Keywords

Developing Country, SMEs, Manufacturing, Export Performance, Zimbabwe.

Introduction

Several factors, inter-alia, de-industrialisation, low investment, economic sanctions, global competition, low levels of exports and low (34.3%) capacity utilisation in the manufacturing sector (CZI, 2015), highlight the need for a composite export performance enhancement model to guide Zimbabwean SMEs to improve their performance and contribute to the development of the economy. Mudavanhu, Mubata and Mudavanhu (2014) highlight that “SMEs have become so important as they contribute to the growth of the economy and offer alternative sources of employment to the general public, thus this justifies the need to support this sector so that it contributes meaningfully to the development of Zimbabwe”.

Evidence of a decrease in export performance of Zimbabwean SMEs as noted above provides a clear justification for re-thinking export marketing strategies. According to Spirig (2011), for firms to perform better in the dynamic global environment, they need to be customer-focused, possess distinctive organisational capabilities, develop customer value strategies and consider cross functional cooperation as their strategic milestone. The aforementioned which are referred to as ‘market-driven’ strategies (Spirig 2011), are premised on the notion that, all the firms’ strategic decisions should begin by understanding markets, customers and competitors.

Milner and Kubota (2005) highlight that “the success of any company largely depends on how strategically aligned its strategy is with the environment in which it operates”. The internal environment is therefore very important for sustainable performance of a firm (Chadamoyo & Dumbu, 2012). The aforementioned researcher argues that the most crucial feature of the internal environment is the ability of management of SMEs to craft resilient strategies that enhance the firm’s performance. Yan (2010) and Zindiye (2008) argue that SMEs in Zimbabwe lack the necessary skills and knowledge to scan and carry out feasibility studies thereby, working in contrast with the environment leading to their failure. Furthermore, customer focus is viewed as one market-driven strategy that enables firms to produce ‘tailor-made’ products to suit the needs of their customers (Tajeddini, Elg &Trueman, 2013). This therefore seems to raise doubt as to how SMEs can enhance their export performance if “they rarely search for information” about their customers (Ahimbisibwe & Abaho, 2013). Market-driven strategies require firms to coordinate activities and make use of their organisational capabilities (internal assets) (Andrews, Beynon & McDermott, 2015).

Seyed (2011) argues that, to guarantee competitiveness in international markets, firms need to embark on continuous improvement and environmental monitoring to detect customer preferences and the motives of competitors. Furthermore, Manalastas (2009) asserts that in a tough environment, SMEs need to scan the external environment and identify opportunities and threats that affect their businesses. “Research in areas such as international management, evolutionary economics and strategic marketing, becomes crucial in times of uncertainty” (Spirig, 2011). The aforementioned author further states that, strategizing means adopting strategies which gives firms an opportunity to thrive and become successful in the environment in which they operate, through interfacing with their customers and developing long lasting relationships with them.

Damoah (2011) provides evidence that “most of the theoretical frameworks of SMEs’ export performance and export behaviour were mainly focused on developed or western country settings and there has been little empirical evidence in African countries”. Moreover, the aforementioned researcher also states that, “export marketing strategies on these frameworks were developed based on the export behaviour of the developed countries’ firms and so most of the empirical studies were also based on SMEs in the developed countries, such as North America and Europe”. Thus, because “the socio economic contexts of the developed countries differ from those of developing countries such as Zimbabwe, understanding of the export behaviour of SMEs in developing country contexts is still very limited in the literature” (Damoah, 2011).

Moghaddam, Hamid, Rasid & Darestani, (2011) asserts that the growth in global trade and sales activity in the global market results in increasing attention being paid to the effect on export performance and although much research has been conducted on export performance, “the literature is characterised by a lack of consensus among researchers as to what constitutes the export marketing strategy of a firm and its export performance.”

In light of the above, from the perspectives of strategic marketing, this study seeks to explore marketing strategies enhancing the export performance of SMEs and in particular, market-driven strategies that will promote the export performance of SMEs in the manufacturing sector in Zimbabwe. More specifically, the aim of the research was to examine the influence of market-driven strategies on the export performance of SMEs in the manufacturing sector in Zimbabwe.

Literature and Background

For the past decade, Zimbabwe has been facing economic hardships, which resulted in unprecedented closure of many companies and the country itself was also being viewed as unfavourable for foreign investment. Zimbabwe went through a deindustrialisation process which led to an economic meltdown of about 40% (CZI, 2011). The aforementioned highlights that, most manufacturing firms were constrained by capacity utilisation and cited problems of low product demand and lack of working capital and raw materials as contributors, while on the other hand, CZI (2012) identified constraints such as the cost of funding and rigid labour laws as factors having the greatest impact on capacity utilisation and doing business in Zimbabwe. According to CZI (2010) “there are four basic sectors which generate wealth in the Zimbabwean economy and these include manufacturing, mining, construction and agriculture. Of the aforementioned, manufacturing is the key factor contributing growth and prosperity in Zimbabwe”. Therefore, it is self-evident that “there is a strong relationship between manufacturing and exports in Zimbabwe which have a negative multiplier effect and wider impact on consumer confidence” (CZI, 2010). Currently, “capacity utilisation is hovering at 34.3% from 36.5% in 2014” (CZ1, 2015). This clearly shows that the sector is heavily constrained.

There is agreement among certain researchers (Chingwaru & Jakata, 2015; Kahiya, 2015), that the route to economic growth runs through export development, given that export is factored into the generic equation for calculating gross domestic product. The Zimbabwean economy has struggled to push export revenue past the US$4 billion mark as annual exports stood at US$3.5 billion (Kahiya, 2015). These statistics show that Zimbabwean exports are roughly half of Namibia’s and about one-third of Zambia’s. While the ratio 29.5% of exports to GDP in Zimbabwe seems to be respectable, this is very low as compared to sub-Saharan countries such as Botswana (55.1%), Angola (55.8%), Swaziland (56.3%), Zambia (41.9%), Ghana (42.2%) and Congo Republic (76.5%) (Kahiya, 2015).

Zimbabwe is an agro-based economy which relies on exports of processed food, particularly those from SMEs, as they dominate most industries in the country (RBZ, 2015). The 21^st century has brought about globalisation where national boundaries are removed and companies trade with one another easily (Powell, 2015). The increase in globalisation requires companies to produce and export products that are innovative, low priced and meet international standards (Fariza, 2012). Products manufactured by Zimbabwean SMEs mainly in the food processing industry among others, are not doing well and some of these products are almost extinct in the international markets such as the UK and in some African markets, although these products used to dominate these markets in the period between 1980-1999 (SEDCO, 2012). The Government of Zimbabwe through its industrial development policy (IDP) tried to assist the SMEs through export led policies and a number of export support mechanisms, such as concessionary funding, training, tax relief, discounts and rebates (MIC, 2012). Although some of these SMEs presumably have exporting expertise, there was no significant improvement in the export performance of SMEs in the manufacturing sector.

Research (Makanyeza, 2014) shows that Zimbabwe’s trade deficit actually widened in the first quarter of 2011. Spirig (2011) argued that “companies expanding abroad initially experience a reduction in returns due to the so called ‘liability of foreignness’, dividends on trade will only be realised after gaining experience in foreign operations”. Furthermore, Fariza (2012) argues that, SMEs need to evaluate their capabilities before expanding into new geographic locations and engaging internationally. In this context Mascherpa (2011:2) states that, “engaging internationally comes with severe and unprecedented pressure emanating from globalisation, this globalisation has accelerated competition among firms, thereby increasing barriers to entry in many markets”. Under such circumstances, “firms need to respond to these complex environments” (Spirig, 2011).

Ocloo, Akaba and Brown (2014) highlight that, the survival of companies in this increasing globalisation and competition lies in their ability to create and implement sustainable competitive marketing strategies. The aforementioned authors further state that, to facilitate this, firms need to make an effort in developing comprehensive methods of gathering information related to the status of global markets. This idea of gathering information about customers is also supported by Spirig (2011), who provided evidence that firms need to be market-driven to effectively compete in the global market. The aforementioned researcher further states that, firms which have market information respond to market requirements and anticipate the changing environmental conditions. In line with this argument, other researchers (Sciarelli, 2008) argued that, SMEs should develop an “outside-in” thinking process that must always scan the marketing environment in order to enact strategies that suit changing environmental conditions and deliver superior value to the customers, thus being market-driven. In summary, it can therefore be inferred that adopting market-driven strategies has been accepted as one strategy which could enable SMEs to perform better than their competitors.

Customer focus as a market-driven strategy is “a component of market orientated firms that focuses on putting the customers at the centre of strategic focus” (Yaacob, 2014). Similarly, Verhoef and Lemon (2013) assert that firms that are customer focused not only provides products that meet customer specification but, place overall attention on the whole supply chain operations such as seeking for qualified suppliers, designing customer ‘tailor made’ products, timely delivery to the customer, reasonably priced products and a well thought after-sale service. In line with the aforementioned, Tajeddini, Elg and Trueman (2013) argue that, customer focus requires firms to continuously update and improve entire processes associated with improving the total welfare of the customer, since customer needs are continuously changing due to the dynamic environment and thus processes and products to meet those customer needs also have to change. The benefits accruing from this customer focus have been reported to be the increase in the firm’s performance and cost related benefits (Tajeddini, Elg and Trueman, 2013).

Organisational capability is also a market-driven strategy which is premised on “identifying an organisation’s distinctive capabilities” (Spirig, 2011). Capabilities can be defined as “complex bundles of skills and accumulated knowledge, exercised through organizational processes that enable firms to coordinate activities and make use of their internal assets (Andrews, Beynon & McDermott, 2015). Certain types of resources which are owned and controlled by the firm have the promise of generate competitive advantages which eventually lead to superior firm performance (Barney, Ketchen & Wright, 2011). However, another argument based on this strategy is whether the manufacturing SMEs in Zimbabwe can make full use of their internal organisational capability to enhance their export performance?

Customer value is also a market-driven strategy which is premised on the notion that, “it (customer value) is the overall assessment of the utility of the product taking into consideration what is received in comparison with what is given” Allahham (2013). Shanker (2012) views customer value as “customers’ perceptions of what they receive, in return for what they sacrifice”. According to Shanker (2012), customer value is categorised into two, namely “desired value and perceived value”. The desired value refers to the functionality that the customer would want to see in a product, while perceived value is the benefit that the customer believes he/she would receive after purchasing the product. Customer value is not only a crucial determinant in maintaining long-term customer relationships, but also plays a key role in affecting purchases intentions and creating mutual trust (Zhuang, Cumiskey, Xiao & Alford, 2010). Allahham (2013) is of the view that, “companies that deliver higher value to customers have greater chances of satisfying them to increase their loyalty” and consequently better performance. In line with the above views, the question is whether SMEs in Zimbabwe can produce products that yield maximum utility to the export markets to enhance their export performance?

Finally, cross functional cooperation is also a component of market-driven strategies which determines a firm’s performance. Blindenbach-Driessen (2015) argues that cross functional teams are defined as groups formed with the intention of jointly working together in developing opportunities that require diverse expertise and include personnel from different functions. Pimenta, Lago da Silva and Tate (2014) are of the view that “cross-functional cooperation relates to the use of integrating mechanisms in activities that require expertise from different functions”. However, Pylväs (2012) argues that it is expected that the joint occurrence of cross-functional cooperative ability has a positive effect on a firm’s customer performance and financial performance.

In summary, the aforementioned, namely, customer focus, organisational capability, customer value and cross functional cooperation which were named ‘market-driven strategies’ are internal determinants of export performance (Spirig, 2011). However, no mention is made of contingency factors which are external to the firm, namely, export market characteristics and industry characteristics, which are also regarded as key in determining export performance of a firm (Islam & Hu, 2012). Spirig (2011) submits that market-driven strategies are premised on the notion that, all firms’ strategic decisions should begin by understanding markets, customers and competitors. Cravens and Pierce (2008) opine that, the prominent feature of market-driven organisations involves “market sensing and better methods of linking customers and this involves learning about customers, channel members, competitors and their present and future markets”.

Although much research (Spirig, 2011; Mascherpa, 2011; Ismail, 2012) has been conducted on market-driven strategies in developed countries, Damoah (2011) argues that there has been little empirical evidence in African countries in general and Zimbabwe in particular, more especially on exporting manufacturing companies classified as SMEs. Moreover, the aforementioned researcher states that, “export marketing strategies were developed based on the export behaviour of the developed countries’ firms and most of the empirical studies on market-driven strategies were also based on SMEs in the developed countries, such as North America and Europe”. Therefore, a comprehensive empirical study is required to determine the effect of market-driven strategies on export performance of SMEs in the manufacturing sector in Zimbabwe. In order to address the aforementioned, it is hypothesized with respect to the SMEs in Zimbabwe that:

H1: There is a positive and significant relationship between customer focus and the export performance of SMEs.

H2: There is a positive and significant relationship between distinctive capabilities and the export performance of SMEs.

H3: There is a positive and significant relationship between customer value and the export performance of SMEs.

H4: There is a positive and significant relationship between cross-functional cooperation and involvement and the export performance of SMEs.

H5: Export market characteristics negatively affect the export performance of SMEs.

H6: Industry characteristics negatively affect the export performance of SMEs.

H7: Export Marketing barriers negatively affect the export performance of SMEs

The aforementioned hypotheses will be tested using the methodology described below.

Research Methodology

The Small Enterprise Development Corporation’s (SEDCO) (2014) definition of an SME was adopted in this study, which was “an enterprise employing not more than 75 people and with a fixed asset base not exceeding US $500000” a sample was drawn from the population from each exporting segment using Krejcie and Morgan’s (1970) model. Samples were drawn at a 95% confidence level and a 5.0% margin of error, as these are acceptable levels in a research (The Research Advisors, 2006). The sample was stratified as follows: leather 44, food processing 132, textiles169 his in total resulted in having a sample size of 350 respondents out of a population of 558, which represented 37% of the total population of export manufacturing SMEs in Harare and falls within the acceptable limits (Ibrahim, 2014). The sample was selected from companies are based in Harare, the capital city, as recent evidence suggests that, “Harare city would give diverse responses relatively large enough to be representative of the national views” (Chingwaru & Jakata, 2015).

Data Collection

Self-administered questionnaires were used to collect data from the SME owner-managers. While research has been conducted to explain export performance and its antecedents, there have been no generally accepted conceptualisation on how best it can be measured (Chen, 2015). Thus, this study used summated scales to measure the following constructs: customer focus, distinctive organisational capabilities, customer value, cross-functional cooperation, export performance, export market characteristics and industry characteristics, since “there are no unifying scales to measure them” (Mascherpa, 2011). Therefore, specific scale items or questions were typically used to measure the construct, using a five point Likert scale to elicit the degree of agreement or disagreement with each statement. According to Zikmund and Babin, (2007), when measuring customer focus, “a number of variables may be used and these may be captured on a scale of 1-5.” In this case variables that can measure the ‘customer focus’ construct are export tailor-made products; export timely delivery; reasonably priced products; after-sale export services; and continuous improvement. Using the Likert scale, “respondents were asked to indicate the extent to which they agreed or disagreed with statement(s) concerning a particular object” (Makanyeza, 2014).

Research Findings

In terms of the response rate, of the 345 distributed, 332 were usable questionnaires, resulting in a 96% response rate. Most of the participants were female (61.7%) and managers, representing 37.7% of the total sample. The results showed that most (68.7%) of the SMEs exported to regional markets, which highlight that most of the SME’s products are most probably being exported to regional markets in the SADC region.

Reliability

Table 1 reveals that the Cronbach’s Alpha values for each research construct ranges from 0.781 to 0.899 and since these are above 0.6 as recommended by Tavakol and Dennick (2011), the reliability of the model is confirmed. Furthermore, the item to total values was from 0.795 to 0.943 and above the cut-off point of 0.5 (Stylidis, Shani & Belhassen, 2017). The Cronbach’s Alpha results indicated in Table 1 therefore validate the reliability of measures used in the current study.

Table 1: Scale Accuracy
Research constructs		Scale item		Cronbach’s test		CR	AVE	Factor loadings
		Mean	SD	Item-total	a value
CF	CF1	3.79	0.888	0.663	0.917	0.934	0.674	0.613
	CF2	4.02	0.771	0.795				0.848
	CF3	3.77	0.922	0.727				0.920
	CF4	4.30	0.741	0.690				0.610
	CF5	3.60	0.791	0.770				0.952
	CF6	3.49	1.142	0.849				0.932
	CF7	3.79	0.888	0.663				0.792
CFCI	CFCI3	4.35	0.573	0.761	0.864	0.894	0.629	0.748
	CFCI4	3.84	1.045	0.799				0.886
	CFCI5	4.37	0.578	0.623				0.772
	CFCI6	3.95	0.815	0.692				0.849
	CFCI7	3.67	1.085	0.740				0.695
CV	CV1	3.81	0.958	0.622	0.758	0.861	0.675	0.751
	CV6	3.86	0.889	0.690				0.860
	CV7	4.40	0.695	0.503				0.850
DC	DC1	3.44	1.098	0.684	0.761	0.831	0.507	0.815
	DC2	4.28	0.591	0.606				0.761
	DC3	3.98	1.012	0.827				0.701
	DC4	4.00	1.047	0.698				0.637
	DC5	4.00	0.655	0.683				0.556
	DC6	4.21	0.600	0.550				0.540
EMB	EMB2	4.28	0.666	0.610	0.870	0.901	0.605	0.604
	EMB3	3.86	0.915	0.578				0.786
	EMB4	4.00	0.951	0.502				0.810
	EMB5	4.14	0.804	0.506				0.696
	EMB6	3.91	0.895	0.509				0.898
	EMB7	3.70	0.989	0.500				0.838
EMC	EMC1	3.88	0.879	0.574	0.926	0.942	0.731	0.788
	EMC2	3.63	1.176	0.594				0.802
	EMC3	4.14	1.014	0.780				0.862
	EMC4	3.79	0.940	0.707				0.888
	EMC5	3.84	0.615	0.678				0.919
	EMC6	3.88	0.662	0.735				0.862
IC	IC1	4.19	0.794	0.835	0.921	0.931	0.580	0.761
	IC2	4.16	0.843	0.707				0.805
	IC3	4.12	0.851	0.827				0.855
	IC4	4.02	0.771	0.795				0.828
	IC5	3.77	0.922	0.727				0.802
	IC6	4.30	0.741	0.690				0.858
	IC7	3.60	0.791	0.770				0.844
	IC8	3.49	1.142	0.849				0.609
	IC9	3.79	0.888	0.663				0.586
	IC10	4.35	0.573	0.761				0.592
SEP	EP1	3.84	1.045	0.799	0.845	0.878	0.545	0.675
	EP2	4.37	0.578	0.623				0.751
	EP3	3.95	0.815	0.692				0.805
	EP4	3.67	1.085	0.740				0.735
	EP5	3.81	0.958	0.622				0.689
	EP6	3.86	0.889	0.690				0.768

When the appropriate overall fit was established, the following procedure was used to assess reliability and validity, under the guidance of previous literature (Nusair & Hua, 2010). As advocated by Chinomona (2011), the squaring of factor loadings was performed in order to assess the item reliability. Item reliability recognizes “the amount of variance in an item due to underlying construct rather than to error” (Chau 1997).

Composite Reliability test was performed so as “to examine the internal reliability of the research constructs” (Chinomona, 2011). Bryman and Bell (2015) assert that a Composite Reliability index that is greater than 0.7 signifies sufficient internal consistency of the construct. The results reported in Table 1 above confirm the internal reliability of all the research constructs since the Composite Reliability results range from 0.831 to 0.942.

Validity

In this study, discriminant and convergent validity were also examined using the AVE, following the suggestion by Fornell and Larcker (1981). According to Nusair and Hua (2010) “a low-cross correlation signifies discriminant validity while the strong loading of items on their familiar construct is an indication of convergent validity”. Sarstedt, Ringle, Smith, Reams & Hair (2014) assert that “discriminant validity is the degree to which a construct is empirically different from other constructs in the model, both in terms of how it links with other constructs and in terms of how specifically the items represent only this single construct”. Convergent validity in other words refers to “the degree to which a construct is represented by its measurement items” (Sarstedt, 2014). Table 1 reflects a summary of the scale accuracy analysis.

Besides “assessing the convergent validity of items through checking correlations in the item-total index” (Nusair and Hua, 2010), factor analysis was also performed so as to identify “convergent validity of measurement items” as recommended by Sarstedt (2014). According to Nusair and Hua (2010) “items exhibit good convergent validity when they load strongly on their common construct”. The literature maintains that “a loading that is above 0.5 signifies convergent validity” (Anderson & Gerbing, 1988). In this regard, the final items used in the current study loaded well on their respective constructs with the values ranging from 0.540-0.952 (Table 2). This therefore indicates good convergent validity where items are explaining more than 65% of their respective constructs. Furthermore, since CR values are above the recommended threshold of 0.7, this substantiates the existence of convergent validity (Anderson & Gerbing, 1988).

Chinomona (2011) asserts that “the average variance extracted estimate reflects the overall amount of variance in the indicators accounted for by the latent construct”. In this case “a good representation of the latent construct by the item is identified when the variance extracted estimate is above 0.5” (Sarstedt, Ringle, Smith, Reams & Hair, 2014; Fornell, 1981). Thus, in this study, the results of AVE that range from 0.507 to 0.675 (Table 1) signify a good representation of the latent construct by the items.

Hair, Hult, Ringle and Sarstedt (2014) assert that “when determining if there is discriminant validity or not, what must be done is to identify whether the observed variable displays a higher loading on its own construct than on any other construct included in the structural model”. Furthermore, a recommendation made by Chinomona (2011) affirms that “to check if there is discriminant validity is to assess if the correlation between the research constructs is less than 1.0”. Table 2 below shows that, the inter-correlation values for all paired latent variables are less than 1.0, hence confirming the existence of discriminant validity.

Table 2: Correlation Between The Constructs
RESEARCH CONSTRUCTS	CF	CFC	CV	DC	EMB	EMC	IC	SEP
(CF)	1
(CFC)	0.318	1
(CV)	0.136	0.326	1
(DC)	0.479	0.451	0.303	1
EMB	0.059	0.083	0.094	0.035	1
EMC	0.174	0.065	0.026	0.069	0.135	1
IC	0.153	0.034	0.061	0.035	0.138	0.789	1
SEP	0.318	0.110	0.082	0.195	0.547	0.251	0.190	1

Structural Model Assessment

The data was analysed using structural equation modelling (SEM) since it has gained popularity as a statistical technique to test theory in several fields of knowledge (Hair, Anderson, Tatham & Black, 1998). SEM is perceived as “being similar to regression analysis but more predominant in that, it assesses the casual relationships among constructs while concurrently accounting for measurement error” (Sarstedt, Ringle, Smith, Reams & Hair, 2014). In an endeavour to statistically analyze the measurement and structural model, the Smart PLS software for SEM technique (Ringle, Wende & Will, 2005) was used. Unlike AMOS and LISREL which are covariance based approaches, Smart PLS is a regression based technique that originates from path analysis (Chinomona & Surujal, 2012) and it is capable of handling complex predictive models in studies involving small-to-medium sample sizes. In this case, the sample size of this study is relatively small (345), thus Smart PLS was considered as an appropriate tool and giving more value to the current study. In this regard, Bootstrapping resampling method was used to test the statistical significance of the relationships. This procedure involved generating 200 sub-samples of cases selected randomly, with replacement, from the original data.

The next phase of data analysis involved the use of SEM and in particular, path analysis (Beran & Violata, 2010; Stein, 2012), since path modelling describes “the relationships between observed or measured variables and theoretical constructs” (Roche, Duffield & White 2011) and “tests the structural paths of the conceptualized research model” (Anderson & Violata, 1988). The SEM procedure was performed in order to test the theoretical underpinnings of the study and the significance of the relationships between the model’s constructs. The structural model was evaluated by examining the p-values as well as standardized regression coefficients. While conducting path modelling, standardized regression coefficients were explained as well as their predictive ability. Figure 1 below shows the structural model for the study.

Table 3: Hypothesis Tests Results
Proposed hypothesis relationship	Hypothesis	Path Coefficients	T-Statistics	P-Values	Rejected/Supported
CF→  SEP	H1	0.247	2.663	0.004	Supported and significant
DC → SEP	H2	0.092	0.681	0.497	Rejected
CV →SEP	H3	0.082	0.753	0.453	Rejected
CFCI→SEP	H4	0.042	0.234	0816	Rejected
EMC     →   SEP	H5	-0.203	1.981	0.005	Supported
IC →  SEP	H6	0.087	0.384	0.702	Rejected
EMB  →SEP	H7	-0.509	5.985	0.000	Supported

Table 3 below which provides the t-statistics for the hypothesised relationships reveals that the minimum t-statistic is 4.445, which exceeds the recommended threshold of 2 (Hulland, 1999), which further confirms that all the hypothesised relationships are statistically significant. Overall, the R² for LI and SMEP in Figure 1 above indicates that the research model explains more than 56% of the variance in the endogenous variables. Following the formulae provided by Tenenhaus, Vinzi, Chatelin and Lauro (2005), the global goodness-of-fit (GoF) statistic for the research model was calculated using the equation:

Using the above formula, the calculated global goodness of fit (GoF) is 0.69, which is above the threshold of GoF>0.36, as confirmed by Wetzels, Odekerken-Schröder and Van Oppen (2009). Thus, it is concluded that the research model has a good overall fit.

Further to assessing the hypothesized measurement and structural model, the next step was to examine causal relationships among the latent variables through path analysis (Nusair and Hua, (2010). According to Nusair and Hua (2010) SEM reveals that “particular latent variables directly or indirectly influence certain other latent variables with the model, resulting in estimation results that portray how these latent variables are related”. The estimation results elicited through hypothesis testing are indicated in Table 3 below, which also reflects the hypotheses, path coefficients, t-statistics and the decision on the hypotheses. Chinomona, Lin, Wang & Cheng (2010) highlight that “t>1.96 are indicators of relationship significance and that higher path coefficients indicate strong relationships among latent variables”.

From the results reflected in Table 3 above, it may be deduced that:

• There is a positive and significant relationship between Customer Focus and Export Performance (path coefficient of 0.247; t=2.66.3); hence, H1 is accepted.

• There is a positive, yet insignificant association between Distinctive Capabilities and Export Performance (path coefficient of 0.092; t=0.681), hence, H2 is rejected.

• There is an insignificant association between Customer Value and export performance (path coefficient of 0.082; t=0.753); hence, H3 is rejected.

• There is a relationship between Cross-Functional Cooperation and Involvement and Export Performance, since a path coefficient of 0.042 and a t=0.234 was realized; hence, H4 is rejected.

• There is a negative association between Export Marketing Characteristics and Export Performance of SMEs (path coefficient of -0.203; t=1.981), which implies that Export Marketing Characteristics has a negative influence on the export performance of SMEs; hence, H 5 is accepted.

• H6 was rejected in that there is a positive yet insignificant relationship between Industrial Characteristics and the export performance of SMEs (path coefficient of 0.087; t=0.384).

• H7 is accepted since there is a significant association between Export Marketing Barrier and the export performance of SMEs (path coefficient of -0.509; t=5.985), which means that Export Marketing Barrier has a negative influence on the export performance of SMEs.

Discussion

Internal Influences of the Export Performance of SMEs

The findings reveal that Customer Focus and the export performance of the SMEs are significantly positively related, which finding is consistent with that reported by other researches, inter-alia, Asikhia and Binuyo (2012), Verhoef and Lemon (2013) and Yaacob (2014). However, the findings seem to be different to that reported by Mahmoud, (2011), who argued that although the idea of being customer focused is very noble, it is not quite relevant for SMEs in developing countries as it is difficult to achieve.

The relationship between Distinctive Capabilities and the export performance of SMEs is positive, but not in a significant way, evidenced by the fact that the majority of respondents stated that they do not have manufacturing experience, their products are imitable and not quite reputable, they do not have credible reputation in the export market and, they do not have marketing resources to undertake exports. However, this was expected since some researchers inter-alia, Freeman and Styles (2014) opine that “due to the lack of resources, SMEs exhibit a haphazard approach with regards to the acquisition and use of export market information and consequently, the firm’s risk to exporting increases, as decisions are often based on pure ‘gut feeling’ as opposed to informative strategic decision making.” Furthermore, there is a notion that firms should selectively target different marketing capabilities to improve their export performance (Al-Aali, Lim, Khan & Khurshid, 2013). Thus, the results confirm that this notion cannot specifically apply to SMEs’ in the developing countries as capable and reliable human resources are needed to target different marketing capabilities that will result in enhanced export performance (Khan, 2013).

It was also ascertained that there is a positive relationship between Customer Value and the export performance of SMEs, however the relationship is insignificant. This could have been attributed to the fact that the majority of the respondents highlighted that they do not export products that meet the expectations of the target customers, their exports do not encompass desired value and perceived value, they do not export products that create long lasting impressions, they do not export products that attaches a psychological meaning to the product and their products have no co-creation value among others. The results of this study differ somewhat from those reported by other researchers (Shanker, 2012; Yu-Shan & Ching-Hsun, 2012), since the aforementioned results were positive and significant. This could be attributed to the availability of resources and location of the study for example, since developed countries tend to have adequate resources to finance exports, compared to developing countries and in particular Zimbabwe, which does not have adequate resources. Ngugi, Johnsen and Erdélyi (2010) argue that SMEs in developing countries may not be able to match customer value and organisational capability due to the nature of their size and resource constraints.

It also emerged that cross-functional cooperation and involvement influenced the export performance of SMEs however, the influence is insignificant, implying that the effect of cross-functional cooperation on the export performance of SMEs is minimum. The aforementioned findings are different to that reported by other researchers (Topolšek & ?urin, 2012), who reported positive and significant relationships between Cross-functional Cooperation and firm performance. This could be linked to the size of the firms considered for the study as indicated by Mahmoud (2011). Drawing from the research findings, it can be pointed out that SMEs in Zimbabwe have not fully embraced Cross-functional Cooperation to enhance their export performance.

External Influences on the Export Performance of SMEs

The findings confirmed a positive and significant relationship between the Export Market Characteristics and the export performance of SMEs, which results are similar to Hashem and Irshaidat (2014) and Uzoma (2014). Upon further analysis, a path coefficient of -0.203 was realized, which means that Export Marketing Characteristics have a negative influence on the export performance of SMEs. Based on these findings, it can therefore be confirmed that when there are more restrictive laws, high taxes, cultural perceived barriers and high exchange rate, the export performance of SMEs is subdued.

The results indicated a positive relationship between Industrial Characteristics and the export performance of SMEs, which implies that Industry Characteristics (competitor networks, technology and export market turbulence), influence the export performance of SMEs, albeit not in a significant way. These results are different to that reported by Ricci and Trionfetti (2012), Uzoma, Chukwu & Mirdi, (2014) and Lengler, Sousa, Perin, Sampaio & Martínez-López. (2016), all of who confirmed a significant relationship.

Although the Export Marketing Barriers tend to be internal and external, the researcher only looked at the external barriers since these are a major drawback to export performance and also the fact that this study only dealt with exports. The research results revealed that Export Marketing Barriers have a negative influence on the export performance of SMEs. The results are similar to what Madsen (1989) reported, namely, there is a negative association between commercial barriers and export performance, once the collinear relationship between commercial barriers and external market growth rate had been controlled.

Conclusion

Drawing from the results, it can be pointed out that by being ‘Customer Focused’ SMEs in Zimbabwe could improve their export performance. Distinctive Capabilities are also one of the internal influences that determine a firm’s performance and are classified as one of the market-driven strategies that can be adopted by a firm to enhance its performance.

The findings also confirm that the direct effects of Customer Value on the export performance of SMEs in Zimbabwe are insignificant. The results also indicate that the relationship between Distinctive Capabilities and the export performance of SMEs is positive, but not in a significant way, evidenced by the fact that the majority of respondents who stated that they do not have manufacturing experience, their products are imitable and not quite reputable, they do not have credible reputation in the export market and, they do not have marketing resources to undertake exports.

It was also evident that SMEs in Zimbabwe have not fully embraced cross-functional cooperation to enhance their export performance. It was ascertained that export market characteristics have a strong impact on the export performance of SMEs, which implies that export market characteristics are negatively affecting the export performance of SMEs. Based on these findings, it can therefore be confirmed that when there are more restrictive laws, high taxes, cultural perceived barriers and high exchange rate, the export performance of SMEs is subdued. The research results also revealed that export marketing barriers have a negative influence on the export performance of SMEs.

Recommendations

Since Customer Focus is positively associated with the export performance of SMEs, this implies that the management of SMEs should emphasize customer focus in order to positively influence the export performance of SMEs. Furthermore, since it became apparent that the Distinctive Capabilities do not influence the export performance of SMEs in a significant way, the SME owner-managers should prioritise investing in techniques that foster the acquisition of more knowledge that enhance distinctive capabilities.

It also became evident from this study that Customer Value does not influence the export performance of SMEs in any significant way, which implies that the SMEs owner-managers should focus on other factors which have a strong influence on the export performance of SMEs such as Customer Focus and Export Market Characteristics.

It also became evident that Export Market Characteristics and the export performance of SMEs are negatively related in a significant way, which implies that the SMEs owner-managers should prioritise by investing more on strategies that help to reduce the effect of Export Market Characteristics (restrictive laws, high taxes, cultural perceived barriers and high exchange rate), in order to positively influence the export performance of SMEs.

It was ascertained that the Industrial Characteristics do not influence the export performance of SMEs in a significant way, which implies that the export performance of SMEs is not strongly influenced by Industrial Characteristics (perceived competition, competitor networks, etc.), when compared to other factors. Therefore, the SMEs owner-managers should focus on other factors that have a strong influence on the export performance of SMEs such as customer focus and export market characteristics.

The findings reveal that Export Marketing Barriers have a negative influence on the export performance of SMEs, which implies that the export performance of SMEs is strongly negatively influenced by Export Marketing Barriers, compared to the direct effect of Distinctive Capabilities and Cross-Functional Cooperation and Involvement. Therefore, the SMEs owner-managers should prioritise in finding ways to minimise the export marketing barriers in order to enhance the export performance of SMEs.

It can therefore be accepted that when SMEs owner-managers attempt to enhance their exports, they must invest heavily in areas that effectively meet the value expectations of their target customers and produce products with co-creation value. Thus, the SMEs owner-managers should attempt to create vibrant teams with diverse expertise that work to create an enabling environment to boost exports, hence resulting in sustainable export competitive advantage of SMEs. Furthermore, SMEs should have networks, relationships, partnerships and strategic alliances with larger organisations in order to develop their capabilities.

Furthermore, SMEs should improve their internal relationships so that each employee is influenced to make meaningful contributions that will enhance export performance. Also SMEs owner-managers should create an effective system that enables free transfer of knowledge across all departments in an organisation and this will help in enhancing export performance.

It is recommended that SME owner-managers should take into consideration the negative effect of export market characteristics on the export performance of SMEs so as to enact strategies that are resilient to environmental torment. The SMEs owner-managers should also prioritise finding ways to minimise export marketing barriers in order to improve the export performance of SMEs.

Future Research

The study limited itself to only SMEs in the manufacturing sector notably, the food, leather and textile processing. Perhaps, future studies should consider surveying unregistered SMEs and all the sectors in which SMEs participate in Zimbabwe. The current research only investigated the relationships between the internal influences and in particular the market-driven strategies and, the external influences, on the export performance of SMEs in Zimbabwe. Future researchers should consider other countries in Africa and also expand the current research model and include other research variables/other marketing strategies, without concentrating on market-driven strategies only. Such an expanded conceptual model can be expected to yield more interesting insights for practitioners studying the export performance of SMEs and as well as generating and adding new knowledge to the existing body of academic literature. The focus in this paper was on the SME owner-managers; it may be interesting to ascertain the views of SME policy makers and export marketing strategists/analysts.

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