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International Journal of Innovation Studies 8 (2024) 154–171
Available online 7 February 2024
2096-2487/© 2024 China Science Publishing & Media Ltd. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is
an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The future of competitive advantage in Oman: Integrating green
product innovation, AI, and intellectual capital in
business strategies
Fadi Abdelfattah
a
,
*
, Mohammed Salah
a
, Khalid Dahleez
b
, Riyad Darwazeh
c
,
Hussam Al Halbusi
d
a
Modern College of Business and Science (MCBS), Muscat, Oman
b
Dean College of Business Administration, A’Sharqiyah University, Ibra, Oman
c
Head of Accounting Department, Business School, Al-Ahliyya Amman University, Amman, Jordan
d
Management Department, Ahmed Bin Mohammed Military College (ABMMC), Doha, Qatar
ARTICLE INFO
Keywords:
Green product innovation
R&D investments
AI adoption
Intellectual capital
Government involvement
Knowledge-driven culture
Oman
ABSTRACT
This study delves into the dynamics of green product innovation, articial intelligence (AI)
adaption, and intellectual capital, investigating their impact on the competitiveness of rms in
Oman. It emphasizes the crucial role of government intervention and R&D investments in this
process. Based on the responses of 214 top managers in Oman, the research employs structural
equation modeling to analyze the intricate relationships between these factors. The ndings
underscore a signicant positive correlation between green innovation, AI implementation, and
intellectual capital, with government involvement and R&D investments as vital moderators. This
study provides a novel perspective on the synergy of technology, innovation, and intellectual
capital in developing economies. It offers essential insights for business leaders, policymakers,
and scholars, highlighting the necessity of integrating advanced technologies and sustainable
practices in business strategies to achieve competitive advantage. The research adds to the
existing body of knowledge on innovation and competitiveness. It offers practical implications for
enhancing rm performance in Oman and similar emerging markets.
1. Introduction
The global business landscape is experiencing an unprecedented transformation, driven by the imperatives of sustainability, the
rapid pace of technological advancements, and the increasingly inuential role of government policies in sculpting competitive dy-
namics (Chin et al., 2019; Hitt et al., 1998). In this complex milieu, organizations must strategically harness these multi-faceted forces
to secure long-term viability and achieve a sustainable competitive edge (Rockwell, 2019). This study delves into the intricate web of
relationships that intertwine sustainability, technological investments, and governmental actions, concentrating on their collective
impact on a rm’s competitive advantage in the Omani context. We mainly focus on the critical moderating roles of these factors,
especially intellectual capital and government involvement.
* Corresponding author.
E-mail addresses: fadi.Abdelfattah@mcbs.edu.om (F. Abdelfattah), mohammed.salah@mcbs.edu.om (M. Salah), Khalid.Dahleez@asu.edu.om
(K. Dahleez), r.darwazeh@ammanu.edu.jo (R. Darwazeh), hussam.mba@gmail.com (H. Al Halbusi).
Contents lists available at ScienceDirect
International Journal of Innovation Studies
journal homepage: www.keaipublishing.com/en/journals/international-
journal-of-innovation-studies
https://doi.org/10.1016/j.ijis.2024.02.001
Received 4 August 2023; Received in revised form 15 November 2023; Accepted 20 December 2023
International Journal of Innovation Studies 8 (2024) 154–171
155
Consider the case of Tesla, Inc., a company that has become synonymous with sustainable innovation in the automotive industry.
Tesla’s foray into the Middle Eastern market, including Oman, is an example of how a rm can leverage green product innovation,
advanced research and development (R&D), and supportive government policies to gain a competitive advantage. Tesla’s success in
introducing electric vehicles (EVs) and clean energy products in these markets highlights the intricate interplay between technological
innovation, sustainability, and policy frameworks, making it an ideal case for this study.
Tesla’s green product innovation, particularly its electric vehicles, represents a signicant shift toward sustainable solutions in an
industry historically dominated by fossil fuels (Moshood et al., 2022). The company’s commitment to R&D in battery technology and
AI-driven autonomous driving systems exemplies the pivotal role of technological advancement in shaping a rm’s competitive
landscape (Hammer, 2001; Ng et al., 2022). Tesla’s journey provides crucial insights into the dynamics of sustainable innovation and
technological evolution (Kavanagh, 2019; Li et al., 2020).
Green product innovation performance, epitomizing a rm’s prociency in creating and introducing eco-friendly products, has
emerged as a pivotal factor in the competitive arena (Soewarno et al., 2019). As global consciousness about environmental sustain-
ability heightens and consumer preferences increasingly tilt toward green solutions, the ability of rms to innovate sustainably is not
only an operational necessity but also a strategic imperative (Moshood et al., 2022). Businesses today, propelled by escalating demands
from a broad spectrum of stakeholders, including consumers, investors, and regulatory bodies, nd themselves at a juncture where
embracing sustainable practices is as much about survival as it is about ethical responsibility (Aureli et al., 2020; Baah et al., 2020;
Sharma and Henriques, 2005).
Parallel to the sustainability trajectory, technological innovation, mainly through R&D investment and AI adoption, is a corner-
stone of contemporary corporate competitiveness (Bal and Gill, 2020; Horowitz et al., 2018). These advancements are critical for
pioneering groundbreaking products and instrumental in streamlining operations and elevating overall performance metrics
(Hammer, 2001; Ng et al., 2022). Understanding these dynamics becomes essential in an era of rapid technological evolution and
shifting industry paradigms (Kavanagh, 2019; Li et al., 2020).
The role of government, through its policies and support mechanisms, is pivotal in shaping the trajectory of business growth and
innovation (Pergelova and Angulo-Ruiz, 2014; Storey, 2017; Hassan et al., 2021). The nuanced inuences that governmental actions
can exert on the synergy between green product innovation, R&D, AI adoption, and rm competitiveness are especially pertinent for
policymakers and business strategists (Dong et al., 2022; Mohammed Salah et al., 2023). This aspect assumes added signicance in the
backdrop of Oman’s evolving economic landscape and policy framework, offering a rich context for exploration.
Central to this investigation is the concept of intellectual capital — the reservoir of collective knowledge, skills, and innovative
capabilities within an organization (Cabrilo and Dahms, 2020; Subramaniam and Youndt, 2005). The potential interplay between
intellectual capital and other key variables—such as green product innovation, R&D investment, and AI adoption—in shaping a rm’s
competitive advantage presents a fertile ground for academic inquiry (Usai et al., 2021; Wang et al., 2009). Furthermore, unraveling
how intellectual capital can be strategically leveraged to augment technological innovations and bolster sustainable practices offers
crucial insights for businesses aiming to fortify their competitive positioning in the market.
This study introduces a novel integrative framework that examines the synergistic impact of green product innovation, R&D in-
vestments, AI adoption, intellectual capital, and government policies on a rm’s competitive advantage, focusing on Oman’s evolving
market. Utilizing a mixed-methods approach that merges qualitative case studies with quantitative surveys across Omani rms, our
structural equation modeling (SEM) analysis unveils groundbreaking insights. Our ndings underscore the pivotal role of intellectual
capital in enhancing green innovation and technological investments, alongside the signicant inuence of government intervention
in shaping competitive dynamics. This research marks a substantial departure from existing literature by presenting a holistic
perspective of these complex interactions and their implications for business strategies and governmental policies, especially pertinent
to emerging markets like Oman. We aim to substantially enrich the existing body of knowledge in business administration, innovation
management, and competitive strategy. This study lls gaps in the current research by delving into how these elements collectively
shape a rm’s competitive advantage. It brings a unique viewpoint on the synergistic effects of technological advancements and
human capital in the dynamic business landscape. We offer novel insights into the moderating roles of intellectual capital and gov-
ernment policies, providing a comprehensive understanding of how these factors inuence the outcomes of green innovation and AI
implementation. Poised to give strategic guidance to businesses and policy implications for governments, particularly in developing
economies; our study represents a signicant leap forward in understanding sustainable business practices and technology-driven
competitiveness.
2. Literature review
2.1. Resource-based view theory and its application to the framework
This study anchors itself in the resource-based view (RBV) of strategic management, a theory emphasizing the critical role of a
rm’s internal resources and capabilities in forging and sustaining a competitive edge (Barney, 1991; Raduan et al., 2009). The RBV
postulates that a rm can attain a competitive advantage by effectively harnessing its unique resources that are valuable, rare,
inimitable, and non-substitutable (VRIN) (Barney, 1991). Within our framework, the RBV theory provides a lens through which several
critical connections are discerned.
•Green Product Innovation, R&D Investments, and AI Adoption as VRIN Resources: These elements embody the VRIN
characteristics, acting as pivotal resources or capabilities that enable rms to position themselves in the competitive landscape
F. Abdelfattah et al.
International Journal of Innovation Studies 8 (2024) 154–171
156
distinctively (Mariani et al., 2023; Usai et al., 2021; Yin et al., 2022). Their value is accentuated in sustainability, technological
innovation, and the burgeoning signicance of AI in modern business practices.
•Intellectual capital as a Core Intangible Resource: Intellectual capital, encompassing the collective knowledge, skills, and
expertise within an organization, emerges as a vital intangible resource. It inuences the efcacy of other resources and capa-
bilities, playing a central role in our proposed framework (Luthy, 1998; Marr et al., 2004; Muwardi et al., 2020). As a moderating
variable, intellectual capital is pivotal in determining how green product innovation, R&D investment, and AI adoption are
leveraged to secure a competitive advantage. This highlights the imperative of considering an organization’s knowledge assets to
understand the dynamics of these variables.
•Government Involvement and External Environment Factors: Consistent with the RBV, the external environment, which is
inclusive of government policies, regulations, and supportive actions, is acknowledged as a critical factor inuencing the value and
utility of organizational resources and capabilities (Khan et al., 2022; Lut et al., 2022). In our framework, Government
Involvement acts as a moderating variable, impacting the interplay between the independent and dependent variables. This un-
derscores the necessity of acknowledging the governmental role in sculpting business landscapes and modulating rm
competitiveness.
By weaving the RBV theory into the fabric of our study, we offer a robust theoretical foundation for the proposed framework. This
theoretical perspective enriches our understanding of the interrelations between green product innovation, R&D investments, AI
adoption, intellectual capital, and government involvement in the context of competitive advantage. It guides the formulation of our
hypotheses, steers the empirical investigation, and signicantly contributes to the existing discourse in business administration and
strategic management, particularly in the evolving contexts of technological innovation and sustainable practices.
2.2. Green product innovation performance and competitive advantage
In today’s environmentally conscious market, organizations must embed green practices within their core strategies (Athwal et al.,
2019; Hasan, 2013). Green product innovation performance, dened as a rm’s capability to develop and launch eco-friendly
products, is increasingly recognized as a pivotal aspect of competitive differentiation (Soewarno et al., 2019).
Organizations committed to sustainable innovation often see enhanced customer loyalty and improved brand reputation, as today’s
consumers are more inclined toward environmentally sustainable products and services (Weng et al., 2015; Peattie and Charter, 1992;
Pickett-Baker and Ozaki, 2008). By innovating and offering green products, companies meet these evolving consumer demands and
effectively distinguish themselves from competitors, securing a competitive advantage (Fernando et al., 2019; Johne, 1999).
Furthermore, green product innovation ensures compliance with increasingly stringent environmental regulations. It positions
rms as proactive market leaders, favorably inuencing their regulatory landscape while avoiding penalties and the negative publicity
associated with non-compliance (Camilleri, 2022; Handeld et al., 1997). Embracing eco-friendly technologies and practices leads to
cost savings and improved operational efciency. By re-engineering production processes, organizations can utilize resources more
efciently and minimize waste, enhancing both cost-effectiveness and competitiveness (Khan et al., 2022; Santos et al., 2019; Ford and
Despeisse, 2016; Bj¨
orkdahl, 2020; Rao and Holt, 2005).
Moreover, green product innovation positively inuences employee engagement and motivation. In an era where employees are
increasingly drawn to companies that demonstrate a commitment to sustainability and social responsibility, fostering a culture of
environmental awareness and innovation can attract and retain talent, bolstering long-term competitive advantage (Mirvis, 2012; Raza
et al., 2021; Lado and Wilson, 1994; Yong et al., 2020).
In summary, the role of green product innovation in shaping a rm’s competitive edge is multi-faceted. It encompasses aspects
ranging from customer loyalty and brand reputation to regulatory compliance, operational efciencies, and human resource benets.
As global businesses navigate the complexities of sustainability, the signicance of green product innovation in achieving competitive
advantage continues to gain prominence among practitioners and scholars alike.
2.3. R&D investments, AI adoption, and competitive advantage
R&D investment is an essential driver of innovation and competitive advantage (Porter and Stern, 2001; Skordoulis et al., 2020),
enabling rms to develop new products and services, enhance existing offerings, and improve operational efciency (Edeh et al.,
2020). By remaining at the forefront of technological advancement, companies can gain a competitive edge through constant inno-
vation, faster time-to-market, and better alignment with customer needs (Battisti et al., 2022).
Recent advancements in technology, particularly the rise of AI, are also crucial in shaping a rm’s competitiveness (Lee et al.,
2019). AI adoption can signicantly improve productivity, decision-making, and customer experience, ultimately contributing to a
rm’s competitive advantage (Gupta et al., 2020; Leszkiewicz et al., 2022). For example, AI can help companies analyze vast amounts
of data to identify trends, optimize processes, and enhance their decision-making capabilities (Rathore, 2023). AI-powered tools can
also improve the customer experience by offering personalized recommendations, automating routine tasks, and providing instant
support (Nwachukwu and Affen, 2023).
The synergistic effect of R&D investment and AI adoption on competitive advantage can be explained by dynamic capabilities
(Mikalef et al., 2021), that is, a rm’s ability to sense, seize, and recongure resources to respond to changing market conditions and
maintain competitiveness (Linde et al., 2021). Firms can build and strengthen their dynamic capabilities by investing in R&D and
adopting AI technologies, which allows them to adapt more effectively to a rapidly evolving business environment (Garbellano and Da
F. Abdelfattah et al.
International Journal of Innovation Studies 8 (2024) 154–171
157
Veiga, 2019).
This integration can help develop new business models and value propositions, further enhancing a rm’s competitive position
(Chen et al., 2021). For instance, companies that leverage AI to create innovative products or services, such as autonomous vehicles or
AI-driven medical diagnostics, can disrupt existing markets and create entirely new industries (Strusani and Houngbonon, 2019).
To summarize, R&D investment and AI adoption play critical roles in determining a rm’s competitive advantage by driving
innovation, improving operational efciency, and enabling the development of dynamic capabilities. As technology advances rapidly,
understanding the impact of R&D investment and AI adoption on competitive advantage has become vital for businesses, policy-
makers, and researchers.
2.4. Intellectual capital as a moderating variable
Intellectual capital, the collective knowledge, skills, and expertise within an organization, is a vital intangible asset for driving
innovation and maintaining competitiveness (Choudhury, 2010; Salehi et al., 2022). It encompasses human capital (employees’
knowledge and skills), structural capital (organizational systems and processes), and relational capital (external relationships and
networks) (Hsu and Fang, 2009; Mahmood and Mubarik, 2020). These components work together to create value and facilitate the
effective use of resources, including R&D investment and technology adoption (Huang et al., 2010).
Youndt et al. (2004) suggest that intellectual capital can moderate the relationship between various organizational resources,
including R&D investments and innovation performance, ultimately inuencing competitive advantage. Firms with high intellectual
capital are better positioned to leverage their green product innovation, R&D, and AI investments to achieve superior competitive
outcomes (Subramaniam and Youndt, 2005). For instance, a robust human capital base enables companies to generate novel ideas,
solve complex problems, and effectively implement innovative solutions (Kutieshat and Farmanesh, 2022; Nonaka et al., 1995).
Structural capital, in turn, can facilitate the efcient dissemination and application of knowledge across organizations (Nonaka et al.,
1995). Finally, relational capital allows rms to access external resources and expertise, thus fostering collaboration and co-innovation
with partners and stakeholders (Albort-Morant et al., 2018; Leal-Mill´
an et al., 2016).
Regarding the role of intellectual capital in enhancing the effectiveness of R&D investment and AI adoption, Roper et al. (2017) nd
that a solid intellectual capital base can improve the returns on R&D investments, resulting in more extraordinary innovation per-
formance (Roper and Turner, 2020). Similarly, Hughes and Wareham (2010) argue that rms with higher levels of intellectual capital
are better equipped to exploit the opportunities offered by AI technologies, translating these investments into a competitive advantage.
The moderating effect of intellectual capital on the relationships between green product innovation performance, R&D in-
vestments, AI adoption, and competitive advantage highlights the importance of fostering a knowledge-driven culture within orga-
nizations (Mehralian et al., 2018). This includes investing in employee training and development, implementing efcient knowledge
management systems, and nurturing external relationships and networks (Chen and Huang, 2009; Mehralian et al., 2018).
In summary, intellectual capital is crucial for moderating the relationships between organizational resources, including green
product innovation, R&D investments, AI adoption, and competitive advantage. By understanding and leveraging their intellectual
capital, rms can maximize the benets of their investments in sustainability, technology, and innovation, ultimately enhancing their
competitive position in the market.
2.5. Government involvement as a moderating variable
Governments play a crucial role in shaping the business environment through policies, regulations, and support measures (Liu,
2023; Luo et al., 2010). Government involvement can signicantly inuence rm performance and competitiveness (Songling et al.,
2018), for example, through policies promoting sustainable practices and technological innovation (Khan et al., 2021). As a moder-
ating variable, government involvement can affect the strength or direction of the relationship between green product innovation
performance, R&D investments, and AI adoption with competitive advantage (Dong et al., 2020, 2022).
Governments can provide nancial incentives, such as grants, subsidies, or tax breaks, to encourage rms to invest in sustainable
initiatives and adopt innovative technologies (Mohammed et al., 2023; Jaffe et al., 2005). These incentives can lower barriers to entry
for rms, allowing them to allocate resources more effectively toward green product innovation, R&D, and AI adoption (Chien et al.,
2021). They can create regulatory frameworks that promote sustainable business practices and support the diffusion of innovative
technologies, such as setting emission targets, implementing product labeling schemes, and establishing industry standards (Ceschin
and Vezzoli, 2010; Wiegmann et al., 2017).
In addition to direct support measures, government involvement can inuence rm competitiveness by fostering a conducive
innovation ecosystem (Smorodinskaya et al., 2017). This includes investing in public R&D infrastructure, promoting
industry-academia collaboration, and facilitating access to knowledge and resources (Lundvall, 1992; Zhou and Wang, 2023). By
creating an enabling environment for innovation, governments can enhance the effectiveness of rms’ investments in green product
development, R&D, and AI adoption, ultimately contributing to competitive advantage (Khalil and Nimmanunta, 2022; Laosir-
ihongthong et al., 2014).
The moderating effect of government involvement on the relationships of green product innovation performance, R&D in-
vestments, and AI adoption with competitive advantage highlights the importance of collaborative efforts between governments and
businesses (Luo et al., 2010; Saberi and Hamdan, 2019). Governments can design and implement policies that foster sustainable and
innovative practices by understanding and responding to private sector needs, ultimately driving competitiveness and economic
growth.
F. Abdelfattah et al.
International Journal of Innovation Studies 8 (2024) 154–171
158
In summary, government involvement can signicantly inuence the relationships between green product innovation perfor-
mance, R&D investments, and AI adoption with a competitive advantage by providing nancial incentives, creating a conducive
regulatory environment, and fostering a supportive innovation ecosystem. As a moderating variable, government involvement un-
derscores the importance of public-private partnerships in promoting sustainable and innovative practices, ultimately enhancing
rms’ competitiveness in the market. Based on the above literature review of theories and essential constructs, this study proposes the
conceptual framework shown in Fig. 1.
Based on the literature review and the proposed theoretical framework, we present the following hypotheses.
1. H1: Green product innovation performance is positively associated with a rm’s competitive advantage.
•Rationale: This hypothesis is premised on the idea that rms engaging in green product innovation can gain a competitive edge
due to increasing consumer demand for sustainable products and potential regulatory benets.
2. H2: R&D investment positively correlates with a rm’s competitive advantage.
•Rationale: The hypothesis suggests that rms investing in research and development can create innovative products or processes,
enhancing market position and protability.
3. H3: AI adoption positively affects a rm’s competitive advantage.
•Rationale: This hypothesis posits that implementing articial intelligence within business operations enhances efciency,
innovation, and market responsiveness, thereby boosting competitive advantage.
4. H4a: Intellectual capital positively moderates the relationship between green product innovation performance and competitive
advantage.
•Rationale: The hypothesis implies that substantial intellectual capital (like skilled workforce knowledge assets) enhances the
effectiveness of green innovation efforts in improving competitive advantage.
5. H4b: Intellectual capital positively moderates the relationship between R&D investment and competitive advantage.
•Rationale: This suggests that intellectual capital strengthens the impact of R&D investment on competitive advantage, likely
through better innovation management and knowledge application.
6. H4c: Intellectual capital positively moderates the relationship between AI adoption and a rm’s competitive advantage.
•Rationale: The hypothesis argues that intellectual capital (e.g., tech-savvy staff, innovative culture) boosts the benets of
adopting AI in enhancing a rm’s competitiveness.
7. H5a: Government involvement positively moderates the relationship between green product innovation performance and
competitive advantage.
•Rationale: This hypothesis assumes that government support, through policies or incentives, amplies the positive impact of
green product innovation on competitive advantage.
8. H5b: Government involvement positively moderates the relationship between R&D investment and competitive advantage.
Fig. 1. Research model.
F. Abdelfattah et al.
International Journal of Innovation Studies 8 (2024) 154–171
159
•Rationale: It suggests that governmental policies and assistance can enhance the effectiveness of R&D investments in boosting a
rm’s competitive position.
9. H5c: Government involvement positively moderates the relationship between AI adoption and competitive advantage.
•Rationale: The hypothesis postulates that government support, through initiatives or funding, can enhance the impact of AI
adoption on a rm’s competitive edge.
3. Method
This study employed a survey-based research design and used SEM with the SmartPLS program to investigate the relationships
between green product innovation performance, R&D investments, AI adoption, intellectual capital, government involvement, and the
rm’s competitive advantage. The following sections describe the methodology, including the sample, data collection procedure,
measures, and data analysis.
3.1. Sample and data collection
The target population comprised top management personnel from various rms in Oman, including founders, presidents, vice
presidents, and top management; 214 top management members representing a diverse range of industries and rm sizes participated.
Owing to time and resource constraints, the sample was selected using a non-probability convenience sampling method.
The data were collected through an online survey that included structured questionnaires designed to measure the constructs of
interest. Participants were informed of the purpose of the study, the voluntary nature of their participation, and the condentiality of
their responses. Informed consent was obtained from all participants before they began the survey.
3.2. Measures
All the constructs were measured using a 5-point Likert scale adapted from the existing literature to ensure their validity and
reliability. The following measures were employed in this study:
First, green product innovation performance was assessed using a scale adapted from Chen et al. (2006). This scale includes items
related to environmental performance, customer satisfaction, and adopting green technologies, providing a comprehensive view of a
rm’s green product innovation efforts.
Second, R&D investment was measured using a scale adapted from Clausen (2013), with items related to the amount of nancial
resources dedicated to R&D activities, the number of R&D projects, and the proportion of employees engaged in R&D, thereby
capturing the extent of a rm’s commitment to R&D.
Third, AI adoption was assessed using a scale adapted from Chen et al. (2021) and based on Chau and Tam (1997), with items
related to the extent of AI implementation in various business processes, the level of AI integration, and the use of AI-driven deci-
sion-making, providing insights into how deeply AI has been adopted within a rm.
Fourth, intellectual capital was measured using a scale adapted from Alrowwad et al. (2020), with items related to human capital
(knowledge, skills, and expertise of employees), structural capital (organizational structures, processes, and systems), and relational
capital (relationships with customers, suppliers, and other stakeholders), offering a comprehensive assessment of a rm’s intellectual
resources.
Fifth, government involvement was assessed using a scale adapted from Oliveira et al. (2014), with items related to government
support for innovation, regulatory incentives for sustainable practices, and public-private collaboration initiatives. This measure
captures how government policies and actions affect rms’ innovation and sustainability practices.
Finally, competitive advantage was measured using a scale adapted from Chen et al. (2006), with items related to a rm’s market
position, protability, and growth, providing an understanding of its performance compared with its competitors.
4. Data analysis
This study employed the partial least squares (PLS) method for SEM. The research model was analyzed using SmartPLS 4 software
(Ringle et al., 2015). The two-stage analytical approach suggested by Hair et al. (2019, 2017) was employed. The initial stage involved
an assessment of the measurement model, which included an evaluation of the reliability and validity of the constructs. The second
stage entailed executing a structural model assessment that examined the relationships proposed in the hypotheses.
4.1. Demographic analysis
The respondents’ age distribution revealed that most were between 25 and 50. Specically, 53.3% (114 respondents) were in the
25–30 age group, whereas 41.1% (88 respondents) were in the 41–50 age group. A small percentage (5.6%, 12 respondents) were
younger than 25.
An analysis of the gender composition of the respondents showed a relatively even distribution between male and female par-
ticipants. Among the respondents, 52.3% (112) were male, while 47.7% (102) were female.
Regarding job experience, 50.5% (108 respondents) had 6–10 years of experience, followed by 29% (62 respondents) with 11–15
years of experience. The remaining 20.6% (44 respondents) had 3–5 years of job experience.
F. Abdelfattah et al.
International Journal of Innovation Studies 8 (2024) 154–171
160
Regarding job titles, most respondents held top management positions, such as CEO (chief executive ofcer), COO (chief operating
ofcer), or CFO (chief nancial ofcer), accounting for 80.4% (172 respondents) of the sample. Founders constituted 7% (15 re-
spondents), while 12.6% (27 respondents) held the title of president or vice president. The education level of the respondents was
predominantly undergraduate, with 70.1% (150) having completed their bachelor’s degrees. Moreover, 29.9% (64 respondents) held
postgraduate degrees (Table 1).
4.2. Measurement model assessment
The measurement model was assessed by examining its construct validity, encompassing convergent and discriminant validity and
construct reliability. For construct reliability, Cronbach’s
α
composite reliability (CR) values were used to evaluate the reliability of
each principal variable in the measurement model. The item reliability results showed no issues, with most items surpassing the
recommended threshold of 0.70 (Hair et al., 2017), as illustrated in Table 2. The ndings revealed that all individual Cronbach’s
α
composite reliability values ranged between 0.767 and 0.827, exceeding the suggested 0.70 (Hair et al., 2017, 2019). Consequently,
construct reliability was successfully achieved, as shown in Table 2.
All AVE values ranging from 0.508 to 0.655 and exceeding the recommended 0.50 were examined to assess convergent validity
(Hair et al., 2017). This outcome validates the successful attainment of convergent validity, as shown in Table 2.
Discriminant validity was evaluated using two Heterotrait–Monotrait ratio (HTMT) approaches. Discriminant validity arises when
the HTMT surpasses the HTMT0.85, which is 0.85. Table 3 demonstrates that the HTMT values are below the 0.85 threshold, con-
rming discriminant validity for each construct pair (Henseler et al., 2015).
4.3. Structural model assessment
In this section, direct hypotheses H1 to H3 are presented. Table 4 presents the ndings; we note no multicollinearity concerns
because the variance ination factor values are much lower than the 5.0 cutoff (Hair et al., 2017) as seen in Table 4.
Hypothesis testing offers an initial indication of the direct effect (H1). A signicant relationship exists between green product
innovation and competitive advantage. Consequently, H1 was accepted, with β =0.386, t =4.530, and p <0.000. The second direct
effect (H2) examined the relationship between R&D investment and competitive advantages, which was positively signicant with
values of β =0.298, t =3.884, and p <0.000. Similarly, for H3, AI adoption has a signicant relationship with competitive advantage,
yielding β =0.275, t =3.750, and p <0.000. The results are summarized in Table 5.
The moderation test results, which aimed to determine whether government involvement and intellectual capital moderate the
relationship between the independent variables (i.e., green product innovation, R&D investment, and AI adoption) and the dependent
variable (i.e., competitive advantages), are presented in Table 6. The moderation effect was tested in line with the study’s objective,
yielding the following results for the six interactions.
The rst assesses the inuence of green product innovation and government involvement on competitive advantage, revealing a
signicant interaction with β =0.171, t =3.640, and p <0.000. Consequently, H4a is supported. The second interaction examines the
impact of R&D investment and government involvement on competitive advantage, revealing a substantial interaction with values of β
=0.138, t =2.471, and p <0.000. Thus, H4b is accepted. The third interaction explored the relationship between AI adoption and
government involvement in competitive advantage, with the statistical analysis indicating a positive interaction with values of β =
0.098, t =2.166, and p <0.001. Therefore, H4c was supported.
Table 1
Description of respondents.
Demographic Item Categories Frequency Percentage
Age Below 25 Years 12 5.6
25–30 Years 114 53.3
41–50 Years 88 41.1
Total 214 100.0
Gender Female 102 47.7
Male 112 52.3
Total 214 100.0
Job Experience 3–5 Years 44 20.6
6–10 Years 108 50.5
11–15 years 62 29.0
Total 214 100.0
Job Title Top Management (CEO, COO, CFO) 172 80.4
Founder 15 7
President or Vice president 27 12.6
Total 214 100
Type of University Public University 150 70.1
Private University 64 29.9
Total 214 100
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Table 2
Measurement model, loading, construct reliability and convergent validity.
Construct Reliability
Constructs Items Loading (>0.5) Cronbach’s
α
Composite reliability (rho_c) AVE (>0.5)
Green Product Innovation GPI1 0.718 0.825 0.832 0.653
GPI2 0.840
GPI3 0.819
GPI4 0.741
GPI5 0.898
R&D Investment RDI1 0.747 0.809 0.860 0.508
RDI2 0.844
RDI3 0.779
RDI4 0.895
RDI5 0.824
AI adoption AI1 0.811 0.767 0.789 0.620
AI2 0.753
AI3 0.758
AI4 0.786
AI5 0.803
Government Involvement GOVI1 0.806 0.827 0.883 0.655
GOVI2 0.826
GOVI3 0.785
GOVI4 0.768
GOVI5 0.719
Intellectual Capital INP1 0.794 0.808 0.824 0.621
INP2 0.854
INP3 0.792
INP4 0.748
INP5 0.835
Completive Advantages FCA1 0.750 0.814 0.827 0.514
FCA2 0.825
FCA3 0.806
FCA4 0.720
FCA5 0.794
FCA6 0.714
FCA7 0.746
FCA8 0.811
Note: AVE refers to the average variance extracted.
Table 3
HTMT - matrix.
Variables AI
Adaption
Completive
Advantages
Government
Involvement
Green Product
Innovation
Intellectual
Capital
R&D
Investment
AI Adaption 0.591
Completive Advantages 0.267 0.874
Government
Involvement
0.273 0.438 0.546
Green Product
Innovation
0.213 0.349 0.724 0.675
Intellectual Capital 0.161 0.521 0.482 0.545 0.623
R&D Investment 0.162 0.443 0.320 0.450 0.719 0.711
Notes: Bold values on the diagonal in the correlation matrix are square roots of AVE (variance shared between the constructs and their respective
measures). Off-diagonal elements above the diagonal are the HTMTs and their respective condence intervals at the 95% condence level.
Table 4
Multicollinearity.
Variables Multicollinearity Results
Product Innovation Performance 1.664
R&D Investments 2.275
AI Adoption 1.834
Government Involvement 1.359
Intellectual Capital 2.140
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Regarding the second moderation (i.e., intellectual capital), the rst interaction between green product innovation and intellectual
capital on competitive advantages exhibited a signicant interaction with β =0.113, t =4.521, and p <0.000, supporting H5a. The
interaction between R&D investment and intellectual capital on competitive advantages yielded β =0.162, t =5.785, and p <0.000,
conrming H5b. Surprisingly, the interaction between AI adoption and intellectual capital on competitive advantages was insigni-
cant, displaying β =0.045, t =1.024, and p <0.153. As a result, H5c is not supported. The results are summarized in Table 6.
Generally, the distinction between high and low interactions in a moderation analysis is not easily discernible by merely examining
the coefcients. Dawson (2014) suggests that visualizing these interactions could provide further insights. This study employed
interaction plots for each of the four interactions to investigate slope gradients.
As illustrated in Fig. 2, the line labeled “high government involvement” for the rst interaction exhibits a steeper gradient than “low
government involvement.” This nding indicates that the positive relationship between green product innovation and competitive
advantage is more robust when government involvement is high (Fig. 2). The second interaction among R&D investment, government
involvement, and competitive advantage demonstrates that the positive relationship between R&D investment and competitive
advantage is more signicant when government involvement is higher (Fig. 3).
Fig. 4 presents the interactions between AI adoption, government involvement, and competitive advantage. As observed in the
interaction, a high government involvement value reinforces the positive relationship between AI adoption and competitive
Table 5
Structural path analysis: Direct effect.
Bias and Corrected Bootstrap
95% CI
Hypothesis Relationship Std Beta Std
Error
t-
value
p-
values
[Lower Level; Upper
Level]
Decision
H1 Green Product Innovation - >Completive
Advantages
0.386 0.060 4.530 0.000 [0.275; 0.475] Yes
H2 R&D Investment - >Completive Advantages 0.298 0.066 3.884 0.000 [0.075; 0.293] Yes
H3 AI Adaption- >Completive Advantages 0.275 0.073 3.750 0.000 [0.166; 0.404] Yes
Notes: VIF=Variance Ination Factor.
Table 6
Structural path analysis: The interaction effect.
Bias and Corrected
Bootstrap
95% CI
Hypothesis Relationship Std
Beta
Std
Error
t-
value
p-
values
[Lower Level; Upper
Level]
Decision
H4a Green Product Innovation ×Government Involvement- >
Completive Advantages
0.171 0.056 3.640 0.000 [0.041; 0.203] Yes
H4b R&D Investment ×Government Involvement - >
Completive Advantages
0.138 0.050 2.471 0.000 [0.064; 0.232] Yes
H4c AI Adaption ×Government Involvement - >Completive
Advantages
0.098 0.048 2.166 0.001 [0.027; 0.176] Yes
H5a Green Product Innovation ×Intellectual Capital- >
Completive Advantages
0.113 0.055 4.521 0.000 [0.041; 0.203] Yes
H-5b R&D Investment ×Intellectual Capital - >Completive
Advantages
0.162 0.028 5.785 0.000 [0.064; 0.232] Yes
H-5c AI Adaption ×Intellectual Capital- >Completive
Advantages
0.045 0.044 1.024 0.153 [-0.117; 0.020] No
Fig. 2. Green Product Innovation ×Government Involvement interaction on the Completive Advantages.
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advantage, strengthening the relationship when government involvement is high (Fig. 4). Additionally, Fig. 5 indicates that the
relationship between green product innovation and competitive advantage is more signicant when intellectual capital is high than
when it is low. Finally, intellectual capital enhances the positive relationship between R&D investment and competitive advantage
(Fig. 6).
Concerning the overall explanatory power of the model, R
2
=0.665 for competitive advantage, which can be characterized as
having a moderate to substantial effect (Hair et al., 2017). The Stone–Geisser blindfolding sample reuse technique revealed Q
2
values
greater than zero, indicating that the research model in this study is suitable for predicting competitive advantages (Q
2
=0.233) (Hair
et al., 2017).
In terms of overall goodness-of-t (GoF), the standardized root mean square (SRMR) residual index yields a value of 0.042, which is
Fig. 3. R&D Investment ×Government Involvement interaction on the Completive Advantages.
Fig. 4. AI Adaption ×Government Involvement interaction on the Completive Advantages.
Fig. 5. Green Product Innovation ×Intellectual Capital interaction on the Completive Advantages.
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considerably below the 0.08 cutoff (Henseler et al., 2015). The SRMR 95% bootstrap quantile was also 0.059, higher than the SRMR
value (0.042), implying that the model exhibited a good t (Hair et al., 2017). Finally, the discrepancy indexes dULS (unweighted least
squares discrepancy) and dG (geodesic discrepancy) are also beneath the bootstrap-based 95% percentile (dULS =1.371 <HI 95 of
dULS =2.852; dG =0.462 <HI 95 of dG =0.881) (Hair et al., 2017). In summary, the discrepancy between the empirical and
model-based correlation matrices is insignicant, suggesting that the model is likely to be accurate (Henseler et al., 2015).
5. Discussion
The results of this study reveal a positive relationship between all the variables, namely, green product innovation performance,
R&D investments, AI adoption, intellectual capital, government involvement, and the rm’s competitive advantage.
Consistent with the ndings of previous research (Chang, 2011; Nanath and Pillai, 2017), the positive relationship between green
product innovation performance and a rm’s competitive advantage can be explained by several factors contributing to the
competitive advantage of rms engaging in environmentally friendly product development.
The results of this study support the notion that green product innovation can increase customer loyalty (Chang and Fong, 2010; Le,
2022). As consumer awareness of environmental issues increases, companies that demonstrate a commitment to sustainability are
more likely to attract and retain environmentally conscious customers. This phenomenon can be explained through the value congruity
theory, which posits that consumers are more loyal to brands that align with their values (Sharma, 2019; Varadarajan, 2017). In this
context, green product innovation signals a rm’s commitment to environmental stewardship, resulting in stronger customer loyalty
and improved competitive advantage (Cronin et al., 2011; Fattah et al., 2023).
This study’s positive relationship between green product innovation and improved brand reputation aligns with the ndings of
previous research (Gangi et al., 2020; Sezen and Cankaya, 2013). By developing and marketing environmentally friendly products,
companies can enhance their brand image and showcase their commitment to corporate social responsibility (CSR). This improved
brand reputation can strengthen customer relationships, attract new customers, and contribute to competitive advantages. Signaling
theory can help explain this relationship because green product innovation signals stakeholders’ commitment to sustainability (Khan
et al., 2021; Vesal et al., 2021; Yu et al., 2017).
Our ndings also suggest that green product innovation can help rms comply with government regulations, thus contributing to
their competitive advantage (Aguilera-Caracuel and Ortiz-de-Mandojana, 2013; Eiadat et al., 2008; Sahoo et al., 2023). Proactive
engagement in sustainable product development enables rms to anticipate regulatory changes and adapt accordingly, thus avoiding
penalties and outperforming less proactive competitors. Additionally, by aligning with government policies that promote sustainable
practices, rms can benet from incentives and subsidies to encourage eco-friendly innovation. The positive relationship between
green product innovation performance and competitive advantage emphasizes the strategic importance of sustainability in the modern
business landscape (Doran and Ryan, 2016; Fernando et al., 2019). Companies prioritizing sustainable practices are better equipped to
adapt to changing consumer preferences, comply with environmental regulations, and manage resource constraints. In addition,
embracing sustainability as a core business strategy can unlock new market opportunities and drive innovation, ultimately enhancing a
rm’s competitiveness (Klein et al., 2022).
Companies can leverage these ndings to develop and market environment-friendly products, leading to increased customer
loyalty, improved brand reputation, regulatory compliance, and enhanced competitiveness. These ndings contribute to the growing
body of literature on the strategic importance of sustainability and its impact on rm performance, providing a basis for future research
in this area.
The positive relationship between R&D investments, AI adoption, and the rm’s competitive advantage corroborates the ndings
conrming the signicance of technological innovation in driving competitiveness (Nicodemus and Egwakhe, 2019). The results of this
study support the notion that investing in R&D enables rms to develop innovative products, leading to competitive advantage (Lim
et al., 2010).
Companies prioritizing R&D investments are more likely to create cutting-edge products that address customer needs, differentiate
themselves from competitors, and secure a leading position in the market. The positive relationship between R&D investments and
Fig. 6. R&D Investment ×Intellectual Capital interaction on the Completive Advantages.
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competitive advantage can be explained through the dynamic capabilities framework, which posits that rms can create and maintain
competitive advantages by continuously adapting to and developing new competencies (Skordoulis et al., 2020; Tubbs, 2007).
This study also nds a positive relationship between AI adoption and a rm’s competitive advantage, which aligns with previous
research ndings (Brynjolfsson and McAfee, 2014; Krakowski et al., 2022). Adopting AI technologies can enable rms to streamline
their operations by automating repetitive tasks, optimizing resource allocation, and improving process efciency. Consequently,
companies can reduce costs, increase productivity, and enhance overall competitiveness (Brynjolfsson and Mcafee, 2017). A rm’s
resource-based view can help explain this relationship because AI adoption allows rms to exploit valuable, rare, and inimitable
resources in the form of advanced technologies (Mikalef et al., 2019). The positive relationship between AI adoption and competitive
advantage can also be attributed to improved decision-making capabilities (Li et al., 2022).
AI technologies can process vast amounts of data, identify patterns, and generate insights that support strategic decision-making.
By leveraging AI-driven insights, companies can make better-informed decisions, react quickly to market changes, and outperform
their competitors. The results underscore the importance of continuous innovation and technological adoption in maintaining
competitiveness (Brynjolfsson and Mcafee, 2017; Martini et al., 2013).
As the market landscape evolves rapidly, businesses that prioritize R&D investments and adopt advanced technologies such as AI
are better positioned to adapt to changing customer preferences, capitalize on emerging opportunities, and stay ahead of their
competitors. Firms can leverage the present ndings to prioritize R&D investments and adopt AI technologies, enabling them to
develop cutting-edge products, streamline operations, and enhance their decision-making capabilities (Horowitz et al., 2018; Nwa-
chukwu and Affen, 2023; Teo and Pian, 2003).
The results also indicate that intellectual capital positively moderates the relationships among green product innovation perfor-
mance, R&D investments, AI adoption, and the rm’s competitive advantage. This nding aligns with previous research, underlining
the pivotal role of intellectual capital in driving innovation and maintaining competitiveness (Deb and Wiklund, 2017; Mention and
Bontis, 2013; Rehman et al., 2023). We further explore the underlying mechanisms through which intellectual capital moderates these
relationships and offer insights into the implications for practitioners and researchers.
The positive moderating effect of intellectual capital suggests that organizations with high intellectual capital are better positioned
to capitalize on their investments in green product innovation, R&D, and AI adoption (Nirino et al., 2022; Secundo et al., 2020). Firms
with solid intellectual capital can effectively utilize their knowledge, resources, expertise, and innovative capabilities to create value
from these investments, enhancing their competitive advantage.
The importance of intellectual capital in the examined relationships underscores the need to foster a knowledge-driven culture
within organizations (Chahal and Bakshi, 2015; Mehralian et al., 2018). Creating an environment that encourages knowledge sharing,
learning, and collaboration can help organizations maximize the benets of their investments in green product innovation, R&D, and
AI adoption. A knowledge-driven culture can facilitate the continuous improvement and adaptation required to maintain a competitive
edge in rapidly changing markets.
These ndings highlight the signicance of investing in human capital development to enhance the effectiveness of green product
innovation, R&D, and AI adoption efforts (Agyabeng-Mensah and Tang, 2021). Organizations can harness their intellectual capital to
drive innovation and competitiveness by nurturing employees’ skills, competencies, and creativity. Investments in human capital
development can include targeted training programs, mentorship opportunities, and the creation of a supportive work environment
that encourages personal and professional growth (Debrah et al., 2018; Lengnick-Hall, 2002).
The moderating role of intellectual capital in the relationship between green product innovation performance, R&D investments,
and competitive advantage demonstrates the interplay between knowledge resources and technological innovation (Tze San et al.,
2022; Wang et al., 2009). This interplay suggests that organizations can achieve superior competitive outcomes by strategically
combining their intellectual capital with investment in innovative technologies and sustainable practices. Organizations can leverage
the above ndings to foster a knowledge-driven culture, invest in human capital development, and strategically combine their in-
tellectual capital with green product innovation, R&D, and AI adoption. Moreover, these ndings contribute to the growing body of
literature on the role of intellectual capital in shaping rm competitiveness and innovation and offer a foundation for future research in
this area (Dakhli and de Clercq, 2004; Wang et al., 2021; Mohammed Salah et al., 2023).
However, when examining the moderating role of intellectual capital on AI adoption and a rm’s competitive advantage, the
results obtained in the context of this study do not provide robust evidence to substantiate the claim that the interplay between AI
adoption and competitive advantage has a signicant bearing on a rm’s competitive advantage. Although it is plausible that AI
adoption and intellectual capital independently inuence competitive advantage, their combined impact appears to be inconse-
quential in the context investigated.
Many factors could explain the absence of signicant relationships between these variables. One potential explanation is that the
degree of AI adoption and the quality of intellectual capital in the sampled rms might not have reached a level sufciently advanced
to engender a discernible impact on competitive advantage. This could be attributed to variations in the stages of AI adoption or the
heterogeneity of intellectual capital across rms, potentially rendering the relationship between these factors and competitive
advantage more nuanced than initially hypothesized (Cerbioni and Parbonetti, 2007; Chen and Chen, 2022).
Another possibility is that external factors such as industry dynamics, market conditions, or rm-specic characteristics may
substantially inuence competitive advantage, eliminating the potential combined effect of AI adoption and intellectual capital. For
example, industry competition, the regulatory environment, or a rm’s strategic focus might play a more critical role in shaping
competitive advantage, which, in turn, could diminish the relative importance of the interaction between AI adoption and intellectual
capital (Petricevic and Teece, 2019; Szalavetz, 2022).
The positive relationship between government involvement and the other variables demonstrates the signicant role that
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governments, particularly the Omani government, play in shaping the business environment and promoting competitiveness
(Al-Masroori, 2006; Alqassabi, 2020; Saberi and Hamdan, 2019). We delve deeper into the mechanisms through which the Omani
government’s involvement moderates these relationships and offer insights into the implications for practitioners and researchers in
the Omani context.
The Omani government can play a pivotal role in encouraging green product innovation, R&D investments, and AI adoption
through supportive policies, funding, and incentives (Alqassabi, 2020; Ghouse et al., 2019). It can help local rms prioritize envi-
ronmentally friendly innovations, invest in R&D, and adopt AI technologies by providing a regulatory framework that encourages
sustainable practices and technological advancements. This, in turn, can enhance the competitive advantage of Omani rms in the
global market.
The positive moderating effect of government involvement on the relationships between the independent variables and a rm’s
competitive advantage underscores the importance of collaboration between the Omani government and businesses in promoting
innovation and competitiveness (Al-Kharusi, 2022). By establishing public–private partnerships, the Omani government can work
closely with rms to identify and address challenges related to green product innovation, R&D investments, and AI adoption, thus
maximizing the impact of these initiatives on rms’ competitive advantage.
The Omani government can also contribute to developing a supportive ecosystem for innovation and competitiveness by investing
in education, infrastructure, and research institutions (Sanyal and Hisam, 2018). These investments can help create a skilled work-
force, enhance the country’s technological capabilities, and foster a culture of innovation, ultimately beneting Omani rms and
contributing to their competitive advantages.
The ndings of this study in the Omani context offer insights into the role of government involvement in shaping rm competi-
tiveness in developing economies (Al-Mataani et al., 2017; Al Badi, 2019). The positive moderating effect of government involvement
in Oman highlights the potential benets of tailored government interventions and support mechanisms to address local rms’ specic
needs and challenges in pursuing innovation and competitiveness.
Discussing government involvement as a moderating variable in Omani provides valuable insights for practitioners, researchers,
and policymakers. By understanding how the Omani government’s involvement moderates the relationships among green product
innovation, R&D investments, AI adoption, and competitive advantage, stakeholders can develop and implement effective policies and
strategies to promote innovation and competitiveness among Omani rms. These ndings contribute to the growing body of literature
on the role of government involvement in shaping rm competitiveness in developing economies, thereby offering a foundation for
future research in this area.
The ndings of the present study have several practical implications. Businesses should prioritize green product innovation, invest
in R&D, and embrace AI technologies to enhance competitiveness. Moreover, they should develop intellectual capital through
employee training, knowledge sharing, and collaboration. Policymakers should design and implement supportive policies encouraging
sustainable practices, technological innovation, and collaboration between the public and private sectors.
This study contributes to the existing literature by exploring the complex relationships between green product innovation per-
formance, R&D investments, AI adoption, intellectual capital, government involvement, and a rm’s competitive advantage. The
positive relationships observed between these variables offer valuable insights for businesses, policymakers, and researchers interested
in understanding the interplay among sustainability, technological investments, and the role of governments in shaping rm
competitiveness. Future research should investigate the causal mechanisms of these relationships and explore how different industries
or contexts affect the observed relationships.
5.1. Theoretical contributions
This research transcends traditional boundaries in business competitiveness by constructing and exploring an integrative frame-
work. This framework uniquely coalesces green product innovation performance, R&D investment, AI adoption, intellectual capital,
and government involvement — elements pivotal to contemporary business strategy. This study enriches the existing business
administration and innovation studies corpus by dissecting and synthesizing the intricate interplay among these elements. It in-
troduces fresh perspectives on achieving competitive advantage in the modern business milieu.
A notable theoretical contribution of this research is elucidating the moderating roles of intellectual capital and government
involvement. These insights are crucial because they illuminate how these factors potentiate or constrain the effectiveness of in-
vestments in green innovation, R&D, and AI adoption. Such an understanding is critical for conceptualizing how rms can leverage
their unique resources and navigate regulatory landscapes to maximize competitive gain. This focus on the interaction between these
variables deepens our understanding of rm competitiveness’s nuances, moving beyond the traditional one-dimensional analysis of
business strategies.
Furthermore, this study’s cross-disciplinary approach, drawing from innovation management, strategic management, and eco-
nomics, represents a signicant leap in theory development. By interweaving principles and insights from these diverse elds, this
research creates a more comprehensive and multi-faceted view of what drives competitiveness in the contemporary business era. This
melding of disciplines bridges previously isolated theoretical discussions and provides a more encompassing lens through which the
determinants of competitive advantage can be understood and analyzed.
This research offers a groundbreaking approach to understanding rm competitiveness, weaving diverse theoretical strands into a
cohesive and comprehensive framework. Its contributions lie in highlighting the nuanced and multi-faceted nature of competitive
advantage, advancing our knowledge in the eld, and setting the stage for future explorations into the synergistic effects of technology,
intellectual assets, and policy on business success.
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5.2. Practical contributions
The practical implications of this study are manifold, providing a rich source of insights for business leaders, policymakers, and
industry practitioners. Central to its contributions is delineating how green product innovation, R&D investments, and AI adoption
interlink and collectively inuence a rm’s competitive edge. This knowledge is invaluable for corporate decision-makers, enabling
them to strategize more effectively and allocate resources with a nuanced understanding of these critical variables.
For businesses, especially those at the forefront of technological and environmental innovation, this study offers a roadmap for
integrating green initiatives with technological advancements. By revealing these elements’ strategic importance and interplay, this
study guides rms in prioritizing nancially sound and environmentally sustainable investments. Furthermore, the emphasis on in-
tellectual capital as a moderating factor underscores the signicance of nurturing human capital. This insight is pivotal for organi-
zations aiming to foster a culture of innovation and continuous learning, enhancing their competitive market position.
On the policy front, the study serves as a valuable resource for government bodies and regulatory agencies. The ndings underscore
the critical role of government involvement in shaping an innovation-friendly environment. Policymakers can leverage this research to
design and implement policies encouraging sustainable practices and supporting R&D and AI initiatives. These policies could accel-
erate economic growth and foster sustainable development, aligning with broader societal and environmental goals.
Moreover, the research highlights the essentiality of public–private collaborations in driving innovation and competitiveness. This
insight is particularly relevant in today’s complex business landscape, where challenges and opportunities in sustainability and
technology require a concerted effort from both sectors. The study, therefore, acts as a catalyst for such collaborations, providing a
framework for how governments and businesses can jointly navigate and capitalize on these emerging elds.
In summary, this study extends beyond academic circles, offering pragmatic guidance for businesses and policymakers alike. Its
insights into the synergistic effects of green innovation, technological investments, and government policy provide a solid foundation
for strategic decision-making, fostering a more sustainable and competitive business environment.
6. Limitations and recommendations
One of the primary limitations of this study is the sample size. First, the data were collected from 214 top managers in Oman.
Although this sample size is adequate for the analysis conducted in this study, it may not represent the entire business landscape in
Oman. Therefore, the generalizability of the ndings to a broader population may be limited. Future research with a more extensive
and diverse sample could help validate and extend the current ndings. Second, the data for this study were collected through a survey.
While surveys are a common and efcient means of data collection, they can be subject to response bias and self-reporting inaccuracies.
Respondents may provide socially desirable answers rather than reecting their behavior or attitudes. Despite efforts to mitigate these
biases, they remain a potential study limitation. Third, this study relies on cross-sectional data, which provides a snapshot of the
relationships at a specic point in time. Longitudinal or experimental designs could offer a more dynamic understanding of how the
variables interact and change over time. This study does not capture the evolving nature of these relationships, and causality cannot be
denitively established. Finally, the ndings of this study are based on data collected in the context of Oman. Oman’s business
environment, cultural factors, and government policies may differ from other regions or countries. Consequently, the transferability of
these ndings to other contexts may be limited, and caution should be exercised when applying these results in diverse settings.
Ethics statement
This study was approved by the Modern College of Business and Science Academic Integrity and Ethics Committee.
Declaration of competing interest
The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
Acknowledgments
The research leading to these results has received funding from the Research Council (TRC) of the Sultanate of Oman under the
Block Funding Program. TRC Block Funding Agreement No [MoHERI/BFP/ASU/01/2021].
References
Aguilera-Caracuel, J., Ortiz-de-Mandojana, N., 2013. Green innovation and nancial performance: an institutional approach. Organ. Environ. 26 (4), 365–385.
Agyabeng-Mensah, Y., Tang, L., 2021. The relationship among green human capital, green logistics practices, green competitiveness, and social and nancial
performance. J. Manuf. Technol. Manag. 32 (7), 1377–1398.
Al Badi, K., 2019. Implementation of marketing concept and organizational culture in SMEs in Al Buraimi–Oman. Benchmark Int. J. 26 (7), 2401–2414.
Al-Kharusi, S., 2022. The Role of Inward FDI in Enhancing Sultanate of Oman’s National Competitiveness. King’s College London.
Al-Masroori, R.S., 2006. Destination Competitiveness: Interrelationships between destination planning and development strategies and stakeholders’ support in
enhancing Oman’s tourism industry. In: Unpublished Doctoral Dissertation. Grifth University, Australia.
Albort-Morant, G., Leal-Mill´
an, A., Cepeda-Carrion, G., Henseler, J., 2018. Developing green innovation performance by fostering of organizational knowledge and
coopetitive relations. Review of Managerial Science 12 (2), 499–517.
F. Abdelfattah et al.
International Journal of Innovation Studies 8 (2024) 154–171
168
Alqassabi, M.A., 2020. Insights on sustainability of small and medium enterprises in Oman: a conceptual framework. Int. J. Econ. Financ. Issues 10 (1), 209–218.
Alrowwad, A.a., Abualoush, S.H., Masa’deh, R.e., 2020. Innovation and intellectual capital as intermediary variables among transformational leadership,
transactional leadership, and organizational performance. J. Manag. Dev. 39 (2), 196–222.
Al-Mataani, R., Wainwright, T., Demirel, P., 2017. Hidden entrepreneurs: informal practices within the formal economy. Eur. Manag. Rev. 14 (4), 361–376.
Athwal, N., Wells, V.K., Carrigan, M., Henninger, C.E., 2019. Sustainable luxury marketing: a synthesis and research agenda. Int. J. Manag. Rev. 21 (4), 405–426.
Aureli, S., Del Baldo, M., Lombardi, R., Nappo, F., 2020. Nonnancial reporting regulation and challenges in sustainability disclosure and corporate governance
practices. Bus. Strat. Environ. 29 (6), 2392–2403.
Baah, C., Jin, Z., Tang, L., 2020. Organizational and regulatory stakeholder pressures friends or foes to green logistics practices and nancial performance:
investigating corporate reputation as a missing link. J. Clean. Prod. 247, 119125.
Bal, R., Gill, I.S., 2020. Policy Approaches to Articial Intelligence Based Technologies in China. European Union, and the United States.
Barney, J., 1991. Firm resources and sustained competitive advantage. J. Manag. 17 (1), 99–120.
Battisti, S., Agarwal, N., Brem, A., 2022. Creating new tech entrepreneurs with digital platforms: meta-organizations for shared value in data-driven retail ecosystems.
Technol. Forecast. Soc. Change 175, 121392.
Bj¨
orkdahl, J., 2020. Strategies for digitalization in manufacturing rms. Calif. Manag. Rev. 62 (4), 17–36.
Brynjolfsson, E., McAfee, A., 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. WW Norton & Company.
Brynjolfsson, E., Mcafee, A., 2017. Articial intelligence, for real. Harv. Bus. Rev. 1, 1–31.
Cabrilo, S., Dahms, S., 2020. The role of multidimensional intellectual capital and organizational learning practices in innovation performance. Eur. Manag. Rev. 17
(4), 835–855.
Camilleri, M.A., 2022. Strategic attributions of corporate social responsibility and environmental management: the business case for doing well by doing good.
Sustain. Dev. 30 (3), 409–422.
Cerbioni, F., Parbonetti, A., 2007. Exploring the effects of corporate governance on intellectual capital disclosure: an analysis of European biotechnology companies.
Eur. Account. Rev. 16 (4), 791–826.
Ceschin, F., Vezzoli, C., 2010. The role of public policy in stimulating radical environmental impact reduction in the automotive sector: the need to focus on product-
service system innovation. Int. J. Automot. Technol. Manag. 10 (2–3), 321–341.
Chahal, H., Bakshi, P., 2015. Examining intellectual capital and competitive advantage relationship: Role of innovation and organizational learning. Int. J. Bank
Market. 33 (3), 376–399. https://doi.org/10.1108/IJBM-07-2013-0069.
Chang, C.-H., 2011. The inuence of corporate environmental ethics on competitive advantage: the mediation role of green innovation. J. Bus. Ethics 104, 361–370.
Chang, N.-J., Fong, C.-M., 2010. Green product quality, green corporate image, green customer satisfaction, and green customer loyalty. Afr. J. Bus. Manag. 4 (13),
2836.
Chau, P.Y., Tam, K.Y., 1997. Factors affecting the adoption of open systems: an exploratory study. MIS Q. 1–24.
Chen, C.-H.V., Chen, Y.-C., 2022. Inuence of intellectual capital and integration on operational performance: big data analytical capability perspectives. Chin.
Manag. Stud. 16 (3), 551–570.
Chen, C.-J., Huang, J.-W., 2009. Strategic human resource practices and innovation performance—the mediating role of knowledge management capacity. J. Bus. Res.
62 (1), 104–114.
Chen, Y.-S., Lai, S.-B., Wen, C.-T., 2006. The inuence of green innovation performance on corporate advantage in Taiwan. J. Bus. Ethics 67, 331–339.
Chen, H., Li, L., Chen, Y., 2021. Explore success factors that impact articial intelligence adoption on telecom industry in China. Journal of Management Analytics 8
(1), 36–68.
Chen, Y., Visnjic, I., Parida, V., Zhang, Z., 2021. On the road to digital servitization–The (dis) continuous interplay between business model and digital technology. Int.
J. Oper. Prod. Manag. 41 (5), 694–722.
Chien, F., Ngo, Q.-T., Hsu, C.-C., Chau, K.Y., Iram, R., 2021. Assessing the mechanism of barriers towards green nance and public spending in small and medium
enterprises from developed countries. Environ. Sci. Pollut. Control Ser. 28 (43), 60495–60510.
Chin, T., Li, G., Jiao, H., Addo, F., Jawahar, I., 2019. Career Sustainability during Manufacturing Innovation: a Review, a Conceptual Framework, and Future Research
Agenda. Career Development International.
Choudhury, J., 2010. Performance impact of intellectual capital: a study of Indian IT sector. Int. J. Bus. Manag. 5 (9), 72.
Clausen, T.H., 2013. External knowledge sourcing from innovation cooperation and the role of absorptive capacity: empirical evidence from Norway and Sweden.
Technol. Anal. Strat. Manag. 25 (1), 57–70.
Cronin, J.J., Smith, J.S., Gleim, M.R., Ramirez, E., Martinez, J.D., 2011. Green marketing strategies: an examination of stakeholders and the opportunities they
present. J. Acad. Market. Sci. 39, 158–174.
Dakhli, M., de Clercq, D., 2004. Human capital, social capital, and innovation: a multi-country study. Enterpren. Reg. Dev. 16 (2), 107–128.
Dawson, J.F., 2014. Moderation in management research: what, why, when, and how. J. Bus. Psychol. 29 (1), 1–19.
Deb, P., Wiklund, J., 2017. The effects of CEO founder status and stock ownership on entrepreneurial orientation in small rms. J. Small Bus. Manag. 55 (1), 32–55.
Debrah, Y.A., Oseghale, R.O., Adams, K., 2018. Human capital, innovation and international competitiveness in Sub-Saharan Africa. Africa’s Competitiveness in the
Global Economy 219–248.
Dong, Y., Wei, Z., Liu, T., Xing, X., 2020. The impact of R&D intensity on the innovation performance of articial intelligence enterprises-based on the moderating
effect of patent portfolio. Sustainability 13 (1), 328.
Dong, Q., Wu, Y., Lin, H., Sun, Z., Liang, R., 2022. Fostering Green Innovation for Corporate Competitive Advantages in Big Data Era: the Role of Institutional Benets.
Technology Analysis & Strategic Management, pp. 1–14.
Doran, J., Ryan, G., 2016. The importance of the diverse drivers and types of environmental innovation for rm performance. Bus. Strat. Environ. 25 (2), 102–119.
Edeh, J.N., Obodoechi, D.N., Ramos-Hidalgo, E., 2020. Effects of innovation strategies on export performance: new empirical evidence from developing market rms.
Technol. Forecast. Soc. Change 158, 120167.
Eiadat, Y., Kelly, A., Roche, F., Eyadat, H., 2008. Green and competitive? An empirical test of the mediating role of environmental innovation strategy. J. World Bus.
43 (2), 131–145.
Fattah, F.A., Al_Alawi, A., Salah, M., Alwishahi, M., 2023. Evaluating Strategic Implementation and Sustaining Therapeutic Lifestyle Changes in Sultanate of Oman:
the Impact of COVID-19 Pandemic.
Fernando, Y., Jabbour, C.J.C., Wah, W.-X., 2019. Pursuing green growth in technology rms through the connections between environmental innovation and
sustainable business performance: does service capability matter? Resour. Conserv. Recycl. 141, 8–20.
Ford, S., Despeisse, M., 2016. Additive manufacturing and sustainability: an exploratory study of the advantages and challenges. J. Clean. Prod. 137, 1573–1587.
Gangi, F., Daniele, L.M., Varrone, N., 2020. How do corporate environmental policy and corporate reputation affect risk-adjusted nancial performance? Bus. Strat.
Environ. 29 (5), 1975–1991.
Garbellano, S., Da Veiga, M.d.R., 2019. Dynamic Capabilities in Italian Leading SMEs Adopting Industry 4.0. Measuring Business Excellence.
Ghouse, S.M., McElwee, G., Durrah, O., 2019. Entrepreneurial success of cottage-based women entrepreneurs in Oman. Int. J. Entrepreneurial Behav. Res.
Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T., Potapov, D., 2020. Digital analytics: modeling for insights and new methods. J. Interact. Market. 51 (1), 26–43.
Hair, J.F., Sarstedt, M., Ringle, C.M., Gudergan, S.P., 2017. Advanced Issues in Partial Least Squares Structural Equation Modeling. SAGE Publications.
Hair, J.F., Risher, J.J., Sarstedt, M., Ringle, C.M., 2019. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31 (1), 2–24.
Hammer, M., 2001. The superefcient company. Harv. Bus. Rev. 79 (8), 82–93.
Handeld, R.B., Walton, S.V., Seegers, L.K., Melnyk, S.A., 1997. ’Green’value chain practices in the furniture industry. J. Oper. Manag. 15 (4), 293–315.
Hasan, M., 2013. Sustainable supply chain management practices and operational performance. Am. J. Ind. Bus. Manag. 3 (1), 42–48.
Hassan, M.S., Raja Arifn, R.N., Mansor, N., Al Halbusi, H., 2021. An examination of street-level bureaucrats’ discretion and the moderating role of supervisory
support: evidence from the eld. Adm. Sci. 11 (3), 65.
F. Abdelfattah et al.
International Journal of Innovation Studies 8 (2024) 154–171
169
Henseler, J., Ringle, C.M., Sarstedt, M., 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Market. Sci.
43 (1), 115–135.
Hitt, M.A., Keats, B.W., DeMarie, S.M., 1998. Navigating in the new competitive landscape: building strategic exibility and competitive advantage in the 21st
century. Acad. Manag. Perspect. 12 (4), 22–42.
Horowitz, M.C., Allen, G.C., Kania, E.B., Scharre, P., 2018. Strategic Competition in an Era of Articial Intelligence. Center for a New American Security.
Hsu, Y.-H., Fang, W., 2009. Intellectual capital and new product development performance: the mediating role of organizational learning capability. Technol.
Forecast. Soc. Change 76 (5), 664–677.
Huang, C., Arundel, A., Hollanders, H., 2010. How Firms Innovate: R&D, non-R&D, and Technology Adoption.
Hughes, B., Wareham, J., 2010. Knowledge arbitrage in global pharma: a synthetic view of absorptive capacity and open innovation. R&D Management 40 (3),
324–343.
Jaffe, A.B., Newell, R.G., Stavins, R.N., 2005. A tale of two market failures: technology and environmental policy. Ecol. Econ. 54 (2–3), 164–174.
Johne, A., 1999. Successful Market Innovation. Innovationsmanagement, pp. 163–170.
Kavanagh, C., 2019. New Tech, New Threats, and New Governance Challenges: an Opportunity to Craft Smarter Responses? Carnegie Endowment for International
Peace.
Khalil, M.A., Nimmanunta, K., 2022. Conventional versus green investments: advancing innovation for better nancial and environmental prospects. Journal of
Sustainable Finance & Investment 1–28.
Khan, P.A., Johl, S.K., Johl, S.K., 2021a. Does adoption of ISO 56002-2019 and green innovation reporting enhance the rm sustainable development goal
performance? An emerging paradigm. Bus. Strat. Environ. 30 (7), 2922–2936.
Khan, S.A.R., Razzaq, A., Yu, Z., Miller, S., 2021b. Industry 4.0 and circular economy practices: a new era business strategies for environmental sustainability. Bus.
Strat. Environ. 30 (8), 4001–4014.
Khan, M.T., Idrees, M.D., Rauf, M., Sami, A., Ansari, A., Jamil, A., 2022a. Green supply chain management practices’ impact on operational performance with the
mediation of technological innovation. Sustainability 14 (6), 3362.
Khan, S.A.R., Yu, Z., Farooq, K., 2022b. Green Capabilities, Green Purchasing, and Triple Bottom Line Performance: Leading toward Environmental Sustainability.
Business Strategy and the Environment.
Klein, N., Ramos, T.B., Deutz, P., 2022. Factors and strategies for circularity implementation in the public sector: an organisational change management approach for
sustainability. Corp. Soc. Responsib. Environ. Manag. 29 (3), 509–523.
Krakowski, S., Luger, J., Raisch, S., 2022. Articial intelligence and the changing sources of competitive advantage. Strat. Manag. J.
Kutieshat, R., Farmanesh, P., 2022. The impact of new human resource management practices on innovation performance during the COVID-19 crisis: a new
perception on enhancing the educational sector. Sustainability 14 (5), 2872.
Lado, A.A., Wilson, M.C., 1994. Human resource systems and sustained competitive advantage: a competency-based perspective. Acad. Manag. Rev. 19 (4), 699–727.
Laosirihongthong, T., Prajogo, D.I., Adebanjo, D., 2014. The relationships between rm’s strategy, resources, and innovation performance: resources-based view
perspective. Prod. Plann. Control 25 (15), 1231–1246.
Le, T.T., 2022. Corporate social responsibility and smes’ performance: mediating role of corporate image, corporate reputation and customer loyalty. Int. J. Emerg.
Mark.
Leal-Mill´
an, A., Rold´
an, J.L., Leal-Rodríguez, A.L., Ortega-Guti´
errez, J., 2016. IT and relationship learning in networks as drivers of green innovation and customer
capital: evidence from the automobile sector. J. Knowl. Manag.
Lee, J., Suh, T., Roy, D., Baucus, M., 2019. Emerging technology and business model innovation: the case of articial intelligence. Journal of Open Innovation:
Technology, Market, and Complexity 5 (3), 44.
Lengnick-Hall, M., Lengnick-Hall, C., 2002. Human Resource Management in the Knowledge Economy: New Challenges, New Roles, New Capabilities. Berrett-Koehler
Publishers.
Leszkiewicz, A., Hormann, T., Krafft, M., 2022. Smart business and the social value of AI. In: Smart Industry–Better Management, vol. 28. Emerald Publishing Limited,
pp. 19–34.
Li, X., Hui, E.C.-m., Lang, W., Zheng, S., Qin, X., 2020. Transition from factor-driven to innovation-driven urbanization in China: a study of manufacturing industry
automation in Dongguan City. China Econ. Rev. 59, 101382.
Li, L., Lin, J., Ouyang, Y., Luo, X.R., 2022. Evaluating the impact of big data analytics usage on the decision-making quality of organizations. Technol. Forecast. Soc.
Change 175, 121355.
Lim, J.N., Schultmann, F., Ofori, G., 2010. Tailoring competitive advantages derived from innovation to the needs of construction rms. J. Construct. Eng. Manag. 136
(5), 568–580.
Linde, L., Sj¨
odin, D., Parida, V., Wincent, J., 2021. Dynamic capabilities for ecosystem orchestration: a capability-based framework for smart city innovation
initiatives. Technol. Forecast. Soc. Change 166, 120614.
Liu, H.Y., 2023. The role of the state in inuencing work conditions in China’s internet industry: policy, evidence, and implications for industrial relations. J. Ind.
Relat. 65 (1), 3–21.
Lundvall, B.-A., 1992. National Systems of Innovation: towards a Theory of Innovation and Interactive Learning.
Luo, Y., Xue, Q., Han, B., 2010. How emerging market governments promote outward FDI: experience from China. J. World Bus. 45 (1), 68–79.
Lut, A., Alkelani, S.N., Al-Khasawneh, M.A., Alshira’h, A.F., Alshirah, M.H., Almaiah, M.A., Alrawad, M., Alsyouf, A., Saad, M., Ibrahim, N., 2022. Inuence of digital
accounting system usage on SMEs performance: the moderating effect of COVID-19. Sustainability 14 (22), 15048.
Luthy, D.H., 1998. Intellectual capital and its measurement. In: Proceedings of the Asian Pacic Interdisciplinary Research in Accounting Conference (APIRA).
Mahmood, T., Mubarik, M.S., 2020. Balancing innovation and exploitation in the fourth industrial revolution: role of intellectual capital and technology absorptive
capacity. Technol. Forecast. Soc. Change 160, 120248.
Mariani, M.M., Machado, I., Nambisan, S., 2023. Types of innovation and articial intelligence: a systematic quantitative literature review and research agenda.
J. Bus. Res. 155, 113364.
Marr, B., Schiuma, G., Neely, A., 2004. The dynamics of value creation: mapping your intellectual performance drivers. J. Intellect. Cap.
Martini, A., Laugen, B.T., Gastaldi, L., Corso, M., 2013. Continuous innovation: towards a paradoxical, ambidextrous combination of exploration and exploitation. Int.
J. Technol. Manag. 61 (1), 1–22.
Mehralian, G., Nazari, J.A., Ghasemzadeh, P., 2018. The effects of knowledge creation process on organizational performance using the BSC approach: the mediating
role of intellectual capital. J. Knowl. Manag.
Mention, A.L., Bontis, N., 2013. Intellectual capital and performance within the banking sector of Luxembourg and Belgium. J. Intellect. Cap. 14 (2), 286–309.
Mikalef, P., Fjørtoft, S.O., Torvatn, H.Y., 2019. Developing an articial intelligence capability: a theoretical framework for business value. Business Information
Systems Workshops: BIS 2019 International Workshops, Seville, Spain. June 26–28, 2019, Revised Papers 22.
Mikalef, P., Conboy, K., Krogstie, J., 2021. Articial intelligence as an enabler of B2B marketing: a dynamic capabilities micro-foundations approach. Ind. Market.
Manag. 98, 80–92.
Mirvis, P., 2012. Employee engagement and CSR: transactional, relational, and developmental approaches. Calif. Manag. Rev. 54 (4), 93–117.
Mohammed, Salah Hassan, Raja Noriza Raja, Arifn, Mansor, Norma, Al Halbusi, Hussam, 2023. The moderating role of willingness to implement policy on street-
level bureaucrats’ multidimensional enforcement style and discretion. Int. J. Publ. Adm. 46 (6), 430–444. https://doi.org/10.1080/01900692.2021.2001008.
Mohammed, Salah, Abdelfattah, Fadi, Halbusi, Hussan Al, 2023. Generative articial intelligence (ChatGPT & bard) in public administration research: a double-edged
sword for street-level bureaucracy studies. Int. J. Publ. Adm. https://doi.org/10.1080/01900692.2023.2274801.
Moshood, T.D., Nawanir, G., Mahmud, F., Mohamad, F., Ahmad, M.H., AbdulGhani, A., Kumar, S., 2022. Green product innovation: a means towards achieving global
sustainable product within biodegradable plastic industry. J. Clean. Prod. 363, 132506.
F. Abdelfattah et al.
International Journal of Innovation Studies 8 (2024) 154–171
170
Muwardi, D., Saide, S., Eko Indrajit, R., Iqbal, M., Siti Astuti, E., Herzavina, H., 2020. Intangible resources and institution performance: the concern of intellectual
capital, employee performance, job satisfaction, and its impact on organization performance. Int. J. Innovat. Manag. 24 (5), 2150009.
Nanath, K., Pillai, R.R., 2017. The inuence of green is practices on competitive advantage: mediation role of green innovation performance. Inf. Syst. Manag. 34 (1),
3–19.
Ng, T.C., Lau, S.Y., Ghobakhloo, M., Fathi, M., Liang, M.S., 2022. The application of industry 4.0 technological constituents for sustainable manufacturing: a content-
centric review. Sustainability 14 (7), 4327.
Nicodemus, T., Egwakhe, J.A., 2019. Technology transfer and competitive advantage: the managers’ perspective. Technology 11 (28).
Nirino, N., Ferraris, A., Miglietta, N., Invernizzi, A.C., 2022. Intellectual capital: the missing link in the corporate social responsibility–nancial performance
relationship. J. Intellect. Cap. 23 (2), 420–438.
Nonaka, I., o Nonaka, I., Ikujiro, N., Takeuchi, H., 1995. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, vol. 105.
OUP USA.
Nwachukwu, D., Affen, M.P., 2023. Articial Intelligence Marketing Practices: the Way Forward to Better Customer Experience Management in Africa (Systematic
Literature Review).
Oliveira, T., Thomas, M., Espadanal, M., 2014. Assessing the determinants of cloud computing adoption: an analysis of the manufacturing and services sectors. Inf.
Manag. 51 (5), 497–510.
Peattie, K., Charter, M., 1992. Green Marketing. The marketing book, p. 726.
Pergelova, A., Angulo-Ruiz, F., 2014. The impact of government nancial support on the performance of new rms: the role of competitive advantage as an
intermediate outcome. Enterpren. Reg. Dev. 26 (9–10), 663–705.
Petricevic, O., Teece, D.J., 2019. The structural reshaping of globalization: implications for strategic sectors, proting from innovation, and the multinational
enterprise. J. Int. Bus. Stud. 50, 1487–1512.
Pickett-Baker, J., Ozaki, R., 2008. Pro-environmental products: marketing inuence on consumer purchase decision. J. Consum. Market. 25 (5), 281–293.
Porter, M.E., Stern, S., 2001. Innovation: Location Matters. MIT Sloan management review.
Raduan, C., Jegak, U., Haslinda, A., Alimin, I., 2009. Management, strategic management theories and the linkage with organizational competitive advantage from
the resource-based view. Eur. J. Soc. Sci. 11 (3), 402–418.
Rao, P., Holt, D., 2005. Do green supply chains lead to competitiveness and economic performance? Int. J. Oper. Prod. Manag.
Rathore, B., 2023. Digital transformation 4.0: integration of articial intelligence & metaverse in marketing. Eduzone: International Peer Reviewed/Refereed
Multidisciplinary Journal 12 (1), 42–48.
Raza, A., Farrukh, M., Iqbal, M.K., Farhan, M., Wu, Y., 2021. Corporate social responsibility and employees’ voluntary pro-environmental behavior: the role of
organizational pride and employee engagement. Corp. Soc. Responsib. Environ. Manag. 28 (3), 1104–1116.
Rehman, S.U., Giordino, D., Zhang, Q., Alam, G.M., 2023. Twin Transitions & Industry 4.0: Unpacking the Relationship between Digital and Green Factors to
Determine Green Competitive Advantage. Technology in Society, 102227.
Ringle, C., Da Silva, D., Bido, D., 2015. Structural equation modeling with the SmartPLS, 2014. In: Bido, D., da Silva, D., Ringle, C. (Eds.), Structural Equation
Modeling with the Smartpls. Brazilian Journal of Marketing, 13, 2.
Rockwell, S., 2019. A resource-based framework for strategically managing identity. J. Organ. Change Manag.
Roper, S., Turner, J., 2020. R&D and innovation after COVID-19: what can we expect? A review of prior research and data trends after the great nancial crisis. Int.
Small Bus. J. 38 (6), 504–514.
Roper, S., Love, J.H., Bonner, K., 2017. Firms’ knowledge search and local knowledge externalities in innovation performance. Res. Pol. 46 (1), 43–56.
Saberi, M., Hamdan, A., 2019. The moderating role of governmental support in the relationship between entrepreneurship and economic growth: a study on the GCC
countries. Journal of Entrepreneurship in Emerging Economies 11 (2), 200–216.
Sahoo, S., Kumar, A., Upadhyay, A., 2023. How do green knowledge management and green technology innovation impact corporate environmental performance?
Understanding the role of green knowledge acquisition. Bus. Strat. Environ. 32 (1), 551–569.
Salehi, M., Fahimi, M.A., Zimon, G., Homayoun, S., 2022. The effect of knowledge management on intellectual capital, social capital, and rm innovation. J. Facil.
Manag. 20 (5), 732–748.
Santos, H., Lannelongue, G., Gonzalez-Benito, J., 2019. Integrating green practices into operational performance: evidence from Brazilian manufacturers.
Sustainability 11 (10), 2956.
Sanyal, S., Hisam, M.W., 2018. The role of business incubators in creating an entrepreneurial ecosystem: a study of the Sultanate of Oman. Indian J. Commer. Manag.
Stud. 9 (3), 10–17.
Secundo, G., Ndou, V., Del Vecchio, P., De Pascale, G., 2020. Sustainable development, intellectual capital and technology policies: a structured literature review and
future research agenda. Technol. Forecast. Soc. Change 153, 119917.
Sezen, B., Cankaya, S.Y., 2013. Effects of green manufacturing and eco-innovation on sustainability performance. Procedia-Social and Behavioral Sciences 99,
154–163.
Sharma, E., 2019. A review of corporate social responsibility in developed and developing nations. Corp. Soc. Responsib. Environ. Manag. 26 (4), 712–720.
Sharma, S., Henriques, I., 2005. Stakeholder inuences on sustainability practices in the Canadian forest products industry. Strat. Manag. J. 26 (2), 159–180.
Skordoulis, M., Ntanos, S., Kyriakopoulos, G.L., Arabatzis, G., Galatsidas, S., Chalikias, M., 2020. Environmental innovation, open innovation dynamics and
competitive advantage of medium and large-sized rms. Journal of Open Innovation: Technology, Market, and Complexity 6 (4), 195.
Smorodinskaya, N., Russell, M., Katukov, D., Still, K., 2017. Innovation Ecosystems vs. Innovation Systems in Terms of Collaboration and Co-creation of Value.
Soewarno, N., Tjahjadi, B., Fithrianti, F., 2019. Green innovation strategy and green innovation: the roles of green organizational identity and environmental
organizational legitimacy. Manag. Decis. 57 (11), 3061–3078.
Songling, Y., Ishtiaq, M., Anwar, M., Ahmed, H., 2018. The role of government support in sustainable competitive position and rm performance. Sustainability 10
(10), 3495.
Storey, D., 2017. Six Steps to Heaven: Evaluating the Impact of Public Policies to Support Small Businesses in Developed Economies. The Blackwell handbook of
entrepreneurship, pp. 176–193.
Strusani, D., Houngbonon, G.V., 2019. The Role of Articial Intelligence in Supporting Development in Emerging Markets.
Subramaniam, M., Youndt, M.A., 2005. The inuence of intellectual capital on the types of innovative capabilities. Acad. Manag. J. 48 (3), 450–463.
Szalavetz, A., 2022. The digitalisation of manufacturing and blurring industry boundaries. CIRP Journal of Manufacturing Science and Technology 37, 332–343.
Teo, T.S., Pian, Y., 2003. A contingency perspective on Internet adoption and competitive advantage. Eur. J. Inf. Syst. 12 (2), 78–92.
Tubbs, M., 2007. The relationship between R&D and company performance. Res. Technol. Manag. 50 (6), 23–30.
Tze San, O., Latif, B., Di Vaio, A., 2022. GEO and sustainable performance: the moderating role of GTD and environmental consciousness. J. Intellect. Cap. 23 (7),
38–67.
Usai, A., Fiano, F., Petruzzelli, A.M., Paoloni, P., Briamonte, M.F., Orlando, B., 2021. Unveiling the impact of the adoption of digital technologies on rms’ innovation
performance. J. Bus. Res. 133, 327–336.
Varadarajan, R., 2017. Innovating for sustainability: a framework for sustainable innovations and a model of sustainable innovations orientation. J. Acad. Market. Sci.
45, 14–36.
Vesal, M., Siahtiri, V., O’Cass, A., 2021. Strengthening B2B brands by signalling environmental sustainability and managing customer relationships. Ind. Market.
Manag. 92, 321–331.
Wang, H.C., He, J., Mahoney, J.T., 2009. Firm-specic knowledge resources and competitive advantage: the roles of economic-and relationship-based employee
governance mechanisms. Strat. Manag. J. 30 (12), 1265–1285.
Wang, Z., Cai, S., Liang, H., Wang, N., Xiang, E., 2021. Intellectual capital and rm performance: the mediating role of innovation speed and quality. Int. J. Hum.
Resour. Manag. 32 (6), 1222–1250.
F. Abdelfattah et al.
International Journal of Innovation Studies 8 (2024) 154–171
171
Weng, H.-H., Chen, J.-S., Chen, P.-C., 2015. Effects of green innovation on environmental and corporate performance: a stakeholder perspective. Sustainability 7 (5),
4997–5026.
Wiegmann, P.M., de Vries, H.J., Blind, K., 2017. Multi-mode standardization: a critical review and a research agenda. Res. Pol. 46 (8), 1370–1386.
Yin, S., Zhang, N., Ullah, K., Gao, S., 2022. Enhancing digital innovation for the sustainable transformation of manufacturing industry: a pressure-state-response
system framework to perceptions of digital green innovation and its performance for green and intelligent manufacturing. Systems 10 (3), 72.
Yong, J.Y., Yusliza, M.Y., Ramayah, T., Chiappetta Jabbour, C.J., Sehnem, S., Mani, V., 2020. Pathways towards sustainability in manufacturing organizations:
empirical evidence on the role of green human resource management. Bus. Strat. Environ. 29 (1), 212–228.
Youndt, M.A., Subramaniam, M., Snell, S.A., 2004. Intellectual capital proles: an examination of investments and returns. J. Manag. Stud. 41 (2), 335–361.
Yu, H.-C., Kuo, L., Kao, M.-F., 2017. The relationship between CSR disclosure and competitive advantage. Sustainability Accounting, Management and Policy Journal
8 (5), 547–570.
Zhou, J., Wang, M., 2023. The role of government-industry-academia partnership in business incubation: evidence from new R&D institutions in China. Technol. Soc.
72, 102194.
F. Abdelfattah et al.
