinkula (1999b). Finally, BP was assessed from six subjective criteria: return on investment (ROI), profits (P), sales (S), customer satisfaction (CS), employee satisfaction (ES) and overall results (OR), all in comparison with company objectives.

5 Results

Importantly, in our research scenario, we distinguished between companies with the lowest LO intensity scores and those with the highest, to define two groups of companies with below average and above average LO intensity.

A multi-group structural equation model was used, taking into consideration one of the dominant focal points for analysis of the multi-group data (Hair et al., 2006) to facilitate analysis of the proposed model and to study the moderator effect of LO on the proposed causal relationships.

The procedure that was followed proposes dividing the sample of companies into two groups, according to low- or high-LO intensity.

To create the intensity index, we first developed a confirmatory factor analysis jointly for the LO construct with the aim of assessing measurement reliability and validity by applying structural equation modelling (SEM) techniques using the statistics package EQS 6.1.b. LO scale is a second order factor measured by three first order factors: Commitment to learning (CL), shared vision (SV) and open-mindedness (OM). To confirm the existence of multidimensionality in the LO scale, a rival models strategy was developed (Anderson and Gerbing, 1988; Hair et al., 2006). We compared a second order

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model, in which various dimensions measured the multidimensional construct under consideration with a first order model, in which all of the items were weighed on a single factor (Steenkamp and van Trijp, 1991). The results showed that the second order model had a much better fit than the first order model. These results allow us to conclude that the LO variable showed a multidimensional nature.

The resulting factor scores of the measurement model were used as a measure of the LO indicator; therefore, this variable is continuously defined in real intervals (Allred et al., 2006; Brown et al., 2003; Mäenpää, 2006). This new variable allowed us to divide the sample into two segments: 102 companies with a low LO value (below the median) and 101 companies with an LO value above the median.

5.1 Measurement model

A SEM model is analysed and interpreted in two stages: assessment of reliability and validity of the measurement model, and the assessment of the structural model.

The first analysis performed was an analysis of the validity and reliability of the scales employed in our model in both groups using confirmatory factorial analysis (CFA). Development of the scales was based on a review of the most relevant literature and guaranteed the content validity of the measurement instruments.

MO scale is a second order factor measured by three first order factors: customer orientation (CO), competitor orientation (COMO) and interfunctional coordination (IC). To confirm the existence of multidimensionality in the MO scale, a rival models strategy was developed. The results showed that the second order model had a much better fit than the first order model. These results allow us to conclude that the MO variable showed a multidimensional nature.

We then conducted a confirmatory factor analysis jointly for all the constructs in the model, with the aim of assessing the measurement reliability and validity, by applying SEM techniques using the statistics package EQS 6.1.b. The estimation method used was maximum likelihood (Bentler, 1995). The scale refinement process continued with the three criteria proposed by Jöreskog and Sörbom (1993). Following these recommendations, we eliminated three items from the CO scale (CO1, CO4 and CO5), two items from the EO scale (EO1 and EO5) and two from BP (CS and ES). The results of the final confirmatory factor analysis for the entire sample are reported in Table 1 for companies with low LO, and for companies with high LO. The results suggested that our re-specified measurement model provides a good fit to the data based on several fit statistics.

Reliability of the constructs is presented in Table 1 and demonstrates high-internal consistency of the constructs. With an exploratory analysis, we found that the item-total correlation that measures the correlation of each item with the sum of the remaining items that constitute the scale is above the minimum of 0.3 recommended by Nurosis (1993). In each case, Cronbach’s alpha (α) exceeded Nunnally and Bernstein’s (1994) recommendation of 0.70. Composite reliability (CR) represents the shared variance among a set of observed variables measuring an underlying construct (Fornell and Larcker, 1981). Generally, a composite reliability of at least 0.60 is considered to be desirable (Bagozzi and Yi, 1988). This requirement is met for every factor. Average variance extracted (AVE) was also calculated for each construct, resulting in AVEs that were greater than 0.50 (Fornell and Larcker, 1981). Therefore, the six scales demonstrate acceptable levels of reliability.

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Strategic orientation and new venture performance in the hospitality industry 273

Table 1 Internal consistency and convergent validity

Ove

rall

sam

ple

Low

LO

H

igh

LO

Vari

able

In

dica

tor

Fact

or lo

adin

g*

α C

R AV

E

Fact

or lo

adin

g*

α C

R AV

E

Fact

or lo

adin

g*

α C

R AV

E

CO

C

O2

0.67

2 0.

82

0.83

0.

62

0.

500

0.74

0.

77

0.55

0.65

4 0.

70

0.71

0.

50

C

O3

0.83

8

0.

750

0.78

2

C

O6

0.84

5

0.

914

0.55

3

CO

MO

C

OM

O1

0.78

6 0.

86

0.86

0.

62

0.

693

0.81

0.

82

0.54

0.83

6 0.

84

0.85

0.

59

C

OM

O2

0.68

2

0.

644

0.64

1

C

OM

O3

0.82

0

0.

777

0.79

9

C

OM

O4

0.84

4

0.

810

0.79

4

IC

IC1

0.70

3 0.

85

0.84

0.

58

0.

746

0.79

0.

79

0.50

0.54

0 0.

78

0.78

0.

47

IC

2 0.

820

0.72

4

0.

782

IC3

0.75

1

0.

645

0.70

8

IC

4 0.

765

0.64

8

0.

703

EO

EO

2 0.

635

0.85

0.

85

0.59

0.51

4 0.

80

0.81

0.

52

0.

679

0.83

0.

83

0.55

EO3

0.78

9

0.

786

0.70

1

EO

4 0.

760

0.68

7

0.

742

EO6

0.87

6

0.

854

0.84

6

IS

IS1

0.85

0 0.

89

0.89

0.

73

0.

859

0.90

0.

90

0.74

0.79

9 0.

85

0.85

0.

65

IS

2 0.

880

0.87

1

0.

848

IS3

0.83

9

0.

858

0.77

8

BP

RO

I 0.

838

0.92

0.

92

0.75

0.88

0 0.

91

0.91

0.

73

0.

751

0.90

0.

90

0.70

P 0.

899

0.88

0

0.

895

S 0.

851

0.82

1

0.

823

OR

0.

866

0.82

9

0.

860

Not

es: G

oodn

ess o

f fit

indi

ces:

O

vera

ll: χ

2 (203

gl)

= 35

0.22

; χ2 /g

l = 1

.72;

NFI

= 0

.885

; NN

FI =

0.9

40; C

FI =

0.9

47; R

MSE

A =

0.0

60

Low

LO

: χ2 (2

03 g

l) =

296.

84; χ

2 /gl =

1.4

6; N

FI =

0.7

87; N

NFI

= 0

.908

; CFI

= 0

.919

; RM

SEA

= 0

.061

H

igh

LO: χ

2 (203

gl)

= 26

2.77

; χ2 /g

l = 1

.29;

NFI

= 0

.793

; NN

FI =

0.9

34; C

FI =

0.9

42; R

MSE

A =

0.0

54

*p <

0.0

1

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274 M-d-C. Alarcón-del-Amo et al.

Convergent validity is verified by analysing the factor loadings and their significance. The t scores obtained for the coefficients in Table 1 indicate that all factor loadings were significant (p < .001). Additionally, the size of all the standardised loadings are higher than 0.50 (Steenkamp and Geyskens, 2006), and the average of the item-to-factor loadings are higher than 0.70 (Hair et al., 2006). This finding provides evidence supporting the convergent validity of the indicators (Anderson and Gerbing, 1988).

Evidence for discriminant validity of the measures was provided in three ways (see Table 2 for the overall sample showing companies with low LO and with high LO). First, none of the 95% confidence intervals of the individual elements of the latent factor correlation matrix contained a value of 1.0 (Anderson and Gerbing, 1988). Second, the shared variance between pairs of constructs was always less than the corresponding AVE (Fornell and Larcker, 1981), except for scales related to EO and innovation, whose AVE is lower and/or equal to the squared correlation (0.73 for the overall sample, 0.71 for companies with low LO and 0.66 for companies with high LO). Bagozzi (1994) argues that existing discriminant validity is acceptable if the correlations between the variables in the confirmatory model are not significantly higher than 0.8 points; this argument is supported in this study. Table 2 Discriminant validity of the theoretical construct measures

Overall sample CO COMO IC EO IS BP

CO 0.62 [0.45, 0.71] [0.64, 0.86] EO 0.59 [0.80, 0.92] [0.43, 0.66] COMO 0.34 0.62 [0.60, 0.83] IS 0.73 0.73 [0.43, 0.67] IC 0.56 0.51 0.58 BP 0.30 0.30 0.75

Low LO

CO COMO IC EO IS BP

CO 0.55 [0.19, 0.60] [0.36, 0.73] EO 0.52 [0.75, 0.93] [0.33, 0.68] COMO 0.15 0.54 [0.54, 0.85] IS 0.71 0.74 [0.32, 0.66] IC 0.29 0.48 0.50 BP 0.26 0.24 0.73

High LO CO COMO IC EO IS BP

CO 0.50 [0.27, 0.69] [0.52, 0.87] EO 0.56 [0.71, 0.92] [0.19, 0.58] COMO 0.23 0.59 [0.40, 0.75] IS 0.66 0.73 [0.25, 0.63] IC 0.48 0.33 0.47 BP 0.15 0.20 0.75

Note: The diagonal represents the AVE, while above the diagonal, the 95% confidence interval for the estimated factors correlations is provided, below the diagonal, the shared variance (squared correlations) is represented.

Based on these criteria, we concluded that the measures in the study provided sufficient evidence of reliability, convergent and discriminant validity. Thus, the revised measurement model was retained as the study’s final measurement model.

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5.2 Measurement invariance

Once the validity and reliability of the scales were confirmed, we had to assure the measurement invariance of the measurement instrument to compare the two groups (Hair et al., 2006). In our case, the differences that could exist between the ratings given by the scales in companies with low and high LO could be the result of real differences between the companies or due to systematic errors produced by the manner in which individuals in different companies respond to certain items. As Horn (1991, p.119) proposed, “without evidence of invariance of the measurement instrument, the study conclusions would be weak”.

To analyse the invariance of the measurement instrument, we will follow three steps that correspond to the three invariance levels that we must comply with.

Step 1 First, we will evaluate the loose cross-validation or single group solution. This is the least demanding equivalence form that estimates the CFA separately in each of the two samples. A good fit in both samples is required.

When we evaluated the validity and reliability of the model companies with low LO and with high LO, the CFA fit was good for both samples (summarised in Table 3, in the rows of simple group solutions).

Step 2 This step is designed to verify that the factorial structure (number of factors) is the same in the two samples, which is called equal form or factor structure or configurational invariance. The method is similar to that used in Step 1 except that instead of estimating the model of each sample separately, a multi-group estimation is performed. The model is estimated simultaneously in the two groups. This model will serve as a basis for checking whether the restrictions that are incorporated deteriorate the adjustment.

We check whether the chi-squared and the degrees of freedom are the sum of the two previous ones (see Table 3) and, while they are still significant, the remainder of the indicators show that it is reasonable to assume the same factorial structure in the two samples (RMSEA = 0.061; CFI = 0.930; NNFI = 0.921).

Step 3 The invariance of the factorial loadings (also called equal factor loadings or metric invariance) is subsequently checked. This invariance implies that it would be reasonable to assume that the factorial loadings that join each factor with its indicator are the same in both samples. We ensure that the concepts have been measured in the same way in both cases by comparing the chi-squared of the results of Steps 2 (equal form) and 3 (equal factor loadings) to analyse whether the fit of the new model is significantly worse. The difference of the chi-squared is 45.81, which is significant (see Table 3).

Thus, we can conclude that imposing restrictions on the equality of factorial loadings significantly deteriorates the fit and that such restrictions are therefore not plausible. Thus, we cannot affirm the factorial invariance of the measurement instrument.

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Table 3 Measurement invariance test

Model χ2 g.l. Dif. χ2 ∆g.l p RMSEA CFI NNFI

Single group solution

Low LO (n=102) 296.84* 203 0.061 0.919 0.908

High LO (n=101) 262.77* 203 0.054 0.942 0.934

Measurement invariance (n = 203)

Equal form 559.61* 406 0.061 0.930 0.921

Equal factor loadings 605.43* 428 45.81 22 0.0 0.064 0.919 0.913

Note: *p < 0.01.

Nevertheless, if a partial invariance exists, or, if there were at least two invariable factorial loadings for each factor, we could continue and evaluate the moderator effect of the LO on the relationships proposed (Byrne, 2006; Byrne et al., 1989; Hair et al., 2006; Muthén and Christoffersson, 1981). We analyse this partial invariance with the Lagrange multipliers for each of the restrictions proposed, and we find that only the restrictions corresponding to four factorial loads would improve the fit if they were eliminated. It is likely that these restrictions are different in the two samples. The other 18 restrictions have significances higher than 1% (p > 0.01). Eliminating the parameter equality restriction does not improve the fit and it is therefore likely that they are the same in both samples. We also found that in these 18 restrictions, there are at least two loads per factor. In summary, we can affirm the partial invariance and proceed to evaluate the significance of the LO moderator effect in the structural relationships. We estimated the multi-group model, and the next section describes the structural model.

5.3 Structural model and the LO moderator effect

The fit of the multi-group model, in which the structural part is added, is good (χ2(g.l. = 414) = 556.20 (p = 0.00); χ2/g.l = 1.34; RMSEA = 0.058; CFI = 0.935; NNFI = 0.928). It will be taken as a reference for comparing the fit with the models in which the restrictions that interest us have been added, such as the equality relationship between factors, to analyse whether that difference is significant and to determine the moderator effect.

We have compared the multi-group model without restriction with each of the models with each of the restrictions, i.e., with six different models. Table 4 shows the results of this multi-group analysis.

We can see that no significant differences exist, which means that the fit is not significantly different when a restriction with equal parameters is added. Therefore, it is logical to consider these restrictions, and we can conclude that the parameters are not significantly different, which confirms the non-existence of a LO moderator effect in the hypothesised sense. We therefore consider the overall sample to test the causal hypotheses. Figure 1 shows a synthesis of the results obtained for the causal hypothesis testing shown in Table 4.

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Table 4 Hypotheses testing

Path Overall sample LO moderator effect

Stand coefficients Robust t-value ∆χ2 (∆g.l. = 1) p

H1/H1a MO →EO 0.508 ** 5.591 0.135 ns 0.713 H2/H2a MO →IS 0.011 ns 0.156 0.231 ns 0.631 H3/H3a EO →IS 0.852 ** 7.452 0.564 ns 0.452 H4/H4a MO →BP 0.324 ** 3.896 0.146 ns 0.702 H5/H5a EO → BP 0.131 ns 0.878 0.237 ns 0.627 H6/H6a IS → BP 0.293 * 1.840 0.113 ns 0.737

Notes: *p < 0.01; *p < 0.1; ns = no significant. Goodness of fit indices: χ2 (198 gl) = 284.88; χ2/gl = 1.43; NFI = 0.906; NNFI = 0.964; CFI = 0.969; RMSEA = 0.047.

Figure 1 Structural model results

Table 4 and Figure 1 show that the overall model results obtained allow us to state that that MO has a clear, positive, direct and significant influence on performance. Moreover, MO has an indirect effect on performance through the EO, which has a significant effect on IS that in turn influences performance.

6 Conclusions

The rationale for the direct relationship between MO and performance is based on the idea that high quality customer service is fundamental to new venture success (Brettel et al., 2009; Kakati, 2003). Given that new ventures typically have resource constraints that do not allow transaction marketing approaches such as those pursued by large and established companies, new ventures will have to rely on personal networks to develop business and obtain information. Although we have not proved the dependency of new venture success on personal contact networks, some results in previous literature highlight this relationship (Birely, 1985; McEvily and Zaheer, 1999).

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Moreover, our empirical results suggest that MO is directly related to EO with respect to new ventures. Knowledge and information from marketing are frequently very important for the entrepreneurial process, and these factors affect entrepreneurial behaviour (Bhuian et al., 2005; Liu et al., 2002; Luo et al., 2005). Although we have not detected a direct connection between EO and performance, such a connection had been considered in the theoretical section. Businesses with high EO can target premium market segments, charge high prices, and ‘skim’ the market ahead of competitors. These behaviours should provide such businesses with higher profits and allow them to expand faster (Rauch et al., 2009; Zahra and Covin, 1995). EO is directly related to IS, and IS is also related to performance in new ventures; furthermore, being market-oriented guarantees higher performance in two different ways.

EO is related to IS because entrepreneurial values allow firms to capitalise on emerging opportunities and are therefore important drivers of new products and organisational growth (Bhuian et al., 2005; Lumpkin and Dess, 1996; Naman and Slevin, 1993; Rauch et al., 2009; Slater and Narver, 1995). Thus, firms with strong EO are likely to develop new product concepts or breakthrough innovations (Baker and Sinkula, 2009; Lumpkin and Dess, 1996; Zhou et al., 2005) as well as organisational innovation (Kakati, 2003; Rhee et al., 2010). Likewise, the greater the use of innovative products, the greater the competitive advantage of the firm and the more difficulties the firm’s competitors will face in developing effective responses (Sandvik and Sandvik, 2003). Therefore, in the case of new ventures, innovation is positively linked to organisational performance (Li and Atuahene-Gima, 2001; Rhee et al., 2010).

No connections were found between MO and innovation and between EO and performance in the context of new ventures. Although unexpected, this result is consistent with certain results and debates in academic literature. For example, different studies reported lower correlations between EO and performance (Lumpkin and Dess, 2001; Zahra, 1991) or were unable to find a significant relationship between them (Covin et al., 1994).

When LO is considered in the model to be moderating the relationships established among MO, EO, IS and performance, the evidence does not allow us to find the predicted moderation effect in new ventures. To be more or less learning-oriented does not impact the relationships among MO, EO, IS and performance generally identified. We believe that this result could be related to the empirical context analysed. A LO is likely to increase the rate of internal and external change in a company, but the process of establishing a LO takes time. Changes in a firm’s LO, as Garvin (1993, p.91) notes, are “the product of carefully cultivated attitudes, commitments and management processes that have accrued slowly and steadily over time”. From our perspective, it would be interesting to study these same companies several years later and to analyse the levels of OM, OE and IS of the most learning-oriented companies because leveraging the effect of learning in other orientations requires time.

Although this study has several relevant contributions to the field, it also has important limitations that should be considered with respect to future research. First, this study is focused on one industry only. Second, the sample used is limited to one country: Spain. Moreover, we interviewed only one person per firm; in future investigations, information should be obtained from more than one respondent per organisation.

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7 Managerial implications

Relevant implications with respect to strategic management include the suggestion that new ventures have to be market-oriented to obtain high BP. This implication is based on two effects of MO on BP: first, a direct positive effect, and second, an indirect effect on BP through EO and IS.

Therefore, a connection must also exit between EO and IS. It is not enough to have an EO, or other capability, to obtain innovations in an isolated way that improves BP. The results suggest a sequence that starts with MO and that provides information and/or other inputs to take advantage of EO. EO then leverages the innovation capacity of a firm to facilitate subsequent successful innovation that increases company performance. Therefore, firms should nurture their MO but also reinforce the connection among different orientations and capabilities.

Moreover, managers should take into account that simply adopting an entrepreneurial-oriented strategy is not enough for new venture performance. By definition, new ventures should be the result of a certain amount of EO on the part of the company, however, a company cannot depend on its entrepreneurialism for business success because new ventures should be primarily market-oriented.

We suggest, therefore, that managers concentrate on developing dynamic capabilities that connecting different orientations and capabilities and that can provide superior customer value and, consequently, obtain higher performance. As Martelo et al. (2011) note, firms are aware of customer demand for superior value, and firms could benefit from combining their existing capabilities to offer superior value.

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