The dynamic capabilities framework provides a useful strategic management framework for understanding how firms harness their internal and external capabilities to enhance their performance and long-term competitiveness. Capabilities denote the ability to adapt, reconfigure and integrate skills, resources and functional competences (Mohamud & Sarpong 2016; Teece & Pisano 1994) in ways that increase the economic value of firms. The dynamic capabilities framework postulates that firms which emphasise repetitive processes and harness their internal and external competences to address the changing environment have more potential to attain competitive advantage than those that do not (Mohamud & Sarpong 2016). Inan and Bititci (2015) posited that dynamic capabilities allow firms to extend or modify their existing resources, alter their operational capabilities so that they can adapt. The present study argues that dynamic capabilities manifest in the capacity of small firms to harness their knowledge resources and their operational efficiencies prudently and competently in ways that reduce the cost of production, augment their sales and exert dominance in markets, thereby increasing their competitive advantage.

The dynamic capabilities framework provides a useful theoretical lens for grasping the effects of KM on firm competitiveness, as it lays a foundation for comprehending how strategic resources at the disposal of the firm (e.g. capacity to manage knowledge and operational efficiency) can be harnessed as useful vehicles for unleashing the competitive edge of firms. Nielsen (2006:60) presented dynamic capabilities as ‘integrated sets of KM activities that changes, renews and exploits the knowledge-based resources of the firm’. Effective KM (acquisition, conversion, application and protection), a key dynamic capability in organisations directly impacts strategic organisational performance, which in turn influences financial outcomes (Zack, Mckeen & Singh 2009). Effective KM, for example, creates a competitive advantage (Sook-Ling, Choo-Kim & Razak 2013), enables effective service delivery (Nenungwi & Garaba 2022) and adds value to client engagement, according to empirical studies based on economic entities from various economic sectors (Govender, Mearns & Du Plessis 2022). Given the agility and versatility of small firms in terms of resources appropriation and exploitation (e.g. bootstrapping, collapsing of managerial and ownership functions as cost-recovery strategies), one would expect such capabilities to be advanced to the benefit of the firm in terms of competing with rival firms.

Although there is no generally accepted definition of term ‘competitiveness’, Ocloo et al. (2014) perceived competitiveness as the extent to which a firm’s products and services exhibit an edge over its competitors for continued survival and competition for markets, resources and revenues. Firm competitiveness is therefore usually measured by a firm’s performance against other firms and depends on its ability to manage knowledge and cultivate operational efficiency (Kiptalam et al. 2016). Institutionalised competitive intelligence is critical to achieving strategic business objectives such as firm competitiveness in the information and knowledge economy age (Maritz & Du Toit 2018). An effective competitive intelligence system converts information about the business environment into new business knowledge that business leaders can use to make decisions that will improve their enterprises’ competitiveness (Chevallier et al. 2016). Ghannay and Zeineb (2012) argued that the combination and alignment of competitive intelligence and KM policies and systems allows businesses to gain a long-term competitive advantage.

Knowledge management is an emerging set of organisational design and operational principles, processes, organisational structures, applications and technologies that help knowledge workers to dramatically leverage their creativity and ability to deliver business value to customers and assist organisations to acquire improved business competitiveness and improved operations (Jantarajaturapath, Imsuwan & Wongsim 2016). Because organisations in the global economy no longer rely on just tangible assets and production factors to remain competitive (Omotayo 2015), KM has evolved as the main source of competitive advantage. Hence, SMMEs need this strategic shift to KM to capitalise on the innovation and competence prospects associated with it (Dube & Ngulube 2012; Krajnovic, Covo & Jasic 2012).

Knowledge management can be broadly categorised into two dimensions, namely knowledge recognition and knowledge implementation (Mokoena 2019; Ndolo 2015). These dimensions are relevant because they are concerned with the identification of knowledge gaps and the constitution of KM practices (Robertson 2016; Zieba et al. 2016). The fundamental importance of the dimensions under study is underpinned by their recursive processes that permit SMMEs to compete globally.

There is a clear lack of consensus on the definition of operational efficiency. On the one hand, operational efficiency is expressed as an operational excellence management system (OEMS) consisting of a set of rules that will guide a company in its operations to achieve operational competence (Al-Qubaisi & Ajmal 2018). On the other hand, the concept is conceived as a ratio of actual input measured against maximum output and behaves like financial leverage (Sharma et al. 2014). The authors contend that operational efficiency identifies and eliminates wasteful processes and resources that threaten organisational profits and facilitates remedial design of new work processes that improve the quality and productivity of the firm. In Njoroge’s (2012) view, a firm is operating efficiently when it is generating sales revenue and minimising costs, as evidenced by increasing its sales volumes at minimal cost.

Operational efficiency is extensively linked with firm competitiveness. Kiptalam et al. (2016) validated this observation by contending that operational efficiency and capabilities available to the firm determine whether the firm will be competitive over its rivals or not. Thus, Okwang’a, Mungania and Karanja (2015) maintained that operating efficiently ensures that firms produce at lower costs, increase customer satisfaction and stay ahead of the competition in the market.

This study employed an explorative, cross-sectional design to explore the influence of KM and firm competitiveness, with operational efficiency as an intermediary. A positivistic and quantitative approach was adopted to test the associative relationships between variables under study. A survey instrument was developed based on extensive literature covering KM, operational efficiency and competitiveness. A cross-section survey was administered on retail SMMEs in Mangaung Metropolitan Area (MMA), Free State province, a large administrative area with a fairly large concentration of small business in the central region of South Africa. As one of the traditional methods of research, a survey instrument was considered most appropriate appropriate for the study because of its ease of use and ability to yield large amounts of analysable data (Young 2016) in a short time.

The target population for this study was retail SMMEs, business entities that have a strong presence in the central region of South Africa (SEDA 2016). Moreover, the choice of SMMEs was driven by their overall strategic importance in boosting the country’s economy through employment creation and poverty alleviation (Mbuyisa 2017), despite their apparent weak competitiveness (Rambe & Khaola 2020), a concern which necessitates due attention. The Wholesale and Retail Sector Training Authority (W&RSTA) of South Africa estimated that there are approximately 3165 registered SMMEs in the Free State province. The W&RSTA was deemed the most credible source because it is made up of predominantly SMMEs, which account for approximately 90% of the retail sector.

Drawing on the identified population, the researchers extracted a sample comprising a manageable number of SMMEs. Using an online sample size calculator¹ set at 95% confidence level and a margin of error of 5%, a population of 3165 SMMEs will generate a sample of 343 SMMEs. Even though the sample was drawn from SMMEs, the authors drew their information from SMME owners and managers because of their close proximity to the relevant variables under study and their knowledge of routine business operations. A structured questionnaire comprising closed-ended, five-point Likert scale questions was developed to establish the extent of respondents’ agreement with the provided statements (Maree & Pietersen 2016). The questionnaire was deemed most appropriate as it provided convenience of completion in view of the SMME owners and managers’ competing roles and responsibilities. The instrument covered demographic data and business profiles, SMME’s KM dimensions, operational efficiency and competitiveness.

A total of 343 survey questionnaires were distributed by hand by the main author and two research assistants to SMME owners and managers in the MMA. Some SMME owners and managers completed and handed over their copies instantly to the researchers, while others asked them to collect it later. Over a period of 2 months, 300 out of the 343 questionnaires (representing acceptable 87.5% response rate) were correctly completed and collected from SMME owner and managers. Small, medium and micro enterprise owners and managers were accessible directly from their respective workplaces, while those who were not physically available were reached through their e-mails. Those who completed the form electronically submitted it via e-mail to the researcher or research assistants.

The results indicate male dominance (60%) in SMME ownership, while the female counterparts accounted for 40%. This finding corroborates Bhorat et al.’s (2018) assertion that men typically own the majority of SMMEs. About 46.7% of the respondents were between the ages of 36 and 55 years, 42.3% were between 18 and 35 years, while only 11.0% were individuals over 56 years. This demonstrates the presence of mature adult population as either owners or managers of SMMEs. Black people were the most represented (49%) racial group, followed by mixed race people, who comprised 27.7% of the sample. A significant percentage (35.3%) of owners and managers possessed the highest qualification of matric or lower high school grade and 27.7% professed to having tertiary qualification, while 30% were postgraduates. The important statistics to note are that almost half the number of respondents (47.3%) were owners and managers of the business and 42% of businesses were in existence for 6–10 years.

The scales used to measure the variables were adapted from the existing literature.

Knowledge management: Two dimensions (recognition and implementation) were used to assess SMME KM (Cardoni et al. 2020). On a five-point Likert scale, respondents were asked to indicate their extent or agreement with an available set of statements. Items included in the measurement scale were knowledge resources available in operations and sales, training and personnel, including knowledge sharing and transfer (Baporikar 2014; Robertson 2016). Sample questions around KM recognition (items) were how critical the recognition of KM was in business operations and sales and whether the business supported employees with training and role specification. Sample question items on KM implementation were the extent to which the business deployed other resources and personnel to support the implementation of KM and how the business encouraged knowledge sharing and transfer. Overall, KM was measured by 11 items covering two dimensions.

Operational efficiency: The construct was subdivided into two dimensions: proactiveness and intelligence generation (Bindl & Parker 2010; Ndolo 2015). On a five-point Likert scale, respondents were asked to indicate their extent or agreement with a provided set of statements. Sample items used to measure proactiveness covered the firm’s ability to leverage technology, enhance employee skills and promote efficient delivery of goods and services to customers (Kalluru & Bhat 2009). Intelligence generation was measured using sample questions (items) such as the gathering and assessing of competitor information, gathering of customer information, business discussions with suppliers and maintaining corporate relations with other firms. Overall, operational efficiency was measured by 11 items covering two dimensions.

Firm competitiveness: On a five-point Likert scale, respondents were asked to indicate their extent or agreement with a provided set of statements. Sample items used to measure firm competitiveness were whether the firm forecasted better return on investment (Arslan & Tathdil 2012; Ricardo 2015), the company enjoyed better market share, the firm had increased gross sales (SME Competitiveness Outlook 2015). Measuring SME competitiveness the firm had retained existing customers and attracted new customers and the business could attract talented employees.

Ethical clearance to conduct this study was obtained from the Faculty of Management Sciences Research and Innovation Committee of the Central University of Technology (ref. no. FMSEC05/18).

Asssessment of the measurement model involves ascertaining whether proposed indicator variables adequately represent the latent constructs, which they are purpoted to measure. In this regard, the following issues are tested: internal consistency (reliability), convergent and discriminant validity. Firstly, the researchers used the Cronbach’s alpha (α) coefficient to access reliability. A Cronbach’s alpha coefficient of at least 0.7 confirms acceptable internal consistency of measuring items. Secondly, average variance extracted (AVE) was used to measure convergent validity. Convergent validity of a latent construct is confirmed when its indicator values demonstrate high levels of positive correlation – AVE greater than 0.5. The results of the two tests are presented in Based on the data presented in the table, all the constructs in the proposed conceptual model satisfied the minimum conditions of reliability and convergent validity.

While testing the suitability of the measurement model, the extent to which the constructs in the model differed from each other was also evaluated. This is known as discriminant validity. Discriminant validity exists when indicators exhibit high factor loadings on the specific constructs, which they represent and there are no cross-loading of items across constructs. Table 2 shows no evidence of cross-loadings, and this confirms discriminant validity.

The structural model was assessed using the following indicators: (1) the coefficient of determination (R-squared), a measure of predictive power, and (2) path coefficients, measures of strength of relationships between variables. R-squared measures the variance of an endogenous variable explained by a set of predictor variables. Its value ranges from 0 to 1. As presented in Figure 2 and Table 3, KM (recognition) and KM (implementation) accounted for 75.4% of the variance in operational efficiency (proactiveness) and 53.1% of the variance in operational efficiency (intelligence gathering). Moreover, KM (recognition), KM (implementation), operational efficiency (proactiveness) and operational efficiency (intelligence gathering) collectively explained 64.6% of the variance in firm competitiveness. Hair et al. (2014) suggested that R² values ranging from 0.5 to 0.75 represent moderate predictive power of a proposed model. Therefore, the model that was proposed in this study has moderate predictive power.

Path coefficients were used to test the strength of the direct and indirect relationships between the independent and dependent variables. The statistics in Table 4 shows that data supported the following hypothesised direct relationships: H1: KM (recognition) -> firm competitiveness (B = 0.662, p = 0.000) and H2: KM (implementation) -> firm peformance (B = −0.1402, p = 0.0335). This means KM (recognition) had a strong, positive and significant explanatory influence on firm competitiveness, while KM (implementation) had a weak but negative, significant explanatory effect on the same outcome variable.

A bootstrapping procedure based on 500 samples was carried out to ascertain the mediation effects of operational efficiency on the influence of KM (recognition) and KM (implementation) on firm competitiveness. The mediation effects describe a situation where a third variable is added and intervenes in the relationship between two variables (MacKinnon, Fairchild & Fritz 2007). The result of the tests are presented in Table 4. The indirect relationship proposed in H3: KM (recognition) -> operational efficiency -> firm peformance (B = 0.1545, p = 0.019) was significant. This proves complementary partial mediation because both the direct and indirect relationships between KM (recognition) and firm competitiveness were statistically significant. The indirect relationship in H4: KM (implementation -> operational efficiency -> firm peformance was also supported (B = 0.0697, p = 0.050), confirming mediation. As the direct and total effects and/or relationships were negative while the indirect effect and/or relationship was positive, the nature of the mediation relationship was partial and positive.