MO, however, evolves as a function of organisational core competency only when it is amalgamated with a learning climate (Slater & Narver 1995). Proposed as antecedent of MO, organisational learning, in its basic sense, refers to (Jerez-Gómez, Cespedes-Lorente & Valle-Cabrera 2005):

Such learning norms strengthen market-driven firms with a better sensing of new information in the marketplace (Sinkula 1994; Slater & Narver 1995) and being more flexible and responsive in value creation than the competitors (Day 1994). Put differently, a high degree of learning within an organisation constitutes critical sources for long-term superior performance (Alegre & Chiva 2008; Baker & Sinkula 1999; Hurley & Hult 1998; Jimenez-Jimenez & Sanz-Valle 2011). Any organisation, overtly, might eventually end with dysfunction without the relentless pursuit of learning (DiBella 1995).

Mere aspiration to transform into a learning organisation is inadequate and, additionally, firms must attempt to turn their learning into capabilities (Hamel & Prahalad 1994): ‘creating a market orientation is only a start’ (Slater & Narver 1995:63). Inspired by these implicative assertions, various scholars have exposed that much of the firm’s competitive capability hinges on the managerial coordination to realise the synergistic effect of MO and organisational learning to result in the aforementioned capability (Baker & Sinkula 1999; Farrell & Oczkowski 2002; Hult, Hurley & Knight 2004; Hurley & Hult 1998; Keskin 2006; Lertpachin, Wingwon & Noithonglek 2013; Sinkula 1994; Sinkula, Baker & Noordeweir 1997). Echoing the importance of the combinative view, however, the prior findings did not examine in particular whether specific antecedents, such as organisational learning, would nurture small firms’ market-oriented competence across their routine business activities (Raju et al. 2011). Addressing this need, it is postulated that:

In evaluating the preceding research, it is uncovered that innovation mostly goes along with the aim of achieving some sort of competitive advantage to outperforms rivals (Salunke et al. 2011). While the conventional conceptualisation of innovation dominates over product or production means (Drejer 2004; Slaughter 2000), such a tangible approach has increasingly attracted criticism for the risk of disintegrating other relevant metrics (Xue et al. 2018). Joining this debate, recent scholarly efforts reform the dynamism of innovation in the role of non-technical innovation (Brochner 2010; Hipp & Grupp 2005; OECD 2005) that is suggested as one of the pivotal pillars of organisational competitiveness (Evangelista & Vezzani 2010; Greenan 2003; Mol & Birkinshaw 2009). Tracked through the extant literature, theoretically and empirically, non-technical advancements are depicted to entail organisational (or managerial) and marketing innovations (Camison & Villar-Lopez 2011; OECD 2005).

Structurally, organisational innovation includes changes in responsibilities or the information flow and divisional structure of a firm; procedurally, organisational innovation involves the new implemented routines, processes and operations within firm (Armbruster et al. 2006). In project business, new business practices would enable firms to acquire a greater competitive advantage (Seaden et al. 2003), and enhance problem-solving and profitability (Pellicer et al. 2012). Firms pursuing advanced practices in management could better manage projects over a range of related activities, resources and business linkages (Hakansson & Ingemansson 2013). Marketing innovation, on the other hand, relates to new marketing methods that manifest significant changes in design or packaging, placement, promotion and pricing (OECD 2005). From a practical standpoint, innovative marketing efforts are especially important for project-based companies in aiding business development (Dikmen, Birgonul & Ozcenk 2005). A recent study has also demonstrated that construction organisations that carefully and systematically integrated their pricing and promotion strategies with their firm’s specific resources had a higher chance of successful tender bidding (Coskun et al. 2013).

To sum up, both organisational and marketing innovations have a substantial impact on business success. Potentially, non-technical innovation produces similar beneficial effects as those depicted in cost-reducing process enhancements or customer value-generating product developments (Evangelista & Vezzani 2010). To date, how non-technical measures enable firms of a project-based nature to seize greater market positional advantage is underexplored (Brochner 2010). Using a small firm as the focal point, this study accordingly advances an analysis of non-technical innovation to appropriately capture its impact on businesses that focus on project-based provisioning.

Innovation is essentially an outcome of strong market-oriented culture (Hurley & Hult 1998). Underlined as one of the ‘core-value creating capabilities’ (Slater & Narver 1994), MO enables access to valuable inputs required for innovation such as accumulated knowledge from customers, rivals and technologies (Deshpande, Farley & Webster 1993). In this connection, innovative decision-makers rely on customer input to secure improved outcomes through the strategic alignment of customer-oriented needs with organisational innovation efforts (Alam 2002). Not only does market-oriented culture foster an optimal environment for continuous value creation (Han et al. 1998), it also allows firms to capitalise on market-based success through customising the benefits of novel offerings for customers (Baker & Sinkula 1999; Matear et al. 2002; Tsiotsou 2010). Accordingly, knowledge sources relating to clients or competitors can essentially inspire new organisational practices (Birkinshaw & Mol 2006). To better accommodate customer needs, both intra-organisational and inter-organisational innovation could support firms with greater efficiency, effectiveness and flexibility in their service deliveries (Armbruster et al. 2006). Therefore, the present study conjectures that firms integrate both marketing and organisational innovation into their quest to better accommodate market demands:

Literature has long acknowledged the role of knowledge-based capabilities in driving the discovery of value-creating opportunities in the form of innovation (Alegre & Chiva 2008; Hurley & Hult 1998; Jimenez-Jimenez & Sanz-Valle 2011; Pesamaa et al. 2013). Nonetheless, rarely are these recognised in the domain of non-technical innovation (Nyström et al. 2016; Zubizarreta et al. 2017). Exceptionally, a recent empirical analysis conducted on industrial firms views both organisational memory and learning capabilities as the key to discover novel organisational and marketing practices (Camison & Villar-Lopez 2011). Likewise, some studies suggest that different forms of learning, such as market-focused, internally focused and relationally focused learning, exert different impacts on the development of innovative managerial practices and marketing instruments (Weerawardena 2003; Weerawardena et al. 2006). Even in high-tech industries, new aspects of organisational practice are essential in nurturing an optimum learning culture that fosters firms’ competitiveness (Lee, Tan & Chiu 2008). These empirical investigations were in consensus about the value derived from both organisational learning and non-technical advancement to achieve positive organisational outcomes (Camison & Villar-Lopez 2011; Lee et al. 2008; Weerawardena 2003; Weerawardena et al. 2006).

In relation to innovation, the importance of learning has also been discussed in project-based business literature (Drejer & Vinding 2006; Gann & Salter 2000; Salunke et al. 2011). Along with the impedance of knowledge loss between various firms associated with boundary-crossing projects, Gann and Salter (2000) suggest that firms fail to coherently integrate their on-off project experience into the repetitive business routine to achieve sustained innovative opportunities. Also, Drejer and Vinding (2006) accentuate that innovative initiatives are anchored through firms’ continuous efforts to collect, diffuse and assimilate project-specific knowledge at the firm level. Interestingly, Manley (2008) argues that the known liabilities of smallness could turn into positive features of small firms, where the knowledge transposal processes are relatively easier and therefore promote the generation of new ideas despite the resource-poor environment. In multiple case studies, Salunke et al. (2011) decipher the dynamic mode of different learning activities in connection with the occurrence of project-oriented innovation. However, it is important to note that these studies are exclusive to the conceptualisation of innovation over non-technical means. Given that project-based firms innovate technically and non-technically (Brochner 2010), the likely impact of organisational learning on non-technical measures is posited as follows:

The extant research offering evidence of the practical relevance of non-technical innovation in regard to entrepreneurship is inconclusive. While some researchers echo the strong positive impact that non-technical innovation has on performance (Caroli & Van Reenen 2001; Evangelista & Vezzani 2010; Greenan 2003; Hamel 2006; Mazzanti, Pini & Tortia 2006; Mol & Birkinshaw 2009; Shergill & Nargundkar 2005), others present contradictory conclusions to the empirical relationship (Cappelli & Neumark 2001; Staw & Epstein 2000; Walker, Damanpour & Devece 2010). Furthermore, the incongruous findings are complicated by varying metrics of organisational outcomes, such as brand performance (Weerawardena et al. 2006), improvement in future productivity (Mol & Birkinshaw 2009), reputational advantage (Staw & Epstein 2000), organisational effectiveness (Jimenez-Jimenez & Sanz-Valle 2011) and others. Positing non-technical innovation as a predictor of positive entrepreneurship, which has been argued to be better measured from both financial and market perspectives (Cheng & Krumwiede 2012; Matear et al. 2002; Shergill & Nargundkar 2005), the present study proposed the following hypotheses:

The research framework of this study is presented in Figure 1.

This study follows Nasution and Mavondo’s (2008) work to operationalise MO as four dimensions: customer orientation, competitor orientation, inter-functional coordination and latent need fulfilment. The additional dimension of latent need fulfilment is a crucial focus on customers’ unexpressed needs in order to discern and anticipate potential market-based opportunities by proactive means. In consideration of previous works (Day 1994; Slater & Narver 1995) that omitted the component of inter-functional coordination, the final MO scale consists of customer orientation (4 items), competitor orientation (4 items) and latent need fulfilment (4 items), structured on a five-point Likert-type scale (anchored by ‘strongly disagree’ and ‘strongly agree’).

This study adopts the scale developed by Jerez-Gómez et al. (2005) in interpreting the observed OL across four dimensions: managerial commitment (5 items), systems perspective (3 items), openness and experimentation (4 items) and knowledge transfer (4 items). Like MO, OL is assessed using a five-point Likert-type scale (anchored by ‘strongly disagree’ and ‘strongly agree’).

This study adapted the scales previously developed by Dikmen et al. (2005) and OECD (2005). Two dimensions, organisational innovation (4 items) and marketing innovation (4 items), were self-assessed by respondents on a five-point Likert-type scale (anchored by ‘never practice’ and ‘always practice’). All eight items are self-reported by respondents to conquer the difficulties in requesting sensitive information pertaining to organisations (Im & Workman 2004). Finally, age of the firm was viewed as the extraneous variable given its documented effect on innovation-related performances (Jimenez-Jimenez & Sanz-Valle 2011; Sinkula 1994).

Some preliminary analyses were performed in the first place. Primarily, in assessing the univariate skewness and kurtosis for each construct, an acceptable level of data distribution was detected. The reliability of each construct, as indicated by the Cronbach’s alpha values, was well above the threshold value of 0.7 (Nunnally 1978). Further, both the Kaiser–Meyer–Olkin (KMO) index and Bartlett’s test of sphericity were computed to determine the sufficiency of the data set for exploratory factor analysis (EFA) (Chu & Murrmann 2006). The result, with a KMO index of 0.885 and Bartlett’s sphericity test of high significance (p < 0.001), indicated the appropriateness of EFA in the subsequent analysis. In this regard, a principal component method with oblique rotation was performed for all the measures to examine their respective factor structures. Regardless of the theoretical underpinning, Hair et al. (2006) suggested the removal of an item if: (1) its factor loading is less than 0.5, (2) it cross-loads in two different groups at a time or (3) it loads in a group that it does not belongs to. Accordingly, two items from MO constructs and three items from OL constructs were dropped. While the OL construct was restructured into two new related groups, the remaining indicators (i.e. three components of MO, two non-technical innovation types and two metrics of entrepreneurship) have loaded on their respective latent construct as expected. In the next step, all the multi-item constructs were assessed by confirmatory factor analysis (CFA) using partial least square (PLS) path modelling.

The fitting index of measurement for each construct was computed via CFA. To assess the composite reliability, the factor loadings of each item should be above the suggested threshold of 0.708 (Hair et al. 2014). Accordingly, one item in the construct of MO and three items in the construct of OL were dropped due to low and insignificant factor loadings. As recommended by Nunnally (1978), all the multi-item constructs should fit a minimum value of 0.8 for the demonstration of construct reliability. Ranging from 0.86 to 0.90 (Table 1), the result indicated good reliabilities across the measures. Meanwhile, the average variance extract (AVE) values of the key constructs were higher than 0.5 (Fornell & Larcker 1981) and therefore in support of convergent validity. Finally, the discriminant validity was determined by the value of the key construct in its square root of AVE (SQRT AVE) to be greater than its correlation with any other factors in the framework (Hair et al. 2006). The result, diagonal from 0.71 to 0.84, showed adequate discriminant validity for each construct. Table 1 presented the specification of the adequacy of the measurement model in all cases.

Rather than goodness-of-fit indices (Henseler & Sarstedt 2013), the robustness of a structural model is computed via its explanatory power on the hypothesised paths (Hair et al. 2014). Both MO and OL are the exogenous constructs, with organisational and marketing innovation as the intermediate endogenous constructs, and entrepreneurship as the endogenous outcome. As indicated in Figure 2, the square multiple correlations (R²) value of the structural model revealed a very satisfactory level of predictability for the framework. The respective R² value for the intermediate endogenous constructs (i.e. organisational innovation = 0.65; marketing innovation = 0.48) and the endogenous latent constructs (i.e. entrepreneurship = 0.52) largely exceeded the 0.1 threshold value (Falk & Miller 1992).

Next, each standardised path coefficient (β) and the associated t-values of each latent construct were examined (as indicated in Table 2). Accordingly, MO has a moderate significant positive effect on entrepreneurship (β = 0.25, t = 2.644), supporting H1. A strong and significant positive effect exists between MO and OL (β = 0.76, t = 19.708), supporting H3. The significant effect of MO on organisational innovation (β = 0.21, t = 2.435) and marketing innovation (β = 0.45, t = 4.756) supports H4 and H5. Organisational learning has a significant positive effect on organisational innovation (β = 0.33, t = 4.937) and marketing innovation (β = 0.29, t = 3.181), supporting H6 and H7. The effects of organisational innovation (β = 0.21, t = 2.482) and marketing innovation (β = 0.23, t = 3.171) on entrepreneurship are also significant, giving support to H8 and H9. Contrary to prediction, however, no support is indicated for the hypothesised path of OL and entrepreneurship (H2). The control variable of firm age was significant as well. Nevertheless, an interesting significant effect is detected between marketing innovation and organisational innovation (β = 0.38, t = 5.013).

Beyond the direct hypothesised paths, the present study disentangled the indirect paths to understand the mediating function of non-technical innovation in its causal relationships with its antecedents and consequences. Consistent with Cheng and Krumwiede’s (2012) recommended component-wise procedure, we dismantle each of the key constructs into their respective components to better scrutinise the specific mediated effects of the mediators. The significance of each path was computed using the Sobel test (Preacher & Hayes 2008) assisted with bootstrapping procedure. As indicated in Table 3, both the first-order and second-order analysis of structural models offered support for the existence of a mediation effect in the proposed framework. For instance, in observing the second-order structural model, the path of market orientation to entrepreneurship significantly indicated a direct effect (β = 0.25, p < 0.01) and an indirect effect (β = 0.18, p < 0.05). Similarly in the path of organisational learning to entrepreneurship, the direct effect (β = 0.17, p < 0.05) and indirect effect (β = 0.16, p < 0.05) are statistically significant. Further to this line of assessment, the variance accounted for (VAF) in the proposed model was computed to determine the mediating strength of the mediator. The respective variances of less than 20%, between 20% and 80%, or more than 80% equate to no mediation, partial mediation and full mediation (Hair et al. 2014). As such, non-technical innovation was observed to exert a partial mediating effect in both MO to entrepreneurship (VAF = 42%) and OL to entrepreneurship (VAF = 41%) paths.

Figure 2 presents the developed structural model of innovation based on the quantitative strand. Through the use of PLS structural equation modelling, the structural model indicated the factors that significantly relate to innovation practices of the firms. These included organisational capabilities (all except human resource practice construct), innovations (include both technological and organisational innovation activities) and entrepreneurship. Importantly, it depicted that firms affiliated with certain capabilities could competently pursue the undertaking of innovations across projects and within the firm to acquire higher performance.

Moving a step further into the first-order structural model, several interesting findings were suggested (Table 3). Under the MO construct, customer orientation was insignificantly associated with both financial performance (β = 0.03) and market performance (β = 0.02), suggesting no mediation in the paths. Meanwhile, competitor orientation was significantly and positively related to financial performance (β = 0.13, p < 0.05) and market performance (β = 0.15, p < 0.05), both of which, in turn, imply partial mediated effects in the paths. Likewise, the indirect effects of latent needs fulfilment regarding financial performance (β = 0.14, p < 0.05) and market performance (β = 0.11, p < 0.05) were significant, suggesting partial mediation as well. Under the OL construct, managerial commitment and openness were significantly related to financial performance (β = 0.13, p < 0.05) and market performance (β = 0.12, p < 0.05), suggesting partial mediated effects in the paths. Additionally, knowledge transfer and integration had a significant indirect effect on financial performance (β = 0.17, p < 0.05) but a non-significant effect on market performance (β = 0.04). Figure 3 presents the structural equation model.

The optimum number of extracted components for each key construct was determined. Traditional methods included a latent root test (that identified all factors to have eigenvalues > 1) a scree test (that examines the cut-off point in the curve graph). A syntax named ‘Tsplot’ was conducted for parallel analysis, which was a plot with horizontal lines crossing the main line of the eigenvalue. The number of factors lying above the crossing lines determining the appropriate number of extracted factors. The parallel analysis was used to determine the appropriate number of extracted factors for all eight the constructs, including: integrated market orientation (IMO), organisational learning (OL), human resource practice (HRP), entrepreneurship (EO), inter-organisational network (IN), technological innovation (TI), organisational innovation (OI) and entrepreneurship (FP). As depicted in Figure 4, only factors lying above the extraction line were considered. The y-axis denotes the eigenvalue while the x-axis denotes the number of variables. Appendix G showed O’Connor’s syntax and the steps of conducting parallel analysis.

Via the PLS technique, the full model was depicted by connecting all constructs according to the conceptual model. All manifest variables were assigned to their latent constructs based on the results of analyses in the previous sections. As shown in Figure 5, the full model comprised both measurement and structural models. The measurements models displayed the relationship between the constructs (i.e. circles) and the indicator variables (i.e. rectangles) while the structural model displayed the relationship (or paths) between the constructs (e.g. OL and TI; TI and FP, etc.). The current study used the following criteria to run the algorithm in PLS to obtain the results of the full model:

This study has informed several new insights to academic research. Firstly, unlike previous studies that predominantly anchored on the technical sense of innovation, this study examines the offerings of non-technical innovation by specifically drawing their relation with the firm’s capabilities. Based on the empirical results, capabilities are important determinants of non-technical innovation. Both market-oriented and learning postures, as disclosed in their mutually complementary effects (β = 0.76, t = 19.709), synergistically spur on the coupled development of non-technical innovation. Remarkably, the leverage of market-oriented efforts within the learning perspective not only favours the growth of technical innovation (Hurley & Hult 1998; Lertpachin et al. 2013; Nasution & Mavondo 2008), but also constitutes impetus for new organisational and marketing practices. These findings have unlocked interesting extensions to previous works (Camison & Villar-Lopez 2011; Weerawardena, 2003; Weerawardena et al. 2006) that advocate the antecedent role of the firm’s capabilities in exploiting the novel value creation within the framework of non-technical initiatives.

Secondly, this study advances the inconclusive empirical findings that have sought to model non-technical innovation in affiliation with the preponderance of business performance. As noted earlier, the innovation literature frequently interweaves entrepreneurship with diverse types of outcome metrics. Featuring entrepreneurship in both financial and market forms, as depicted in the major stream of empirical research (Cheng & Krumwiede 2012; Matear et al. 2002; Shergill & Nargundkar 2005), the result reveals the nearly significant positive effect between the two varying non-technical innovation dimensions with regard to entrepreneurship. Specifically, organisational innovation (β = 0.21, t = 2.486) and marketing innovation (β = 0.23, t = 3.171) play a favouring role in engendering beneficial sources of opportunities that directly impact on business performance. In addition to this line of evidence, both innovative approaches unravel their interdependent linkage (β = 0.38, t = 5.013) in further establishing the performance breadth. As stressed by Gambatese and Hallowell (2011), some innovations have intangibly occurred throughout routine business activities although they are more difficult to recognise in contrast to tangible product-based innovations. Our study represents a supporting response to the call of Brochner (2010) for the inclusion of non-technical innovation in a setting that captures small firms’ pursuit of innovation-based performance.

Thirdly, this study specifically scrutinised the potential mediating impact of non-technical innovation in the capabilities-performance linkage. In particular, the findings were in contrast to earlier studies that ued customers’ explicit thoughts as the basis of innovative capability among small and medium enterprises (Akman & Cengiz 2008). In contrast, non-technical innovative offerings were observed to substantiate beneficial entrepreneurship through firms’ efforts to adapt to implicit customer expectations and integrate knowledge linkages across peer competitors. Similar results were found in prior studies that captured organisational business advantage in affiliation with the appropriate input of latent needs of customers (Nasution & Mavondo 2008) and competitor orientation (Cheng & Krumwiede 2012). Likewise, the favouring role of non-technical innovation is found to partially intertwine with both the components of managerial commitment and openness, as well as knowledge transfer and integration in relation to entrepreneurship. In this regard, the sturdier the integral trait of project-centred learning at the firm level, the greater the performance value sustained in firms (Manley 2008; Salunke et al. 2011). The current study presents more refined findings in discerning the impact of innovative offerings upon their antecedents and consequences.

Notably, the findings offer valuable implications for small service firms. To secure new service offerings, managers should pay special attention to the role of non-technical innovation in connection with both organisational and marketing dimensions. Specifically, given the constrained resources and uncertain site-based operations, firms affiliated with certain strategic capabilities could competently pursue the undertaking of non-technical innovation. Managerial emphasis should therefore persistently deploy to seize new marketplace opportunities in alignment with various active learning-based practices. Accordingly, both MO and OL need to be considered, interdependently, in enabling the non-technical feasibility of innovation delivery. In addition, non-technical innovation has emerged as a favourable source for the overall success of business performance. Even with finite resources, managers should strive to capitalise the two distinctive types of innovation while concatenating them into routine business operation and project activities, without which superior firm success is difficult to underpin. Our thorough empirical analysis should enlighten practitioners that new service offerings should not be based solely on a product and process viewpoint, but organisational and marketing prospects should be included in strategic innovation.