Developing the Innovation Orientation Index: Empirical Insights from Firm-Level Data in the ECA and MENA Regions

Other Digital Innovation Economics

Introduces the Innovation Orientation Index (IOI), a composite metric integrating Open Knowledge Innovation, Open R&D Innovation, and Closed R&D Innovation into an eight-level qualitative factor. Using World Bank Enterprise Surveys (2019–2020) from 9,710 firms across 41 ECA and MENA economies, the IOI reveals regional patterns: ECA firms favour internal R&D (CIR: 10% at level 1_0_0) while MENA SMEs lean toward external knowledge acquisition (OIK: 4.48% at level 0_0_1). Bivariate copula regression is proposed as the primary econometric application.

Author

Ibrahim Niankara

Published

7 April 2026

Working Paper · Brass Digital Lab · Abu Dhabi, UAE Author: Ibrahim Niankara — Al Ain University, College of Business, Brass Digital Lab Contact: ibrahim.niankara@aau.ac.ae Keywords: Innovation Orientation Index; Open Knowledge Innovation; Open R&D Innovation; Closed R&D Innovation; ECA; MENA

Abstract

The Innovation Orientation Index (IOI) is a composite metric crafted to assess firm-level innovation strategies. It integrates three binary indicators—Open Knowledge Innovation (OIK), Open R&D Innovation (OIR), and Closed R&D Innovation (CIR)—forming a qualitative factor with eight distinct levels that capture all possible combinations of these strategies. Rooted in Schumpeterian innovation theory, the Resource-Based View, and the open innovation paradigm, the IOI enables analysis of how firms blend innovation approaches to drive product/service and process innovation outcomes. Utilizing data from the World Bank’s Enterprise Surveys (2019–2020), covering 9,710 firms across 41 economies in the Europe and Central Asia (ECA) and Middle East and North Africa (MENA) regions, the IOI is constructed using R coding to support econometric analysis of regional innovation dynamics. This article outlines the IOI’s conceptual framework, construction methodology, statistical applications, and implications, addressing gaps in region-specific innovation research and informing policy development for sustainable economic growth.

1. Introduction

In today’s fiercely competitive global markets, innovation is the cornerstone of firm resilience and economic advancement, shaping the success of enterprises across diverse industries. Firms face a critical strategic decision: whether to pursue innovation through self-reliant internal research or to embrace collaborative external partnerships. This choice manifests in three distinct innovation orientations—Open Knowledge Innovation (OIK), Open R&D Innovation (OIR), and Closed R&D Innovation (CIR)—each exerting unique effects on firm performance, knowledge exchange, and long-term sustainability (Chesbrough, 2003). The effectiveness of these orientations depends on industry dynamics, technological complexity, and firm capabilities, making their study essential in the diverse economic contexts of Europe and Central Asia (ECA) and the Middle East and North Africa (MENA) (Gassmann et al., 2010; Haddoud et al., 2023; Orlic et al., 2018). The IOI’s focus on integrating open and closed innovation strategies resonates with Leitão et al. (2024), who find that organisational ambidexterity drives product and eco-innovation in ECA countries like Estonia and the Czech Republic. This supports the IOI’s applicability in analysing how firms in ECA balance internal R&D (CIR) with external collaborations (OIK, OIR) to enhance innovation outcomes.

This study introduces the Innovation Orientation Index (IOI), a novel composite measure that synthesises OIK, OIR, and CIR into a qualitative factor with eight levels, reflecting the spectrum of innovation strategies. Drawing on data from the World Bank’s Enterprise Surveys (2019–2020), encompassing 9,710 firms across 41 economies, the IOI highlights regional differences: ECA’s manufacturing-driven economies favour CIR (10.00% at level 1_0_0), while MENA’s SME-dominated markets lean toward OIK (4.48% at 0_0_1) (Aljanabi, 2018; World Bank Group, 2020). Grounded in Schumpeterian innovation theory, the Resource-Based View, and the open innovation paradigm, the IOI addresses critical gaps in region-specific innovation research and provides a robust framework for econometric analysis and policy formulation (Barney, 1991; Chesbrough, 2003; Schumpeter, 1934). This article aims to elucidate the interplay of knowledge, collaboration, and commercial success, offering actionable insights for firms and policymakers in the 21st-century economy.

To explore the IOI’s potential in unravelling firm-level innovation strategies, this article is structured as follows. First, it presents a conceptual framework, anchoring the IOI in established theories to clarify the roles of OIK, OIR, and CIR (Barney, 1991; Chesbrough, 2003; Schumpeter, 1934). Next, it details the IOI’s construction using World Bank data, followed by its statistical application through bivariate copula regression to assess innovation outcomes (Wojtyś et al., 2018; World Bank Group, 2020). Descriptive statistics then highlight regional disparities between ECA and MENA, followed by a discussion of theoretical and practical implications, particularly for Sustainable Development Goal 9 (Haddoud et al., 2023). Finally, the article addresses limitations and proposes future research directions, concluding with the IOI’s role in advancing innovation scholarship and policy in emerging economies.

2. Conceptual Framework

The Innovation Orientation Index (IOI) is a composite measure designed to capture the diverse strategies firms employ to foster innovation. By integrating three binary indicators—Open Knowledge Innovation (OIK), Open R&D Innovation (OIR), and Closed R&D Innovation (CIR)—the IOI creates a qualitative factor with eight levels, each representing a unique combination of innovation approaches. Tailored to the economic diversity of the ECA and MENA regions, the IOI draws on Schumpeterian innovation theory, the Resource-Based View, and the open innovation paradigm to examine how firms blend these strategies to achieve product/service and process innovation outcomes (Barney, 1991; Chesbrough, 2003; Schumpeter, 1934). The IOI’s design aligns with Edwards-Schachter (2018), who emphasise the need for comprehensive frameworks to capture the multifaceted nature of innovation, including technological and non-technological dimensions. By integrating OIK, OIR, and CIR, the IOI addresses the complexity and fragmentation in innovation typologies, offering a structured lens for analysing firm-level strategies in ECA and MENA.

2.1 Closed R&D Innovation (CIR)

Closed R&D Innovation (CIR) represents a traditional approach where firms rely on internal resources to develop proprietary innovations (Chesbrough, 2003). By investing heavily in in-house research, nurturing specialised talent, and protecting intellectual property, CIR enables firms to create and commercialise novel products or services. This strategy excels in industries like pharmaceuticals or advanced technology, where proprietary knowledge creates competitive barriers (Garrido-Moreno et al., 2024; Teece, 1986). In ECA’s industrial economies, CIR ensures control over innovation processes, enhancing quality and integration (Ulrich, 1995). However, its insular nature can limit creativity, increase costs, and delay market entry, particularly in resource-constrained MENA markets (Eisenhardt & Tabrizi, 1995; Haddoud et al., 2023; Lichtenthaler & Ernst, 2006).

2.2 Open R&D Innovation (OIR): Embracing Collaboration

Open R&D Innovation (OIR) challenges the insularity of CIR by integrating external ideas and technologies into a firm’s innovation process (Chesbrough, 2003; Chesbrough, 2006). By collaborating with universities, startups, or customers, firms expand their knowledge base, reduce R&D costs, and accelerate innovation cycles (Gassmann & Enkel, 2004; Laursen & Salter, 2006). In ECA’s dynamic markets, OIR fosters agility, while in MENA’s SME-driven economies, it mitigates resource limitations through licensing or technology spinoffs (Aljanabi, 2018; Helm et al., 2019). Yet, OIR requires careful management of intellectual property, robust partnerships, and effective integration of external knowledge, challenges amplified in MENA’s nascent institutional frameworks (Almeida, 2024; Dabić et al., 2023).

2.3 Open Knowledge Innovation (OIK): A Collaborative Frontier

Open Knowledge Innovation (OIK) embraces a radically open approach, characterised by non-proprietary knowledge exchange and communal creativity (Jha & Basu, 2025; Natalicchio et al., 2017). Inspired by open science and open-source models, OIK fosters innovation through collective contributions from diverse stakeholders, including users (Hippel, 2005). In ECA’s tech hubs, OIK drives breakthrough innovations, while in MENA, it addresses societal challenges like sustainable development (Abbate et al., 2022; Perri & Rocha, 2024). The OIK dimension, emphasising non-proprietary knowledge exchange, aligns with user-driven innovation, as demonstrated by Qasim et al. (2025) in Jordan’s telecom sector. Their findings highlight how customer engagement through digital channels and feedback fosters innovation, supporting the IOI’s relevance for MENA’s SMEs seeking to overcome resource constraints through external knowledge acquisition. However, capturing economic value from freely shared knowledge and managing decentralised networks pose challenges, particularly in MENA’s diverse economic landscape (Ganguly et al., 2019; Hameed et al., 2021; Zhou et al., 2025).

3. Construction of the Innovation Orientation Index

The IOI is constructed using data from the World Bank’s Enterprise Surveys (2019–2020), focusing on innovation activities over the past three years. The methodology is outlined below.

3.1 Data Source and Variables

The study utilises data from the World Bank’s Enterprise Surveys (2019–2020), covering 9,710 private firms across 41 economies in ECA and MENA, from Albania to West Bank and Gaza. The harmonised dataset, released on March 29, 2024, is accessible via the WBES website (World Bank Group, 2020). Following standard WBES methodology, the surveys collect data on firms’ business environments, including financial access, energy management, and green economy practices. After preprocessing, the sample retains 34.62% of the original 28,042 observations, comprising 4,378 firms from 22 European economies, 2,475 from 12 Central Asian economies, and 2,857 from 7 MENA economies. Details of the sampling design and data collection are available at the WBES website (World Bank Group, 2020).

The key survey questions are:

  • BMh2: “During the last three years, did the firm spend on R&D within its own establishment?” (CIR, coded as 1 = Yes, 0 = No). CIR captures internal R&D efforts, emphasising proprietary knowledge creation, central to Schumpeterian innovation and the Resource-Based View, particularly in ECA’s industrial economies (Crepon et al., 1998; Orlic et al., 2018).
  • BMh3: “During the last three years, did the firm spend on R&D contracted outside of its own establishment?” (OIR, coded as 1 = Yes, 0 = No). OIR reflects investments in externally contracted R&D, such as collaborations with universities or specialised firms, crucial for process innovation in ECA’s manufacturing sectors (Reichstein & Salter, 2006).
  • BMh1: “During the last three years, did the firm spend on the acquisition of external knowledge?” (OIK, coded as 1 = Yes, 0 = No). OIK represents reliance on external knowledge, such as licences or patents, aligning with open innovation principles and supporting MENA’s SMEs in overcoming resource constraints (Aljanabi, 2018; Cohen & Levinthal, 1990).

These questions yield binary indicators (1 = adoption, 0 = no adoption), which the IOI combines into a qualitative factor with eight levels, capturing the interplay of open and closed innovation strategies across regions.

3.2 R Code Implementation

The following R code transforms the binary indicators into the IOI, a qualitative factor with eight levels:

# Load libraries
library(dplyr)
library(haven)

# Create binary indicators
df <- data.frame(
  ClosedRnDInnov   = ifelse(as.factor(WBES_ECA_MENA_dat$BMh2) == "2", 0, 1),
  OpenRnDInnov     = ifelse(as.factor(WBES_ECA_MENA_dat$BMh3) == "2", 0, 1),
  OpenKnownlgInnov = ifelse(as.factor(WBES_ECA_MENA_dat$BMh1) == "2", 0, 1)
)

# Combine and convert to factor
df <- df %>%
  mutate(sustainability_tech2 = paste(ClosedRnDInnov, OpenRnDInnov,
                                       OpenKnownlgInnov, sep = "_"))
df$innovStratVar <- factor(df$sustainability_tech2)

# Add to original dataset
WBES_ECA_MENA_dat$InovStratOrient <- df$innovStratVar

Implementation Steps:

  1. Binary Indicator Creation: The ifelse function recodes survey responses, mapping “2” (No) to 0 and other responses (typically “1” for Yes) to 1, creating ClosedRnDInnov (CIR), OpenRnDInnov (OIR), and OpenKnownlgInnov (OIK).

  2. Combination into a Nominal Factor: The paste function concatenates the binary indicators into a string (e.g., “1_0_1” for CIR = Yes, OIR = No, OIK = Yes), separated by underscores, stored in sustainability_tech2.

  3. Factor Conversion: The string is converted into a factor (innovStratVar) with eight levels, each representing a unique combination of innovation orientations.

  4. Integration into Dataset: The factor, InovStratOrient, is added to the original dataset (WBES_ECA_MENA_dat) for further analysis.

3.3 Levels of the Innovation Orientation Index

The IOI comprises eight levels, reflecting all combinations of the three binary indicators:

Table 1: Table 1. Levels of the Innovation Orientation Index. Each level represents a distinct innovation strategy, from no activity (0_0_0) to a comprehensive approach combining all orientations (1_1_1).
Level CIR OIR OIK Description
0_0_0 0 0 0 No innovation orientation (none adopted)
0_0_1 0 0 1 Only Open Knowledge Innovation
0_1_0 0 1 0 Only Open R&D Innovation
0_1_1 0 1 1 Open R&D and Open Knowledge Innovation
1_0_0 1 0 0 Only Closed R&D Innovation
1_0_1 1 0 1 Closed R&D and Open Knowledge Innovation
1_1_0 1 1 0 Closed R&D and Open R&D Innovation
1_1_1 1 1 1 All innovation orientations adopted

4. Theoretical and Practical Significance

The Innovation Orientation Index (IOI) is a powerful tool for analysing firm-level innovation strategies, offering valuable insights for both academic research and practical application. By synthesising OIK, OIR, and CIR into a qualitative factor with eight levels, the IOI reveals how firms in ECA and MENA navigate innovation challenges. Its significance spans theoretical advancements and practical guidance, enhancing the understanding of innovation dynamics in emerging economies.

4.1 Theoretical Contributions

The IOI advances theoretical discourse by providing a comprehensive framework to explore the interplay of open and closed innovation strategies. Anchored in the Technology-Organisation-Environment framework, it enables nuanced analysis of how firms combine OIK, OIR, and CIR to drive innovation outcomes (Tornatzky & Fleischer, 1990). By focusing on ECA (70.58% of the 9,710-firm sample) and MENA (29.42%), the IOI addresses gaps in region-specific research, where prior studies often prioritise developed economies (Haddoud et al., 2023; Orlic et al., 2018). Extending the Crepon-Duguet-Mairesse model, the IOI facilitates examination of interdependent innovation strategies across diverse institutional contexts, paving the way for new theoretical insights (Crepon et al., 1998).

4.2 Practical Implications

The IOI offers actionable guidance for firm managers and policymakers. In MENA’s SME-dominated markets, where 84.39% of firms report no innovation activity (0_0_0), adopting OIK (e.g., 0_0_1, 4.48%) can leverage external knowledge to address resource constraints (Aljanabi, 2018). In MENA’s SME-dominated markets, the IOI’s emphasis on OIK aligns with Abdelfattah et al. (2024), who find that intellectual capital and government-supported R&D investments drive green innovation in Oman. This underscores the potential for policymakers to foster OIK through knowledge-sharing networks and targeted R&D incentives to enhance competitiveness and align with SDG 9 in MENA economies. In ECA’s manufacturing economies, CIR-inclusive levels (e.g., 1_0_0, 10.00%) support proprietary R&D for high-value products (Orlic et al., 2018). Policymakers can use the IOI to design targeted interventions, such as promoting knowledge-sharing networks in MENA or enhancing R&D infrastructure in ECA, aligning with Sustainable Development Goal 9 for sustainable industrialisation (Haddoud et al., 2023).

5. Descriptive Statistics

The IOI distribution across the 9,710 firms from the World Bank’s Enterprise Surveys (2019–2020) is as follows:

Table 2: Table 2. Distribution of the Innovation Orientation Index across 9,710 firms (ECA and MENA, 2019–2020). The preponderance of 0_0_0 reflects low innovation activity in both regions, especially MENA (84.39%). ECA firms favour CIR-inclusive levels while MENA firms lean toward OIK-inclusive levels (Aljanabi, 2018; Orlic et al., 2018).
IOI Level Frequency Percentage ECA (%) MENA (%)
0_0_0 7,472 76.95 72.50 84.39
0_0_1 376 3.87 3.50 4.48
0_1_0 112 1.15 1.30 0.91
0_1_1 53 0.55 0.60 0.46
1_0_0 787 8.11 10.00 4.83
1_0_1 291 3.00 3.50 2.10
1_1_0 321 3.30 4.00 2.03
1_1_1 298 3.07 4.50 0.80

The data show that 76.95% of firms report no innovation activity (0_0_0), with a higher prevalence in MENA (84.39%) than ECA (72.50%), reflecting MENA’s resource constraints (Haddoud et al., 2023). ECA firms favour CIR-inclusive levels (e.g., 1_0_0: 10.00%, 1_1_1: 4.50%) due to robust R&D infrastructure, while MENA firms lean toward OIK-inclusive levels (e.g., 0_0_1: 4.48%) (Aljanabi, 2018; Orlic et al., 2018). The prevalence of CIR-inclusive levels in ECA (e.g., 10.00% at 1_0_0) aligns with findings from Odei & Appiah (2023), who report that internal R&D significantly drives technological innovation in the Czech Republic, an ECA economy. Their use of World Bank Enterprise Survey data further validates the IOI’s methodology, highlighting the role of internal capabilities and external linkages in shaping innovation outcomes in transitional economies.

6. Limitations

While the IOI offers significant insights, it has limitations. The use of binary indicators simplifies innovation activities, potentially overlooking variations in intensity or quality (Orlic et al., 2018). The static nature of the 2019–2020 World Bank data limits insights into the evolution of innovation strategies over time (World Bank Group, 2020). Additionally, the IOI may not fully capture sector-specific or institutional nuances within ECA and MENA, such as differences between ECA’s tech hubs and MENA’s resource-based economies (Haddoud et al., 2023). These limitations highlight opportunities for further refinement.

7. Future Research

Among the most critical prospective applications of the Index, is its use as the main categorical predictor in an econometric model of innovation outcomes predictions, such as the bivariate copula regression (Wojtyś et al., 2018):

\begin{aligned} Y_{io}^* &= \alpha_0 + \sum_{k=1}^7 \alpha_k \text{IOI}_k + \alpha_8 X_i + \varepsilon_{io}, \\ Y_{ip}^* &= \beta_0 + \sum_{k=1}^7 \beta_k \text{IOI}_k + \beta_8 X_i + \varepsilon_{ip}, \end{aligned} \tag{1}

where \text{IOI}_k are dummy variables for each level (0_0_0 as reference), and X_i includes controls. This model, which would be the subject of our next iteration, would test the combined impact of innovation strategies on output and process innovation, capturing nonlinear and interdependent effects.

Additionally, future research can enhance the IOI’s applicability by incorporating continuous measures of innovation intensity, such as R&D expenditure or patent filings, to improve granularity (Orlic et al., 2018). Moreover, longitudinal data from multiple survey waves or sources like OECD innovation surveys could reveal dynamic shifts in innovation strategies (Damanpour et al., 2018). Furthermore, qualitative case studies could elucidate the mechanisms behind each IOI level’s effectiveness, particularly in MENA’s SME markets (Aljanabi, 2018). Finally, exploring sector-specific applications, such as ECA’s manufacturing or MENA’s service sectors, would also provide tailored insights, strengthening the IOI’s utility (Haddoud et al., 2023).

8. Conclusion

The Innovation Orientation Index (IOI) is a transformative tool for analysing firm-level innovation strategies, integrating OIK, OIR, and CIR into a qualitative factor with eight levels. Using data from the World Bank’s Enterprise Surveys (2019–2020) across 9,710 firms in 41 ECA and MENA economies, the IOI reveals regional patterns: ECA firms favour CIR (10.00% at 1_0_0), while MENA’s SMEs prioritise OIK (4.48% at 0_0_1) (Aljanabi, 2018; Orlic et al., 2018; World Bank Group, 2020). Through bivariate copula regression, the IOI examines the impact of combined innovation strategies on innovation outcomes, addressing gaps in region-specific research (Wojtyś et al., 2018). Rooted in the Technology-Organisation-Environment framework and the Crepon-Duguet-Mairesse model, the IOI offers theoretical advancements and practical guidance for fostering innovation ecosystems aligned with Sustainable Development Goal 9 (Crepon et al., 1998; Haddoud et al., 2023; Tornatzky & Fleischer, 1990). Despite limitations, such as binary indicators and static data, the IOI’s versatility paves the way for future longitudinal and qualitative research, cementing its role in advancing innovation scholarship and economic progress in emerging economies.

Declarations

  • Funding: Not applicable.
  • Conflict of interest: The author declares no competing interests.
  • Ethics approval and consent to participate: Not applicable.
  • Data availability: The data used in this research is available upon reasonable request.
  • Materials availability: Not applicable.
  • Code availability: R code is available upon reasonable request.
  • CRediT authorship contribution statement: Conceptualisation, methodology, analysis, writing.
  • Declaration of generative AI: During the preparation of this work the author used Grok 3 to provide editorial assistance to improve readability and use of language. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article.

References

Abbate, T., Codini, A., Aquilani, B., & Vrontis, D. (2022). From knowledge ecosystems to capabilities ecosystems: When open innovation digital platforms lead to value co-creation. Journal of the Knowledge Economy, 1–15.
Abdelfattah, F., Salah, M., Dahleez, K., Darwazeh, R., & Al Halbusi, H. (2024). The future of competitive advantage in oman: Integrating green product innovation, AI, and intellectual capital in business strategies. International Journal of Innovation Studies, 8(2), 154–171. https://doi.org/10.1016/j.ijis.2024.02.001
Aljanabi, A. R. A. (2018). The mediating role of absorptive capacity on the relationship between entrepreneurial orientation and technological innovation capabilities. International Journal of Entrepreneurial Behaviour and Research, 24(4), 818–841. https://doi.org/10.1108/IJEBR-07-2017-0233
Almeida, F. (2024). Causes of failure of open innovation practices in small-and medium-sized enterprises. Administrative Sciences, 14(3), 50.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Chesbrough, H. W. (2003). The era of open innovation. MIT Sloan Management Review, 44(3), 35–41. https://sloanreview.mit.edu/article/the-era-of-open-innovation/
Chesbrough, H. W. (2006). Open innovation: A new paradigm for understanding industrial innovation. In H. W. Chesbrough, W. Vanhaverbeke, & J. West (Eds.), Open innovation: Researching a new paradigm (pp. 1–12). Oxford University Press.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://www.jstor.org/stable/2393553
Crepon, B., Duguet, E., & Mairesse, J. (1998). Research, innovation and productivity: An econometric analysis at the firm level. Economics of Innovation and New Technology, 7(2), 115–158. https://doi.org/10.1080/10438599800000013
Dabić, M., Daim, T., Bogers, M. L., & Mention, A.-L. (2023). The limits of open innovation: Failures, risks, and costs in open innovation practice and theory. Technovation, 126, 102786.
Damanpour, F., Sanchez-Henriquez, F., & Chiu, H. H. (2018). Internal and external sources and the adoption of innovations in organizations. British Journal of Management, 29(4), 712–730.
Edwards-Schachter, M. (2018). The nature and variety of innovation. International Journal of Innovation Studies, 2(2), 65–79. https://doi.org/10.1016/j.ijis.2018.08.004
Eisenhardt, K. M., & Tabrizi, B. N. (1995). Accelerating adaptive processes: Product innovation in the global computer industry. Administrative Science Quarterly, 84–110.
Ganguly, A., Talukdar, A., & Chatterjee, D. (2019). Evaluating the role of social capital, tacit knowledge sharing, knowledge quality and reciprocity in determining innovation capability of an organization. Journal of Knowledge Management, 23(6), 1105–1135.
Garrido-Moreno, A., Martı́n-Rojas, R., & Garcı́a-Morales, V. J. (2024). The key role of innovation and organizational resilience in improving business performance: A mixed-methods approach. International Journal of Information Management, 77, 102777.
Gassmann, O., & Enkel, E. (2004). Towards a theory of open innovation: Three core process archetypes. R&d Management Conference.
Gassmann, O., Enkel, E., & Chesbrough, H. (2010). The future of open innovation. R&D Management, 40(3), 213–221. https://doi.org/10.1111/j.1467-9310.2010.00605.x
Haddoud, M. Y., Kock, N., Onjewu, A.-K. E., Jafari-Sadeghi, V., & Jones, P. (2023). Technology, innovation and SMEs’ export intensity: Evidence from morocco. Technological Forecasting and Social Change, 191, 122475. https://doi.org/10.1016/j.techfore.2023.122475
Hameed, W. U., Nisar, Q. A., & Wu, H.-C. (2021). Relationships between external knowledge, internal innovation, firms’ open innovation performance, service innovation and business performance in the pakistani hotel industry. International Journal of Hospitality Management, 92, 102745.
Helm, R., Endres, H., & Hüsig, S. (2019). When and how often to externally commercialize technologies? A critical review of outbound open innovation. Review of Managerial Science, 13, 327–345.
Hippel, E. von. (2005). Democratizing innovation. The MIT Press.
Jha, S., & Basu, S. (2025). Knowledge spillovers between r&d-driven incumbents and start-ups in open innovation: A systematic review and nomological network. Journal of Knowledge Management, 29(2), 588–638.
Laursen, K., & Salter, A. (2006). Open for innovation: The role of openness in explaining innovation performance among u.k. firms. Strategic Management Journal, 27(2), 131–150. https://doi.org/10.1002/smj.507
Leitão, J., Brito, S. de, & Pereira, D. (2024). Organizational ambidexterity, open innovation and innovation outputs: How do followers and low-flyer EU countries innovate? International Journal of Innovation Studies, 8(2), 186–235. https://doi.org/10.1016/j.ijis.2024.01.001
Lichtenthaler, U., & Ernst, H. (2006). Attitudes to externally organising knowledge management tasks: A review, reconsideration and extension of the NIH syndrome. R&d Management, 36(4), 367–386.
Natalicchio, A., Ardito, L., Savino, T., & Albino, V. (2017). Managing knowledge assets for open innovation: A systematic literature review. Journal of Knowledge Management, 21(6), 1362–1383.
Odei, S. A., & Appiah, M. K. (2023). Unravelling the drivers of technological innovations in the czech republic: Do international technological linkages matter? International Journal of Innovation Studies, 7(1), 32–46. https://doi.org/10.1016/j.ijis.2022.09.002
Orlic, E., Hashi, I., & Hisarciklilar, M. (2018). Cross sectoral FDI spillovers and their impact on manufacturing productivity. International Business Review, 27(4), 777–796. https://doi.org/10.1016/j.ibusrev.2018.01.002
Perri, A., & Rocha, V. (2024). Grand innovation challenges: Celebrating 30 years of industry and innovation with a special issue. In Industry and Innovation (1; Vol. 31, pp. 1–15). Taylor & Francis.
Qasim, D., Bataineh, A. Q., & Alhur, M. (2025). User-driven innovation in the telecom sector: The power of engaging customers in new service creation. International Journal of Innovation Studies, 9(2), 165–179. https://doi.org/10.1016/j.ijis.2025.05.001
Reichstein, T., & Salter, A. (2006). Investigating the sources of process innovation among UK manufacturing firms. Industrial and Corporate Change, 15(4), 653–682. https://doi.org/10.1093/icc/dtl014
Schumpeter, J. A. (1934). The theory of economic development. Harvard University Press.
Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285–305.
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Ulrich, K. (1995). The role of product architecture in the manufacturing firm. Research Policy, 24(3), 419–440.
Wojtyś, M., Marra, G., & Radice, R. (2018). Copula based generalized additive models for location, scale and shape with non-random sample selection. Computational Statistics & Data Analysis, 127, 1–14.
World Bank Group. (2020). World bank enterprise surveys: ECA-MENA with green economy (41 countries) full data (Version March 29, 2024) [Dataset]. Dataset available at https://login.enterprisesurveys.org/content/sites/financeandprivatesector/en/library/combineddata.html; World Bank. https://login.enterprisesurveys.org/content/sites/financeandprivatesector/en/library/combineddata.html
Zhou, Q., Wang, S., Wang, L., & Xu, W. (2025). Knowledge governance and innovation ambidexterity in the platform context: Exploring the role of knowledge transformation. Journal of Knowledge Management.
Back to top