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Methodology Article | | Peer-Reviewed

A Conceptual Framework and Research Methodology for Analyzing Ethiopia's Export Performance Determinants

Eshetu Chalachew,  Sam Lubbe*,  Flip Schutte
Published in International Journal of Business and Economics Research (Volume 14, Issue 6)
Received: 30 October 2025     Accepted: 19 November 2025     Published: 17 December 2025
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Abstract

This study developed and empirically tested a Resource-Based View (RBV) and Contingency Theory model to investigate the determinants of export performance among Ethiopian manufacturing firms (textile, garment, leather). It posited that export marketing mix strategy acts as a crucial mediator linking internal (managerial, firm-specific) and external (competitive, mar-ket) factors to overall export success. Employing a sequential mixed-methods design (QUANT → QUAL), the research ana-lyzed data from a large sample of 389 exporting firms. Quantitative analysis utilized Structural Equation Modeling (SEM), alongside specialised econometric techniques—including the Tobit model (for export intensity) and the Probit model (for di-chotomous outcomes)—to ensure appropriate dependent variable handling. Findings confirmed the significant, mediating role of strategic marketing choices in translating resources and environmental pressures into performance. The results offer valua-ble, actionable insights for managers optimizing resource allocation and policymakers seeking to enhance national export development programs, providing a robust, context-specific framework for emerging economies.

Published in International Journal of Business and Economics Research (Volume 14, Issue 6)
DOI 10.11648/j.ijber.20251406.12
Page(s) 240-253
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2025. Published by Science Publishing Group
Previous article
Keywords

Export Performance, Resource-Based View (RBV), Export Marketing Strategy, Ethiopian Manufacturing Firms, Structural Equation Modeling (SEM), Mediating Role, Contingency Theory, Tobit Model

1. Introduction
Global competition and the drive for market expansion have made export performance
[13]
a critical strategic imperative for manufacturing firms worldwide. In emerging economies like Ethiopia, where industrialisation and export-led growth are central to national policy, understanding the factors that drive or inhibit success in international markets is crucial for both policymakers and business leaders. This article investigates the complex array of internal (firm-specific and managerial) and external (market and competitive) determinants that shape the export performance of medium and large-scale Ethiopian manufacturing firms in the textile, garment, and leather sectors. Drawing upon the theoretical foundations of the Contingency Theory and the Resource-Based View (RBV), this research proposed a comprehensive conceptual framework that captured the interplay between a firm’s unique resources, its strategic choices, and the dynamic external environment.
A key limitation in prior export research has often been the failure to fully account for the intricate process through which various determinants translate into performance outcomes. While many studies confirmed direct links, they often overlooked the mediating role of strategic management decisions. This conceptual model explicitly addresses this gap by claiming that a firm's export marketing mix strategy (encompassing product, pricing, promotion, and distribution decisions) acts as a significant mediator between the antecedent internal and external factors and the ultimate measure of export success. By focusing on this mechanism, the study provided a more nuanced explanation of how and why specific firm capabilities and market conditions influence export profitability, growth, intensity, goal achievement, and manager satisfaction.
The methodological approach for this investigation was designed to achieve robust and triangulated findings. Employing a sequential mixed-methods design (QUANT → QUAL), the study utilised a large-scale, cross-sectional survey of 389 Ethiopian exporters, supplemented by in-depth semi-structured interviews with key informants. The quantitative data were rigorously analysed using Structural Equation Modelling (SEM) and Partial Least Squares (PLS-SEM) to test the hypothesised relationships, measured latent constructs, and validating the mediating effects. Furthermore, the selection of specialised econometric models—the Tobit model for export intensity and the Probit model for binary outcome variables like goal achievement and satisfaction—ensured that the unique characteristics of the performance metrics are appropriately handled, avoiding the statistical biases associated with using ordinary least squares (OLS) for censored or dichotomous data.
Ultimately, this article aims to make a substantial contribution to both the academic literature and management practice by offering a statistically verified and context-specific model of export performance for Ethiopia’s vital manufacturing sector. The findings clarified the relative weight of strategic, organisational, and environmental factors, identifying which export marketing mix strategies yield the greatest returns, and provided empirical evidence to guide strategic decision-making for firms entering or expanding in global markets. Moreover, the study’s adoption of a critical realism paradigm validated the use of combined methods to achieve a superior, objective understanding of the phenomena, making the conclusions both statistically generalisable and richly interpretable.
2. The Projected Framework
The projected framework was supported by the Contingency Theory and the Resource-Based View theory (RBV). These theories provided a foundation for exploring both external and internal environmental determinants of export performance
[24, 27, 38]
and their relative effect on export performance. They attempted to identify the factors that explain why firms achieve varying levels of export performance
[3, 9]
.
The theories further claimed that a firm’s export performance is shaped by both internal factors (such as managerial characteristics, firm characteristics, and export marketing strategy determinants) and external factors (such as the extent of competition, foreign market characteristics, and technological intensity)
[24]
. Cavusgil et al., 1994) also state that a firm’s export performance is directly determined by firm characteristics and is moderated by marketing strategy.
Hence, the commonly applied conceptual framework for the determinants of firm export performance, based on the RBV and Contingency View, classified the factors into internal and external categories
[3, 9]
. Therefore, export performance tended to be conditioned by both environmental and firm-specific characteristics
[3, 9, 24]
.
In the research conceptual framework, export marketing strategy functions as a significant intermediate variable
[9]
. It is formulated based on a firm’s internal resources and the external forces it faces, ultimately influencing competitive advantage and determining export performance
[9]
. Empirical findings, therefore, supported the central and direct influence of export marketing strategy on export performance
[9]
.
Prior studies have found that research tends to focus on the direct influence of determinant factors on export performance while neglecting their intermediate influence
[9]
. Building upon this, this study took advanced steps to advocate for considering more intermediate influences
[9]
. In doing so, it refined the core theoretical conceptual framework and offered comprehensive perspectives
[9]
.
In the conceptual framework model presented below in Figure 1, all relationships between concepts, dimensions, components, and indices were defined as measurement relationships in regression models. Here, all hypotheses of the conceptual models were defined in correlation form due to their measurement nature. The conceptual framework was also based on the constructive and interrelated nature of export marketing strategies alongside internal and external environmental forces
[43]
.
Export performance, a key element of the model, is the degree to which a firm’s objectives—both objective/financial and subjective/non-financial—with respect to exporting a product into international markets are achieved through the planning and execution of the export marketing strategy
[9, 43]
. Export market objectives can be economic (e.g., profit, growth, or intensity) and/or subjective (e.g., satisfaction and goal achievement)
[6, 9, 43]
. The research framework proposed for the study is presented below.
Figure 1. The conceptual framework of the research, Researchers view 2020.
Furthermore, the level of influence exerted by the export marketing strategy was reflected in the firm's export performance success
[9, 43]
. The internal/controllable determinants in the conceptual framework—managerial characteristics and firm characteristics—influenced the use of export marketing strategy and the capability to select and implement a specific marketing strategy
[9, 43]
. Crucially, the strategy aligned with the exporter’s strengths, leverage market opportunities, offset weaknesses, and overcame threats
[6, 9, 43].
Conversely, the external/uncontrollable determinants in the conceptual framework included the intensity of foreign competition across markets, foreign market characteristics (such as the country’s political environment and sociocultural forces), and technological forces. These factors influenced the export marketing strategy, which in turn influenced export performance
[9, 43]
. Thus, a firm can respond to the interaction of external and internal forces in various ways to attain its objectives
[9, 43]
.
Consequently, the firm’s reaction may take the form of a marketing plan that considers all aspects of product, pricing, distribution, and promotion
[9, 43]
. Additionally, the current situation of the export market, including prospective demand, cultural differences, brand and product awareness, and dissimilarity of legal systems, can influence the selection of a marketing strategy
[6, 43]
.
3. Mediating Variables
In the research conceptual model, mediators intervened between antecedents and outcomes
[9]
. Mediating variables explained the indirect association between determinants and export performance and also highlighted how and why such a relationship occurs
[9]
. In this article, the export marketing strategy acted as an internal mediator that linked the association between internal and external determinants and export performance
[9]
.
Hence, export strategy choices were based on the firm’s assets, organisation, and managerial characteristics, as well as external forces, all of which directly affected the level of export performance
[9]
. Although various studies employed marketing strategies as mediators in their conceptual models, they often do not explicitly test or acknowledge mediating effects in their review
[9, 39]
.
This omission leads to incomplete theorisation and empirical bias in the results of hypothesis testing
[9]
. In support of this notion,
[50, 51]
indicated that environmental diversity had a positive influence on price adoption, and price adoption, in turn, has a reversed quadratic influence on a firm’s export performance
[9]
.
In this context, analysing only the direct relationship between environmental difference and export performance concealed the mediating influence of the price adoption strategy and may lead to biased findings
[9]
. Consequently, the current study considered export marketing strategies as the mediating influence of determinants to enhance research precision and consistency
[9, 40]
.
Thus, the goal of this article was to design and explain a model for identifying the determinant factors of export performance in Ethiopian manufacturing firms using the Contingency Theory and Resource-Based View approach. This model was tested based on the opinions of general managers or marketing managers of exporting firms. With respect to the above conceptual model, the following propositions were formulated:
4. Formulation of Propositions
H1: The adoption of the export marketing mix strategy (product, price, promotion, and channel selection) contributed positively to export performance.
H2: Intensive competition contributed positively to export performance.
H2a: Intensive competition contributed positively to the adoption of the export marketing strategy.
H3: Political and legal environments had a positive influence on export performance.
H3a: Political and legal environments contributed positively to the adoption of the export marketing mix strategy.
H4: Cultural environments contributed positively to export performance.
H4a: Cultural environments contributed positively to the adoption of the export marketing strategy.
H5: The appropriate use of technology contributed positively to export performance.
H5a: The appropriate use of technology contributed positively to the adoption of the export marketing strategy.
H6: Firm size contributed significantly to export performance.
H6a: Firm size contributed positively to the adoption of the export marketing mix strategy.
H7a: Firm export experience contributed positively to export performance.
H7a: Firm export experience contributed positively to the adoption of the export marketing strategy.
H8: Manager’s international experience contributed positively to export performance.
H8a: Manager’s international experience had a positive association with the adoption of the export marketing strategy.
H9: Manager’s foreign language skill contributes positively to export performance.
H9a: Manager’s foreign language skill had a positive association with the adoption of the export marketing mix strategy.
H10: Export Marketing Strategy mediated the association between internal determinants and export performance.
H11: Export Marketing Strategy mediated the association between external determinants and export performance.
H12: All external factors influenced export performance equally.
H12a: Some external factors influenced export performance more than others.
H13: All internal factors influenced export performance equally.
H13a: Some internal factors influenced export performance more than others.
H14: All export marketing mix strategies influenced export performance equally.
H14a: Some export marketing mix strategies influence export performance more than others.
5. Research Methodology
The study employed a mixed research approach, using both quantitative and qualitative methods for data triangulation. Empirical findings demonstrated the level of significance of the relationships, while qualitative information explored the interpretation of the phenomena
[18, 59]
. Quantitative research excels at summarising large data sets to enable generalisation of findings
[45]
.
Conversely, the qualitative data were used to provide causal support for the quantitative findings through rich interpretation
[25]
. The mixed research method was used not only to develop, extend, and test theories but also to achieve triangulation between methods by enhancing quantitative results with rich interview data
[11, 45]
Accordingly, a basic quantitative outcome was reinforced through in-depth investigation
[3]
Combining these two approaches overcame some inherent limitations when each methodology was used alone
[45]
.
Moreover, among the quantitative methods, the present study employed questionnaires to collect data. Thus, sequential mixed method procedures were involved, beginning with a quantitative method where a theory or concept was tested, followed by a qualitative method involving detailed exploration via interviews for explanatory purposes
[12]
.
Hence, this was a multi-strategy research that sequentially combined both qualitative and quantitative research
[10, 18, 59]
. A sequential mixed method design (QUANT → QUAL sequence) was employed
[45]
. The nature of the research was explanatory and primarily involved quantitative research tools and techniques. Nevertheless, for greater conceptual validation, qualitative information was useful in the research approach
[45]
. Consequently, qualitative data were collected to generate insight into validating the trust and commitment building process
[12, 45]
.
Accordingly, the mixed research method allowed the researchers to achieve a better understanding of how managers perceived the contribution of determinant factors on export performance (qualitative approach) while simultaneously producing empirical evidence on the relationships between variables through systematic and formal quantification (quantitative approach). In this way, the weaknesses of each process are balanced by the strengths of the other, providing a holistic view of the research problem
[45]
.
5.1. Population, Sample Size, and Sampling Methods
Population
The research population included medium and large-scale manufacturing exporting firms in Ethiopia. Ethiopia was selected as the country background for this research because the nation has actively adopted policies to develop firms’ export performance in the value-added export sector
[47]
. The present study used a multi-industry sample (textile and garment; leather and leather products) to enhance observed variance and strengthen the generalisability of the results, thereby improving the external validity of the research
[55]
.
Categorising a firm as small, medium, or large is complex. It depends on a number of variables, such as manufacturing capacity, technological intensity, number of employees, turnover rates, capital investment, and sub-sectors
[2, 47, 55]
.
Consequently, according to the Central Statistical Agency (CSA) of Ethiopia
[7]
, manufacturing firms in the country are considered Large and Medium Manufacturing Industries if they use electricity (power-driven machinery). Also included are if they have a fixed location, produce at least partially marketable products, are led by a manager/administrator, and employ ten workers or more
[1, 6, 47]
.
5.2. Sample Size
The sample size was based on the 2020 Ethiopian Exporter Directory, which contained a list of approximately 389 eligible firms for this study. Since Structural Equation Modelling (SEM) was the proposed analysis technique, a sample size of 200 or more was statistically recommended for the proposed testing and analysis
[23]
. Therefore, the entire population (389) was sampled.
The selected firms were contacted in person to announce the research to key informants who possessed knowledge of and access to the type of data required for the study. The researchers also inquired about voluntary participation. This directory is considered a reliable and legitimate source because it was developed by the Ethiopian Textile and Garment Industry Development Institute (ETGIDI) and the Ethiopian Leather Industry Development Institute (ELIDI), which administer and support firm exporters in Ethiopia.
Accordingly, it provided the most complete set of manufacturing export firms in Ethiopia. Data was collected only from those sectors and firms (textile & garment, leather & leather products) that were directly involved in export activities and located in the Addis Ababa region, making them the target population. In Ethiopia, ETGIDI and ELIDI have the mandate to register firms that directly sell their products to foreign buyers.
5.3. Sampling Method
The sampling strategy involved contacting firms sorted by sector and location. Thus, the unit of analysis for the study was Ethiopian medium and large-scale manufacturing export firms in the textile and garments, and leather and leather products sectors, which were directly involved in the international market.
The questionnaire data were collected using a purposive sampling method, taking all firms as the sample unit with their knowledgeable informants. This method allowed the researchers to select cases that specifically satisfy the research questions and meet the research objectives
[45, 57]
. From the textile and leather manufacturing exporting firms found in Addis Ababa, the researchers collected data from 389 key informants who had relevant knowledge and held high managerial positions. Furthermore, the researchers conducted semi-structured interview questions with five (5) Policymakers and five (5) Export analysts who willingly participated after being informed of the nature and purpose of the study.
The reason for choosing purposive sampling is that it produced a sample knowledgeable about the population, which led to accurate results
[10, 18, 30, 45]
. For SEM, many statisticians asserted that a sample size above 200 is often recommended
[22, 26]
. Therefore, the sample size of 389 meets the criteria for using SEM for data analysis in the study.
5.4. Research Plan
The basic principle of research design was to align the research design with the research questions and objectives
[18, 25, 26]
The present study adopted exploratory and confirmatory factor analysis and a descriptive survey research designed to carry out a mixed-method research by collecting data via quantitative and qualitative methods from Ethiopian export manufacturing firms.
Consequently, five explanatory variables were included in the questionnaire and interview for export performance. Objective indicators such as a firm’s export growth, export profits, and export intensity were measured by an ordinal scale (five-point Likert scales, 1 to 5). The other subjective indicators, like goal achievement and satisfaction, were measured based on the respondent’s idea or perception on a five-point Likert scale (e.g., strongly agree to strongly disagree; very successful to very unsuccessful; very satisfied to very dissatisfied)
[18]
.
One of the concerns related to research design was the choice between a longitudinal and a cross-sectional study
[42, 56]
. A cross-sectional study typically focuses on collecting data from a given sample of the population at a single point in time
[26, 45, 59]
. A cross-sectional study appeared fitting for export performance research that aimed to capture dynamic relationships between determinants and export performance from a single-point-in-time perspective by collecting data during the study
[3, 15, 22, 25, 41, 45, 51, 56]
.
This study followed a problem-solving approach to determine factors that influence the export performance of Ethiopian manufacturing exporters using the methodology outlined in
[30]
. In the problem-solving research method, the aim was to resolve recognised problems in detail and design a multidisciplinary model for doing so
[52]
.
Applying a critical realism paradigm validated the use of combined quantitative and qualitative research to achieve the objectives
[16]
. The research instrument was developed and pre-tested using a small sample of export firms. After the pilot test, the final questionnaire was distributed to collect data from respondents of all Ethiopian textile and garment and leather products manufacturing firms engaging in exporting. Then refined instruments containing the sets of dependent variables intended to measure export performance were set (Export intensity, export growth and profitability, goal achievement, distributor satisfaction).
To substantiate the preference of the research paradigm, the spirit and discrepancy between the three interconnected assumptions (methodology, ontology and epistemology) and the four paradigms (Positivism, critical realism, interpretive and critical theory) were reviewed.
Ontology: refers to the reality that scholars discover
[16, 19]
.
Epistemology: refers to the association between the researchers and reality
[16, 19]
.
Methodology: refers to the practice employed by the researchers to investigate reality
[16, 19]
.
Critical Realism: recognises the inconsistency between the scholar’s views of reality and reality itself which will guide to triangulate of confirm a superior and objective consideration of the observable facts being investigated
[16]
.
Quantitative and qualitative data from a variety of sources were collected in an attempt to find out what is known concerning the centre of the research. The data were enumerated in order to get value from the findings
[16]
. Consequently, the study was supplemented by qualitative and quantitative research methods (triangulation) that were intended to support the rationale of choosing the proposed methodology of the research to get better internal validity
[16]
. So, the researchers employed a critical realism paradigm for this study.
Figure 2. An eagle's-eye point of view of the phases of the research process adopted from
[30, 52]
.
Figure 3. The alignment of the research problems, research objectives and research questions in problem-solution approach research, which is taken from
[30]
.
In summary, the research study started with the research design, the planning phase of research, when a research problem was devised and decomposed into specific sub-problems, followed by preparing objectives for each of the sub-problems
[29, 52]
. Thus, to answer the above questions, a mixed research method, that is, questionnaires and semi-structured interviews with the firms‘ responsible managers, was developed. The study was designed as a problem-solution-oriented approach. As adopted from
[30]
in problem-solution oriented research, the research questions, research problems and objectives needed to align as stated in the figure on the previous page.
As shown from the figure, the problem solution approach begins with the general problem, which is generated from a general aim and decomposed into sub-problems. Also, the general problem is split into a number of sub-problems from which specific objectives were generated, and in turn, a number of specific research questions were derived. The research sub-questions form the basis of sections in the research instrument, where each sub-question is taken out in a series of more detailed survey questions. There would be as many subsections in the survey instrument as there are research sub-questions
[52]
. Moreover, the general research questions derived from general objectives were decomposed into sub-problems via the research questions as a source for the research instrument. The research instruments were questionnaires in the case of quantitative research and an interview in the case of qualitative research to help answer the research questions through the interpretation of empirical data to meet the research objectives and in general to solve the research problems
[52]
.
Data Source and Collection Methods
The data was collected from the firms’ knowledgeable, higher managerial positions, directors or owners of manufacturing firms in Ethiopia. However, the selection of appropriate respondents to be contacted within the firm was an important issue
[18, 26, 31, 55]
. This was carried out by distributing the questionnaires and seeking responses from the key staff within the firm [18; 45].
The study used secondary and primary sources of data. Primary data were collected specifically for this research via direct contact with export managers
[18, 31, 55]
. The primary data were collected through self-designed questionnaires and semi-structured interviews with export firm managers. On the other hand, the secondary data were collected from existing literature, different research journals and papers, relevant books, and the internet.
The qualitative method was used to analyse the data collected through interviews (Gloria, 2016). However, the quantitative technique was used to analyse the data collected through questionnaires
[18]
. Two university graduates, who were familiar with business research methods, were recruited as data enumerators. They have had previous data collection experience. Before the field survey, they were trained regarding the purpose of the research, sampling method, and contents of the questionnaire and on how to approach the respondents. They collected the data under the supervision of the researchers.
Thus, the present study investigated a face-to-face survey with owners or senior managers, who were in charge of exporting activities for their respective firms. This method was also suitable when the respondents needed some clarifications in relation to the content of the questionnaire
[18, 26, 55, 59]
.
5.5. Key Informant
Identifying the right informant was an essential framework to collect high-quality data and also a requirement for discovering precise research findings
[18, 56]
. This was performed by delivering the questionnaires and asking for responses from the key informants within the firm
[18]
. Key informants were not chosen as they are representatives, but because of the exceptional knowledge with a special position they had in a firm at the time of the survey
[26, 56]
.
Thus, in relation to export performance, key informants would be likely to have the highest knowledge with firm’s information and key informants targeted for this study were either owners or managers of the firm or any other responsible person within the firm who was actively involved in the export-making decision process. As a result, it was decisive to collect data from a single knowledgeable respondent. They were also integral to the firm’s policymaking regarding international business operations to explain the occurrence of questioning
[18, 32]
. Consequently, the single key-informant technique seemed to be well-favoured and as a result the present study centred on a single knowledgeable respondent from every export firm
[18, 34, 50, 51, 58]
.
5.6. Questionnaire Design
The questionnaire was designed to collect information on managers’ points of view in relation to the export performance
[18, 35]
. Hence, in the present study, the questionnaires were categorised into ten (10) parts as: (1) respondents’ demography; (2) firms’ profile: (3) Firm’s competitive intensity; (4) technological intensity (5) foreign market characteristics; (6) firm characteristics; (7) managerial determinants; (8) Export Marketing mix Strategy, (9) Export performance and (10) general questions.
The researchers decided on the measurement scales recognised as being valid and reliable from the literature
[18, 35, 56]
. A Likert scale was used to measure the theoretical constructs on multiple items (a five-point Likert scale) that required respondents to point out a degree of satisfaction, agreement, applicability, importance, rate of success, etc., within a range of statements allied with the research
[18, 35]
. All measures were performed with fixes 1 = very dissatisfied and 5 = very satisfied, or 1 = strongly disagree and 5 = strongly agree. The study also depended on previously validated scales published in journals. The entire measures provided a strong indication of reliability and validity and an acceptable Cronbach’s Alpha
[17, 28]
. The nature of the survey questionnaire employed in this study was depicted as an abundantly structured design with closed-ended questions.
5.7. Instrument Validation Process
Pre-testing was the groundwork for a questionnaire in a small pilot study to find out the quality of the survey instrument before using it in a mass survey
[26]
. A pre-test of ten to twenty representative respondents was usually sufficient to identify problems with a questionnaire
[26]
. As a result, 20 firms’ executives were randomly selected from the list of exporters with similar characteristics as the target population but who would not be taking part in the final survey. They were approached to review the questionnaire in order to identify which questions were difficult to answer, which ones were ambiguous, which terms were misinterpreted, and which sections were too long. After obtaining their feedback, the final version of the questionnaire was presented.
Several researchers recommended that the pre-test ought to be carried out through personal interviews since they enabled the researchers to become aware of the respondents’ responses and uncertainties, which might not be found via other techniques
[26, 59]
. Besides, the instrument was also discussed with content experts and practitioners in the field of international marketing research to evaluate and comment on the instrument, including scale items, question content, wording, form layout, question difficulty, instructions, sequencing and timing
[18, 35]
. The preliminary questionnaire was reviewed by two academicians and four doctoral students who were knowledgeable in the field of study to evaluate the questionnaire items for face validity of the constructs.
5.8. Interview Questions
The interviews were carried out via face-to-face at the experts’ office and audio recorded, as it would be conducive for discussion of the role of the export market
[57]
. The qualitative interviews were investigated and supplemented insights into determinants of export performance to complement the quantitative study for triangulation of findings and might advance theory in international markets
[17]
. Thus, the researchers employed semi-structured interviews to collect primary qualitative data to smooth the progress of the researchers’ comprehensive investigation and increase the validity of information
[18, 57]
. Moreover, the centre of the interview was to discover and gain exploratory knowledge on export practices in Ethiopia. Ten interviews were deemed adequate as several ideas were designed from the primary data to give broader insights into exporting practices of firms, which was consistent with other studies in the field, but data saturation was kept in mind
[8, 31, 51]
. Each interview lasted approximately 60 minutes.
5.9. Validity and Reliability of the Research Study
The multi-item scales used in the study were evaluated to ensure that they can realise sufficient levels of reliability and validity
[26]
. The researchers have designed questionnaires based on the literature review and their knowledge of the study area.
Reliability
Reliability refers to the consistency of the results obtained from a research study
[26]
.. This research study contained a significant amount of triangulation, which demanded both quantitative and qualitative approaches to collect data. It measured the consistency of a questionnaire and the Cronbach’s alpha values
[14, 26]
.
Reliability addresses whether repeated assessments consistently come up with the same results when performed under the same circumstances
[26]
. The data from the questionnaires were easily quantifiable, as the data would not be open to subjective interpretation
[26]
. The researchers believed that independent researchers, on reanalysing the data, would come to the same conclusions. If an independent researcher utilised this data collection method, the researcher would be likely to reach the same conclusion
[26]
.
5.10. Validity
Validity analysis is an essential part of an empirical study
[4, 14, 26, 44]
. Validity refers to the extent to which a research study investigates what the researchers claim to investigate
[28]
. Internal validity concerns whether the understanding of the study design supported clear conclusions from the results of the research
[22]
. To assure the internal validity of the research, triangulation of data collection sources was used
[22, 26]
. Construct validity was accessed through Confirmatory Factor Analysis (CFA) and Exploratory Factor Analysis (EFA)
[26, 28]
. Exploratory Factor Analysis provided different factor solutions, which would let the data suggest possible factors.
Where the research was new and emerging, the researchers needed to use EFA at the first stage to explore these factors and to search in the literature for a validated questionnaire
[4]
. This could then be tested by Confirmatory Factor Analysis
[3, 4, 10]
. In this research, the collected data were tested by exploratory factor analysis (EFA) to prove the structural validity of the scale (the capability of items of measuring the variables)
[4]
. For this reason, exploratory factor analysis (EFA) was suitable to examine the factor structure within each construct group
[4, 14, 26, 44]
.
Therefore, for a scale development study, first an EFA was used in order to discover the underlying latent structure and then CFA using the same data set
[20, 46]
. In order for a study to be generalisable in a wider context, it should be possible to assume that the sample used in the research is representative of the general population to which the research results were tied
[5, 21, 22, 26]
. Consequently, the researchers believed that the sample population was defined and that the findings could be applied to other populations of export firms and their instruments and measures could be reliable and valid
[3, 4]
.
Furthermore, the researchers attempted to influence respondents to change their responses on the questionnaires or during interviews. The design and content validity of this questionnaire were scrutinised by the supervisor, two academicians and four doctoral students believed knowledgeable and have had ample experience in international business research. And then the required modifications were practised on the questionnaire.
6. Model Specification
The regression model, Probit model, and Tobit model have been employed to investigate factors that determine export performance. The dependent variable, export performance, was represented by five constructs: annual export intensity, export profit, export growth, goal achievement, and satisfaction. On the other hand, the independent or explanatory variables were managerial factors, firm characteristics, export marketing mix strategy, competitive intensity, foreign market characteristics, and technological factors.
6.1. The Tobit Model for Export Intensity
A multinomial Tobit model has been adopted in order to model factors that determine the export intensity variable
[36, 54]
. The Tobit model was used to find out the factors that influence the intensity of export performance
[36, 54]
. At this time, the binary dependent variable—successful or not successful—was not suitable
[36, 54]
. Furthermore,
[54]
, in his study, acknowledged that important information is lost because of the use of a binary dependent variable. The dependent variable employed at this point was then censored at achievement
[54]
. Since not all firms necessarily earn aggregated export sales revenue, there is a possibility that the dependent variable may have zero values
[36, 54]
.
With so many zero values for the dependent variable, using Ordinary Least Squares (OLS) to estimate the model would lead to biased and inconsistent results
[36, 54]
. The Censored Tobit model was an appropriate tool to obtain unbiased and consistent estimations
[36, 54]
.
Thus, to find out intensity-dependent variables for analysis, the mean index (the mean performance score) was subtracted from the average score of every firm’s collective (aggregate) performance score
[54]
. Hence, scores with negative outcome values were labelled as zero (0) and those with positive outcomes were traced in their absolute terms
[36, 56]
. Therefore, the intensity of export performance represented the degree to which a firm’s average score deviates from the mean
[36, 54]
.
6.2. The Probit Model for Goal Achievement and Satisfaction
A multinomial Probit model was used to identify the effect of independent variables on goal achievement and satisfaction. The Probit model was preferred for dichotomous (0−1) dependent variables rather than continuous ones
[54]
. Regression models that include yes/no or present/absent type of response are known as dichotomous or dummy dependent variables, and the determinants of an event happening or not happening were identified.
Thus, in the estimation of determinants of goal achievement or satisfaction on export, the probit model was going to be employed since Probit was a powerful tool in its ability to estimate the individual effects of the categorical variables on a qualitative dichotomous dependent variable, whether the success/satisfaction was perceived or not. In this regard, this model lent itself to meaningful interpretations when the dependent variable was a dichotomous outcome.
Regression through ordinary least squares (OLS) is a commonly applied statistical technique. Though when the dependent variable is dichotomous (0−1) rather than continuous, OLS becomes an inefficient prediction method and the underlying linear probability model (LPM) that was being estimated signifies a poor choice of model specification.
Hence, when noncontiguous dependent variables were used, for instance, in a 5-point Likert scale ranging from very satisfied to very dissatisfied, satisfied and very satisfied responses are almost similar. So, in this case, instead of creating confusion, it would be better to change the binary result variable to either satisfied or dissatisfied. That was why the Probit model was preferred for dichotomous (0−1) rather than continuous. So, the satisfaction marks for each element were clustered in either unsatisfied (0) or satisfied (1) to form a binary result variable.
The Manager’s satisfaction level composite index formula (MSI) was computed as follows:
MSI=ST∑i=1nWi
Where:
M = stands for Managers for a given export firm
MSI = is the overall satisfaction index for each respondent that ranges from 0 to 1,
Wi = is the level of satisfaction in each attribute for each respondent (with a series of choices ranging from “very satisfied” to “very dissatisfied”),
ST = is the maximum relative satisfaction level of the respondent.
Manager’s Satisfaction Index (MSI) is the yardstick or standard to measure the level of satisfaction in numerous attributes of export performance. Moreover, the index of managers’ satisfaction would also be a complementary dependent variable, which was useful to identify factors affecting managers’ satisfaction level with vigorous export performance. In order to measure the level of respondents’ satisfaction, the researchers identified the most important predictors.
The qualitative nature of the indicators was measured by scoring that could be organised to develop a satisfaction index by simply adding the scoring from the Likert model and dividing it by the total possible maximum score in order to measure the overall managers’ satisfaction index. Based on the overall satisfaction index, the dependent variable was formed by changing the values into one (those values greater than or equal to the mean value of the dependent variable) and zero (those values less than the mean value of the dependent variable).
The same procedures were applied for measuring managers’ goal achievement measurements, and a binary outcome variable was formed in the same manner. Finally, the regression model (OLS) was an appropriate technique for identifying export profit and export growth variables since the dependent variable takes a continuous measure where growth/profit data are commonly treated as continuous variables
[12]
.
General Research Model
Thus, the general research model for this study (adapted from 12) is:
Yi=f(Xi, βi,ϵ)(1)
Where:
Yi = indicates the dependent variables, it was export performance and represented by five constructs (export intensity, export growth, export profitability, goal achievement and satisfaction).
Xi = was a vector of factors affecting export performance: manager’s foreign language skills, firm size, manager’s international experience, firm export experience, product adaptation strategies, pricing competitiveness, distribution strategies, promotion intensity, competitive intensity, government rules and regulations, cultural dissimilarity, and technological factors.
βi = a vector of parameters to be estimated/ was a vector of Tobit maximum likelihood estimates.
ϵ = is the error term, which was assumed to have a normal distribution.
To investigate the determinants of a firm’s export performance, the study considered export performance as defined as the amount earned from export across different products
[12, 36]
. This study used the simple regression model to determine the factors that affect export performance (EP). For the simplicity and accuracy of the results of the study, twelve factors were identified as variables that influence the export performance of firms.
Therefore, the export Performance equation was expressed as follows:
EP=f(CI,TF,FMC,FS,FIE,MLS,MIE,PDA,PRA,PMA,PLA,ϵ)(2)
Where:
EP = Export Performance
CI = Competitive Intensity
TF = Technological Factors
FMC = Foreign Market Characteristics
FS = Firm Size
FIE = Firm International Experience
MLS = Manager’s Language Skill
MIE = Manager’s International Experience
PDA = Product Adaption;
PRA = Price Adaption;
PMA = Promotion Adaption;
PLA = Place (distribution) Adaptation)
ϵ = Dummy variable to capture other factors that influence export performance
Thus, the export performance model could be specified in a linear relationship through a regression equation as follows:
Yi=β0+β1CI+β2TF+β3FMC+β4FS+β5FIE+β6MLS+β7MIE+β8PDA+β9PRA+β10PMA+β11PLA+ϵ(3)
Where: β1 to β11>0 (implying the expected positive relationship between the independent factors and export performance).
The present study adopted the same approaches that were employed by previous researchers who investigated the determinants of a firm’s export performance
[12]
. Though some adaptations were made to make known the firm's situation.
7. Statistical Analysis
The qualitative and quantitative data obtained through questionnaires and interviews were analysed using both qualitative and quantitative analysis tools. In this study, quantitative data were collected by the survey method
[48]
. While the qualitative data was analysed through interpretation and conceptual generalisation, the quantitative data was analysed by descriptive statistics (frequencies, percentages, means and standard deviation) and correlation coefficient. The outcome for both the descriptive and quantitative analysis was presented by using tables, figures and graphs.
In previous studies, a range of data analysis methods have been employed, like partial least squares, regressions and structural equation modelling
[1, 6, 14, 23, 28, 33, 53]
. The growing exercise of Structural Equation Modelling (SEM) could perhaps be enlightened by the increasing involvement of the models employed to evaluate export performance
[9, 22, 23, 28, 35, 37, 51]
.
Thus, Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to consider the interrelationships amongst the theoretical constructs to evaluate export performance
[9, 5, 14, 21, 23, 35]
. To analyse the data, edited and coded responses were entered into Statistical Package for the Social Sciences (SPSS) version 25, for descriptive analysis. AMOS 18 was engaged to do measurement model analysis, structural model analysis and Factor analysis.
Also, factor analysis (both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA)), descriptive statistics, Cronbach’s Alpha (reliability measure), analysis of variance (ANOVA), and correlation analysis were the statistical methods applied in the quantitative study
[23, 49]
. All data collected were analysed in an objective manner, and the findings reflected the actual data obtained from the respondents. The researchers used a combination of quantitative and qualitative approaches. High-quality data was acquired from key informants merely when a successful survey instrument was constructed
[26]
. The questions were clearly declared and simply recognised to get the interest and concentration of prospective respondents
[26]
.
Thus, SEM allowed the scholars to identify structural associations with the latent variables, as a result creating more precise representations
[9, 23, 26]
. Though the first generation models were incomplete to investigate a single association simultaneously between independent and dependent variables, SEM can investigate all the associations in one process
[26, 28, 1]
. Also, it would have the capability to measure indirect effects of variables via other (mediating) variables
[22, 23]
.
In addition, factor analysis (EFA and CFA) were developed in the measurement model to ascertain the weight of every measured variable against the latent variable
[9, 35]
. It could also ascertain the validity and reliability of the construct
[1, 26, 28]
. Therefore, SEM signified a rational combination of factor analysis and regression models
[21, 28, 53]
. Here, the measurement model indicates associations between latent variables or constructs and the observed measures and holds information about how theoretical constructs are measured and operationalised in the study
[23, 26, 28, 53]
.
Under SEM, the coefficient estimates are more valid since they clearly integrate the error of measurement in its analysis
[9, 28, 53]
. Hence, exogenous variables (independent variables) are measured with no error, an assumption which is unlikely to be true in reality
[23, 28, 53]
.
8. Conclusion
This study successfully investigated the multifaceted determinants of export performance among medium and large-scale Ethiopian manufacturing firms, grounding its approach in the Contingency Theory and the Resource-Based View (RBV). By employing a robust mixed-methods design and advanced econometric techniques, including PLS-SEM, the Tobit model for intensity, and the Probit model for binary outcomes, we were able to transcend the limitations of simpler regression analyses and provide a more comprehensive, rigorously tested model. The empirical results confirm the significant, complex interplay between internal managerial/firm-specific factors and external market/competitive factors in shaping export success. Crucially, the findings validate our central proposition regarding the strategic role of the export marketing mix strategy, demonstrating that it serves as a powerful mediating mechanism through which a firm's inherent resources and environmental pressures are translated into measurable performance outcomes, such as export growth, profitability, and goal achievement.
The practical implications of this research are substantial for both firms and policymakers in Ethiopia's developing economy. For exporters, the identified model offers a clear, validated roadmap, highlighting which internal capabilities and market adaptation strategies yield the highest returns on export performance. By quantifying the differential effects of factors like managerial experience, firm size, and specific product/price adaptation strategies, this study enables firms to strategically allocate resources, prioritise investments in human capital and technology, and tailor their market entry and engagement approaches. Furthermore, the findings on external factors, such as competitive intensity and foreign market characteristics, provide a sound empirical basis for government agencies to design and implement more effective export development and support programs, focusing on market intelligence, trade finance, and capacity building to mitigate external challenges.
In conclusion, this research makes a definitive contribution to the international business and export performance literature by introducing and validating an integrated conceptual framework in the critical context of a sub-Saharan African emerging market. The adoption of the critical realism paradigm ensures that our conclusions are not only statistically sound but also grounded in a deep, contextual understanding of firm behaviour. While the cross-sectional nature of the primary data offers a snapshot in time, future research should build upon this model by adopting a longitudinal design to track the evolution of these determinants and their long-term impact on export sustainability. Ultimately, this work provides a solid empirical and theoretical foundation for future studies aiming to unpack the complexities of export success in similar developing market contexts.
Abbreviations

CFA

Confirmatory Factor Analysis

CSA

Central Statistical Agency

ETGIDI

Ethiopian Textile and Garment Industry Development Institute

ETLIDI

Ethiopian Leather Industry Development Institute

EFA

Exploratory factor analysis

FMA

Foreign Market Characteristics

LPM

Linear probability model

FIE

Firm International Experience

MIE

Manager’s International Experience

MLS

Manager’s Language Skill

MSI

Manager’s Satisfaction Index

OLS

Ordinary least squares

PLS

Partial Least Squares

RBV

Resource-Based View

SEM

Structural Equation Modelling
Author Contributions
Eshetu Chalachew: Writing – original draft
Sam Lubbe: Supervision
Flip Schutte: Writing – review & editing
Conflicts of Interest
The authors declare no conflicts of interest.
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    Chalachew, E., Lubbe, S., Schutte, F. (2025). A Conceptual Framework and Research Methodology for Analyzing Ethiopia's Export Performance Determinants. International Journal of Business and Economics Research, 14(6), 240-253. https://doi.org/10.11648/j.ijber.20251406.12

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    Chalachew, E.; Lubbe, S.; Schutte, F. A Conceptual Framework and Research Methodology for Analyzing Ethiopia's Export Performance Determinants. Int. J. Bus. Econ. Res. 2025, 14(6), 240-253. doi: 10.11648/j.ijber.20251406.12

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    Chalachew E, Lubbe S, Schutte F. A Conceptual Framework and Research Methodology for Analyzing Ethiopia's Export Performance Determinants. Int J Bus Econ Res. 2025;14(6):240-253. doi: 10.11648/j.ijber.20251406.12

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  • @article{10.11648/j.ijber.20251406.12,
  author = {Eshetu Chalachew and Sam Lubbe and Flip Schutte},
  title = {A Conceptual Framework and Research Methodology for Analyzing Ethiopia's Export Performance Determinants},
  journal = {International Journal of Business and Economics Research},
  volume = {14},
  number = {6},
  pages = {240-253},
  doi = {10.11648/j.ijber.20251406.12},
  url = {https://doi.org/10.11648/j.ijber.20251406.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20251406.12},
  abstract = {This study developed and empirically tested a Resource-Based View (RBV) and Contingency Theory model to investigate the determinants of export performance among Ethiopian manufacturing firms (textile, garment, leather). It posited that export marketing mix strategy acts as a crucial mediator linking internal (managerial, firm-specific) and external (competitive, mar-ket) factors to overall export success. Employing a sequential mixed-methods design (QUANT → QUAL), the research ana-lyzed data from a large sample of 389 exporting firms. Quantitative analysis utilized Structural Equation Modeling (SEM), alongside specialised econometric techniques—including the Tobit model (for export intensity) and the Probit model (for di-chotomous outcomes)—to ensure appropriate dependent variable handling. Findings confirmed the significant, mediating role of strategic marketing choices in translating resources and environmental pressures into performance. The results offer valua-ble, actionable insights for managers optimizing resource allocation and policymakers seeking to enhance national export development programs, providing a robust, context-specific framework for emerging economies.},
 year = {2025}
}

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  • TY  - JOUR
T1  - A Conceptual Framework and Research Methodology for Analyzing Ethiopia's Export Performance Determinants
AU  - Eshetu Chalachew
AU  - Sam Lubbe
AU  - Flip Schutte
Y1  - 2025/12/17
PY  - 2025
N1  - https://doi.org/10.11648/j.ijber.20251406.12
DO  - 10.11648/j.ijber.20251406.12
T2  - International Journal of Business and Economics Research
JF  - International Journal of Business and Economics Research
JO  - International Journal of Business and Economics Research
SP  - 240
EP  - 253
PB  - Science Publishing Group
SN  - 2328-756X
UR  - https://doi.org/10.11648/j.ijber.20251406.12
AB  - This study developed and empirically tested a Resource-Based View (RBV) and Contingency Theory model to investigate the determinants of export performance among Ethiopian manufacturing firms (textile, garment, leather). It posited that export marketing mix strategy acts as a crucial mediator linking internal (managerial, firm-specific) and external (competitive, mar-ket) factors to overall export success. Employing a sequential mixed-methods design (QUANT → QUAL), the research ana-lyzed data from a large sample of 389 exporting firms. Quantitative analysis utilized Structural Equation Modeling (SEM), alongside specialised econometric techniques—including the Tobit model (for export intensity) and the Probit model (for di-chotomous outcomes)—to ensure appropriate dependent variable handling. Findings confirmed the significant, mediating role of strategic marketing choices in translating resources and environmental pressures into performance. The results offer valua-ble, actionable insights for managers optimizing resource allocation and policymakers seeking to enhance national export development programs, providing a robust, context-specific framework for emerging economies.
VL  - 14
IS  - 6
ER  -

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  • Abstract
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    1. 1. Introduction
    2. 2. The Projected Framework
    3. 3. Mediating Variables
    4. 4. Formulation of Propositions
    5. 5. Research Methodology
    6. 6. Model Specification
    7. 7. Statistical Analysis
    8. 8. Conclusion
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