Technological capability refers to an enterprise’s ability to identify, acquire, operate, assimilate, adapt, and improve technology in response to production requirements. It extends beyond ownership of machinery and equipment to include technical knowledge, employee skills, managerial practices, organizational routines, and learning processes required for effective technology use [1][9]. Technology transfer primarily concerns the movement of equipment, knowledge, or technical assistance from external sources, whereas technological capability concerns the recipient enterprise’s ability to use, internalize, modify, and improve the transferred technology [1][2].

Technology acquisition alone does not guarantee improved manufacturing performance, particularly in developing economies. Enterprises also require absorptive capacity, understood as the ability to recognize the value of external knowledge, assimilate it, and apply it to productive activities [3]. In this study, absorptive capacity is treated as a related theoretical concept rather than as a separately validated measurement scale. Technological capability develops through practical experience, employee training, technical problem-solving, knowledge sharing, maintenance, learning-by-doing, learning-by-using, and adaptation to enterprise-specific production conditions [1][9]. Although related to innovation capability, technological capability is broader because it includes the ability to identify, assess, select, acquire, implement, operate, learn from, adapt, and improve technology [9].

This study applies the Technological Capability Assessment Framework developed by Arnold and Thuriaux (1997) [9]. Figure 1Figure 1 presents the technological capability assessment framework used in this study. The framework organizes technological capability into three interrelated domains: strategic capability, internal capability, and external capability.

Note. Adapted from Arnold and Thuriaux (1997) [9].

Figure 1. Technological capability assessment framework.

Strategic capability includes technology awareness, technology search, core competence, and technology strategy. These dimensions concern the enterprise’s capacity to recognize technological needs and opportunities, search for relevant technologies, identify internal technical strengths, and align technology decisions with organizational objectives. Internal capability comprises assessment and selection, acquisition, implementation, and learning capabilities. Together, these dimensions reflect how technology is evaluated, obtained, implemented, operated, and incorporated into organizational routines. External capability is represented by linkages with suppliers, customers, other enterprises, universities, research institutions, government agencies, technology networks, and industry associations [9].

The nine dimensions provide a structured basis for assessing technological-capability strengths and gaps. They are used in this study as analytical dimensions for assessing MIE-Gelan’s capability profile; they are not treated as independently validated causal variables.

Three complementary perspectives inform the study. The Resource-Based View explains how technical knowledge, employee skills, organizational routines, and other internal resources may function as strategic capabilities [10]. Dynamic Capability Theory emphasizes the ability to sense technological opportunities, seize them through appropriate decisions, and reconfigure resources as technological and production conditions change [11]. Organizational Learning Theory explains how knowledge is acquired, shared, retained, and converted into improved routines and problem-solving capacity [12][13].

These theories serve as interpretive lenses rather than variables tested through causal analysis. The Resource-Based View is particularly relevant to core competence, implementation, and learning; Dynamic Capability Theory relates to technology awareness, search, strategy, assessment, acquisition, and adaptation; and Organizational Learning Theory informs the interpretation of knowledge sharing, learning-by-doing, absorptive processes, and continuous improvement [10]-[14].

Figure 2Figure 2 integrates the nine technological-capability dimensions with the three theoretical perspectives [14]. Strategic, internal, and external capabilities are treated as interconnected components that contribute to overall technological capability development [15][16]. The framework further presents organizational learning, including knowledge acquisition, knowledge sharing, absorptive processes, and continuous learning, as a mechanism through which technological capability may be developed and strengthened [17][18].

The framework identifies potential links between technological capability, organizational learning, and broader outcomes such as productivity improvement, product quality, innovation performance, operational efficiency, technological upgrading, process innovation, product innovation, and sustainable competitiveness [17]-[19]. Because these outcomes were not directly measured in this study, they are presented as potential implications rather than demonstrated causal effects. The framework therefore guides the assessment and interpretation of MIE-Gelan’s technological-capability profile without claiming to statistically test the proposed relationships.

Note. Developed by the author based on Arnold and Thuriaux (1997) [9], the resource-based view, dynamic capability theory, and organizational learning theory.

Figure 2. Integrated conceptual framework of the study.

The reviewed literature demonstrates broad agreement that technological capability is a critical determinant of manufacturing performance, innovation, productivity improvement, and long-term competitiveness [1][2][15]. Existing studies consistently emphasize that technological capability extends beyond the acquisition of machinery and equipment to include organizational routines, employee skills, knowledge management, learning processes, technology strategy, and external knowledge linkages [1][3].

The Resource-Based View, Dynamic Capability Theory, and Organizational Learning Theory further suggest that sustainable technological development depends on the effective integration of internal resources, adaptive organizational processes, and continuous learning [10]-[12].

Despite this consensus, several limitations remain in the existing literature. First, empirical evidence on technological capability development in African manufacturing enterprises remains limited, particularly at the firm level. Most studies focus on productivity, innovation outcomes, technology adoption, or investment performance, while providing limited insight into the internal capability-building mechanisms that support technological development [7][8]. Second, although the Technological Capability Assessment Framework of Arnold and Thuriaux (1997) has been widely recognized as a comprehensive framework for assessing enterprise-level technological capability, its applicability within least-developed-country manufacturing environments remains insufficiently examined [9]. Third, limited research has specifically investigated technological capability gaps within metal-manufacturing enterprises, despite their strategic importance for industrial development and industrial upgrading.

These limitations indicate the need for firm-level empirical studies capable of examining technological capability as a multidimensional organizational phenomenon rather than as a simple outcome of technology acquisition. In response to these gaps, the present study applies the Arnold and Thuriaux (1997) framework to assess technological capability at Mesfin Industrial Engineering PLC-Gelan Manufacturing Plant. The study focuses on nine technological-capability dimensions and examines their strengths, weaknesses, and implications for future industrial upgrading.

Accordingly, the conceptual framework presented in Figure 2Figure 2 serves as the analytical foundation of this study. It links strategic capability, internal capability, external capability, and organizational learning to overall technological capability development and provides the basis for the research design, data collection, analysis, and interpretation of the technological capability profile of MIE-Gelan Manufacturing Plant.

This study employed a mixed-methods case-study design to assess the technological capability of Mesfin Industrial Engineering PLC-Gelan Manufacturing Plant (MIE-Gelan) in Ethiopia.

MIE-Gelan was purposively selected as the case-study site. Mesfin Industrial Engineering, established in 1993, is one of Ethiopia’s leading metal-construction and electromechanical-engineering enterprises, serving the energy, mining, construction, transportation, and agricultural sectors [20][21]. As an established manufacturing facility integrating fabrication, machining, maintenance, engineering, and quality-management functions, MIE-Gelan operates within a technologically intensive production environment. The plant was selected because it provides a suitable setting for examining how technological capabilities are acquired, applied, and developed in manufacturing operations. Furthermore, the case reflects many of the technological-learning, resource, skills, and industrial-upgrading challenges faced by manufacturing firms in developing economies. Although the study focuses on a single manufacturing plant, the findings are intended for analytical rather than statistical generalization and may provide insights relevant to similar manufacturing enterprises operating under comparable conditions [22].

Quantitative data were collected using a structured questionnaire to measure technological capability levels across the nine dimensions of the Technological Capability Assessment Framework developed by Arnold and Thuriaux (1997). Qualitative data were collected through semi-structured interviews, direct observation, and document review to explain the organizational practices, constraints, and contextual conditions underlying the quantitative results.

The study followed an explanatory mixed-methods approach in which quantitative results were analyzed first to identify capability strengths and gaps across the nine technological-capability dimensions. Qualitative interviews, observations, and document reviews were then used to explain, interpret, and contextualize the quantitative findings. Integration occurred during the interpretation stage by comparing qualitative evidence with the quantitative capability scores for each dimension. Areas of convergence strengthened the interpretation of the findings, while any inconsistencies were examined to provide additional contextual understanding of the enterprise’s technological-capability conditions [23]-[25].

Both primary and secondary data were used in this study. Primary data were collected through questionnaires, semi-structured interviews, and direct observation. Secondary data were obtained from company reports, operational records, and technology-related documents. These multiple sources of evidence supported triangulation of the findings.

A census survey was conducted among all 72 employees in the production, maintenance, and quality departments who were directly involved in technology utilization, equipment operation and maintenance, quality control, or day-to-day production activities and who were available and willing to respond. The respondents included 45 production employees, 19 maintenance employees, and 8 quality employees.

Engineering personnel were excluded from the questionnaire because their number was limited and their duties differed substantially from those of production, maintenance, and quality employees. However, their perspectives were included through key-informant interviews.

Five key informants were purposively selected: the General Manager, Production Manager, Maintenance Manager, Quality Control Manager, and Senior Technical Expert. They were selected because of their direct involvement in technology management, technical decision-making, production, maintenance, and quality activities.

The interviews focused on technology planning, acquisition, implementation, utilization, organizational learning, external collaboration, technological constraints, and opportunities for capability improvement.

The questionnaire was adapted from the Technological Capability Assessment Framework of Arnold and Thuriaux (1997) [9]. The framework consists of nine dimensions: technology awareness, technology search, core competence, technology strategy, assessment and selection capability, acquisition capability, implementation capability, learning capability, and external linkages.

A structured questionnaire containing 24 items was developed and contextualized to the operational conditions of MIE-Gelan Manufacturing Plant. The questionnaire items were adapted to reflect the terminology, organizational structure, and technological activities of the case-study enterprise while preserving the conceptual meaning of the original framework dimensions. Each item was assigned to one technological-capability dimension according to its primary conceptual emphasis. This item allocation avoided double counting in the calculation of dimension-level and overall technological-capability scores. The operationalization of the nine technological-capability dimensions, including the corresponding questionnaire items and number of items assigned to each dimension, is presented in Table 1.

The complete questionnaire, including questionnaire items, source information, adaptation rationale, and allocation to the nine technological-capability dimensions, is provided in Appendix. All questionnaire items were adapted from Arnold and Thuriaux’s (1997) Technological Capability Assessment Framework and contextualized to the operational environment of MIE-Gelan Manufacturing Plant.

The number of questionnaire items differs across dimensions because the allocation follows the conceptual structure of the Technological Capability Assessment Framework developed by Arnold and Thuriaux (1997). Broader dimensions, such as External Linkages, require multiple indicators to capture relationships with suppliers, customers, research institutions, government agencies, and industry networks, whereas more focused dimensions, such as Technology Awareness, require fewer indicators. To avoid bias arising from unequal item numbers,

Table 1. Operationalization of technological capability dimensions.

Source: Developed by the researcher based on Arnold and Thuriaux (1997).

dimension scores were calculated using average item scores rather than total scores, ensuring that no dimension received greater weight solely because it contained more questionnaire items.

Content validity was assessed through expert review by three specialists with expertise in technology management, manufacturing engineering, and industrial research. The reviewers evaluated the relevance, clarity, contextual suitability, and alignment of the questionnaire items with the nine technological-capability dimensions. Based on their feedback, several items were reworded, overlapping statements were refined, and minor modifications were made before final administration of the questionnaire [26].

A pilot test was conducted with 20 participants who had characteristics similar to those of the target respondents. The pilot assessed item clarity, comprehensibility, and contextual suitability. Based on participant feedback, ambiguous terms were simplified, and minor formatting adjustments were made to improve readability and consistency. No major structural changes to the questionnaire were required [27].

Reliability was assessed using Cronbach’s alpha. The overall 24-item questionnaire produced a Cronbach’s alpha coefficient of 0.855, indicating good internal consistency for the instrument as a whole. However, the technological-capability assessment framework comprises nine conceptually distinct dimensions rather than a single unidimensional construct. Therefore, the overall Cronbach’s alpha should not be interpreted as evidence that all dimensions form one internally consistent scale.

Several dimensions were measured using one or two questionnaire items, making dimension-specific reliability estimation impractical. Consequently, these dimensions were treated as descriptive capability indicators rather than multi-item psychometric scales. The primary purpose of the instrument was to assess technological-capability conditions across the nine dimensions of the Arnold and Thuriaux (1997) framework rather than to develop a single latent measurement scale. Reliability evidence was therefore interpreted in conjunction with the conceptual structure of the framework, expert review, and pilot testing.

Ethical principles were observed throughout the study. Participants were informed about the purpose of the research, and participation was voluntary. Personal identifiers were excluded from questionnaires, interview records, analysis, and reporting to protect confidentiality.

Permission to conduct the study was obtained from the management of Mesfin Industrial Engineering PLC-Gelan Manufacturing Plant. Participants were informed of the study objectives, the voluntary nature of participation, and the confidential treatment of their responses. Verbal informed consent was obtained before questionnaire administration and interviews.

All information collected was used only for academic purposes. Participation or non-participation did not affect employees’ work status, job responsibilities, or relationship with enterprise management.

Quantitative and qualitative data were analyzed using complementary procedures. Quantitative analysis included frequencies, percentages, proportionally weighted scores, item-level capability scores, dimension-level capability scores, and an overall technological capability score. Qualitative data from interviews, observations, and document reviews were analyzed thematically to explain and triangulate the quantitative findings.

The questionnaire contained 24 items representing the nine technological-capability dimensions of Arnold and Thuriaux’s (1997) framework. Item-level response frequencies, proportionally weighted scores, and capability scores were calculated for all 24 questionnaire items. The results obtained from the item-level, dimension-level, and overall technological-capability analyses are presented and discussed in Chapter 4.

Responses were measured using a four-point Likert scale: Strongly Disagree, Disagree, Somewhat Agree, and Strongly Agree. The four-point scale was used to avoid neutral responses and encourage respondents to provide a clear assessment of technological-capability conditions in the enterprise [28]. However, because there was no neutral or “do not know” option, the results were interpreted as perception-based indicators rather than direct measures of the enterprise’s actual technological capability. The questionnaire response categories, corresponding scale values, and proportional weights used in the analysis are presented in Table 2.

The proportional weights were derived directly from the four-point Likert scale by dividing each scale value by the maximum scale value (4), producing equivalent scores of 0.25, 0.50, 0.75, and 1.00. This linear transformation converts Likert-scale

Table 2. Questionnaire response categories and proportional weights.

responses into percentage-based capability scores while preserving the relative ordering and proportional differences among responses. Consequently, the resulting percentage scores are mathematically equivalent to the original Likert-scale means and do not alter the ranking of technological-capability dimensions.

3.5.1. Item-Level Capability Score

The item-level capability score was calculated using the following formula:

where:

I C S i = item-level capability score for item i ; S D i = number of respondents selecting Strongly Disagree for item i ; D i = number of respondents selecting Disagree for item i ; S W A i = number of respondents selecting Somewhat Agree for item i ; S A i = number of respondents selecting Strongly Agree for item i ; N = total number of respondents.

Because all 72 respondents completed the questionnaire, N = 72 was used in the item-level calculations.

3.5.2. Dimension-Level Capability Score

The capability score for each technological-capability dimension was calculated by averaging the item-level capability scores assigned to that dimension:

where:

D C S j = capability score for the technological-capability dimension j ; I C S i = item-level capability scores for item i ; K j = number of questionnaire items assigned to dimension j .

For example, the Technology Strategy dimension consisted of four items: Q1, Q7, Q8, and Q9. Its capability score was calculated as:

3.5.3. Overall Technological Capability Score

The overall technological capability score was calculated from the total proportional weighted score across all 24 questionnaire items:

where:

O T C S = overall technological capability score;

T W S = total proportional weighted score from all questionnaire items;

N = total number of respondents;

K = total number of questionnaire items.

For this study O T C S = ( 948 72 × 24 ) × 100

Mesfin Industrial Engineering PLC-Gelan Manufacturing Plant obtained an overall technological capability score of 54.86%. As a sensitivity check, percentage-based capability scores were compared with the corresponding original Likert-scale means. The comparison produced identical rankings across all nine technological-capability dimensions, indicating that the identification of capability strengths, weaknesses, and improvement priorities was not affected by the score-transformation procedure. Based on the classification in Table 3, this score indicates a low level of technological capability.

Table 3. Technological capability classification.

The capability-classification thresholds were derived from the percentage equivalents of the four-point Likert scale used in the study. Scores below 60% correspond approximately to responses concentrated between Strongly Disagree and Disagree and were classified as low capability. Scores between 60% and 79.99% correspond approximately to responses between Disagree and Somewhat Agree and were classified as moderate capability. Scores of 80% and above correspond to responses approaching Strongly Agree and were classified as high capability. These thresholds were used as analytical criteria for interpreting relative capability levels within the case-study enterprise and do not represent universal industrial benchmarks.

Based on Table 3, the overall score of 54.86% indicates a low level of technological capability.

3.5.4. Qualitative Data Analysis

Qualitative data from interviews, observations, and document reviews were analyzed thematically [29]. Semi-structured interviews were conducted in Amharic with five purposively selected key informants. Interview responses were documented through detailed interview notes and, where permission was granted, audio recordings. The interview records were subsequently translated from Amharic into English for analysis. The researcher reviewed the interview records, observation notes, and organizational documents and conducted thematic coding based on the nine technological-capability dimensions of Arnold and Thuriaux’s (1997) framework. Codes were reviewed repeatedly during analysis to ensure consistency between the interview records, observation notes, documentary evidence, and the resulting themes. Documentary sources included available organizational reports, operational records, maintenance-related records, and technology-related documents relevant to technology management, production, maintenance, and organizational learning activities.

The evidence was coded and organized according to the nine technological-capability dimensions of Arnold and Thuriaux’s (1997) framework.

The qualitative findings were used to explain and triangulate the quantitative results [30]. Integration occurred at the interpretation stage, where qualitative evidence was compared with the quantitative scores across the nine dimensions.

A total of 72 employees participated in the study, resulting in a response rate of 100%. All questionnaires were complete; therefore, no missing-data treatment was required.

The respondents were drawn from the production, maintenance, and quality departments. Production employees constituted the largest proportion of respondents (62.5%), followed by maintenance employees (26.4%) and quality employees (11.1%). These departments were included because they are directly involved in technology utilization, equipment operation, production, maintenance, quality control, and day-to-day technological problem-solving.

Regarding educational level, most respondents possessed Technical and Vocational Education and Training (TVET) qualifications, followed by bachelor’s and master’s degrees. Most respondents had between three and seven years of work experience, indicating familiarity with the plant’s operational and technological activities.

Overall, the respondent profile was appropriate for assessing the plant’s technological capability. The demographic characteristics of the respondents are presented in Table 4.

Research Question 1

What is the aggregated employee-assessed level of technological capability at Mesfin Industrial Engineering PLC-Gelan Manufacturing Plant?

Table 4. Demographic characteristics of respondents.

Source: Survey Data, 2026.

The overall technological capability of Mesfin Industrial Engineering PLC-Gelan Manufacturing Plant was assessed using 24 questionnaire items covering nine technological capability dimensions based on the framework of Arnold and Thuriaux (1997). Each item was assessed using a four-point response scale with proportional weights of 0.25, 0.50, 0.75, and 1.00.

The study included 72 respondents. The maximum possible weighted score for each item was therefore 72, assuming that all respondents selected the highest weight of 1.00. The total weighted score across the 24 assessment items was 948.00.

The maximum possible total score was calculated as follows:

Maximum Possible Total Score = N × K = 72 × 24 = 1728

where N is the total number of respondents and K is the total number of questionnaire items.

The overall technological-capability score was calculated using the formula presented previously in Equation (3.4):

where:

O T C S = Overall Technological Capability Score; T W S = Total Weighted Score; N = Number of Respondents; K = Number of Questionnaire Items.

Substituting the study values into Equation (3.4), where TWS = 948, N = 72, and K = 24, gives:

The plant achieved an overall technological capability score of 54.86%. According to the classification adopted in this study, scores between 25.00% and 59.99% represent low capability. Therefore, the plant’s overall technological capability was classified as low. The detailed results of the overall technological-capability assessment are presented in Table 5.

Table 5. Overall technological capability assessment result.

The score indicates that the plant has some technological capability foundations, but these capabilities are not yet sufficiently developed or systematically integrated. The findings suggest limitations in technology awareness and search, technology assessment and selection, acquisition, organizational learning, and external technological collaboration.

The overall score provides a general assessment of the plant’s technological capability but does not reveal the relative performance of the nine capability dimensions. Section 4.3 therefore examines each dimension separately to identify the plant’s relative strengths and weaknesses.

Research Question 2

What are the major strengths and capability gaps across the nine technological-capability dimensions at Mesfin Industrial Engineering PLC-Gelan Manufacturing Plant?

The nine technological capability dimensions were assessed using 24 questionnaire items developed from the technological capability framework of Arnold and Thuriaux (1997). Each item was assigned to the dimension that best reflected its primary conceptual meaning.

For each dimension, the Average Weighted Score was first obtained from the weighted scores of the questionnaire items belonging to that dimension. This Average Weighted Score is the raw value obtained directly from the survey data before any further transformation. It represents the average weighted score of all questionnaire items assigned to a particular technological-capability dimension.

The dimension-level capability score was then calculated using the procedure presented in Equation (3.2) in Chapter Three. In operational terms, the Average Weighted Score for each dimension was converted into a percentage by dividing it by the total number of respondents and multiplying the result by 100. This operational expression was used to convert the Average Weighted Score of each technological-capability dimension into its corresponding percentage capability score. The calculation was expressed as follows:

where:

D C S j = dimension-level capability scores for dimension j ; A W S j = average weighted score j ; N = total number of respondents.

Since the study included 72 respondents, N = 72 was used in calculating the percentage score for each dimension.

For example, the Technology Awareness score was calculated as follows:

This calculation shows that the raw Average Weighted Score of 37.50 corresponds to a dimension-level capability score of 52.08%. The same calculation procedure was applied to the remaining eight technological-capability dimensions.

The results for all nine technological capability dimensions are presented in Table 6.

The results show limited variation among the nine dimensions, as eight dimensions fall within the low-capability category. This indicates that improvement is needed across most technological capability areas.

Implementation Capability recorded the highest score (62.85%) and was the only dimension classified as moderate capability. It is therefore the plant’s main relative strength. This result suggests that the plant is comparatively better able to organize and carry out technology-related activities.

However, this moderate score does not indicate a fully developed implementation system. It only shows that Implementation Capability performed better than the other dimensions. The plant should strengthen its implementation practices by linking them with technology planning, assessment and selection, organizational learning, and external collaboration.

Acquisition Capability recorded the second-highest score (57.47%), followed by

Table 6. Dimension-level technological capability assessment.

Note: Low capability = 25.00% - 59.99%; Moderate capability = 60.00% - 79.99%; High capability = 80.00% - 100.00%. Source: Survey Data, 2026.

Core Competence (57.29%). Although these dimensions performed relatively better than most low-capability dimensions, both remain below the 60.00% threshold for moderate capability. The findings indicate that the plant has some ability to acquire technological resources and maintain internal technical competence, but further improvement is required.

Technology Strategy scored 55.21%, while Technology Search scored 54.86%. These results indicate that the plant has some, but limited, capacity to establish technology priorities and identify technological opportunities. However, technology planning may not yet be systematic or sufficiently integrated with the plant’s long-term objectives.

Learning Capability recorded a score of 54.34%. This indicates weaknesses in post-project review, knowledge documentation, experience sharing, and the transfer of lessons between technology-related activities. These weaknesses may reduce the plant’s ability to absorb new technologies, address implementation problems, and improve through operational experience.

External Linkages scored 52.33%. This suggests that relationships with suppliers, universities, research institutions, consultants, government agencies, and other support organizations are not sufficiently developed or strategically managed. Strengthening these relationships could improve access to technical information, training, technology transfer, technical assistance, and collaborative problem-solving.

Technology Awareness scored 52.08%. This indicates limited awareness of technological developments, emerging technologies, risks, and opportunities. Improved technology awareness would enable the plant to identify relevant changes earlier and respond more effectively.

Assessment and Selection Capability recorded the lowest score (51.91%). This is the plant’s most significant capability gap. The result indicates weaknesses in evaluating, comparing, prioritizing, and selecting technology alternatives. Weak assessment and selection procedures may result in technology choices that do not adequately match the plant’s operational needs, financial capacity, technical skills, or long-term objectives.

Research Question 3

What is the level of capability in technology assessment and selection, technology acquisition, technology implementation, organizational learning, and external technological linkages?

The plant’s capability to acquire, absorb, adapt, and improve transferred technologies was assessed using five related technological-capability dimensions: Assessment and Selection Capability, Acquisition Capability, Implementation Capability, Learning Capability, and External Linkages. Together, these five dimensions reflect the organizational capabilities required to assess and select appropriate technologies, acquire them from relevant sources, implement them effectively, learn from operational experience, and obtain external technical support. These capabilities are essential for the effective acquisition, absorption, adaptation, and improvement of transferred technologies.

The five dimensions were measured using 15 questionnaire items. Since the dimensions contained different numbers of assessment items, an item-weighted average was used to calculate the composite score. This ensured that each questionnaire item contributed equally to the composite assessment.

The composite average weighted score was calculated using the following formula:

where:

C A W S = composite average weighted score; A W S j = average weighted score for dimension j ; K j = number of assessment items assigned to dimension j ; K = total number of assessment items included in the composite score; m = number of technological capability dimensions included in the composite assessment.

For this study:

The composite capability score was then calculated by applying the percentage-conversion procedure presented in Equation (3.2):

where:

C C S = composite capability score; C A W S = composite average weighted score; N = total number of respondents.

Therefore:

The item-weighted calculation produced a composite average weighted score of 39.34, reported to two decimal places. The final composite capability score was calculated using the intermediate value before it was rounded, resulting in a composite capability score of 54.63%. According to the classification adopted in this study, this score represents low capability.

The component scores used to calculate the composite result are reported in Table 6. Assessment and Selection Capability scored 51.91%, Acquisition Capability scored 57.47%, Implementation Capability scored 62.85%, Learning Capability scored 54.34%, and External Linkages scored 52.33%. Because these dimensions contained different numbers of questionnaire items, the composite score was calculated using an item-weighted average rather than a simple average of the five-dimension scores.

Implementation Capability recorded the highest score (62.85%) and was the only dimension classified as moderate capability. It therefore represents the plant’s main relative strength within this capability group. This finding suggests that the plant is comparatively better at implementing technology-related activities than at selecting technologies, acquiring technological resources, learning from implementation experience, and obtaining external technical support.

However, the moderate Implementation Capability score does not indicate a fully developed implementation system. It shows only that this dimension performed better than the other four dimensions included in the assessment.

Acquisition Capability recorded the second-highest score (57.47%) but remained within the low-capability category. This indicates that the plant has some capacity to acquire technological resources, although this capacity requires further development.

Learning Capability scored 54.34%, indicating limitations in employee learning, technical knowledge documentation, experience sharing, and the application of lessons from previous technology-related activities. These limitations may restrict the plant’s ability to absorb, adapt, and improve transferred technologies.

External Linkages scored 52.33%, suggesting limited collaboration with suppliers, universities, research institutions, consultants, government agencies, and other technology-support organizations. This may restrict access to specialized knowledge, training, technical assistance, and collaborative problem-solving.

Assessment and Selection Capability recorded the lowest score (51.91%). It therefore represents the most significant capability gap among the five dimensions. Weak technology-assessment and selection procedures may result in the acquisition of technologies that do not adequately match the plant’s operational requirements, employee skills, financial capacity, infrastructure, or long-term objectives.

Overall, the composite score of 54.63% indicates that the plant’s capability to acquire, absorb, adapt, and improve transferred technologies is low. Although the plant has a relatively stronger ability to implement technology-related activities, weaknesses in assessment and selection, learning, and external linkages limit its ability to achieve sustainable technological upgrading.

Therefore, the plant should prioritize the formal evaluation and selection of technology alternatives, employee training, technical knowledge documentation, post-implementation review, internal knowledge sharing, and collaboration with external technology-support organizations. Its relatively stronger Implementation Capability should be used as a foundation for strengthening these weaker areas and supporting continuous technological improvement.

ResearchQuestion4

What strategic interventions may strengthen technological capability and support future industrial upgrading and competitiveness at the plant?

Mesfin Industrial Engineering PLC-Gelan Manufacturing Plant achieved an overall technological capability score of 54.86%, indicating low capability. Of the nine assessed dimensions, Implementation Capability was the only dimension with moderate capability (62.85%), while the remaining dimensions were classified as low capability, ranging from 51.91% to 57.47%.

To determine intervention priorities, the dimensions were ranked according to their relative scores, as presented in Table 7. Lower scores generally indicate larger capability gaps and greater urgency for improvement. However, dimensions with very similar scores should not be interpreted as representing substantially different capability levels. Therefore, the priority ranking is intended as a practical guide for improvement planning rather than a precise statistical ordering of the dimensions.

The findings show that Assessment and Selection Capability (51.91%), Technology Awareness (52.08%), and External Linkages (52.33%) represent the lowest-scoring dimensions and therefore warrant particular attention. However, the differences among these scores are very small. Consequently, these dimensions should be interpreted as belonging to a similar capability-gap group rather than as distinctly ranked priorities. Improvement efforts may therefore address these

Table 7. Capability priority matrix for technological capability development.

Note: Priority levels reflect relative improvement needs and do not replace the formal capability classifications. Where score differences are minimal, the rankings should be interpreted cautiously as indicative rather than definitive priorities. Source: Survey Data, 2026.

areas concurrently rather than sequentially. Improvement in these areas is necessary to strengthen the plant’s ability to identify technological opportunities, evaluate alternatives, select appropriate technologies, and obtain external technical support.

Learning Capability, Technology Search, and Technology Strategy are high-priority areas because they support systematic technology planning, information gathering, knowledge sharing, and continuous improvement. Core Competence and Acquisition Capability also require improvement, although their scores were relatively higher than those of the other low-capability dimensions. Implementation Capability should be consolidated and integrated with planning, learning, acquisition, and external collaboration activities.

Based on these findings, the plant should establish formal procedures for technology assessment, comparison, selection, and long-term planning. It should also strengthen technology awareness and search activities through regular scanning of technological developments, market information, opportunities, and threats. Employee training, post-project reviews, knowledge documentation, mentoring, and experience-sharing practices should be improved to enhance organizational learning and human-resource development.

Technology acquisition procedures should be strengthened through clear sourcing mechanisms, supplier support, and effective transfer of operational and maintenance knowledge. Collaboration with suppliers, universities, research institutions, consultants, government agencies, and technology providers should also be enhanced to improve access to technical information, expertise, training, and technical assistance. Existing implementation capability should be maintained while linking project implementation with continuous improvement, employee learning, technology planning, and external support.

The proposed interventions are interrelated. Technology strategy provides direction for technology awareness, search, assessment, acquisition, and implementation. Organizational learning and human-resource development enable employees to use, maintain, adapt, and improve acquired technologies. External collaboration supports access to technical knowledge, expertise, and technology-support services.

Figure 3Figure 3 presents the relationship between identified capability gaps and strategic interventions in technology strategy, organizational learning, external collaboration, technology acquisition, implementation, and human resource development. These interventions are expected to support improved technological capability, industrial competitiveness, and technological upgrading. The framework is an analytical guide based on the study findings and does not establish statistically confirmed causal relationships.

Source: Developed by the researcher based on the technological capability assessment findings, 2026.

Figure 3. Strategic intervention framework derived from technological capability assessment findings.

Qualitative data were collected through semi-structured interviews with five purposively selected key informants and direct observations at Mesfin Industrial Engineering PLC-Gelan Manufacturing Plant. The purpose was to explain the organizational, managerial, and technological conditions underlying the quantitative findings. The evidence revealed interconnected challenges related to technology strategy, technology acquisition and modernization, organizational learning, external collaboration, and continued reliance on conventional production practices.

Interview findings indicated that technology-related decisions were largely driven by immediate operational needs rather than systematic long-term planning. One interview participant noted that technology investments were often made in response to immediate production requirements rather than long-term technology plans (Interviewee 1). This reactive approach is consistent with the low Technology Strategy score of 55.21% and may limit the plant’s ability to anticipate future technological requirements, plan modernization activities, and align technology investments with organizational objectives.

The interviews and observations also revealed aging equipment, limited upgrading, restricted investment, and difficulties in accessing advanced manufacturing technologies.Available maintenance and operational records reviewed during the study also indicated continued emphasis on equipment maintenance activities rather than major technology-upgrading initiatives. Direct observations showed continued use of aging production equipment, while maintenance activities focused primarily on repairing existing machinery rather than upgrading technological systems. Maintenance activities focused mainly on repairing existing equipment and addressing immediate operational problems. These conditions are consistent with the low Acquisition Capability score of 57.47%, indicating limitations in acquiring new technologies, upgrading existing equipment, and utilizing technological assets effectively.

Organizational learning was another significant limitation. Employees developed technical skills mainly through practical work experience, while structured training, mentoring, knowledge documentation, and formal knowledge-sharing mechanisms remained limited. As one participant explained, most technical knowledge was acquired through workplace experience rather than formal training programs (Interviewee 3). This finding supports the low Learning Capability score of 54.34% and highlights the need to strengthen employee development, knowledge management, and post-project learning.

The qualitative evidence further showed limited interaction with universities, research institutions, suppliers, consultants, and technology providers. Several interview participants reported infrequent collaboration with universities, research institutions, and external technology-support organizations. This finding corresponds with the low External Linkages score of 52.33%. Weak external collaboration may restrict access to technical information, specialized expertise, training, technology-transfer opportunities, and collaborative problem-solving.

Direct observations indicated continued reliance on conventional production practices, with limited evidence of systematic technological innovation and continuous improvement. This finding should be considered alongside the Implementation Capability score of 62.85%, which was the highest score and the only dimension classified as moderate capability. Although the plant demonstrates a relatively stronger ability to implement existing technology-related activities, implementation practices are not yet sufficiently integrated with innovation, technology adaptation, organizational learning, and continuous improvement.

Overall, the qualitative findings provide explanatory support for the quantitative results. The plant’s low technological capability appears to be associated with interconnected weaknesses in technology planning, equipment modernization, organizational learning, external collaboration, and continuous improvement. These issues should therefore be addressed through coordinated interventions rather than isolated actions. The findings reinforce the strategic priorities presented in Table 7 and Figure 3Figure 3, particularly the need to improve long-term technology planning, employee training, knowledge sharing, external collaboration, technology acquisition, equipment modernization, and implementation practices.

Addressing these areas would strengthen the plant’s ability to acquire, absorb, adapt, and improve transferred technologies, thereby supporting technological capability development, industrial competitiveness, and technological upgrading. However, the qualitative findings are explanatory in nature and should not be interpreted as statistically confirmed causal relationships.

The qualitative and quantitative findings were largely convergent across the nine technological-capability dimensions. For example, the low scores for Assessment and Selection Capability, Learning Capability, and External Linkages were supported by interview evidence describing limited technology-evaluation procedures, weak knowledge-sharing practices, and restricted collaboration with external technology-support organizations. Similarly, the relatively stronger Implementation Capability score was consistent with observations indicating practical experience in operating existing technologies. No major contradictions were identified between the qualitative and quantitative findings. The qualitative evidence therefore served primarily to explain and contextualize the capability strengths and gaps identified through the survey results.

First, the findings indicate that technological capability at MIE-Gelan is characterized by relatively stronger implementation capability than strategic, learning, and technology-management capabilities. This suggests that operational use of technology may develop faster than technology planning, assessment, acquisition, and organizational learning functions in late-industrializing manufacturing firms.

Second, the study indicates that technology acquisition alone does not guarantee technological upgrading. Sustainable capability development depends on complementary organizational capabilities, including technology planning, employee development, knowledge transfer, maintenance systems, organizational learning, and post-implementation evaluation.

Third, technological capability development appears to be a systemic and interdependent process. Technology awareness, technology search, assessment and selection, acquisition, implementation, learning, and external linkages function as mutually reinforcing capabilities. Weaknesses in one area may reduce the effectiveness of the others and limit overall technological upgrading.

Overall, the findings suggest that sustainable technological upgrading at MIE-Gelan requires coordinated improvement in technology strategy, human-resource development, organizational learning, external collaboration, maintenance planning, and continuous improvement.

Mesfin Industrial Engineering PLC-Gelan Manufacturing Plant should establish a formal technology strategy aligned with its production objectives, modernization priorities, workforce requirements, and long-term development goals. A dedicated technology-management team should coordinate technology planning, assessment, acquisition, implementation, and performance evaluation.

The plant should strengthen organizational learning through regular technical training, mentoring, knowledge-sharing activities, and documented operating procedures. Particular attention should be given to equipment operation, preventive maintenance, quality management, technical problem-solving, and the retention of organizational knowledge.

Technology acquisition and equipment modernization should be guided by systematic technical and economic assessments, including supplier evaluation, compatibility analysis, operator training, maintenance preparation, and post-acquisition performance review. Modernization efforts should prioritize critical and obsolete equipment that constrains production performance.

The plant should also expand collaboration with universities, technical institutions, research organizations, suppliers, and technology providers to improve access to training, technical expertise, maintenance support, innovation, and technology-transfer opportunities.

To sustain capability development, continuous-improvement practices should be institutionalized through preventive maintenance, root-cause analysis, employee participation, and regular performance monitoring. Key indicators such as equipment utilization, downtime, training participation, maintenance completion, and improvement-project outcomes should be tracked.

At the policy level, relevant Ethiopian industrial-development institutions should strengthen support for manufacturing firms through modernization financing, technical-training programs, advisory services, testing facilities, and university-industry collaboration. Future research should examine technological capability across multiple firms and sectors using larger samples, longitudinal designs, and objective performance indicators.