SME development strategy and product/service innovation intention: A NCaRBS analysis of the role of uncertainty

Abstract

Small and medium-sized enterprises (SME) are tasked with driving economic recovery globally, in terms of contribution to economic growth. Understanding the determinants of SME innovation is essential in clarifying this phenomena. This study investigates the link between SME strategies and intention to undertake future innovation, using Federation of Small Businesses data. The analysis employs the novel N-State Classification and Ranking Belief Simplex (NCaRBS) technique, investigating relationships between changes in SME strategies, including staffing levels, importing/exporting and client base, and future (including uncertain) innovation intentions. NCaRBS can analyse an incomplete data set, with missing values in the considered characteristic variables, without the need to manage their presence. NCaRBS can also generate results providing insights into SME behaviour regarding strategy and innovation, while also increasing learning about potential reasons behind SMEs uncertainty regarding innovation intention. The study provides novel perspectives into how SMEs develop innovation intentions and the strategies required to support/exploit such intentions, of value to academia, enterprise support agencies and policymakers.

Keywords

Don’t Know innovation NCaRBS SME strategy uncertainty

Introduction

Small and medium-sized enterprises (SMEs) are increasingly tasked with driving economic recovery globally (Arshed et al., 2016; Beynon et al., 2016), particularly through knowledge diffusion (Braunerhjelm et al., 2010). Here, innovation is defined ‘as the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organisational method in business practices, workplace organisation or external relations’ (Eurostat, 2018). Innovation is regarded as critical in enabling growth and improving performance (Harris et al., 2013; Keizer et al., 2002). Consequently, government policymakers strive to encourage innovation activity to benefit their economies (Hausman, 2005). Understanding SME innovation determinants is therefore essential.

SMEs are also required to demonstrate technological sophistication in competitive markets (Dibrell et al., 2008). Keizer et al. (2002) claim SMEs need to be innovative to overcome relatively limited resources, vulnerability to uncertainty, turbulence in business environments and extensive customer and supplier power. However, there is often uncertainty surrounding potential future SME innovation activity (Sawyer et al., 2003), to design future innovation policies. Foreman-Peck et al. (2006) find a positive relationship between innovative orientation and growth, but also that limited financial or management resources could lead innovation activity to deprive other business activities of required resources (Heimonen, 2012; Hewitt-Dundas, 2006).

This highlights the importance of investigating SME characteristics, both internal and environmental, of relevance in driving innovation activity (O’Regan et al., 2006). In this study, the technique is used to investigate the relationship between planned changes in potentially relevant strategic-based characteristics (identified in the literature), including staffing levels, importing/exporting and client base, and whether they intend to undertake future innovation. Such nuanced understanding of innovation intention behaviour within the SME sector is currently absent from the extant literature and is offered here as a contribution.

Unsworth et al. (2012) also identify factors such as environmental uncertainty as important in determining innovation adoption. If an SME provides a Don’t Know (DK) response to innovation intention, there is often an assumption that the SME is, in reality, more inclined to be more ‘No’ to such intended innovation, Gilljam and Granberg (1993) using the term ‘easy out’. In terms of analysing uncertainty, Francis and Busch (1975) suggest, however, that respondents with non-substantive responses, such as DK, should not generally be excluded from analysis, arguing such responses are not random, exclusion introducing bias in any undertaken analysis. Turner and Michael (1996) argue a DK is not always a sign of knowledge deficit (i.e. uncertainty or ambivalence), the business context also needs to be considered (in our analysis whether an SME manager would want to admit to saying No to intended innovation – preferring instead to say DK in their response).

This study investigates the relationship between SME development strategy-based characteristics and enterprise intention to undertake future innovation. The analysis technique used, crucially, permits the inclusion of ignorance in the development strategy-based characteristics of SMEs, and allowance for uncertainty in the innovation intentions of SMEs (inclusive of DK responses), employing an analysis technique called N-State Classification and Ranking Belief Simplex (NCaRBS). Introduced in Beynon and Kitchener (2006) and Beynon et al. (2015a, 2015b), NCaRBS is a development from the original CaRBS (Beynon, 2005a, 2005b; Beynon et al., 2014). As a novel analysis technique, it, or versions of it, has been employed in a range of research areas including credit rating analysis (Beynon, 2005b), organizational strategy theory (Beynon et al., 2015a) and SME training satisfaction (Jones et al., 2013), among others.

The NCaRBS technique has the unique ability to analyse incomplete data sets, with missing values in the characteristic variables included, without needing to manage their missingness. Further, it allows inclusion of responses from SMEs, which answered DK to their innovation intention, and graphical exposition of such results.

The analysis uses data from the Federation of Small Businesses (FSB) 2010 survey. The FSB is the United Kingdom’s largest campaigning pressure group promoting the interests of the self-employed and owner/managers of SMEs with over 200,000 members. The FSB biannually surveys a wide range of SME behaviour and attitudes, and is the largest representative survey of UK firms available for academic research purposes (Beynon et al., 2015b). The survey was sent out to the entire membership, garnering 11,367 SME responses. The paper authors were granted access to use the data for academic research purposes after representation to the FSB, with 8420 responses being usable for the research. Because the research uses secondary data from a pre-existing survey, however, it does produce limitations (in terms of the variables created) in the strategies that can be assessed in relation to innovation intention.

The study uses data from the same FSB data set used in Beynon et al. (2015b) and, as with that study, uses the NCaRBS technique to explore innovation intention and uncertainty. However, while Beynon et al. (2015b) focused on demonstrating the usefulness of NCaRBS as a technique, using general SME characteristic variables (such as firm age, firm size, firm growth, owner education, etc.) this study focuses on the strategic orientation of the firm, providing pertinent additional evidence towards a number of debates. First analysis offers insights into SME behaviour in terms of firm strategy and its relationship with innovation intention, as well as the specific strategies most closely related to SME uncertainty about innovation intention, adding to limited literature in this area highlighted by McAdam et al. (2004). It also addresses Love and Roper’s (2015) argument that evidence evaluating SMEs is limited and characterized by small sample sizes and simplistic econometric analysis, providing relevant evidence to the ongoing debate regarding how government programmes and policies can support innovation-orientated entrepreneurship (Mason and Brown, 2013).

The structure of the rest of the article is as follows: A brief literature review discusses the variables directly of relevance to the study. The methodology is then presented, including a brief elucidation of the NCaRBS technique, the incomplete FSB-innovation data set described and research problem articulated. Results generated using NCaRBS, level of classification fit and contribution of characteristic variables are then identified, followed by a discussion of inferences that can be drawn in respect to SME innovation and business analytics, including the results related to innovation uncertainty. Finally, conclusions are given and the research contribution identified.

Literature: Innovation, potential drivers and the role of uncertainty

While Lee et al. (2010) claim SMEs have the capacity for innovation, lack of technology competencies for new product development, cost-effectiveness, operational efficiency, emerging market niches and process innovation are often seen to affect SME ability to innovate (Appiah-Adu and Singh, 1998). Van de Vrande et al. (2009) note SMEs often lack the resources and key business capabilities required to transform inventions into products or processes. Keskin (2006) suggests, when considering SME’s innovativeness potential, one must also consider environmental uncertainty, Edwards et al. (2005) identify SME flexibility and specificity as advantageous in accelerating innovation. Overall, however, there remains uncertainty regarding drivers of innovation activity within SMEs and further evidence is required (Love and Roper, 2015).

This debate can also be seen as relevant in the context of SME intentions, an issue previously researched in terms of internationalization (Kundu and Katz, 2003), information communication technology (ICT) (Daniel and Wilson, 2002; Sin Tan et al., 2010), strategic alliances (Lohrke et al., 2006), growth (Morrison et al., 2003) and adoption of externally sourced innovations (Lin and Ho, 2008). While there is an established innovation literature on the determinants of firms’ actual innovation activities, there has, however, been minimal prior research on SME intention to innovate, and specifically what might drive a firm to be uncertain regarding its future commitment. Such knowledge is useful in understanding how SMEs can be encouraged to embrace innovation as a driver of future growth.

In terms of potential characteristic/explanatory variables which may drive/inhibit innovation or cause uncertainty about it, these can be categorized into several groupings. This study broadly adopts the Edwards et al. (2005) view of innovation intention, specifically in terms of overlapping, constraining and enabling, aspects of the firm’s existing dispositions towards relevant internal resources, as well as strategic orientations including those related to uncertainty and novelty, and relations with customers.

As with all businesses, SMEs require resources, knowledge and skills to enhance the efficiency and effectiveness of their operations. Dollinger’s (1995) typology classified these resources as financial capital, human capital, social capital, technological resources, reputational capital and organizational resources, identifying a range of resource-related strategies of potential relevance to innovation intentions.

Research and development

Love and Roper (2015) suggest in-house research and development (R&D), unsurprisingly, is critical to firm ability to generate the knowledge base for proprietary intellectual property and innovation (see also Griffith et al., 2003). Raymond and St-Pierre (2010) also identify a linkage between R&D and product innovation, while R&D investment is argued by Baldwin and Hanel (2003) to be a key mechanism, along with development of knowledge and competencies, in determining overall levels of innovation in a sector/industry. Conversely, Laforet’s (2008) literature review highlights the link between R&D and innovation but also identifies that SMEs cannot always convert their R&D into successful innovation; Hall et al. (2009) suggest innovation often occurs without the presence of formal R&D activity, the lack of such formal activity often prevalent within SMEs.

Investment in staff training

Jones et al. (2013), summarizing, note that staff training positively influences performance and capabilities, through enhanced productivity, firm survival rates, quality, labour turnover, financial results and potential growth. Antonioli and Della Torre (2016) argue technological innovations are linked to increased external training activities, while organizational innovation adoption (job rotation, multitasking and decentralization) is linked to increases in internal training activities. Garcıa-Morales et al. (2007) also suggest a firm committed to learning increases its innovation capability and is less likely to miss opportunities. MacDonald et al. (2007) identify, therefore, that government policy links training policy as a means to improve innovation outcomes, while Muscio (2007) illustrates that training assists in developing SMEs’ human capital absorptive capacity, of crucial importance in innovation application.

Evidence, however, remains inconclusive and contradictory (Aragón-Sánchez et al., 2003; Jayawarna et al., 2007), potentially discouraging policymakers and SMEs from investing in training provision as a way of encouraging innovation activity. MacDonald et al.’s (2007) study, for example, failed to establish a positive association between training and innovation; De Saá-Pérez et al. (2012) also note that investment in training alone does not provide greater levels of innovation capacity for SMEs, only when training interacts with firm knowledge does its effect become highly significant, highlighting the potential importance of R&D.

Investment in equipment and machinery

An obvious capital of relevance to a firm is productive capital, in the form of equipment and machinery. Pellegrino et al. (2012) identify that investment in new machinery and equipment can be classified as embodying technological change; their study of innovative SMEs found that such investment was vital in generating innovative outputs. Love and Roper (2015) also argue that updating firm production technology via such capital investment may help innovative SMEs overcome internal resource limitations. Given the potential for such SMEs to be resource constrained, however, it is also possible that investment in equipment and capital may also be a substituting activity with innovation, the outcomes from investment and innovation suffering from uncertainty created by further technological advance and competitive pressures, as found by Skuras et al. (2008). This suggests, therefore, that there is uncertainty over whether both can be undertaken simultaneously, while reduced investment in these areas may imply greater general resource constraint again making innovation activity more unlikely or at least more uncertain.

In terms of strategic orientation more broadly, within the literature, the most obviously relevant orientation relates to firm growth aspirations (Uhlaner et al., 2013). Prior studies suggest that rapid growth can occur in labour- and knowledge-intensive industries, manufacturing and service industries (Davidsson and Delmar, 1997), in SMEs of all ages (Smallbone et al., 2002), high growth competency being related to higher levels of innovativeness (Akgün et al., 2007; Allen and Stearns, 2004).

An outstanding question remains, however, regarding what metrics effectively measure growth aspirations (Hudson et al., 2001), impacting variables including employment (Hoffman et al., 1998), turnover (Moreno and Casillas, 2007), market share (O’Gorman, 2001) and profits (Jayawarna et al., 2007). Other measures that proxy for such growth aspirations include SME size as measured by the numbers of outlets, customer base and so on. (Dobbs and Hamilton, 2007). The analysis below discusses the three most obviously associated growth aspirations that are also available in the FSB survey.

Staffing levels

Hoffman et al. (1998) suggest SMEs ability to attract, develop and retain employees impacts upon their capability to effectively pursue growth. Roper (1997) and Sheehan et al. (2014) also suggest SME staffing levels, innovation and productivity are positively associated. Thus, SMEs need to effectively manage their staffing levels (Thakur, 1999) to pursue innovative activity, as well as being a measure of firm growth in its own right. Bougrain and Haudeville (2002) further identify that research and production managers are essential to the innovation process, sales managers also enhancing firm ability to respond to the market.

Number of premises/branches

The literature on the influence of firm size towards innovative capacity is inconclusive. Schumpeter (1942) and Laforet (2008) suggest large businesses possess advantages over SMEs due to superior financial capabilities allowing them to be more effective innovators. Contrastingly, Bertschek and Entorf (1996) and Cohen and Klepper (1996) note that large firms are inhibited by excessive complexity and bureaucracy, impacting negatively upon creativity and flexibility in contrast to SMEs, suggesting that firms with greater numbers of premises or branches may be adversely affected by such problems. Pla-Barber and Alegre (2007) found that firm size is not a determinant for innovation.

Client base

Expanding the client base can be regarded as a direct measure of growth. Prior literature also recognizes the importance of managing the client base effectively to establish professional relationships (Ceci and Iubatti, 2012). Professional interactions between firms generate interdependent capabilities and routines regarding production, logistics and quality management, facilitating effective coordination and resources allocation to improve productivity (Petersen et al., 2003). De Propris’s (2002) UK SMEs study also found a positive relationship between firms engaging in such cooperation with client firms and suppliers and product innovation. Tomlinson and Fai (2013), however, identify the need for further research to explore the nature and intensity of cooperative ties and their impact over innovation activity. The expansion of the client base, while being a measure of growth, may also introduce uncertainty for the firm in terms of its innovation, or at least less effective relationships while the relationships are being built.

The final set of strategy variables considered are also more externally focused, and often related to the exploitation of innovation.

Importing and exporting

Kocak and Abimbola (2009) found firms deemed to be innovative sought to gain enhanced competitive performance from the application of knowledge-based resources to marketing their outputs in several countries. Higón and Driffield’s (2011) study also found product innovation impacting positively on the decision to export, and Pickernell et al. (2016) found innovation focus consistently and positively linked to exporting for SMEs. Conversely, Salomon and Shaver (2005) postulated that exporting allowed access to knowledge not available in the domestic market, promoting learning that can foster increased innovation, their results finding that exporting is associated with innovation. Girma et al. (2008) also found that previous exporting experience enhances innovative capability. The literature suggests both that a possible relationship exists and that it could be bidirectional in nature, therefore requiring further study.

Online presence

Pickernell et al. (2013) confirmed a significant positive relationship between SME online presence and innovation. Simpson and Docherty (2004) also suggest innovation and online presence adoption are positively related, while Simmons et al. (2011) claim online presence represents an enabling mechanism for SME innovation activity, improving efficiency of processes, enhancing communication, and revolutionizing existing business models, increasing competitiveness and improving performance.

Examples of highly innovative SMEs prepared to adopt higher levels of online presence (Loebbecke and Schäfer, 2001), and change their business operations are also apparent. Lazoi et al. (2011), for example, found a link between SMEs in the aerospace sector adopting higher levels of online activity (ICT) and innovation through the supply chain, via enabling enhanced networking effects. Higón (2012) also found website development, in particular, demonstrated the potential to create competitive advantage through product innovation. Online presence adoption in SMEs remains an under-researched topic, especially with regard to recognizing the antecedents to successful deployment (Eggers et al., 2017; Fink and Disterer, 2006).

Investment in marketing/advertising

According to Simmonds (1986), marketing itself can be viewed as an innovation activity. Marketing also interacts with other functional departments, such as R&D, significantly impacts new product success, marketing and R&D seemingly having equivalent influence on new product decisions (Atuahene–Gima and Li, 2000). Investment in reputational capital through marketing (Morris and Paul, 1987) is also important in making the firm more able to exploit innovation. Actually bringing innovation to market is a key activity involving marketing and market research (Galindo and Méndez, 2014). Investment in marketing can therefore be seen as part of the innovation ‘pipeline’ (McCarthy et al., 2014).

Methodology

Background

The FSB survey (including data from previous versions of the survey) has already been utilized to analyse a wide range of small business issues, see for example, Mason et al. (2006, 2011), Carter et al. (2009), Pickernell et al. (2011a, 2011b, 2013, 2016), Jones et al. (2013) and Beynon et al. (2015b). The FSB (2010) survey was an evolution of prior FSB surveys developed in consultation with FSB members. The survey data collection process was managed by a private sector consultancy, the paper authors were granted access to the data for academic research purposes after representation to the FSB.

Survey instrument

Individual SMEs were considered the unit of analysis, and owner/managers were asked to complete the questionnaire. The 2010 survey was sent to the FSB’s entire UK membership of approximately 200,000 SMEs. Of the 11,367 SMEs that responded, 8420 provided responses usable for the research discussed in this article (for reasons discussed further below, usable respondents have to contain a response to the outcome variable and at least one of the considered characteristic variables). Analysis of background data for the 8420 respondents identified showed the median and mode age range of respondents was 45–54, two-thirds of whom were male, 35% having a degree or higher, with the median number of employees for firms being four.

Three perspectives on future SME innovation intention are considered in this study, see Figure 1. In Figure 1(a), the survey question of SME innovation intention in the next 12 months is presented, including the three response perspectives of Yes, No and DK able to be chosen (as given in the FSB questionnaire). In Figure 1(b), the same three perspectives are shown, this time across a simplex plot, demonstrating how the substantive (Yes and No) and non-substantive (DK) responses can be represented on the same domain (used in the NCaRBS analysis undertaken).

Figure 1.

Innovation intention question with response options (a) and response representation in simplex plot domain (b).

Beyond the SME viewpoint on innovation intention, their respective views on characteristics of their future strategy were also taken from the FSB survey, as described in the previous section, see Figure 2.

Figure 2.

Development strategy questions with response options.

In Figure 2, nine SME-based characteristics of development strategy are considered, namely, staffing levels (Stff_Levl), number of premises/branches (Prms_Brch), investment in equipment/machinery (Eqpt_Mchn), investment in staff training (Stff_Trng), R&D (Rsch_Dvlp), importing/exporting (Impt_Expt), investment in marketing/advertising (Mrkt_Advt), client base (Clnt_Base) and online presence (Onln_Prsn). Consideration of these strategies is also over the next 12 months, the same as innovation intention. The nine presented facets of strategies cover a range of issues, Stff_Levl, Prms_Brch and Clnt_Base can all be seen as growth-related variables, four measures relating to SME investment in resources, namely Eqpt_Mchn, Stff_Trng, Rsch_Dvlp and Mrkt_Advt, with Impt_Expt and Onln_Prsn seen as variables related to widening potential markets/potential resource sources.

Coding of variables

In the development strategy questions, inspection of Figure 2 shows three substantive responses, namely ‘Increase’, ‘Stay the same’ and ‘Decrease’. There are also two available responses which are not directly substantive. First, ‘DK’ is a well-known non-substantive response. Second, ‘Not applicable to my business’ by definition does not offer any substantive information towards specific evidence to development strategy.

Beyond these two non-substantive responses, there is another version of response to be considered, that is non-response, where for one or more of the described development strategy variables given, none of the five presented response options was selected. Termed a missing value, the presence of a missing value here means no evidence is given on that specific strategy.

These three responses, ‘DK’, ‘Not applicable to my business’ and ‘No response’, offer no substantive evidence towards development strategy. Instead, for different reasons offer only ignorance in terms of evidence. Ignorance here simply means there is no substantive evidence being offered towards a development strategy. A breakdown of the presence of each response type is next considered, from the 8420 finally considered SMEs, see Table 1.

Table 1.

Frequency of responses to nine development strategy survey questions (for 8420 considered SMEs).

	Substantive			Non-substantive
	Decrease	Stay the same	Increase	Don’t Know	Not applicable to my business	No response
Stff_Levl	573	4656	2140	268	598	185
Prms_Brch	189	5743	528	104	1357	499
Eqpt_Mchn	361	3878	2373	373	988	447
Stff_Trng	299	3911	2082	266	1399	463
Rsch_Dvlp	215	2826	1380	265	3016	718
Impt_Expt	101	1772	1134	178	4369	866
Mrkt_Advt	685	3577	2407	336	844	571
Clnt_Base	144	1407	6095	119	272	383
Onln_Prsn	67	2582	4550	181	454	586

Note: Stff_Levl: staffing levels; Prms_Brch: number of premises/branches; Eqpt_Mchn: investment in equipment/machinery; Stff_Trng: investment in staff training; Rsch_Dvlp: research and development; Impt_Expt: importing/exporting, Mrkt_Advt: investment in marketing/advertising; Clnt_Base: client base; Onln_Prsn: online presence.

In Table 1, the number of responses of each type possible are given for each development strategy variable considered. Of particular note here is the frequency of responses to each of the non-substantive responses ‘DK’, ‘Not applicable to my business’ and ‘No response’. The percentages of non-substantive responses for each development strategy variable are Stff_Levl – 12.482%, Prms_Brch – 23.278%, Eqpt_Mchn – 21.473%, Stff_Trng – 25.273%, Rsch_Dvlp – 47.494%, Impt_Expt – 64.287%, Mrkt_Advt – 20.796%, Clnt_Base – 9.192% and Onln_Prsn – 14.501%.

These non-substantive percentage totals also indicate how much information (evidence) is available among the considered respondents across the different development strategy variables. A noticeable case is for Impt_Expt, where there is 64.287% of non-substantive respondents, but this is mostly due to the fact that this strategy variable is not applicable to the responding SME (51.888% of respondents). When all development strategy variables are considered together, the total number of respondents with at least one non-substantive response among its strategy responses was 6484 (77.007%) which in a traditional analysis, such as using regression, would all be removed from the data set.

The substantive and non-substantive frequency breakdown results presented here represent an often encountered problem of data quality. However, the notion of data quality is heavily dependent on what standard of data is required for the intended analysis, specifically the analysis technique intended to be employed. The NCaRBS technique employed here is able to analyse incomplete data without the need to manage the missing values present. Here, importantly, all 8420 SME respondents are retained in the analysis (many with non-substantive responses among their development strategy variable values).

NCaRBS analysis

This section presents results from the NCaRBS analysis of the defined FSB strategy-innovation data set. Summary details of the NCaRBS technique are given below, for further elucidation the reader is directed to Beynon et al. (2015a, 2015b).

Fundamental in this analysis is the intention, using NCaRBS, to undertake regression type fit analysis, whereby the development strategy variables (Stff_Levl, Prms_Brch, etc.) are used to best fit respondent (SME) responses to their known innovation intention (Yes, No, DK – in one analysis). Within NCaRBS, each response value to a strategy question is transformed into a body of evidence (BOE), a triplet of values, mass values (m_i _{, j, h}(·)) to be specific, associating its evidence towards certain subsets of the limits of the outcome variable, here innovation intention, with limits Yes, No and DK (see Figure 1(b) for representation of this domain view). These mass values offer levels of belief towards respondents association to the subsets of the Yes, No and DK responses. To consider the results, and enable the opportunity to fit the analysis, the mass value-based structure is evolved to a probability type form of values (pignistic probability (BetP _i (·)), which give evidence specific to the individual limits of response, Yes, No and DK (see Beynon et al., 2015a, 2015b – for further detail). The results presented include the ability to have fitted respondents based on their responses to the development strategy questions and their outcome-based innovation intention response.

Strategic characteristic-innovation fit

The fit results presented here come from the system configuration process within the NCaRBS analysis (see Beynon et al., 2015b). The optimization necessary to configure a NCaRBS system is based on minimizing the error between each respondent’s actual innovation intention response and their predicted response, here measured by OB^{NCaRBS, w}. The OB^{NCaRBS, w} fit is dependent on the respective triplet of BetP _i (·) values, BetP _i (Ys), BetP _i (No) and BetP _i (DK), associating each respondent to the three outcome responses of Yes, No and DK, respectively.

In this study, following Beynon et al. (2015a, 2015b), five runs were undertaken, the run with best fit chosen to further consider. In Beynon et al. (2015b), resampling results showed fit and subsequent variable contributions were robust (relatively insensitive) across the different runs. Since the sum of these triplets of BetP _i (·) values for each respondent total to one, they can also be represented as points in a simplex plot (as previously shown in Figure 1(b)). In the analysis of the 8420 SMEs, Figure 3 shows the predicted outcome classifications of the individual SMEs, to the three outcome responses, No, Yes and DK.

Figure 3.

Simplex plot-based representation of predicted outcome variable, innovation intention.

In Figure 3, the three simplex plots shown describe separately the predicted outcomes of those SMEs originally known to be associated with the outcome response, 1969 No (Figure 3(a)), 5338 Yes (Figure 3(b)) and 1113 DK (Figure 3(c)). Each point represents a respondent, and specifically their triplet of BetP _i (·), in vector form [BetP _i (Ys), BetP _i (No), BetP _i (DK)].¹

From the description of the NCaRBS analysis, the variation in the numbers of SMEs associated with each outcome was taken into account, allowing each group of SMEs equal weighting in achieving their correct classification (see description around description of OB^NCARBS,w objective function). The grey shaded regions in each simplex plot (triangle) indicate where there is majority association to the correct classification (e.g. in Figure 3(a), for known respondents responding No to innovation intention, the grey shaded region indicates those points for which BetP _i (No) > BetP _i (Ys) and BetP _i (No) > BetP _i (DK).

A numerical breakdown of the correct/incorrect classification of SMEs is given in Table 2, the actual and predicted classifications of the 8420 SMEs provided, for each group of SMEs the spread of these across the three possible outcome responses given. From this table, the overall level of correct classification is found to be 4906 out of 8420 (58.266%) SMEs.

Table 2.

Confusion matrix of classification results.

Actual/predicted	No	Yes	Don’t Know	Total
No	1164	461	344	1969
Yes	1065	3420	853	5338
Don’t Know	426	365	322	1113
Total	2655	4246	1519	8420

One comment worth mentioning here is in relation to the level of incorrect classification of the respondents with actual DK outcome responses (only 322 (28.931%) correctly classified). Within Table 2, it is noted 426 (38.275%) of DK respondents to innovation intention were predicted as being No to this question, indicating some evidence of the ‘easy out’ sentiment in regard to the role played by DK responses (supporting the view of Groothuis and Whitehead, 2002).

Development strategy characteristic contribution

Beyond the described classification fit, this subsection considers the contribution of the individual characteristics; variables used to fit respondents to their innovation intention response (see Beynon et al., 2015b – for technical details). For a specific development strategy variable, a variable BOE can be constructed, through combining the evidence in the constituent BOEs, m_i _{, j, h}(·) h = 1,…n _D, termed a variable BOE, defined m_i _{, j,-}(·). As for the final object BOEs found, the respective pignistic probability form of the m_i _{, j,-}(·) h = 1,…n _D can be found. Since they sum to one, they can also be described within a simplex plot domain, see Figure 4.

Figure 4.

Simplex plot-based contribution of SME development strategy characteristics (responses Decreasing – D, Stay the same – S and Increasing – I) in regard to future innovation intention.

Discussion

Strategic orientation: Growth-related variables

Stff_Levl (staffing levels)

From the simplex plot contribution graph for a response of decreasing or staying the same in terms of staffing levels there is close association with No to future innovation intention, there is then a noticeable change to Yes to innovation intention where the strategy is to increase staffing levels in the next 12 months (Figure 4(a)). This confirms Roper (1997) and Sheehan et al. (2014) studies suggesting an association between SME staffing levels and innovation.

Prms_Brch (numbers of premises/branches)

The simplex plot shows a similar result for branch premises as for staffing levels (Figure 4(a)), decreasing or staying the same in terms of numbers of premises/branches closely associated with No to future innovation intention, with a notable change to Yes for future intended innovation as the strategy to increase premises/branches in the next 12 months is suggested (Figure 4(b)). This conflicts with Pla-Barber and Alegre (2007) study suggesting firm size was not associated with innovation.

Clnt_Base (client base)

From the simplex plot contribution graph for a response of decreasing or staying the same in terms of client base there is close association with No to intended innovation (Figure 4(h)). There is then a very noticeable change to DK to innovation intention as the strategy to increase client base in the next 12 months is suggested. This finding is consistent with De Propris (2002), potentially related to the likelihood that new client relationships take time to develop into innovation enhancing ones.

The evidence from these three growth-related variables highlights strategies that require an internal growth focus, namely staffing levels and branch premises, being positively related to innovation while the externally focused client base variable is more linked to uncertainty over innovation intention. This may suggest that SMEs are more confident with managing their internal business environment with regard to developing innovation intention but are more risk adverse in relation to the more uncertain outcomes possible in the external environment impacting on innovation intention, potentially through impacts upon resources.

Strategic orientation: Widening potential markets/potential resource sources variables

Impt_Expt (importing/exporting)

The simplex plot shows, interestingly, that decreasing or staying the same in terms of importing/exporting activity is more related to an ambiguous outcome for innovation, while there is a strong relationship between investing in importing/exporting activity and Yes to innovation intention, potentially indicating a reluctance for respondents to say No (Figure 4(f)). Conversely, it may also be the case that only a clear increasing international exposure-related strategy fits with a clear innovation strategy, static or reduced international exposure being symptomatic of a more uncertain strategic direction which is consistent with Pickernell et al. (2016).

Onln_Prsc (online presence)

For online presence (Figure 4(i)), there is clearly a strong relationship between static or decreasing online presence being related to No for innovation intention and increased online presence linked to a Yes for intended innovation, consistent with Pickernell et al. (2013).

Mrkt_Advt (investment in marketing/advertising)

In the case of investing in the reputational capital of the SME through marketing/advertising, there is found to be a strong relationship between increasing intended investment in these activities and Yes to innovation intention, with decreasing or static levels more related to an ambiguous/DK response to innovation intention (Figure 4(g)).

While these three variables therefore show differences in the relationships between static or decreasing support for these activities and the relationship with innovation intention, increased activities in these areas are all strongly linked to a Yes for intended innovation. Conversely, decreasing or static marketing and international activity are more strongly linked with uncertainty over innovation, while for online activity there is a more certain (negative) link between static or declining activity. This suggests therefore that firm strategy with regard to online activity has a more direct relationship with innovation intention, while non-positive marketing and international activity create more uncertainty for innovation, which again may be indicative of a more uncertain external environment.

Resource-related variables

Rsch_Dvlp (R&D)

For R&D (Figure 4 (e)), there is, unsurprisingly, a clear relationship between intending to increase these activities and Yes for future innovation intention as previously mooted by Love and Roper (2015) and Griffith et al. (2003). Staying the same or decreasing R&D activity is, however, increasingly more related to DK for innovation intention.

Eqpt_Mchn (investment in equipment/machinery)

From the simplex plot contribution graph for a response of decreasing investment in equipment/machinery there is much more ambiguity (No, Yes or DK) in intended innovation (Figure 4(c)). Where investment stays the same there is clear association to No as the intended innovation outcome, then for increasing investment there is a return to ambiguity on the intended innovation.² So here there is minimal discernible evidence to associate investment in equipment/machinery with increased innovation intention which is a notable finding.

Stff_Trng (investment in staff training)

In the case of staff training (investment in human capital; Figure 4(d)), decreasing intended training is more strongly associated with No to innovation intention, with static intended staff training more closely linked to an ambiguous response to intended innovation and increased staff training more closely linked with Yes to innovation intention supporting Roper’s (1997) and Sheehan et al.’s (2014) prior evidence.

Overall, this suggests that increased intended investment in R&D, but also increased staff training are positively associated with increased innovation intention (Galindo and Méndez, 2014). These strategic activities can be seen as at the very least complementary with innovation, in terms of supporting and facilitating it (R&D and staff training) or exploiting it (marketing). Conversely, proposed changes to investment in equipment and/or machinery seem to suggest greater uncertainty over innovation, which may be linked to the less obvious direct link between this and innovation.

Conclusions

The study offers a novel perspective into SMEs innovation focus, specific strategies related to this, and their role in uncertainty around strategic direction in relation to innovation. For example, staffing levels, number of premises and branches and online presence have unambiguous relationships with innovation intention, reduced levels of these variables are associated with a lower likelihood of innovation, and higher levels associated with higher likelihood in a linear relationship. Increasing R&D, staff training, importing/exporting and marketing and advertising are also associated with positive innovation intention but also reduced uncertainty over innovation intention.

More broadly, evidence here suggests SMEs are more certain about embracing innovation in relation to internal environment-focused strategies and issues within their immediate control. This might suggest an external environment risk adverse SME mindset with greater reluctance to invest resources in innovation where there may be less guarantee of return on investment. This might support the argument that innovation is more attainable for larger SMEs (Ettlie and Rubenstein, 1987; Laforet, 2008); further research is required to explore this phenomenon.

The simplex plot domain shown in Figure 1(b), and in later NCaRBS results, could also be used to facilitate future analysis of more ‘uncertain’ response from SMEs on future innovation intention. Respondents could be asked to judge their SME’s future innovation intention by placing a mark (cross) inside the simplex plot (innovation intention domain), allowing the inclusion of ambiguity between Yes and No and also the potential to self-acknowledge a level of uncertainty in their position (signified by how near to the DK vertex their mark may be). This potentially innovative way of representing SME respondents’ ambiguous/uncertain perspectives on a subject may have new enlightening impacts on this important research issue.

This study provides enhanced understanding of strategies and resource investments most closely related to unambiguous innovation intention within SMEs. In terms of implications for policy and practice, this study is of value to academia, enterprise support agencies and policymakers supporting SMEs achieve economically sustainable growth. For example, the above results potentially allow greater prioritization of policy resources, particularly regarding how government can look to effectively promote SME innovation both directly and indirectly (Simpson and Docherty, 2004), depending on whether the aim is increasing innovation or assisting SMEs reduce their own uncertainty over this strategy. For enterprise support agencies, the evidence here suggests greater prioritization is placed on encouraging SME investment in R&D and training and internationalization activity to enable further development of innovation cultures. This evidence regarding the nuanced behaviour of SMEs towards innovation further informs academic debate and understanding. For SMEs, innovation must be regarded as a process to enable change and growth. This requires SME owner/managers to adopt a strategic orientation with regard to a range of issues such as technology adoption, training and seeking entry into international markets.

In terms of limitations, this study recognizes that this analysis has considered specific variables and alternatives could be substituted or extended in further studies. In addition, there is a lack of comparison between NCaRBS and alternative, more traditional methods of handling such data, which is again a potential avenue for further research. Further quantitative studies are required to supplement this evidence evaluating a range of SME behaviours in different national and industry contexts.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Paul Jones

Notes

References

Akgün

Keskin

Byrne

, et al. (2007) Emotional and learning capability and their impact on product innovativeness and firm performance. Technovation 27(9): 501–513.

Allen

Stearns

(2004) Technology entrepreneurs. In: Gartner

Shaver

Carter

, et al. (eds) Handbook of Entrepreneurial Dynamics: The Process of Business Creation. Thousand Oaks: Sage, pp. 438–448.

Antonioli

Della Torre

(2016) Innovation adoption and training activities in SMEs. The International Journal of Human Resource Management 37(3): 311–337.

Appiah-Adu

Singh

(1998) Customer orientation and performance: a study of SMEs. Management Decision 36(6): 385–394.

Aragón-Sánchez

Barba-Aragon

Sanz-Valle

(2003) Effects of training on business results. International Journal of Human Resource Management 14(6): 956–980.

Arshed

Mason

Carter

(2016) A case of enterprise policy in England. Environment and Planning C: Government and Policy 34(8): 1582–1611.

Atuahene–Gima

(2000) Marketing’s influence tactics in new product development: a study of high technology firms in China. Journal of Product Innovation Management 17(6): 451–470.

Baldwin

Hanel

(2003) Innovation and Knowledge Creation in an Open Economy: Canadian Industry and International Implications. Cambridge: Cambridge University Press.

Bertschek

Entorf

(1996) On nonparametric estimation of the Schumpeterian link between innovation and firm size: evidence from Belgium, France and Germany. Empirical Economics 21(3): 401–426.

10.

Beynon

(2005a) A novel technique of object ranking and classification under ignorance: an application to the corporate failure risk problem. European Journal of Operational Research 167: 493–517.

11.

Beynon

(2005b) Optimizing object classification under ambiguity/ignorance: application to the credit rating problem. International Journal of Intelligent Systems in Accounting, Finance and Management 13: 113–130.

12.

Beynon

Andrews

Boyne

(2015a) Evidence-based modelling of hybrid organizational strategies. Computational and Mathematical Organization Theory 21: 1–23.

13.

Beynon

Jones

Packham

, et al. (2014) Investigating the motivation for enterprise education: a CaRBS based exposition. International Journal of Entrepreneurial Behaviour and Research 20(6): 584–612.

14.

Beynon

Jones

Pickernell

, et al. (2015b) A NCaRBS analysis of SME intended innovation: learning about the Don’t Knows. OMEGA - International Journal of Management Science 59(Part A): 97–112.

15.

Beynon

Jones

Pickernell

(2016) Country-based comparison analysis using fsQCA investigating entrepreneurial attitudes and activity. Journal of Business Research 69(4): 1271–1276.

16.

Beynon

Kitchener

(2006) Analyzing strategic stance in public services management: an exposition of NCaRBS in a study of U.S. states’ long-term care systems. In: Voges

Pope

(eds) Business Applications and Computational Intelligence. Hershey: IDEA Group, Chapter 17, pp. 344–359.

17.

Bougrain

Haudeville

(2002) Innovation, collaboration and SMEs internal research capacities. Research Policy 31: 735–747.

18.

Braunerhjelm

Acs

Audretsch

, et al. (2010) The missing link: knowledge diffusion and entrepreneurship in endogenous growth. Small Business Economics 34(2): 105–125.

19.

Carter

Mason

Tagg

(2009) Perceptions and experience of employment regulation in UK small firms. Environment and Planning C: Government and Policy 27: 263–278.

20.

Ceci

Iubatti

(2012) Personal relationships and innovation diffusion in SME networks: a content analysis approach. Research Policy 41(3): 565–579.

21.

Cohen

Klepper

(1996) Firm size and the nature of innovation within industries: the case of process and product R&D. The Review of Economics and Statistics 78: 323–343.

22.

Daniel

Wilson

(2002) Adoption intentions and benefits realised: a study of e-commerce in UK SMEs. Journal of Small Business and Enterprise Development 9(4): 331–348.

23.

Davidsson

Delmar

(1997) High-growth firms: characteristics, job contribution, and method observations. Paper presented at the RENT XI Conference, Mannheim, Germany.

24.

De Propris

(2002) Types of innovation and inter-firm co-operation. Entrepreneurship and Regional Development 14: 337–353.

25.

De Saá-Pérez

Díaz-Díaz

Luis Ballesteros-Rodriguez

(2012) The role of training to innovate in SMEs. Innovation 14(2): 218–230.

26.

Dibrell

Davis

Craig

(2008) Fueling innovation through information technology in SMEs. Journal of Small Business Management 46(2): 203–218.

27.

Dobbs

Hamilton

(2007) Small business growth: recent evidence and new directions. International Journal of Entrepreneurial Behavior & Research 13(5): 296–322.

28.

Dollinger

(1995) Entrepreneurship: Strategies and Resources. Boston: Irwin.

29.

Edwards

Delbridge

Munday

(2005) Understanding innovation in small and medium-sized enterprises: a process manifest. Technovation 25(10): 1119–1127.

30.

Eggers

Hatak

Kraus

, et al. (2017) Technologies that support marketing and market development in SMEs – evidence from social networks. Journal of Small Business Management 55(2): 270–302.

31.

Ettlie

Rubenstein

(1987) Firm size and product innovation. Journal of Product Innovation Management 4(2): 89–108.

32.

Eurostat (2018) Concepts and Definitions: Eurostat’s Concepts and Definitions Database. Available at: http://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?%20TargetUrl=DSP_GLOSSARY_NOM_DTL_VIEW&StrNom=CODED2&StrLanguageCode=EN&IntKey=16501035&RdoSearch=BEGIN&TxtSearch=innovation&CboTheme=&IsTer=&IntCurrentPage=1&ter_valid=0 (accessed 10 March 2018).

33.

Federation of Small Businesses (FSB) (2010). Lifting the Barriers to Growth in UK Small Businesses. London: Federation of Small Businesses.

34.

Fink

Disterer

(2006) International case studies: To what extent is ICT infused into the operations of SMEs? Journal of Enterprise Information Management 19(6): 608–624.

35.

Foreman-Peck

Makepeace

Morgan

(2006) Growth and profitability of small and medium-sized enterprises: some Welsh evidence. Regional Studies 40(4): 307–319.

36.

Francis

Busch

(1975) What we now know about “I Don’t Knows.” The Public Opinion Quarterly 39(2): 207–218.

37.

Galindo

Méndez

(2014) Entrepreneurship, economic growth, and innovation: Are feedback effects at work? Journal of Business Research 67(5): 825–829.

38.

Garcıa-Morales

Llorens-Montesa

Verdu-Jover

(2007) Influence of personal mastery on organizational performance through organizational learning and innovation in large firms and SMEs. Technovation 27(9): 547–568.

39.

Gilljam

Granberg

(1993) Should we take don’t know for an answer? Public Opinion Quarterly 57: 348–357.

40.

Girma

Görg

Hanley

(2008) R&D and exporting: A comparison of British and Irish firms. Review of World Economics 144(4): 750–773.

41.

Griffith

Redding

Van Reenen

(2003) R&D and absorptive capacity: theory and empirical evidence. The Scandinavian Journal of Economics 105(1): 99–118.

42.

Groothuis

Whitehead

(2002) Does don’t know mean no? Analysis of ‘don’t know’ responses in dichotomous choice contingent valuation questions. Applied Economics 34(15): 1935–1940.

43.

Hall

Lotti

Mairesse

(2009) Innovation and productivity in SMEs: empirical evidence for Italy. Small Business Economics 33(1): 13–33.

44.

Harris

McAdam

McCausland

, et al. (2013) Knowledge management as a source of innovation and competitive advantage for SMEs in peripheral regions. International Journal of Entrepreneurship and Innovation 14(1): 49–61.

45.

Hausman

(2005) Innovativeness among small businesses: theory and propositions for future research. Industrial Marketing Management 34(8): 773–782.

46.

Heimonen

(2012) What are the factors that affect innovation in growing SMEs? European Journal of Innovation Management 15(1): 122–144.

47.

Hewitt-Dundas

(2006) Resource and capability constraints to innovation in large and small plants. Small Business Economics 26(3): 257–277.

48.

Higón

(2012) The impact of ICT on innovation activities: evidence for UK SMEs. International Small Business Journal 30(6): 684–699.

49.

Higón

Driffield

(2011) Exporting and innovation performance: analysis of the annual small business survey in the UK. International Small Business Journal 29(1): 4–24.

50.

Hoffman

Parejo

Bessant

, et al. (1998) Small firms, R&D, technology and innovation in the UK: a literature review. Technovation 18(1): 39–55.

51.

Hudson

Smart

Bourne

(2001) Theory and practice in SME performance measurement systems. International Journal of Operations & Production Management 21(8): 1096–1115.

52.

Jayawarna

Macpherson

Wilson

(2007) Training commitment and performance in manufacturing SMEs: incidence, intensity and approaches. Journal of Small Business and Enterprise Development 14(2): 321–338.

53.

Jones

Beynon

Pickernell

, et al. (2013) Evaluating the impact of different training methods on SME business performance. Environment and Planning C: Government and Policy 31(1): 56–81.

54.

Keizer

Dijkstra

Halman

(2002) Explaining innovative efforts of SMEs: an exploratory survey among SMEs in the mechanical and electrical engineering sector in The Netherlands. Technovation 22(1): 1–13.

55.

Keskin

(2006) Market orientation, learning orientation, and innovation capabilities in SMEs: an extended model. European Journal of Innovation Management 9(4): 396–417.

56.

Kocak

Abimbola

(2009) The effects of entrepreneurial marketing on born global performance. International Marketing Review 26(4/5): 439–452.

57.

Kundu

Katz

(2003) Born-international SMEs: BI-level impacts of resources and intentions. Small Business Economics 20(1): 25–47.

58.

Laforet

(2008) Size, strategic, and market orientation effects on innovation. Journal of Business Research 61(7): 753–764.

59.

Lazoi

Ceci

Corallo

, et al. (2011) Collaboration in an aerospace SMEs cluster: innovation and ICT dynamics. International Journal of Innovation and Technology Management 8(3): 393–414.

60.

Lee

Park

Yoon

, et al. (2010) Open innovation in SMEs – an intermediated network model. Research Policy 39(2): 290–300.

61.

Lin

(2008) An empirical study on logistics service providers’ intention to adopt green innovations. Journal of Technology Management & Innovation 3(1): 17–26.

62.

Loebbecke

Schäfer

(2001) Web portfolio based electronic commerce: the case of transtec AG. Logistics Information Management 14(1/2): 54–67.

63.

Lohrke

Kreiser

Weaver

(2006) The influence of current firm performance on future SME alliance formation intentions: a six-country study. Journal of Business Research 59(1): 19–27.

64.

Love

Roper

(2015) SME innovation, exporting and growth: a review of existing evidence. International Small Business Journal 33(1): 28–48.

65.

McAdam

McConvery

Armstrong

(2004) Barriers to innovation within small firms in a peripheral location. International Journal of Entrepreneurial Behaviour & Research 10(3): 206–221.

66.

McCarthy

Packham

Pickernell

(2014) The potential of and framework for promoting a business angel university and intellectual property exploitation: a case study from Wales. In: Alain

Fayolle

Dana

Redford

(eds) Handbook on the Entrepreneurial University. Cheltenham, UK: Edward Elgar, p.323.

67.

MacDonald

Assimakopoulos

Anderson

(2007) Education and training for innovation in SMEs: a tale of exploitation. International Small Business Journal 25(1): 77–95.

68.

Mason

Brown

(2013) Creating good public policy to support high-growth firms. Small Business Economics 40(2): 211–225.

69.

Mason

Carter

Tagg

(2006) The effect of the national minimum wage on the UK small business sector: a geographical analysis. Environment and Planning C: Government and Policy 24(1): 99–116.

70.

Mason

Carter

Tagg

(2011) Invisible businesses: the characteristics of home-based businesses in the United Kingdom. Regional Studies 45(5): 625–639.

71.

Moreno

Casillas

(2007) High-growth SMEs versus non-high-growth SMEs: a discriminant analysis. Entrepreneurship & Regional Development 19(1): 69–88.

72.

Morris

Paul

(1987) The relationship between entrepreneurship and marketing in established firms. Journal of Business Venturing 2(3): 247–259.

73.

Morrison

Breen

Ali

(2003) Small business growth: intention, ability, and opportunity. Journal of Small Business Management 41(4): 417–425.

74.

Muscio

(2007) The impact of absorptive capacity on SMEs’ collaboration. Economics of Innovation and New Technology 16(8): 653–668.

75.

O’Gorman

(2001) The sustainability of growth in small- and medium-sized enterprises. International Journal of Entrepreneurship Behaviour & Research 7(2): 60–75.

76.

O’Regan

Ghobadian

Sims

(2006) Fast tracking innovation in manufacturing SMEs. Technovation 26(2): 251–261.

77.

Pellegrino

Piva

Vivarelli

(2012) Young firms and innovation: a microeconometric analysis. Structural Change and Economic Dynamics 23(4): 329–340.

78.

Petersen

Handfield

Ragatz

(2003) A model of supplier integration into new product development. Journal of Product Innovation Management 20(4): 284–299.

79.

Pickernell

Jones

Thompson

, et al. (2016) Determinants of SME exporting: insights and implications. The International Journal of Entrepreneurship and Innovation 17(1): 31–42.

80.

Pickernell

Kay

Packham

, et al. (2011a) Competing agendas in public procurement: an empirical analysis of opportunities and limits in the UK for SMEs. Environment and Planning C: Government and Policy 29(4): 641–658.

81.

Pickernell

Packham

Jones

, et al. (2011b) Graduate entrepreneurs are different: they access more resources. International Journal of Entrepreneurial Behaviour & Research 17(2): 183–202.

82.

Pickernell

Senyard

Jones

, et al. (2013) New and young firms entrepreneurship policy and the role of government – evidence from the Federation of Small Businesses survey. Journal of Small Business and Enterprise Development 20(2): 358–382.

83.

Pla-Barber

Alegre

(2007) Analysing the link between export intensity, innovation and firm size in a science-based industry. International Business Review 16(3): 275–293.

84.

Raymond

St-Pierre

(2010) R&D as a determinant of innovation in manufacturing SMEs: an attempt at empirical clarification. Technovation 30(1): 48–56.

85.

Roper

(1997) Product innovation and small business growth: a comparison of the strategies of German, UK and German Companies. Small Business Economics 9(6): 523–537.

86.

Salomon

Shaver

(2005) Learning by exporting: new insights from examining firm innovation. Journal of Economics & Management Strategy 14(2): 431–460.

87.

Sawyer

McGee

Peterson

(2003) Perceived uncertainty and firm performance in SMEs. International Small Business Journal 21(3): 269–284.

88.

Schumpeter

(1942) Capitalism, Socialism and Democracy. New York: Harper.

89.

Sheehan

Garavan

Carbery

(2014) Innovation and human resource development (HRD). European Journal of Training and Development 38(1/2): 2–14.

90.

Sin Tan

Choy Chong

Lin

, et al. (2010) Internet-based ICT adoption among SMEs: demographic versus benefits, barriers, and adoption intention. Journal of Enterprise Information Management 23(1): 27–55.

91.

Simmonds

(1986) Marketing as Innovation the eighth paradigm. Journal of Management Studies 23(5): 479–500.

92.

Simmons

Armstrong

Durkin

(2011) An exploration of small business website optimization: enablers, influencers and an assessment approach. International Small Business Journal 29(5): 534–561.

93.

Simpson

Docherty

(2004) E-commerce adoption support and advice for UK SMEs. Journal of Small Business and Enterprise Development 11(3): 315–328.

94.

Skuras

Tsegenidi

Tsekouras

(2008) Product innovation and the decision to invest in fixed capital assets: evidence from an SME survey in six European Union member states. Research Policy 37(10): 1778–1789.

95.

Smallbone

Baldock

Burgess

(2002) Targeted support for high-growth start-ups: some policy issues. Environment and Planning C: Government and Policy 20(2): 195–209.

96.

Thakur

(1999) Size of investment, opportunity choice and human resources in new venture growth. Journal of Business Venturing 14(3): 283–309.

97.

Tomlinson

Fai

(2013) The nature of SME co-operation and innovation: a multi-scalar and multi-dimensional analysis. International Journal of Production Economics 141(1): 316–326.

98.

Turner

Michael

(1996) What do we know about don’t knows? Or, contexts of ‘ignorance’. Social Science Information 35(1): 15–37.

99.

Uhlaner

van Stel

Duplat

, et al. (2013) Disentangling the effects of organizational capabilities, innovation and firm size on SME sales growth. Small Business Economics 41(3): 581–607.

100.

Unsworth

Sawang

Murray

, et al. (2012) Understanding innovation adoption: effects of orientation, pressure and control on adoption intentions. International Journal of Innovation Management 16(1): 1250004.

101.

Van de Vrande

de Jong

JPJ

Vanhaverbeke

, et al. (2009) Open innovation in SMEs: trends, motives and management challenges. Technovation 29(6): 423–437.