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Marketing capabilities: Antecedents and implications for B2B SME performance Bill Merrilees ⁎, Sharyn Rundle-Thiele, Ashley Lye Department of Marketing, Grifﬁth University, Gold Coast Campus, Queensland, Australia, 4222 abstract article info Article history:

Received 15 March 2009 Received in revised form 26 November 2009 Accepted 25 March 2010 Keywords:

B2B marketing strategy Marketing capabilities Support capabilities Branding Innovation The study provides a new perspective on SME marketing strategies in the B2B context. Using a resource-based view of theﬁrm, the study develops a structural model linking marketing capabilities and marketing performance. A study of 367 SME Australianﬁrms reveals that two key marketing capabilities, namely branding and innovation, have major performance outcomes in the SME B2B context. This is theﬁrst SME study to evaluate concurrentlythe contribution of innovation and branding marketing capabilities, with innovation capability the strongest determinant of SME performance. The study alsoﬁnds market orientation and management capability act as enabling mechanisms for building marketing capabilities. Disaggregation tests indicate that the same ﬁndings apply to three size categories denoting microﬁrms (less than 20 staff), smallﬁrms (20–99 staff) and medium-sizedﬁrms (100–499 staff).

Current approaches usually focus on a single marketing capability, often innovation (O'Dwyer, Gilmore, & Carson, 2009) or branding (Abimbola & Vallaster, 2007) as potential key drivers of SME performance. While such approaches are useful and indicate that each driver is important, they do not establish a pecking order of importance, nor do they indicate a clear mechanism for improving performance. The current study provides a more comprehensive approach to assessing the relative contribution of two major, higher- level marketing capabilities, innovation and branding, to marketing performance.

One objective of the current paper is to progress the literature in terms of assessing the contribution of higher-level marketing capabilities to performance. In particular, it is unclear which of the higher-order capabilities are most important to performance. A second objective is to model such assessments in the context of B2B SMEﬁrms. Very little is known about the relative contribution of different higher-order marketing capabilities in the SME context. A third objective is to examine the relative contribution of marketing support capabilities to each of the higher-level marketing capabilities.

Evaluating such relative contributions clariﬁes the mechanisms by which marketing capabilities are created and managed. A fourth objective is a speciﬁc variation of the third, namely in terms of explaining innovation capability, to test whetherﬁrm size is amoderatorrather than anantecedentto the innovation process (Harmancioglu, Droge, & Calantone, 2009).

To address these objectives, weﬁrst provide a broad theoretical context. Then we conceptualize directional relationships between particular higher-order marketing capabilities (innovation and brand- ing) and marketing performance. Next, we conceptualize the relation- ship between marketing support capabilities (market orientation and management) and each of the marketing capabilities. The structural model is developed by conceptualizing the relationship from marketing performance toﬁnancial performance. The total structural model is estimated using structural equation modeling (AMOS software) with a sample of 367 Australian SMEs in the B2B sector. Additional tests use multiple regression analysis at a disaggregated level, with three differentﬁrm size levels. In particular, the disaggregation method tests whetherﬁrm size moderates marketing capabilities.

Firm size is an important dimension of the study. The upper size boundary for a small and medium-sizedﬁrm lacks consensus, but the current study adopts a common choice, namely 499 (Berthon, Ewing, & Napoli, 2008; Hooley, Greenly, Cadogan, & Fahy, 2005; Motwani, Dandridge, Jiang, & Soderquist, 1999; Pﬁrrmann, 1995). The range of 1–499 covers a potentially wide diversity of resource endowments and behaviors, leading many researchers to distinguish betweensmall (deﬁned here as less than 100 employees), andmedium-sized (100– 499)ﬁrms. Selection of the 100 cutoff-level is commonly used (Bonaccorsi, 1992; Coviello, Brodie, & Munroe, 2000; Motwani et al., 1999). Within the small category, a further division between small (deﬁned as 20– 99 in our study) and very small or micro (deﬁned as less than 20 employees in our study), allows for further potential size- based differences in behavior. The aim is to make the micro size cutoff as small as practical, noting a range of cutoff selections in the literature, 20 (Bonaccorsi, 1992; Cobbenhagen, 2000; Hooley et al., Industrial Marketing Management 40 (2011) 368–375 ⁎Corresponding author. Tel.: + 61 7 5552 7176; fax: + 61 7 5552 9039.

E-mail address:bill.merrilees@grifﬁth.edu.au(B. Merrilees).

doi:10.1016/j.indmarman.2010.08.005 Contents lists available atScienceDirect Industrial Marketing Management 2005; Lybaert, 1998), 25 (Berthon et al., 2008) and 50 (Motwani et al., 1999).

2. Theoretical context The resource-based view of theﬁrm argues that competitive advantage and hence performance depend on resource endowments (Hooley & Greenley, 2005).Newbert (2007), in his review of empirical research on the resource-based view of theﬁrm, emphasizes capabilitiesrather thanresources, in terms of relevance and potential impact on performance. Resourcesper secannot do anything. What is important is the capacity to utilize resources effectively, that is, a capability. Recent work (Liao, Kickul, & Ma, 2009) also emphasizes the greater relevance and importance of capabilities compared to resources.

Day (1994)especially focuses on marketing capabilities, with a special focus on market sensing and customer-linking capabilities.

Subsequent conceptual literature has endorsed the relevance of marketing capabilities to understandingﬁrm strategy and perfor- mance (Srivastava, Shervani, & Fahey, 1998, 1999; Varadarajan & Jayachandran, 1999).Varadarajan and Jayachandran (1999), in their seminal marketing strategy review, propose a way forward in terms of understanding and explainingﬁrm behavior in the realm of deploying marketing resources for competitive advantage. Their review article frequently refers to intangible market based assets (capabilities) such as brand equity and customer equity. The subsequent decade of academic research (for example:Berthon et al., 2008; Li & Mitchell, 2009; Weerawardena, O'Cass, & Julian, 2006; Wong & Merrilees, 2008) has picked up the two main suggestions for moving forward, namely emphasizingﬁrstly marketing capabilities in what is usually referred to as the resource-based theory of marketing strategy and secondly the link from marketing capabilities to performance. These insights guide the theoretical foundation for the current study, which links marketing support capabilities to marketing capabilities, and then to marketing performance.

Empirically, evaluation of the contribution of marketing capabil- ities toﬁrm performance is surprisingly scant. Two early studies, Capron and Hulland (1999) and Grewal and Tansuhaj (2001) demonstrate the role of marketing capabilities in two very speciﬁc circumstances, namely horizontal acquisitions and managing an economic crisis. Other studies seeking a more general assessment of the contribution of marketing capability to performance divide into two approaches. One approach, especially the earlier studies, conceptualize marketing capabilities in terms of themid-level marketing processes supporting strategy and include the marketing mix (4Ps) elements, market research and market management (Vorhies, 1998; Vorhies & Morgan, 2005). A limitation of this stream, recognized by one of the proponents (Vorhies & Morgan, 2005) is that it precludes any assessment ofhigher-levelintegrative capabilities, such as brand management, innovation and customer relationship management, which is the second approach.Vorhies and Morgan (2005)leave this opportunity to future researchers. However, in the same year,Hooley et al. (2005), building on preliminary work in Hooley et al. (1999)provide an assessment of several higher-level marketing capabilities, including brand reputation, customer rela- tionship marketing and innovation.

A recent Journal of Marketing study (Krasnikov & Jayachandran, 2009) provides a meta-analysis to demonstrate the link between marketing capabilities and performance. Although reassuring, this state of the artﬁnding is in the mould of theﬁrst approach where marketing capability is the sum of manymid-levelmarketing activities. There is limited research sinceHooley et al. (2005) evaluating two or more higher-level marketing capabilities compared to studies of either asingle higher-levelmarketing capability (Berthon et al., 2008; Li & Mitchell, 2009; Weerawardena et al., 2006; Wong &Merrilees, 2008) or focusing onmid-levelmarketing capabilities (Krasnikov & Jayachandran, 2009; Vorhies and Morgan (2005).

3. Theoretical development: marketing capabilities and performance As noted, initial studies conceptualize marketing capabilities as the sum ofmid-levelmarketing activities, such as advertising and distribution (Vorhies, 1998; Vorhies & Harker, 2000; Vorhies & Morgan, 2005; Weerawardena, 2003). Although our emphasis is on higher-levelmarketing capabilities, such as innovation and branding, the aforementioned studies are used when relevant, such as the link from innovation to performance.

Discerning the B2B and SME literatures for salient higher-level marketing capability studies that examine the impact of marketing capability on performance suggests that two prevail, namely innovation and branding. In the interests of parsimony, the current paper conﬁnes itself to examining just two higher-level marketing capabilities, innovation and branding. We summarize the potential links as follows.

Innovation receives a dominant share of attention in performance studies. Innovation capabilities are seen as critical for competitive advantage and superior marketing performance (Han, Kim, & Srivastava, 1998; Hooley et al., 2005). Innovation seems to be a particularly powerful determinant of marketing performance. In the B2B context, innovation has a major positive inﬂuence on performance (Hult, Hurley, & Knight, 2004; Weerawardena, 2003; Weerawardena & O'Cass, 2004).

In the SME context, similar studies have shown the importance of innovation on performance (Weerawardena et al., 2006). Another recent study of dynamic B2B industries in Chinaﬁnds that SME innovation is very favorable relative to large enterprise innovation (Li & Mitchell, 2009). Thus, in sum, we have hypothesis one:

H1.SME marketing performance is positively related to innovation capability.

Branding is a second major higher-level marketing capability that is a potential determinant of marketing performance. In the B2Bﬁeld, Gordon, Calatone, and diBenedetto (1993), Mudambi, Doyle, and Wong (1997) and Kim, Reid, Plank, and Dahlstrom (1998)highlight the beneﬁts of branding. The importance of branding in the B2B context is also emphasized inMitchell, King, and Reast (2001), Webster and Keller (2004), Bendixen, Bukasa, and Abratt (2004), McQuiston (2004) and Van Riel de Mortanges and Streukens (2005).

SME branding is a relatively recent approach. Historically, branding was considered the domain of largeﬁrms and little research was undertaken in the SME area. Most of the research in this area is less than ten years old (Abimbola, 2001; Abimbola & Vallaster, 2007; Berthon et al., 2008; Doyle, 2003; Krake, 2005; Rode & Vallaster, 2005; Wong & Merrilees, 2005). One study indicates that branding is the major determinant of SME marketing performance (Wong & Merrilees, 2008). The direction and strength of this branding research leads us to our second hypothesis:

H2.SME marketing performance positively relates to branding capability.

4. Theoretical development: marketing support capabilities and marketing capabilities The potential link of marketing capabilities to marketing perfor- mance is important, but it is also necessary to explain the mechanisms leading to the creation and management of marketing capabilities. It seems likely that aﬁrm needs a certain mix of capabilities in order to create marketing capabilities, that is, capabilities create other capabilities. A neglected, but nonetheless very useful exposition of the 369 B. Merrilees et al. / Industrial Marketing Management 40 (2011) 368–375 inter-dependencies between capabilities isPerry, Stott, and Small- wood (1993). They distinguish betweenﬁrstly,core capabilitiesthat directly lead to giving aﬁrm a competitive advantage; secondly,value added support capabilitiesare those that facilitate the core capabilities; and thirdly,essential capabilitieswhich neither create advantage nor facilitate the work that creates advantage but must be done for business to continue. In our context, marketing capabilities are core capabilities, with our attention shifting to identifying the capabilities that directly inﬂuence them.

Although a number of variables are potential candidates for inclusion as marketing support capabilities, we followHooley et al. (2005)and use two speciﬁcally, namely market orientation and management capability.

There is strong support in the literature for a nexus between market orientation and innovation (Han et al., 1998; Li & Calantone, 1998; Menguc & Auh, 2006).Han et al. (1998)spells out the basis for a link between market orientation and innovation. They argue that market orientation is essentially a business philosophy, a culture that requires operationalization by innovation, among other things, if it is to impact performance. Ideally, the innovation aligns to the market orientation culture and vice versa. A number of studies while not explicitly modeling the link between market orientation, nonetheless demonstrate high levels of association between the two constructs (Brooksbank, Kirby, & Wright, 1992; Brooksbank & Taylor, 2002; Brooksbank, Kirby, Tompson, & Taylor, 2003; Nasution & Mavondo, 2008) suggesting a probable link between greater research and market intelligence on one hand and greater innovation on the other.

Although most studies point to a positive link between market orientation and innovation, there is some contrary evidence, including a weak link inHooley et al. (2005). In the special case of biotech new ventures, product innovativeness is paramount (Renko, Carsrud, & Brannback, 2009). That study found no prior inﬂuence of market or entrepreneurial orientation, perhaps reﬂecting the radical and disruptive nature of new technology in the biotech industry. Based on most (but not all) of the literature, we posit the third hypothesis:

H3.Innovation capability positively relates to market orientation.

Hooley et al. (2005)proposes management capability as an antecedent of innovation capability. They argue that superior management capabilities, through integration and teamwork, will enhance innovation. Their empirical results strongly support this relationship. Innovation combines not just new idea creation but also systematic and structured management processes or steps (Tidd, Bessant, & Pavitt, 2001; Trott, 1998).Cooper (2001)“stage-gate”steps are a special case of the process of managing innovation.Cobbenha- gen (2000), Motwani et al. (1999) and Pﬁrrmann (1995)give evidence of the importance of management capabilities in the context of SMEs. In total, the well-documented role of management processes for innovation suggests that management capabilities are likely to inﬂuence innovation success. Thus, we posit the fourth hypothesis:

H4.Innovation capability positively relates to management capability.

A number of authors have argued that stronger market orientation can contribute to brand building by basing the brands on consumer needs ( de Chernatony & MacDonald, 1992; Doyle, 2000). On the other hand, there may be tension between the concepts of market orientation and brand orientation, because the former requires constant adaption to consumer needs while the latter is moreﬁrm- driven and notﬁckle to variations in consumer needs (Urde, 1999).

Additionally, theHooley et al. (2005)results are not supportive.Reid, Luxton, and Mavondo (2005)suggest a link between market orientation and brand orientation. On balance, the literature tenta- tively supports theﬁfth hypothesis:

H5.Branding capability positively relates to market orientation.Theﬁnal marketing support link to branding emanates from management capability.Hooley et al. (2005)propose management capability as an antecedent of branding marketing capability. They argue that superior management capabilities reach through all functions and not just direct customer interface functions, such as marketing and sales, to enhance innovation. Their empirical results strongly support this relationship. Perhaps in a similar way to innovation, brand building combines not just new idea creation but also systematic and structured management processes or steps (Berthon et al., 2008; de Chernatony, 2001; Wong & Merrilees, 2005).Panigyrakis and Veloutsou (1999)show that brand managers undertake various management tasks, including market analysis, planning, implementation and coordination of activities, evaluation of the marketing mix and training of colleagues in brand related issues.

Collectively theseﬁve studies suggest that management capabilities are likely to inﬂuence branding success. Thus, we posit the sixth hypothesis:

H6.Branding capability positively relates to management capability.

5. Theoretical development: marketing performance and ﬁnancial performance Performance is the consequence of effective marketing capabilities.

Marketing performance represents items like sales, growth and market share whereasﬁnancial performance more explicitly refers to proﬁt- ability and rate of rate of return on investment. Sometimes marketing performance andﬁnancial performance are combined as part of a single, one-dimension performance measure (Weerawardena, 2003).

Alternatively, marketing andﬁnancial performance can be speciﬁed as two separate dimensions of a total performance measure (Vorhies & Morgan, 2005). As a third approach, the two types of performance can be speciﬁed as two separate constructs (Hooley et al., 2005).

For conceptual reasons, the current study adopts the last approach.

It makes sense that the initial outcomes of applying marketing capabilities relate to direct customer effects, in terms of selling more units to existing customers, repeat purchasing or gaining new customers. That is, the initial link will be from marketing capabilities to marketing performance (H1 and H2). The initial link may or may not be proﬁtable. Some companies may try to“buy”sales through discounts or excessive advertising, resulting in the potential of pro ﬁtless growth. Thus, it is appropriate to modelﬁnancial perfor- mance as a separate sequence from marketing performance (Buzzell & Gale, 1987; Homburg & Pﬂesser, 2003; Hooley et al., 2005; Reicheld & Sasser, 1990; Wong & Merrilees, 2007). Although the marketing performance toﬁnancial performance link is likely to be strong, it will not be perfectly correlated. Thus, we have the seventh hypothesis:

H7.SMEﬁnancial performance positively relates to marketing performance.

6. An overall structural model linking antecedents and consequences of marketing capabilities The above conceptual relationships specify both consequences (H1 and H2) and antecedents (H3, H4, H5 and H6) of two marketing capabilities. The four marketing support capabilities are exogenous in the model. Additionally,H7indicates a subsequent consequence from marketing performance toﬁnancial performance.

For parsimony reasons, just two marketing capabilities (innovation and branding) are modeled here.Hooley et al. (2005)estimates a more complex model, but their results indicate a number of problems, including two incorrectly signed paths. We argue that it is better to start with a simpler model and get the fundamental relationships correct.

The seven paths associated with the seven hypotheses can be embedded into a formal structural model, given byFig. 1. This model 370B. Merrilees et al. / Industrial Marketing Management 40 (2011) 368–375 will be tested with the full sample of 367 B2B SMEs using SEM analysis (AMOS).

A feature of the overall structural model is that the effects of market orientation and management capability on marketing performance are mediated through branding capability and innovation capability. The theoretical justiﬁcation for this is discussed above in terms of the rationale forH1 and H2. Other studies, such asHan et al. (1998),with innovation mediating the market orientation to performance relation- ship, have taken a similar position. After the model is formally established, it is appropriate that we test for mediation tests, using the Baron and Kenny (1986)statistical approach.

7. Methodology A quantitative (structured survey) research method was deemed appropriate to enable actual measures to be calculated. A commercial database (Dun and Bradstreet Australia, Business Who's Who of Australia) is used for the sample frame, with the researchers specifying industry types from the Standard Industrial Classiﬁcation 87 andﬁrm size (less than 500 employees). A mail-based survey was the basis for randomly distributing the surveys.

The survey instrument asked respondents to indicate their perceptions with regard to the items pertaining to brand capability, innovation capability, management capability,ﬁrm characteristics, market orientation, marketing performance andﬁnancial perfor- mance. Items were measured through a seven-point scale, with scale poles ranging from“strongly disagree”to“strongly agree”. Most of the scales are drawn from or adapted fromHooley et al. (2005).

A total of 374 usable responses were obtained from owners or managers in a variety of Australianﬁrms, representing a response rate of 13%. Although a size limit of 500 was stipulated, sevenﬁrms exceeded the limit (perhaps they had grown since being included in the database) and were not included in the analysis. Thus, theﬁnal sample is 367ﬁrms in the SME category. About half of the respondents (median) had sales turnover greater than $8 million and the median number of employees in theﬁrm was 36, suggesting that both small and medium-sizedﬁrms were both well represented in the sample.

More speciﬁcally theﬁnal sample includes 108 micro sizedﬁrms (less than 20 staff), 173 small-sizedﬁrms (20–99 staff) and 86 medium- sizedﬁrms (100–499 staff).

Table 1summarizes the descriptive statistics. Each Australian state is represented more or less in proportion to population, with 132 of the 367 responses from the most populous state, New South Wales, and so forth for the remaining states. There is a good industry spread (manufacturing, construction, services, etc.) and a good spread across market type (fast-moving goods, consumer durables, capital indus-trial goods, repeat industrial goods and services). The distribution of ﬁrms classiﬁed by market status includes 27% market leaders, 16% market challengers, 20% market followers and 37% nicheﬁrms. The survey was mailed to the“Business owner/Manager”. Marketing managers and owners are the most common titles.

8. Results 8.1. Non-response bias TheArmstrong and Overton (1977)test for non-response bias is conducted. The test is based on splitting the sample by time of response, early and late. ANOVA tests are used for each item between the early and late respondents. The argument is that the late respondents have similar traits to those who do not respond at all.

The ANOVA tests suggest that non-response bias is not likely to be a major problem. Additional checks indicate that the responses by State were more or less in proportion to the population of each State, consistent with no major non-response bias. Similarly, there was a good spread ofﬁrms across a range of demographic variables.

8.2. Factor adequacy Before conducting the analysis, tests of sampling adequacy areﬁrst conducted. The Kaiser–Meyer–Olkin (KMO) statistic is 0.92, appro- priately greater than the recommended cut off of 0.60. Additionally the Bartlett test of sphericity was signiﬁcant, at the 1% level.

8.3. Reliability of scales All the scales were reliable, with the composite reliabilities ranging from 0.77 to 0.91, all greater than the benchmark of 0.70.Table 2 shows the reliability level for each scale and factor loadings for each item in a scale. H6 H3 H2 H7 Market Orientation Management Capability Branding Capability Innovation Capability Financial Performance Marketing Performance H5 H4 H1 Fig. 1.Structural model. Table 1 Descriptive statistics of respondents.

Firm size Microﬁrms (less than 20 employees) 108 Smallﬁrms (20–99 employees) 173 Medium-sizedﬁrms (100–499 employees) 86 Industry Services 159 Manufacturing 133 Wholesaling 39 Construction 21 IT/software 13 Agriculture 12 State New South Wales 132 Victoria 103 Queensland 63 Western Australia 33 South Australia 21 Austrian Capital Territory & Northern Territory 10 Tasmania 5 Type of market Services 159 Fast moving consumer goods 71 Consumer durable goods 59 Repeat industrial goods 55 Capital industry equipment 23 Title of respondent Marketing manager 134 Owner98 Manager 81 Chief executive ofﬁcer 43 Finance manager 11371 B. Merrilees et al. / Industrial Marketing Management 40 (2011) 368–375 8.4. Common method bias Common method bias occurs when the measurement technique introduces systematic variance into the measures (Doty & Glick, 1998). The study uses the suggestion ofPodsakoff, Mackenzie, and Lee (2003)to vary the formatting (through clearly delineated sections) as a way of preventing or minimizing common method bias. Further, the Harmon single factor post-hoc test suggests that no same-source factor exists (Podsakoff & Organ, 1986). Using the unrotated factor solution of including all of the items together,ﬁvefactors had an Eigen Value greater than one, notonefactor as per the Harmon test. Further, although there is always a strongest factor, it is not overly dominant in the current study, explaining 38% of the variance out of a total 64% extracted.

Another test consistent with no common method bias is the discriminant analysis test (seeTable 3); the current study fulﬁlls this requirement.

The authors propose an additional test based on our inferences from Table 2in the study byAgustin and Singh (2005).ThestudybyAgustin and Singh (2005)shows that common method biases decreases as additional signiﬁcant explanatory variables are added to the equation, although they themselves do not detect or highlight this pattern. We therefore propose the following additional test for common method bias, namely that studies with equations having two or more signiﬁcant explanatory variables are unlikely to suffer from major common method bias. The current study fulﬁlls this requirement.8.5. Discriminant validity Discriminant validity of the constructs is tested using theFornell and Larcker (1981)test, namely that the square root of the average variance extracted for a givenconstruct is greater than the standardized correlation of the given construct with each of the other constructs.Table 3presents the relevant table, with the square root of the average variance extracted represented by the diagonal. In all cases, as can be seen by the information inTable 3, theFornell and Larcker (1981)test is met for all pairs of constructs. That is, there is discriminant validity; the constructs are distinctly different from each other.

8.6. Convergent validity Convergent validity refers to a level of coherency across the items within each construct. In theﬁrst instance, convergence requires that the average variance extracted for each construct is greater than 0.50 (Fornell & Larcker, 1981), which is met in the current study (see Table 3). Secondly, all the factor loadings are statistically signiﬁcant and load on to their particular construct. This condition is also met.

Overall, there is support for convergent validity.

8.7. Construct validity (measurement model) Six constructs comprise theﬁnal model and include marketing performance,ﬁnancial performance, innovation capability, manage- ment capability, branding capability and market orientation. Using conﬁrmatory factor analysis, the measurement model works satisfac- torily. Theﬁt indices are greater than the 0.90 benchmark, with GFI = 0.96, AGFI = 0.92, TLI = 0.97 and CFI = 0.98. These indices indicate that the data ﬁts the model. Similarly, the levels of misﬁt were tolerable, with RMSEA = 0.064 and RMR = 0.035, all below the relevant benchmark of 0.08. The chi-square is 98.0 (39 degrees of freedom), statistically signiﬁcant, which is not desirable but common in samples with more than 300. Additional tests including a normed chi-square of 2.51 (less than the benchmark of 5) and SRMR = 0.027 (less than the benchmark of 0.08), all supporting construct validity.

8.8. Structural model estimation Theﬁt indices are greater than the 0.90 benchmark, with GFI = 0.95, AGFI = 0.91, TLI = 0.96 and CFI = 0.97. These indices indicate that the dataﬁts the model. Similarly, the levels of misﬁt were tolerable, with RMSEA = 0.071 and RMR = 0.048, all below the relevant benchmark of 0.08. The chi-square is 130.5 (with 46 degrees of freedom) and statistically signiﬁcant, not desirable but common in samples more than 300. Additional tests including a normed chi-square of 2.84 (less than the benchmark of 5) and SRMR = 0.037 (less than the benchmark of 0.08), indicating a goodﬁt between the data and the model.

The explanatory power (R-square) of the four equations in the structural model are very satisfactory: 0.47 for explainingﬁnancial Table 2 Factor loadings and composite reliability (CR).

Factor items and composite reliability (CR) Factor loading Marketing performance (CR = 0.83) Stronger growth in sales revenue 0.88 Better able to acquire new customers 0.83 Has a greater market share 0.80 Able to increase sales to existing customers 0.71 Source:Hooley et al. (2005) Financial performance (CR = 0.91) Is more proﬁtable 0.92 Has a better return on investment 0.92 Is better able to reachﬁnancial goals 0.89 Source:Hooley et al. (2005) Management capabilities (CR = 0.81) Has better operational management expertise 0.87 Has better overall management capabilities 0.85 Is more able to execute marketing strategies quickly 0.75 Manages its supply chain better 0.75 Source:Hooley et al. (2005) Market orientation (CR = 0.80) More likely to plan ahead to satisfy customers in the future 0.83 Responds more quickly to changing customer requirements 0.81 Places a priority on making changes to improve customer satisfaction 0.75 More likely to involve employees in planning and decision making 0.71 Has better market intelligence 0.65 Is more likely to target customers where we have a competitive advantage 0.62 Undertakes market research to measure satisfaction 0.60 Source:Farrelly and Quester (2003) Branding capability (CR = 0.86) Better able to identify a simple brand meaning 0.90 Uses branding as an operational tool 0.88 Better able to communicate a consistent brand meaning 0.86 Is more likely to treat its brands as assets 0.85 Able to get staff to support the brand 0.81 Source:Wong and Merrilees (2008) Innovation capability (CR = 0.77) Better at developing new ideas to help customers 0.84 More able to fast track new offerings to customers 0.80 Better able to manage processes to keep costs down 0.73 More able to package a total solution to solve customer problems 0.72 Source:Hooley et al. (2005) Table 3 Discriminant validity matrix.

Construct 1 2 3 4 5 6 1. Marketing performance0.81 2. Financial performance 0.570.91 3. Market orientation 0.48 0.350.82 4. Brand capability 0.52 0.31 0.570.86 5. Innovation capability 0.45 0.29 0.71 0.490.77 6. Management capability 0.50 0.44 0.63 0.59 0.610.81 Note: Theﬁgures corresponding to square root of AVE for each column construct is captured in bold along the diagonal. Otherﬁgures are the correlation between two constructs. 372B. Merrilees et al. / Industrial Marketing Management 40 (2011) 368–375 performance; 0.36 for explaining marketing performance; 0.44 for explaining brand capability; and 0.87 for explaining innovation capability.

Table 4gives the SEM structural model parameter estimates. All of the parameters are statistically signiﬁcant at the one percent level and all seven hypotheses are supported. Marketing performance is mainly explained by innovation capability, but with a strong contribution from brand capability. Both of the marketing support capabilities (market orientation and management capability) strongly explain each marketing capability, namely innovation capability and branding capability.

8.9. Mediation tests Although the phases in the structural model are theoretically derived, mediation tests were run to conﬁrm the sequence of effects.

Speciﬁcally, the two marketing capability variables (innovation capability and brand capability) mediate the inﬂuence of the two marketing support variables (market orientation and management capability).

The three-equation test ofBaron and Kenny (1986)is used, with the results shown inTable 5. Step 1 of the approach requires that the independent variables (market orientation and management capa- bility) statistically affect the mediating variables (brand orientation and innovation capability). This condition is met, as shown inTable 5.

Step 2 of the approach requires that the independent variables directly and statistically affect marketing performance, the dependent variable. This condition is met as shown inTable 5. Step 3 allows both the mediators and independent variables to inﬂuence the dependent variable and with an emphasis on the mediator variables (brand capability and innovation capability) statistically inﬂuencing the dependent variable. This condition is met as shown inTable 5. Step 4 compares the second and third set of regressions. The requirement is that the inﬂuence of the independent variables declines substantially.

This condition is met because there is a halving of the beta coefﬁcientsfor both the independent variables; the market orientation beta drops from 0.26 to 0.11 and the management capability beta drops from 0.34 to 0.18. All four conditions are met, indicating that brand capability and innovation capability mediate the inﬂuence of market orientation and management capability.

8.10. Disaggregation tests using multiple regression analysis Having conﬁrmed the aggregate model in terms of both measure- ment validity and estimation, further analysis uses multiple regres- sion analysis (because of sub-sample size limitations) disaggregated byﬁrm size: the three size groups of micro (under 20 staff), small (20–99 staff) and medium-sizedﬁrms (100–499 staff).

Multiple regression analysis is applied to each sub-component of the structural model, one equation at a time. All of the regressions have reasonably high coefﬁcient of determination (R-square). Further, there is no evidence of multicollinearity problems, with all VIFs less than three (appropriately less than the benchmark of 10).

In terms of explaining marketing performance in terms of innovation capability and brand capability, there is a high level of consistency across the threeﬁrm sizes. The sub-model of marketing performance estimated at the aggregate level is mirrored in eachﬁrm size multiple regression. There is noﬁrm size effect, indicating a robust explanation for this important outcome variable. The same pattern of results applies in terms of explainingﬁnancial performance.

Firm size clearly does not moderate this explanatory sub-model.

Table 6A seems to reveal a different story when the determinants of innovation marketing capability are examined. As can be seen, there seems to be a moderating inﬂuence ofﬁrm size in this sub- model. In particular, market orientation plays the greatest role in micro-sizedﬁrms and becomes less inﬂuential asﬁrm size increases.

Table 6A is relevant for testing the hypothesis noted earlier that ﬁrm size is amoderatorrather than anantecedentto the innovation process (Harmancioglu et al., 2009). Thus, we hypothesize:

H8.The multiple regression model of innovation capability varies acrossﬁrm size categories.

Although the apparent sizes of the beta coefﬁcients inTable 6A seem to indicate different regression parameter estimates, especially between the micro and the medium-sizedﬁrms, a formal Chow test indicates that there is not a statistically different regression model across the size categories. Formally, the Chow test calculates an F- score of 1.8, less than the critical F-score of 2.60 at the 0.05 level, statistically insigniﬁcant (Studenmund, 2001). That is, the two most diverse size samples donothave different regression model parameters.

All of the remaining Chow tests are also statistically insigniﬁcant.

There are no cases where size ofﬁrm, using the three size categories, moderates the multiple regression results. Expressed differently, the aggregate multiple regression results (and by inference the aggregate SEM results) apply broadly and similarly to the threeﬁrm size categories, with minor, insigniﬁcant differences.

Disaggregation is also applied with the data split intoﬁve industry groupings: manufacturing, wholesale, services, agriculture and con- struction. The differences in the sub-models had just minor differences. A third disaggregation split the sample intoﬁve different types of markets: fast-moving consumer goods, capital industry equipment, consumer durables, services and repeat industrial goods.

There is a slight tendency for the fast-moving consumer goodsﬁrms to make greater use of market orientation, but otherwise the differences across theﬁve types were minor. Table 4 Path estimates for structural model.

Hypothesized linkβt-value Sig. Supported?

H1. Innovation Capability→Mkt Perform. 0.48 6.63 0.000 Supported H2. Branding Capability→Mkt Perform 0.17 2.65 0.008 Supported H3. Mkt Orientation→Innovation Capability 0.51 6.97 0.000 Supported H4. Manag. Capability→Innovation Capability 0.49 6.80 0.000 Supported H5. Mkt Orientation→Branding Capability 0.31 4.21 0.000 Supported H6. Manag. Capability→Branding Capability 0.40 5.32 0.000 Supported H7. Mkt Perform.→Financial Perform 0.69 13.97 0.000 Supported Note:‘β’values refer to standardized path coefﬁcients.

All hypotheses are supported at the 1% level.

Table 5 Mediation tests usingBaron and Kenny (1986)three-equation approach. NB: All the beta coefﬁcients are signiﬁcant at the 5% level except that of market orientation in step 3.

Step Multiple regression result Step 1 Brand Capability = 0.32 Market Orientation + 0.38 Management Capability Innovation Capability = 0.44 Market Orientation + 0.32 Management Capability Step 2 Marketing Performance = 0.26 Market Orientation + 0.34 Management Capability Step 3 Marketing Performance = 0.11 Market Orientation + 0.18 Management Capability + 0.28 Brand Capability + 0.14 Innovation Capability373 B. Merrilees et al. / Industrial Marketing Management 40 (2011) 368–375 9. Discussion A major area of contribution to the literature is the evaluation of alternative SME B2B capabilities as determinants of marketing performance. A multi-path model is proposed and tested as a structural equation model. The overall model works well in terms of explanatory power of key variables and provides broad support for the resource-based view of theﬁrm framework. Two key marketing capabilities, branding and innovation, are the central players in explaining marketing performance. Both marketing capabilities seem to be core to the way marketing works, but no previous study has conﬁrmed their concurrent contribution in the SME B2B context. Even in the broader literature, regardless of sector orﬁrm size, few previous studies have concurrently supported bothH1 and H2. For comparison, Hooley et al. (2005)supportsH1but notH2.

Two marketing support capabilities, management capabilities and market orientation, support the two key marketing capabilities. This ﬁnding is broadly consistent withHooley et al. (2005), except they do not supportH5. Market orientation has a strong inﬂuence on innovation marketing capability and, as expected, a weaker inﬂuence on branding marketing capability. For effective marketing capabilities, SMEs need to tap into the creative, market-sensing processes associated with market orientation, but also closely control the more disciplined, structured management processes.

The results have similarities withNasution and Mavondo (2008), who highlight the importance of innovation and human resources in explaining customer value (not the same, but akin to performance). In the current study innovation remains, but branding capability replaces human resources.

The study also contributes to the literature by delineating the role ofﬁrm sizein strategic decisions. Disaggregation of the sub-models by ﬁrm size reveals only minor differences, none of which is statistically signiﬁcant. Broadly, the aggregate SEM results are likely to apply to each of the three size groups deployed in the current study: micro ﬁrms (under 20 staff), smallﬁrms (20–99 staff) and medium-sized ﬁrms (100–499 staff).

Table 6A provides interesting results acrossﬁrm sizes, despite the differences not being statistically signiﬁcant. Notionally at least, the results suggest that smallerﬁrms (especially microﬁrms) build their innovation capability through market sensing (market orientation).

Smallerﬁrms seem to be in closer touch with their customers, compared to largerﬁrms, and incorporate this customer feedback into innovation development. In contrast and understandably, largerﬁrms have greater reliance on structured management processes to build innovation capability.10. Managerial implications Establishing strategic objectives is critical to marketing perfor- mance. It is important to set priorities because of resource constraints.

In the sample studied, the most popular strategies were based on branding and innovation. In planning the future,ﬁrms need to specify which strategic area is to given the highest priority and more precise goals or targets set.

The study highlights theimportanceto SME B2Bﬁrms in developing two key marketing capabilities: branding and innovation.

The study guides the SMEs inhowthey can develop these marketing capabilities, essentially through the marketing support capabilities of market orientation and management capabilities.

11. Limitations and future research The sample is reasonably diverse in terms of many demographics, ﬁrm location,ﬁrm size, industry type and market type. Disaggregation tests generally show a robust result in terms of model parameters.

However, for some disaggregation tests, sizes of sub-samples are necessarily small. Larger sub-samples, including studies dedicated to just one sub-sample (say manufacturing or information technology) might give a different set of results. Additionally, the results are restricted to one country, Australia. Some variables, such as market orientation, may play a different role in other countries.

Future research should initially target different countries. Such research could then test the generalizability of the results. The current study suggests innovation and branding are dominant marketing capabilities—would the same results appear in most other countries?

Deliberately, the current study is restricted to micro, small and medium-sized B2Bﬁrms. Future research could investigate whether largeB2Bﬁrms use similar marketing strategy behavior.

12. Conclusions In theﬁrst instance, the study provides insight into how micro, small and medium-sizedﬁrms in the B2B sector undertake marketing strategies. The study uses a resource-based view of theﬁrm framework, which provides robust structural equation modeling results. In a pioneering contribution for the SME B2B sector, the study highlights the major contribution of two key marketing capabilities to explaining marketing performance. The two key marketing capabil- ities are branding and innovation. No previous single SME B2B study has indicated the concurrent, combined importance of these two marketing capabilities.

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Bill Merrileesis a Professor in Marketing in the Grifﬁth Business School. He has published in IMM, JBR, EJM and JAR. He specializes in branding, strategy and SMEs.

Sharyn Rundle-Thieleis an Associate Professor in Marketing in the Grifﬁth Business School. She specializes in social marketing, loyalty, branding, marketing management and services.

Ashley Lyeis a Canadian-based entrepreneur and an adjunct associate professor in marketing in the Grifﬁth Business School. His special interest is marketing strategy and marketing management.375 B. Merrilees et al. / Industrial Marketing Management 40 (2011) 368–375