Introduction
Learning orientation is surmised as a company-level pledge toward ‘creating and using knowledge to enhance competitive advantage’ (Calantone, Cavusgil, & Zhao, Reference Calantone, Cavusgil and Zhao2002, p. 516). Learning-oriented companies are characterized by the copresence of four specific values: (a) encouragement of commitment to learn, (b) nourishment of a shared vision, (c) support of open-mindedness, and (d) promotion of intraorganizational knowledge sharing procedures (Baker, Mukherjee, & Perin, Reference Baker, Mukherjee and Perin2022; Calantone et al., Reference Calantone, Cavusgil and Zhao2002). Such an orientation represents a founding condition for the development of successful organizational learning initiatives. Still, the pursuit of learning orientation necessitates the development of a culture rooted on the creation of a favourable disposition toward learning, whereas organizational learning simply describes the descending mechanisms and processes pertaining how an organization and its member learn (Hu, Reference Hu2014; Sinkula, Baker, & Noordewier, Reference Sinkula, Baker and Noordewier1997). Being a learning-oriented company also implies overture to information coming from external sources, that is pivotal to support increments in the internal knowledge base (Baker & Sinkula, Reference Baker and Sinkula1999a, Reference Baker and Sinkula1999b).
Pertinent literature associates learning orientation with innovation performance (Alerasoul, Afeltra, Hakala, Minelli, & Strozzi, Reference Alerasoul, Afeltra, Hakala, Minelli and Strozzi2022; Baker et al., Reference Baker, Mukherjee and Perin2022; Calantone et al., Reference Calantone, Cavusgil and Zhao2002). It has specially been observed how learning orientation influences companies’ absorptive capacity (Gutiérrez, Bustinza, & Molina, Reference Gutiérrez, Bustinza and Molina2012), business model innovation (Bae & Choi, Reference Bae and Choi2021), employees competencies’ acquisition (Alerasoul et al., Reference Alerasoul, Afeltra, Hakala, Minelli and Strozzi2022), and technology selection and adoption (Schilling, Reference Schilling2002), all factors consequently affecting firm innovation performance – i.e., the degree to which a company is proficient in improving internal processes or the usefulness and usability of its material and immaterial outputs (Calantone et al., Reference Calantone, Cavusgil and Zhao2002). These findings are consistent across the organizational stream of knowledge management research (Addicot et al., Reference Addicott, McGivern and Ferlie2006); indeed, learning orientation explains why some companies are better than others in seeking/finding/collecting knowledge, creating new one to support modernization, and have information permeate to any functional area requiring knowledge to complete an innovative task (Calantone, Harmancioglu, & Droge, Reference Calantone, Harmancioglu and Droge2010).
Learning orientation has also been deemed related to product innovation. Companies with greater absorptive capacity and better execution of knowledge-related activities are more inclined to generate creative ideas potentially delving into early proofs of concept (Pellegrini, Ciampi, Marzi, & Orlando, Reference Pellegrini, Ciampi, Marzi and Orlando2020; Xie, Gao, Zang, & Meng, Reference Xie, Gao, Zang and Meng2020). Innovative products arise from companies’ ability to combine market information with productive best practices (Hervas-Oliver, Sempere-Ripoll, & Boronat-Moll, Reference Hervas-Oliver, Sempere-Ripoll and Boronat-Moll2021). Hereafter, learning-oriented companies are more performant in launching new products on the market (Calantone et al., Reference Calantone, Cavusgil and Zhao2002), as they are capable to combine internal and external knowledge to support the development of something different via existing resources.
In spite of abundant research on learning orientation and product innovation, scholars scantly explored the connection between these two constructs, frequently assuming the existence of a direct and unmediated relationship (Calisir, Altin Gumussoy, & Guzelsoy, Reference Calisir, Altin Gumussoy and Guzelsoy2013). This research attempts to fill this research gap by observing whether strategic flexibility represents a mediating factor between learning orientation and product innovation. We also speculate that innovation culture – i.e., the nurturing of a culture aiming at creating transparency and proactiveness in respect of innovating (Martin-de Castro, Delgado-Verde, Navas-López, & Cruz-González, Reference Martin-de Castro, Delgado-Verde, Navas-López and Cruz-González2013) – and market acuity – i.e., companies enhanced capabilities to observe market changes (Rosenzweig & Roth, Reference Rosenzweig and Roth2007) – are reenforcing the hypothesized relationship. The research consequently wishes to reply to the following research question:
RQ: Does strategic flexibility explicate the relationship between learning orientation and product innovation? Do innovation culture and market acuity also play a role?
While product innovation is vital for any company, the choice to rely predominantly on external or internal knowledge anyhow depends on company characteristics (Annosi, Marzi, Ciampi, & Rialti, Reference Annosi, Marzi, Ciampi and Rialti2022). Large or multinational companies, for instance, tend to have formally defined procedures for innovation such as investigating competition, collecting customer preferences, and analysing data (Rhee, Park, & Lee, Reference Rhee, Park and Lee2010; Spithoven, Vanhaverbeke, & Roijakkers, Reference Spithoven, Vanhaverbeke and Roijakkers2013). By contrast, SMEs often lack the financial resources of larger counterparts, the numbers of employees dedicated to R&D to create bespoke teams, and the ability to manage multiple simultaneous product innovation endeavours (Del Giudice et al., Reference Del Giudice, Scuotto, Papa, Tarba, Bresciani and Warkentin2021).
SMEs’ product innovation processes have been thoroughly investigated in relation to the importance of these companies in any economic landscape. Van de Vrande, De Jong, Vanhaverbeke and De Rochemont (Reference Van de Vrande, De Jong, Vanhaverbeke and De Rochemont2009) asserts that the most common strategy for SMEs to innovate pertains the exploitation of existing knowledge repositories with limited usage of external information. Research consistently stresses the role of dynamic capabilities to sense market opportunities and for the reconfiguration of existing assets, even in absentia of ad hoc explorative units (Chakma & Dhir, Reference Chakma and Dhir2023; Valenza, Caputo, & Calabrò, Reference Valenza, Caputo and Calabrò2023). SMEs innovation processes, hereby, incorporate some of the values of learning orientation spontaneously to cope with resource and staff shortage (D’Angelo & Presutti, Reference D’Angelo and Presutti2019). In this vein, SMEs were deemed a suitable context to evaluate the intermediate factors between learning orientation and product innovation (Ayoko, Caputo, & Mendy, Reference Ayoko, Caputo and Mendy2021; Keskin, Reference Keskin2006).
We draw on social complexity theory and dynamic capabilities to answer the aforementioned research question (Antonacopoulou & Chiva, Reference Antonacopoulou and Chiva2007; Eisenhardt & Martin, Reference Eisenhardt and Martin2000). Social complexity theory explains the social nature of interaction within businesses and how each person within a business may be an agent of change through his/her interactions to the strategic renewal of the company (Chiva, Grandío, & Alegre, Reference Chiva, Grandío and Alegre2010). Dynamic capabilities framework describes which capabilitie support re-configuration processes occurring in the wake of the integration of new knowledge (Eisenhardt & Martin, Reference Eisenhardt and Martin2000).
Empirical evidence is drawn from data of 300 UK SMEs’ managers. Through the use of structural equation modelling, it was possible to understand the cause-and-effect relationships among the hypothesized variables and to confirm or reject our hypotheses.
The rest of the paper is structured as follows. The second paragraph describes the theoretical background and the main hypotheses underlying our research. The third paragraph describes the two methods and why they were deemed suitable and compatible to assess results veracity through comparison. The fourth one deals with results. The final two paragraphs, instead, present the discussions and the conclusions of the research respectively. Results show the fundamental role of strategic flexibility in mediating the relationship between learning and product innovation. Innovation culture has instead a moderating function. Practical implications about the importance for SMEs to cultivate a learning orientation entrenched culture emerged, as through this orientation it may be possible for company to adjust resources and structures to address increasing complexity deriving from turbulent environments.
Theoretical background and hypothesis development
Learning orientation: A social complexity perspective
Learning orientation involves a company-wide critical evaluation of current norms and knowledge (Sinkula et al., Reference Sinkula, Baker and Noordewier1997). Learning-oriented companies challenge the established organizational structures guiding their activities and actions aiming to think outside the box (Baker & Sinkula, Reference Baker and Sinkula1999a; Reference Baker and Sinkulab). Over the years, the debate around learning has significantly expanded, offering new analytical perspectives to its interpretation (Real, Roldán, & Leal, Reference Real, Roldán and Leal2014; Wang, Reference Wang2008).
Learning orientation articulates around four principal values. Commitment to learning, the first, refers to company efforts to develop an environment focused on learning. The second is shared vision, that deepens the motivations that lead organizational members to learn. Its presence allows the overcoming of communication barriers and encourages cross-functional flow of information by forming a common sense within the company boundaries. Then, open-mindedness refers to the ability to evaluate and accept new approaches. It implies the willingness to question past methods through the integration of new knowledge and allows to renew ideas and knowledge. Finally, intra-organizational knowledge sharing explores behavioural routines that foster learning within different organizational units. Knowledge sharing within the organization is necessary to prevent information loss and promote repositories’ increments (Calantone et al., Reference Calantone, Cavusgil and Zhao2002).
Various research has demonstrated learning orientation positive effects on performance, competitive advantage, and innovation (Sawaean & Ali, Reference Sawaean and Ali2020; Sheng & Chien, Reference Sheng and Chien2016). From a strategic perspective, many authors associated the concept of learning orientation with constructs such as market orientation and entrepreneurial orientation (Anderson, Covin, & Slevin, Reference Anderson, Covin and Slevin2009; Wang, Reference Wang2008). Baker and Sinkula (Reference Baker and Sinkula1999a) firstly explored these relationships, concluding that commitments to customer value creation (market orientation) and higher learning processes were essential for achieving superior performance. According to Wang (Reference Wang2008) market-oriented culture facilitates the learning and application of new knowledge, which can be used to exploit opportunities and create competitive advantages.
Despite the strong support for these perspectives, disagreement exists about the assumption that all market-oriented firms have strong learning orientations (Baker et al., Reference Baker, Mukherjee and Perin2022). Market orientation focuses on gathering and responding to market information for strategies and tactics, while learning orientation centres on continuous internal improvement (Wilson & Liguori, Reference Wilson and Liguori2023). These differences can lead a company to be effective in the market without adopting a learning-oriented approach. Following the perspective of Baker et al. (Reference Baker, Mukherjee and Perin2022), strategic learning helps a company better implement a strategic orientation rather than cause it.
Research anyway highlights that this strategic perspective on learning orientation does not adequately address the role of capabilities (Baker et al., Reference Baker, Mukherjee and Perin2022). If learning involves questioning existing procedures and organizational values, then the attitude to reconfigure a firm’s tangible assets in response to changing demands becomes crucial. The development of strategic capabilities is supported by deep market insights and a culture of openness. Besides these perspectives, literature sometimes treated learning orientation as phenomenon that manifests through the learning activities of individual actors (i.e., Antonacopoulou & Chiva, Reference Antonacopoulou and Chiva2007; Chiva & Habib, Reference Chiva and Habib2015). Yet, considering learning orientation as an individual-level process is limiting, as companies should be viewed as groups of persons that learn collectively (Akella, Reference Akella2010).
The relevance of groups and multi-level analysis is stressed by the social complexity approach, according to which learning orientation require the consideration of individual as participant to broader social structures (Caputo & Pellegrini, Reference Caputo and Pellegrini2020; Huang, Lai, Kao, & Sung, Reference Huang, Lai, Kao and Sung2014). The social complexity perspective considers the relevance of internal relationships as facilitators of knowledge exchanges occurring between the company and its environment (Chiva et al., Reference Chiva, Grandío and Alegre2010; Singh, Charan, & Chattopadhyay, Reference Singh, Charan and Chattopadhyay2022). This perspective of learning describes in an appropriate fashion how SMEs need to operate in current dynamic environment, as these companies look after increments in their knowledge repositories through multiple levels iterations (i.e., among individuals, groups, and the environment). Engaging with the social context enables the questioning of imbedded norms and paradigms. However, to effect change, companies must combine external stimuli with internal components through self-reorganization capabilities, a crucial aspect of the complex approach.
Self-organization is the key to adjustment, as it is an action-oriented interpretive schema capable of determining how firms view and interact with other agents and the environment (Chiva et al., Reference Chiva, Grandío and Alegre2010).
The framework provided by social complexity offers a pathway for understanding learning orientation that addresses the limitations of the existing debate. Specifically, this perspective proposes viewing learning orientation as a dynamic process shaped by both internal and external dynamics, rather than as an individual phenomenon. Furthermore, the concept of self-organization offers a means of integrating capabilities into the learning orientation debate.
According to these assumptions, learning orientation, observed through a social complexity view, could be interpreted as the development of knowledge deriving from a multiplicity of connections in which enterprises constantly reconfigure their practices in an incremental fashion. The environmental context in which SMEs operate creates a set of possibilities for learning, and the development of new knowledge and innovation depends on the way companies encode and integrate it internally (Sheng & Chien, Reference Sheng and Chien2016).
Therefore, companies’ ability to create and use knowledge, is the result of a non-linear process involving internal and external exchanges capable of generating specific outcomes (Antonacopoulou & Chiva, Reference Antonacopoulou and Chiva2007). In the present research, we assume that learning orientation can produce better performance in terms of product innovation whether it is capable to stimulate self-organization in the form of strategic flexibility capability (Černe, Jaklič, Škerlavaj, Aydinlik, & Polat, Reference Černe, Jaklič, Škerlavaj, Aydinlik and Polat2012; Martin-de Castro et al., Reference Martin-de Castro, Delgado-Verde, Navas-López and Cruz-González2013; Zhou & Wu, Reference Zhou and Wu2010). In addition, we postulate that this mediation is simultaneously conditioned by endogenous dynamics as innovation culture and the capability to seek and select exogenous information in the form of market acuity (Guinot, Chiva, & Mallén, Reference Guinot, Chiva and Mallén2013; Rosenzweig & Roth, Reference Rosenzweig and Roth2007). The research then attempts to observe how the strategic effects of learning orientation unfold, i.e., how company-wide learning orientation unleashes its generative potential in term of product innovation through achievement of greater flexibility in relation to endogenous conditioning and capability to get exogenous information.
Hypothesis development
Early contributions on learning orientation agreed that learning arouses from individuals’ actions (Antonacopoulou & Chiva, Reference Antonacopoulou and Chiva2007). However, more recent research shifted the attention toward the role of people’s collective practices within organizations, thus, locating learning at the group level by considering the connections and interconnections that exist in the organization (Park & Kim, Reference Park and Kim2018). Such a perspective is coherent with the social complexity view, which reinforces the idea that a company develops learning orientation only when new knowledge is created, combined, assimilated, and used by the multiplicity of agents that compose the company itself (Chiva et al., Reference Chiva, Grandío and Alegre2010).
Companies following this modus operandi develop a continuous improvement-oriented climate capable of producing changes in terms of innovation. By being open to new ideas and new ways of working, the organization is more likely to be exposed to new technologies, new processes and new approaches (Manesh, Pellegrini, Marzi, & Dabic, Reference Manesh, Pellegrini, Marzi and Dabic2020). Donate and Guadamillas (Reference Donate and Guadamillas2011) show that learning orientation is the prerequisite toward a culture oriented at the consideration of internal information while at the same time pursuing openness, which is essential for successful product innovation. The relationship between learning orientation and product innovation was further strengthened by Kumar, Jabarzadeh, Jeihouni and Garza-Reyes (Reference Kumar, Jabarzadeh, Jeihouni and Garza-Reyes2020), which showed that learning orientation encourages the organization to see product innovation as an ongoing process, rather than a one-time event (Caputo & Ayoko, Reference Caputo and Ayoko2021). The investigation of the relationship between learning orientation and product innovation has been fundamentally limited to large companies in specific industries (Calantone et al., Reference Calantone, Cavusgil and Zhao2002), yet in today’s competitive business environment, the ability to innovate and shape new products is also critical for SMEs (Hervas-Oliver et al., Reference Hervas-Oliver, Sempere-Ripoll and Boronat-Moll2021). As demonstrated by Keskin (Reference Keskin2006) when SMEs exhibit learning orientation, they are more likely to identify customer needs, understand the competition, and develop innovative products by uniting external insights and internal knowledge in a purposeful way. Learning orientation also helps SMEs keep abreast of market trends and understand the customer perspective, which is critical to developing successful products (Keskin, Reference Keskin2006). Still, to nurture learning orientation in product innovation SMEs should create a learning environment that encourages experimentation and exploration (Rhee et al., Reference Rhee, Park and Lee2010). This can be done by providing employees with the resources and support they need to explore new ideas and develop innovative solutions (Mori, Cavaliere, Sassetti, & Caputo, Reference Mori, Cavaliere, Sassetti and Caputo2022). Moreover, SMEs should take advantage of external sources of knowledge. This can be done by forming partnerships and alliances with other organizations and leveraging their knowledge and expertise (Caloghirou, Kastelli, & Tsakanikas, Reference Caloghirou, Kastelli and Tsakanikas2004). Hereby, we propose:
Hypothesis 1: Learning orientation has a positive effect on product innovation.
Learning is then a relevant source of competitive advantage for SMEs (Tyre & Von Hippel, Reference Tyre and Von Hippel1997). To what concern competitive and uncertain environments, it was observed how companies characterized by the aptitude to learn better detect emerging trends, understand competition, identify internal inefficiencies in an easier way, and consequently develop timely and appropriate responses (Van de Vrande et al., Reference Van de Vrande, De Jong, Vanhaverbeke and De Rochemont2009).
Hereafter, learning orientation is related to the ability of the organization to change according to the outcome of the learning process. High levels of learning orientation are not just about the organizational capabilities to learn something new but regards the possibility for the organization to critically observe internal mistakes and external change and gather opportunities to change in a more suitable way (Calantone et al., Reference Calantone, Cavusgil and Zhao2002).
This vision is consistent with extant literature on dynamic capabilities (Baker et al., Reference Baker, Mukherjee and Perin2022; Eisenhardt & Martin, Reference Eisenhardt and Martin2000). Capabilities related to learning orientation are fundamental to stimulate reconfiguration of companies. Through learning orientation employees identify what could be improved about how processes are run, share their opinion, and improve processes according to feedbacks from their peers (Wang, Reference Wang2008). The sum of employees’ learning processes contributes in such a regard to the overall learning of the company and contribute to the increase of its capabilities (Mori et al., Reference Mori, Cavaliere, Sassetti and Caputo2022; Wilhelm, Maurer & Ebers, Reference Wilhelm, Maurer and Ebers2022). Likewise, thanks to learning orientation information from external sources could be absorbed and then combined with new internal insights to shape and validate the final product decisions (Pellegrini & Ciappei, Reference Pellegrini and Ciappei2015). Following these perspectives, learning orientation, contributes to the development of generative capabilities, which in turn provide that a business may renew its strategic approach to innovation thanks to multiple iterations occurring internally (Sun & Zou, Reference Sun and Zou2018).
As learning orientation is an integral element of the renewal process of a company, it has been deemed linked to strategic flexibility of a company (Kumar et al., Reference Kumar, Jabarzadeh, Jeihouni and Garza-Reyes2020). Strategic flexibility was defined by Zhou and Wu (Reference Zhou and Wu2010, p. 511) as the sum of organizational capabilities aiming at ‘structuring and coordinating various resources and functional units’ through the absorption and validation of new information and the subsequent willingness to adjust the way a process is run. Hence, it is possible to consider how through learning orientation a company may be more prone to change its structure and use existing knowledge and resources in a different way (Rialti, Marzi, Caputo, & Mayah, Reference Rialti, Marzi, Caputo and Mayah2020). Following these perspectives and in accordance with social complexity theory, learning orientation allows for the reassessment of existing norms and serves as an essential prerequisite for developing strategic flexibility, that explicates internal re-organization attitude.
Hereby, we propose:
Hypothesis 2: Learning orientation has a positive effect on strategic flexibility.
The relationship between strategic flexibility and innovation performance has long been explored in literature (Zhou & Wu, Reference Zhou and Wu2010). Bock, Opsahl, George and Gann (Reference Bock, Opsahl, George and Gann2012) observed how thanks to flexibility companies are more eager to innovate their business model. Flexible companies are more capable to address emerging problems (Kapasuwan, Rose, & Tseng, Reference Kapasuwan, Rose and Tseng2007). Such a trait is related to increased capability to identify inefficiency and to act accordingly. Thanks to flexibility, companies could better integrate new technologies being more culturally adjustable in their processes (Rialti et al., Reference Rialti, Marzi, Caputo and Mayah2020).
Due to their characteristics, flexible companies may coordinate existing resources to develop something new (Hull & Covin, Reference Hull and Covin2010). Then, strategic flexibility has been related to explorative innovation (Ali, Reference Ali2021). With advanced exploration capabilities these companies may identify new markets in an easier fashion and reap the benefits deriving from increasing competitiveness (Rialti, Zollo, Ferraris, & Alon, Reference Rialti, Zollo, Ferraris and Alon2019). Recent studies have also emphasized how strategic flexibility is critical to cope with environmental dynamism and consequently to respond to increasingly complex market demands (Haarhaus & Liening, Reference Haarhaus and Liening2020).
As such, SMEs embedded with learning orientation are favourable to allocate more resources to experiment the development of new products through self-organization (Chapman & Hyland, Reference Chapman and Hyland2004; Yuan, Feng, Lai & Collins, Reference Yuan, Feng, Lai and Collins2018). Even product design may be affected in such contexts, as in these companies’ employees may perceive a culture where they can contribute to experimentation and the proposal of innovative solutions or new product features (Zhou & Wu, Reference Zhou and Wu2010). Thus, the emphasized perspective is that innovative performance outcomes can be achieved through internal re-adaptation driven by learning orientation.
Hereby, we propose:
Hypothesis 3: Strategic flexibility has a positive effect on product innovation.
Social complexity theory provides that learning orientation pertains to two domains: multilevel learning and self-organization (Antonacopoulou & Chiva, Reference Antonacopoulou and Chiva2007). The first regards how learning spans from individuals to groups and the whole organization. The second observes how through learning processes external information permeates in the system and it is used to reconfigure the way the organization is working. Self-organization, hence, is the internal mechanism of learning orientation enacting internal capabilities to integrate and absorb external knowledge (Chiva et al., Reference Chiva, Grandío and Alegre2010). In detail, self-organization describes how internal learning orientation contributes to the development of internal knowledge management capabilities; thus, by self-organization companies may detect which external information is relevant, absorb it, interpret it, and make it reach the right individual, which could in turn disseminate it in the organization (Antonacopoulou & Chiva, Reference Antonacopoulou and Chiva2007).
This process has therefore been deemed as related to the ways in which the organization could reinvent itself during turbulence through strategic flexibility. In such a vein, we assumed the importance of innovation culture in this latter relationship. Innovation culture is a system of value which describes how much an organization is open and ready in respect of innovative solutions (Martin-de Castro et al., Reference Martin-de Castro, Delgado-Verde, Navas-López and Cruz-González2013). Organizations characterized by innovation culture are aware of innovation and ways to pursue it. Hereby, innovation culture may reinforce how an organization identifies opportunities and exploits them thanks to the internal integration (Bock et al., Reference Bock, Opsahl, George and Gann2012).
For this reason, endogenous innovation culture could strengthen the relationship between learning orientation and strategic flexibility. In accordance with social complexity, we therefore assume that innovative culture represents the expression of the set of interactions that occur within an enterprise which are necessary to promote learning.
At the same time, the importance of market acuity capability in the relationship between strategic flexibility and product innovation may emerge. In the wake of the re-configuration occurring due to strategic flexibility achievement in organization characterized by learning orientation, market acuity could strengthen the success of product innovation endeavours (Rosenzweig & Roth, Reference Rosenzweig and Roth2007). This phenomenon occurs as market acuity pertains to the capability in which an organization interacts with the markets. As product innovation aims at customers, businesses wishing to succeed need to be receptive toward the market and customers. Market acuity then represents a proxy to observe how learning-oriented companies obtain exogenous information to integrate customers’ information in product development processes. Although market acuity signifies a firm’s ability to comprehend the market in which it operates, this capability can evolve based on the organization’s interactions with the external environment.
For these reasons, we propose the two following moderation hypotheses:
Hypothesis 4: Innovation culture strengthens the relationship between learning orientation and strategic flexibility.
Hypothesis 5: Market acuity strengthens the relationship between strategic flexibility and product innovation.
Figure 1 graphically represents the proposed research model.
Figure 1. Research model.
Methodology
This study employs a survey method to collect data from managers of SMEs. Data has been gathered in April 2023 through an online questionnaire distributed via the Prolific platform (www.prolific.ac). The accuracy of participant profiles and the reliability of this approach are supported by numerous studies on SMEs that have utilized this platform (Marzi, Manesh, Caputo, Pellegrini, & Vlačić, Reference Marzi, Manesh, Caputo, Pellegrini and Vlačić2023; Schweitzer & Mai, Reference Schweitzer and Mai2021). Additionally, Prolific allows for the selection of participants based on specific characteristics required for the research, ensuring the suitability of the sample (Marzi et al., Reference Marzi, Manesh, Caputo, Pellegrini and Vlačić2023; Schweitzer & Mai, Reference Schweitzer and Mai2021). We deem the identified context particularly appropriate for testing the proposed hypotheses for three main reasons. First, the UK is a thriving environment for SMEs, with many government policies aiming at supporting the success of this category of business and their strategic renewal. Second, the UK is a normative and culturally homogeneous context to consider, differently for example from UE and the US. Third, SMEs in the UK represent almost the entirety of businesses in terms of numerosity (Calabrese, Cowling & Liu, Reference Calabrese, Cowling and Liu2022).
The sample included a set of respondents with significantly different experience in the sector in which they were currently operating. Also, we examined SMEs of different sizes to minimize single-source biases (Groves et al., Reference Groves, Fowler, Couper, Lepkowski, Singer and Tourangeau2011). Furthermore, to avoid ex ante biases due to directional responses to accomplish the intended objective of the study, we did not disclose our research model, and we put the constructs in a random order, not suggesting a particular configuration of causality (Groves et al., Reference Groves, Fowler, Couper, Lepkowski, Singer and Tourangeau2011).
In total, 321 questionnaires have been completed. Of them, 21 were removed due to missing data or engagement as emerged from an attention check introduced in the survey (Groves et al., Reference Groves, Fowler, Couper, Lepkowski, Singer and Tourangeau2011). Thus, the final database of SMEs consisted of 300 observations (Table 1).
Table 1. Sample characteristics
Source: Authors’ elaboration.
In order to ensure the validity of our data and reduce the potential for response bias, we conducted several robustness checks (Podsakoff, MacKenzie, Lee, & Podsakoff, Reference Podsakoff, MacKenzie, Lee and Podsakoff2003). We used independent sample t-tests to assess non-response bias, comparing early and late respondents, as well as groups based on gender, industry experience, company position, firm size, and technology levels. We utilized Harman’s single factor test to measure common method variance, which showed a total variance of 38.33%, which was below the recommended threshold of 50%. Additionally, we cross-checked our results with a marker variable and correlation matrix procedure, as recommended by Podsakoff et al. (Reference Podsakoff, MacKenzie, Lee and Podsakoff2003). We also used a comparison of the answers of the eliminated respondents with the included respondents to check for self-selection bias and observed no unusual patterns.
Measures
The scales used in the study were derived from pertinent published research to ensure validity (see Table 2). All items were measured on a 7-point Likert scale, ranging from ‘(1) Strongly disagree’ to ‘(7) Strongly agree’.
Table 2. Items and reliability of latent variables
Source: Authors’ elaboration.
Learning orientation was measured with the 8-item scale from Kumar et al. (Reference Kumar, Jabarzadeh, Jeihouni and Garza-Reyes2020). Product innovation was measured with a 3-item scale from Martin-de Castro et al. (Reference Martin-de Castro, Delgado-Verde, Navas-López and Cruz-González2013). Strategic flexibility was measured through Zhou and Wu (Reference Zhou and Wu2010) 6-item scale. Innovation culture was measured with 3-item scale from Martin-de Castro et al. (Reference Martin-de Castro, Delgado-Verde, Navas-López and Cruz-González2013) and market acuity was tested through 4-item scale from Rosenzweig and Roth (Reference Rosenzweig and Roth2007).
Results
Demographic information
Table 1 presents the characteristics of our respondents. As can be seen, the majority of the sample are men (63.33%) and hold strategic and managerial positions (70.67%). All the companies operate in the manufacturing sector, with the two highest percentages of employees being in the ranges of 10–20 and 51–125. Regardless of their size, more than half of the companies have a high level of technology.
Psychometric properties and correlation analysis
Once received our responses, we checked the reliability of each construct and the correlations, as presented in Tables 2 and 3. All constructs showed satisfactory Cronbach’s α values, ranging from .871 (product innovation) to .939 (learning orientation). These results show that the sample understood the meaning of the constructs and that there is a high reliability of the responses obtained. Prior to hypothesis testing, the results of average variance extracted (AVE) for each of the adopted latent constructs are reported, and it was found that all latent constructs had a satisfactory level of AVE, as shows in Table 2. All constructs had acceptable levels of internal consistency, reliability, and convergent validity. All constructs exhibited AVE values above the minimum recommended threshold of .50, demonstrating the convergent validity of the construct measures.
Table 3. Correlation among variables
Source: Authors’ elaboration.
Numbers are rounded to the nearest thousand.
* *p < .05; *p < .1. n = 300.
Regarding the Pearson correlation, all values show optimal results. Specifically, all constructs are correlated with the outcome variable, with strategic flexibility showing a correlation of r = .541 and learning orientation showing a correlation of r = .401. These results indicate that both learning, and re-organization capabilities have a decisive impact on the independent variable. The highest value is represented by the interaction between learning orientation and innovation culture, demonstrating that endogenous variables are essential for re-evaluating existing procedures and values. Market acuity is correlated with learning at r = .685, further reinforcing the idea that learning can result from both internal interactions and interactions with the target market.
Structural model
After conducting the analysis of the descriptive statistics, we employed the structural equation modelling methodology using AMOS software (v.26) to test our hypotheses. The objective was to assess the regression weights – paths or static influences between latent variables – in order to empirically verify the hypothesized relationships (see Figure 2).
Figure 2. Results of the multiple regression.
The findings indicate that our Hypothesis 1 posits a positive effect of learning orientation on product innovation. This hypothesis is empirically supported (β = +.397, p < .01). Regarding the effect of learning orientation on strategic flexibility (Hypothesis 2), we identified a positive and significant relationship (β = +.286, p < .01).
In terms of Hypothesis 3, which asserts that strategic flexibility positively influences product innovation, the coefficient is positive and significant (β = +.453, p < .01). Thus, Hypothesis 3 receives strong empirical support. The strategic flexibility variable, in addition to being influenced by learning orientation and affecting product innovation, serves as a mediating factor in the primary relationship. Specifically, it functions as a partial mediator, reducing the effect of learning orientation on product innovation to β = +.154, p < .05.
The mediation analysis of strategic flexibility followed Baron and Kenny’s (Reference Baron and Kenny1986) four-step procedure: the first condition was met as learning orientation (the independent variable) significantly impacted product innovation (the dependent variable); the second phase requires that the independent variable impacts the mediating variable, i.e., strategic flexibility. This condition was supported, and Hypothesis 2 was confirmed; the third step necessitates that the mediator significantly impacts the dependent variable (see Hypothesis 3); finally, the original relationship between the independent and dependent variables (β = +.397, p < .01) must be diminished or rendered non-significant due to the mediator, indicating partial or full mediating effects. Since the original effect was diminished, we can consider strategic flexibility a partially significant mediating variable in the core relationship.
Concerning the moderating effects, Hypothesis 4 posits a positive moderating effect of innovation culture on the relationship between learning orientation and strategic flexibility. The model demonstrates that the coefficient is positive and significant (β = .183, p < .01). Therefore, Hypothesis 4 also receives empirical support from our sample. Hypothesis 5 posits a positive moderating effect of market acuity on the relationship between strategic flexibility and product innovation. However, this hypothesis does not achieve a satisfactory p-value to warrant empirical support (β = .017, p > .10).
Our model exhibits robust explanatory power for the strategic flexibility variable. Utilizing the multiple square correlation index (R 2), we were able to explain 33% of the variance in flexibility due to the combined effects of learning orientation and innovation culture. Additionally, we accounted for 32% of the variance in the independent variable.
Table 4 summarizes the obtained findings per each hypothesis, while Figure 2 shows the result contextualized in our theoretical model.
Table 4. Summary of hypothesized effects
Source: Authors’ elaboration.
Discussion and implications
Aligning with existing organizational knowledge management literature, the first finding of our research pertains that learning orientation has a positive effect on product innovation (Calisir et al., Reference Calisir, Altin Gumussoy and Guzelsoy2013; Kumar et al., Reference Kumar, Jabarzadeh, Jeihouni and Garza-Reyes2020). This relationship exists in a direct form also whether SMEs represent the unit of analysis. Learning-oriented SMEs are in fact more capable to learn from experiences and external occurrences due to their openness, thus these businesses may be better than competitors in turning this knowledge into new products (Calantone et al., Reference Calantone, Cavusgil and Zhao2002).
Still, the second finding shows strategic flexibility’s mediating role in this relationship (Real et al., Reference Real, Roldán and Leal2014). Particularly, the results posit that strategic flexibility is capable to explain the majority of the hypothesized relationship between learning orientation and product innovation in SMEs. It is therefore assumable that product innovation derives from learning orientation whether the company is capable to exploit learning to apprehend response mechanisms to change, and to improve its adjusting capabilities by achieving strategic flexibility (Rialti et al., 2019, 2020). This occurrence may be explicated by considering how through learning unexpressed dynamic capabilities ensuring reconfiguration can awaken (Benassi & Rialti, Reference Benassi and Rialti2024; Jurksiene & Pundziene, Reference Jurksiene and Pundziene2016). Such a finding is coherent with social complexity perspective (Chiva et al., Reference Chiva, Grandío and Alegre2010). Learning orientation is related to improved greater self-organization (Antonacopoulou & Chiva, Reference Antonacopoulou and Chiva2007), the generation of new knowledge can indeed promote a better attitude toward reconfiguration of internal resources and capabilities to take the fullest advantage of the benefits yielded from internal interactions and the relationship with external stakeholders. Learning-oriented SMEs are actually characterized by an environment that encourages experimentation, which can lead to breakthroughs in product development while mostly relying on existing knowledge and resources and, in this context, strategic flexibility illustrates the capacity of companies to adapt when addressing new stimuli (Chakma & Dhir, Reference Chakma and Dhir2023; Zhou & Wu, Reference Zhou and Wu2010).
The third finding regards the positive influence of innovation culture on the relationship between learning orientation and strategic flexibility. According to the social complexity perspective, companies tend to learn based to the sum of individual propensity to pursue innovation (Chiva et al., Reference Chiva, Grandío and Alegre2010). Specifically, this finding shows that the presence of an innovative culture helps in the generation to create the necessary conditions for having an effective organizational learning orientation and the capabilities to be strategically flexible. Innovative culture encourages employees to take risks and be open to change, which is necessary for an organization to have the ability to adapt to external changes and reorganize resources for assimilating new knowledge (Martin-de Castro et al., Reference Martin-de Castro, Delgado-Verde, Navas-López and Cruz-González2013). Finally, market acuity, which is the ability to anticipate changes in the market, understand customer behaviour, and make decisions that maximize profits (Rosenzweig & Roth, Reference Rosenzweig and Roth2007), does not have a moderating effect on the relationship between strategic flexibility and product innovation. Such an occurrence may be interpreted in relation to the overreaching role of learning orientation. Companies embedded with this orientation are indeed prone to gather information from external stakeholders autonomously, consequently they may not need additional information sources related specifically to customers and market (Caputo & Ayoko, Reference Caputo and Ayoko2021).
The paper therefore contributes to organizational knowledge management literature and social complexity research by explicating the intermediate factors playing a role in the relation among learning orientation and product innovation in SMEs (Alerasoul et al., Reference Alerasoul, Afeltra, Hakala, Minelli and Strozzi2022). It contributes to the first among the aforementioned streams by providing a more detailed perspective about learning orientation importance as an antecedent of strategic flexibility and product innovation. In detail, it emerged how learning make reconfiguration easier, which in turn may drive product innovation through a different utilization of existing knowledge and resources (Teece, Reference Teece2018). Then, it contributes to the second stream by stressing how collaborative processes deriving from learning orientation may support SMEs to maintain their competitiveness and overcome the limitations deriving from low resource levels or lack of structured innovation processes (Calantone et al., Reference Calantone, Cavusgil and Zhao2002; Chiva et al., Reference Chiva, Grandío and Alegre2010).
In term of practical suggestions, it is possible to delineate some managerial implications for SMEs.
SMEs are structured around the relationships and the network they can create with stakeholders; it is possible to suggest to strengthen relationships to invest in practices fostering learning orientation aimed at external knowledge integration. To do so, SMEs managers may incentives employees to seek innovation outside the company (i.e., through the participation to events or to training courses). Managers may also participate more actively to discussion about product innovation with employees. The nature of SMEs promotes lean communication, which can be harnessed to foster dialogic exchange about new ideas via better feedback mechanisms (Rialti & Filieri, Reference Rialti and Filieri2024). Additionally, SMEs managers may promote adaptive and responsive processes targeting a greater understanding past experiences and mistakes (Mori et al., Reference Mori, Cavaliere, Sassetti and Caputo2022). Such a strategy may promote the adaptation of existing knowledge for new objectives thanks to the integration of new information related to the needs of the environment in which they operate. Therefore, managers should try to view learning orientation not as an inclination, but as a core value that guides the company toward improving the system in which it operates. It is also fundamental to stress how flexibility needs to be maintained over time. For this reason, it is possible to recommend SMEs managers to simulate situations which may be stressful for the company. A solution might be represented by constant innovation exercises and simulations aiming at developing new products. Monetary incentives such as ‘idea bounties’ can potentially allow for a greater involvement of the workforce (Madanaguli et al., Reference Madanaguli, Dhir, Talwar, Clauss, Kraus and Kaur2023).
Conclusions and limitations
The present research observes the importance of learning orientation in businesses wishing to embark on product innovation initiatives (Černe et al., Reference Černe, Jaklič, Škerlavaj, Aydinlik and Polat2012). Learning orientation allows SMEs, through innovation culture, to achieve strategic flexibility, thus making these businesses better in adjusting and reconfiguring existing knowledge and resources to create something which may suit the market. In the context of SMEs, such an occurrence is relevant as these businesses frequently lack explorative units, and creating an environment oriented toward the maximum valorization of internal knowledge may represent the discriminant between success and demise (Marzi et al., Reference Marzi, Manesh, Caputo, Pellegrini and Vlačić2023). Learning-oriented SMEs are henceforth capable to draw advantage from complexity by having improved mechanisms to use together internal and external knowledge and apply it to develop new products (Chiva & Habib, Reference Chiva and Habib2015).
Notwithstanding our findings, the research is still limited in at least some aspects. First, we focused on UK SMEs (Calabrese et al., Reference Calabrese, Cowling and Liu2022). The consideration of diverse contexts (i.e., by running cross-countries research; Hu, Filieri, Acikgoz, Zollo, & Rialti, Reference Hu, Filieri, Acikgoz, Zollo and Rialti2023) may generate diverse results. Second, we focused on manufacturing businesses. In such a regard, it may be relevant to consider technological product-oriented businesses, which have diverse mechanisms to generate product innovation. Third, we have not considered the potential impact of technologies such big data analytics or artificial intelligence on the processes of knowledge collection. Hence, future research should potentially consider how these elements may influence successful product innovation in organizations characterized by high learning orientation. Fourth, a gap still exists in respect of other capabilities that may be relevant in affecting the relationship between learning orientation and product innovation. A common example may be represented by capabilities related to the realm of organization ambidexterity research (Rialti et al., Reference Rialti, Zollo, Ferraris and Alon2019). Finally, the research is rooted on survey-based methods and regression. In such a regard, a qualitative observation of the phenomenon may shed some light on new micro-mechanisms not observable using surveys. As an example, a comparison between multiple cases can shed further lights about micro-foundational capabilities necessary to support integration of external knowledge, or about the process underlying the origin of flexibility and adjustment through co-specialization (Benassi & Rialti, Reference Benassi and Rialti2024).
Anna Marrucci is a Post-Doctoral Research Fellow and Adjunct Professor of Management at the University of Florence (IT), as well as an Adjunct Lecturer at Kent State University (USA). Anna got her PhD in Management and Business Administration from the University of Pisa (IT) in 2024. Her principal research interest concerns the role of human relationships in SMEs management. She has been a visiting researcher at University of Seville (ES) and Prague University of Economics and Business (CZ). She has published several book chapters for international publishers such as Taylor and Francis, Emerald and Palgrave; likewise, she wrote a monograph about relational learning in SMEs for Emerald. Additionally, her researches have been published in international journals such as Industrial Marketing Management, Management Decisions, Current Issues in Tourism, International Journal of Emerging Markets, and British Food Journal. Since the last year, she serves in the organizing board of 2025 EURAM Conference.
Riccardo Rialti, PhD is Senior Assistant Professor of Management at the Department of Management and Corporate Law of the University of Siena (IT). Previously, he was an Assistant Professor of Management at the University of Milan (IT). Riccardo got his PhD in Management and Business Administration from the University of Pisa (IT) in 2019. He has been a visiting researcher at University of Lincoln (UK), Middlesex University London (UK), Sophia University (JAP), ESCP Europe (FR), and Assumption University (USA). His main research interests are related to digital transformation and dynamic capabilities. He has authored multiple books on the topic for several international publishers. His papers have been published international journals such as California Management Review, Journal of Business Research, IEEE-Transactions on Engineering Management, Technological Forecasting and Social Change, Business Horizons, Management Decision. He serves in the editorial boards of several international management journals and has been one of the chairs of the 2024 Global Fashion Management Conference at Milan.
Open access
