The Role of Open Innovation in Enhancing Organizational Resilience and Sustainability Performance Through Organizational Adaptability

Abstract

This study aims to investigate the influence of open innovation on both resilience and sustainability, with the mediating effect of adaptability. This investigation is conducted through the lens of dynamic capabilities theory. Although many researchers regard OI as a crucial detector for performance enhancement, the mediating effect of OA in shaping the pathways in which it plays a mediating role in both OR and SP remains unexplored. While the majority of previous studies approached open innovation through inbound and outbound innovation positioned as a mediator or by investigating its direct impact either on OR or overall performance, few have concurrently approached it from the breadth and depth dimensions with respect to either performance or resilience. This study offers a comprehensive approach through its unique elements in which all underexplored factors are combined in one theoretical framework. To the best of our knowledge, this study is a pioneer in the UAE business market, providing insights from multisector employee-level data that differs from previous management-focused research. The data was analyzed using the SmartPLS 4 SEM approach. The findings indicate that OID directly influences OR, underscoring the significance of deep and sustained external collaboration. Meanwhile, OIB indirectly contributes to OR through OA, highlighting the significance of the mediating impact of OA. Moreover, both OIB and OID influence SP, positioning OI as a strategic lever for long-term, sustainable performance. This study contributes to the existing body of research by offering nuanced insights into and details on how organizations can benefit from various OI strategies that enhance resilience and sustainability in today’s dynamic business environments.

1. Introduction

“We want the UAE to become the world’s most prepared country for Artificial Intelligence,” said His Highness Sheikh Mohammed bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai [1].

The United Arab Emirates (UAE) has launched a groundbreaking national transformation, driven by long-term strategies such as “UAE Vison 2031 of UAE Artificial Intelligence Strategy” [1] and “Environment Vision 2030” [2]. These frameworks place innovation, adaptability, resilience, and sustainability as the key pillars of the country’s future. The UAE’s 2031 vision aims to position the country as a global hub for knowledge-based development and innovation, while its artificial intelligence strategy calls for national readiness to adopt and apply AI across various sectors. Complementing this, the environmental vision focuses on integrating environmental, economic, and social sustainability through the preservation of the natural legacy, improved resource efficiency, and an enhanced quality of life. These interconnected objectives present a dual-edged path, offering both opportunities and challenges for companies in the UAE. Firms need to adopt innovation to be ready to implement AI. This requires enhanced capabilities, such as adaptability and resilience. On the other hand, achieving sustainability requires more innovative activities. Therefore, to maintain a competitive edge and still contribute to national progress, companies are compelled to embrace innovation, build adaptive capacities, and enforce long-term sustainability directions in their strategic goals.

In today’s rapidly evolving global economy, organizations face a remarkable convergence of technological shifts, a volatile operating environment, and rapidly shifting market dynamics. These multifaceted difficulties position innovation not only as a source of competitiveness but also as an essential element for long-term survival and sustainability. In response, companies must develop adaptive capabilities that enable them to forecast and respond to changes while continuing to deliver environmental, economic, and social value. However, the role of innovation strategies in fostering sustainability and resilience remains unexplored.

Historically, companies have been dependent on closed innovation systems focused merely on internal research and development. While these approaches are practical in stable environments, they are increasingly being challenged by globalization, digital transformation, and accelerated product cycles. In contrast, the Open Innovation (OI) model boosts collaboration beyond organizational boundaries, leveraging external sources such as universities, suppliers, customers, and even competitors to improve learning and responsiveness [3,4]. Open innovation enhances strategic flexibility and supports the integration of diverse knowledge sources, improving a firm’s adaptability and sustainability. Resilience refers to an organization’s ability to sustain its performance and withstand disruption [5]. Sustainability performance encompasses an organization’s social and environmental outcomes [6], and adaptability refers to the firm’s ability to change in response to challenges [7,8].

While prior studies have acknowledged the general role of open innovation, few have explicitly differentiated between its breadth and depth as dimensions of open innovation or examined their dissimilar effects on sustainability and resilience, particularly regarding the mediating mechanism of adaptability. Despite the popularity of open innovation in research, the two key elements of organizational capabilities (resilience and sustainability) have not yet been sufficiently linked to open innovation academically or empirically. The impact of open innovation on performance has been examined in the literature [9,10]. However, a recent systematic review paper indicated that sustainability performance had not yet been investigated in the literature [11]. Therefore, to the best of our knowledge, it remains unclear how open innovation contributes to these outcomes within a unified theoretical framework. Additionally, research has tended to test open innovation, resilience, and sustainability in isolation, while the mediating effect of adaptive capacity has been frequently overlooked [11,12]. In addition, previous studies have collected data from management teams, while the perspectives of employees remain underexplored, as suggested by Garrido et al. [13]. Moreover, to the best of our knowledge, research on aligning corporate innovation efforts with national development agendas remains limited. Furthermore, even with the compelling empirical context of the UAE, where the 2031 vision is transforming the country through innovation, sustainability, and resilience while simultaneously aiming to become a sustainable business model and knowledge-based global hub, the UAE context remains underexplored, especially in terms of how businesses in the UAE can benefit from innovation to increase adaptability while aligning strategic outcomes with national objectives, as indicated by Sikander et al. [14,15]

This research aims to investigate how open innovation, measured by both depth and breadth, impacts organizational resilience and sustainability performance, with the mediating role of organizational adaptability.

This research addresses the literature gap in several aspects. It empirically tests a unified conceptual framework that links open innovation, in terms of both depth and breadth, with resilience, sustainability, and adaptability. At the same time, this research is grounded on dynamic capabilities theory in which adaptability is conceptualized as an intermediate capability. This research provides empirical evidence from employee data in the context of the rapidly transforming emerging market of the UAE, contributing to the linkage between business practices and the national innovation strategy. Furthermore, this study enables the synchronization of policies and procedures for implementation and practices at the firm level through the lens of the UAE’s vision and its focus on innovation and growth.

Therefore, the main objectives of this study are the following: (1) to assess how open innovation, through depth and breadth, affects organizational resilience and sustainability performance and (2) to examine the mediating role of organizational adaptability in these relationships. Accordingly, this research aims to answer two questions: What is the impact of open innovation on organizational resilience and sustainability performance? And what role does organizational adaptability play in mediating these impacts?

The structure of the remainder of this paper is as follows: Section 2 presents a literature review of the theoretical and relevant foundations. Section 3 explains the research methodology, including sampling, measures, and analysis. Section 4 illustrates the empirical results. Section 5 presents this study’s findings within the context of theory and practice, followed by a conclusion, limitations, and future research directions.

2. Literature Review

2.1. Conceptual Review

2.1.1. Open Innovation

Open innovation as a model promotes and encourages organizations to leverage resources, whether they are in-house (internal) or external technologies and knowledge, to enhance their innovation process [7]. Open innovation measurements involve different approaches, two of which are OID (Open Innovation Depth) and OIB (Open Innovation Breadth) [16]. Open Innovation Breadth commonly refers to the level at which an organization expands its scope of innovation activities beyond its boundaries [16]. Chesbrough assumes that organizations can tap into external sources of knowledge, thereby increasing creativity and inspiration to accelerate the translation of product innovation into market offerings [3]. In addition, breadth enables organizations to leverage a variety of perspectives and expertise to foster collaborative networks that improve innovation outcomes [17]. While the depth perspective of open innovation captures the scope and depth of integration, organizations often apply external collaboration to their innovation process [16]. As highlighted by Laursen and Salter [16], to achieve more substantial innovation breakthroughs, organizations need in-depth collaboration with external groups, such as business partners, academic institutions, and consumers. Thus, these relationship depths are usually an indicator of the company’s capacity to adopt and implement external sources of knowledge effectively [18].

Furthermore, the interaction between breadth and depth is critical. Whereas a wide range of networks can introduce a diversity of ideas, the viability and quality of these ideas can be enhanced via the depth of collaboration [19]. Subsequently, to harness the full capacity of innovation, firms must balance the combination of depth and breadth [16].

2.1.2. Organizational Adaptability

Organizational adaptability is crucial for firms seeking to enhance their strategic advantage in a rapidly evolving business environment. This adaptability can be described as an organization’s ability to adapt its processes, structure, and strategies in response to internal and external challenges [7,8]. As described by Teece [20,21,22,23] dynamic capabilities play a significant role in achieving adaptability. Firms that cultivate dynamic capabilities are in a better position to seize opportunities, predict threats, and subsequently reconstruct their resources accordingly [24].

2.1.3. Organizational Resilience

Organizational resilience is progressively seen as crucial for steering disruptions and unpredictable environments. It refers to a firm’s capacity to predict, react to, and recover from substantial challenges while sustaining its primary functions [5,25]. As organizations face threats such as natural disasters or economic crises, resilience is the key to overcoming these challenges. Furthermore, dynamic capabilities theory centers around this concept, as described by Teece; with a strong dynamic capability, organizations can adapt rapidly and effectively, changing circumstances and subsequently increasing resilience [20].

Resilience has a crucial impact on positive organizational culture, as collaboration and trust improve collective efficacy and allow for rapid responses during disruptions [26]. In addition, leadership also plays a critical role, as teams are inspired to foster innovation and embrace change through transformational leaders [27]. Moreover, when firms leverage their resources creatively, they can recover quickly from disruptions, which is why resourcefulness is essential to resilience [28]. Therefore, resilience requires evolving and learning rather than simply recovering from a crisis.

2.1.4. Sustainability Performance

Sustainability performance is identified as an organization’s capability to simultaneously achieve economic success and operate in a socially and environmentally responsible manner [6,29,30]. The importance of collaborating with external stakeholders to improve sustainability, especially with customers, has been underscored [31,32]. As Hart highlighted, organizations gain access to the “voice of the environment” while engaging customers in operational-related activities, and this will allow them to align their sustainability-related goals with their strategies [33].

Additionally, global citizens, communities, employees, and members who represent social identity act as a crucial source of knowledge in sustainability-related demands [34,35]. Moreover, this collaboration supports the early-phase inclusion of environmental and social considerations in the development of services and products, ensuring that sustainable solutions fulfill social expectations and individual economic needs [36]. Furthermore, engaging customers in evaluating product development and sustainability helps reduce uncertainty regarding marketing and technology, which will subsequently enable the smooth adoption of the product and services and reduce the risk of rejection [37,38]. In addition, customer feedback can help organizations adapt to the product life cycle and dynamic consumer behavior patterns, which strengthens their competitive advantage [39,40,41]. Thus, organizations need to utilize customer integration to maintain sustainability, as it is the key to success by addressing sustainability-related demands effectively.

2.2. Theoretical Framework

Dynamic Capabilities Theory (DCT)

Dynamic capabilities theory was embraced in this research as a theoretical and conceptual framework to gain insight into the relationships between open innovation, organizational resilience, sustainability performance, and organizational adaptability. DCT was developed by Teece et al. [23] and posits that firms with solid dynamic capabilities are more likely to leverage favorable conditions, monitor and sense changes, and rebuild their competencies and resources in response to environmental transformation [21,23]. According to Teece, dynamic capabilities can be described as a firm’s capacity to construct, incorporate, and rebuild external and internal competencies and resources in responding to a rapidly changing environment. Therefore, dynamic capabilities are crucial to ensure a sustainable competitive advantage [20]. Moreover, dynamic capabilities are not just about adapting to environmental changes but also about rethinking and reshaping the internal processes of the organization to maintain competitive advantage and preserve sustainability [42].

Additionally, in the context of this work, innovation is regarded as a dynamic capability that allows firms to integrate and source external knowledge, which enables organizations to innovate and maintain competitiveness [42]. Thus, leveraging innovation enables organizations to build the flexibility and agility needed to embrace recent technological advancements and market disruptions, which ultimately improve resilience [5,43]. On the other hand, resilience through the lens of DCT, and in this research, captures the firm’s ability to survive and withstand disruptions while maintaining strategic and operational objectives [5,44]. Additionally, the more firms embrace open innovation, the better their resilience is, as this enhances their knowledge diversification and ability to plan in response to external changes [45].

Furthermore, sustainability is inherently linked to dynamic capabilities, as continuous innovation and adaptation are key to long-term success [46]. Moreover, innovation plays a vital role in this by enhancing productivity [47], efficiency [48], and strategic alignment with environmental transformations [49], through fostering new technologies, which will ultimately improve SP [48,50].

Lastly, organizational adaptability is an essential element of DCT [51], acting as a mediator between innovation and resilience [52]. As Daghfous highlighted, organizations with high adaptability can better comprehend and apply new knowledge [53], which will then enable them to withstand and recover from setbacks and sustain their performance throughout time [54,55,56].

Therefore, based on DCT and previous empirical studies, Figure 1 illustrates the conceptual framework approach adopted in this study. This framework illustrates how open innovation influences organizational adaptability, resilience, and sustainability performance, with organizational adaptability being positioned as a key mediating capability.

This conceptual framework provides the foundation for the development of the hypothesis presented in the next section, where the proposed interrelationships are developed, examined, and analyzed empirically.

2.3. Hypothesis Development

2.3.1. Open Innovation and Organizational Resilience

DCT highlights the significance of restructuring organizational resources to adapt to external challenges and maintain a competitive edge [20]. As stated earlier, OI is conceptualized as a dynamic capability [22,23], allowing organizations to leverage new technological solutions and external knowledge [17,23]. Therefore, adopting OI will allow firms to increase their flexibility [43], embrace new changes in the market [45,57,58], and enhance their ability to respond to disruptions [9]. Thus, the following hypotheses are made:

H1a. 

Open Innovation Breadth (OIB) positively impacts Organizational Resilience (OR).

H1b. 

Open Innovation Depth (OID) positively impacts Organizational Resilience (OR).

2.3.2. Open Innovation and Sustainability Performance

Dynamic capabilities theory can also be used to help understand the link between sustainability performance and open innovation [46]. As organizations can innovate more effectively, they use external knowledge and sources through open innovation [46], thereby improving sustainability practices, efficiency [48], and overall performance [49]. Furthermore, the integration of new technologies and practices enhances long-term sustainability by addressing operational and environmental challenges [50]. Therefore, based on this, the following hypotheses are made:

H2a. 

Open Innovation Breadth (OIB) positively impacts Sustainability Performance (SP).

H2b. 

Open Innovation Depth (OID) positively impacts Sustainability Performance (SP).

2.3.3. Open Innovation and Organizational Adaptability

OA is another key component of dynamic capabilities, which allows companies not only to anticipate and exploit opportunities [51] but also to withstand and recover from a crisis [52]. OI, on the other hand, offers organizations access to diverse practices, technologies, and ideas that improve their adaptability and help them respond to changing conditions [53]. Meanwhile, external collaboration, particularly with universities, suppliers, and customers, enables organizations to maintain their flexibility and responsiveness [59]. Hence, the following hypotheses can be made:

H3a. 

Open Innovation Breadth (OIB) positively impacts Organizational Adaptability (OA).

H3b. 

Open Innovation Depth (OID) positively impacts Organizational Adaptability (OA).

2.3.4. Organizational Adaptability and Organizational Resilience

As a core aspect of dynamic capabilities, organizational adaptability directly impacts an organization’s ability to respond to challenges [44], as organizations with a high level of adaptability can more effectively restructure their systems, resources, and processes in response to external threats [54,56]. Therefore, adaptive organizations have a better ability to recover from and withstand disruptions, thereby strengthening and enhancing organizational resilience while sustaining competitive advantage [44,54]. Thus, the following hypothesis can be made:

H4. 

Organizational Adaptability (OA) positively impacts Organizational Resilience (OR).

2.3.5. Role of Organizational Adaptability (Mediation Role)

As explained in the theoretical framework, organizational adaptability is expected to play a mediating role between innovation and resilience. Notably, dynamic capabilities are fundamental to maintaining a competitive edge, and they are described as a company’s ability to restructure, construct, and incorporate both external and internal resources in response to rapidly changing environments [22,23]. On the other hand, innovation, which involves utilizing external knowledge sources, leverages dynamic capabilities by encouraging innovation and expanding its knowledge base [17]. Additionally, leveraging dynamic capabilities increases an organization’s adaptive capacity, allowing firms to predict and seize new opportunities and effectively realign resources [9]. Consequently, this adaptability enhances resilience, enabling firms to better survive and recover from environmental disruptions [54]. Additionally, empirical research supports this connection, emphasizing that organizations with adaptive strategies and strong dynamic capabilities have more opportunities to achieve resilience and sustainable competitive edge in dynamic environments [60]. Therefore, adaptability is crucial for organizations to integrate new external knowledge, which represents open innovation, and then transform it into sustainable performance and resilience [52]. As a result, adaptability enables organizations to strengthen their innovation process management, respond to disruptions, and achieve long-term success.

Thus, the following hypotheses are made:

H5a. 

Organizational Adaptability (OA) mediates the relationship between Open Innovation Breadth (OIB) and Organizational Resilience (OR).

H5b. 

Organizational Adaptability (OA) mediates the relationship between Open Innovation Depth (OID) and Organizational Resilience (OR).

Therefore, based on the above arguments, the research model is shown in Figure 2 below:

3. Materials and Methods

3.1. Sampling and Data Collection

The focus of this research is on private sector companies in the United Arab Emirates, which is known for its diverse and cosmopolitan culture. Therefore, we employed a cross-sectional study research design to examine the relationships between open innovation, organizational resilience, sustainability performance, and organizational adaptability. To collect data, a structured quantitative survey was utilized, targeting employees across various industries. Based on recent data published by the UAE government, the number of private sector companies in the UAE (United Arab Emirates) is approximately 450,000 [61]. To determine the minimum sample size required for this research, Cochran’s formula was used, as recommended by Kotrlik et al. [62], with the following parameters: a confidence level of 95% (Z = 1.96), a margin of error of 5% (E = 0.05), and an estimated proportion of 0.5 (p = 0.5). The following formula was used to calculate the sample size:

$$n_0={\displaystyle \frac{Z^2\times p\times \left(1-p\right)}{E^2}}$$

Substituting the values into the formula, we obtain the following:

$$n_0={\displaystyle \frac{{\left(1.96\right)}^2\times 0.5\times \left(1-0.5\right)}{{\left(0.05\right)}^2}}$$

$$n_0={\displaystyle \frac{3.8416\times 0.25}{0.0025}}=384.16$$

Therefore, the initial sample size is around 384. Because the sample size is less than 5% of the population (452,110), no limited population correction is required. Thus, the final sample size required for this research is approximately 384 respondents. This size ensures the reliability of the statistical outcomes and enables an accurate statistical analysis of the correlations between the key variables of this study.

Furthermore, the SurveyMonkey platform was used to collect data online from employees across various sectors. As a starting point, approximately 500 surveys were sent, and 420 successful responses were received, yielding a satisfactory response rate. Additionally, to ensure equal opportunity for participants among all employees, a simple random sampling method was used, enhancing the representativeness of this study. While the UAE officially contains 254 sectors [61], the sampling frame included employees across a diverse range of industries, such as airlines, technology, oil and gas, retail, healthcare, and services. We actively pursued sectoral diversity to ensure the robustness and objectivity of the findings and to achieve a breadth of representation relevant to this study’s objectives.

Moreover, the target respondents were employees who are actively involved in business processes and daily operational practices, instead of the top management team (TMT). The focus of the sample was on midlevel operational staff and professionals, as they are impacted by and engage directly with open innovation, resilience, sustainability practices, and adaptability. Their opinions provide valuable insights into the practical application of these concepts.

To ensure the questionnaire is relevant and clear, the survey items were reviewed by experts prior to distribution to confirm their relevance and clarity. To ensure comprehension and accessibility for the participants, as the majority of them were native Arabic speakers, we translated the survey to Arabic by adopting the translation procedure recommended by Brislin, which involves translating English to Arabic and then back again to English [63].

Furthermore, back-translation techniques were employed during the translation process, and bilingual experts subsequently reviewed the outcome to ensure cultural appropriateness and linguistic accuracy. Additionally, the questionnaires were divided into sections, each with a detailed description of what it measures, providing participants with clear guidance before responding. Moreover, all participants were aware of the survey’s aim, as well as the anonymity and confidentiality of their responses. Notably, to improve response quality and reduce errors and the difficulty of understanding for the participants, the initial survey message included a brief recommendation on how to complete the survey. Therefore, the use of SurveyMonkey ensured efficiency in data collection, including the ease of distribution, anonymization features, and automatic response tracking, which enhanced the reliability of the data collection.

3.2. Measurement and Analytical Techniques

The items used in this study’s survey were adopted from previously established and validated scales in the literature. However, we made a few minor wording adaptations to enhance readability and clarity and ensure cultural and linguistic appropriateness, primarily during the translation process from English to Arabic and back to English, while also maintaining the item’s meaning and original measurement purpose. Therefore, we did not construct any new items in the survey.

Open Innovation (OI) was evaluated through ten items from an instrument developed initially by Chesbrough and Laursen from a breadth and depth perspective [3,16], and each dimension was addressed through five items [64]. Organizational Adaptability (OA) was assessed using a five-item scale [24]. Organizational Resilience (OR) was measured with a five-item scale [65]. Sustainability performance (SP) was assessed using a five-item scale [66].

Furthermore, a five-point Likert scale was adopted to assess all constructs, where one represents “strongly disagree”, and five indicates “strongly agree”, to ensure ease of response for participants and uniformity. Moreover, a complete questionnaire with all items is provided in Appendix A for reference.

Finally, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to examine the correlations between the variables and test the proposed hypotheses. In addition, descriptive statistics were computed to summarize the key variables and the demographics, offering an overview of the dataset in

Table 1. Descriptive statistics.

Demographic Variable	N	%
Gender
Female	224	53%
Male	196	47%
Age (years)
18–24	90	21%
25–34	200	48%
34–44	98	23%
45–54	22	5%
55+	10	2%
Educational level
High School
Associate Degree	67	16%
Bachelor’s Degree	79	19%
Master’s Degree	150	36%
Doctorate/PhD	50	12%
Other	10	2%

. Thus, this strict approach ensures that the measurement items are valid and reliable while enabling an in-depth analysis of the research model.

4. Results

4.1. Analysis of Results

Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the data, testing both the measurement model and the structural model simultaneously. We utilized SmartPLS 4.0 for this purpose. This method’s efficacy was proven due to PLS’s capability of producing reliable and accurate results even with a small sample size [67]. Construct reliability was evaluated by calculating Cronbach’s α, Composite Reliability (CR), and Average Variance Extracted (AVE). Then, the factor loadings of all indicators were analyzed to test convergent validity and to investigate irregularities. Furthermore, to assess discriminant validity, a comparison was conducted between the square root of AVE for each construct with the other constructs, ensuring that the first is the highest [68]. Passing this set of evaluations is crucial to ensure the strength of the proposed model for measurement. Once the assessment was satisfactory, PLS-SEM was used for hypothesis testing, as followed by other studies and widely accepted, such as in [69].

4.2. Assessing the Measurement Model

In this study, we tested the measurement model using the internal consistency measure to evaluate the reliability of the construct. Notably, during the first run through SmartPLS, four indicators were removed due to unsatisfactory outer loading (OIB2 = 0.411; OID2 = 0.641; SP5 = 0.675). Therefore, after removing these four indicators, all remaining indicators were above the 0.718 threshold, which thus proves the reliability of the indicators, as shown in

Table 2. Factor loadings and confirmatory factor analysis.

Model Construct	Loadings (λ)	CA (α)	Rho-A	CR	AVE
Open Innovation Depth		0.889	0.904	0.923	0.752
OID1	0.782
OID2 *
OID3	0.896
OID4	0.843
OID5	0.938
Open Innovation Breadth		0.884	0.901	0.920	0.743
OIB1	0.892
OIB2 *
OIB3	0.782
OIB4	0.871
OIB5	0.899
Organizational Resilience		0.867	0.878	0.905	0.657
OR1	0.722
OR2	0.749
OR3	0.894
OR4	0.863
OR5	0.813
Sustainability Performance		0.912	0.943	0.944	0.849
SP1	0.929
SP2	0.916
SP3	0.919
SP4 **
SP5 *
Organizational Adaptability		0.903	0.903	0.932	0.775
OA1	0.891
OA2	0.891
OA3 *
OA4	0.837
OA5	0.901

Note: *: Item was removed due to low factor loading. **: Item was removed due to collinearity.

[70].

All constructs reflected a Cronbach’s alpha value that exceeds 0.7, and the composite reliability was above 0.7, which helps ensure the construct’s reliability as well [71]. The value of AVE was higher than 0.5 for all constructs, which means that there is no evidence of convergent validity abnormality [72]. Moreover, the discriminant validity assessment was conducted by comparing the square root of the Average Variance Extracted (AVE) for each construct with the correlations among constructs. Thus, as displayed in

Table 3. Discriminant validity matrix.

Variables	OA	OIB	OID	OR	SP
Organizational Adaptability (OA)	0.880
Open Innovation Breadth (OIB)	0.541	0.862
Open Innovation Depth (OID)	0.441	0.737	0.867
Organizational Resilience (OR)	0.860	0.508	0.460	0.811
Sustainability Performance (SP)	0.425	0.497	0.616	0.507	0.921

, the AVE’s square root for each construct is the highest among the correlations of the other constructs. Thereby, as per Fornell and Larcker, this indicated a lack of discriminant validity [73].

To assess collinearity among the indicators, we used the Variance Inflation Factor (VIF) values. The initial assessment results indicated that SP4 exhibits high collinearity (VIF = 11.512), which exceeds the recommended threshold of 5 and reflects multicollinearity in the measurement model. Therefore, this item was removed from the model to enhance its reliability. Subsequently, after removing SP4, all remaining items reflected an acceptable value (VIF > 5), as illustrated in

Table 4. Collinearity (VIF).

Indicators	VIF
OA1	2.755
OA2	3.074
OA4	2.059
OA5	3.197
OIB1	4.108
OIB3	1.975
OIB4	2.600
OIB5	3.691
OID1	2.071
OID3	3.540
OID4	2.290
OID5	4.499
OR1	1.668
OR2	1.754
OR3	2.946
OR4	3.757
OR5	2.979
SP1	4.159
SP2	3.717
SP3	2.500

Note: All VIF values are below 5, indicating acceptable collinearity. SP4 was removed due to high collinearity (VIF > 10).

, which confirms that there is no multicollinearity in the measurement model.

4.3. Assessing the Structure Model

We used SmartPLS 4.0 to continue testing the hypotheses proposed, as shown in Figure 2. As shown in

Table 5. Hypothesis testing.

Hypotheses	β	STDEV	T-Value	p-Value	Results
Direct effect
H1a: OIB → OR	−0.022	0.033	0.675	0.250	Not supported
H1b: OID → OR	0.106	0.040	2.609	0.005	Supported
H2a: OIB → SP	0.091	0.043	2.130	0.017	Supported
H2b: OID → SP	0.550	0.050	11.105	0.000	Supported
H3a: OIB → OA	0.472	0.063	7.446	0.000	Supported
H3b: OID → OA	0.093	0.067	1.383	0.083	Not supported
H4: OA → OR	0.825	0.015	53.355	0.000	Supported
Indirect effect (Mediation)
H5a: OIB → OA → OR	0.389	0.055	7.079	0.000	Supported
H5b: OID → OA → OR	0.077	0.055	1.396	0.081	Not supported

, the analysis demonstrated that H1a was not supported (β = 0.022; t = 0.675; p = 0.250), indicating that OIB has no relationship with OR. H1b, on the other hand, was supported (β = 0.106; t = 2.6.9; p = 0.005), demonstrating that OID has a positive influence on OR. Hence, only OID has a direct relationship with OR, while OIB has no direct relationship with OR. Moving forward with the analysis results, H2a was supported (β = 0.091; t = 2.130; p = 0.017), indicating that OIB has a positive impact on SP. Similarly, H2b was supported (β = 0.550; t = 11.105; p = 0.000), revealing that OID also has a positive impact on SP. Hence, both OID and OIB have a direct relationship with SP. Likewise, H3a was supported (β = 0.472; t = 7.446; p = 0.000), demonstrating that OIB has a positive impact on OA. H3b was not supported (β = 0.093; t = 1.383; p = 0.083), which suggests that OID has no relationship with OA. However, H4 was supported (β = 0.825; t = 53.355; p = 0.000), demonstrating that OA has a positive influence on OR.

Furthermore, a significant indirect mediation effect was revealed through the assessment of the relationship between OIB and OR (β = 0.389; t = 7.079; p = 0.000). However, the indirect effects between OID and OR reflected no mediation effect (β = 0.077; t = 1.396; p = 0.081). This shows the full mediation influence of OA on the relationship between OIB and OR, while there was no mediation impact of OA on the relationship between OID and OR.

Moreover, Figure 3 below displays the structure model assessment outcome obtained from SmartPLS 4.0. Open Innovation Depth (OID), Open Innovation Breadth (OIB), and Organizational Adaptability (OA) collectively account for 74.6% of the variance in Organizational Resilience (OR). Additionally, Open Innovation Breadth (OIB) and Open Innovation Depth (OID) explain 38.4% of the variance in Sustainability Performance (SP) and 29.6% of the variance in Organizational Adaptability (OA). Therefore, this reflects a high explanatory power, which means that the visual illustration complements the summarized results in Table 5. Hence, the proposed model provides support for the key theoretical relationships presented in this study.

5. Discussion

5.1. Open Innovation and Organizational Resilience

This research hypothesized that both Open Innovation Depth (OID) and Open Innovation Breadth (OIB) would have a positive influence on Organizational Resilience (OR). Interestingly, the results revealed that OIB (H1a) did not have a significant relationship with OR, whereas OID (H1b) did have a significant relationship with OR. This means that while diverse external sources are explored in OIB, they may not increase OR. At the same time, deep integration with technologies and other partners’ OID can enhance the organization’s capacity to withstand and survive a crisis, reflecting OR. This finding complements the existing literature, as a recent study conducted in China by Li et al. [74] measured OI through inbound and outbound innovation, and OR served as a mediator between OI and SP. The results revealed a positive relationship between OI and OR as well. Also, another study was conducted in Morocco by Guennoun et al. [75], which investigated the influence of innovation on OR. Qualitative data were collected from CEOs through semi-structured interviews, and it was concluded that innovation improves resilience. Furthermore, additional research was recently conducted by Mirghaderi et al. [76]. Intending to address open innovation in textile startups based on OR attitudes, the findings reflected a significant relationship between OI and OR. Having said that, as far as we are aware, no previous research was found that addresses the relationship between OI and OR from both the breadth and depth lenses; thus, we could not compare our findings with those of similar studies on the same dimension of the OI construct.

Furthermore, the unsupported hypothesis (H1a), which proposed a direct effect of OIB on OR, may represent the complexities and challenges inherent in a wide range of innovation partners. While a broad network of external partners can stimulate new ideas, they may also lead to challenges in coordination or an overload of information, weakening the potential influence on OR unless organizations effectively integrate these inputs into an adaptive process. Moreover, additional costs may also affect this direct relationship. Practically, managing a broad range of external innovation partners may require significant integration, coordination, and operational costs. Thereby, these additional costs may strain the firm’s resources, subsequently reducing the potential leverage that can be derived from a broad range of external networks, thus diluting its impact on OR. In addition, from a theoretical perspective, the unsupported hypothesis can be justified by the theory of the resource-based view, which proposes that organizations operate within available resource constraints [77]. Hence, an extra cost that might require diverting a critical resource away from building an adaptive capability could potentially impact the organization’s ability to sustain financial flexibility and reduce its capacity to invest in other crucial opportunities for resilience. In simple terms, transaction cost economics assumes that higher coordination costs may reduce the firm’s internal collaboration efficiency, which may explain the unsupported direct effect seen between OIB and OR in this context [16,17].

5.2. Open Innovation and Sustainability Performance

The hypothesis of this research posits that Open Innovation (OI) has a direct, positive relationship with Sustainability Performance (SP). On the other hand, the research findings supported this hypothesis, as they reflect a significant positive direct relationship between both OI dimensions, Open Innovation Breadth (OIB) (H2a) and Open Innovation Depth (OID) (H2b), and SP. Therefore, this suggests that integrating external knowledge deeply into a firm’s process (OID) and engaging broadly in an innovation network (OIB) will better position these firms to achieve sustainability. Moreover, the strong effect seen for OIB on SP (H2b) suggests that a deep and focused integration of external resources can be significantly more impactful on SP than simply engaging in a wide range of networking activities. Additionally, recent peer-reviewed research found that open innovation practices can significantly improve performance. For instance, Rauter et al.’s study found that firms engaging in open innovation through collaborating with external partners simultaneously improve their sustainability innovation performance and economics [78]. Nonetheless, the literature consistently recommends that proper open innovation management will enable firms to utilize it as a strategic driver for improving sustainability outcomes and achieving economic gains [79].

5.3. Organizational Adaptability and Organizational Resilience

The hypothesis of this study posits that Organizational Adaptability (OA) has a positive direct relationship with Organizational Resilience (OR). Meanwhile, the findings of the hypothesis testing supported this hypothesis and reflected a strong positive relationship between OA and OR (H4), which indicates that organizations with high adaptability are better positioned to withstand and recover from disruptions. Therefore, this revealed the crucial role of enhancing dynamic capabilities in enabling organizations to respond to business environmental changes and sustaining their resilience. Additionally, recent peer-reviewed studies have revealed that OA is a fundamental antecedent of OR, as adaptability enables firms not only to absorb shocks and recover from disruptions but also to reshape their resources, innovate, and transform in the aftermath of crises. For instance, Duchek conceptualized resilience as a dynamic capability rooted in an organization’s adaptive process, emphasizing that organizations that continuously restructure their resources and learn from each experience are better equipped to overcome adverse events and crises [5]. Similarly, Lengnick et al. [80] suggested that high-adaptive-capacity organizations are highly effective at optimizing internal resources and cultivating learning, which directly improves their resilience. Moreover, another study by Linnenluecke suggested that a continuous learning and flexible decision-making culture is a crucial key that enables firms to predict and react effectively to potential threats, thereby sustaining long-term performance [80]. Overall, empirical evidence confirms that investing in adaptive capabilities is crucial for building and sustaining organizational resilience OR in today’s volatile environment.

5.4. Open Innovation and Organizational Adaptability

This study hypothesizes that Open Innovation (OI) has a direct positive relationship with Organizational Adaptability (OA). The findings of this study supported this hypothesis and revealed a strong positive relationship between Open Innovation Breadth (OIB) and Organizational Resilience (OR) (H3a). This study suggests that organizations exploring a wide range of external sources have highly improved abilities to cope with dynamic environments. On the contrary, the relationship between Open Innovation Depth (OID) and Organizational Adaptability (OA) (H3b) was not statistically significant. Therefore, this suggests that even if firms integrate external knowledge deeply, it may not necessarily translate into direct OA enhancement. Having said so, for literature comparison, and to the best of our knowledge, OID and OIB were not adopted as a measurement dimension of OI in a direct relationship with OA. However, research suggests that OI, through the strategic utilization of knowledge sources—whether internal or external—has been shown to have a significant influence on OA. Moreover, studies indicate that firms with OI practices tend to be more agile, consistently updating and restructuring their capabilities, cultures, and internal processes. For example, Chesbrough [3] and Huizingh [81] illustrated that OI enables organizations to absorb external technologies and ideas, thereby transforming them into more adaptive capabilities. Subsequently, these capabilities enable firms to sense and respond to disruptions and market changes rapidly. Additionally, empirical studies by Cui et al. [82] and Naruetharadhol et al. [83] offer evidence that external knowledge integrated through networks and technology collaboration significantly improves an organization’s ability to adapt. Therefore, adaptability not only improves competitive advantage but also supports a more balanced innovation strategy and dynamic decision-making. Overall, the proactive approach of using an open innovation strategy has been consistently highlighted in the literature as a key to driving a culture of agility and continuous learning, which is essential for prospering in dynamic environments.

5.5. The Mediating Role of Organizational Adaptability

The final hypothesis of this research posits that Organizational Adaptability (OA) has a fully mediating impact on the relationship between Open Innovation (OI) and Organizational Resilience (OR). Surprisingly, the findings supported the full mediation effect on the relationship between Open Innovation Breadth (OIB) and Organizational Resilience (OR) (H5a). Organizational adaptability (OA) did not mediate the relationship between Open Innovation Depth (OID) and Organizational Resilience (OR) (H5b). This suggests that OIB improves resilience by increasing OA; further investigation may help explore other mediating factors that influence this relationship. At the same time, empirical studies in the existing literature suggest a conceptual link, with particular emphasis on the mediating role of organizational adaptability or flexibility between OI and OR. Additionally, open innovation practices can enhance organizational flexibility by introducing firms to a diverse range of knowledge sources and promoting adaptability. In turn, this enhanced flexibility enables firms to respond quickly to environmental challenges and disruptions, thereby enhancing their resilience. Similarly, Yuan et al. asserted the mediation role of adaptability between innovation and resilience [52]. At the same time, Olaleye et al. [84] illustrated that strategic flexibility is a crucial mediator linking innovation to OR. However, the insignificant direct and mediation effects of OID on OA call into question some previous claims that deep innovation enhances all aspects of organizational capabilities. Having said that, this discrepancy may be related to contextual factors, such as industry-specific variations or dynamics in how OID is put into operational practice.

Similarly, the lack of support for the direct relationship between Open Innovation Depth (OID) and Organizational Adaptability (OA) (H3b) and its subsequent indirect influence on Organizational Resilience (OR) (H5b) suggests that deep innovation practices have their benefits in structuring key capabilities but do not necessarily transform into enhanced OA. Moreover, firms engaging in deep innovation may focus too heavily on operational improvements or specific technological development, which can limit their ability to adapt to unpredictable challenges quickly. Therefore, it is important not only to foster deep innovation but also to develop a complementary mechanism that promotes agility and responsiveness.

Practically, the findings of this study suggest that organizations should prioritize deeper strategic innovation integrations to enhance resilience while simultaneously ensuring that innovation depth and breadth are balanced to foster sustainable performance. Moreover, enhancing organizational adaptability through continuous learning, agile decision-making, and flexible structures can support the leveraging of open innovation benefits in an uncertain and dynamic environment.

6. Conclusions

6.1. Theoretical Contribution

Theoretically, this research enhances the comprehensiveness of the impact of Open Innovation (OI) on Organizational Resilience (OR) and Sustainability Performance (SP) through distinguishing between Open Innovation Depth (OID) and Open Innovation Breadth (OIB). Unlike previous studies that explored direct relationships only [3], this study combines both dimensions of OI as breadth and depth, showcasing their influence on a firm’s ability to absorb knowledge, adapt, and effectively innovate [85].

Additionally, the results of this study extend existing knowledge by illustrating that OID plays a more crucial role in improving OR, whereas both OIB and OID contribute to SP. Moreover, the mediating role of OA recommends that breadth-oriented strategies affect OR through OA as a key mechanism for firms, and it positions OA as a mediator that enriches the theoretical explanation of how OID impacts OR. Therefore, this nuanced point of view highlights the role of OA as a dynamic process that transforms OI into an actionable strategy for OR [86].

Furthermore, this study not only extends and validates knowledge about OID and its impact on SP and OR, while OA mediates the relationship between OIB and OR within a rapidly evolving organizational environment, but also builds a strong theoretical foundation for explaining these relationships.

Overall, this study further adds to the literature by presenting empirical confirmation of the nuanced and complex interplay between OID and OIB, OA, OR, and SP. Notably, the results confirm the pivotal role of OID in directly influencing OR and SP. Therefore, organizations engaging in sustained and in-depth collaboration with external partners will subsequently be enabled to leverage their resilient strategies and foster sustainability performance. Furthermore, OR plays a crucial role in enhancing resilience, particularly in environments that involve a diverse range of technologies and external partnerships. Additionally, this study highlights the importance of striking a balance between depth and breadth in OI strategies while also enhancing adaptability to navigate rapidly changing environments effectively. Therefore, these perspectives offer valuable guidance for organizations seeking to structure resilience and promote sustainability through strategic OI practices.

Finally, this study contributes to the generalization of the conceptual framework and enriches the expanding body of knowledge by applying it to the context of the United Arab Emirates, a region known for its diverse industries and dynamic economic development. In this regard, this study addresses a key gap in understanding broader organizational insights by collecting data at the employee level, rather than focusing solely on management [87].

6.2. Practical Contribution

From a management perspective, and to align with the national objectives of the UAE’s vision, the results highlight that organizations aiming to leverage their resilience should place greater emphasis on open innovation depth by fostering sustainable and intensive innovation engagements and collaborations, rather than merely focusing on open innovation breadth through expanding their external partnerships. Additionally, investing in strategic, long-term collaborations that focus on critical stakeholders accelerates the sharing of resources, transformation of knowledge, and development of problem-solving capabilities, all of which are crucial in addressing disruptions and crises.

Moreover, to achieve sustainable performance, organizations can leverage the utilization of strategies that focus on a balanced approach of breadth and depth. This includes the deep integration of innovation across the organization while simultaneously increasing the diversity of external partners, such as universities, suppliers, and other innovation-related institutes, which will help translate innovation into sustainable practices. Finally, organizational adaptability plays a vital role in strengthening organizational resilience and maximizing the benefits of open innovation. Therefore, firms should enhance their adaptability by developing flexible structures, fostering continuous learning systems, and promoting dynamic decision-making processes.

6.3. Limitations and Future Research

Although this research provided valuable insights, several limitations should be noted. First, adopting open innovation through its breadth and various other dimensions, such as outbound and inbound innovation and open and closed innovation, has yet to be explored within the context of this framework. Future research could explore these proposed dimensions to contribute to a broader understanding of open innovation practices.

Second, because this study relied on a self-reporting method, biases may be present, such as social desirability or memory-related issues. Hence, data triangulation from diverse sources or the use of objective performance metrics may improve the reliability of future results. Additionally, as data was collected from employees only, it may not reveal the process by which strategic decision-making is navigated. Hence, gathering multilevel data, such as additional insights from top management teams, will allow a comparative group analysis to be performed and subsequently introduce a more holistic and reliable viewpoint.

Third, as this study relies on a cross-sectional design, which limits causal interpretations, conducting longitudinal studies will be beneficial for tackling the evolution of innovative practices and tracing their impact on performance and resilience. Furthermore, studying AI users as an industry or as a unique focus group may contribute to a deeper understanding of how open innovation in the AI sector influences sustainable performance and resilience, providing valuable guidance for future studies. Moreover, introducing additional mediating factors, such as digital transformation or organizational learning, provides a clearer understanding of how open innovation impacts sustainability and resilience.

Additionally, while a wide range of innovation partners’ networks will potentially improve the knowledge base of organizations, the additional cost involved can be a mitigating element that challenges the transformation of this diversity into better resilience. Thus, this mitigating element can be addressed and tested empirically to expand the body of knowledge in comprehending up to what extent, and under what conditions, this additional cost may mitigate the benefits of scaling up open innovation.

Finally, while the methodology of this study offers a foundation for future international replication, a few limitations warrant consideration. First, the focus of this study is exclusively on the UAE, with its distinguished cultural, regulatory, and economic context, which may limit the generalizability of the findings to other regions with different regulations, cultures, and levels of digitalization. Therefore, future research should examine the potential of replicating the model in diverse geographic regions, such as Southeast Asia or Central and Eastern Europe, adapting the measurement instrument to satisfy the cultural and linguistic considerations. Furthermore, to improve practical relevance and replicability, future research may conduct comparative analysis across diverse countries or industries to collect data from multi-organizational levels, including the management team, instead of focusing on employees only, to produce a more holistic understanding of the relationship between open innovation, organizational resilience, and sustainability.