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Article

Organizational Factors, Ambidextrous Green Innovation Strategy, and Technology Orientation: An Integrated Framework for Green Competitiveness

by
Yarui Gao
1,2,
Jianhua Zhang
1,* and
Muhammad Usman Shehzad
3
1
School of Management, Zhengzhou University, Zhengzhou 450001, China
2
Business School, SIAS University, Zhengzhou 451150, China
3
School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(2), 565; https://doi.org/10.3390/su18020565
Submission received: 31 October 2025 / Revised: 6 December 2025 / Accepted: 22 December 2025 / Published: 6 January 2026
(This article belongs to the Special Issue Greening the Future: Business Innovations for Sustainable Growth)
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Abstract

This study examines the role of green information technology capital (GITC) and knowledge source on firms’ green competitive advantage (GCA), with the mediating role of ambidextrous green innovation strategy (AGIS), and the moderating role of technological orientation (TO). Research employed partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) to analyze data gathered from 367 respondents from Chinese manufacturing firms. The results revealed a significant direct effect of GITC and knowledge sources on GCA, whereas AGIS partially mediated the relationships. Moreover, TO significantly moderates the impact of GITC on AGIS, whereas it does not moderate the relationship between knowledge sources and AGIS. fsQCA results revealed that a varied combination of GITC, knowledge sources, and AGIS dimensions, along with TO, can lead to high GCA. This study advances the literature by offering insightful perspectives on enhancing GCA by leveraging organizational resources to stimulate AGIS.

1. Introduction

In the manufacturing sector, prioritizing green practices for competitive gain has moved from a reputation bonus to an essential business strategy. Due to stricter regulations, more environmentally conscious consumers, and increasing environmental costs, firms are making sustainability a key part of their strategies [1]. Green initiatives at the corporate level save energy and material costs, help face challenges with resources and rules, and distinguish companies in markets where buyers value products with a low carbon footprint [2]. Such initiatives help control industrial emissions and waste, supporting the country’s carbon reduction targets and the United Nations Sustainable Development Goals. Recent studies indicate that manufacturers employing sustainable human-resource systems, green monitoring, and circular design achieve better financial results and visible environmental benefits, supporting the notion that being green provides businesses a competitive advantage (GCA) [1]. However, pursuing GCA is challenging due to the dual externalities associated with green innovation, including complexity, uncertainty, and the lengthy process of GI, which makes many firms hesitant or encounter difficulties [3].
The resource-based view (RBV) and the knowledge-based view (KBV) hold that resources, whether tangible (e.g., green technology) or intangible (e.g., knowledge sources), play a crucial role in enabling enterprises to attain sustainable competitive advantage [4,5]. In this context, green information technology capital (GITC) offers a unique and comparable set of benefits that contribute to reducing energy waste, minimizing electronic waste, and providing timely ecological data to support better environmentally conscious decisions [6]. The effective use of such resources enables organizations to lower their costs and emissions, thereby signaling regulators and consumers that their activities are environmentally responsible [7]. The competitive advantage is largely reliant on how an organization can use both internal and external sources of knowledge [8]. GITC resources are particularly beneficial in the creation of an efficient knowledge management system [9,10], and the combination of GITC with various knowledge sources enables GI, which the literature highlights is needed to convert resource gains into competitive gains, strong compliance, and a stronger reputation [11,12]. Organizational resources are considered significant to GCA [3,13], yet little research has examined their effects on GCA [14] from the combined perspective of GITC and knowledge sources. Therefore, to address this research gap and contribute to a better understanding of how resource-oriented factors influence a firm’s competitiveness, the current study examines the impact of GITC and knowledge sources on GCA.
As environmental challenges grow, organizations are increasingly likely to go beyond individual sustainability efforts and implement strategic plans that integrate ecological objectives into their innovation efforts. In this regard, ambidextrous green innovation approaches (AGIS), balancing efficiency-driven and exploratory green initiatives, have become critical in attaining competitiveness and environmental responsiveness [15]. GITC development helps companies to gain access to real-time data, sophisticated analytics, and collaboration tools that increase eco-efficiency and directly implement environmental opportunities and risks into digital systems [6,16]. Concurrently, the development of various internal and external knowledge using green knowledge management, employee expertise, and interfirm partnerships increases the learning capability, which leads to new eco-solutions and finding new green opportunities [3]. In light of RBV and KBV perspectives, these factors imply that resources are just inert in the absence of strategic integration. Knowledge diversity and GITC are complementary sources of AGIS that convert passive resources into active capabilities of generating incremental and radical green innovations [14]. The existing literature suggests that green capital, coupled with knowledge bases, is beneficial in complementing AGIS, environmental legitimacy, cost-effectiveness, and first-mover benefits, especially when operating in changing regulations [15,17]. Nevertheless, although Hameed et al. [6] and Guckenbiehl et al. [8] discovered that organizations with strong digital and knowledge infrastructure produce more compliant and innovative green solutions, the mediating effect of AGIS between organizational resources (GITC and knowledge diversity) and green competitive advantage (GCA) is insufficiently researched. This research fills this theoretical gap by explaining how these resource-based factors contribute to the green competitiveness of firms.
Integrating GITC and knowledge sources helps spark AGIS [15,16]. The AGIS approach enables organizations to become more efficient by enhancing existing green initiatives and gradually adapting through the development of new sustainable processes [14]. A company’s strategic orientation toward technology significantly influences the effectiveness of converting organizational GI into tangible advantages and benefits [18]. Technology orientation (TO), characterized by proactive use, exploration, and application of emerging technologies, can maximize the effective use of organizational resources in global innovation [6]. Empirical research indicates that a firm’s capital can either amplify or mitigate the impact of resources on ambidexterity [15]. Conversely, digital cultures facilitate knowledge absorption, blend ideas, and aid in scaling green solutions [19]. Recent findings by Wang et al. [20] highlight that when firms deal with significant technological change, TO becomes a key strategy for the organization. Moreover, organizational capabilities, including resource orchestration capability [21], green absorptive capacity [14], and combinative capability [22], are widely recognized as playing a crucial role in supporting innovation. However, Wang and Juo [23] point out that TO is rarely studied concerning its impact on the relationship between organizational resource-oriented factors and AGIS. Given the significance of TO, it may either hinder or strengthen the role of GITC and knowledge sources for AGIS. Therefore, this research seeks to determine whether TO positively or negatively moderates the relationships between GITC, knowledge sources, and AGIS.
Given the identified research gaps, this study seeks to address the following research questions:
RQ1: Do organizational resource factors (GITC and knowledge sources) significantly influence firms’ GCA, and to what extent do AGIS mediate this relationship?
RQ2: Does technology orientation moderate the relationship between organizational resource factors and AGIS?
RQ3: What are the numerous pathways to attaining a higher level of GCA?
This study fills research gaps in the GI and strategic management literature by proposing a framework grounded in the RBV [24] and the KBV [25]. In particular, this study analyzes how organizational resources (GITC and knowledge sources), combined with an AGIS, affect a firm’s competitive edge in the eco-conscious environment, while also examining the impact of TO. The PLS-SEM method is used to analyze the direct, mediating, and moderating relationships proposed in the study model. Coupled with the PLS-SEM approach, fsQCA is employed to investigate how various combinations of key antecedents contribute to firms achieving a high level of GCA. By integrating PLS-SEM and fsQCA, this study aims to provide valuable insights and practical guidance for firms seeking to enhance GCA by establishing key resources and capabilities that stimulate AGIS.

2. Literature Review and Hypotheses Development

2.1. Theoretical Underpinnings

According to the resource-based view (RBV) and its knowledge-based view (KBV) extension, the research finds that GCA occurs when companies have valuable, rare, inimitable, and non-substitutable (VRIN) resources and the routines needed to use and blend knowledge within them [24,25]. GITC consists of green IT human capital (GITHC), green IT structural capital (GITSC), and green IT relational capital (GITRC), encompassing eco-efficient hardware, software, human capital, and processes that save money and reduce greenhouse gas emissions, making them difficult to copy [6]. In other words, RBV predicts that GCA will rise, whereas KBV highlights the unspoken environmental knowledge in these systems that helps set the company apart. According to KBV, when firms connect with various sources, their knowledge base expands, enabling them to solve more problems and innovate more quickly in eco-friendly areas. If a company has heterogeneity and RBV’s VRIN logic, its knowledge flow helps it gain competitive advantages [1]. AGIS transforms the impacts of GITC and knowledge sources into GCA, applying both the capability conversion ideas in RBV and the stress on knowledge combination in KBV [15,16]. The research model is presented in Figure 1.

2.2. Green It Capital and Green Competitive Advantage

GCA is the advantage a firm gains when integrating greener approaches and sustainable practises in its business [26]. This is primarily because it enables the company to save on costs, raise the visibility of its brand, and access new customers, which enhances long-term findings [3]. Hart [27] argues that embedding environmental concerns within the strategy of a firm can enable it to acquire a competitive edge. By integrating sustainability into their day-to-day practices, corporations can outpace competitors, cut costs, attract eco-friendly customers, and enhance competitiveness. Technology has an impact on the environment through the production of e-waste products, the use of a lot of energy in data centers, the mining of raw materials, and the creation of carbon emissions when using the internet [6]. Based on these environmental conditions, organizations ought to strive to improve their GITC capacity and resources that use green thinking to their IT infrastructure, their IT staff, and their IT management [28]. Chuang and Huang [28] state that the GITC comprises three dimensions: (a) GITHC, which implies knowledge, skill, and expertise of individuals who apply green practises using energy saving technologies and ideas in their work; (b) GITSC, which implies basic infrastructure including hardware, software, networks, and technology, founded on green practices; and (c) GITRC, which implies users who consume green products or services, and maintain close cooperative relationships throughout the partnership.
Based on the RBV, the literature indicates that GITC, as a strategic resource, which is manifested by green human capital, structural capital, and relational capital, is a significant element in the GCA of a firm. Environmental competence and preparedness of the employee will enable the employees to apply sustainable IT policies, and both energy-efficient facilities and systems in the environment will ensure efficiency and lawfulness [29]. Relational capital, including green suppliers and networks of stakeholders, enables the exchange of sustainable ideas, thereby enhancing eco-innovation and appropriate responses to regulations and consumer demands [30]. With such combined advantages, the companies are able to introduce eco-friendly products and services, consume less of what they require, and be eco-conscious in the current environmentally conscious markets [18]. Companies that employ Green IT Capital seem to experience reduced ecological impact, lower expenses, and enhanced trust and reputation, which are significant for sustainable competitive advantage [31]. Consequently, the study proposed the following:
H1. 
GITC is positively associated with a firm’s GCA.

2.3. Knowledge Sources and Green Competitive Advantage

The manufacturing firms use several different sources of knowledge, both external and internal to the company, to create new ideas, simplify their activities, and remain competitive [29]. Internal knowledge sources include the mass expertise, experience, and discoveries generated by the individuals of a firm (through interaction and documentation) and their R&D and routine operations. Instead, the data is collected externally into the firm in terms of customer, supplier, and competitor data, and through the current connections with universities and other research-based organizations [30]. Studies have shown time and again that companies depend on both internal and external knowledge resources to be innovative in their environment and stay competitive [31]. Internal sources of knowledge contribute to the development of green capabilities within a company, and external knowledge makes a firm more versatile and better able to utilize other eco-innovative ideas [32]. How a firm is able to harness the power of knowledge depends on its capacity to gather, interpret, apply, and profit from the knowledge it holds [33]. Concisely, the green competitive advantage should be maintained and enhanced by an active and agile status on information utilization as the world increasingly focuses on the environment. Thus, the following hypotheses were put forward in this study.
H2. 
Knowledge sources are positively associated with a firm’s GCA.

2.4. Mediating Role of Ambidextrous Green Innovation Strategy

Based on AGIS, GI is a key means to encourage sustainability and reduce the environmental footprint of a business by promoting both profit-driven and learning-based innovation [21,34]. Exploitative GI improves and upgrades green processes and products using what is already known and feasible, while exploratory GI develops original green technologies and markets by integrating new environmental research with technical expertise [35]. Extending the RBV, this study suggests that an AGIS, involved in both the utilization and invention of green technology, links GITC and GCA. Although GITC provides firms with critical environmental abilities through its people, technology, and stakeholder ties [15], the company’s approach to AGIS helps turn those abilities into actual performance outcomes. Exploitative GI builds on earlier knowledge to boost eco-friendly practices, while exploratory GI introduces new, radical concepts that will eventually transform products, technologies, and markets [17,36]. Linking these two innovation styles enables companies to enhance their sustainability outcomes and adapt quickly to future changes. The literature highlights that utilizing both innovative approaches enables companies to transfer their implicit potential in GITC into valuable growth [37]. For these reasons, we propose the following hypothesis:
H3. 
AGIS mediates the relationship between GITC and GCA.
AGIS states that the combination of research may help companies to find new green technologies, and old experience could help to refine the routines and achieve outcomes that neither of the two approaches could reach alone [38]. In-house research and practical training, along with staff competence, provide a good understanding of certain areas [29]. External learning through partnership, cooperation with other institutions, and critical customers allows entry to new ideas and proven technologies [16]. Through knowledge assets organization, companies are able to use it to improve their capacity to consume information, swiftly experiment with new concepts in sustainability, and improve on the predecessor ones [14]. AGIS links knowledge and performance, optimizing both forms of firm activity, combining them within the market and leveraging them to serve sustainability-sensitive customers, regulators, and brand [21]. Thus, with sufficient, adequately structured knowledge sources, firms can gain an advantage in better environmental and competitive outcomes through the indirect AGIS-reliant technique.
H4. 
AGIS mediates the relationship between knowledge sources and GCA.

2.5. Moderating Role of Technology Orientation

TO is the ability and intention of a firm to use and introduce new technology to create new products and services [39]. This idea suggests that a company adopts and utilizes new technologies to meet market demand [6]. GITC comprises jointly important green IT assets, such as people, structures, and relationships. With these in place, firms are enabled to work on both improving eco-friendly current routines and researching new eco-friendly ideas [38]. However, the impact of these resources depends on the TO of a firm [40]. Building on this, TO enhances GITC’s cooperation and employee skills: being technologically involved leads to regular updates in environmental competencies and stronger ties between suppliers and customers, which increase green integration and readiness for improvement [41]. As a result, firms with impressive TO can more successfully turn GITC into innovation that endures and stays sustainable than those with low TO levels. Thus, the study proposes the following hypotheses.
H5. 
TO positively moderate the relationship between GITC and AGIS.
TO implies innovation, activity, and boldness to use resources to develop advanced technologies and convert knowledge into AGIS. The knowledge needed to enhance existing green processes is mostly internal (R&D and employee knowledge), and the diversity essential to major advancements is external (suppliers, customers, and research institutions) [29]. Even though these findings are linked to TO, it is only technology-oriented companies that can employ technology and digital configurations to enable assimilation, remixing, and commercial exchange of knowledge types [39]. Research has established that TO increases the relationship between knowledge and innovation by converting the environmental plans into tangible green IS projects and advancing the performance of innovations [42]. TO, conversely, assists in resource management, assisting firms to combine both internal and external resources, solve conflicts between innovations and repetitions, and reach more successful AGIS performance in harsh conditions [16]. Therefore, the following hypothesis is proposed:
H6. 
TO positively moderate the relationship between knowledge sources and AGIS.

2.6. Equifinality

Linking the theorized relationships in this study, we suggest that GITC (GITHC, GITSC, and GITRC) and knowledge sources (internal and external knowledge sources) help firms enhance GCA via TO and AGIS (EXGI and ERGI). Previous studies have demonstrated that IT capabilities and knowledge sources can positively impact a firm’s performance and enhance competitiveness [6,17]. However, it remains unclear how these resources interact to contribute value to the firm’s strategy, and the literature in this area is still insufficiently developed. Consequently, this research builds and investigates a novel relationship among GITC, knowledge sources, AGIS, and TO to help advance the GCA. Figure 2 present the configuration model of the study and based on these considerations, the following hypothesis is proposed:
H7. 
Varied combinations of GITC (GITHC, GITSC, and GITRC), knowledge sources (internal and external knowledge sources), AGIS (EXGI and ERGI), and TO are associated with superior GCA.

3. Data Collection and Procedure

The quantitative research approach is best suited when the most important aim of the study is the identification of the influential relationship of variables [43]. The methodological approach of the study is deductive because its main purpose is to test the hypothesis developed on the grounds of the existing theory [44]. A structured questionnaire was used to gather cross-sectional data of Chinese manufacturing companies and explore the research model hypothesis. We utilized Qualtrics through the internet and paper packets through the post to achieve the highest possible number of responses and more valuable feedback. In order to analyze green competitive advantage, we selected the manufacturing industry of China, as it constitutes over half of the entire national economy and a significant portion of the job market [45]. Based on the earlier research, our sample incorporated a set of ISO-certified companies listed on the Shanghai and Shenzhen Stock Exchanges, to which separate manufacturing processes were applied [46]. With certification, environmental management systems became similar across firms and assisted in eradicating certain sources of noise. We sampled broad industry groups using a sample of 1140 firms, so that we could have a representative sample.
The senior sustainability or operations managers of each company were the respondents. These executives are knowledgeable about organizational practices and are well-equipped to realize green IT capital, knowledge-sharing practices, and innovation strategies. Further, the English version of the questionnaire was translated into Chinese and back into English to ensure there was no difference in meaning [47]. A pilot study with 18 managers helped refine the wording and showed that the scale is not difficult to comprehend.
Data collection took place over four months (January to April 2025). Participants received a letter first, which guaranteed confidentiality, stated that there was no right or wrong answer, and said the results would be presented in group form. Each of the questionnaire attachments was delivered to responders via email, WeChat, WhatsApp, and social media links. The companies that did not choose to participate were eliminated, and 389 out of the 723 returned the questionnaires. The 21 surveys that had excessive missing data were not analyzed further, and this left 367 responses and an effective response rate of 50.76. In order to determine the early and late reactions, Armstrong and Overton’s [48] procedure was used, and no differences (p > 0.10) were found between early and late respondents on all factors of the study. Table 1 gives the demographic profile of respondents.

Instrument Development

We operationalized constructs by using questions from established research. We measured GITC (as a higher-order construct) through green-IT human, structural, and relational capital, and 10 items were adapted from Chuang and Huang [28]. The 13 items for knowledge source are drawn from Laursen and Salter [30], who measured internal knowledge sources with 7 items and external sources of knowledge with 6 items. Moreover, the study adapted 8 items from Shehzad et al. [21] to measure AGIS (exploitative and exploratory GI). The four items for GCA are derived from Chen and Chang [26], while the four items for technological orientation were drawn from Hameed et al. [6]. All statements were adjusted for the chosen level of analysis and were scored between 1 and 5 on a Likert scale.
It is essential to check for common method bias (CMB) before commencing the empirical analysis, as this is a common issue in survey-based research [21]. We used Harman’s single-factor test [49] and Kock’s method for comprehensive collinearity analysis [50] to evaluate CMB. The main varimax factor explained 27.16% of the total variance, which is well below the threshold of 40% suggested by Hair et al. [51]. A thorough collinearity check using SmartPLS, a widely used tool in social science, revealed that all VIFs were below 3.3, indicating no sign of common method bias [50].

4. Data Analysis

The research uses a two-fold strategy of integrating PLS-SEM with fuzzy set Qualitative Comparative Analysis (fsQCA) to understand how the key antecedents facilitate AGIS and, consequently, GCA. The researchers selected PLS-SEM that could estimate the hierarchically nested model, non-normal data, and enhance the out-of-sample accuracy [51,52]. This is considered to be the standard method of research on sustainability and innovation [15]. Moreover, the asymmetric patterns of causation were explored using the method of fsQCA to reveal the various combinations of antecedents that may lead to high or non-high GCA [53]. The combination of the findings of PLS-SEM and those of fsQCA allows the study to give a straightforward and consistent explanation of the way that GITC and knowledge sources, including tech strategies, can attain GCA.

4.1. Measurement Model Evaluation

Following the established guidelines in PLS-SEM, we assessed the measurement model by verifying the reliability and convergent and discriminant validity of the constructs (both first-order and higher-order) [54]. The measurement assessment was conducted in two steps because the study model involved higher-order constructs. Measurement model results are presented in Table 2.
  • At the first step, first-order constructs demonstrated appropriate reliability and validity. The majority of item scores exceed 0.70, indicating high indicator reliability. All values of composite reliability fell between 0.851 and 0.909, which proves that the items in the scales are consistent. Values of average variance extracted (AVE) were all higher than 0.50, between 0.534 and 0.770. Though the constructs internal sources of knowledge (0.534) and green IT relational capital (0.594) have AVEs lower than the rest, they still meet the minimum requirement. Since the Cronbach’s alpha (Cα) for all these constructs was over 0.70, this justifies the theoretical significance and is regarded as acceptable [52,54].
  • In the second step, the two-stage method was used to check the higher-order constructs in the model [51,55]. All higher-order constructs proved to be reliable and valid, which shows their lower-order dimensions are both appropriate and meaningful. AGIS, which consists of exploitative and exploratory GI, had strong factor loadings at stage two (0.893–0.906), a Cα value of 0.894, and an average variance extracted of 0.808. Likewise, the knowledge sources’ higher-order construct, which comprises internal and external sources of knowledge, exhibits good reliability (CR = 0.919) and very high convergent validity (AVE = 0.851), supported by strong second-stage loadings (0.920–0.925). The GITC, which includes human, relational, and structural capital, was considered well-structured and conceptually consistent, as evidenced by its Cα of 0.873 and its AVE of 0.697.
As shown in Table 3 and Table 4, the heterotrait–monotrait ratio of correlations (HTMT) was used to check for discriminant validity, as it is a strong measure for variance-based SEM [56]. Values that are below 0.85 (strict) or 0.90 (relaxed) usually confirm that a construct is distinct. Strong discriminant validity was found for all the HTMT values for the lower-order constructs, which were well below the recommended threshold. Similarly, at the higher-order construct level, all HTMT values were also below the stricter criterion of 0.85, and the highest correlation (0.822) between AGIS and GITC indicated that the model is robust.

4.2. Structural Model Assessment

In the second phase, the structural model is examined to assess its predictive ability and the statistical relevance of the relationships between variables. This study employed bootstrap resampling with 5000 samples to estimate the path coefficients and their 95% bias-corrected confidence intervals, as well as the mediation, following the recommendations of Preacher and Hayes [57].
As shown in Table 5, we assessed the model’s explanatory and predictive power by examining three indices—coefficient of determination (R2), effect size (f2), and predictive relevance (Q2)—guided by existing recommendations [52]. The antecedents accounted for nearly half of the variation in both key outcomes, indicating they had moderate explanatory power for AGIS (R2 = 0.497; adjusted R2 = 0.490) and for GCA (R2 = 0.496; adjusted R2 = 0.492). Analysis of effect size indicated that GITC had the most significant impact on AGIS (f2 = 0.361, large effect) and knowledge sources had a medium influence (f2 = 0.146). TO and its interactions displayed very little influence on the results (f2 ≤ 0.031) (Cohen, 1988 [58]). Knowledge sources were found to have the most significant impact on the GCA (f2 = 0.130, medium), followed by AGIS (f2 = 0.100, small to medium) and GITC (f2 = 0.052, small). By assessing predictive relevance while blindfolding, the results of the previous analyses were confirmed: there was strong predictive relevance for AGIS (Q2 = 0.392) and medium predictive relevance for GCA (Q2 = 0.298).

Hypotheses Results

Based on the initial analysis, the control variables of industry type, ownership structure, firm scale, and firm age had no impact on either AGIS or GCA, indicating that the difference between AGIS and GCA was not observed among firms of different types, ownership structures, sizes, or ages.
Next, as shown in Table 6, the results from the direct hypothesized relationships (H1 and H2) indicate that GITC and knowledge sources have a significant impact on GCA. Specifically, knowledge sources’ influence on GCA (β = 0.312, p < 0.001) is more robust and significant than GITC’s effect (β = 0.216, p < 0.001). Thus, H1 and H2 are supported.
Next, conducting bootstrap analyses with 5000 samples (as recommended by Preacher and Hayes [57]) confirmed that the indirect paths were positive and statistically significant (see Table 7). GITC strongly affects GCA through AGI, as shown by β = 0.152 (p < 0.001; 95% CI = 0.084–0.227), while it still has a significant direct effect at β = 0.216. Since both the direct and indirect paths have the same sign and the indirect impact makes up 41% of the whole influence (0.152/0.368), this pattern is considered complementary or “partial” mediation [59]. Also, a comparable but slightly weaker mechanism is found for H4. Knowledge sources impact GCA indirectly through AGIS, with a coefficient of β = 0.096 (p < 0.001; 95% CI = 0.051–0.153), and they have a significant direct effect of 0.312. Although only a small portion of the total impact (24%) can be attributed to mediation, this still exceeds Cohen’s [58] threshold for a small effect, demonstrating the benefits of using both types of knowledge input through ambidextrous innovation for firms.
The findings from the moderation analysis indicate that TO plays a significant role in moderating the link between GITC and AGIS. There is a significant and positive effect of the interaction term TO × GITC on AGIS (β = 0.134, p = 0.041), suggesting that the positive effect of GITC on AGI grows as TO increases. On the other hand, the TO × Knowledge–sources interaction is negative and statistically significant (β = −0.078, p = 0.153; f2 = 0.009), which means TO does not strengthen or weaken the link between knowledge sources and AGIS. Therefore, the evidence supports Hypothesis 5 but not Hypothesis 6.

4.3. Fsqca Approach

The research employed fsQCA to examine how specific antecedent conditions of GITC (GITHC, GITSC, GITRC), knowledge sources (ISK and ESK), and AGIS (EIGI and ERGI) dimensions, along with technology orientation, contribute to generating high or non-high GCA. All condition and outcome variables were calibrated using the direct method described by [60], selecting the 5th, 50th (median), and 95th percentiles to set the membership scores for each variable. Full non-membership (0) was placed at the 5th percentile, maximum ambiguity (0.5) was at the median, and full membership (1) was at the 95th percentile (Figure 3).

4.3.1. Necessity-Conditions Analysis

We employed fsQCA necessity analysis to identify the conditions necessary for GCA, considering a factor as required only if its consistency exceeds the standard 0.90 benchmark [61]. As Table 8 demonstrates, the highest consistency value for the high-GCA outcome is 0.8714 (for exploitative GI), and all the other are below the required level. Similarly, for the non-high-GCA outcome, the highest match (0.7687 for ~GITRC) does not reach the minimum threshold. None of the single factors meets the 0.90 criterion, so none can be viewed as necessary to achieve high, or non-high, GCA.

4.3.2. Solutions

The findings from the fsQCA analysis for GCA are shown in Table 9 using the notation developed by [53,60]. The high-GCA configurations demonstrate a robust empirical fit (consistency = 0.911; coverage = 0.622) and have six sufficient configurations. EXGI and ERGI are core present in two of the six configurations and peripherally present in three out of the six configurations. GITHC and GITRC are core in 1/6 and peripheral in 4/6 configurations. GITSC is present as a peripheral condition in 5/6 configurations and absent in 1/6 configurations. From the knowledge sources, ISK appears as a core resource in 1/6 and as a peripheral resource in 3/6, whereas ESK is a core resource in 2/6, a peripheral resource in 3/6, and does not appear as a core resource in one configuration. In 4/6 configurations, TO is absent, in 1/6 it is present, and in 1/6 it does not matter, meaning it plays a supportive role when GITC, innovation, and knowledge sources are well matched.
The non-high-GCA solution (consistency = 0.944; coverage = 0.498) comprises three sufficient configurations, all of which AGIS (EXGI and ERGI) is absent. Configurations 7 and 8 indicate that in the absence of either AGIS, firms are considered in the non-high category, whether they possess no GIT resources (Configuration 7) or have a peripheral TO (Configuration 8); thus, being a technology primary focus or merely having one type of IT resource is insufficient by itself. Confirmation 9 also affirms this fact since GITHC, GITSC, GITRC, ISK, and ESK are in place, yet AGIS is absent; therefore, non-high GCA proves that the presence of numerous capabilities may not substitute for the lack of innovation efforts. According to all three configurations, lacking AGIS is the main cause of failing to attain a high GCA.

5. Discussion

This research investigates how GITC and knowledge sources contribute to achieving a GCA and also explores how the firm’s participation in an AGIS and its TO affect this relationship. By doing so, this research has significantly enhanced and contributed to broadening the understanding of theoretical and practical initiatives in GITC, sources of knowledge, AGIS, and competitiveness in various ways.
First, this study’s findings indicated that the GCA of a firm is strongly dependent on the possession of green IT resources. Recent studies indicate that the presence of green IT-human, infrastructure, and relational resources helps a firm to incur savings, readily adjust to changes in regulations and reputation that are difficult to imitate by competitors [6,17]. The most probable cause of the study results is that the environmental analysis of green IT systems can help organizations to recognize waste problems. The availability of the IT experts and green partners to assist in green IT also enables the firms to embrace low-carbon technologies faster, save the cost of abatement, and gain additional stakeholder confidence. In the same way, there is a strong and positive association between sources of knowledge (both internal organization and external partners) and GCA. Recent research suggests that the successful use of green knowledge in business extends the number of green solutions to problems and increases the speed at which green products are produced [14,62]. The reason could be that the presence of a rich information network assists the firms in noticing developments in regulations or markets, and they learn promptly and are the first to seize opportunities in sustainability niches.
Second, the results imply that companies that use an AGIS (both exploration and exploitation of eco-innovations) partly mediate the correlation between the organizational resource factor (GITC and knowledge sources) and competitive advantage. Consistent with previous research, the results indicate that companies with AGIS can effectively leverage organizational resources (i.e., GITC and knowledge sources) to increase GCA [3,14]. The most probable reason is that green IT analytics and a variety of inputs can only work at the strategy level when businesses integrate them into two streams: one stream of immediate gains (exploitation) and the other of future discoveries (exploration), which together provide both short-term and long-term benefits. Semi-mediations indicate that demonstrating effort in green IT and knowledge investments may lead to a better reputation until innovation methods are fully applied. The results also show that the partial mediation of AGIS implies that expressing a commitment to green IT and knowledge through investments directly contributes to a company developing a reputation before the entire routine of innovation can be established.
Third, the research results showed that focusing more on TO improves green IT’s ability to support AGIS, while it does not significantly impact the knowledge source–AGIS relationship. In line with prior research, findings demonstrate that when companies focus on new technologies, they are more successful in creating green process and product solutions using IT resources [6,40]. A possible justification is that the culture supports technology, encouraging the testing of digital solutions, reducing resistance to change, and enabling IT experts to combine digital assets into two streams of innovation. The insignificant impact of the TO on sourcing knowledge and AGIS relationship highlights that it may be the firm’s capacity to integrate various knowledge sources, rather than its usual technology preferences, that matters most in effective knowledge recombination [63]. Probably, the breadth of knowledge within an organization already adds cognitive diversity, so having only a strong technology culture is not enough for diverse thinking to be directed towards successful green initiatives.
Lastly, the analysis of fsQCA shows that alone, none of the eight antecedent conditions (GITHC, GITSC, GITRC, ISK, ESK, EXGI, ERGI, and TO) could predict a high degree of GCA. Instead, the best empirical path to a high GCA (Configuration 1 in Table 9) shows this finding, with GITHC, GITSC, GITRC, ISK, ESK, and both EXGI and ERGI present in 51.4% of cases. The finding indicates that more firms gain a superior green advantage when applying dual GI and having a wide range of IT skills and knowledge. In non-high GCA (Configuration 7), the solution leading is supported by 41.5 percent of the cases, and it is worth noting that both GI modes are absent, as well as all GIT abilities and knowledge bases. This shows that it is because of a lack of innovation. In each of the two configurations, EXGI and ERGI are present in two of six high-GCA pathways and are not observed in all three non-high-GCA pathways. In addition, other factors change in order of importance, demonstrating that many combinations can lead to achievement only when GI is included. Such results are the continuation of the previous research as they reveal that dual GI, accompanied by the appropriate IT and knowledge resources, is the key to high GCA [3,14].

5.1. Theoretical Contributions

By integrating RBV and KBV, this study provides valuable theoretical insights into the strategic management literature. First, using both RBV and KBV approaches, this research redefines GITC (human, structural, and relational) and knowledge sources (internal storage and external links) as a single ecological VRIN bundle. Previously, these resources were studied individually [6,14]; however, we have found that combining them leads to better green-competitive performance. Second, this study contributes to the literature by considering AGIS as a key mediating mechanism in enhancing the relationship between green IT capital and knowledge sources, as well as competitive advantage. This finding embeds RBV/KBV frameworks in the logic of dynamic capabilities, demonstrating that firms only obtain value from resource stocks when such stocks are activated through routines that integrate both exploratory and exploitative eco-innovation [21].
Third, this paper further develops the resource-based view (RBV) in terms of the asymmetric moderating effect of TO that serves as a boundary condition determining the value realization of organizational resources. Notably, the results show that TO enhances the effects of IT-based resources on AGIS but not the impact of knowledge-sourced capabilities [40]. This asymmetric aspect of the study narrows RBV by showing that the efficiency of various forms of resources depends on their alignment with particular contextual orientations. Whereas IT-based resources do well in technology-driven cultures that encourage experimentation and digital integration, knowledge-based resources seem to rely more on integrative processes like absorptive capacity to make productive use of them.
Finally, the configurational findings complement the RBV, as they demonstrate that AGI is a non-substitutable hub capability, supporting the notion that sustainable competitive advantage can result from resource complementarity and situational fit, but not from monolithic assets. The different impact of IT and knowledge aspects of configurations highlights dynamism in the relationship among organizational, technological, and environmental factors [40]. Collectively, these contributions address mounting academic predilections to unpack the interactions among various resource domains to create green competitive advantage, thereby advancing the RBV toward a more refined, systems-based conceptualization of resource integration in the context of environmental innovation.

5.2. Practical Implications

Based on the empirical findings, there are three main implications for managers to help firms increase GCA by utilizing GITC, knowledge sources, and configurational strategies. First, companies should unify their investments by linking green IT infrastructure (including eco-analytics, eco-friendly IT employees, and connections with green technology businesses) with well-structured knowledge-sharing processes both within and outside the organization [17,62]. Companies may establish interdepartmental centers to collect environmental data and share insights with R&D, operations, and supply chain departments. Second, since AGIS plays a mediating role in the relationship between resources and advantage, companies should design their corporate governance to encompass both the search for new opportunities and the utilization of existing assets [14]. For instance, firms can (a) establish different budgets and targets for new eco-projects and ongoing process improvements, (b) allow people to work in different roles in both innovation and operations, and (c) include environmental assessment in decisions about moving forward in product development. As a result of these governance and incentive changes, organizations ensure that their green IT and knowledge assets are consistently leveraged in both immediate and long-term ways to benefit the environment.
Finally, configurational analysis highlights that various combinations of resources and capabilities can yield similarly high levels of GCA [61]. Therefore, managers should conduct internal reviews to identify their strengths, such as GITC, knowledge sources, or TO focus, and then determine the pathway that best fits their available resources. Firms with abundant resources may choose to develop both IT and knowledge resources. For companies short on resources, focusing on TO can help compensate for weak IT capabilities. Taking a flexible approach enables practitioners to develop sustainability activities tailored to their specific business context, rather than applying a one-size-fits-all approach.

5.3. Study Limitations

Despite the research’s significance, this study is subject to some limitations. First, the cross-sectional survey method does not allow for causal conclusions; a longitudinal study could discover the mutual development of resource bundles and ambidextrous routines over time. Second, the study only includes Chinese manufacturing firms, which may limit its applicability to other sectors and regions, including other industries and countries, and expand the generalizability of the findings. Third, the study also relies on subjective measures of how companies perform on green issues, rather than concrete performance data. Therefore, future research might involve using both financial and environmental audit data to verify the findings. Finally, this study model explores GITC, knowledge sources, ambidextrous strategy, and TO. However, it omits other organizational factors, such as environmental leadership, green dynamic capabilities, or regulatory pressure, that might influence AGIS and affect GCA [64].

Author Contributions

Conceptualization, M.U.S.; Validation, Y.G. and J.Z.; Formal Analysis, M.U.S.; Investigation, M.U.S.; Writing—Original Draft, Y.G.; Writing—Review and Editing, J.Z.; Supervision, J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by SIAS University’s new round of Henan Province Key Discipline “Tourism Management”, Research and Teaching [2023] No. 414.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Business School, SIAS University (5 January 2025).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in this article. Further inquiries can be directed to the first author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 18 00565 g001
Figure 1. Research model.
Figure 1. Research model.
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Figure 2. Configurational model.
Figure 2. Configurational model.
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Figure 3. GITC × TO on AGIS.
Figure 3. GITC × TO on AGIS.
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Table 1. Demographic profile of respondents.
Table 1. Demographic profile of respondents.
FrequencyPercent
Firm type
Chemical and petroleum5615.26%
Fertilizer4211.44%
Cement4913.35%
Textile5815.80%
Auto part manufacturing5113.90%
Sports goods5514.99%
Leather4311.72%
Others133.54%
Firm size
<1008422.89%
100–2006116.62%
201–5007620.71%
501–10005715.53%
>10008924.25%
Firm age
<5 Years7720.98%
6–10 Years6818.53%
11–20 Years8322.62%
21–40 Years7019.07%
>40 Years6918.80%
Ownership
State-owned enterprise11731.88%
Foreign-owned11631.61%
Private-owned enterprise12032.70%
Sino-foreign joint ventures143.81%
Table 2. Reliability and validity results.
Table 2. Reliability and validity results.
First-Order ConstructsHigher-Order ConstructsItems Loading (Ranges)VIF (Ranges)CαRhoAVE
Exploitative green innovation 0.796–0.8711.753–2.4200.8640.9070.710
Exploratory green innovation 0.862–0.9042.346–2.3460.8510.9090.770
Ambidextrous green innovation strategy0.893–0.9061.615–1.6150.7630.8940.808
Internal sources of knowledge 0.672–0.7601.535–2.2450.7810.8510.534
External sources of knowledge 0.606–0.8662.331–2.8190.8550.8980.642
Knowledge sources0.920–0.9251.971–1.9710.8250.9190.851
Green IT human capital 0.819–0.9001.862–2.3100.8140.8900.730
Green IT relational capital 0.723–0.8321.413–1.6860.7720.8540.594
Green IT structural capital 0.808–0.9141.674–2.5160.8170.8920.734
Green IT capital0.755–0.8811.391–2.0240.7800.8730.697
Green competitive advantage 0.752–0.8231.605–2.1310.8100.8740.634
Technology orientation 0.777–0.8401.704–2.4050.8340.8880.664
Table 3. Heterotrait–monotrait ratio (for lower-order constructs).
Table 3. Heterotrait–monotrait ratio (for lower-order constructs).
Constructs1234567891011
1. Exploitative GI
2. Exploratory GI0.716
3. External sources of knowledge0.5850.405
4. Green competitive advantage0.6580.6380.638
5. Green IT human capital0.4710.4690.4390.540
6. Green IT relational capital0.6460.6690.4870.5810.552
7. Green IT structural capital0.6040.5750.4700.5820.6290.834
8. Internal sources of knowledge0.5880.5540.8610.6610.4660.4760.452
9. Technology orientation0.0760.1110.0600.1010.0510.0710.0610.078
10. Technology orientation × KS0.1050.0530.0680.1240.0180.0930.1420.0460.064
11. Technology orientation × Green IT capital0.0600.0620.0810.1120.1230.0210.1320.1020.0590.426
Table 4. Heterotrait–monotrait ratio (for higher-order constructs).
Table 4. Heterotrait–monotrait ratio (for higher-order constructs).
Constructs1234567
1. Ambidextrous GI strategy
2. Green competitive advantage0.767
3. Green IT capital0.8220.689
4. Knowledge sources0.6790.6990.608
5. Technology orientation0.1050.1010.0630.042
6. Technology orientation × Knowledge sources0.0940.1260.1020.0600.063
7. Technology orientation × Green IT capital0.0310.1130.1120.0980.0580.425
Table 5. Predictive power of the model.
Table 5. Predictive power of the model.
Effect SizeCoefficient of DeterminationBlindfolding
Ambidextrous GI StrategyGreen Competitive AdvantageR-SquareR-Square AdjustedSSOSSEQ2 (=1 − SSE/SSO)
Ambidextrous GI strategy 0.1000.4970.490734.000446.4950.392
Green competitive advantage 0.4960.4921468.0001030.0110.298
Green IT capital0.3610.052
Knowledge source0.1460.130
Technology orientation0.010
Technology orientation × Knowledge source0.009
Technology orientation × Green IT capital0.031
Table 6. Hypotheses results.
Table 6. Hypotheses results.
HypothesesRelationshipsβSTDEVT Statisticsp Values2.5%97.5%Supported
IV or Mod→DV
Control effects
+VeFirm type →Green competitive advantage−0.0490.025−1.9250.055−0.0980.001No
+VeOwnership→Green competitive advantage−0.0260.059−0.4470.655−0.1420.090No
+VeFirm_size →Green competitive advantage0.0190.0350.5460.585−0.0500.089No
+VeFirm_age→Green competitive advantage0.0200.0370.5350.593−0.0540.094No
+VeFirm_type→Ambidextrous GI strategy−0.0450.025−1.7800.076−0.0950.005No
+VeOwnership→Ambidextrous GI strategy0.0100.0590.1730.863−0.1060.126No
+VeFirm_size→Ambidextrous GI strategy−0.0200.035−0.5580.577−0.0890.050No
+VeFirm_age→Ambidextrous GI strategy0.0220.0380.5820.561−0.0520.096No
Direct effects
H1Green IT capital→Green competitive advantage0.2160.0583.7260.0000.1000.328Yes
H2Knowledge sources→Green competitive advantage0.3120.0555.7000.0000.2120.424Yes
Moderating effects
H5Technology orientation × Green IT capital→Ambidextrous GI strategy0.1340.0662.0390.041−0.0120.249Yes
H6Technology orientation × Knowledge sources→Ambidextrous GI strategy−0.0780.0541.4300.153−0.1740.037Yes
Table 7. Mediation results.
Table 7. Mediation results.
Indirect EffectsDirect EffectsTotal Effects
Relationshipsβ (p-Values)T Valuesβ (p-Values)T Valuesβ (p-Values)T ValuesConclusion
H3Green IT capital → Ambidextrous GI strategy → GCA0.152 (0.000)4.1790.216 (0.000)3.7260.368 (0.000)6.822Partial mediation
BCI-LL0.084 0.100 0.258
BCI-UL0.227 0.328 0.468
H4Knowledge sources → Ambidextrous GI strategy→ GCA0.096 (0.000)3.7370.312 (0.000)5.7000.408 (0.000)7.726Partial mediation
BCI-LL0.051 0.212 0.312
BCI-UL0.153 0.424 0.517
Note: GITC = green IT capital; AGIS = ambidextrous green innovation strategy; KS = knowledge sources; GCA = green competitive advantage
Table 8. Necessary conditions analysis.
Table 8. Necessary conditions analysis.
ConditionsHigh GCANon-High GCA
ConsistencyCoverageConsistencyCoverage
GITHC0.80720.71760.61540.5566
~GITHC0.50130.56160.68780.7840
GITSC0.78290.74660.58640.5689
~GITSC0.54790.56560.73880.7759
GITRC0.78960.77040.57460.5704
~GITRC0.55970.56390.76870.7880
ISK0.79350.76620.61100.6003
~ISK0.58600.59690.76200.7896
ESK0.79450.78280.59540.5968
~ESK0.59080.58930.78330.7950
EIGI0.87140.76860.65320.5861
~EIGI0.53070.60070.74210.8545
ERGI0.81140.77760.58250.5680
~ERGI0.54920.56390.77190.8064
TO0.67150.66830.63900.6469
~TO0.64520.63720.67240.6756
Table 9. Configurations for GCA.
Table 9. Configurations for GCA.
Causal ConditionsConfigurations for High GCAConfigurations for Non-High GCA
123456789
Green IT human capital ⬤●●●●Sustainability 18 00565 i002Sustainability 18 00565 i002●
Green IT structural capital●●Sustainability 18 00565 i002●●●Sustainability 18 00565 i002 ●
Green IT relational capital●⬤●● ●Sustainability 18 00565 i002Sustainability 18 00565 i002●
Internal sources of knowledge●●⬤ ● Sustainability 18 00565 i002Sustainability 18 00565 i002●
External sources of knowledge●●⬤⬤●Sustainability 18 00565 i001Sustainability 18 00565 i002Sustainability 18 00565 i002●
Exploitative GI●● ●⬤⬤Sustainability 18 00565 i001Sustainability 18 00565 i001Sustainability 18 00565 i001
Exploratory GI● ●●⬤⬤Sustainability 18 00565 i002Sustainability 18 00565 i002Sustainability 18 00565 i002
Technology orientation Sustainability 18 00565 i002Sustainability 18 00565 i002Sustainability 18 00565 i002Sustainability 18 00565 i002● ●Sustainability 18 00565 i002
Consistency0.9330.9440.9380.9440.9450.9410.9770.9770.912
Raw coverage0.5140.3750.3650.3710.3750.3090.4150.3420.221
Unique coverage0.1160.0190.0090.0090.0190.0460.0780.0160.065
Overall solution consistency0.911 0.944
Overall solution coverage0.622 0.498
Note: ⬤ = core condition present; ● = peripheral condition present; Sustainability 18 00565 i001 = core condition absent; Sustainability 18 00565 i002 = peripheral condition absent; Blank spaces indicate “do not care”.
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Gao, Y.; Zhang, J.; Shehzad, M.U. Organizational Factors, Ambidextrous Green Innovation Strategy, and Technology Orientation: An Integrated Framework for Green Competitiveness. Sustainability 2026, 18, 565. https://doi.org/10.3390/su18020565

AMA Style

Gao Y, Zhang J, Shehzad MU. Organizational Factors, Ambidextrous Green Innovation Strategy, and Technology Orientation: An Integrated Framework for Green Competitiveness. Sustainability. 2026; 18(2):565. https://doi.org/10.3390/su18020565

Chicago/Turabian Style

Gao, Yarui, Jianhua Zhang, and Muhammad Usman Shehzad. 2026. "Organizational Factors, Ambidextrous Green Innovation Strategy, and Technology Orientation: An Integrated Framework for Green Competitiveness" Sustainability 18, no. 2: 565. https://doi.org/10.3390/su18020565

APA Style

Gao, Y., Zhang, J., & Shehzad, M. U. (2026). Organizational Factors, Ambidextrous Green Innovation Strategy, and Technology Orientation: An Integrated Framework for Green Competitiveness. Sustainability, 18(2), 565. https://doi.org/10.3390/su18020565

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