From Crisis to Opportunity: Digital Transformation, Digital Business Models, and Organizational Resilience in the Post-Pandemic Era

Abstract

This study investigates how digital transformation, digital culture, innovation capabilities, and organizational resilience influence organizational performance in the post-pandemic era. Grounded in Resilience Theory, the Dynamic Capabilities Framework, and Organizational Learning, this research analyzes how digital capabilities—such as innovation, digitalization, telework, and investment strategies—influence organizational performance. Data were collected through a structured online survey with 320 valid responses from decision-makers across various sectors. Using descriptive statistics, Pearson correlations, and multiple linear regression analysis, the results reveal that innovation, organizational resilience, and investment strategies are significant predictors of performance, together explaining over 52% of its variance. Interestingly, while digitalization correlates strongly with innovation and strategic adaptation, its direct effect on performance was not statistically significant in the regression model. These findings underscore the importance of an integrated approach to digital transformation and resilience-building strategies for navigating crises and fostering long-term performance. The study contributes to the literature on digitalization, crisis response, and strategic management, offering practical insights for managers and policymakers committed to strengthening organizational adaptability in the post-pandemic era.

1. Introduction

The COVID-19 pandemic has fundamentally reshaped the global business landscape, compelling organizations to rapidly adapt their operational models, technological infrastructures, and crisis management strategies (Kraus et al., 2020; Margherita et al., 2021; Haq & Huo, 2023; Bürgel et al., 2023). As governments worldwide implemented restrictions to contain the virus, businesses faced unprecedented disruptions, necessitating a rapid shift towards digital solutions (Larios-Hernández, 2021; Donthu & Gustafsson, 2020). To sustain operations under unprecedented constraints, businesses accelerated their adoption of digital solutions to ensure continuity and resilience (Guo et al., 2020; Bolisani et al., 2020; Dwyer et al., 2021; Krzywdzinski et al., 2022; Sofic et al., 2022; Portela et al., 2022; Heredia et al., 2022; Yorulmaz et al., 2023; Yorulmaz et al., 2023; Browder et al., 2023; Trieu et al., 2023; Wang et al., 2024; Xu et al., 2024).

This shift gave rise to new work practices, including the widespread adoption of remote work, e-commerce, and digital collaboration platforms (Savić, 2020; Kudyba, 2020; Rublev, 2024). Tools such as Zoom, Microsoft Teams, and Google Meet became essential for communication and workflow management (Oliveira et al., 2021; Santos & Mendonça, 2021), highlighting the transformative potential of Information and Communication Technologies (ICT) to redefine how firms operate. As flexible, goal-oriented work environments gained traction (Bolisani et al., 2020), organizations faced a new generation of employees—tech-savvy, multitasking, and fast-moving—who challenged traditional paradigms (Savić, 2020).

Despite the surge in digital adoption, many firms struggled to fully leverage digital transformation. Barriers such as weak infrastructure, cybersecurity concerns, and digital skill gaps hindered effective implementation (Gkeredakis et al., 2021; Stalmachova et al., 2022; Mancuso et al., 2023; Cardoso et al., 2024; Pozhueva & Shchegolevatykh, 2024). In contrast, others used the crisis as a springboard to accelerate innovation, implement new business models, and boost competitiveness (Klein & Todesco, 2021; Anthony, 2023a, 2023b; Kahveci et al., 2024). These divergent outcomes point to the importance of contextual factors—such as culture, leadership, and strategic alignment—in determining the success of digital strategies (Crespo et al., 2023; Kronblad & Pregmark, 2024).

In the Portuguese context, the pandemic catalyzed digital transformation across multiple sectors, driving organizations to reassess how they adopt technology, respond to disruption, and foster innovation (Roque & Alves, 2023; Oliveira et al., 2021; Santos & Mendonça, 2021). Although Portugal saw a marked increase in digital tool adoption (Oliveira et al., 2021; Portela et al., 2022; Roque & Alves, 2023), few studies have examined how Portuguese firms used digital strategies to build resilience and adapt their business models in response to crisis-induced pressures (Durão et al., 2019; Pereira et al., 2020; Bürgel et al., 2023).

This study addresses that gap by exploring how digital transformation, measured through innovation, digital adoption, crisis response, and post-pandemic strategy, has influenced organizational resilience and performance in Portuguese companies. The analysis is grounded in Resilience Theory (Hollnagel, 2014), the Dynamic Capabilities Framework (Teece, 2007), and Organizational Learning Theory (Argyris & Schon, 1978), which together provide a robust foundation for understanding how firms navigate uncertainty and build adaptive capacity.

Using a quantitative survey design and multiple linear regression analysis, we assess how digital adoption, innovation practices, strategic responses, and resilience relate to organizational performance. By collecting data from decision-makers in Portuguese firms, this study offers insight into how organizations align technology, strategy, and learning to overcome crisis conditions.

This research contributes to both theoretical and practical domains by elucidating how firms embed digital initiatives within broader strategic and cultural frameworks in the post-crisis context. It also offers actionable insights for policymakers and business leaders aiming to strengthen long-term resilience through digitally supported innovation-oriented organizational strategies.

2. Literature Review

We live in a highly technological era, where the reliance on digital tools has reached unprecedented levels (Kudyba, 2020; Pereira et al., 2020; Cardoso et al., 2024). Simultaneously, the frequency and intensity of global crises, such as the COVID-19 pandemic, have surged, prompting significant shifts in how societies, firms, and individuals leverage digital technologies. Faced with lockdowns and disrupted supply chains, organizations rapidly accelerated the adoption of digital solutions—including cloud computing, remote work platforms, and e-commerce systems—to ensure operational continuity and foster resilience (Bolisani et al., 2020; Guo et al., 2020; Dwyer et al., 2021; Crespo et al., 2023). However, although digitalization’s value was widely acknowledged, implementation was often hindered by infrastructural gaps, cybersecurity vulnerabilities, and limited digital competencies (Gkeredakis et al., 2021; Mancuso et al., 2023; Cardoso et al., 2024).

The pandemic served as a powerful catalyst for digitalization across sectors. Firms swiftly adopted subscription-based models, digital marketplaces, and data-driven decision-making to remain operational during lockdowns. The COVID-19 pandemic accelerated digitalization across sectors, compelling firms to implement subscription-based models, digital platforms, and data-driven strategies to sustain operations (Mancuso et al., 2023). Tools like Zoom and Microsoft Teams became central to collaboration (Oliveira et al., 2021; Santos & Mendonça, 2021; Crespo et al., 2023), while emerging technologies, such as AI, IoT, and blockchain, supported efficiency and resilience (Amankwah-Amoah et al., 2021; Rublev, 2024; Bürgel et al., 2023; Wang et al., 2024).

Nonetheless, not all firms benefited equally from digital transformation. Factors such as technological infrastructure, leadership commitment, and human capital readiness significantly influenced outcomes (Gkeredakis et al., 2021; Pozhueva & Shchegolevatykh, 2024). Firms that integrated digital adoption with strategic planning, executive support, and targeted training reported superior performance and competitiveness (Guo et al., 2020; Klein & Todesco, 2021; Haq & Huo, 2023; Kahveci et al., 2024).

This disparity is particularly evident among small and medium-sized enterprises (SMEs). Many SMEs utilized big data analytics, cloud solutions, and online platforms to enhance transparency, agility, and strategic decision-making (Guo et al., 2020; Rublev, 2024; Pozhueva & Shchegolevatykh, 2024). Dynamic capabilities, specifically sensing opportunities, seizing digital investments, and reconfiguring resources, have been highlighted as critical to navigating turbulence (Karimi & Walter, 2015; Kahveci, 2021; Fariq et al., 2022; Martins, 2022). Yet, according to Ghimire et al. (2023), many SMEs remain at an early stage of digital maturity, indicating the need for sustained alignment between strategy and digital investment.

Sectoral and regional differences further shape the priorities, pace, and impact of digitalization. For example, service industries such as tourism and retail have emphasized customer engagement through digital platforms (Browder et al., 2023; Tang & Huang, 2023), whereas manufacturing firms focused on automation and supply chain digitization to ensure operational efficiency (Sofic et al., 2022; Xu et al., 2024). In developing economies, SMEs often lag in digital adoption but can leapfrog with the support of targeted policies (Khalil et al., 2022; Mandviwalla & Flanagan, 2021). Rublev (2024) notes that structured digital roadmaps, supported by leadership and funding, enhance resilience. The Portuguese public sector, for instance, has adopted electronic contracting to boost transparency and efficiency (Rosa & de Almeida, 2017). High-tech firms, by contrast, use digitalization to improve governance and attract capital (Liu & Qi, 2024).

National factors also mediate digital adoption. In China and Turkey, government support has advanced teleworking and labor flexibility (Heredia et al., 2022), while countries like Vietnam and Serbia have focused on IT competency development. In Central America, digitalization has been a lever for managing economic volatility (Browder et al., 2023). These patterns emphasize the importance of contextualizing digital transformation strategies within specific national and sectoral environments.

Despite clear evidence linking digital transformation to resilience, innovation, and performance (Ben-Zvi & Luftman, 2022; Browder et al., 2023; Jangjarat & Jewjinda, 2023; Nosike et al., 2024), three critical tensions endure. First, it remains unclear whether digitalization drives performance directly or only indirectly via innovation, dynamic capabilities, and learning. Second, the role of digital maturity levels and strategic alignment in shaping outcomes demands further investigation. Third, most large-firm research leaves a gap in understanding SME and context-specific patterns, particularly in national settings like Portugal.

Research remains divided. While some studies suggest that digital tools enhance performance through efficiency and innovation (Teece, 2007; Witschel et al., 2019), others argue that without alignment and capability, digitalization may yield no benefit or even overload organizations (Reuschl et al., 2022; Klein & Todesco, 2021). This debate raises the following question: Is digitalization a universal solution or one contingent on organizational and regional conditions?

Digitalization is consistently associated with enhanced resilience (Browder et al., 2023), though performance outcomes are inconsistent. For example, Çallı and Çallı (2021) found that digital maturity improved agility in Turkish SMEs, whereas Ghimire et al. (2023) observed that digital efforts in Nepalese firms did not translate into performance gains. These contradictions suggest the presence of moderators such as sectoral context or strategic coherence.

The resilience literature underscores that technological adaptation must be paired with strategic innovation (Corvello et al., 2023). Key capabilities, such as virtual access, remote collaboration, data analytics, and algorithmic reprogrammability, enhanced firms’ crisis responses and adaptation (Browder et al., 2023; Tang & Huang, 2023; Nosike et al., 2024). Organizational resilience is defined as the capacity to anticipate, respond to, and adapt to change while sustaining operations and growth (Hollnagel, 2014; Teece, 2007; Argyris & Schon, 1978).

Organizational adaptation during the pandemic included the following: (i) accelerated digitalization to maintain business continuity (Krzywdzinski et al., 2022; Browder et al., 2023); (ii) adoption of telework and collaboration tools (Heredia et al., 2022; Yorulmaz et al., 2023); (iii) reliance on real-time data for decision-making; (iv) supply chain resilience through digital integration (Xu et al., 2024); (v) strategic reorientation towards technology-centric models (He et al., 2022; Puspita et al., 2023; Anthony, 2023a).

This study draws on three foundational theoretical frameworks: Resilience Theory (Hollnagel, 2014), the Dynamic Capabilities Framework (Teece, 2007), and Organizational Learning Theory (Argyris & Schon, 1978). Digitalization enhances dynamic capabilities by enabling real-time analytics, innovation, and agile operations (Ghimire et al., 2023; Browder et al., 2023). The development of such capabilities allows firms not only to respond to disruptions but to emerge stronger.

Several studies also examine digitalization’s ambivalent impact. Gkeredakis et al. (2021) identify three perspectives: opportunity (innovation potential), disruption (transformation of work), and exposure (inequality risks). Others such as Larios-Hernández (2021), Kronblad and Pregmark (2024), and Jovanović (2020) highlight the transformative effect of digital tools on resilience and project execution. Kronblad and Pregmark (2024), for instance, identify four organizational archetypes (crisispreneurs, accelerators, endurers, thrivers) and argue that COVID-19 permanently altered digital transformation trajectories.

The literature ultimately suggests that digital business models and resilience capabilities are fundamental for navigating crises and enabling sustainable growth (Guo et al., 2020; Crespo et al., 2023). However, rapid digitalization may exacerbate disparities between digital leaders and laggards, posing long-term challenges (Krzywdzinski et al., 2022).

This study addresses these gaps by integrating Resilience Theory, Dynamic Capabilities, and Organizational Learning into a coherent framework, and by empirically exploring how digital adoption, innovation practices, crisis responses, and post-pandemic strategies collectively shape resilience and performance in Portuguese organizations.

Based on the integrated theoretical foundations discussed (Resilience Theory, the Dynamic Capabilities Framework, and Organizational Learning Theory), this study proposes the following hypotheses to empirically test the theorized relationships among digital transformation, innovation, resilience, and performance in the post-pandemic organizational era:

H1: 

Innovation is positively associated with organizational performance.

Innovation is widely recognized as a critical driver of competitive advantage, particularly in dynamic and volatile environments. Ben-Zvi and Luftman (2022) assert that firms with strong innovation capabilities are better positioned to adapt to changing market conditions, resulting in enhanced performance. Empirical evidence from Klein and Todesco (2021) and Corvello et al. (2023) demonstrates that innovative firms achieve superior market positioning and financial outcomes. Additional support from studies like Witschel et al. (2019) reinforces the link between innovation and improved performance metrics.

H2: 

Digitalization is positively associated with organizational performance.

Digital transformation facilitates the integration of digital technologies that enhance knowledge integration, experimentation, and process improvement, thus fostering innovation. Teece (2007) and Karimi and Walter (2015) argue that digital tools enable firms to sense and seize new opportunities, which catalyzes innovation. Ghimire et al. (2023) further emphasize that digital transformation creates agile and flexible processes conducive to innovation. Additionally, studies such as Guo et al. (2020) and Crespo et al. (2023) highlight the role of digitalization in driving innovation across various sectors. A supportive digital culture, characterized by leadership support and openness to digital initiatives, is crucial for fostering innovation and resilience within organizations. Argyris and Schon (1978) emphasize the role of cultural enablers in promoting organizational learning and adaptation. Cardoso et al. (2024) argue that a strong digital culture enhances adaptive capacity, facilitating innovation and increasing resilience to environmental changes. This is further supported by Gill and VanBoskirk (2016) and Diogo et al. (2019), who highlight the impact of digital culture on organizational transformation and success. Digital tools (e.g., cloud computing, AI) improve efficiency, decision-making, and market responsiveness (Guo et al., 2020; Wang et al., 2024). Digitalization’s impact is amplified when firms leverage it for innovation and build resilient processes (Teece, 2007; Hollnagel, 2014).

H3: 

Investment strategy is positively associated with organizational performance.

Strategic investment in digital infrastructure, talent development, and innovation capabilities is widely acknowledged as a key driver of long-term organizational performance. Davenport et al. (2020) emphasize that proactive investment strategies foster growth, enable technological adoption, and secure competitive advantage. Further, Soto-Acosta (2023) argues that investment aligned with a coherent digital and organizational strategy significantly enhances performance by improving agility, productivity, and customer responsiveness. This view is supported empirically by studies showing that firms prioritizing innovation-focused investments outperform those with reactive cost-cutting strategies (Ben-Zvi & Luftman, 2022; Pozhueva & Shchegolevatykh, 2024).

H4: 

Telework strategy is positively associated with organizational performance.

Effective telework strategies enhance employee flexibility, satisfaction, and continuity of operations—factors that contribute to organizational resilience and performance (Waizenegger et al., 2020). Recent research by Härtel et al. (2023) demonstrates that organizations with well-defined remote work structures, digital collaboration tools, and leadership support report significant gains in productivity and employee engagement. Moreover, Martin and MacDonnell (2012), through a meta-analytic review, confirm that telework is associated with improvements in task performance, job satisfaction, and organizational commitment. These benefits are especially evident when telework policies are strategically managed and embedded within organizational workflows.

H5: 

Cost reduction strategy is positively associated with organizational performance.

Cost reduction is often positioned as a tactical response to crisis, but when executed strategically, it can lead to efficiency gains and long-term sustainability. Bharadwaj et al. (2013) highlight that digital-enabled cost control mechanisms, such as automation and process optimization, allow firms to maintain competitiveness even under pressure. Empirical evidence from Duchek (2020) and Cardoso et al. (2024) supports the idea that resilient and cost-conscious organizations are better equipped to absorb economic shocks while preserving performance. However, the literature also cautions that excessive cost-cutting without strategic vision can undermine innovation and adaptability (Teece, 2007).

H6: 

Organizational resilience is positively associated with organizational performance.

Resilience is defined as an organization’s capacity to anticipate, absorb, and adapt to environmental changes, ensuring continuity and growth. Hollnagel (2014) posits that resilient organizations are better equipped to withstand and recover from disruptions, leading to sustained performance. Studies by Xu et al. (2024) and Duchek (2020) further corroborate the link between resilience and enhanced operational and strategic outcomes. Additional empirical support is provided by Browder et al. (2023), who highlight the role of resilience in sustaining organizational success during crises.

H7: 

Post-pandemic strategy is positively associated with organizational performance.

Strategic initiatives undertaken in response to crises, such as business model adjustments and process optimizations, are critical for long-term performance. Puspita et al. (2023) underscore that organizations proactively implementing strategic responses to crises are more likely to achieve sustained performance improvements. Crespo et al. (2023) stress the importance of strategic agility in navigating post-crisis environments, while Margherita et al. (2021) highlight the role of digital strategies in enhancing organizational adaptability and performance.

H8: 

Post-pandemic digital transformation is positively associated with organizational performance.

The continuation and deepening of digital transformation initiatives post-pandemic are critical for sustaining performance gains achieved during the crisis. Browder et al. (2023) show that digital maturity correlates with superior operational agility, customer satisfaction, and data-driven decision-making. Complementing this, Soto-Acosta (2023) concludes that organizations that institutionalize digital transformation—moving beyond emergency digitization—realize improved strategic alignment, innovation output, and financial outcomes. Additionally, Ghimire et al. (2023) and Heredia et al. (2022) identify digital transformation as a mediating factor between environmental disruption and organizational performance, particularly in sectors heavily affected by COVID-19.

To synthesize the theoretical propositions and guide the empirical analysis, the following conceptual framework (Figure 1) illustrates the hypothesized relationships among digitalization, innovation, strategic responses, organizational resilience, and performance.

3. Methodology

This study adopts a quantitative approach to examine how digital transformation contributes to organizational resilience and performance in Portuguese companies during the pandemic. This research integrates survey-based data collection with descriptive and inferential statistical analyses, including multiple linear regression, to understand the relationships between digital capabilities, innovation, strategic responses, and organizational outcomes.

Based on the guiding research question—How do digital transformation strategies, grounded in dynamic capabilities, organizational learning, and resilience theory, influence organizational performance and adaptability in the post-pandemic context?—the following specific objectives were defined:

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Evaluate the level of digital technology adoption and respondents’ perceptions of their relevance and effectiveness during the crisis;

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Identify the most widely adopted technologies and highlight areas that may require further investment;

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Analyze the influence of the pandemic on organizational operations, business models, costs, and revenue;

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Assess the crisis response strategies adopted by companies and their perceived effectiveness;

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Examine the relationship between digitalization, innovation, and organizational performance;

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Determine the contribution of innovation, resilience, and digital and strategic variables to organizational performance using multiple linear regression analysis;

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Explore the alignment between post-pandemic strategies and the challenges faced during the crisis;

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Identify opportunities for strengthening innovation culture and digital transformation in the post-pandemic context.

To answer to the research question, and the objectives, an online questionnaire was developed based on similar studies identified in the literature (Gill & VanBoskirk, 2016; Pereira et al., 2019; Durão et al., 2019; Lousã & Lousã, 2019; Gkeredakis et al., 2021; Guo et al., 2020), using Google Forms, which was distributed and shared through social network (Linkedin, Facebook) and via e-mail among the researchers’ contact network to a not probabilistic sample for convenience (Pestana & Gageiro, 2014).

The questionnaire consisted of a set of questions to characterize the respondents and the companies where they worked (gender, age groups, sector, function, service time, and business model). The survey included multiple-choice, 5-point Likert scale, and open-ended questions focusing on the following: (i) the degree of digital technology adoption; (ii) degree on innovation; (iii) digital culture; (iv) the impact of the COVID-19 pandemic on business operations; (v) organizational resilience strategies; (vi) the effectiveness of digital business models; (vii) future digital transformation plans. The indicator used to measure organizational performance was revenue status (decrease more than 50%; decrease by 30–50%; decrease less than 30%; no change; increase).

In this study, organizational resilience is operationalized through survey items that align with Hollnagel’s (2014) dimensions: anticipation (strategic foresight and risk assessment), absorption (ability to maintain operations during disruption), recovery (rebound speed), and adaptation (strategic changes post-disruption). These elements are empirically captured through our measures of operational flexibility, resource reallocation, crisis response strategies, and post-pandemic planning. We expect that digital transformation initiatives aligned with these resilience dimensions, particularly those enhancing anticipation (e.g., big data analytics) and adaptation (e.g., platform development), will be positively associated with organizational performance.

At the “Dynamic Capabilities Framework” level (Teece, 2007), we seek to associate it with investments in technology. Organizations that invest in digital technologies (e.g., AI or platform technologies) demonstrate ‘sensing’ and ‘seizing’ emerging opportunities, while their ability to reconfigure operations (e.g., through teleworking or digital business model changes). It is expected that firms demonstrating higher dynamic capabilities—operationalized through innovation adoption, investment strategy, and digital business models—will report stronger performance outcomes.

In relation to “Organizational Learning” (Argyris & Schon, 1978), we seek to highlight feedback cycles, lessons learned after the crisis, as well as the association with continuous improvement practices. Organizations that systematically reviewed their crisis responses and updated strategies accordingly embody principles of double-loop learning. This learning process fosters resilience by embedding new routines and decision-making logics aligned with environmental uncertainty.

To strengthen construct transparency and enhance measurement validity, we provide detailed descriptions of the survey items and theoretical sources used to operationalize key constructs. As constructs such as digital transformation, innovation, and organizational resilience are inherently multidimensional, we adopted validated multi-item scales adapted from the previous literature and tailored them to the post-pandemic era. A comprehensive overview of construct definitions, example items, measurement scales, and references is included in Appendix A.

In line with these theoretical foundations, this study adopts a theoretically grounded associative modeling approach to investigate the interrelationships among digital transformation, innovation, resilience, and organizational performance. In consequence, we examine associative relationships among the following constructs:

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Digital Transformation and Innovation: Digital technologies are posited to facilitate innovation by enabling experimentation, enhancing knowledge integration, and accelerating product and process development. This relationship is grounded in the dynamic capabilities perspective, which emphasizes the role of digital tools in opportunity sensing and organizational reconfiguration (Teece, 2007; Karimi & Walter, 2015; Kahveci, 2021; Ghimire et al., 2023).

-

Innovation and Organizational Performance: Innovation is conceptualized as a critical intermediary through which firms achieve superior outcomes, especially under conditions of environmental volatility. Prior research demonstrates that firms with higher innovation capacity are better positioned to maintain competitiveness and adaptability (Klein & Todesco, 2021; Corvello et al., 2023; Ben-Zvi & Luftman, 2022).

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Digital Transformation and Organizational Resilience: The deployment of digital technologies enhances firms’ resilience capabilities, particularly in terms of real-time decision-making, operational flexibility, and crisis responsiveness. This association is substantiated by empirical findings showing how digitalization underpins adaptive and absorptive capacities (Browder et al., 2023; Tang & Huang, 2023; Rublev, 2024).

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Organizational Resilience and Performance: Organizational resilience is expected to contribute positively to performance by enabling firms to anticipate disruptions, absorb shocks, and adapt strategically. This is consistent with Resilience Theory and related empirical studies emphasizing the operational and strategic benefits of resilient capabilities (Soto-Acosta, 2023; Xu et al., 2024; Hollnagel, 2014).

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Strategic Response and Performance: Post-crisis strategic responses, including the adoption of digital platforms, process optimization, and business model innovation, are anticipated to be positively associated with performance outcomes. This aligns with literature on strategic agility and post-crisis transformation (Crespo et al., 2023; Puspita et al., 2023; Anthony, 2023a).

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Digital Culture and Innovation and Resilience: A supportive digital culture, characterized by leadership commitment, continuous learning, and openness to experimentation, is hypothesized to enhance both innovation and resilience. This association is underpinned by the organizational learning literature and empirical studies on cultural enablers of transformation (Argyris & Schon, 1978; Roque & Alves, 2023; Cardoso et al., 2024).

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Strategic Alignment and Innovation, and Performance: The congruence between digital initiatives and strategic objectives is posited to influence innovation capacity and performance outcomes. Strategic alignment ensures that digital investments are leveraged effectively and supports coherent organizational change (Teece, 2007; Amankwah-Amoah et al., 2021; Pozhueva & Shchegolevatykh, 2024).

To empirically examine the hypothesized relationships, this study adopted a structured quantitative approach. Initially, descriptive statistics (mean, standard deviation, frequency analysis) were used to assess digital adoption trends. After, Pearson correlation analysis was used to explore bivariate associations among key variables, providing preliminary insights into the direction and strength of the proposed relationships (e.g., digitalization and innovation; innovation and performance). Subsequently, multiple linear regression (MLR) analysis was employed to formally test the eight hypotheses. Each independent variable in the regression model corresponds to a theoretical construct, was used to tests the direct effects of the following: innovation (H1), digitalization (H2), investment strategy (H3), telework strategy (H4), cost reduction strategy (H5), organizational resilience (H6), post-pandemic strategy (H7), and post-pandemic digital transformation (H8) on organizational performance. This approach allows for a robust evaluation of the theorized associations and supports the assessment of the extent to which empirical data confirms or challenges the proposed theoretical relationships.

After a pre-test of the questionnaire (Malhotra, 2019), with 15 respondents, and there being no reported problems regarding understanding the questions and completing them, the questionnaire was available online between January and May 2021. As recommended in the literature (Harindranath & Sivakumaran, 2023), periodic reminders were sent to encourage respondents’ participation.

All respondents held leadership positions and indicated that they knew the strategy, business model, and digital transformation management process in their organizations. A filter question was asked, and if they did not have access to this information, it was not possible to continue responding to the questionnaire. They were informed of the voluntary nature of their participation and provided their informed consent before completing the survey. The research was conducted following the ethical principles outlined in the Declaration of Helsinki, ensuring the anonymity, confidentiality, and integrity of all respondents throughout the data collection process.

A total of 320 valid responses were collected. The sample consists of 46.3% male and 53.7% female respondents, with the majority aged 46–55 years (42.5%). Participants represented diverse sectors, including services (43.8%), education (18.8%), industry (15.0%), trade (11.9%), and healthcare (10.8%). Most respondents had over 16 years of work experience (42.5%), and 70.6% of businesses operated primarily offline, while 29.4% had an online presence (

Table 1. Sample.

		F	%
Gender	Male	148	46.3
	Female	172	53.7
Age Groups	25–35	62	19.4
	36–45	82	25.6
	46–55	136	42.5
	56–65	40	125
Setor	Trade	38	11.9
	Education	60	18.8
	Industry	48	1.0
	Health	34	10.8
	Services	140	43.8
Service time	1–5 years	102	31.9
	6–10 years	52	16.3
	11–15 years	30	9.4
	>16 years	136	42.5
Business Model	Online	94	29.4
	Offline	226	70.6

).

These data indicate that the majority of business activity (70.6%) is conducted through offline channels, while the remaining 29.4% is conducted online. This distribution provides insights into the company’s current operational focus and the extent to which it utilizes online channels in its business operations.

The data were imported from Google Forms and analyzed using the SPSS 26.0 statistical software. To ensure internal consistency (Pestana & Gageiro, 2014; Malhotra, 2019), Cronbach’s Alpha was calculated for Likert-scale questions, yielding high reliability (α = 0.932). The Kaiser–Meyer–Olkin (KMO) test confirmed sampling adequacy (KMO = 0.835), while Bartlett’s test of sphericity (p < 0.001) validated the factorability of the dataset.

This methodology provides robust insights into the role of digital transformation in business resilience, offering a data-driven foundation for strategic recommendations in the post-pandemic era.

4. Data Analysis

Respondents consider that the organizations where they work have a high (40.6%) or moderate (33.1%) level of adoption of digital technologies (M = 3.49; SD = 0.952). It should be noted that for 40 respondents (12.5%) the degree of adoption is very high. Only 10 (3.1%) and 34 (10.6%) respondents consider that, respectively, the degree of adoption is very low or low (

Table 2. Degree of adoption of digital technologies and the impact that COVID-19 is having on the company.

	Very Low	Low	Moderate	High	Very High	M	SD
Degree of adoption of digital technologies	10 (3.1)	34 (10.6)	106 (33.1)	130 (40.6)	40 (12.5)	3.49	0.952
Impact that pandemic crisis is having on the company’s business	36 (11.3)	58 (18.1)	116 (36.3)	74 (23.1)	36 (11.3)	3.04	1.139

).

Overall, respondents consider that the pandemic crisis influenced company activity (M = 3.04; SD = 1.139). For 116 of the respondents (36.3%), the impact was moderate, but 74 considered the impact to be high (23.1%) and very high (11.3%). Only 36 respondents (11.3%) considered that the impact was very small or reduced (18.1%).

The data indicate that the sample tends to agree with the adoption of digital technologies (M = 3.78; SD = 1.015), digital platforms (M = 3.79; SD = 0.988), digital business models (M = 3.52; SD = 1.088), and digital management models (M = 3.40; SD = 1.084). Agreement is strongest in the first two areas (technologies and platforms), while digital business and management models also have a significant level of agreement, albeit slightly lower. The high Cronbach’s Alpha coefficient (0.932) suggests high internal consistency in the data, reinforcing the reliability of the conclusions drawn from these data (

Table 3. Degree of digitalization.

Alpha Cronback: 0.932 Items	Strongly Disagree 1	Disagree 2	Undecided 3	Agree 4	Strongly Agree 5	M	SD
	F (%)	F (%)	F (%)	F (%)	F (%)
We fully adopt digital technologies (products or services)	10 (3.1)	28 (8.8)	62 (19.4)	140 (43.8)	80 (25.0)	3.78	1.015
We fully embrace digital platforms that support digital products or services	8 (2.5)	28 (8.8)	64 (20.0)	142 (44.4)	78 (24.4)	3.79	0.988
We fully embrace digital business models	14 (4.4)	42 (13.1)	90 (28.1)	110 (34.4)	64 (20.0	3.52	1.088
We adopt fully digital management models	18 (5.6)	42 (13.1)	104 (32.5)	102 (31.9)	54 (16.9)	3.40	1.084
TOTAL Mean	12 (3.75)	35 (10.94)	80 (25.0)	124 (38.8)	69 (21.51)	3.62	1.043
PCA—83.37%	KMO = 0.835/Bartlett test = 1106.234/Sig < 0.001

).

The data results suggest that the company is perceived as innovative by respondents, with efforts to differentiate products and services and integrate the latest technologies into its business model (

Table 4. Innovation.

Alpha Cronback: 0.894 Items	Strongly Disagree 1	Disagree 2	Undecided 3	Agree 4	Strongly Agree 5	M	SD
	F (%)	F (%)	F (%)	F (%)	F (%)
Our products and services are more innovative than our competitors’ products and services in the domestic market	10 (3.1)	40 (12.5)	110 (34.4)	106 (33.1)	54 (16.9)	3.47	1.010
Our products and services have a unique market value proposition	14 (4.4)	32 (10.0)	106 (33.1)	102 (31.9)	66 (20.6)	3.54	1.064
We are known for being an innovative company and we integrate the latest technologies into our business model	10 (3.1)	34 (10.6)	98 (30.6)	118 (36.9)	60 (18.8)	3.58	1.007
TOTAL Mean	11 (3.4)	35 (10.9)	105 (32.8)	109 (34.1)	60 (18.8)	3.53	1.027
PCA—82.52%	KMO = 0.742/Bartlett test = 570.067/Sig < 0.001

). Overall, the company’s innovation efforts seem to be positively perceived, with average scores ranging from 3.47 to 3.58, indicating a moderate to high level of agreement with various innovation-related statements. The PCA results indicate that 82.52% of the variance in the data is explained by the components extracted, suggesting a relatively high level of explanation by the factors (KMO = 0.742; Bartlett test p < 0.001) indicate that the innovation items are correlated and may reflect underlying dimensions of company innovation.

The results suggest that there is a moderate level of agreement among respondents that the digitization of the company depends on both in-house R&D and external purchases (

Table 5. Digitalization method.

Items	Strongly Disagree 1	Disagree 2	Undecided 3	Agree 4	Strongly Agree 5	M	SD
	F (%)	F (%)	F (%)	F (%)	F (%)
(Digitalization Method) Digitization of the company depends on in-house R&D	14 (4.4)	46 (14.4)	100 (31.3)	110 (34.4)	50 (15.6)	3.42	1.056
(Digitalization Method) Digitization of the company depends on external purchases	34 (10.6)	56 (17.5)	110 (34.4)	76 (23.8)	44 (13.8)	3.13	1.173

). However, there seems to be slightly stronger agreement regarding the reliance on in-house R&D (M = 3.42; SD = 1.056) compared to external purchases (M = 3.13; SD = 1.173).

Overall, the results suggest that the company has a positive digital culture (M = 3.46; SD = 1.114) characterized by a focus on digital strategy, leadership prioritization, investment in education and training, clear communication of digital vision, moderate risk-taking for innovation, and customer-centric digital design and development. The PCA results indicate that 70.4% of the variance in the data is explained by the components extracted, suggesting a relatively high level of explanation by the factors. The KMO value of 0.906 (Bartlett test p < 0.001) indicate that the digital culture items are correlated and may reflect underlying dimensions of company digital culture (

Table 6. Digital culture.

Alpha Cronback: 0.929 Items	Strongly Disagree 1	Disagree 2	Undecided 3	Agree 4	Strongly Agree 5	M	SD
	F (%)	F (%)	F (%)	F (%)	F (%)
We believe that our competitive strategy depends on digital technologies	12 (3.8)	48 (15.0)	82 (25.6)	102 (31.9)	76 (23.8)	3.56	1.115
The company’s management considers the digital strategy a priority	18 (5.6)	40 (12.5)	86 (26.9)	100 (31.3)	76 (23.8)	3.54	1.144
We have the right leaders to execute the organization’s digital strategy	20 (6.3)	48 (15.0)	76 (23.8)	118 (36.9)	58 (18.1)	3.45	1.138
We invest in digital education and training at all levels of our organization	20 (6.3)	42 (13.1)	94 (29.4)	100 (31.3)	64 (20.0)	3.45	1.138
We communicate clearly, internally and externally, our digital vision	22 (6.9)	34 (10.6)	104 (32.5)	106 (33.1)	54 (16.9)	3.42	1.103
We take moderate risks to enable innovation and digital transformation	16 (5.0)	34 (10.6)	116 (36.3)	106 (33.1)	48 (15.0)	3.43	1.032
Customer perceptions are considered in the organization’s digital design and development	24 (7.5)	40 (12.5)	98 (30.6)	104 (32.5)	54 (16.9)	3.39	1.132
TOTAL Mean	19 (5.9)	41 (12.8)	94 (29.4)	105 (32.8)	61 (19.1)	3.46	1.114
PCA—70.4%	KMO = 0.906/Bartlett test = 1682.931/Sig < 0.001

).

There is variability in responses across different technologies, indicating differences in perceptions of their adoption within the company (

Table 7. Technologies adoption.

Items	Strongly Disagree 1	Disagree 2	Undecided 3	Agree 4	Strongly Agree 5	M	SD
	F (%)	F (%)	F (%)	F (%)	F (%)
Big data technology (database, data analysis technology)	37 (11.6)	49 (15.3)	66 (20.6)	88 (27.5)	80 (25.0)	3.40	1.320
Artificial intelligence (AI), such as machine learning (learning machine)	94 (29.4)	64 (20.0)	66 (20.6)	58 (18.1)	38 (11.9)	2.64	1.382
Mobile technology (e.g., mobile internet, wireless)	34 (10.6)	35 (10.9)	47 (14.7)	62 (19.4)	141 (44.4)	3.76	1.393
Cloud computing technology (Cloud computing)	55 (17.2)	46 (14.4)	56 (17.5)	56 (17.5)	107 (33.4)	3.36	1.500
IoT technology (e.g., network distribution technology)	56 (17.5)	52 (16.3)	52 (16.3)	68 (21.3)	92 (28.7)	3.28	1.473
Social technologies (e.g., e-commerce, instant messaging)	54 (16.9)	49 (15.3)	68 (21.3)	56 (17.5)	93 (30.0)	3.28	1.465
Platform development technology (e.g., network platforms)	51 (15.9)	40 (12.5)	62 (19.4)	72 (22.5)	95 (29.7)	3.38	1.433
RFID technology	102 (31.9)	56 (17.5)	66 (20.6)	54 (16.9)	42 (13.1)	2.62	1.419
Sensors	120 (37.5)	56 (17.5)	54 (16.9)	48 (15.0)	42 (13.1)	2.49	1.451
Virtual reality	146 (45.6)	50 (15.6)	46 (14.4)	42 (13.1)	36 (11.3)	2.29	1.440
3D Printing	151 (47.2)	54 (16.9)	46 (14.4)	34 (10.6)	35 (10.9)	2.21	1.414
Drones	186 (58.1)	46 (58.1)	40 (12.5)	34 (10.69	14 (4.4)	1.89	1.236
Augmented Reality	172 (53.8)	44 (13.8)	56 (17.5)	26 (8.1)	22 (6.9)	2.01	1.291
TOTAL Mean	97 (30.5)	49 (15.3)	56 (17.5)	54 (16.9)	64 (20.0)	2.81	1.401

). Among the listed technologies, mobile technology (M = 3.76; SD = 1.393) big data (M = 3.40; SD = 1.320), and platform development technology (M = 3.38; SD = 1.433) have the highest mean scores, indicating a moderate level of agreement with the adoption of these technologies. The technologies with lower mean scores include drones (M = 1.89; SD = 1.236), 3D printing (M = 2.21; SD = 1.414), virtual reality (M = 2.29; SD = 1.440), sensors (M = 2.49; SD = 1.451), RFID (M = 2.62; SD = 1.419), and AI (M = 2.64; SD = 1.382).

The majority of respondents (59.4%) perceive the impact of the pandemic crisis on the company’s business to be between “Moderate” (36.3%) and “High (23.1%)” (M = 3.04; SD = 1.139). However, for 58 respondents (18.1%), the impact was very high. Even so, in percentage equality, 36 respondents (11.3%) consider that the impact was very low and very high (

Table 8. Impact that the pandemic is having on the company’s business.

	Very Low	Low	Moderate	High	Very High	M	SD
F (%)	36 (11.3)	58 (18.1)	116 (36.3)	74 (23.1)	36 (11.3)	3.04	1.139

). These results suggest that the pandemic crisis is having a significant impact on the company’s business, with a majority of respondents perceiving the impact as moderate to high.

The results suggest that the company is considering various strategies as part of its crisis response plan (

Table 9. Crisis response strategy.

Items	Strongly Disagree 1	Disagree 2	Undecided 3	Agree 4	Strongly Agree 5	M	SD
	F (%)	F (%)	F (%)	F (%)	F (%)
Reduction in production and operating costs	70 (21.9)	46 (14.4)	70 (21.9)	88 (27.5)	46 (14.4)	2.97	1.364
Divestment in loss-making/less profitable business units	68 (21.3)	60 (18.8)	100 (31.3)	56 (17.5)	36 (11.3)	2.77	1.266
Adoption of telework	36 (11.3)	28 (8.8)	52 (16.3)	82 (25.6)	122 (38.1)	3.72	1.338
Optimization of business models to capture new customer needs	18 (5.6)	26 (8.1)	108 (33.8)	98 (30.6)	70 (21.9)	3.54	1.088
Development of online marketing channels and reduction in dependence on offline transactions	36 (11.3)	46 (14.4)	78 (24.4)	94 (29.4)	66 (20.6)	3.33	1.263
Active investment in technological innovation	30 (9.4)	32 (10.0)	96 (30.0)	112 (35.0)	50 (15.6)	3.36	1.142
Diversification into new business areas	48 (15.0)	44 (13.8)	94 (29.4)	96 (30.0)	38 (11.9)	3.09	1.222
Supply chain integration	50 (15.6)	52 (16.3)	106 (33.1)	80 (25.0)	32 (10.0)	2.96	1.193
TOTAL Mean	44 (13.8)	42 (13.1)	88 (27.5)	88 (27.5)	58 (18.1)	3.21	1.234

), with some strategies receiving higher levels of agreement than others (M = 3.21; SD = 1.234). The strategies with the highest mean scores include adoption of telework (M = 3.72; SD = 1.338) and optimization of business models (M = 3.54; SD = 1.088), active investment in technological innovation (M = 3.36; SD = 1.142), and Development of online marketing channels and reduction in dependence on offline transactions (M = 3.33; SD = 1.263) indicating a relatively high level of agreement with these strategies. The strategies with the lowest mean scores include supply chain integration (M = 2.96; SD = 1.193) and divestment in loss-making/less profitable business units (M = 2.77; SD = 1.266).

The effectiveness of each strategy may depend on the specific circumstances and challenges faced by the company during the crisis.

The company has implemented various strategies to protect employees’ rights during the crisis (

Table 10. Strategies to protect employees’ rights.

Items	Strongly Disagree 1	Disagree 2	Undecided 3	Agree 4	Strongly Agree 5	M	SD
	F (%)	F (%)	F (%)	F (%)	F (%)
Payment of wages in accordance with employment contracts	22 (6.9)	4 (1.3)	48 (15.0)	78 (24.4)	168 (52.5)	4.15	1.152
Pay basic subsistence allowance for lay-off workers	64 (20.0)	12 (3.8)	60 (18.8)	78 (24.4)	106 (33.1)	3.48	1.473
Negotiating with employees or unions to defer payment	180 (56.3)	30 (9.4)	56 (17.5)	32 (10.0)	22 (6.9)	2.02	1.331
Payment of wages to employees who are in quarantine	32 (10.0)	8 (2.5)	74 (23.1)	58 (18.1)	148 (46.3)	3.87	1.299
Introduction of compensatory measures or payment of overtime for employees who had to remain with the company	74 (23.1)	30 (9.4)	76 (23.8)	64 (20.0)	76 (23.8)	3.13	1.472
Compensatory measures for telecommuting employees (technological support, provision of equipment, support for internet costs)	84 (26.3)	38 (11.9)	62 (19.4)	66 (20.6)	70 (21.9)	3.01	1.505
TOTAL Mean	76 (23.7)	20 (6.3)	63 (19.7)	63 (19.7)	98 (30.6)	3.27	1.372

). The strategy with the highest mean score is “Payment of wages in accordance with employment contracts” (M = 4.15), indicating a strong agreement with this strategy. Strategies such as “Payment of wages to employees who are in quarantine” (M = 3.87) and “Basic subsistence allowance for laid-off workers” (M = 3.48) also have moderate levels of agreement. The strategies with the lowest mean scores include “Negotiating with employees or unions to defer payment” (M = 2.02) and “Compensatory measures for telecommuting employees” (M = 3.01).

As can be seen in

Table 11. Post-pandemic strategy.

Items	Strongly Disagree 1	Disagree 2	Undecided 3	Agree 4	Strongly Agree 5	M	SD
	F (%)	F (%)	F (%)	F (%)	F (%)
Change existing product lines	100 (31.3)	40 (12.5)	84 (26.3)	66 (20.6)	30 (9.4)	2.63	1.346
Change regional market coverage	90 (28.1)	40 (12.5)	86 (26.9)	68 (21.3)	36 (11.3)	2.74	1.355
Changing external cooperation relationships	78 (24.4)	26 (8.1)	104 (32.5)	70 (21.9)	42 (13.1)	2.90	1.335
Change the business model	110 (34.4)	44 (13.8)	82 (25.6)	64 (20.0)	20 (6.3)	2.48	1.300
Implement and/or accelerate the digital transformation process	38 (11.9)	26 (8.1)	92 (28.7)	96 (30.0)	68 (21.3)	3.40	1.241
TOTAL Mean	83 (25.9)	35 (10.9)	90 (28.2)	73 (22.8)	39 (12.2)	2.83	1.315

, the company is considering various strategies for the post-pandemic period (Table 11). The strategy with the highest mean score is “Implement and/or accelerate the digital transformation process” (M = 3.40; SD = 1.241), indicating a relatively high level of agreement with this strategy. Strategies such as “Change external cooperation relationships” (M = 2.90; SD = 1.335) and “Change regional market coverage” (M = 2.74; SD = 1.333) also have moderate levels of agreement. The strategies with the lowest mean scores include “Change the business model” (M = 2.48; SD = 1.300) and “Change existing product lines” (M = 2.63; SD = 1.346).

The company is considering various digital transformation strategies for the post-pandemic period (

Table 12. Post-pandemic digital transformations.

Items	Strongly Disagree 1	Disagree 2	Undecided 3	Agree 4	Strongly Agree 5	M	SD
	F (%)	F (%)	F (%)	F (%)	F (%)
Strengthen online office task application	28 (8.8)	22 (6.9)	98 (30.6)	100 (31.3)	72 (22.5)	3.51	1.167
Improve the digitization of supply chain channels	34 (10.6)	24 (7.5)	96 (30.0)	94 (29.4)	72 (22.5)	3.45	1.218
Adopt digital technologies, such as digital products or services	30 (9.4)	24 (7.5)	78 (24.4)	110 (34.4)	78 (24.4)	3.56	1.202
Adopt digital platforms, such as Marketing and digital communication platforms	30 (9.4)	26 (8.1)	82 (25.6)	106 (33.1)	76 (23.8)	3.53	1.204
Adoption of digital infrastructures, such as digital technology systems	28 (8.8)	32 (10.0)	74 (23.1)	118 (36.9)	68 (21.3)	3.51	1.183
Adopt the telework regime	72 (22.5)	42 (13.1)	80 (25.0)	68 (21.3)	58 (18.1)	2.98	1.401
TOTAL Mean	37 (11.5)	28 (8.8)	85 (26.6)	99 (30.9)	71 (22.2)	3.42	1.229

). The strategies with the highest mean scores include adopting digital technologies (M = 3.56; SD = 1.202) and adopting digital platforms (M = 3.53; SD = 1.204), indicating a relatively high level of agreement with these initiatives. Strategies such as improving the digitization of supply chain channels (M = 3.45; SD = 1.218) and adopting digital infrastructures (M = 3.51; SD = 1.183) also have moderate levels of agreement. The strategies with the lowest mean score are adopting the telework regime (M = 2.98; SD = 1.401).

Furthermore, to assess organizational resilience, exploratory factor analysis was conducted using Principal Component Analysis (PCA) with varimax rotation to confirm the multidimensionality. Although the original scale (Argyris & Schon, 1978; Teece, 2007; Hollnagel, 2014) suggests a multidimensional structure (Adaptability, Resourcefulness, Situational Awareness, and Learning and Continuous Improvement), PCA revealed that all items align under a single factor (see Appendix A,

Table A2. Organizational resilience (PCA).

Component	Auto Initial Values			Extraction Sums of Squared Loadings			Loading
	Total	% Variance	% Cumulative	Total	% Variance	% Cumulative
1	5.136	64.203	64.203	5.136	64.203	64.203	0.857
2	0.749	9.368	73.571				0.850
3	0.558	6.970	80.541				0.831
4	0.492	6.149	86.691				0.817
5	0.358	4.469	91.160				0.816
6	0.289	3.609	94.769				0.759
7	0.241	3.011	97.780				0.739
8	0.178	2.220	100.000				0.730

Psychometric summary: total variance explained (1 factor: 64.2%; Kaiser–Meyer–Olkin (KMO): 0.899; Bartlet’s test of sphericity: χ² = 1682.89, p < 0.001.

). This factor explains 64.2% of the total variance, indicating that, in practice, the items operate collectively to measure a unified concept of organizational resilience (Kaiser–Meyer–Olkin (KMO): 0.899; Bartlet’s Test of Sphericity: χ² = 1682.89, p < 0.001) (

Table 13. Organizational resilience.

Organizational Resilience α: 0.918	Items	M	SD	Item-Total Correlation	Loading
1. Adaptability	Our organization adapted quickly to the changing demands of the pandemic.	3.79	1.044	0.653	0.857
	We modified existing operations to ensure continuity.	3.92	0.907	0.796	0.850
2. Resourcefulness	We developed new products or services in response to the crisis.	3.85	0.954	0.766	0.831
	We reallocated resources to areas of greater need.	3.57	0.969	0.741	0.817
3. Situational Awareness	We identified potential risks and acted preemptively.	3.65	0.906	0.796	0.816
	We maintained good awareness of external changes affecting our business.	3.95	0.925	0.656	0.759
4. Learning and Continuous Improvement	We evaluated our crisis response to identify lessons learned.	3.79	0.940	0.756	0.739
	We improved our strategy based on post-crisis reviews.	3.92	1.042	0.686	0.730

Psychometric summary: total variance explained (1 factor: 64.2%; Kaiser–Meyer–Olkin (KMO): 0.899; Bartlet’s test of sphericity: χ² = 1682.89, p < 0.001.

).

The individual items also show strong psychometric properties. Mean (M) scores across the items range from 3.57 to 3.95, reflecting generally positive perceptions of organizational resilience during the pandemic. Item-total correlations (>0.7) and factor loadings (>0.653) are consistently high, indicating that each item contributes meaningfully to the overall construct.

The majority of companies surveyed (28.7%) reported no change in their organizational performance in 2020 (M = 3.17; SD = 1.282). Significant portions of companies reported decreases in organizational performance, with 27.5% reporting a decrease in less than 30%, 16.3% reporting a reduction in more than 50%, and 11.9% reporting a decrease by 30% to 50%. A smaller proportion of companies (15.6%) reported an increase in organizational performance in 2020 compared to 2019 (

Table 14. Impact the pandemic had on the company’s organizational performance (revenue status) in 2020, compared to 2019.

	F	%	M	SD
Decrease more than 50%	52	16.3	3.17	1.282
Decrease by 30–50%	38	11.9
Decrease less than 30%	88	27.5
No change	92	28.7
Increase	50	15.6

).

To explore the interrelationships between various organizational variables, including Performance (PER), Innovation (IN), Digitalization (DIG), and different business strategies, such as Investment (BSI), Teleworking (BST), Cost Reduction (BSCR), Post-Pandemic Strategy (PPS), and Post-Pandemic Digital Transformation (DTPP), Pearson correlations were analyzed to discern the strength and direction of these relationships (

Table 15. Correlations.

	(PER)	(IN)	(DIG)	(BSI)	(BST)	(BSCR)	(PPS)	(DTPP)
Performance (PER)	1
Innovation (IN)	0.606 **	1
Digitalization (DIG)	0.541 **	0.696 **	1
Business strategy: Investment (BSI)	0.456 **	0.419 **	0.589 **	1
Business strategy: Telework (BST)	0.146 **	0.193 **	0.314 **	0.000	1
Business strategy: Cost reduction (BSCR)	0.119 *	0.110 *	0.182 **	0.000	0.000	1
Pos-pandemic Strategy (PPS)	0.183 **	0.245 **	0.395 **	0.472 **	0.079	0.373 **	1
Digital transformation pos-pandemia (DTPP)	0.272 **	0.324 **	0.565 **	0.558 **	0.394 **	0.155 **	0.633 **	1

**. The correlation is significant at the 0.01 level (2-tailed). *. The correlation is significant at the 0.05 level (2-tailed).

).

Performance (PER) demonstrated a significant positive correlation with Innovation (IN) (r =.606, p < 0.01) and Digitalization (DIG) (r = 0.541, p < 0.01). This suggests that enhancements in innovation and digitalization are strongly associated with improved organizational performance. Additionally, Performance showed a moderate positive correlation with Business Strategy: Investment (BSI) (r = 0.456, p < 0.01) and a weaker, albeit significant, correlation with Post-Pandemic Digital Transformation (DTPP) (r = 0.272, p < 0.01). The correlations with Business Strategy: Telework (BST) and Cost Reduction (BSCR) were positive but weak (r = 0.146, p < 0.01; and r = 0.119, p < 0.05, respectively), indicating a lesser impact on performance.

Innovation (IN) exhibited a very strong positive correlation with Digitalization (DIG) (r = 0.696, p < 0.01), implying that innovative practices are often intertwined with digitalization efforts. Moreover, Innovation correlated moderately with Business Strategy: Investment (BSI) (r = 0.419, p < 0.01) and Post-Pandemic Digital Transformation (DTPP) (r = 0.324, p < 0.01), further emphasizing the linkage between innovation, strategic investment, and digital transformation.

Digitalization (DIG) was strongly correlated with Business Strategy: Investment (BSI) (r = 0.589, p < 0.01) and Post-Pandemic Digital Transformation (DTPP) (r = 0.565, p < 0.01). This denotes that digitalization is a critical component of investment strategies and post-pandemic digital transformation. The moderate correlation with Post-pandemic Strategy (PPS) (r = 0.395, p < 0.01) suggests that digitalization plays a significant role in shaping post-pandemic strategic responses.

Business Strategy: Investment (BSI) showed moderate to strong correlations with Post-Pandemic Digital Transformation (DTPP) (r = 0.558, p < 0.01) and Post-pandemic Strategy (PPS) (r = 0.472, p < 0.01). These findings highlight the importance of strategic investment in driving digital transformation and adapting to post-pandemic challenges.

Business Strategy: Telework (BST) had generally weak correlations with other variables, except for a moderate positive correlation with Post-Pandemic Digital Transformation (DTPP) (r = 0.394, p < 0.01). This suggests that while telework alone might not be a significant factor, it gains importance when integrated with broader digital transformation efforts. Although teleworking is not a key factor in isolation concerning other variables, combining it with digital transformation initiatives makes it more relevant. This emphasizes the need to view teleworking as part of a more comprehensive approach to digitalization. The moderate illumination between teleworking and post-pandemic digital transformation indicates that organizations that implement teleworking also tend to adopt other digital technologies more effectively. This suggests that the effectiveness of teleworking can be amplified when there is a digital infrastructure that supports its goals and practices.

Business Strategy: Cost Reduction (BSCR) was weakly correlated with most variables, with the highest correlation observed with Post-Pandemic Strategy (PPS) (r = 0.373, p < 0.01). This indicates that cost reduction strategies are moderately aligned with post-pandemic strategic initiatives.

Finally, Post-Pandemic Strategy (PPS) and Post-Pandemic Digital Transformation (DTPP) exhibited a strong positive correlation (r = 0.633, p < 0.01), underscoring the critical role of digital transformation in formulating effective post-pandemic strategies.

The results highlight the central role of innovation and digitalization in improving organizational performance. Strategic investments, particularly related to digital transformation, will be critical to meeting post-pandemic challenges. Remote working and cost-cutting strategies, while beneficial, appear to have a more subtle impact when considered alongside broader digital and innovation initiatives.

To assess the influence of various strategic and organizational factors on firm performance (PER) in the post-pandemic era, a multiple linear regression (MRL) analysis was performed. The analysis included eight independent variables: Innovation, Digitalization, Investment Strategy, Telework Strategy, Cost Reduction Strategy, Organizational Resilience, Post-Pandemic Strategy, and Post-Pandemic Digital Transformation. The dependent variable was organizational performance.

The model presents a strong correlation coefficient (R = 0.726), indicating a solid linear relationship between the predictors and the dependent variable (performance). The overall regression model was statistically significant, F (8. 311) = 43.36, p < 0.001, indicating that the combination of predictors reliably explains variability in organizational performance. The model yielded a coefficient of determination R² = 0.527, and the adjusted R² = 0.515, suggesting that approximately 52.7% of the variance in performance is accounted for by the predictors included in the model (

Table 16. Multiple linear regression predicting organizational performance.

Predictor	B	SE B	β	t	p	VIF
(Constant)	−0.985	0.194	—	−5.085	<0.001	—
Innovation	0.324	0.056	0.324	5.755	<0.001	2.087
Digitalization	0.005	0.067	0.005	0.068	0.946	2.960
Investment Strategy	0.169	0.057	0.169	2.985	0.003	2.107
Telework Strategy	0.102	0.047	0.102	2.191	0.029	1.430
Cost Reduction Strategy	0.056	0.044	0.056	1.265	0.207	1.271
Organizational Resilience	0.405	0.051	0.400	7.928	<0.001	1.678
Post-Pandemic Strategy	−0.085	0.041	−0.101	−2.096	0.037	1.520
Post-Pandemic Digital Transformation	−0.028	0.041	−0.034	−0.681	0.496	1.686

Note: R = 0.726; R² = 0.527, Adjusted R² = 0.515, F (8, 311) = 43.36, p < 0.001.

).

Diagnostic tests for multicollinearity indicated acceptable levels, with all variance inflation factors (VIFs) below 3 and tolerance values exceeding 0.2. This suggests the model estimates are stable and not distorted by multicollinearity.

Among the independent variables, Organizational Resilience demonstrated the strongest positive standardized effect (β = 0.400, p < 0.001), followed by Innovation (β = 0.324, p < 0.001) and Investment Strategy (β = 0.169, p = 0.003). The Telework Strategy also had a statistically significant, albeit smaller, positive effect (β = 0.102, p = 0.029). Interestingly, the Post-Pandemic Strategic Orientation was negatively associated with performance (β = −0.101, p = 0.037), suggesting potential misalignment or unintended consequences in its implementation.

In contrast, Digitalization (β = 0.005, p = 0.946), Cost Reduction Strategy (β = 0.056, p = 0.207), and Post-Pandemic Digital Transformation (β = −0.034, p = 0.496) did not show statistically significant effects on organizational performance. These findings indicate that while certain strategic orientations are effective drivers of performance, others may require reevaluation or improved alignment with organizational goals. The non-significance of Digitalization may reflect its indirect influence via Innovation. Post-Pandemic Strategy has a negative effect, possibly indicating misalignment with revenue status. The results highlight the importance of fostering innovation and resilience within organizations to enhance performance outcomes, especially in dynamic and uncertain post-pandemic environments. Conversely, some strategic efforts may need to be refined to better support performance goals.

5. Discussion

The findings of this study reinforce the argument that digital transformation is a vital enabler of organizational resilience and innovation, particularly during times of crisis. As highlighted in the literature, the adoption of digital technologies has become indispensable for navigating uncertainty and fostering adaptability (Guo et al., 2020; Jovanović, 2020; Gkeredakis et al., 2021; Ghimire et al., 2023; Haq & Huo, 2023).

This study conceptualizes organizational resilience as the capacity to absorb shocks, adapt dynamically, and maintain continuity in adverse conditions. Rooted in Resilience Theory and the Dynamic Capabilities Framework, our approach positions resilience not merely as crisis response, but as a strategic capability linked to long-term transformation and learning. The empirical evidence confirms that digital leadership and a robust digital culture play a decisive role in supporting operational efficiency and cash flow management during turbulent periods—echoing earlier findings that link digital transformation with sustainability and competitive advantage (Amankwah-Amoah et al., 2021; Kahveci, 2021; Klein & Todesco, 2021; Mancuso et al., 2023; Kahveci et al., 2024).

Consistent with prior research, our study confirms the pivotal role of digital technologies in enabling organizational agility and continuity during crises (Çallı & Çallı, 2021; Wang et al., 2024). These technologies have proven to be critical tools for sustaining operations and enhancing stakeholder communication. However, as Gkeredakis et al. (2021) caution, rapid digitalization can also produce unintended effects, such as increased digital inequality and challenges in managing remote workforces.

The concept of entrepreneurial resilience (Bürgel et al., 2023; Corvello et al., 2023) is particularly relevant in this context, as it extends traditional notions of resilience beyond mere survival or recovery to encompass proactive opportunity-seeking and strategic innovation in the face of adversity. Our results demonstrate that digital technologies enabled Portuguese firms to pursue innovation, differentiate products, and access new markets—key elements of antifragility and opportunity-driven resilience in uncertain environments.

Although firms acknowledge the strategic relevance of digital transformation, implementation remains uneven, particularly among SMEs. This is consistent with prior studies that point to barriers such as limited resources, skills gaps, and lack of strategic alignment (Durão et al., 2019; Cardoso et al., 2024). Nonetheless, the pandemic acted as a catalyst, accelerating digital adoption and encouraging the emergence of new business models and interaction paradigms (Shafi et al., 2020; Ssenyonga, 2021; Haq & Huo, 2023).

Our findings confirm that digital maturity is positively associated with organizational agility and performance, echoing the conclusions of Çallı and Çallı (2021). Although Portuguese organizations recognize the importance of digital transformation, they face implementation challenges. Nonetheless, there is an acknowledgment of the need for digital transformation to explore new opportunities and enhance competitiveness. Despite its adverse effects on the economy and companies, the pandemic also brought opportunities for reforms aimed at mitigating the effects of the crisis and promoting rapid economic recovery: it accelerated the digitalization process, allowed the creation of new business models, new methods of work and new forms of relationships with consumers (Shafi et al., 2020; Ssenyonga, 2021; Haq & Huo, 2023).

However, achieving digital maturity entails more than technology adoption; it requires a deep cultural and strategic transformation. From the perspective of the Dynamic Capabilities Framework (Teece, 2007), this transformation reflects the development of sensing, seizing, and reconfiguring capabilities that enable firms to respond proactively to environmental changes. Digital maturity enhances a firm’s ability to sense opportunities, rapidly seize them through innovation, and reconfigure resources to maintain competitiveness. Simultaneously, Organizational Learning Theory emphasizes that this transformation is rooted in continuous learning and knowledge integration across the organization. Digital maturity evolves when firms embed digital tools within learning processes, enabling them to experiment, adapt, and institutionalize new routines. Without these adaptive learning structures and cultural shifts, technological tools remain underutilized and fail to deliver performance outcomes. Thus, digital maturity represents a convergence of technological capabilities, strategic intent, and an organizational culture that fosters agility and learning.

During the pandemic, organizations adopted a range of digital tools, including teleworking platforms, e-commerce systems, data analytics, and cloud-based solutions. These tools were essential for maintaining continuity and customer relationships under adverse conditions (Gkeredakis et al., 2021; Wang et al., 2024). Moreover, they facilitated innovative approaches to marketing, payment systems, and service delivery (Larios-Hernández, 2021; Haq & Huo, 2023). However, their effectiveness depended on how well they were integrated into broader strategies. Our data suggests that the most resilient organizations were those that aligned digital tools with long-term innovation and learning goals, rather than deploying them reactively.

Interestingly, while telework became a widely adopted practice during the crisis, our analysis indicates that it does not, by itself, significantly influence performance. This supports previous findings (Durão et al., 2019; Haq & Huo, 2023), suggesting that the benefits of teleworking emerge when it is part of a comprehensive digital strategy that includes process innovation and employee engagement.

The multiple linear regression (MRL) provides meaningful insights into the strategic factors influencing organizational performance in the post-pandemic era. The strong predictive value of innovation (H2) and organizational resilience (H6) (β = 0.324 and 0.400, respectively) reinforces the Dynamic Capabilities Framework, particularly the reconfiguring capability. Investment strategies correlated with performance support the seizing dimension, suggesting that resource reallocation during crises is a critical determinant of resilience. While digitalization correlated with innovation, its lack of direct significance in the regression suggests that technological tools must be embedded within organizational routines and learning structures, emphasizing that technologies alone do not constitute capabilities unless activated by learning and strategic alignment.

Our study, similar to the results of research by Gkeredakis et al. (2021), shows how digital technologies enable experimentation and innovation during crises. Digital technologies help organizations and professions quickly adapt to new digital environments, ensuring business continuity and introducing new working methods.

However, H2 and H8 received only partial or no empirical support. Specifically, the non-significant relationship between digitalization and organizational performance contradicts the assumptions underlying H8. The findings reveal a nuanced relationship between digitalization and organizational performance. While bivariate analysis showed a significant positive correlation (r = 0.541, p < 0.01), the regression results demonstrated no direct effect of digitalization on performance (β = 0.005, p = 0.946).

This apparent contradiction aligns with recent studies suggesting that digital technologies alone are insufficient to drive performance outcomes (Ghimire et al., 2023; Çallı & Çallı, 2021). The results support the notion that digitalization’s value is contingent on complementary organizational capabilities, particularly innovation and resilience.

The mediation analysis provides critical insights into how digital transformation ultimately impacts performance. Digitalization showed strong associations with both innovation (r = 0.696, p < 0.01) and organizational resilience, which emerged as the strongest predictor in the regression model (β = 0.400, p < 0.001). These findings corroborate the Dynamic Capabilities Framework (Teece, 2007), demonstrating that digital tools must be activated through innovation processes and organizational learning to create value. The full mediation effect suggests that Portuguese firms benefit from digitalization primarily when they can leverage these technologies to develop innovative products and services while simultaneously building adaptive capacity.

The nonsignificant direct effect of digitalization may reflect implementation challenges common among Portuguese SMEs. Prior research indicates that many Portuguese organizations remain in early stages of digital maturity (Durão et al., 2019; Cardoso et al., 2024), potentially limiting their ability to translate technological adoption into performance gains. Furthermore, the negative association between post-pandemic strategy and performance (β = −0.101, p = 0.037) suggests that some digital initiatives may have been implemented reactively without proper strategic alignment, echoing concerns raised by Reuschl et al. (2022) about “rushed digitalization” during crises.

Moreover, the significance of investment strategy (H3) and telework strategies (H4) suggests that forward-looking initiatives and operational flexibility continue to shape performance outcomes. Interestingly, while these factors yielded significant positive effects, other commonly advocated strategies, such as digitalization (H2) and cost reduction (H5), did not demonstrate a measurable impact in this model. This could indicate either a misalignment between the implementation of these strategies and performance metrics, or a time lag in realizing their benefits.

Regarding the adoption of teleworking (BST) the data suggests that while telework alone might not be a significant factor, it gains importance when integrated with broader digital transformation efforts. Although teleworking is not a key factor in isolation concerning other variables, combining it with digital transformation initiatives makes it more relevant. This emphasizes the need to view teleworking as part of a more comprehensive approach to digitalization. The moderate illumination between teleworking and post-pandemic digital transformation indicates that organizations that implement teleworking also tend to adopt other digital technologies more effectively (Durão et al., 2019; Corvello et al., 2023; Haq & Huo, 2023; Bürgel et al., 2023). This suggests that the effectiveness of teleworking can be amplified when there is a digital infrastructure that supports its goals and practices.

The negative relationship between post-pandemic strategic orientation and performance (H7) adds a nuanced layer to the analysis. It may reflect strategic confusion or insufficient adaptation of new approaches to actual organizational needs. Alternatively, it may lead to the unintended consequences of rushed or poorly integrated changes. The absence of significant effects from post-pandemic digital transformation supports this interpretation, highlighting a potential gap between digital initiatives and their operational embedding.

H8 (Post-Pandemic Digital Transformation) showed a marginal positive effect but lacked statistical significance, suggesting that digital efforts after the crisis, though promising, may still be in early stages of implementation or lacking integration.

Overall, these results underscore that while innovation and resilience are central to post-pandemic performance, other strategies must be contextually embedded and strategically aligned to be effective. The non-significant findings, particularly with respect to H2 (Digitalization), highlight the importance of considering indirect pathways, organizational readiness, and the role of dynamic capabilities in translating digital investments into tangible performance gains.

The results of this study reveal a network of theoretically grounded and statistically supported associations that enhance our understanding of how digital transformation contributes to organizational resilience and performance. Drawing on Resilience Theory, the Dynamic Capabilities Framework, and Organizational Learning Theory, the findings indicate that digital transformation is positively associated with innovation and, indirectly, with organizational performance. Firms that effectively integrate digital tools demonstrate higher innovation capacity, supporting the notion that digitalization enables dynamic sensing and opportunity exploitation. In turn, innovation shows a strong positive association with performance, highlighting its mediating role in translating digital investments into competitive advantage.

Despite the clear benefits, digital transformation poses challenges, including the need for a strategic vision, adaptability, and a human-centric approach. Organizations must navigate these challenges to create value and drive sustainable growth in the post-pandemic world (Soto-Acosta, 2023; Nosike et al., 2024). Continuous innovation and the integration of new and legacy technologies are essential for maintaining business resilience and competitive advantage (Akib et al., 2022). The digital transformation is a critical enabler of resilience in the post-pandemic era. It supports financial sustainability, enhances organizational adaptability, and fosters long-term success across various sectors. As businesses continue to navigate the evolving landscape, a well-defined digital strategy will be crucial for sustaining resilience and growth (Ben-Zvi & Luftman, 2022; Nosike et al., 2024).

6. Conclusions

This study offers a comprehensive analysis of how Portuguese organizations navigated the COVID-19 crisis through digital transformation, innovation, and strategic adaptation. It contributes to the growing literature by integrating Resilience Theory, Dynamic Capabilities, and Organizational Learning to examine post-pandemic organizational responses. The empirical findings reveal that innovation and resilience are the most significant predictors of performance, while digitalization alone does not exert a direct effect. Instead, digitalization serves as an enabling factor—its performance impact is mediated by how it fosters innovation and resilience.

These results suggest that digital maturity, innovation capacity, and strategic coherence are central to organizational resilience. The effectiveness of digital transformation is contingent upon its alignment with cultural readiness, learning capacity, and strategic intent.

The empirical findings offer a nuanced picture of firm adaptation: while many organizations adopted digital tools and crisis strategies such as telework and business model optimization, the most impactful outcomes occurred when these actions were embedded within broader organizational capabilities. Rather than acting as direct drivers of performance, digital tools functioned as enablers, supporting adaptability, learning, and opportunity-seeking behaviors that are central to antifragility and entrepreneurial resilience. Innovation capabilities and organizational resilience, in turn, emerged as stronger predictors of performance, underscoring that the value of digital transformation lies in how technologies are strategically integrated and leveraged within the firm.

For practitioners, this suggests that technology adoption must be complemented by investments in people, culture, and long-term strategy. For policymakers, the results underline the importance of targeted support for SMEs to develop the dynamic capabilities required to sustain innovation and resilience.

Despite its contributions, this study has limitations. First, while it addresses the strategic relevance of digital transformation, it does not offer a granular analysis of specific technologies or their differential impacts on resilience. Future research could benefit from examining technology types by industry or organizational function. Second, this study is based on a non-probabilistic convenience sample, which limits generalizability to all Portuguese firms. Finally, the cross-sectional design constrains causal inference and limits analysis of longer-term outcomes.

The limitations identified in the present study highlight several avenues for future scholarly inquiry. Longitudinal research designs are warranted to evaluate the durability and long-term implications of digital strategies on organizational resilience and performance. Comparative analyses across national contexts could elucidate both context-specific dynamics and shared challenges inherent in digital adaptation processes. Sector-specific investigations—in domains such as healthcare, retail, education, and manufacturing—would facilitate the identification of tailored resilience strategies suited to distinct operational environments. Moreover, exploring the implications of digital transformation on employee experience, well-being, and productivity could yield valuable insights into the interplay between human factors and technological change under conditions of crisis. Notwithstanding, these limitations necessitate a cautious interpretation of the current findings. Subsequent research could mitigate such constraints by employing longitudinal methodologies, integrating diverse data sources (e.g., objective performance indicators), and implementing robustness checks, including instrumental variable regression and structural equation modeling (SEM) with temporal sequencing, to more rigorously establish causal relationships.

In conclusion, this study reinforces that the true value of digital transformation lies not in the tools themselves, but in how they are integrated into strategy, supported by leadership, and sustained through learning and innovation. Building resilience in a digital era is not just about technology—it is about strategy, culture, and the capacity to adapt.