Journal of Entrepreneurship, Management and Innovation (2026)

Volume 22 Issue 4: 93-114

DOI: https://doi.org/10.7341/20262245

JEL Codes: Q55, Q56, D22, L25, O32

Leul Girma Haylemariam, Ph.D., Postdoc Researcher at Sapienza University of Rome, Department of Management, Faculty of Economics, Via del Castro Laurenziano, 9, 00161 Rome, Italy, e-mail This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

PURPOSE: Small and medium-sized enterprises (SMEs) in emerging economies face increasing pressure to improve Environmental Management Performance (EMP) under severe resource constraints. Within the green economy paradigm, green dynamic capabilities (GDCs), a firm’s ability to sense environmental opportunities, seize green markets, and reconfigure processes, are important organizational capabilities associated with improving environmental outcomes. Although green technologies (GT) are widely promoted as a solution, adoption alone may be insufficient. This study examines the association between GT and EMP in SMEs by investigating the mediating role of GDCs and the moderating role of green innovation (GI), drawing on green resource orchestration (GRO) and dynamic capabilities view. METHODOLOGY: A quantitative, cross-sectional design using survey data from 266 Green SMEs in Addis Ababa was employed. Data from owner-managers were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). FINDINGS: The total GT–EMP association (estimated from a model without the mediator GDCs) was β = 0.280 (p < 0.001). GDCs show a statistically significant but substantively modest complementary role in the GT–EMP relationship, with an indirect effect through GDCs of β = 0.030 (95% CI [0.008, 0.062]). GI negatively moderates the GDCs–EMP relationship (β = -0.186, p = 0.002) in the main model. However, this effect is not robust to the inclusion of firm-level controls.The cross-sectional design precludes causal claims. IMPLICATIONS: The study contributes to the GRO and GDCs literature by providing findings consistent with a GRO interpretation, suggesting that EMP is associated with managerial coordination beyond mere resource possession. For practitioners and policymakers, the findings highlight the importance of balancing technology adoption and innovation with internal capability development. ORIGINALITY & VALUE: This research provides novel empirical evidence from an underexplored African emerging economy and demonstrates a negative moderating role for GI in the sustainability capability nexus.

Keywords: green technologies, adoption, environmental management performance, environmental performance, green dynamic capabilities, green innovation, green resource orchestration, SMEs, resource-constrained firms, emerging economies, Ethiopia, PLS-SEM, sustainability management

INTRODUCTION

Environmental Management Performance (EMP) has become a strategic imperative for businesses worldwide, including small and medium-sized enterprises (SMEs) in emerging economies (Van Tulder et al., 2021). This imperative reflects growing stakeholder expectations, tightening environmental regulations, and the recognition that long-term competitiveness depends on sustainable practices (Franzen & Bahr, 2024). Despite their vulnerability to environmental challenges due to resource constraints, SMEs possess the agility and potential to drive positive environmental change, as their structural flexibility, faster decision-making, and closer stakeholder relationships support adaptation to environmental demands. (Putri et al., 2025; Lee et al., 2024; Omar et al., 2024). In this context, green technologies (GT) are widely discussed in relation to EMP and competitiveness (Raihan et al., 2022). However, the mere adoption of GT is often insufficient for SMEs in resource-scarce environments, as resource orchestration theory suggests that value creation depends not on resource possession but on how resources are managed (Sirmon et al., 2011; Andersén, 2023).

The green economy paradigm, which seeks to integrate economic growth, social equity, and environmental preservation, provides the overarching framework for understanding these dynamics (Alsmadi & Alzoubi, 2022). This integration is critical for organizational growth, as SMEs must balance economic viability with inclusive social benefits and environmental stewardship, a challenge particularly acute in emerging economies, where resource constraints often create trade-offs between these dimensions, leaving the mechanisms for achieving integration underexplored (Ayaz & Tatoglu, 2024). Within this paradigm, the concept of green resource orchestration (GRO) has emerged as a critical theoretical lens (Andersén, 2023; Sirmon et al., 2011). Complementing this perspective, green dynamic capabilities (GDCs), defined as a firm’s ability to sense environmental opportunities, seize emerging green markets, and reconfigure internal processes, represent organizational capabilities that support SMEs’ responses (Qiu et al., 2020; Teece, 2007, 2018). Together, GRO and GDCs offer an integrated framework for understanding how SMEs in resource-constrained contexts may improve environmental outcomes through the coordination of green investments and capabilities.

This study is grounded in two complementary theoretical perspectives: GRO and GDCs. The relationship between GRO and GDCs is symbiotic and central to this study. GDCs represent important organizational capabilities that support adaptive change, the underlying organizational ability to sense, learn, and transform in response to environmental pressures. GRO, in turn, represents the strategic steering of the managerial routines and leadership actions that purposefully deploy and leverage these dynamic capabilities. In other words, GDCs are what a firm can do to adapt its green resources, while GRO is what managers do to orchestrate those capabilities. This integration is essential because, as Andersén (2023) notes, EMP depends not only on technology or capability possession but also on how these abilities are structured and executed systemically.

Despite this integrated view, significant gaps remain. First, although the direct association between GT and EMP is often assumed, the underlying mechanism, particularly the mediating role of GDCs, remains underexplored, especially in developing countries. Empirical research has rigorously examined the drivers of GT adoption (Sanz-Torro et al., 2025) and the role of GDCs as antecedents of sustainability performance (Kalyar et al., 2024), but the theoretical link between GT and the development of GDCs is strikingly absent. Second, the role of Green innovation (GI) as a contextual factor that is associated with changes in the effectiveness of GDCs remains unclear, with the existing literature often presuming a universally positive moderating effect (e.g., El-Kassar & Singh, 2019). This assumption may not hold in emerging economies such as Ethiopia, where resource constraints and evolving institutional frameworks create distinct dynamics (Ayaz & Tatoglu, 2024).

This oversight is critical because assumptions derived from studies in developed economies, where institutional support is robust, financial resources are more accessible, and managerial capabilities are more developed, may not hold in emerging economy contexts such as Ethiopia. In these settings, resource constraints are more severe, institutional frameworks for environmental management are still evolving, and SMEs face unique challenges in accessing green finance and technical expertise (OECD 2025; Klewitz & Hansen, 2014). Moreover, prolific innovation may create severe resource conflicts in resource constrained SMEs, potentially undermining the effective deployment of capabilities and jeopardizing returns on investment. Consequently, there is a limited understanding of how the interplay of GT, GDCs, and GI relates to EMP and the competitive positioning of SMEs in developing economies like Ethiopia (Puppim de Oliveira & Jabbour, 2017; Mitchell et al., 2020).

To address these gaps, this study investigates the relationship between GT and EMP among Green SMEs in Addis Ababa, examining the mediating role of GDCs and the moderating influence of GI. Specifically, it seeks to answer the following research questions:

RQ1: To what extent is the adoption of green technologies associated with Environmental Management Performance in SMEs, and is this relationship mediated by green dynamic capabilities?

RQ2: How is green innovation related to the effectiveness of green dynamic capabilities and Environmental Management Performance?

This study makes several unique contributions. Theoretically, it contributes to the GRO and GDCs literature by using GRO as an interpretive lens, consistent with the observed complementary mediating role of GDCs in the GT–EMP relationship, in an underexplored context: SMEs in a developing African economy. The study does not empirically test GRO as a separate construct but rather draws on GRO principles to interpret the observed relationships. It also provides a nuanced understanding of GI not only as an enabler but also as a potential source of strategic resource-allocation tensions.Thus, the findings are consistent with a GRO interpretation and suggest that technology adoption is more effective when accompanied by green dynamic capabilities, without empirically testing GRO as a measurable construct. It provides a nuanced understanding of GI not only as an enabler but also as a potential source of strategic resource-allocation tensions. Empirically, the findings from 266 Green SMEs in Addis Ababa indicate a statistically significant but substantively modest complementary mediation role of GDCs and reveal a counterintuitive moderating effect, enriching the literature on the complex relationships surrounding EMP and resource management in constrained settings. In practice, the findings provide managers and policymakers in similar contexts with evidence-based insights on how to strategically coordinate technology, capabilities, and innovation to support sustainability goals without overextending limited resources. Direct beneficiaries include SME managers and sustainability consultants, who can apply these insights to improve environmental management practices; indirect beneficiaries include policymakers who can design more effective support programs, local communities that benefit from improved environmental quality, and the broader society.

The remainder of this paper is structured as follows. Section 2 presents the theoretical framework and develops the hypotheses. Section 3 outlines the research methodology. Section 4 presents the empirical results. Section 5 presents the findings, along with their theoretical and managerial implications, followed by limitations, future research directions and conclusions in the final section.

THEORY AND HYPOTHESIS DEVELOPMENT

Green resource orchestration (GRO) and green dynamic capabilities (GDCs)

Resource Orchestration Theory was developed to address the static perspective of the Resource-Based View (Barney, 1991) and to provide a dynamic framework for how leaders allocate and reconfigure resources in response to changing demands (Helfat & Martin, 2015). In the context of environmental sustainability, GRO represents these managerial routines specifically applied to structure, bundle, and leverage environmental resources, such as green technologies, green innovation, and recyclable materials, to address environmental issues and capitalize on green opportunities (Zahoor & Gerged, 2021; Chadwick et al., 2015; Asiaei et al., 2022).

However, the effective execution of GRO requires a specific type of ability for change and adaptation. This is where the concept of GDCs becomes critical. GDCs refer to a firm’s high level capabilities to build, integrate, and reconfigure its internal and external resources in response to rapidly changing environmental requirements, thereby achieving sustainable development (Qiu et al., 2020; Lin & Chen, 2017). They extend the generic concept of dynamic capabilities (Teece et al., 1997) into the environmental domain. As part of dynamic capabilities, GDCs are often conceptualized through microfoundations such as resource integration capability (combining internal and external green resources), resource reconfiguration capability (restructuring existing resources to adapt), and environmental insight capability (sensing and interpreting external environmental trends and regulations) (Dangelico et al., 2016; Teece, 2007, 2018).

These microfoundations are operationalized through three GDCs dimensions: sensing (identifying environmental trends), seizing (mobilizing resources to act), and reconfiguring (adapting processes) (Teece, 2007; Qiu et al., 2020). GRO is operationalized through three managerial actions: structuring (acquiring resources), bundling (combining resources), and leveraging (deploying capabilities) (Sirmon et al., 2011; Andersén, 2023). These dimensional frameworks guide the operationalization of constructs in the measurement model. Therefore, in this study, GRO serves as the interpretive theoretical lens guiding the explanation of the observed relationships, while GDCs are the empirically measured capabilities.

Green Technologies (GT) and Environmental Management Performance (EMP)

GT are technologies that support and reduce environmental impact by reducing emissions and waste, and by improving resource use (OECD, 2011). GT are positively associated with greater resource efficiency through process optimization, waste to resource circularity, and real time monitoring. The convergence of GT with Industry 4.0 IoT sensors, AI analytics, and smart systems creates opportunities for SMEs to optimize energy and material use, capabilities previously accessible only to large firms (Kalyar et al., 2024). For example, Chen and Lee (2020) and Du et al. (2019) demonstrated that GT uptake is associated with lower carbon dioxide emissions, mainly from high energy consuming sectors, due to limited resources, technological, and strategic planning.

In the context of SMEs, EMP is often described as the efficient use of resources (water, energy, materials) and the reduction of pollutants (emissions, waste), related to the size of the business (Hillary, 2004). In emerging countries, SMEs tend to gradually integrate GT into their systems, with environmental values of owner-managers influencing this more than cost reduction (Durrani et al., 2024; Tumpa et al., 2019). External factors, including government regulations, supply chain requirements, and growing consumer demand, significantly influence the adoption of GT (Cirera et al., 2023). Moreover, SMEs in developing countries face unique challenges in implementing GT, such as limited access to green finance (OECD 2025) and a lack of technological know-how and skills (Klewitz & Hansen, 2014). Despite these internal and external challenges, the literature indicates that SMEs that employ GT can still enhance competitive advantage through improved regulatory compliance, stronger brand positioning, and greater cost containment (Hojnik & Ruzzier, 2016).

Crucially, the effectiveness of GT varies according to the managerial profile of owner-managers, as their environmental values and strategic orientation shape how GT is integrated and utilized (Omar et al., 2024; Lee et al., 2024). In SMEs, managers with strong environmental commitment are positively associated with continued GT implementation and environmental initiatives (Durrani et al., 2024). Conversely, weak managerial commitment may reduce GT effectiveness.Thus, the first hypothesis was proposed. It captures the total GT–EMP association (without mediator).

H1: The utilization of GT is positively associated with EMP in SMEs.

GT, GDCs, and EMP

Despite growing scholarly attention to both GT and GDCs, empirical research directly examining the relationship between GT adoption and GDCs development remains limited, particularly in the context of SMEs in emerging economies. This gap is significant, as understanding the relationship between GT and GDCs development is important for understanding variations in EMP. Although the literature has extensively studied the drivers of GT adoption (Sanz-Torro et al., 2025) and the role of GDCs as antecedents of sustainability performance (Kalyar et al., 2024), the direct link between GT and the development of GDCs remains underexplored, a surprising omission given that the dynamic capability view suggests technologies are often associated with the development of new organizational capabilities (Teece, 2014).

This study argues that GT adoption may be associated with GDC development. Potential mechanisms include learning by doing, where implementation supports the development of new monitoring and adaptation skills, path dependency, where prior investments incentivize further capability building; and stakeholder feedback, where new partnerships demand enhanced seizing capabilities. However, these mechanisms remain speculative and require empirical testing (Adomako et al., 2021). Empirical studies are consistent with a positive relationship between GDCs and EMP. For instance, Khairy et al. (2023) observed a positive association between GDCs and environmental management in Egypt, while Qiu et al. (2020) found that GDCs were positively associated with green product innovation, competitive edge, and EMP among Chinese manufacturing firms.

On the other hand, Singh et al. (2022) reported that, in emerging-market SMEs, the effectiveness of organizational capabilities varies across different contextual conditions, including stakeholder motivation and institutional support, suggesting that results may differ across contexts. Similar findings emerge from other emerging economies. In India, Sharma and Sharma (2026) found that sustainable innovation capability mediates the link between collaborative networks and MSME performance under regulatory pressure. In Brazil, Frare and Beuren (2022) showed that green process innovation mediates the relationship between green entrepreneurial orientation and EMP in small firms. In South Africa, Bag et al. (2022) demonstrated that ecoinnovation drives green supply chain management and circular economy capability, enhancing SMEs performance. Together, these studies confirm that GDCs effectiveness is contingent on local institutional and resource conditions.

In the Ethiopian context, family values are associated with SMEs’ environmental practices. Owner-managers’ commitment to community wellbeing and long term stewardship can motivate green action, yet family obligations and risk aversion may constrain investment in innovative technologies (Putri et al., 2025; Durrani et al., 2024). This duality suggests that interventions must consider both the enabling potential of family values and the constraints of family risk perceptions. SMEs represent a distinct innovation space characterized by structural agility, informal communication, and close stakeholder relationships, enabling rapid experimentation with green technologies (Klewitz & Hansen, 2014). However, these same features create vulnerabilities: limited managerial bandwidth and absence of formalized processes for capability development. To transform their environmental context, SMEs must develop systematic learning mechanisms, a challenge requiring deliberate managerial attention despite resource constraints (Sanz-Torro et al., 2025). This oversight is notable, given that technological adoption alone is often insufficient to yield environmental benefits without the supporting capabilities needed to integrate, adapt, and leverage such technologies internally (Sirmon et al., 2011). Accordingly, the study argues that GDCs may play a complementary role in the relationship between GT and EMP. Thus, the second hypothesis was proposed. H2c captures the indirect effect through GDCs.

H2a: GT is positively associated with GDCs.

H2b: GDCs are positively associated with EMP.

H2c: GDCs mediate the relationship between GT and EMPGI, GDCs, GT, and EMP.

GI is defined as „hardware or software innovation that is related to green products or processes, including innovation in technologies involved in energy saving, pollution prevention, waste recycling, green product designs, or corporate environmental management” (Chen et al., 2006). An increasing number of studies emphasize GI’s role as a moderator, shaping the effectiveness of strategic and technological inputs on EMP. Prior studies confirm GI’s moderating role in various contexts: environmental strategy and firm performance (Tariq et al., 2019), green transformational leadership (Singh et al., 2022), the growth-emissions relationship (Cai et al., 2025), and green capabilities (Akhtar et al., 2024). From a GRO perspective, GI can be theorized as a contextual factor related to how firms coordinate structured GT and bundled GDCs with EMP, as effective resource orchestration supports the alignment of innovation efforts with existing capabilities to generate synergistic outcomes (Andersén, 2023; Kalyar et al., 2024).

This logic, drawn from studies often conducted in larger or more resource rich firms, leads to an initial expectation of a positive moderating effect. A positive moderating effect would indicate that GI is positively associated with the relationship between GDCs and EMP, a synergy documented in studies showing that organizational resources and institutional support facilitate this complementarity (Ahmad et al., 2024; Kalyar et al., 2024). This study acknowledges alternative perspectives. Institutional theory suggests external pressures may be associated with EMP independently of internal capabilities (Delmas & Toffel, 2008), while contingency theory posits that capability effectiveness depends on contextual factors (Donaldson, 2001). The resource orchestration perspective complements these views by examining how firms are associated with environmental outcomes. However, in resource constrained SMEs, this study proposes a negative moderating effect. Drawing on explorationexploitation tradeoff theory (March, 1991), GI represents exploration (new innovations) and GDCs represent exploitation (existing capabilities). Ambitious GI initiatives may divert attention and resources from sensing, seizing, and reconfiguring, straining the very capabilities they aim to leverage. Thus, beyond a threshold, GI may create capability-straining tensions rather than synergies (Coad et al., 2022). Accordingly, the third hypothesis was proposed:

H3: Green innovation negatively moderates the relationship between green dynamic capabilities and environmental performance.

The Conceptual framework illustrating the hypothesized relationships among constructs (GT, GDCs, GI, and EMP) is presented in Figure 1.

Figure 1. Conceptual framework

METHODOLOGY

Research design and context

This study employs a quantitative, cross-sectional survey design to investigate the association between GT and EMP, examining the mediating role of GDCs and the moderating role of GI. This design is well suited for understanding relationships among variables and making generalizable inferences within a given population (Creswell & Creswell, 2018). The study employs a deductive reasoning approach, testing hypotheses derived from the GRO perspective and the GDCs framework. Structural equation modeling (SEM), specifically Partial Least Squares SEM (PLS-SEM), was employed. PLS-SEM was selected for its suitability for smaller sample sizes, its ability to handle complex models with mediating and moderating effects, and its focus on prediction and theory development (Hair et al., 2022).

The study was conducted in Ethiopia, a low income country in East Africa, where SMEs constitute approximately 90% of all businesses, account for more than 50% of employment, and formal SMEs contribute up to 40% of GDP (Policy Studies Institute, 2024). Ethiopia’s environmental context is characterized by significant climate vulnerabilities, including recurrent droughts and land degradation, which increase pressure on firms to adopt environmentally sustainable practices (Gebreegziabher et al., 2016; Deressa et al., 2009). At the same time, the country has adopted the Climate Resilient Green Economy (CRGE) strategy to promote environmentally sustainable economic development (Federal Democratic Republic of Ethiopia, 2011). Addis Ababa was chosen as the study site because it is Ethiopia’s primary economic and industrial hub, hosting the country’s highest concentration of SMEs (Policy Studies Institute, 2024). The city is also a focal point for environmental sustainability initiatives, as reflected in the Addis Ababa Climate Action Plan (2021–2025), which commits the city to climate resilience and carbon neutrality by 2050. This vision is supported by the Ethiopian Green Building Council (ETGBC), a membership-based non-profit that promotes sustainable construction through advocacy, professional training, green building certification, and expert consultancy.

The definition of SMEs in Ethiopia is based on sector, employment, and capital investment. A microenterprise has 1-5 employees and less than 100,000 ETB capital; a small enterprise has 6-30 employees and capital of 100,001 to 1.5 million ETB; and a medium enterprise has 31-100 employees and capital of up to 2 million ETB (Abate & Sheferaw, 2023).

Sampling and data collection

The target population for this study comprises Green SMEs in Addis Ababa defined as SMEs that demonstrated at least one verifiable environmental practice identified through administrative environmental records and environmental program participation databases. The units of analysis were individual SMEs, with data collected from owner-managers, who possess comprehensive knowledge of strategic decisions and environmental practices (Ismail et al., 2023). Inclusion criteria required SMEs to be: (1) officially registered; (2) operational for at least one year; (3) have documented environmental initiatives (i.e., at least one green business practice); and (4) have an owner-manager willing to participate. Exclusion criteria included: (1) microenterprises with fewer than five employees; (2) SMEs with no documented environmental initiatives; and (3) incomplete responses exceeding 20%.

According to FeMSEDA administrative statistics reported by Policy Studies Institute (2024), there were 10,061 registered small enterprises and 8,593 micro enterprises in Addis Ababa. Addis Ababa has also been identified as a major center of micro and small enterprise (MSE) development under Ethiopia’s national Micro and Small Enterprise Development Policy and Strategy (MoUDH, 2016). While these data are over a decade old, no comprehensive, publicly accessible, and centralized registry of SMEs in Addis Ababa has been identified. This limitation is acknowledged and reflects the practical challenges of conducting survey research on SME populations in this context.

The study focuses on all registered SMEs, not solely manufacturing SMEs. In the absence of a centralized database, the sampling frame was constructed in three stages, following established survey sampling procedures for building sampling frames from multiple administrative data sources (Levy & Lemeshow, 2013; Groves et al., 2011). First, the researchers obtained SMEs registration records from the Addis Ababa City Administration Micro and Small Enterprise Development Agency, which served as the initial sampling source for identifying registered SMEs in Addis Ababa. Second, this database was integrated with administrative environmental records obtained from the Ethiopian Environmental Protection Authority (EEPA) and participation databases from the Ethiopian Climate Innovation Center (ECIC). The integration process used common organizational identifiers, including firm name, business license number, sector classification, sub-city location, and owner-manager contact information, to verify consistency across datasets and eliminate duplicate entries. Third, firms were retained in the final sampling frame only when the integrated records demonstrated at least one verifiable environmental practice, including energy-efficient technology adoption, waste reduction activities, recycling practices, environmental compliance certification, or participation in recognized environmental support programs. After screening and data-cleaning procedures, 542 eligible Green SMEs were identified. The sampling frame was purposively constructed by applying eligibility criteria to administrative records. From this eligible frame, 300 SMEs were selected using simple random sampling, using a computer-generated randomization procedure that ensured each eligible SME had an equal probability of selection (Levy & Lemeshow, 2013). This sample represented approximately 55.4% of the eligible Green SME population, providing substantial coverage of the final sampling frame while remaining feasible for survey administration and follow-up procedures (Groves et al., 2011). The selected sample size also exceeded recommended thresholds for PLS-SEM analysis involving mediation and moderation models (Hair et al., 2022; Kline, 2023).

Data collection followed a systematic procedure: (1) identification of the sampling frame; (2) purposive sampling to meet inclusion criteria; (3) random selection of 300 SMEs; (4) distribution of an online Google Forms survey with a cover letter assuring confidentiality and stating no right or wrong answers; (5) two follow-up reminders at two-week intervals. A pilot study was conducted with 25 owner-managers to pretest the survey. Pilot participants provided feedback on question clarity and terminology, leading to minor wording adjustments. The survey was translated into Amharic and back-translated to ensure conceptual equivalence. Discrepancies were resolved through consensus between the translators. The complete survey instrument in both English and Amharic is provided in Appendix A. To mitigate common method bias, procedural remedies included respondent anonymity, clear language, and the separation of predictor and criterion variables (Podsakoff et al., 2003). The data collection period spanned three months (May–July 2025). From the 300 SMEs approached, 270 responded; after data cleaning, 266 valid responses were retained, adequate for PLS-SEM analysis (Hair et al., 2022; Kline, 2023).

Measurement

All constructs (GT, GDCs, GI, EMP) were measured using reflective multi-item scales adapted from validated prior literature to ensure construct validity. All items were measured on a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The scales were modified to fit the context of SMEs in Ethiopia. GT was assessed using six items derived from Afum et al. (2023) and Lee et al. (2014). GDCs were measured with six items modified from Ma et al. (2025). GI was assessed using six items borrowed from Ma et al. (2025).

Although GDCs, GT, and GI are related, they are conceptually distinct. GT refers to the adoption of specific environmental technologies and practices (e.g., cleaner production, energy monitoring) (OECD, 2011). GI captures the generation of new or improved green products, processes, or methods (Chen, Lai, & Wen, 2006). In contrast, GDCs represent higherorder organizational abilities to sense environmental opportunities, seize them, and reconfigure resources – they are not the technologies themselves nor the innovation outputs (Teece, 2007; Sirmon et al., 2011; Qiu et al., 2020). Empirically, discriminant validity is confirmed by the Fornell-Larcker criterion and HTMT ratios (Tables 3 and 4), where the square roots of AVEs for GDCs, GT, and GI exceed their interconstruct correlations and all HTMT values are below 0.85. Thus, while the constructs correlate, they measure distinct phenomena. The mapping of the six GDC items to the sensing, seizing, and reconfiguring dimensions is provided in Appendix A (see Table A1).

EMP was evaluated using six items adopted from Eikelenboom and de Jong (2019) and MartinezConesa et al. (2017). The items refer to environmental practices and investments (e.g., energysaving mechanisms, environmental audits, recyclable packaging, water programs). They collectively represent the firm’s EMP, a combination of implemented practices and perceived performance outcomes. The complete survey instrument, including all measurement items, is provided in Appendix A.

Data analysis

Data analysis was performed using SmartPLS 4.0 and Partial Least Squares Structural Equation Modeling (PLS-SEM). PLS-SEM was selected for its suitability for complex models with mediating and moderating effects, its ability to handle smaller sample sizes, and because the study uses reflective constructs and is focused on prediction and theory development rather than model fit confirmation (Hair et al., 2019, 2022).

The analysis followed a comprehensive two-step approach. First, the measurement model was assessed for reliability and validity by examining Composite Reliability (Composite Reliability ≥ 0.7) and Average Variance Extracted ≥ 0.5) (Fornell & Larcker, 1981). Second, the structural model was evaluated. The significance of the path coefficients (for direct, mediating, and moderating effects) was tested using bootstrapping with 5,000 subsamples (Preacher & Hayes, 2008). The model’s explanatory power was assessed using R², and effect size was evaluated with Cohen’s f². Variance Inflation Factor values were examined to ensure the absence of multicollinearity (all Variance Inflation Factors < 5). Harman’s single-factor test was conducted as a statistical check for common method bias.

RESULTS

Descriptive statistics

Table 1 presents descriptive statistics for the study’s central constructs. All variables were measured on a five-point Likert scale. The mean scores range from 3.27 to 3.69, indicating generally positive perceptions across constructs. The skewness and kurtosis values for all variables fall within acceptable limits (±2), supporting the assumption of approximately normal data distribution.

Table 1. Descriptive statistics of key constructs

Construct	mean	std	min	max	Skewness	Kurtosis
GT	3.692	0.961	1.0	5.0	-0.622	-0.658
GDCs	3.542	0.985	1.2	5.0	-0.255	-1.286
GI	3.274	0.939	1.0	5.0	+0.107	-0.966
EMP	3.534	0.993	1.0	5.0	-0.45	-0.749

Note: All variables measured on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Skewness and kurtosis values within ±2 support approximate normality.

Measurement model assessment

Following the two-step approach outlined in Section 3.4, the measurement model was first assessed for reliability, convergent validity, and discriminant validity. Table 2 presents the indicator loadings, Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) for each construct. All indicator loadings exceed 0.7 and are significant at p < 0.001. Cronbach’s alpha values range from 0.902 to 0.921, exceeding the recommended threshold of 0.7. Composite reliability values range from 0.904 to 0.909, also above 0.7. AVE values range from 0.611 to 0.624, exceeding the 0.5 threshold. These results confirm internal consistency and convergent validity.

Table 2. Indicator loadings, reliability, and convergent validity

Construct	Item	Loading	Cronbach’s α	CR	AVE
GT	GT1	0.812	0.921	0.909	0.624
	GT2	0.785
	GT3	0.801
	GT4	0.778
	GT5	0.793
	GT6	0.769
GDCs	GDC1	0.801	0.908	0.907	0.615
	GDC2	0.784
	GDC3	0.792
	GDC4	0.776
	GDC5	0.788
	GDC6	0.765
GI	GI1	0.795	0.902	0.905	0.611
	GI2	0.781
	GI3	0.788
	GI4	0.772
	GI5	0.784
	GI6	0.769
EMP	EMP1	0.802	0.907	0.904	0.617
	EMP2	0.789
	EMP3	0.794
	EMP4	0.775
	EMP5	0.783
	EMP6	0.768

Note: All loadings are significant at p < 0.001 based on bootstrapping with 5,000 subsamples. CR = Composite Reliability; AVE = Average Variance Extracted.

Discriminant validity was assessed using the Fornell-Larcker criterion and the HTMT ratio. Table 3 presents the Fornell-Larcker criterion, where the square root of AVE for each construct (diagonal values) exceeds the interconstruct correlations (offdiagonal values), confirming discriminant validity. Table 4 presents the HTMT ratios; all values are below the conservative threshold of 0.85, providing additional evidence that the constructs are empirically distinct.

Table 3. Fornell-Larcker criterion

Construct	GT	GDCs	GI	EMP
GT	0.790
GDCs	0.421	0.784
GI	0.398	0.412	0.782
EMP	0.459	0.438	0.406	0.785

Note: Diagonal values are square roots of AVE; off-diagonal values are inter-construct correlations. Discriminant validity is established when diagonal values exceed off-diagonal correlations (Fornell & Larcker, 1981).

Table 4. HTMT ratios for discriminant validity

Construct	GT	GDCs	GI	EMP
GT	–
GDCs	0.682	–
GI	0.654	0.671	–
EMP	0.703	0.694	0.665	–

Note: All HTMT values are below the conservative threshold of 0.85, confirming discriminant validity (Henseler et al., 2015).

Structural model evaluation

Following the two-step approach, the structural model was then evaluated to test the hypothesized relationships. Figure 2 presents the PLS-SEM analytical model with standardized path coefficients. Solid arrows represent direct paths (H2a, H2b) and total effect (H1/H2c). The indirect/mediated effect (H2c) is decomposed in Table 6. The dotted arrow represents the moderation path (H3). ***p < 0.001, **p < 0.01, p < 0.05.

Figure 2. PLS-SEM analytical model depicting structural paths and interaction effects

Table 5 summarizes the structural paths tested in the model. The results address RQ1 (the relationship between GT and EMP and the mediating role of GDCs) and RQ2 (the moderating role of GI). The total GT–EMP association (estimated from a model without the mediator GDCs) was β = 0.280 (t = 4.512, p < 0.001, 95% CI [0.162, 0.398]), supporting H1. After including GDCs as a mediator, the direct effect of GT on EMP was reduced to β = 0.250 (t = 4.021, p < 0.001, 95% CI [0.128, 0.372]), while the paths from GT to GDCs (β = 0.181, t = 2.891, p = 0.004, 95% CI [0.058, 0.304]) and from GDCs to EMP (β = 0.165, t = 2.748, p = 0.006, 95% CI [0.047, 0.283]) were both significant. As shown in Table 6, the indirect effect through GDCs is statistically significant (β = 0.030), supporting H2c. However, the mediation magnitude is substantively modest (VAF = 10.7%), indicating that most of the GT– EMP relationship remains direct or is explained by factors outside the present model. These associations are consistent with a statistically significant but substantively modest indirect effect, but the cross-sectional design limits causal interpretation. The main effect of GI on EMP was not significant (β = -0.042, p = 0.483), while the interaction term (GDCs × GI) shows a significant negative moderation effect on EMP (β = -0.186, t = 3.091, p = 0.002, 95% CI [-0.304, -0.068]), supporting H3.

Table 5: Structural path coefficients and hypothesis testing

Hypothesis	Path	β	t-value	p-value	95% CI	Supported
H1 (direct effect with mediator)	GT →EMP	0.250	4.021	<0.001	[0.128, 0.372]	Yes
H2a	GT → GDCs	0.181	2.891	0.004	[0.058, 0.304]	Yes
H2b	GDCs →EMP	0.165	2.748	0.006	[0.047, 0.283]	Yes
GI →EMP	GI →EMP	-0.042	0.702	0.483	[-0.160, 0.076]	No
H3	GDCs × GI→ EMP	-0.186	3.091	0.002	[-0.304, -0.068]	Yes

Note: β = standardized coefficient from the mediated model (including GDCs as mediator). t-values in parentheses. Bootstrap with 5,000 subsamples. Bias-corrected and accelerated (BCa) confidence intervals reported. ***p < 0.001, **p < 0.01, *p < 0.05 (two-tailed). The total effect (GT →EMP without mediator) is reported in Table 6.

Table 6. Mediation analysis results - total, direct, and indirect effects of GT on EMP

Effect Type	β	t-value	p-value	95% CI	VAF
H1 (total effect without mediator) GT → EMP, without mediator	0.280	4.512	<0.001	[0.162, 0.398]	—
Direct effect (GT →EMP, with mediator)	0.250	4.021	<0.001	[0.128, 0.372]	—
Indirect effect (GT → GDCs →EMP)	0.030	2.641	0.008	[0.008, 0.062]	10.7%

Note: Bootstrapping with 5,000 subsamples. Bias-corrected and accelerated (BCa) confidence intervals reported. VAF = Variance Accounted For (indirect effect / total effect). The VAF value (10.7%) indicates a substantively modest indirect effect. The total effect is estimated from a model without the mediator GDCs; the direct effect is from the mediated model.

Mediation analysis

To formally test the mediating role of GDCs, a bootstrapping analysis with 5,000 subsamples was conducted. The indirect effect of GT on EMP via GDCs is significant (β = 0.030, 95% CI [0.008, 0.062], p = 0.008). The variance accounted for (VAF) is 10.7%, indicating that GDCs account for a small but statistically significant share of the GT–EMP association.

Moderation Analysis

To further interpret the negative moderating effect of GI, a simple slopes analysis was conducted using the two-stage approach for latent interaction estimation in SmartPLS 4.0. All indicators were standardized prior to creating the product term. The interaction term (GDCs × GI) was created by multiplying the standardized scores of GDCs and GI for each observation. The model included all lower-order terms (GDCs and GI) as predictors of EMP. The main effect of GI on EMP was not significant (β = -0.042, p = 0.483). Figure 3 illustrates that at low levels of GI (-1 SD), the relationship between GDCs and EMP is positive and significant (β = 0.312, p < 0.001). At high levels of GI (+1 SD), this relationship becomes non-significant (β = 0.018, p = 0.862). One possible explanation is that intensive innovation efforts may create resource-allocation tensions that weaken GDCs effectiveness in resource-constrained SMEs. However, alternative explanations exist, such as reverse causality (firms with weaker GDCs may pursue more GI to compensate) or unmeasured confounding variables. The cross-sectional design precludes causal inference.

Figure 3. Simple slopes plot for the moderating effect of GI on the GDCs – EMP relationship

As shown in Table 7, the conditional indirect effect of GT on EMP through GDCs is strongest and significant at low GI (β = 0.056), remains significant but weaker at mean GI (β = 0.030), and becomes non-significant at high GI (β = 0.003). These findings further support the negative moderating role of GI in the GDCs–EMP relationship.

Table 7. Conditional indirect effects of GT on EMP through GDCs at different levels of GI

GI Level	Conditional Indirect Effect	95% CI	Interpretation
Low GI (Mean − 1 SD)	0.056	[0.021, 0.102]	Significant
Mean GI	0.030	[0.008, 0.062]	Significant
High GI (Mean + 1 SD)	0.003	[-0.015, 0.028]	Not Significant

Note: Conditional indirect effects were estimated using bootstrapping with 5,000 subsamples. Bias-corrected and accelerated (BCa) confidence intervals reported. Low GI = Mean − 1 SD; Mean GI = sample mean; High GI = Mean + 1 SD

Model fit and predictive power

The explanatory power of the structural model was evaluated using R², Adjusted R², and Cohen’s f². For EMP, R² = 0.138 and Adjusted R² = 0.125. For GDCs, R² = 0.033, indicating that GT explains 3.3% of the variance in green dynamic capabilities. Cohen’s f² effect sizes were calculated for each predictor: GT →EMP (f² = 0.082), GDCs →EMP (f² = 0.028), GI →EMP (f² = 0.002), and GDCs×GI →EMP (f² = 0.036). These values represent small to medium effects, with the interaction term showing a small but meaningful moderating effect (Cohen, 1988).

While the R² value for EMP may be considered modest, it is acceptable in behavioral and management research, particularly when studying complex phenomena like EMP, where a substantial portion of variance may be attributable to external factors not included in the model (Hair et al., 2019). The primary objective of this study was not to maximize R² but to test the specific hypothesized mechanisms, the mediating role of GDCs and the moderating role of GI within the GRO framework. The significant paths for both mediation and moderation confirm that the model provides valuable insights into how these variables interact, which is a key theoretical contribution.

Control variables impact

To account for contextual variance, GT Maturity and Firm Age categories were added as control variables. Firm size was examined as a theoretically relevant control variable because organizational size may influence access to resources, managerial capacity, and environmental management practices in SMEs.This indicates that organizational experience with GT enhances EMP, but firm longevity itself does not.

Table 8. Robustness test of the structural model with control variables

Predictor → EMP	β	t-value	p-value	95% CI	Result
GT → EMP	0.231	3.168	0.002	[0.087, 0.374]	Stable
GDCs → EMP	-0.023	-0.310	0.757	[-0.172, 0.125]	Not Stable
GI → EMP	0.078	1.052	0.294	[-0.069, 0.225]	Stable
GDCs × GI → EMP	-0.037	-0.518	0.605	[-0.176, 0.103]	Not Stable
Age 1-5 → EMP	-0.115	-0.801	0.424	[-0.397, 0.168]	Not significant
Age 6 -10 → EMP	-0.125	-1.636	0.103	[-0.275, 0.026]	Not significant
Size Medium → EMP	0.039	0.518	0.605	[-0.111, 0.190]	Not significant
Sector_Services → EMP	-0.177	-2.529	0.012	[-0.316, -0.039]	Significant
Sector Agriculture → EMP	0.012	0.172	0.863	[-0.127, 0.151]	Not significant

To further assess the robustness of the structural model, firm-level control variables were included as direct predictors of EMP. Following the study’s exclusion criteria, microenterprises were excluded from the analysis; therefore, small firms served as the reference category for firm size. Firm age was modeled using two dummy variables (Age 1-5 and Age 6-10), with firms older than 10 years as the reference category, while manufacturing served as the reference category for sector controls. The inclusion of controls materially alters the hypothesized relationships for GDCs→EMP and GDCs×GI→EMP. The GT→EMP association remains robust (β = 0.231, p = 0.002). However, the GDCs→EMP and GDCs×GI→EMP paths become non-significant after adding controls, indicating that H2b and H3 are sensitive to firm-level covariates. Among the controls, only the services sector showed a significant negative association with EMP (β = -0.177, p = 0.012).

Common method bias and multicollinearity

Harman’s Single-Factor Test

To assess the potential for common method bias, Harman’s single-factor test was conducted. The unrotated principal component analysis revealed that the first factor accounted for 25.17% of the total variance, well below the commonly accepted threshold of 50%. However, Harman’s test alone is widely regarded as a weak diagnostic and is not sufficient to fully dismiss common method bias in a same source cross-sectional design (Podsakoff et al., 2003).Therefore, common method bias cannot be ruled out and remains a limitation of this study.

Variance Inflation Factor

Multicollinearity was assessed using Variance Inflation Factors (VIF). All VIF values were below the conservative threshold of 5 (GT = 1.1, GDCs = 1.13, GI = 1.1, GDCs × GI = 1.0, GT Maturity = 1.16), indicating that multicollinearity is not a threat to the estimation or interpretation of the model.

DISCUSSION

This study set out to examine how GT is associated with EMP in Green SMEs in Addis Ababa, with particular attention to the mediating role of GDCs and the moderating role of GI. Drawing on GRO as an interpretive lens and the dynamic capabilities view, the research addressed two primary questions: (RQ1) to what extent GT is associated with EMP and whether this relationship is mediated by GDCs; and (RQ2) how GI relates to the effectiveness of GDCs and EMP. The study does not claim to empirically test GRO as a separate construct; rather, GRO informs the interpretation of how GDCs mediate the GT–EP relationship. The findings offer both confirmatory and counterintuitive insights. The total effect of GT on EMP (from a model without mediator) was β = 0.280 (p < 0.001), supporting H1 and aligning with prior research (Chen & Lee, 2020; Du et al., 2019). After including GDCs as a mediator, the direct association of GT on EMP was β = 0.250 (p < 0.001). GDCs are associated with a statistically significant but substantively modest complementary mediation pattern in the GT–EMP relationship, with an indirect effect through GDCs of β = 0.030 (95% CI [0.008, 0.062]). However, the mediation magnitude remains modest (VAF = 10.7%), indicating that most of the GT–EMP relationship remains direct or may be explained by factors outside the present model.

From a conceptual perspective, the significant positive association between GT and EMP (β = 0.280) suggests that sensing, the ability to identify environmental opportunities, may be related to GT adoption, providing evidence consistent with the first step of the GDCs framework. This finding of a statistically significant but substantively modest indirect effect is consistent with a GRO interpretation, which suggests that resource configurations depend not merely on resource possession but also on how resources are mobilized (Sirmon et al., 2011). Turning to the seizing dimension, the significant path from GT to GDCs (β = 0.181) suggests that technology investments are associated with resource mobilization, the core of seizing. The indirect effect (0.030) indicates a statistically significant but substantively modest indirect effect through GDCs, supported by bootstrapped confidence intervals. This work extends prior research in three ways. First, it positions GDCs as a complementary mediator in the GT–EMP relationship, unlike prior studies that treated GDCs only as antecedents (Kalyar et al., 2024; Qiu et al., 2020). Second, the statistically significant but substantively modest indirect effect contrasts with full mediation findings in resource rich contexts (El-Kassar & Singh, 2019). Third, it supports the GRO framework by suggesting that orchestrating green resources through GDCs may contribute to the environmental outcomes associated with GT investments (Sirmon et al., 2011). Regarding the reconfiguring dimension, the persistent direct association of GT on EMP (β = 0.250) after including GDCs implies that reconfiguration may not be fully developed in these early stage adopters, contrasting with the theoretical assumption that reconfiguration is always necessary (Teece, 2007). Empirically, this statistically significant but modest indirect effect suggests that Green SMEs in Addis Ababa are still building full reconfiguration capabilities, with implications for how resource constrained firms prioritize capability development.

The counterintuitive finding that GI negatively moderates the GDCs–EMP relationship (β = -0.186, p = 0.002) challenges the prevailing assumption that GI universally amplifies green capabilities (El-Kassar & Singh, 2019). Simple slopes analysis reveals that at low GI levels, the GDCs–EMP relationship is positive and significant (β = 0.312, p < 0.001); at high GI levels, it becomes non-significant (β = 0.018, p = 0.862). One possible interpretation is that intensive innovation efforts may create resource allocation tensions that weaken GDCs effectiveness. However, alternative explanations exist, such as reverse causality (firms with weaker GDCs may pursue more GI to compensate) or unmeasured confounding variables. The cross-sectional design precludes causal inference. Alternative explanations should be considered. The self-reported nature of GI data may capture perceived rather than actual innovation intensity (Podsakoff et al., 2003). Reverse causality is also possible: firms with weaker GDCs may pursue more GI initiatives to compensate for capability deficiencies (Durrani et al., 2024). However, the theoretical grounding in exploration-exploitation trade-off theory (March, 1991) and the robustness of the simple slopes analysis support the interpretation that resource allocation conflicts explain the observed effect. From a theoretical standpoint, this negative moderation extends the GRO framework by showing that the interplay between green resources is contingent on organizational context and is not always synergistic. Conceptually, it implies a threshold effect beyond which GI becomes counterproductive. Epistemologically, it challenges the universal positive moderation assumption that is dominant in the literature (e.g., El-Kassar & Singh, 2019) and highlights the need for context-specific theorizing in emerging economies. Empirically, the result is robust to alternative explanations and is illustrated in the simple-slopes plot (Figure 3).

This study applies GRO and GDC frameworks to an underexplored context: SMEs in a developing African economy. Unlike prior research on large firms in developed or advanced emerging economies (Andersén, 2023; Qiu et al., 2020; Khairy et al., 2023), the Ethiopian context indicates that capability performance patterns are contingent upon resource scarcity, institutional frameworks, and family values. (OECD, 2025; Klewitz & Hansen, 2014; Putri et al., 2025; Lee et al., 2024). This responds to calls for theorizing environmental capability development in such settings (Mitchell et al., 2020; Puppim de Oliveira & Jabbour, 2017). The findings confirm that GDCs partially mediate the GT–EMP relationship and that GI negatively moderates GDCs effectiveness, answering both research questions. This suggests that in resourceconstrained Green SMEs in Addis Ababa, technology adoption must be balanced with capability development, and innovation should not be pursued to the extent that it strains the dynamic capabilities needed for EMP.

Theoretical implications

The findings offer three nuanced contributions to entrepreneurship, management, and innovation literature. First, this study contributes to the GRO and GDCs literature by providing findings consistent with a GRO interpretation of the complementary mediating role of GDCs in the GT–EMP relationship among Green SMEs in Addis Ababa. While prior research has established that resource orchestration capabilities are associated with performance outcomes (Sirmon et al., 2011; Andersen, 2023), the present findings suggest that GT is associated with EMP through the complementary role of GDCs in underexplored SMEs contexts. By indicating a statistically significant but substantively modest indirect effect, this study suggests that GT may contribute to EMP both directly and indirectly through capability development, providing a more nuanced understanding of how technology investments are associated with environmental outcomes in SMEs.

Second, this research challenges the prevailing assumption that GI universally enhances the effectiveness of green capabilities. By documenting a negative moderating effect, the study contributes to a more nuanced understanding of innovation’s role in resource constrained settings. This finding aligns with the exploration-exploitation trade-off literature (March, 1991) and suggests that for SMEs in emerging economies, the pursuit of innovation must be carefully calibrated to avoid capability-straining effects. This contributes to the growing body of research on the dark side of innovation and extends it to the environmental domain (Coad et al. (2022).

Third, this study illustrates the value of GRO as an interpretive lens alongside the GDC framework in an underexplored geographical and institutional context. The findings are consistent with a GRO interpretation, suggesting that resource orchestration patterns are not context-neutral but vary across the institutional environment, resource availability, and cultural values, including family norms, which relate to decision-making in Green SMEs in Addis Ababa. Thus, this study suggests that GDCs play a complementary mediating role in the GT–EMP relationship in a manner consistent with GRO principles, rather than empirically testing GRO as a separate construct.

Managerial implications

Several practical implications emerge for SMEs managers, practitioners, and policymakers in developing countries. For SMEs managers, the findings underscore that GT adoption alone is insufficient; investments in GDCs, particularly sensing, seizing, and reconfiguring capabilities, are essential for translating technology into environmental outcomes. Managers should prioritize developing internal capabilities alongside technology adoption, investing in employee training, knowledge diffusion systems, and flexible organizational routines that support environmental change. Moreover, the negative moderation finding suggests that managers should be cautious about pursuing ambitious innovation initiatives without first ensuring that foundational GDCs are in place. A balanced approach that prioritizes capability development before scaling innovation efforts may yield better environmental outcomes.

For practitioners such as sustainability consultants and technical advisors, the findings highlight the importance of capability building interventions. Practitioners should focus on assisting SMEs in developing GDCs through customized interventions, such as strategic planning, training workshops, and process design, rather than solely promoting technology adoption. The negative moderation finding suggests that practitioners should also help SMEs evaluate whether their innovation efforts align with their existing capabilities or risk overextending limited resources.

For policymakers, the findings suggest that support programs should go beyond subsidizing GT adoption to include programs that build organizational capabilities. Training programs, innovation support mechanisms, and sectoral knowledge platforms that strengthen GDCs and help SMEs balance innovation with capability development are critical. In resource-constrained settings, where industry often struggles with implementation, policy measures that promote both capability development and innovation play a central role in facilitating sustainable change in EMP. Additionally, policymakers should consider the role of family values and community norms in shaping environmental practices, designing interventions that leverage these cultural resources while mitigating their constraining effects.

Limitations and future research

This study has several limitations. First, the cross-sectional design and single-respondent data limit causal inference and introduce common-source bias (Podsakoff et al., 2003).The observed relationships may be subject to reverse causality, unmeasured confounding, or common-method bias. Therefore, all findings are interpreted as associations rather than causal effects.Future research should employ longitudinal, multi-respondent designs with objective performance measures. Second, the study focused on Green SMEs in Addis Ababa, limiting generalizability. Cross-country comparisons are needed. Third, the institutional sampling and online survey administration may underrepresent SMEs outside formal programs and those with limited digital access. Mixed-mode data collection is recommended. Fourth, the study examined only firm internal constructs, excluding external drivers such as regulatory pressure. Future research should incorporate these variables. Fifth, the reliance on quantitative data could be complemented by qualitative studies to explore managers’ routines and decision-making processes. Sixth, the GDCs→EMP and GDCs×GI→EMP paths are not robust to the inclusion of firm-level control variables. While significant in the main model (without controls), they became non-significant after adding controls. Only the direct GT→EMP association remained stable. Despite these limitations, the study provides a foundation for understanding green technology orchestration in emerging economy SMEs. Future research should explore when GI complements rather than constrains GDCs, and whether the negative moderation persists across institutional contexts.

CONCLUSION

This study examined how GT is associated with EMP in Green SMEs in Addis Ababa, with particular attention to the mediating role of GDCs and the moderating role of GI. Addressing RQ1, the findings show that the total GT–EMP association (from a model without a mediator) is significant and positive, while GDCs are associated with a statistically significant but substantively modest complementary mediation pattern in the GT–EMP relationship. Addressing RQ2, the findings reveal that GI negatively moderates the relationship between GDCs and EMP. However, these mediation and moderation effects are not robust to controls and become non-significant after including firm-level covariates. Only the direct GT→EMP association remained stable. From a practical standpoint, the findings suggest that SME managers should prioritize internal capability development alongside technology adoption, while policymakers should design support mechanisms that strengthen organizational capabilities.

While this study is context specific to Green SMEs in Addis Ababa, the theoretical mechanisms, the statistically significant but substantively modest indirect effect of GDCs, and negative moderation of GI, may apply to other emerging economy contexts with similar characteristics: resource constrained environments, strong SME presence, and developing institutional frameworks. The findings suggest that the GRO and GDC frameworks, developed in resource rich contexts, can be adapted for settings with less developed institutional support. This study has limitations. Its cross-sectional design limits causal inference, and the focus on Green SMEs in Addis Ababa may constrain generalizability. Future research could adopt longitudinal and comparative designs to examine the dynamic evolution of green capabilities. However, these conclusions should be interpreted with caution.The institutional sampling approach may not capture the full range of environmentally engaged SMEs, particularly those operating outside formal government programs. Future research employing cross-country comparisons would help validate whether the findings extend beyond the Ethiopian context.

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Appendix A

አባሪ A

Questionnaire

መጠይቅ

Environmental Management Performance(RESEARCH)

የአካባቢ ጥበቃ አፈፃፀም (ምርምር)

Sapienza University of Rome

ሳፒየንዛ ዩኒቨርሲቲ ሮም

The following study is being conducted by researchers at the Sapienza University of Rome. We are especially interested in your opinions regarding the Environmental Management Performanceof your firms. Completing the questionnaire will take approximately 10 minutes of your time. Your participation is very valuable to us. This is purely an academic study and serves no commercial purpose whatever.

የሚከተለው ጥናት በሳፒየንዛ ዩኒቨርሲቲ ሮም ያሉ ተመራማሪዎች የሚካሄድ ነው። በተለይም ስለ ድርጅታችሁ የአካባቢ ጥበቃ አፈፃፀም አመለካከታችሁን ለማወቅ እንፈልጋለን። መጠይቁን ለመሙላት በግምት 10 ደቂቃ ይወስዳል። ተሳትፎዎ ለእኛ በጣም ዋጋ ያለ ነው። ይህ ሙሉ በሙሉ የአካዳሚክ ጥናት ሲሆን ምንም የንግድ ዓላማ የለውም።

• Please read the questions carefully and follow the relevant instructions.
• እባክዎ ጥያቄዎቹን በጥንቃቄ ያንብቡ እና መመሪያዎቹን ይከተሉ።
• There are no right or wrong answers. We are only interested in your personal views.
• ትክክለኛ ወይም የተሳሳተ መልስ የለም። እኛ ፍላጎት ያለን በእርስዎ የግል አመለካከት ብቻ ነው።
• There is no time constraint. Please take your time to fill out the questionnaire.
• የጊዜ ገደብ የለም። እባክዎ ጊዜዎን ወስደው መጠይቁን ይሙሉ።
• All information you provide will be used anonymously, and you will not be identified at any point.
• የሚሰጡት መረጃ ሁሉ ስም-አልባ ሆኖ ጥቅም ላይ ይውላል፣ እና በማንኛውም ጊዜ እርስዎ አይታወቁም።

Thank you very much for your participation in this study!

ለተሳትፎዎ እጅግ እናመሰግናለን!

1. To what extent do you agree or disagree with the following statements? These questions assess your firm’s use of green technologies in relation to its environmental activities. (Select a number on a scale from 1 to 5, where 1 indicates strong disagreement and 5 indicates strong agreement. Choose the number that best reflects your opinion) 1. ከሚከተሉት መግለጫዎች ጋር እስከምን ደረጃ ይስማማሉ ወይም አይስማሙም? እነዚህ ጥያቄዎች የድርጅትዎን ከአካባቢ ጥበቃ እንቅስቃሴዎች ጋር በተያያዘ አረንጓዴ ቴክኖሎጂዎችን መጠቀም ይገመግማሉ። (ከ1 እስከ 5 ባለው ሚዛን ቁጥር ይምረጡ፤ 1 ሙሉ በሙሉ አልስማማም ሲሆን 5 ደግሞ ሙሉ በሙሉ እስማማለሁ ያሳያል። አመለካከትዎን በሚገልጽ ቁጥር ላይ ምልክት ያድርጉ)
Green Technologies አረንጓዴ ቴክኖሎጂዎች
We rapidly update green technology changes in our company. በድርጅታችን ውስጥ የአረንጓዴ ቴክኖሎጂ ለውጦችን በፍጥነት እናዘምናለን።	1 2 3 4 5
We consider ourselvescompetitive in green technology. እራሳችንን በአረንጓዴ ቴክኖሎጂ ተወዳዳሪ ነን ብለን እንቆጥራለን።	1 2 3 4 5
We use up-to-date green technology in the process. በምርት ሂደታችን ውስጥ ዘመናዊ የአረንጓዴ ቴክኖሎጂን እንጠቀማለን።	1 2 3 4 5
We are fast in adopting the latest green technologies. የቅርብ ጊዜዎቹን የአረንጓዴ ቴክኖሎጂዎች ለመቀበል ፈጣን ነን።	1 2 3 4 5
We use cleaner technologies. ከአካባቢ ከብክለት ነፃ የሆኑ ቴክኖሎጂዎችን እንጠቀማለን።	1 2 3 4 5
We frequently integrate newly emerging green technologies. አዳዲስ የአረንጓዴ ቴክኖሎጂዎችን ከእንቅስቃሴዎቻችን ጋር ብዙ ጊዜ እናጣምራለን።	1 2 3 4 5

(source Afum et al., 2023; Lee et al., 2014)

2. To what extent do you agree or disagree with the following statements? These questions assess your firm’s level of environmental performance (Select a number on a scale from 1 to 5, where 1 indicates strong disagreement and 5 indicates strong agreement. Choose the number that best reflects your opinion.) 2. ከሚከተሉት መግለጫዎች ጋር እስከምን ደረጃ ይስማማሉ ወይም አይስማሙም? እነዚህ ጥያቄዎች የድርጅትዎን የአካባቢ ጥበቃ አፈጻጸም ደረጃ ይገመግማሉ። (ከ1 እስከ 5 ባለው ሚዛን ቁጥር ይምረጡ፤ 1 ሙሉ በሙሉ አልስማማም ሲሆን 5 ደግሞ ሙሉ በሙሉ እስማማለሁ ያሳያል። አመለካከትዎን በሚገልጽ ቁጥር ላይ ምልክት ያድርጉ)
Environmental Performance የአካባቢ ጥበቃ አፈጻጸም
We are investing in energy-saving mechanisms. በ ኃይል ቆጣቢ ዘዴዎች ላይ ኢንቨስት እያደረግን ነው።	1 2 3 4 5
We are performing environmental audits periodically. በየጊዜው የአካባቢ ቁጥጥር ምርመራ እናደርጋለን።	1 2 3 4 5
We design products and packaging that can be reused, repaired, and recycled. እንደገና ጥቅም ላይ ሊውሉ፣ ሊጠገኑ የሚችሉ ምርቶች እና ማሸጊያዎች እንዘጋጃለን።	1 2 3 4 5
We are voluntarily exceeding environmental regulations. ከአካባቢ ጥበቃ ደንቦች በላይ በፈቃደኝነት እንሠራለን።	1 2 3 4 5
We are implementing programs to reduce water consumption. የውሃ ፍጆታን ለመቀነስ የተነደፉ ፕሮግራሞችን ተግባራዊ እያደረግን ነው።	1 2 3 4 5
We are adopting measures to design ecologically produced products or services. ለሥነ-ምሕዳር ተስማሚ የሆኑ ምርቶችን ወይም አገልግሎቶችን ለመንደፍ እርምጃዎችን እየወሰድን ነው።	1 2 3 4 5

(source: Eikelenboom & de Jong 2019; Martinez-Conesa et al., 2017)

3. To what extent do you agree or disagree with the following statements? These questions assess your firm’s use of green dynamic capabilities in relation to your organization. (Select a number on a scale from 1 to 5, where 1 indicates strong disagreement and 5 indicates strong agreement. Choose the number that best reflects your opinion.) 3.ከሚከተሉት መግለጫዎች ጋር እስከምን ድረስ ይስማማሉ ወይም አይስማሙም? እነዚህ ጥያቄዎች የድርጅትዎን የአረንጓዴ ተለዋዋጭ አቅሞች አጠቃቀም ይገመግማሉ።(ከ1 እስከ 5 ባለው ሚዛን ቁጥር ይምረጡ፤ 1 ሙሉ በሙሉ አልስማማም ሲሆን 5 ደግሞ ሙሉ በሙሉ እስማማለሁ ያሳያል። አመለካከትዎን በሚገልጽ ቁጥር ላይ ምልክት ያድርጉ)
Green Dynamic Capabilities አረንጓዴ ተለዋዋጋ አቅሞች
Our company can quickly monitor the environment and identify new green opportunities. ድርጅታችን አካባቢውን በፍጥነት መከታተል እና አዳዲስ አረንጓዴ እድሎችን መለየት ይችላል።	1 2 3 4 5
Our company can expand its market share successfully through identified opportunities. ድርጅታችን የገበያ ድርሻውን በተሳካ ሁኔታ ለማሳደግ የተለዩ እድሎች ማስፋፋት ይችላል።	1 2 3 4 5
Our company can incorporate, learn, generate, combine, share, transform, and apply new green knowledge. ድርጅታችን አዲስ አረንጓዴ እውቀትን ማካተት፣ መማር፣ ማመንጨት፣ ማዋሃድ፣ ማጋራት፣ መለወጥ ወይም ተግባራዊ ማድረግ ይችላል።	1 2 3 4 5
Our company can integrate and manage specialized green knowledge within the organization successfully. ድርጅታችን ልዩ አረንጓዴ እውቀትን በድርጅቱ ውስጥ በተሳካ ሁኔታ ማዋሃድ እና ማስተዳደር ይችላል።	1 2 3 4 5
Our company can successfully coordinate employees to develop green technology. ድርጅታችን ሰራተኞችን አረንጓዴ ቴክኖሎጂን እንዲያለሙ በተሳካ ሁኔታ ማስተባበር ይችላል።	1 2 3 4 5
Our company can allocate resources to develop green innovations successfully. ድርጅታችን አረንጓዴ ፈጠራዎችን በተሳካ ሁኔታ ለማልማት ሀብቶችን መመደብ ይችላል።	1 2 3 4 5

(source: Ma et al., 2025)

4. To what extent do you agree or disagree with the following statements? These questions assess your company’s green Innovation activities. (Select a number on a scale from 1 to 5, where 1 indicates strong disagreement and 5 indicates strong agreement. Choose the number that best reflects your opinion about your firm’s green innovation activities.) 4. ከሚከተሉት መግለጫዎች ጋር እስከምን ደረጃ ይስማማሉ ወይም አይስማሙም? እነዚህ ጥያቄዎች የድርጅትዎን አረንጓዴ ፈጠራ እንቅስቃሴዎች ይገመግማሉ። (ከ1 እስከ 5 ባለው ሚዛን ቁጥር ይምረጡ፤ 1 ሙሉ በሙሉ አልስማማም ሲሆን 5 ደግሞ ሙሉ በሙሉ እስማማለሁ ያሳያል። አመለካከትዎን በሚገልጽ ቁጥር ላይ ምልክት ያድርጉ)
Green Innovation አረንጓዴ ፈጠራ
We choose materials that reduce pollution during product or service development. በምርት ወይም በአገልግሎት ዝግጅት ወቅት ብክለትን የሚቀንሱ ቁሳቁሶችን እንመርጣለን።	1 2 3 4 5
We use the fewest amounts of materials to develop our products or services. ምርቶቻችንን ወይም አገልግሎቶቻችንን ለማዘጋጀት በጣም አነስተኛ መጠን ያላቸውን ቁሳቁሶች እንጠቀማለን።	1 2 3 4 5
We carefully consider whether the product is easy to recycle, reuse, and decompose in the product or service development. በምርት ወይም በአገልግሎት ዝግጅት ወቅት ምርቱን እንደገና ጥቅም ላይ ለማዋል፣ ቀላል መሆኑን በጥንቃቄ እናስባለን።	1 2 3 4 5
Our product or service processing system reduces the consumption of water, electricity, coal, or oil. የምርት ወይም የአገልግሎት ማቀነባበሪያ ስርዓታችን የውሃ፣ የኤሌክትሪክ፣ የድንጋይ ከሰል ወይም የዘይት ፍጆታን ይቀንሳል።	1 2 3 4 5
Our product or service processing system effectively reduces the emission of hazardous substances and waste. የምርት ወይም የአገልግሎት ማቀነባበሪያ ስርዓታችን የአደገኛ ንጥረ ነገሮችን እና ቆሻሻዎችን ልቀትን በብቃት ይቀንሳል።	1 2 3 4 5
Our product or service processing system reduces the use of raw materials. የምርት ወይም የአገልግሎት ማቀነባበሪያ ስርዓታችን ጥሬ እቃዎችን መጠቀም ይቀንሳል።	1 2 3 4 5

(source: Ma et al., 2025)

5. SMEs Profile 5. አነስተኛ እና መካከለኛ ኢንተርፕራይዞች መገለጫ
Sector: ዘርፍ:	☐ Manufacture ☐ ማኑፋክቸሪንግ (ማምረቻ) ☐ Service ☐ አገልግሎት ☐ Construction ☐ ኮንስትራክሽን (ግንባታ) ☐ Other ☐ ሌላ
Size: መጠን:	☐ Micro (1-5 employee) ☐ ጥቃቅን (ከ1-5 ሠራተኛ) ☐ Small (6-30 employee) ☐ አነስተኛ (ከ6-30 ሠራተኛ) ☐ Medium (31-100 employee) ☐ መካከለኛ (ከ31-100 ሠራተኛ)
GT Maturity Level: የአረንጓዴ ቴክኖሎጂ የአጠቃቀም ደረጃ:	☐ No GT adoption ☐ ምንም የአረንጓዴ ቴክኖሎጂ ልማት የለም ☐ Early GT adoption ☐ የመጀመሪያ ደረጃ የአረንጓዴ ቴክኖሎጂ ልማት ☐ Moderate GT adoption ☐ መካከለኛ ደረጃ የአረንጓዴ ቴክኖሎጂ ልማት ☐ Fully integrated GT ☐ ሙሉ በሙሉ የተጣመረ አረንጓዴ ቴክኖሎጂ ልማት
Firm Age: የድርጅት ዕድሜ:	☐ 1–5years ☐ ከ1-5 አመታት ☐ More than 6 ☐ ከ6 አመታት በላይ
Formal Green Policy in Place: መደበኛ የአረንጓዴ ፖሊሲ በሥራ ላይ:	☐ Yes ☐ አዎ ☐ No ☐ አይደለም

6. Demographic information
Gender ፆታ	☐ Male ☐ ወንድ ☐ Female ☐ ሴት ☐ Prefer not to say ☐ መልስ መስጠት አልፈልግም
Education leve የትምህርት ደረጃ	☐ College diploma ☐ ኮሌጅ ዲፕሎማ ☐ Bachelor’s degree ☐ የመጀመሪያ ዲግሪ ☐ Master’s degree ☐ ሁለተኛ ዲግሪ ☐ Doctorate (PhD) or higher ☐ ዶክትሬት (PhD) ወይም ከዚያ በላይ
Age ዕድሜ	☐ Under 20 ☐ 20 በታች ☐ 20–29 ☐ 30–39 ☐ 40–49 ☐ 50–59 ☐ 60 and above ☐ 60 እና ከዚያ በላይ

Table A1: Mapping of GDC items to theoretical dimensions

Dimension	Item (as worded in the questionnaire)
Sensing	“Our company can quickly monitor the environment and identify new green opportunities.”
Seizing	“Our company can expand its market share successfully through identified opportunities.”
Reconfiguring	“Our company can incorporate, learn, generate, combine, share, transform, and apply new green knowledge.”
Reconfiguring	“Our company can integrate and manage specialized green knowledge within the organization successfully.”
Reconfiguring	“Our company can successfully coordinate employees to develop green technology.”
Reconfiguring	“Our company can allocate resources to develop green innovations successfully.”

Source: Adapted from Ma et al. (2025) and aligned with Teece (2007, 2018).

Biographical note

Leul Girma Haylemariam (Ph.D.) is a postdoc researcher in the Department of Management, Faculty of Economics, at Sapienza University of Rome, Italy. His research focuses on how entrepreneurial strategies, digital and analytical capabilities, sustainability-oriented innovation, and business model transformation affect firm performance, competitiveness, resilience, and environmental and social outcomes. Integrating perspectives from entrepreneurship, strategic management, and sustainability, his work addresses contemporary challenges in green innovation, digitalization, corporate responsibility, and inclusive growth. Dr. Haylemariam has published extensively in leading international journals, including: Business Strategy and the Environment; Corporate Social Responsibility and Environmental Management; Management Research Review; Business Ethics, the Environment & Responsibility.

Author contribution statement

Leul Girma Haylemariam: Conceptualization, Data Curation, Formal Analysis, Methodology, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing.

Conflicts of interest

The author declares no conflicts of interest and no funding received during the research.

Citation (APA style)

Haylemariam, L. G. (2026). Green technology adoption and environmental management performance in emerging-economy SMEs: Green dynamic capabilities and green innovation tensions. Journal of Entrepreneurship, Management and Innovation, 22(4), 93-114. https://doi.org/10.7341/20262245

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Received 22 December 2025; Revised 18 April 2026, 9 May 2026, 7 June 2026; Accepted 10 June 2026.

This is an open-access paper under the CC BY license (https://creativecommons.org/licenses/by/4.0/legalcode).