The effect of entrepreneurial orientation on export performance: Evidence from manufacturing SMEs in Tanzania

Abstract

This paper examines the effect of innovativeness and proactiveness on export performance and the moderating effect of risk-taking on the relationship between innovativeness, proactiveness, and export performance. A quantitative research approach and a cross-sectional survey design were employed. In addition, stratified random sampling was employed to obtain data from 250 managers of manufacturing-exporting SMEs. Hypotheses were examined using the PROCESS macro test. The findings affirm that innovativeness is a significant predictor of export performance (β = 0.3854 and p < 0.001). However, negative effect of proactiveness on export performance was revealed (β = −0.1748 and p < 0.001). Risk-taking was found to be a significant moderator of the relationships between innovativeness and export performance (β = 0.2234 and p = 0.0009) as well as proactiveness and export performance (β = 0.1041 and p = 0.0271).The findings of this study broaden the applicability of RBV theory to an exporting context by examining the process through which entrepreneurial-orientation (EO) as an intangible resource leads to successful export performance. This study also contributes to the debate on the EO-export performance relationship by establishing the interplay effect between EO dimensions in enhancing export performance. The study contributes that risk-taking significantly moderates the relationship between innovativeness, proactiveness and export performance. Furthermore, the study recommends that the owners-managers of exporting SMEs develop and implement more innovative strategies, respond quickly to seize available export market opportunities, cultivate a positive attitude toward risks, and increase their capacity to take risks in order to improve their export performance.

Keywords:

• Export performance
• innovativeness
• proactiveness
• risk-taking
• SMEs
• Tanzania

1. Introduction

Small and medium-sized enterprises (SMEs) play a significant role in the economic development in all nations (Jin & Hurd, 2018). This is because they contribute significantly to jobs creation, wealth, and the reduction of poverty (Ismail & Wright, 2022; Muriithi, 2017). In addition, SMEs make a major contribution to the country’s GDP (Edeh et al., 2020). Furthermore, SMEs are considered to be the birthplace of innovations and entrepreneurship (Agyapong, 2010). As a result, both developed and developing countries have paid close attention to their growth and competitiveness (Ali et al., 2020). For instance, the government of Tanzania has put in place various efforts to support the growth of SMEs. The efforts include the formation of the Small Industries Development Organization in 1973 and the creation of the University of Dar es Salaam Entrepreneurship Center in 2001. Also, the establishment of the SME development policy in 2003, the creation of the SME credit guarantee scheme in 2005, and the establishment of the SME department in the Ministry responsible for Industry and Trade in 2003 (Kiyabo & Isaga, 2019; Mpunga, 2016).

Exporting is the initial step of the internationalization process (Paul & Mas, 2019), and is considered to be the least expensive and easiest mode of SMEs’ internationalization (Gupta & Chauhan, 2020). Likewise, export performance refers to the outcomes (success or failure) of a firm’s operations in export markets (Chen et al., 2016). Zou et al. (1998) suggested that, in measuring export performance, both financial and non-financial measures should be taken into account. Additionally, Beleska-Spasova (2014) contended that the multifaceted EXPERF Scale of Zou et al. (1998) is a valid instrument for measuring export performance in different nationalities. Therefore, the EXPERF Scale was used in this study to measure export performance.

Exporting is regarded as a means for SMEs to grow, increase competitiveness and survival (Haddoud et al., 2017). Unfortunately, SMEs in various economies experience disappointing performance in export markets (Acikdilli et al., 2020; Hossain et al., 2022a; Mkenda & Rand, 2020; Paul, 2020). As a consequence, their contribution to total exports is low, and they often achieve less than expected (Gupta & Chauhan, 2020;). For instance, in OECD economies, although SMEs represent 95 percent of all firms, they only generate around 20–30 percent of the total exports (OECD, , 2019). In Africa, particularly in Sub-Saharan Africa, SMEs generate around 10–15 percent of total exports, as opposed to the planned share of 30 percent, despite they account for 95 percent of all enterprises (International Trade Center, 2018). Likewise, Tanzania is not exempt from such problems. Despite SMEs accounting for 95 percent of all enterprises in Tanzania, their contribution to overall country exports is estimated to be between 10–15 percent (Edwin, 2019; Mkenda & Rand, 2020).

In addition to that, empirical evidence worldwide shows invariably this problem of poor export performance of SMEs. For instance, the studies of Ferreras-Méndez et al. (2019) and Manzanares (2019) in Spain and Rekarti et al. (2018) in Indonesia. Also, Abu-Rumman et al. (2021) in Jordan, Li et al. (2019) in China, Kalinic and Brouthers (2022) in Dutch and Italian. In addition, the studies of Imran et al. (2019) and Mubarik et al. (2020) in Pakistan, Acikdilli et al. (2020) in Turkey, and Kamal et al. (2022b) and Hossain and Azmi (2021) in Bangladesh. Furthermore, the studies by Edeh et al. (2020) in Nigeria, Robb et al. (2020) and Robb and Stephens (2021) in South Africa. Moreover, the studies of Ahimbisibwe et al. (2016) and Ahimbisibwe and Abaho (2013) in Uganda, Cherotich (2021) and Kitonyi et al. (2020) in Kenya and Luge et al. (2021), Mbura (2019), Mkenda and Rand (2020), and Mpunga (2016) in Tanzania. These studies provide evidence of the problem regarding the poor export performance of SMEs worldwide.

Nevertheless, because exporting is an act of entrepreneurship (Ibeh & Young, 2001), researchers have developed an interest in how entrepreneur orientation helps SMEs perform better in export markets (Hernandez-Perlines, 2018; Hossain et al., 2022a). Likewise, Leko-Šimić and Horvat (2006) opined that the entrepreneurship behavior of the firm owner/manager and of the firm itself has a significant impact on the process of internationalization and, export performance. The entrepreneurship behavior of the firm is measured through its entrepreneurial orientation (EO), because EO is widely regarded as the cornerstone and central focus of entrepreneurship (Covin & Slevin, 1991; Wales et al., 2011). EO is a strategic organizational stance comprised of the specific processes, practices, and activities that allow firms to create value through entrepreneurial endeavors (Lumpkin & Dess, 1996). Likewise, EO is a dynamic capability that helps firms to acquire and maintain a competitive edge in ever-changing marketplaces (Rwehumbiza & Marinov, 2020) like export markets and is intimately linked to the firms’ exporting behavior and success (Hossain et al., 2022a).

Based on that, a number of empirical studies have examined the relationship between EO and SMEs’ export performance (Ajayi, 2016; Boso et al., 2018, 2016; Chin et al., 2016; Hossain & Azmi, 2021; Imran et al., 2019; Jin & Cho, 2018; Kalinic & Brouthers, 2022; Kamal et al., 2022b; A. Monteiro et al., 2019; Robb et al., 2020; Rua et al., 2018). However, the studies produced mixed evidence leading to the conclusion that the empirical findings on this relationship are not conclusive (Hossain & Azmi, 2021; Imran et al., 2019). While the majority of studies indicate a significant positive effect (Ajayi, 2016; Boso et al., 2016; Imran et al., 2019; Jin & Cho, 2018; Kalinic & Brouthers, 2022; Kamal et al., 2022b; Karami & Tang, 2019; Monteiro et al., 2019; Rua et al., 2018) some studies found insignificant effect (Baldegger et al., 2021; Chin et al., 2016; Felzensztein et al., 2015; Fernández-Mesa & Alegre, 2015; Imran et al., 2017) and others revealed mixed findings regarding EO dimensions and export performance (Hossain & Azmi, 2021; Robb et al., 2020). To the best of the authors’ knowledge, there is no published study on the relationship between EO and export performance in Tanzania. However, we found few empirical studies that investigated the EO-performance relationship in the context of Tanzania. For instance, Kapaya et al. (2018), Nyello and Kalufya (2021), and Roux and Bengesi (2014) found significant positive effect on the relationship. Also, a study by Kiyabo and Isaga (2020) found a negative effect of EO on the performance of SMEs. Furthermore, studies of Shayo and Uiso (2019) and Okangi (2019) revealed mixed results regarding the dimensions of EO and the performance of tourism firms in Tanzania.

Based on these findings it has been argued that the EO-performance is a complex relationship that needs further explorations (Hossain et al., 2022a; Otache & Mahmood, 2015; Robb et al., 2020). Also, Anderson and Ronteau (2017) and Hossain et al. (2022a) explained that the majority of studies that examined the EO-performance relationship have been conducted in developed nations whereas a dearth of empirical based-evidence exists in developing nations. As a result of this, various authors have called for further research on the EO-performance relationship, particularly in a developing country context, to advance researchers’ knowledge of the mechanisms and contingent factors affecting the relationship (Anderson & Ronteau, 2017; Hossain et al., 2022a; Knight et al., 2020). This study, therefore, responds to those calls by investigating the EO-export performance relationship in a developing country context, Tanzania, and also contributes to the body of knowledge by examining the mechanism underlying the performance impacts of EO.

In this study, unlike the previous studies, we contend that there could be a potential interplay between different dimensions of EO in enabling SMEs to achieve better export performance. By so doing, the current study unpacks the process of how SMEs’ EO improves export performance. Consistent with Miller (1983) and Covin and Slevin (1989) who argued that the three dimensions of EO namely innovativeness, risk-taking and proactiveness positively interact to enhance performance; the current study posits that there would be joint effects of different dimensions of EO leading to successful export performance. For instance, the ability of a firm to innovate a new product or market may need a considerable amount of risk-taking propensity. Similarly, the ability of a firm to identify and exploit market opportunities before other competitors may necessitate a significant level of risk-taking behavior. This study centered on the three EO dimensions namely innovativeness, risk-taking, and proactiveness among others. This is because the literature suggests that EO is majorly composed of these three dimensions (Covin & Slevin, 1989; Miller, 1983). Also, it is acknowledged that an individual or a firm is entrepreneurial-oriented if pursuing innovativeness, risk-taking, and proactiveness (Covin & Slevin, 1991; Karami et al., 2020). Furthermore, the literature identified that SMEs engaging in these three dimensions are more likely to thrive in export markets (Ajayi, 2016; Hossain et al., 2022a). Based on this, the study proposes a moderation model of the EO dimensions on export performance. Specifically, the study suggests that risk-taking moderates the effect of innovativeness and proactiveness on export performance.

To test the developed conceptual model this study collected data from 250 manufacturing exporting SMEs in Tanzania. By so doing, this study contributes significantly to the body of literature and practical standpoint. First, by examining the performance impact of EO, the study conceptualizes the moderating effect of risk-taking on the relationship between innovativeness, proactiveness and SMEs’ export performance which has not yet been done in previous studies. Second, by examining the moderating role of risk-taking on the relationships between innovativeness, proactiveness, and export performance, this study unpacks the process through which EO leads to export achievements. Specifically, the current study adds to the entrepreneurship and export literature a comprehensive insight into how SMEs might develop effective EO leading to successful export performance. Uncovering the process would allow policymakers and business owners/managers to improve and enhance the effectiveness of EO in attaining better export performance.

2. Literature review and hypotheses development

2.1. The resource-based view (RBV) theory

This study employed the resource-based view (RBV) theory by Penrose (1959) consistent with prior studies that examined EO-performance relationship (Abu-Rumman et al., 2021; Hossain & Azmi, 2021; Imran et al., 2019; Jin & Cho, 2018; Kiyabo & Isaga, 2020; Monteiro et al., 2019; Monteiro et al., 2017) in the extant literature. According to the RBV theory, a firm is defined as a bundle of resources integrated into different ways, and it is this combination that gives a firm its uniqueness (Wernerfelt, 1984). The RBV offers a theoretical underpinning for the importance of various types of resources owned by a firm to its competitiveness and overall performance (Roxas & Chadee, 2011). Based on the theory, organizations’ performance varies due to differences in resource endowment (Barney, 1991; Peteraf, 1993). Firm resources can be categorized as tangible, like finance and physical resources, or intangible like capabilities, skills, and knowledge. The theory further asserts that the possession of strategic resources is critical for a firm to achieve competitive advantage and superior performance (Kiyabo & Isaga, 2020; Monteiro et al., 2019). Strategic resources can provide either “cost leadership or differentiation” as a result they have the potential to be a source of sustainable competitive advantage (Bhat & Momaya, 2020). Strategic resources are resources which are valuable, rare, and non-substitutable, and have been recognized as the major differentiators between firms that have advantages and those that do not (Kellermanns et al., 2016).

Extant literature acknowledges intangible resources as strategic resources because they feature distinguishing characteristics (Gupta & Chauhan, 2020; Locket et al., 2009). As a result, recent studies have shifted the emphasis from tangible to intangible resources, which are regarded as more vital and relevant for firm success and performance (Gupta & Chauhan, 2020; Monteiro et al., 2019). Similarly, Racela and Thoumrungroje (2020) claimed that to generate a sustainable competitive advantage and superior performance, strategic intangible resources must be efficiently utilized. For SMEs, EO is viewed as a strategic intangible resource that significantly contributes to competitive advantage and superior performance (Monteiro et al., 2017; Runyan et al., 2006). EO promotes the ability of firms to gain and maintain a competitive advantage in a volatile business environment such as the export markets (Altinay et al., 2015; Hossain et al., 2022a). This is because, SMEs with high level of entrepreneurial management build strong business plans and make the required changes to capitalize on market opportunities (Ipek & Tanyeri, 2020). Therefore, based on RBV theory, EO is a distinctive capability in terms of risk-taking, innovativeness, and proactiveness that provides SMEs with a competitive edge and in turn successful export performance (Jin & Cho, 2018). In light of this, the RBV theory explains why SMEs should strategically utilize EO to gain a competitive advantage and achieve success in export markets.

2.2. Entrepreneurial orientation and export performance

Despite the fact that research on EO in developing economies is increasing, empirical evidence of its effect on export performance, particularly for SMEs in developing countries, is scant (Hossain et al., 2022a; Robb et al., 2020). Given the negative consequences of globalization, SMEs are frequently encouraged to evaluate their existing capabilities, like EO, when developing a strategy for global success (Robb et al., 2020). EO refers to the procedures, practices, and decision-making activities that lead to the new entrance (Lumpkin & Dess, 1996). Also, D. Miller (1983) describes EO as a firm’s ability to engage in product-market innovation, make somewhat risky decisions, and come out on top by outperforming competitors. In addition, D. Miller (1983) further explained that EO is comprised of innovative, proactive and risk-taking characteristics. Among the three dimensions, innovativeness and proactiveness are regarded as the primary elements of EO (Abu-Rumman et al., 2021; Otache & Mahmood, 2015). Therefore, entrepreneurial SMEs should primarily possess the two dimensions.

2.2.1. Innovativeness and export performance

The ability to innovate is regarded as the most important attribute of an entrepreneurial firm (Kuratko et al., 2011). Innovativeness is referred to the tendency of a firm to engage in and promote experimentation and creative activities that may result in new products, processes, or services (Lumpkin & Dess, 1996). Innovative firms expand their business operations easily through exporting (Saridakis et al., 2019). This is because innovative firms can develop improved, modified, or new products and processes that can provide them with a competitive advantage in foreign markets (Ahimbisibwe & Abaho, 2013). Furthermore, highly innovative organizations are likely to experience positive outcomes in their respective markets in terms of new technology, products, services, or processes (Edeh et al., 2020). As a result, firms with high levels of innovation outperform those with lower levels (Covin & Wales, 2019). In addition, the process of innovation is regarded as a vital performance factor, with organizations that encourage innovative behavior performing better in exporting (Calantone et al., 2006). Firm innovation provides a competitive edge in foreign markets by allowing businesses to benefit from economies of scale and scope (Silva et al., 2017). As consequently, innovative SMEs are more likely to flourish in international markets. Thus, it is worth hypothesizing that:

H₁: Innovativeness positively influences the export performance of SMEs.

2.2.2. Proactiveness and export performance

Proactiveness refers to a company’s ability to predict market demand and opportunities through market analysis and develop new products or processes ahead of its rivals to gain first-mover advantages in the marketplace (Hossain et al., 2022a). Proactive SMEs are optimistic about market potential opportunities (Lee & Peterson, 2000). Also, foreign markets have distinct customers with changing requirements and tastes, thus, being proactive in dealing with them is critical. Proactive SMEs concentrate on making their ideas a reality and achieving a competitive edge by being the first to seize on new market opportunities (Lee & Peterson, 2000). In addition, Jafari-Sadeghi and Dana (2022) suggest that firms must be proactive to guarantee competitiveness and superior performance in foreign markets. Moreover, the ability to be proactive may add a degree of proficiency, leading to improved export performance for the firm (Hughes & Morgan, 2007). Therefore, firm proactiveness is a vital instrument, particularly for SMEs in recognizing and responding to varying customer needs which ensures success in foreign markets (Hunt & Arnett, 2006). Thus, the study hypothesizes that:

H₂: Proactiveness positively influences the export performance of SMEs

2.3. Moderating effect of risk-taking

Risk-taking refers to the willingness of the owner-manager to devote a substantial amount of resources while executing strategies and opportunities with unknown expected returns (Lumpkin & Dess, 1996). Risk is regarded as an essential element of the entrepreneurial process (McMullen & Shepherd, 2006) because both innovation and proactivity are related to a significant amount of risks (Kuratko et al., 2011). For instance, the ability of the management to take risks is often a key component of the process when a firm engages in innovative and proactive activities. When a business intends to launch a new product, process, or market, a high degree of managerial risk-taking aptitude is essential. Similarly, a significant level of management risk-taking propensity is needed when a firm seeks to capitalize on discovered opportunities ahead of competitors in order to acquire first-mover advantages in the marketplace. Therefore, when managers have a strong risk-taking mentality, the firm’s entrepreneurial performance improves (Stambaugh et al., 2017). In addition, firm internationalization success is contingent on the entrepreneurial behavior of the firm owners’/managers’ readiness to take risks (Jafari-Sadeghi, 2021). As such, the current study investigates the argument that higher export performance may be attained through innovativeness and proactiveness if managers’ risk-taking attitude is working properly. Therefore, the study hypothesizes that the relationship between innovativeness, proactiveness and export performance may be influenced by the management’s risk-taking ability to commit resources to undertake risky strategies related to the potential opportunities in the export market. The interplay might occur in such a manner that a higher level of risk-taking attitude, the likelihood that, innovativeness and proactiveness will have a strong effect on export performance. It is thus hypothesized that:

H₃: Risk-taking significantly moderates the relationship between innovativeness and export performance of SMEs.

H₄: Risk-taking significantly moderates the relationship between proactiveness and export performance of SMEs.

2.4. The conceptual model

The conceptual model of this study is depicted in Figure 1; it has been developed based on the literature review and hypotheses formulated which have been grounded on the RBV theory. The model proposes that innovativeness and proactiveness lead to export performance. Also, the model suggests that risk-taking moderates the relationship between innovativeness, proactiveness, and export performance.

Figure 1. The conceptual model

Source: Literature review

3. Research methodology

3.1. Study areas and research design

The research was carried out in Tanzania, in the cities of Arusha, Dar es Salaam, Dodoma, Mbeya, and Mwanza. The cities are selected because they hold a large portion of Tanzania’s manufactured outputs (Andreoni, 2017; Kiyabo & Isaga, 2020). Furthermore, these cities were chosen because they have a relatively significant number of manufacturing SMEs in Tanzania (Nyello & Kalufya, 2021; URT, 2016). Consequently, a quantitative research approach was employed in this study to test the hypotheses formulated by the collected data. The study employed a cross-sectional survey design since data from a target population were only gathered once. The design is considered effective and efficient because it enables the collection of a significant amount of data within a short period (Kresmodel, 2018).

3.2. Sampling and data collection

The study’s target population consisted of 958 manufacturing-exporting SMEs in the selected cities, and the list was acquired from the National Bureau of Statistics (NBS), supplemented with a registered list from the regionals’ Small Industries Development Organization (SIDO). The target population comprised exporting SMEs in the food, leather, textile, and wood industries. The four industries were chosen because they account for the majority of Tanzania’s exporting SMEs in the manufacturing sector (Andreoni, 2017). This study includes manufacturing SMEs that have been regularly active in exporting activities for at least three years in order to acquire sufficient and relevant data to answer research hypotheses. Three years has been described as enough time to establish the firm’s direction and performance outcomes (Quaye et al., 2017). In this study, the Tanzanian definition of SMEs was adopted, with micro-enterprises having 1–4 workers, small-firms having 5–49 employees, and medium-sized firms having 50–99 employees United Republic of Tanzania (URT), 2003)

Also, Yamane’s (1967) algorithm was utilized to calculate sample size, with a target population of 958 exporting SMEs, a confidence level of 95%, and a margin of error of 5% as suggested by Leavy (2017), resulting in a sample size of 282. Consequently, proportionate stratified sampling was used to ensure that each city has a representation of SMEs as indicated in Table 1. Following that, SMEs from the chosen cities were included in the sample using a simple random sampling technique. A total of 282 managers of manufacturing-exporting SMEs participated in an actual survey that was conducted from November 2021 to April 2022. The decision to use managers in this study was due to the fact that they are knowledgeable about the study variables and are actively involved in the businesses’ strategic decisions. After distributing 282 questionnaires, only 250 valid responses were gathered, translating to an 88.7% response rate. In addition, data was gathered using a standardized questionnaire. The questionnaire was used because it covers a wide area and gives a large amount of information in a short period. A structured questionnaire was also employed since it shields the researcher and the participants from any potential bias (Kim et al., 2016). Also, to ensure that the content and design were simple for the respondents to grasp, the questionnaire was pre-tested by 20 managers of exporting SMEs before the actual survey, and it was then improved based on their input.

Table 1. Proportional sampling

Region	No of exporting SMEs	Proportion	Sample size
Arusha	153	153/958*250	40
Dar es Salaam	390	390/958*250	102
Dodoma	115	115/958*250	30
Mbeya	115	115/958*250	30
Mwanza	185	185/958*250	48
Total	958	958/958*250	250

Source(s): SPSS output

4.3. Measurements of study variables

The measurement scales for EO dimensions and export performance were adapted from extant literature and they have been validated and used in previous studies. The multi-item measurements with five-point Likert scales ranging from one “strongly disagree” to five “strongly agree” were used to measure the study’s constructs. The measuring items were chosen because they matched the conceptual model of the current study and had sufficient past reliability. The measurement scale for EO dimensions was adapted from Miller (1983) and further modified by Lumpkin and Dess (1996). The scale consists of innovativeness, proactiveness, and risk-taking, making EO a multidimensional formative indicator. Four items were used to measure each of the innovativeness, proactiveness and risk-taking. In addition, export performance (EXPERF) was measured using the EXPERF scale developed by Zou et al. (1998). The scale is used in this study because it is a comprehensive scale that integrates both objective and subjective measures, to ensure the validity of the results (Zou et al., 1998). This scale comprises nine items.

4.4. Data analysis

Confirmatory factor analysis (CFA) in the structural equation model (SEM) was used in this study to assess model measurements like model fit indices, reliability, and validity. The CFA is regarded to be relevant in multivariate data analysis for determining the reliability of indicators for latent variables (Barati et al., 2019). Also, Hayes’ PROCESS macro was used to examine the moderating influence of risk-taking behavior on the effect of innovativeness and proactiveness on export performance. The PROCESS macro was employed since it is well-recognized as a robust and modern tool for conducting regression analysis with extra variables like moderators (Hayes, 2022).

4.5. Common method variance

The term “common method variance” refers to “systematic error that results from employing a common or single technique to measure the study’s constructs” (Podsakoff et al., 2003). In this study data were collected from a single individual who represents an exporting SME, using the same response format for all constructs (i.e., Likert scales), and in a single survey, hence the prospect of a common method bias was raised. In contrast, Harman’s single factor test was used to determine if the data obtained demonstrated common method bias. According to the test results, a single-factor analysis explains 31.084% of the total variation, which is below 50%.Thus, the collected data is considered to be free from common method bias (Kock et al., 2021).

5. Results and discussion

5.1. Validity and reliability results

Cronbach’s alpha was used to assess internal consistency reliability. The results are shown in Table 2. All Cronbach’s alpha values are greater than 0.7, demonstrating that the study’s constructs are internally consistent and reliable (Cronbach, 1951; Davis et al., 1981). The results in Table 2 show that all constructs in the study had composite reliability (CR) values greater than the threshold value of 0.7, implying that the measurement scales are reliable (Hair et al., 2010). In addition, items display adequate level of reliability since all items have a factor loading greater than the acceptable value of 0.5 (Ady Hameme, 2017). Moreover, the results in Table 2 reveal that the Average Variance Extracted (AVE) values for all constructs are greater than the suggested threshold of 0.5, indicating that convergent validity was achieved (Hair et al., 2010). Moreover, the results in Table 4 show that discriminant validity was achieved since the square root of AVE for each of the study’s construct was greater than the values of inter-construct correlations (Fornell & Larcker, 1981). Likewise, the values of AVE were greater than the maximum shared variance (MSV) and average shared variance (ASV), indicating that discriminant validity is achieved (Almén et al., 2018).

Table 2. Confirmatory factor analysis results

Construct and items	Code	Loadings	α	CR	AVE
Innovativeness (INV)			0.856	0.856	0.598
Creativity in methods of operations	INV 1	0.78
Seeks out new ways of doing things	INV 2	0.81
Encourages people to think in unique ways	INV 3	0.78
Constantly experimenting with new products	INV 4	0.71
Proactiveness (PR)			0.855	0.855	0.600
Seeks to exploit anticipated market changes	PR 1	0.77
Opportunistically shape export environment	PR 2	0.88
Positioning to meet export market demands	PR 3	0.74
Excel at identifying opportunities	PR 4	0.69
Risk-Taking attitude (RS)			0.891	0.896	0.686
Invests in higher risk projects	RS 1	0.84
Tolerance for high risk projects	RS 2	0.93
Taking chances is part of business strategy	RS 3	0.66
Takes calculated risks with new ideas	RS 4	0.86
Export Performance (EXPERF)			0.934	0.932	0.603
Export has been very profitable	EXP 1	0.77
Export has generated a high sales volume	EXP 2	0.73
Export has achieved rapid growth	EXP 3	0.87
Export improved our global competitiveness	EXP 4	0.84
Export has strengthened our strategic position	EXP 5	0.76
Export increased our global market share	EXP 6	0.80
The export performance has been satisfactory	EXP 7	0.79
Our firm export has been successful	EXP 8	0.70
Our firm export has fully met our expectation	EXP 9	0.72

Notes: α- Cronbach’s alpha; CR- Composite Reliability; AVE- Average Variance Extracted

Model fit indices: χ²/df = 1.743; GFI = 0.909; NFI = 0.913; RFI = 0.902; TLI = 0.954; IFI = 0.961; CFI = 0.961; RMSEA = 0.055; SRMR = 0.047; PClose = 0.218

Table 3. Descriptive statistics and correlations

Variables	Mean	Std. deviation	INV	PR	RS	EXPERF
INV	3.9108	0.6964	1
PR	3.7644	1.0753	−0.108*	1
RS	4.0240	0.7353	0.365**	0.312**	1
EXPERF	3.8811	0.6908	0.451**	−0.119*	0.416**	1

*P < 0.05

**P < 0.01

5.2. Descriptive statistics and correlations of variables

The mean value of innovativeness (INV) is 3.9108, with a standard deviation of 0.6964, indicating that, on average, respondents agreed that their firms are innovative. In addition, respondents’ opinions indicate that their respective SMEs are proactive (PR), with a mean value of 3.7644 and a standard deviation of 1.0753. Moreover, respondents are in agreement regarding the risk-taking (RS) behavior of their exporting firms. The results for RS indicate a mean value of 4.0240 with a standard deviation of 0.7353. Furthermore, according to the results of export performance (EXPERF), the mean value was 3.8881 and the standard deviation was 0.6908, indicating that respondents agreed that their respective SMEs perform well in export market operations as shown in Table 3. Likewise, bivariate correlations were conducted, and the results are presented in Table 3. It should be noted that the value of the correlation coefficients, which is less than 0.7, suggests the absence of multicollinearity between the two variables (Pallant, 2020). Therefore, based on the bivariate correlation coefficients results, multicollinearity issues were not a concern in this study as all values were below 0.7. The correlation results show that INV and PR are negative correlated (r = −0.108 and p < 0.05). INV and RS are significantly correlated (r = 0.365 and p < 0.01). In addition, RS and PR correlated significantly (r = 0.312 and p < 0.01). Moreover, INV and EXPERF correlated significantly (r = 0.45 and p < 0.01), PR and EXPERF are negative correlated (r = −0.119 and p < 0.05), and lastly, RS and EXPERF correlated significantly (r = 0.416 and p < 0.01). Therefore, the results of the bivariate correlations of the study variables show that the variables are associated, so further regression analysis was performed to obtain the statistical results of the study’s hypotheses.

Table 4. Discriminant validity results

Variables	MSV	ASV	INV	PR	RS	EXPERF
INV	0.291	0.112	0.773
PR	0.198	0.072	−0.097	0.774
RS	0.198	0.105	0.186	0.445	0.828
EXPERF	0.291	0.128	0.540	−0.097	0.289	0.777

Note(s): The square roots of AVE are indicated in diagonals (bolded), with construct inter-correlations in lower half of the table. All construct inter-correlations are less than the corresponding square root of AVEs.

5.3. The model fit results

The model fit indices for Goodness of Fit Index (GFI), Normed Fit Index (NFI), Relative Fit Index (RFI), Tucker-Lewis Index (TLI), Incremental Fit Index (IFI), Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Squared Residual (SRMR), P-value of the null hypothesis (PClose) and Chi-square value to the degree of freedom (χ² /df) were evaluated, and the findings are reported in Table 2. Also, the Chi-square (χ2) value of 313.822 was obtained, with a degree of freedom (df) 180 and a p < 0.001. Based on the CFA results, as shown in Table 2, all of the model fit indices are within acceptable limits (Hair et al., 2010; Hooper et al., 2008), indicating that the model accurately fits the data.

5.4. Testing of hypotheses and discussion

Hayes’ PROCESS macro test was used to evaluate all four hypotheses of this study. Table 5 results display the direct influence of INV and PR on EXPERF. It also indicates the moderating effect of RS on the link between INV and EXPERF (model A) as well as the moderating effect of RS on the link between PR and EXPERF (model B). The r-square value for model A was 0.3082, indicating that the INV accounts for 30.82% of the variance in EXPERF. Model A was significant with P < 0.001 and F value of 36.5357. Two of the study’s hypotheses (H₁ and H₃) were examined in model A. In H₁, it was study hypothesized that INV positively influences EXPERF. Table 5 results show that INV has a significant positive influence on EXPERF (β = 0.3854, P < 0.001). The findings suggest that higher INV results in higher EXPERF. Therefore, H₁ is supported. Also, in H₃ the study hypothesized that RS significantly moderates the effect of INV on EXPERF. The results as indicated in Table 5 shows that the interaction term (INV*RS) is positive and significant with β = 0.2234, P = 0.0009, and confidence intervals between 0.0924 and 0.3544. The absence of zeros in between the confidence interval values suggests that RS significantly alters the relationship between INV and EXPERF. As such, RS is a significant moderator of the relationship between INV and EXPERF. Model A’s r-square was also increased by 3.17 percent due to the interaction effect. This indicates that 3.17% increase the variation of EXPERF was caused by the interaction effect of INV and RS. Furthermore, Figure 2 shows that the effect of INV on EXPERF is lower for exporting SMEs with low levels of RS (standard deviation −0.74) than for exporting SMEs with high levels of RS (Standard deviation 0.74). As a result, the findings of this study confirm H₃, and it is concluded that RS significantly interacts with the INV to enhance EXPERF.

Figure 2. Slope plotting for the interaction effect (INV*RS).

Table 5. Regression results and interaction effects of INV, PR and RS on EXPERF

Variables	Coeff	Se	T	P	LLCI	ULCI
Model A; Main effects
INV	0.3854	0.0579	6.6538	****	0.2713	0.4995
RS	0.3308	0.0572	5.7848	****	0.2182	0.4435
INV*RS	0.2234	0.0665	3.3589	0.0009	0.0924	0.3544
R^2	0.3082
F(sig.)	36.5357			****
R^2 change	0.0317
F(sig.) change	11.2819			0.0009
Model B; Main effects
PR	−0.1748	0.0380	−4.6005	****	−0.2491	−0.1000
RS	0.5551	0.0661	8.3933	****	0.4249	0.6854
PR*RS	0.1041	0.0468	2.2229	0.0271	0.0119	0.1964
R^2	0.2447
F(sig.)	26.5684			****
R^2 change	0.0152
F(sig.) change	4.9413			0.0271

**** p < 0.001

Model B was found to be significant with a P < 0.001 and F-value of 26.5684. The model B’s r-square was 0.2447, suggesting that 24.47% of the variation in EXPERF was explained by the PR. Model B examined hypotheses H₂ and H₄ of this study. In H₂, it was study hypothesized that PR positively influences EXPERF. However, the results in Table 5 show that PR at the mean of RS is negatively related to EXPERF (β = −0.1748 and P < 0.001). The findings suggest that higher PR results in lower EXPERF and vice versa. Also, in H₄ the study hypothesized that RS significantly moderates the effect of PR on EXPERF. The results as shown in Table 5 indicate that the interaction term (PR*RS) is positive and significant with β = 0.1041, P = 0.0271, and confidence intervals between 0.0119 and 0.1964. Thus, the absence of zeros in between the confidence interval values implies that RS significantly moderates the effect of PR on EXPERF. Therefore, RS is a significant moderator of the relationship between PR and EXPERF. Additionally, the interaction effect improved the r-square value for model B by 1.52 percent. This suggests that the interaction effect of PR and RS contributes to a significant change of 1.52% in the variation of EXPERF. Moreover, Figure 3 shows that the effect of PR on EXPERF is weaker for exporting SMEs with low levels of RS (standard deviation −0.74) than for exporting SMEs with high levels of RS (Standard deviation 0.74). Henceforth, the findings of this study support H₄ that RS significantly interacts with the link between PR and EXPERF.

Figure 3. Slope plotting for the interaction effect (PR*RS).

Based on the empirical findings of this study, three hypotheses were supported and one was not supported. In H₁, the study reveals that INV has a significant positive effect on EXPERF. The results in Table 5 indicate that when SMEs increase INV by one unit, EXPERF increases by 0.3854 units. The findings of this study also imply that Tanzanian manufacturing-exporting SMEs have the innovative ability to enhance EXPERF. The findings are consistent with (Ajayi, 2016; Boso et al., 2018; Habib et al., 2020; Hossain & Azmi, 2021). Also, the results of this study support the proposition that innovative SMEs are more likely to succeed in export markets (Bhat & Momaya, 2020). Similarly, the findings of this study support the Guarascio et al. (2017) claim that innovativeness gives firms a competitive edge by introducing new products, processes, and developing of new markets which plays a critical role in the firm’s international success. Furthermore, the findings of this study support the RBV theory that innovativeness as an intangible strategic resource leads to improved export performance. However, due to the paucity of the empirical based-evidence of the relationship in the developing economies context, the current study examines the effect of INV on EXPERF of manufacturing SMEs in Tanzania, a developing economy. Hence, the findings of this study extend the contribution of INV to EXPERF, particularly in the context of SMEs in a developing country.

In H₃, findings of this study reveal that PR is negative related to EXPERF. This implies that when PR increases, EXPERF decreases. More specifically, results in Table 5 imply that when PR increases by one unit, EXPERF decreases by 0.1748 units. The findings of this study are consistent with Okangi (2019) who conducted a study on “the impacts of entrepreneurial orientation on the profitability growth of construction firms in Tanzania” and found a negative effect of PR on the growth of profitability. Francis and Collins-Dodd (2000) found a negative effect of PR on the export performance of Canadian SMEs. Moreover, the study of Skarmeas et al. (2016) revealed a negative effect of PR on the export market exploration of Portuguese export manufacturers. In addition, Frishammar and Hörte (2007) found a negative impact of PR on the new product development performance of Swedish manufacturing firms. Furthermore, the findings of this study support the argument that the majority of SMEs are reactive rather than proactive in seizing foreign market opportunities (Robb et al., 2020). Similarly, Miller (2002) claimed that certain SMEs that undertake overseas transactions benefit from the existing opportunities by being reactive since this would give them time to evaluate market constraints. However, the findings are not consistent with studies (Ajayi, 2016; Hossain & Azmi, 2021; Robb et al., 2020; Robb & Stephens, 2021; Rua & Franca, 2016) that found a positive effect of proactiveness on export performance. Therefore, the results of this study add to the body of knowledge on the effect of PR on EXPERF in the context of SMEs in a developing country context, Tanzania.

The moderating effect of RS on the relationship between INV and EXPERF (H₃) was supported. This implies that the effect of INV on EXPERF significantly increases at the increased level of RS. As depicted in Figure 2, RS significantly strengthens the positive relationship between INV and EXPERF. Therefore, SMEs with higher levels of RS are more likely to enhance EXPERF through INV. This implies that the effect of INV on EXPERF is strong for manufacturing-exporting SMEs that have high levels of RS. Also, the moderating effect of RS on the relationship between PR and EXPERF (H₄) was supported. That is the effect of PR on EXPERF significantly increases at increased level of RS. Also, the results from Table 5 and Figure 3 show that RS dampens the negative effect of PR on EXPERF. This implies that, despite PR having a negative influence on EXPERF, its interaction effect with RS changes the relationship’s direction to positive. Hence, RS is the significant moderator of the relationship between PR and EXPERF. Therefore, exporting SMEs with higher levels of RS are more inclined to increase EXPERF through innovativeness and proactiveness characteristics. This suggests that the effect of innovativeness and proactiveness on EXPERF is strong for SMEs that have high levels of RS. The findings of this study back up Leko-Šimić and Horvat (2006) assertion that firms with risk-taking attitudes outperform those with a lower degree of risk-taking attitude. Furthermore, the study’s findings validate the RBV theory and reveal the process through which EO enhances export performance, particularly how the EO dimensions interact to improve export performance. Therefore, exporting SMEs in developing countries should not be scared to make risky decisions about the creation and introduction of a new product, process, and market to take advantage of the vast opportunities available in foreign markets.

6. Conclusions

The overall objective of this study was to examine the effect of innovativeness and proactiveness on EXPERF and the moderating effect of risk-taking on the relationship between innovativeness, proactiveness and EXPERF. The current study utilizes RBV theory to develop the conceptual model in order to accomplish the objective. The developed conceptual model was empirically tested in the context of Tanzanian manufacturing SMEs. The motive to undertake this study was influenced by the dearth of empirical based-evidence of the relationship between EO and export performance in developing economies and, the absence of the process through which EO leads to better EXPERF. Therefore, this study was carried out to investigate the process through which EO leads to improved EXPERF. The findings of this study reveal that innovativeness is a significant determinant of EXPERF. However, proactiveness negatively affects EXPERF. In addition, the findings reveal that risk-taking significantly moderates the effect of innovativeness, and proactiveness on EXPERF. The findings imply that at higher levels of risk-taking the effect of innovativeness and proactiveness on EXPERF increases. Therefore, it may be concluded that when SMEs in emerging economies adopt risky decisions concerning innovations and exporting operations, and commit a significant amount of resources to them, their EXPERF improves.

7. Implications of the study

7.1. Theoretical implications

The current study examined the relationship between innovativeness, proactiveness, and EXPERF in Tanzanian manufacturing SMEs and also the moderating effect of risk-taking on the effect of innovativeness and proactiveness on EXPERF. Thus, this study contributes to the effect of innovativeness and proactiveness on EXPERF of manufacturing SMEs in a developing economy context. Given the paucity of empirical evidence on the relationship in developing economies, this study offers empirical insights into SMEs in a developing economy context, hence, contributing to the existing literature. Also, the study adds to the already available stream of empirical evidence of the EO-export performance link by widening the current thinking on exporting by affirming that different EO dimensions affect EXPERF in ways that prior studies have not yet addressed. In particular, this study adds to the existing literature that the risk-taking element significantly moderates the influence of innovativeness and proactiveness on EXPERF. Moreover, the findings of this study extend the applicability of RBV theory in the context of exporting on the process by which EO leads to improved EXPERF. The study reveals how EO as an intangible resource results in improved EXPERF. That is the interplay between the dimensions of EO results in improved EXPERF. Furthermore, the results of this study support the RBV theory that strategic resources lead to superior export performance.

7.2. Managerial implications

This study has managerial implications for the owners/managers of the manufacturing SMEs looking to improve their EXPERF. First, the findings of this study affirm that innovativeness is a strategic determinant of EXPERF. Therefore, owners/managers should nurture more innovative strategies and carefully execute them to provide the groundwork for successful EXPERF. This might be done by setting innovation goals and objectives and fostering an innovative culture within their businesses. Through innovation initiatives their firms will benefit from economies of scale and scope. Second, the results of this study reveal that proactiveness is negatively related to EXPERF of SMEs. However, owners/managers and entrepreneurs are urged to have the capacity to respond swiftly and ahead of competitors in order to seize the numerous opportunities present in export markets. This is because being proactive may lead to stronger EXPERF, especially when trends and possibilities are few or unpredictable (Cannavale & Nadali, 2019). Furthermore, the empirical findings of this study emphasize the significance of adopting risk-taking behavior for EXPERF as study’s findings indicate that innovativeness and proactiveness can have a significant influence on EXPERF when SMEs are risk-takers. Thus, owners/managers of exporting SMEs should cultivate a positive attitude toward risks and must increase their capacity to adopt risk-taking behavior for their business operations by conquering the negative bias and developing risk-taking self-efficacy.

8. Limitations and directions for future studies

The results of the current study should be examined in light of a few shortcomings that may present opportunities for future studies. This study was carried out in Tanzania, a single-country context, which might raise doubts about the findings’ generalization. Likewise, the study’s findings may not apply to SMEs in other developing nations since the level of entrepreneurship among countries may vary; thus, future research may replicate this study in other emerging economies to test the veracity of the results. Also, this study covered only manufacturing SMEs; future studies can include large firms, as they may behave differently on innovation, proactivity and risk-taking due to the resources advantage they have. Additionally, the study employed a cross-sectional design; future research can use longitudinal designs to examine how the variables used in this study change over time, which could lead to different results. Moreover, this study reveals the surprising negative effect of proactiveness on EXPERF; hence, future studies might incorporate a qualitative paradigm into the findings of this study to further investigate the causes of such a negative effect. Furthermore, this study examines risk-taking as the moderator variable; future studies may investigate the mediation impact of this variable to widen the existing knowledge. Finally, the study recommends future studies to investigate whether risk-taking moderates the relationship between autonomy, competitive aggressiveness, and EXPERF to reveal its effect on the complete scope of EO dimensions and hence, add to the existing body of knowledge.

Ethical clearance

The ethical clearance for this study was granted by the University of Dodoma (UDOM) ethical committee in accordance with postgraduate regulations of UDOM.

Acknowledgements

The corresponding author wishes to express his gratitude to the administration of the University of Dodoma (where he works) for sponsoring the PhD programme that culminated in the development of this work. In addition, the corresponding author wishes to thank the co-authors who are his supervisors. Furthermore, the authors would like to appreciate the efforts of experts from the department of business administration and management, whose contributions resulted in substantial improvements to this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The corresponding author received funds from the employer (The University of Dodoma) through internal funds designated for long-term training programmes (PhD studies).