Teleworking and hybrid work models are some of the most notable examples of flexible work arrangements (FWAs), the main organizational responses to the COVID-19 crisis that have completely changed the conventional employment patterns ([21]). The sudden change of the physical to the remote working environment forced companies to quickly redesign the workflow, communication systems, and performance evaluation systems. In this regard, teleworking can be described as the performance of job functions outside of the traditional working environment using digital tools, whereas hybrid employment merges the use of remote and physical work, providing more flexibility and adaptability ([29]).

Human resource management has shown that the implementation of FWAs has been linked to a high level of changes in employee job satisfaction. Increased employee well-being and engagement have been linked to greater autonomy, an improved work-life balance, and shorter commuting time ([34]; [18]). Nonetheless, the above gains are not equally applicable, because personal experiences can differ based on organizational support, digital infrastructure, and managerial practices. Simultaneously, FWAs have been shown to correlate positively with productivity, especially in the knowledge-intensive industries where productivity is less tied to the physical location ([9]). The continuity of tasks and even efficiency of work with the integration of digital tools allow employees to work asynchronously in some cases ([20]; [39]).

However, there are significant coordination costs involved with the introduction of flexible work models. The physical scattering of teams can disrupt communication, decrease informal knowledge sharing and make the coordination of tasks more complicated. This leaves organizations to invest in digital collaboration tools, and acquire new managerial skills to curb such setbacks. The effectiveness of FWAs thus depends on the ability of firms to balance flexibility with structured coordination mechanisms ([42]).

One of the critical debates in the literature is the long-term effect of flexible work regarding productivity. Although a number of studies indicate that productivity is increased as a result of greater flexibility and autonomy, other studies indicate that productivity would be affected negatively with employee burnout, social isolation, and decline in organizational cohesion. Such dangers are especially acute in the context of long-distance remote work, where the absence of boundaries between professional and personal lives can have harmful effects on mental health. The quick shift to flexible work amid the pandemic in the Greek setting was directly connected to the more general experiences of digital transformation, which further supports the idea that the success of such an arrangement depends on the technological preparedness and adaptability of the organization.

Digital transformation is a complex process by which organizations incorporate innovative digital technologies into their operations and operations, radically change the business models, processes, and value creation mechanisms ([25]). Fundamentally, digital transformation is a process that involves the implementation of major technological platforms like cloud computing, automation systems, and enterprise resource planning (ERP) platforms ([13]; [4]).

Cloud technologies provide scalability, flexibility and real-time access to data and automation improves efficiency of processes by minimizing the involvement of human intervention and operational errors. ERP systems, in turn, contribute to the unification of organizational activities, enhance the coordination, information flow, and the quality of decisions ([25]).

The COVID-19 pandemic was a strong catalyst of digital transformation, compelling organizations to swiftly adopt digital tools to maintain continuity of operations ([16]). The sudden transition to remote working and the models of digital interactions demanded companies to invest in digital infrastructure and restructure their operational processes in the conditions of the highest level of uncertainty ([48]). This acceleration, as pointed out in the dissertation was not only a technological change but a structural change that touched upon the organizational design, patterns of communication and performance management systems. Companies that could swiftly implement digital solutions were more adaptable and resilient, whereas those that lagged behind experienced a tremendous operational setback ([13]; [47]).

One of the most important aspects of digital transformation is that it directly affects the efficiency of operations. Digital integrations optimize operations and minimize transaction expenses as well as respond faster to market dynamics ([48]). Technological developments such as automation and the use of data to make decisions lead to higher productivity and a more efficient allocation of resources, thus, making organizations more agile. Nevertheless, these advantages depend on the successful fit between the use of technology and organizational strengths ([19]).

In this scenario, the aspect of digital maturity becomes a crucial distinguishing variable ([6]). The dissertation offers empirical data that suggests that companies that were more digitally prepared were in a better position to capitalize on the investments in technology and transform it into better performance outcomes ([7]). This is an indication that digital transformation is not a homogenous process, but it relies on already existing capabilities, strategic orientation and organizational learning. Digital maturity, therefore, can be viewed as a strategic resource, which not only improves efficiency but also long-term competitiveness and resilience ([33]; [4]).

Organizational performance is a multidimensional construct that shows how firms are capable of making sound use of resources, attaining strategic goals, and remaining competitive in changing environments ([1]). In the framework of modern business research, especially the circumstances of crisis and digital transformation, the concept of organizational performance is often understood in three dimensions: efficiency, productivity, and organizational structure, which are interrelated. These dimensions offer a holistic analytical tool to evaluate the reaction of firms to external shocks and adjust their internal operations ([29]; [23]).

Efficiency is the most efficient ways of using the available resources to ensure that the costs are minimized without compromising the input or increasing the output. Efficiency is an important performance indicator during times of disruption, like the COVID-19 pandemic, because the companies have to work within limited conditions of liquidity and lack of operational predictability. The adoption of digital technologies and automation of processes have been found to be one of the major factors that drive efficiency and allow organizations to optimize workflows, minimize redundancies, and speed up decision-making ([32]).

Productivity, in turn, describes the connection between input and output, with the focus on how the organizational resources, especially human capital, are converted into quantifiable outcomes ([10]). Teleworking and other types of flexible work arrangements have also brought new dynamics in productivity measurements that make existing systems of performance measurement ineffective. Although digital tools can enable remote collaboration and continuity, their effects on productivity depend on the organizational preparedness, technological infrastructure, and managerial practices ([28]; [26]; [37]).

Lastly, organization structure is the way in which roles, responsibilities, communication channels and coordination are arranged in a firm. The aspect of structural adaptability has become a determining variable in the performance of organizations, especially in turbulent environments ([22]). Companies that embraced more decentralized, flexible, and digitally enabled systems were better placed to react to quick changes, streamline operations continuity, and promote innovation. In this sense, the pandemic demonstrated the significance of the structural design-technological capabilities and strategic goals alignment, which supported the idea that organizational performance is not only an outcome measure but also a structural and managerial alignment factor ([22]; [11]).

A multi-theoretical approach that incorporates Resource-Based View (RBV), Dynamic Capabilities Theory, and Institutional Theory forms the theoretical basis of the current study as it attempts to elucidate the interplay between flexible work arrangements and digital transformation in the context of organizational performance and resilience. This integrative method allows understanding the interplay of internal resources, adaptive processes, and external pressures in forming firm-level outcomes in a comprehensive manner ([39]).

The Resource-Based View explains that companies gain a sustained competitive advantage by developing and implementing valuable, rare, inimitable, and non-substitutable (VRIN) resources ([17]). In this context, digital infrastructure, technological expertise, and human capital that can work in flexible work settings are strategic resources that lead to better efficiency and productivity in operations. During the COVID-19 crisis, firms with the capability to utilise such resources were highly linked with their continuity and performance under disruption conditions ([27]; [35]).

Yet, the dynamic aspect of transformation based on crisis is not entirely explained by the RBV. The Dynamic Capabilities Theory overcomes this limitation by focusing on the capacity of the firm to incorporate, develop, and restructure internal and external competencies within changing environments at high rates ([37]). The pandemic is exactly such a situation, in which companies not only had to have digital tools, but had to constantly redesign their organizational processes, restructure workflows and reset their strategic priorities ([22]). The dynamism of capabilities is demonstrated by the quick shift to teleworking and the adoption of digital platforms, which allows companies to react to the turbulence of the environment in an effective manner ([36]; [45]).

Complementarily, the Institutional Theory helps in shedding light on the outside pressures that affect the organizational behavior. In the case of the pandemic, regulations, governmental policies, and social expectations increased the use of remote work and digital practices. Not only were firms taking into account efficiency considerations, but also aligning themselves with institutional norms and legitimacy requirements, especially in regulated settings and among firms using the IFRS standards, where transparency and accountability are paramount ([2]).

Making these views converged, the main theoretical advantage of this research is that the ability to implement digital transformation and flexible work arrangements together is a strategic resource ([43]). Such a possibility is not only a technological but an organizational potential inherent in the ability of the firm to organize resources, change structures, and adapt to institutional needs and, as a result, increase productivity, efficiency, and long-term resilience ([46]; [5]).

The paper focuses on the organizational productivity impacts of flexible work arrangements and digital transformation in large companies in the Republic of Greece during 2020-2023, in the light of the above literature and theoretical framework. All three hypotheses have organizational productivity as their dependent variable. The Flexible Work Index and the Digital Transformation Index are considered as main independent variables, and by combining them into an interaction term, the impact of digital transformation on the productivity of flexible work is tested. Based on this, the following hypotheses are drawn up:

H1: Flexible work arrangements are positively and significantly associated with organizational productivity.

H2: The digital transformation has a significant positive impact on organizational productivity.

H3: There is a positive moderation between flexible work arrangements and organizational productivity by digital transformation; that is, flexible work arrangements have a more positive effect in firms that are more digital.

The research adopted a quantitative cross-sectional design to test three theoretically derived hypotheses concerning the relationships among flexible work arrangements, digital transformation and perceived organizational productivity. A quantitative approach has been chosen based on the aim of testing hypotheses with theoretical underpinnings and determining statistically significant associations between variables ([3]). It is a methodological orientation that provides the opportunity to systematically measure complex organizational constructs, e.g., productivity and efficiency, and extend the results to the target population, on the whole ([30]; [8]).

The survey instrument adopted comprised of structured questions, specifically formulated to assess various aspects of organizational adaptation at the time of the pandemic ([49]). The questionnaire included Likert-scale questions that measured perceptions and practices in terms of flexible work models, digital transformation initiatives, and performance outcomes. This method allows measurement consistency and allows the use of more sophisticated statistical methods ([14]). The survey-based design is aligned with the previous empirical studies in the area and aligns with the overall methodology used in the doctoral research, which focuses on the combination of quantitative indicators with the analysis at the organizational level ([3]; [44]).

The sample consisted of 457 large enterprises in Greece that have taken advantage of the financial assistance provided by the state under COVID-19 state-aid programs and prepared their financial statements following the International Financial Reporting Standards (IFRS). The enterprises were identified by using European Commission’s State Aid Transparency Public Search database, which publishes the individual data on state aid awards made by the Member States in compliance with the European transparency requirements. The eligibility criteria were as follows: 1) active operation in the Republic of Greece in 2020-2023; 2) financial support provided under one of the state-aid schemes for COVID-19; 3) being a large enterprise, according to the criteria followed in Europe; and 4) consistent application of IFRS during the period under study. The enterprises’ size and IFRS-reporting status were only verified from the publicly available corporate financial statements.

A stratified sampling procedure was used to maintain the sectoral composition of the sampling frame that was used in proportion. For each stratum, simple random sampling without replacement was used, with the use of a random-number generator in Microsoft Excel. This process provided transparency and reproducibility and excluded the same enterprise from being included more than once. In total 130 enterprises were invited to participate. The questionnaire was sent via e-mail from January to April 2026, with two reminders sent two weeks apart to firms that did not reply to the first invitation to complete the survey.

A knowledgeable representative from each participating firm was invited, with firsthand experience from the enterprise within the 2020-2023 period. Sixty-one financial directors (68.54%), 19 human-resource directors (21.35%), eight accounting-department executives (8.99%) and one information-technology director (1.12%) comprised the final sample. The unit of analysis was the enterprise and only a single completed questionnaire was retained from each participating firm.

A usable response rate of 68.46% (89/130 × 100) was achieved from 130 enterprises contacted, with 89 enterprises returning complete and usable questionnaires. The remaining 41 enterprises (31.54% of contacted enterprises) provided an unusable questionnaire. The 89 companies represented 19.47% of the 457 eligible companies.

Comparing the distribution of responding and non-responding enterprises by sector and enterprise size was used to evaluate potential non-response bias. The statistics results were obtained using the chi-square test, which showed no significant difference in Sectoral Composition (p = 0.418) and an independent-samples t-test, which showed no significant difference in Average Number of Employees (p = 0.327). These findings show that there was no apparent non-response bias for the organizational characteristics available. However, differences between those firms that do and don’t respond are impossible to rule out completely.

The questionnaire included five multi-item scales for flexible work arrangements, digital transformation, organizational productivity, operational efficiency and overall organization performance. The respondents were asked to rate their enterprise’s experience in 2020-2023. All items were measured on a five-point Likert scale ranging from 1 = “Strongly disagree”, 2 = “Disagree”, 3 = “Neither agree nor disagree”, 4 = “Agree” and 5 = “Strongly agree”. A higher score meant the practice was more flexible and/or digital, or the organizational outcome was better.

The flexible work index was composed of the following four items: “Our enterprise widely practiced teleworking in 2020-2023” (FW1); “We systematically adopted hybrid working arrangements” (FW2); “We systemically provided flexibility on where employees work” (FW3); and “We flexibly adjusted working hours to meet our business needs” (FW4). The internal consistency of the scale was satisfactory (α = 0.82).

The Digital Transformation Index included the four questions listed below: “The enterprise expanded its use of cloud-based and enterprise information systems” (DT1); “Core organizational processes became increasingly automated” (DT2); “Digital communication and collaboration platforms were systematically integrated into daily operations” (DT3); and “Data and digital technologies were increasingly used to support managerial decision-making” (DT4). The scale had a high internal consistency (Cronbach’s α = 0.88).

The four items used to measure Organizational Productivity were: “Employee output improved during the period examined” (PR1); “The enterprise completed tasks more rapidly than before” (PR2); “Digital and flexible working practices increased employee productivity” (PR3); and “The enterprise maintained or increased its output despite pandemic-related disruptions” (PR4). The scale tested had high reliability (Cronbach’s α = 0.85).

The four items used to assess Operational Efficiency were: “The enterprise reduced unnecessary operating costs” (EF1); “Internal processes were completed using fewer organizational resources” (EF2); “Coordination among departments improved” (EF3); and “The speed and accuracy of operational procedures increased” (EF4). The scale had a good internal consistency (Cronbach’s α = 0.81).

Overall Organizational Performance consisted of four items: (OP1) The enterprise achieved the main operational goals in 2020-2023; (OP2) The enterprise performed well financially despite the crisis; (OP3) The enterprise adapted well to changes in its business environment; and (OP4) The enterprise enhanced its overall competitiveness during the period covered. Additionally, the internal consistency of this scale was high (Cronbach’s α = 0.87).

A composite index was created for each construct by averaging the individual items. Thus, all indices were kept at the same range (1 - 5), allowing for easy interpretation. If a respondent answered three or four of the four items in the scale, then a composite score was calculated. There were no items reverse coded. The Flexible Work Index and Digital Transformation Index were first mean centred before the product of the two was computed to yield the Flexible Work × Digital Transformation interaction term. Mean-centering eliminated non-critical multicollinearity and was helpful for interpretation of the key regression coefficients.

Data analysis is done in a multi-stage and structured manner that guarantees robustness and validity of the empirical results. First, descriptive statistics are used to give a description of the sample features and distribution of the important variables. This measure will enable outlining the overall trends in terms of the use of flexible working schemes and digital transformation practices.

We then do the reliability analysis by applying the Cronbach’s alpha coefficient to determine the internal consistency of the measurement scales. This will be an important step in making sure that the constructs that are employed in the study are statistically viable and can be analyzed further.

The essence of the empirical study is based on the regression analysis models, which are used to test relationships among the independent and dependent variables. Multiple linear regression analysis was used to test H1 and H2 by estimating the associations of the Flexible Work Index and Digital Transformation Index with perceived organizational productivity, controlling for firm size and industry. A second model incorporating the mean-centered Flexible Work × Digital Transformation interaction term was estimated to test the moderating relationship specified in H3. Also, diagnostic tests, including a multicollinearity test and a residual analysis, are performed to confirm the assumptions under the regression models.

This analysis framework perfectly fits into the methodological concept followed in the doctoral dissertation, which focuses on applying descriptive, reliability, and inferential statistical tools to deliver a holistic and empirically based concept of organizational transformation in the pandemic era.

The descriptive analysis outlines the extent of the uptake of flexible work arrangements and digital transformation practices by the Greek enterprises in the period 2020-2023. The sample comprises 89 large IFRS-reporting enterprises. The descriptive results represent senior respondents’ assessments of flexible work adoption, digital transformation and organizational outcomes during 2020-2023.

The results (Table 1) show that there are relatively high teleworking practices (M = 3.82), as about 68 percent of the firms are moderate to high in terms of the implementation of remote working. Nevertheless, a complete remote model of functioning was not widespread (24%), which indicates that the majority of companies implemented hybrid structures instead of full decentralization.

Digital transformation (Table 2) seems to be ahead of flexible work adoption, where digital infrastructure has a mean score of 4.12. This proves that the pandemic was a robust catalyst of technological integration, and previous findings that companies were focusing on digital preparedness as an important adaptation strategy are supported.

Table 1. Adoption of flexible work arrangements.

Table 2. Level of digital transformation.

Multiple regression analysis was done to determine the effects of flexible work arrangements and digital transformation on the performance of an organization. The dependent variable is organizational productivity, and the independent variables are flexible work index and digital transformation index.

The findings (Table 3) of the multiple regression support H1 and H2. For H1, the flexible work arrangements were positively and statistically significantly related to perceived organization productivity (β = 0.21, p = 0.004). The perceived organizational productivity had a positive and statistically significant relationship with digital transformation (β = 0.52, p < 0.001) in support of H2.

For testing H3, an Extended regression model was created by introducing the interaction term between the mean-centered Flexible Work Index and Digital Transformation Index. Interaction coefficient was positive and statistically significant (β = 0.18, p = 0.009). The result suggests that the positive association between flexible work arrangements and perceived organizational productivity increased with digital transformation. This was therefore supported by H3.

Table 3. Regression results (dependent variable: productivity).

Note. N = 89. Dependent variable: perceived organizational productivity. Predictors: Digital Transformation Index, Flexible Work Index, firm size and industry. R² = 0.504; adjusted R² = 0.481; F (4, 84) = 21.37, p < 0.001.

Regression model has a high power to explain as it explains about 50 percent of the variance in productivity. Digital transformation has the largest standardized coefficient (0.52), which has a very significant positive impact on productivity (p < 0.001). Flexible work is also statistically significant with a positive correlation (0.21, p = 0.004), but with a lesser degree.

Also, the multicollinearity diagnostics (VIF < 2.5 of all variables) ensures the strength of the model, which aligns with the concept of methodological rigor used in the initial empirical framework.

An extended model was estimated to further analyze the effects of interaction (Table 4):

Table 4. Interaction model.

The interaction term is positive and statistically significant, indicating that the impact of flexible work on productivity is strengthened in firms with higher levels of digital maturity.

The findings provide three key insights. First, digital transformation stands out as the most significant predictor of firm productivity, suggesting that digital capabilities represent a key performance driver in times of crisis. Companies that embraced digital tools and automation of processes have experienced a notable increase in productivity, confirming that digital transformation increases efficiency and resilience.

Second, agile working has a positive, but moderate, impact on productivity. Both teleworking and hybrid work arrangements enhance productivity, but not in a uniform or isolated way. Rather, they are dependent on the digital infrastructure of the firm. Flexible work arrangements appear to be less effective in firms with low digital capabilities and in some instances, may even create coordination losses.

Third, the interaction term between digital transformation and flexible work is significant, suggesting complementarity. This reveals that flexible work does not work as an independent productivity enabler but rather as a catalyst for the impact of digital transformation. As such, firms who embraced both digital and flexible work arrangements perform best.

All in all, the results are in line with the other empirical evidence in the dissertation, which highlights that the adaptation of firms during the pandemic was not uniform but rather related to firms’ capabilities, especially digital capabilities and responsiveness.

The four figures complement the descriptive and regression results. The flexible working arrangements adoption rate is shown in Figure 1 for the participating enterprises. Teleworking intensity was the role that had the highest mean score, while fully remote was used much less widely than hybrid. Large Greek businesses were most likely to have hybrid working arrangements, not fully remote, in 2020-2023.

In Figure 2, you will see the main dimensions of digital transformation. Digital infrastructure systems like enterprise resource planning (ERP) and cloud-based systems had the greatest adoption rate. Relatively high scores were obtained for digital customer interaction and process automation, reflecting the relatively high level of technological adaptation among the enterprises surveyed during the period of the pandemic.

Figure 1. Adoption of flexible work arrangements.

Figure 2. Digital transformation adoption.

The standardized regression coefficients of the multiple regression model are shown in Figure 3. Flexible work arrangements and digital transformation were the two positive factors with the greatest association in the study, in this instance with perceived organizational productivity. The coefficients for firm size and industry were weaker in positive direction.

Figure 3. Standardized regression coefficients.

The relationship between flexible work arrangements and digital transformation is shown in Figure 4. As digital transformation increases, the positive slope rises, suggesting that flexible working is correlated with increased perceived productivity, but especially at high levels of digital transformation. This graphical pattern corresponds to the positive and statistically significant interaction coefficient listed in Table 4.

Figure 4. Interaction effect.