The Impact of Organizational Orientations on Medium

Journal of Small Business Management 2015 53(1), pp. 94–113 doi: 10.1111/jsbm.12054

The Impact of Organizational Orientations on Medium and Small Firm Performance: A Resource-Based Perspective by Subhash C. Lonial and Robert E. Carter

Recent studies suggest that market, entrepreneurial, and learning orientations individually improve firm performance. In this study, we suggest that each of the orientations can enhance company success, but the potential of each orientation should not be viewed in isolation. Instead, we draw on the resource-based view of the firm, looking at these three orientations as capabilities of small and medium-sized enterprises (SMEs). The analysis was carried out on a sample of 164 SMEs. The results indicate that market, entrepreneurial, and learning orientations jointly give rise to positional advantage, which, in turn, is positively related to the performance of the firm.

Introduction In the current marketplace, firms confront an intense operating environment where maintaining and improving sales, market share, and profitability are an ongoing challenge. To succeed in this potentially austere setting, firms must effectively deploy tangible and intangible assets that are valuable, unique, and difficult to copy (Day and Wensley 1988). In this resourcebased view (RBV) of the firm (Barney 2001; Barney 1991), companies use their physical assets, human assets, and organizational assets to develop long-term competitive advantages and, in turn, achieve superior company performance (Morgan, Strong, and McGuinness 2003; Wiklund and Shepherd 2003). Intangible organizational assets, such as entrepreneurial orientation (EO), market orientation (MO),

and learning orientation (LO), are particularly difficult for competitors to duplicate and, hence, lead to these sustainable advantages (Atuahene-Gima and Ko 2001; Kropp, Lindsay, and Shoham 2006; Martin, Martin, and Minnillo 2009). Scholars have recorded substantial progress over the past two decades deciphering the impact of various organizational orientations on firm performance; yet gaps remain— especially in regard to the scope of the orientations to investigate, the conceptual framework describing how these orientation(s) affect performance, and the contrasts between large and small/medium-sized firms (Baker and Sinkula 2009; Li et al. 2008; Martin, Martin, and Minnillo 2009). Prior research typically examined the impact of a single organizational orientation, such as MO, on firm performance

Subhash C. Lonia is a professor in the Marketing Department, College of Business, University of Louisville, KY. Robert E. Carter is an associate professor in the Marketing Department, College of Business, University of Louisville, KY. Address correspondence to: Robert E. Carter, University of Louisville, Marketing Department, College of Business, Louisville, KY 40292. E-mail: [email protected].

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(Baker and Sinkula 1999; Bhuian, Menguc, and Bell 2005; Kirca, Jayachandran, and Bearden 2005; Matsuno and Mentzer 2000; Matsuno, Mentzer, and Özsomer 2002; Pelham 2000). Scholars have noted, however, that a univariate analysis provides a curtailed understanding of an orientation’s effect on firm performance (Kropp, Lindsay, and Shoham 2006; Li et al. 2008). Thus, prior outcomes may be incomplete (Wang 2008). In response, a handful of researchers have investigated the impact of EO, MO, and LO on multinational company performance (Hult and Ketchen 2001; Hult, Snow, and Kandemir 2003). However, the generalizability of these finding to small and mediumsized enterprises (SMEs) is questioned (Baker and Sinkula 2009) since SMEs confront restricted access to financial resources (Li et al. 2008), face correspondingly greater financial pressure (Martin, Martin, and Minnillo 2009), use less marketing research (Verhees and Meulenberg 2004), and deploy different innovation and entrepreneurial skills (Verhees and Meulenberg 2004). Finally, the complex associations between organizational orientations and firm performance, in a SME context, are not fully developed (Zhou et al. 2008). Although Kropp, Lindsay, and Shoham (2006) examined the impact of various organizational orientations directly on SME performance, the conceptual model is likely to be more intricate and involve intervening constructs linking these antecedents to company outcomes (Hult and Ketchen 2001). We contribute to this research area by demonstrating that multiple company orientations affect firm performance, in the context of SMEs. We evaluate a conceptual model where EO, MO, and LO concurrently give rise to positional advantage (PA), which is linked to firm performance. We survey senior executives at 164 SMEs, and find that these organizational orientations represent important firm capabilities which are positively associated with PA. In turn, this intervening construct is favorably related to company outcomes. Thus, for superior firm performance, SMEs need to develop wide-ranging expertise, as reflected by the ability to effectively and concurrently institute multiple organizational orientations.

Literature Review and Hypotheses Resource-Based View In a competitive environment, firms deploy their physical, human, and organizational assets to gain an advantage in the marketplace (Day and Wensley 1988). If these resources and capabilities1 are valuable to customers, rare, and difficult to imitate, then these assets give rise to sustainable competitive advantage(s), boosting firm performance (Barney 2001; Barney 1991; Wiklund and Shepherd 2003). Among various intangible assets that a firm possesses, organizational orientations are considered some of the most important because these skills sets are deeply ingrained into the everyday routines of an organization and, as such, are problematic for competitors to copy (Zhou et al. 2008). In turn, organizational orientations may give rise to sustainable advantage and superior company performance. Whereas firms may pursue various orientations, researchers have particularly noted the importance of EO, MO, and LO, and their corresponding relationship to company outcomes (Atuahene-Gima and Ko 2001; Kropp, Lindsay, and Shoham 2006). In summary, companies that effectively deploy these organizational capabilities perform at high levels in the marketplace. Entrepreneurial Orientation Shortened product life cycles, rapidly changing competitive and technological environments, and uncertain profits derived from current entrants require organizations to develop and commercialize new opportunities (Rauch et al. 2009). However, not all companies are equally adept at developing new products; those firms that are able to exploit new product opportunities embrace an EO (Wang 2008). In this context, EO is defined as the “entrepreneurial strategy-making processes that key decision makers use to enact their firm’s organizational purpose, sustain its vision, and create competitive advantage(s)” (Rauch et al. 2009, p. 763). EO has been operationalized in multiple ways; yet there appears to be a growing consensus that this construct reflects

1

Consistent with Day and Wensley (1988) and Day (1994), we view resources as tangible or physical assets of a firm; and capabilities as a bundle of skills that reflect intangible assets of a company. Using this perspective, EO, MO, and LO represent intangible capabilities (or sets of skills or behaviors) of a firm.

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three components—risk taking, proactiveness, and innovation (Baker and Sinkula 1999; Kropp, Lindsay, and Shoham 2006; Matsuno, Mentzer, and Özsomer 2002; Verhees and Meulenberg 2004; Wiklund 1999; Wiklund and Shepherd 2005). Rauch et al. (2009) provide compelling evidence for the link between EO and firm performance, based on their metaanalysis, reporting a correlation of 0.242. The theoretical basis for this EO-performance relationship is multifold. First, these three components of EO are essential for firms dealing with complex environments (Dess, Lumpkin, and Covin 1997). In a related manner, developing sustainable advantages requires an entrepreneurial spirit due to the need to restage an organization by re-examining fundamental work and management processes (i.e., to “destroy the existing order”). This is accomplished by firms that are innovative, proactive, and risk taking (Matsuno, Mentzer, and Özsomer 2002). Further, companies benefit from the introduction of new products before competitors (Rauch et al. 2009). These firstmovers earn monopoly rents, accrue correspondingly higher profits, and deliver superior financial performance (Lumpkin and Dess 1996; Wang 2008). Nonetheless, other researchers have noted that an EO may not necessarily lead to superior organizational performance because EO processes are both costly and time consuming (Dess, Lumpkin, and Covin 1997; Hart 1992; Pelham 2000; Smart and Conant 1994; Wiklund 1999). Further, even if EO demonstrates a positive effect, focusing on EO may still result in a misallocation of resources since these scarce assets may be more effectively deployed in other endeavors. Under this scenario, the suboptimal deployment of resources causes firm performance to suffer (Lumpkin and Dess 2001; Merlo and Auh 2009; Rauch et al. 2009). In trying to explain these conflicting results, scholars have postulated that EO acts on performance in different ways for SMEs (as compared to their larger rivals) because of their limited budgets for conducting new product development research, and other financial constraints (Verhees and Meulenberg 2004; Wang 2008). More specifically, SMEs may be less willing to take risks or to fund uncertain initiatives because of their inability to recover from financial losses. As such, an EO may be less useful at SMEs since they are less able to pursue calculated risks as compared to their larger counter-

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parts. Finally, the impact of EO on firm performance may also depend on other organizational orientations that are included in any given analysis. For example, Matsuno, Mentzer, and Özsomer (2002) report that the impact of EO on performance disappeared when MO was jointly examined. Wang (2008 p. 635) echoes this outcome suggesting that “simply examining the direct EO-performance relationship provides an incomplete picture of performance.” In summary, although EO generally exhibits a positive impact on organization performance, the significance of this link may vary depending on firm size and the inclusion of other orientations in the analysis.

Market Orientation The foundation concept of marketing states that firms should identify and satisfy customer wants and needs more effectively than their competition (Day 1994; Kirca, Jayachandran, and Bearden 2005). The company structure emphasizing the implementation of the marketing concept is referred to as an MO (Matsuno, Mentzer, and Rentz 2005), and it “provides a firm with market-sensing and customer-linking capabilities that lead to superior organizational performance” (Kirca, Jayachandran, and Bearden 2005, p. 25). Although multiple definitions of MO have been put forth (Narver and Slater 1990), this construct typically reflects company behaviors focusing on the generation of market intelligence through decision support systems, information systems, and market research; dissemination of that intelligence across company departments; and responding to changes in the competitive environment based on this intelligence (Kara, Spillan, and DeShields 2005; Kohli and Jaworski 1990). This conceptualization for MO is well supported in the extant literature for large companies as well as SMEs (Kara, Spillan, and DeShields 2005; Martin, Martin, and Minnillo 2009; Renko, Carsrud, and Brännback 2009). Given its focus on creating and delivering value for the customer, MO has been generally (although not universally) identified as a predictor of performance (Baker and Sinkula 1999; Jaworski and Kohli 1993; Li et al. 2008; Matsuno and Mentzer 2000; Pelham 2000). Reflecting this perspective, Kirca, Jayachandran, and Bearden’s (2005) oft-cited meta-analysis reported a correlation of 0.32 between MO and company performance. Although the predominant view reflects the positive association between company


performance and MO, other scholars have reported either negative associations or nonsignificant relationships (Kirca, Jayachandran, and Bearden 2005). Greenly (1995) noted that different moderating conditions or market environments could negate the expected positive connection. More recently, researchers have suggested that the conflicting results linking MO to firm performance may be related to the inherent differences in MO between large firms and SMEs (Verhees and Meulenberg 2004). In support, these researchers identify several ways in which MO is implicitly different for SMEs. Of note, the gathering of market intelligence through market research and decision support systems is limited at SMEs due to constrained budgets. Dissemination of marketing intelligence may be less of an issue since there are fewer employees, and a smaller circle of senior executives are needed to coordinate the company response to the marketing intelligence. Additionally, Pelham (2000) postulates that SMEs are naturally more nimble than their larger counterparts, thereby quickly responding to new marketing intelligence. Finally, SMEs may be more dependent on MO and other customer service orientations since they lack the economies of scale to compete with larger competitors on a price basis, and may be less likely to undertake risky initiatives due to their inability to recover from significant financial losses. In summary, there is strong evidence that MO positively impacts firm performance; however, the strength and/or significance of this link may vary, particularly by firm size.

Learning Orientation In addition to MO and EO, an LO has also been deemed important in terms of understanding firm performance (Baker and Sinkula 1999; Hult and Ketchen 2001; Wang 2008). LO refers to corporate behaviors and activities related to creating, acquiring, and using knowledge to develop or enhance a competitive advantage (Calantone, Cavusgil, and Zhao 2002; Sinkula, Baker, and Noordewier 1997; Wiklund and Shepherd 2003). LO has been conceptualized as being comprised of three components (Baker and Sinkula 1999, p. 1) commitment to learning (i.e., the value an organization places on learning), 2) openmindedness (i.e., the degree to which a firm is receptive to new ideas that challenge current procedures), and 3) shared vision (i.e., a unify-

ing theme which provides guidance on how the firm should target its learning activities). Often new knowledge causes companies to question long-held beliefs about their industry or business which, in turn, results in changes to existing routines and procedures. Organizations with a strong LO encourage employees to challenge current norms by “thinking outside the box” thereby re-inventing themselves in the face of complex, challenging, and dynamic environments (Baker and Sinkula 1999). For example, companies that embrace an LO are better able to adapt to evolving business situations by developing new products that meet emerging consumer needs (Wang 2008). The positive association between LO and performance is based on the belief that firms which are able to learn from their environment are more likely than their respective competitors to quickly adapt to changing business scenarios, providing rapid improvements in product and/or service quality. In turn, these tendencies lead to enhanced firm outcomes and ongoing competitive advantage (Baker and Sinkula 2009). Still, the impact of LO on performance of the organization may vary by company size. In particular, smaller firms may exhibit less commitment to learning than their larger counterparts; as such, the link between LO and performance may be attenuated for SMEs.

Study Hypotheses As noted, prior research provides evidence for the positive impact of EO, MO, and LO on firm performance. Nonetheless, the support for these positive links is not regularly observed. We believe that there are several reasons for the inconsistent findings. First, an organization may possess multiple orientations concurrently. Thus, investigating a single orientation may be incomplete (Wang 2008). Further, a focus on MO (for example) may hinder an organization’s ability to also demonstrate a suitable EO (Matsuno, Mentzer, and Özsomer 2002). To avoid potential biases caused by examining only a single orientation, we study the impact of three critical organizational orientations (i.e., EO, MO, and LO) on firm performance. Second, we postulate that the process by which EO, MO, and LO operate is more complex than a simple link between each construct and performance. In particular, these organizational orientations are thought to be indicators of an intervening latent variable termed PA (Hult

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and Ketchen 2001; Morgan, Strong, and McGuinness 2003). PA collectively represents the unique skills and resources deployed by a firm, which create entry barriers and hinder imitation by competitors (Day and Wensley 1988). Although scholars agree on the importance of PA, and its general definition as a distinctive advantage that is “valuable, rare, and difficult to acquire” (Hult and Ketchen 2001 p. 902), a range of constructs (and corresponding operationalizations) have been used.2 However, a broad-based view of PA is deemed appropriate since firms with a multiple competencies are expected to earn above average profits (Morgan, Strong, and McGuinness 2003). Hence, using EO, MO, and LO as indicators of PA is believed to provide a more robust assessment of PA than operationalizations that are less encompassing. This approach is generally consistent with Hult and Ketchen (2001): these scholars also use multiple indicators of PA including MO, entrepreneurship, innovativeness, and organizational learning. Although the current research relies on a similar model, we operationalize some of the constructs differently, EO in particular. We judged the three dimensional definition of EO to be more appropriate based on its wide acceptance. Indeed, Rauch et al. (2009, p. 763, italics added) state: “three dimensions of EO have been identified and used consistently in the literature: innovativeness, risk taking, and proactiveness.” Nonetheless, given their overarching similarities to the current study, Hult and Ketchen (2001) provide a useful reference point for assessing the impact of multiple organizational orientations among multinational firms. Finally, Verhees and Meulenberg (2004) note that prior outcomes based on large firms may not generalize to SMEs. Because of their limited access to financial resources, SMEs may be less able to fund risky initiatives due to their inability to recover from financial losses (Wang 2008). In a similar manner, MO may be more important for SMEs since they lack the economies of scale enjoyed by their larger rivals. As such, SMEs may be more dependent on customer-service-related orientations (Li et al. 2008) and their inherent nimbleness in terms of responding to new marketing intelligence (Pelham 2000). This ongoing debate reinforces

Figure 1 Conceptual Model and Hypotheses Market Orientation

H1

Entrep Orientation

H2

Learning Orientation

H3

Positional Advantage H4 Firm Performance

the need to provide a greater understanding of the links between multiple organization orientations and performance. To this end, we examine the relationship between EO, MO, and LO and firm performance, via the intervening latent construct of PA, using SMEs as the research scenario. This leads to the four hypotheses investigated in the current study. The conceptual model, with the corresponding hypotheses, is presented in Figure 1. H1: Entrepreneurial orientation is positively related to positional advantage, for SMEs. That is, EO is a first order indicator of PA. H2: Marketing orientation is positively related to positional advantage, for SMEs. That is, MO is a first order indicator of PA. H3: Learning orientation is positively related to positional advantage, for SMEs. That is, LO is a first order indicator of PA. H4: Positional advantage is positively related to organization performance, for SMEs.

Method Sample and Data Collection The data for this study were collected in 2007 using a self-administered mail survey of SMEs using a list from the Small Business

2

For example, Carbonell and Rodriguez (2006) operationalized PA as an intervening variable between innovation speed and new product performance.

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Administration for a major city in the state of ZZZ. Of the 755 companies in the overall data set, 400 businesses with fewer than 500 employees were randomly selected. Using a list size of 400 provides suitably robust results while balancing budgetary constraints. Initially a pre-announcement postcard was mailed to each sample member. Subsequently, a questionnaire was mailed to the CEO/owner of these companies with a request to forward the survey to a senior executive in the firm (or for the owner to complete the survey, if appropriate). Following the procedures described by Dillman (2007), the initial mailing included (1) a cover letter explaining the purpose of the study, (2) an unmarked questionnaire, and (3) a business reply envelope. As a modification to this method, a return postcard was also included, which contained the respondent’s name; and the respondent was asked to return the postcard and survey separately. In this manner, we were able to provide the participant the assurance of confidentiality while enabling the researchers to maintain an accurate follow-up list. The second mailing consisted of a reminder postcard that requested respondents to complete and return their questionnaires if they had not already done so. The third mailing consisted of (another copy of) the questionnaire along with the cover letter and return envelope. Combined, these procedures produced an effective response rate of 41 percent (164 usable responses). The extrapolation procedure (Armstrong and Overton 1977) was used to assess potential nonresponse bias. No significant differences were found between the scores of early and late respondents; hence, nonresponse bias is not a concern. In those rare cases with missing data (~1.5 percent), the missing field was replaced by the corresponding mean value. The responding companies employed from four to 400 workers, with a median firm size of 40 employees; and represent a range of industries including metal working (n = 52), services (n = 28), computer software (n = 20), distribution (n = 20), chemical and medical (n = 16, each), and financial (n = 12).

Measurement Scales The current study includes specific items to measure the exogenous constructs of interest: EO, MO, LO, and firm performance. Each of these constructs is comprised of three subdimensions that are measured by summing from

two to seven individual questions. Each item uses a five-point scale. Summed indicators for these constructs are used due to the large number of individual questions and the overall sample size of 164; and this approach is consistent with the methodology used in a broad range of prior research (Carter 2009; Eroglu, Machleit, and Barr 2005; Raju and Lonial 2001). EO is operationalized using three dimensions: propensity of a company to take calculated risks, be innovative, and demonstrate proactiveness (Baker and Sinkula 2009; Bhuian, Menguc, and Bell 2005; Miller 1983; Wiklund and Shepherd 2005). Three items are used as indicators for innovativeness, three items for risk taking, and two items for proactiveness; for a total of eight questions (Covin and Slevin 1989; Matsuno, Mentzer, and Özsomer 2002; Morris and Paul 1987; Naman and Slevin 1993). Cronbach reliability for EO is 0.86, which is above the generally accepted guideline of 0.80 (Nunnally and Bernstein 1994). Details for this construct, including factor loadings, are provided in Appendix A. We use a total of 20 items as indicators of MO, representing three subdimensions of intelligence generation (six items), intelligence dissemination (seven items), and responsiveness (seven items), consistent with Matsuno, Mentzer, and Özsomer (2002) and Jaworski and Kohli (1993). The Cronbach’s alpha measure for reliability for MO is 0.90 (which is well above the normally accepted value of 0.80; Nunnally and Bernstein 1994). The 20 indicators of MO, and their corresponding factor loadings, are presented in Appendix B. It has been argued that the “conventional” MO items tend to reflect characteristics or actions of larger firms. However, we still chose to use these specific items for several reasons. First, the items are derived from well established research (Jaworski and Kohli 1993; Matsuno, Mentzer, and Özsomer 2002). Second, we placed a priority on being able to compare our results, based on SMEs, to studies derived from larger organizations. Finally, the use of these MO scales to study firms of all sizes is consistent with prior research (Baker and Sinkula 2005). In summary, it was our judgment that changing the scale items would somewhat limit our ability to compare to the area literature; thus, our decision to use existing MO scales and item wording. The LO scale developed by Sinkula, Baker, and Noordewier (1997), and retested and

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validated by Baker and Sinkula (1999), is used in the current study. The LO scale uses a total of 18 items comprising three constructs: commitment to learning (six items), shared vision (six items), and open-mindedness (six items). Consistent with the approach used for MO and EO, the questions are summed for each of these three factors that characterize LO. At a value of 0.94, the Cronbach reliability for LO is clearly above generally accepted levels (Nunnally and Bernstein 1994). Details of the items used as indicators for this construct are included in Appendix C. The final construct is company performance, which is derived from multiple survey measures. Although performance can be indicated using archival or accounting measures, there is considerable precedence for the application of self-reported or survey measures3 to assess organizational performance (Baker and Sinkula 2009; Han, Kim, and Srivastava 1998; Jaworski and Kohli 1993; Kara, Spillan, and DeShields 2005; Kropp, Lindsay, and Shoham 2006; Matsuno, Mentzer, and Özsomer 2002; Matsuno, Mentzer, and Rentz 2005; Wang 2008; Wiklund 1999; Wiklund and Shepherd 2005). Additionally, based on the similarity in the correlations between EO and different assessments of firm performance (i.e., survey measures versus archival assessments), Rauch et al. (2009) conclude that either survey measures or accounting measures of performance are suitable for research purposes. This finding is also consistent with Matsuno, Mentzer, and Özsomer (2002) who found a high correlation between subjective and objective measures of performance. Finally, Wallace et al. (2010, p. 587) note that SMEs (which are often private companies) are unlikely to provide objective financial data, and that accounting measures may also be inferior in this context since survey measures provide a broader scope and conceptualization of performance in line with its multidimensional nature. Consistent with these scholars, we also use survey measures as indicators of organization performance. Respondents are asked to rate their firm on 13 performance variables relative to the competition. These performance items were generated based on literature reviews and

supplemented with interviews with local executives of SMEs to ensure the face validity and content validity of these measures. Subsequently, these performance variables were analyzed using exploratory factor analysis. Three factors were extracted, each with an eigenvalue greater than 1.0, explaining 67.3 percent of the total variance. These dimensions or factors are labeled as financial performance (seven items), market performance (three items), and quality performance (three items); and are shown in Appendix D along with the corresponding factor loadings. The Cronbach reliability for this performance measure is 0.90, which exceeds acceptable levels (Nunnally and Bernstein 1994). The descriptive statistics and correlations for EO, MO, LO, and firm performance are summarized in Table 1.

Measurement Model Given the proposed conceptual model (see Figure 1), structural equation modeling (SEM) is used to estimate the path coefficients. Following the two-step approach recommended by Anderson and Gerbing (1988), we first evaluate a measurement model to assess construct unidimensionality and the items’ correspondence to their respective latent construct. Unidimensionality is assessed by conducting a confirmatory factor analysis (CFA) on each of the exogenous constructs: EO, MO, LO, and firm performance. As previously noted, all three of the constructs exhibit acceptable levels for Cronbach’s alpha reliability (greater than 0.80, Nunnally and Bernstein 1994). Other fit measures also demonstrate acceptable responses (the model fit guidelines are discussed in more detail in an upcoming section), with the exception of goodness of fit (GFI) for MO (see Table 2). However, given the acceptable fit statistics across a broad range of other measures, as well as on Cronbach’s alpha reliability, the unidimensionality of each of these constructs was judged to be acceptable. In the second step (Anderson and Gerbing 1988), discriminant validity is demonstrated by estimating multiple models: (1) the unconstrained model; and (2) three separate constrained models where the correlation between any pair of orientations (MO-EO, EO-LO,

3

This includes self-reported measures of firm performance such as the change in market share, revenue, profit, profit margin, and so forth.

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0.60 0.61 0.63 0.69 0.77 0.78 0.82 0.94 0.83 0.75 0.74 0.74

3.23 3.27 3.58 3.36 3.11 3.49 3.46 3.23 3.44

Standard Deviation

3.32 3.61 3.45

Mean

Descriptive Statistics

Correlations in Bold type are significant at p < .05.

Performance 1. Market Performance 2. Quality Performance 3. Financial Performance Market Orientation 4. Intelligence Generation 5. Intelligence Dissemination 6. Responsiveness Envrionmental Orientation 7. Innovation 8. Risk Taking 9. Proactiveness Learning Orientation 10. Commitment 11. Shared Vision 12. Openmindedness

Parameter

1.00

1

0.34 1.00

2

0.25 0.37 1.00

3

1.00

0.23 0.20 0.23

4

0.60 1.00

0.22 0.23 0.08

5

Table 1 Descriptives and Correlations

0.42 0.44 1.00

0.32 0.41 0.35

6

1.00

0.26 0.34 0.45

0.18 0.24 0.21

7

Correlation

0.46 1.00

0.30 0.27 0.30

0.03 0.01 0.13

8

0.64 0.37 1.00

0.18 0.27 0.35

0.28 0.35 0.20

9

1.00

0.26 0.03 0.10

0.18 0.19 0.39

0.20 0.23 0.37

10

0.68 1.00

0.27 0.00 0.12

0.11 0.12 0.28

0.20 0.19 0.25

11

0.58 0.73 1.00

0.20 0.05 0.13

0.10 0.04 0.25

0.16 0.11 0.28

12

0.072 0.031 0.044 0.040 0.89 0.97 0.91 0.89 0.88 0.99 0.98 0.92 0.83 0.97 0.90 0.89 1.98 1.15 1.32 2.81 0.898 0.863 0.939 0.903 Market Orientation (20) Entrepreneurial Orientation (8) Learning Orientation (18) Firm Performance (13)

Root Mean Square Error of Approximation Tucker–Lewis Index Comparative Fit Index Goodness of Fit CMin/df Cronbach’s Alpha Reliability Dimension (Number of Items as Indicators)

Table 2 Summary of Confirmatory Factor Analysis (CFA) Fit Statistics 102

MO-LO) is fixed at 1.0 (Venkatraman 1989). If the difference in the chi-square statistic (between the unconstrained and constrained model) at one degree of freedom is significant, then the correlation between the two constructs is less than unity; and discriminant validity is supported. The chi-square differences between the unconstrained and three constrained models are 23.7, 11.5, and 7.1 for MO-EO, EO-LO, and MO-LO, respectively. Each of these chi-square values is significant (df = 1, p < .05) and, therefore, discriminant validity is supported. In addition, the pair-wise correlations between orientations are significantly less than one (correlations are: EO-MO: 0.462, EO-LO: 0.146, MO-LO: 0.256) providing further evidence of discriminant validity. This outcome is broadly consistent with Baker and Sinkula (2009) who report that MO and EO are distinct constructs; and Wang (2008) who treats EO and LO as separate constructs. Both independent and dependent measures were asked in the same survey, risking common method variance (Podsakoff et al. 2003; Renko, Carsrud, and Brännback 2009). To minimize its impact on study results, researchers suggest both procedural and statistical methods to reduce this potential bias. Following the recommendations of Podsakoff et al. (2003), we guaranteed firm anonymity since the questionnaire did not ask for the firm name, both dependent and independent measures were interspersed in the survey, specific questions items were worded for simplicity and conciseness, and different response scales were employed in the survey. If common method bias is present, then one latent dimension should account for the variance in the independent variables (Newell et al. 2011; Podsakoff et al. 2003). Ex post, multiple approaches have been proposed to identify the presence of common method variance (Richardson, Simmering, and Sturman 2009). As these scholars note, there are trade-offs corresponding to each approach, and generally recommend using the CFA technique, but only if an ideal marker (or construct that is theoretically unrelated to the independent and dependent measures) is available. This is not the case in the current study. As such, Harman’s onefactor test was used to determine if common method bias was present (Podsakoff and Organ 1986); an approach which has received widespread use over many years across a broad range of marketing, small business, and


business psychology settings (Brettel, Engelen, and Voll 2010; Cohen and Caspary 2011; Liao, Kickul, and Ma 2009; Matthews and Scott 1995; Newell et al. 2011; Wallace et al. 2010; Wang et al. 2011). Last, as part of the Harman one-factor test, all 47 indicators of the predictor variables were entered into an exploratory factor analysis (Podsakoff et al. 2003). Nine factors were extracted with eigenvalues greater than one. The first factor explained only 25.1 percent of the variance; thus, no single factor explained a majority of the variance. As an additional test, we employed a second-order alternative method using CFA. The fit indices were worst for the single-factor model, as shown by the fit indices: c2/df = 14.788, GFI = 0.527, root mean square error of approximation (RMSEA) = 0.291, comparative fit index (CFI) = 0.30. Finally, we examined the correlation matrix of the latent constructs and found that the highest value in the current study is 0.46, which is less than the threshold of 0.90 (Pavlou, Liang, and Xue 2007). In summary, based on multiple approaches and

evaluations, common method bias does not appear to be a major issue in the current study.

Model Estimation and Results Estimation of the Structural Model The conceptual model depicted in Figure 1 was estimated using SEM (AMOS 4.0, Arbuckle and Wothke 1999). All of the path coefficients are significant. To assess the appropriateness of the structural model, several fit measures are assessed, including overall fit (Cmin/df = 1.87), absolute goodness of fit (GFI = 0.93), incremental fit (CFI = 0.93), and absolute “badness” of fit (RMSEA = 0.073). Although researchers have put forth various criteria or cut-off levels for the different fit measures, there is general agreement for the following guidelines: Cmin/df