Nurses’ adaptation to digital transformation in healthcare: A grounded theory of progressive digital adaptation

Research design

The study employed Corbin and Strauss’s ²⁶ grounded theory methodology to develop a theory explaining how nurses adapt to digital transformation in healthcare settings. Grounded theory is an inductive qualitative approach that allows for the development of theory directly from data. It is especially suited for understanding processes that unfold over time and are shaped by interaction, such as nurses’ engagement with digital technologies in clinical environments.

Grounded theory methodology was appropriate for developing a theory explaining the process of nurses’ digital adaptation, including the conditions influencing this process and the strategies employed at different stages. This approach enabled systematic data collection and analysis to generate theory grounded in participants’ lived experiences. ²⁶ The constant comparative method was central to the study, enabling ongoing analysis of data from the first interview through to saturation and theory generation.

The grounded theory methodology employed in this study is underpinned by symbolic interactionism, which posits that individuals act based on the meanings they attribute to their experiences, and these meanings are shaped through interaction with others and their social environment. ²⁷ This theoretical foundation allowed the study to examine how nurses construct meaning around digital transformation and how their interpretations and responses evolve through interaction with technology, colleagues, and organizational structures.

Data collection and analysis were iterative and concurrent. Coding followed Corbin and Strauss’s structured approach, including open coding, axial coding, and selective coding, with continuous refinement of emerging categories. ²⁶ The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines ²⁸ to ensure transparency, rigor, and comprehensive reporting of qualitative methods.

Setting and context of the study

This study was conducted at a well-established private hospital in Egypt, a developing country context where digital health infrastructure is rapidly evolving but faces distinct implementation challenges. Over the past two years, the hospital has actively integrated a range of digital systems into its clinical operations, including electronic health records (EHRs), digital documentation tools, and telehealth services. As a multi-specialty institution serving diverse patient populations, it has positioned itself as a regional leader in healthcare digitization.

The hospital utilizes an integrated electronic health record (EHR) system for patient documentation, computerized physician order entry (CPOE), digital medication administration systems, and telehealth platforms for remote consultations. Clinical practices at the hospital follow national healthcare standards, with nursing staff working in 8–12-hour shifts across medical, surgical, and critical care units. Digital documentation is mandatory for all patient encounters, requiring nurses to input vital signs, assessments, medications, and care plans electronically.

Nurses employed at this hospital had direct exposure to a wide array of digital innovations, making the setting ideal for exploring their adaptation processes. While the hospital administration provided structured training programs and technical support, the extent of digital engagement and the challenges encountered varied across departments and individual nurses.

Researcher context

The research team consisted of female nurse scholars with backgrounds in healthcare, nursing administration, and qualitative research methodology. Prior to data collection, the researchers had no existing relationships with participants. Throughout the research process, the team-maintained reflexivity through regular meetings to discuss emerging insights, examine assumptions, and critically evaluate their positionality in relation to the data. ²⁶ Reflexive memos were maintained to document how the researchers’ backgrounds and perspectives might influence data interpretation, and peer debriefing sessions were conducted to challenge emerging categories and strengthen analytical integrity.

Participants and sampling

Participants were selected using a combination of sampling strategies consistent with grounded theory methodology. ²⁶ Nurses were recruited from medical units, surgical units, and critical care units, with clinical experience ranging from less than two years to over fifteen years, and recruitment continued until data saturation was achieved at thirty interviews.

Inclusion and exclusion criteria

Inclusion criteria consisted of registered nurses employed at the study hospital with a minimum of six months’ experience using digital systems such as electronic health records, telehealth platforms, or digital documentation tools. Participants were required to be currently working in clinical practice, willing to participate, able to provide informed consent, and able to communicate in Arabic. Exclusion criteria included nurses on extended leave or not actively practicing during the study period, nurses who declined to participate, and nurses with less than six months of experience with digital systems.

The recruitment process began by contacting unit nurse managers from the target clinical units to identify potential participants. Unit managers were provided with study information and inclusion criteria. Eligible nurses were then approached individually by the research team, provided with written and verbal information about the study, and invited to participate willingly. Those who expressed interest were given time to consider participation before providing written informed consent.

Initial purposive sampling recruited ten participants to establish foundational insights into the adaptation process, including four staff nurses from medical units, three from surgical units, and three from critical care units. The initial sample varied in clinical experience (two to more than ten years), educational preparation (diploma to master’s degree), and digital system experience (six months to more than two years).

Theoretical sampling involved recruiting new participants who possessed specific characteristics or experiences relevant to developing theoretical concepts. ²⁶ This approach differed from initial purposive sampling in that recruitment decisions were driven by analytical needs. For example, when peer support emerged as significant, six nurses from units with varying collaboration levels were recruited. When managerial support emerged as influential, five participants under different management styles were sought. When sustained resistance patterns appeared, four participants with these experiences were recruited. When generational differences seemed significant, five nurses across different age ranges were added. This theoretical sampling added twenty participants to the initial ten.

Theoretical saturation was achieved after thirty interviews, when additional interviews no longer produced new codes, themes, or properties, and the emerging theory was well-developed and conceptually dense.^29,30 The final sample included eleven nurses from medical units, ten from surgical units, and nine from critical care units. Educational preparation included twelve diploma, fifteen bachelor’s, and three master’s degree holders. The sample included twenty-four female and six male nurses.

Data collection

Data were collected over a four-month period using in-depth, semi-structured interviews developed by the researchers based on literature review.^3,31,32 A pilot interview was conducted with one nurse to test the interview guide; minor adjustments were made based on feedback, and the pilot was not included in the final analysis.

Interview procedures

Face-to-face interviews were conducted in a private hospital setting after obtaining written informed consent. Each session lasted forty-five to sixty minutes and was audio-recorded with participants’ explicit consent for verbatim transcription. Demographic responses were recorded on the interview guide using only participant identifiers (P1, P2, P3); no names, employee IDs, or other identifying information appeared on research materials.

Field notes were maintained throughout data collection. During interviews, brief observational notes captured non-verbal cues, emotional reactions, and contextual details. Within thirty minutes after each interview, these notes were expanded to include reflections on interview dynamics, emerging patterns, and questions for subsequent data collection.

Data analysis

Data analysis in this study followed Corbin and Strauss’s grounded theory methodology, ²⁶ using a systematic, inductive, and iterative approach to generate theory grounded in participants’ experiences. Interviews were transcribed verbatim and reviewed immediately following each session to inform subsequent sampling and questioning. The analysis began alongside data collection, following the principle of constant comparison—a core principle of grounded theory—which involves systematically comparing each segment of data with all others to identify patterns, variations, and relationships, thereby refining emerging categories. ²⁶ See Figure 1.OPEN IN VIEWER

Consistent with Morse’s ²⁹ recommendation, all data were given equal consideration during analysis, with new data continuously compared with existing categories to further refine and expand the developing theory. This iterative process ensured analytical consistency and depth throughout the study.

Open coding

The first phase involved open coding, in which transcripts were examined line-by-line to identify significant statements, actions, or meanings. Each segment of data was labelled with descriptive codes, many of which reflected participants’ own words. These initial codes were written in the margins, colour-coded, and organised manually into clusters based on similarities in content. Through this process, early conceptual categories were identified, such as “fear of making errors,” “lack of confidence,” and “support from peers.”

Focused and axial coding

Following open coding, focused coding grouped significant codes into broader categories. Concurrently, the axial coding phase examined relationships among these categories to develop subcategories and specify conditions, contexts, interactions, and consequences associated with each phenomenon. ²⁶ For example, the subcategories “formal training” and “self-directed learning” were linked under the broader category of “learning and skill acquisition,” revealing how different support mechanisms shaped nurses’ adaptation over time. Throughout this phase, constant comparison continued to ensure that relationships between categories were empirically grounded and theoretically coherent.

Selective coding

The final stage, selective coding, involved identifying the core category that unified all emerging themes and subthemes. This central phenomenon—nurses’ adaptive journey to digital transformation—captured the dynamic process through which nurses navigated technological change, balancing barriers and facilitators to ultimately integrate digital tools into clinical practice.

Memo writing was used throughout all coding phases to support reflexivity, document coding decisions, and capture evolving theoretical insights. ²⁶ Although no qualitative software was employed, the manual coding process facilitated deep immersion in the data and strengthened analytical rigour.

Theoretical saturation was achieved when no new codes or categories emerged from subsequent interviews, and the relationships between concepts were well-developed and clearly understood.^29,30 This process ensured that the emerging theory was both contextually rich and empirically grounded, offering a comprehensive explanation of how nurses adapt to digital transformation within healthcare settings.

Rigour and trustworthiness

To enhance trustworthiness, multiple strategies aligned with qualitative research standards were embedded in the study design.^29,33

Credibility was strengthened through prolonged engagement with participants, iterative data collection, and member checking. After initial analysis, summaries of findings were shared with selected participants to validate interpretations. Dependability was supported by maintaining a detailed audit trail including interview protocols, memo notes, coding decisions, and analytical reflections. The research team held regular reflexive meetings to examine assumptions and critically evaluate positionality. Confirmability was addressed by documenting all analytical decisions, ensuring findings remained rooted in data rather than researcher preconceptions. Memos were used to bracket assumptions, and peer debriefing with an external qualitative expert challenged emerging categories. Field notes were recorded after each interview session rather than during interviews to maintain natural conversational flow and minimise participant distraction; these notes captured contextual observations, non-verbal cues, and initial analytical impressions. Transferability was supported through rich, contextualised descriptions of the study setting, participants, and methods, enabling readers to assess applicability to other healthcare contexts.^29,30

Ethical considerations

This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2008). Ethical approval was obtained from the Ethics Research Committee of the Faculty of Nursing, Alexandria University (IRB approval No. [IRB00013620]). Written informed consent was obtained from all participants prior to data collection. Participants were fully briefed on the study’s purpose, procedures, and their rights, including the right to withdraw at any point without consequences. At the start of each interview, researchers reiterated these points and obtained explicit consent for audio recording. To ensure confidentiality, participants were assigned alphanumeric codes (e.g., P1, P2), and all identifying information was removed from transcripts. All recordings, transcripts, and forms were stored securely in password-protected files and accessed only by the research team.

Participants’ characteristics

The demographic characteristics of the interviewed nurses (N = 30) in Table 1 revealed a predominantly middle-aged group, with 40% aged between 20 and 30 years. The sample was largely female (73.3%). The distribution of years of experience was balanced, with 33.3% having 6–10 years of experience and another 33.3% having more than 10 years. Most participants held a bachelor’s degree in nursing (60%), followed by diploma holders (23.3%) and master’s degree holders (16.7%). Nurses from medical units comprised the largest group (40%), followed by those from critical care (33.3%) and surgical units (26.7%). Marital status was nearly evenly split, with 53.3% married and 46.7% single.

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Table 1.

Demographic characteristics of interviewed nurses (N = 30).

Demographic characteristics	No	%
Age (years)
20-30	12	40.0
30-40	8	26.7
40-50	6	20.0
> 50	4	13.3
Gender
Female	22	73.3
Male	8	26.7
Years of Experience
Less than 1 year	3	10.0
1-5 years	7	23.3
6-10 years	10	33.3
More than 10 years	10	33.3
Education Level
Diploma	7	23.3
Bachelor	18	60.0
Master	5	16.7
Unit of Work
Critical Care	10	33.3
Medical	12	40.0
Surgical	8	26.7
Marital Status
Single	14	46.7
Married	16	53.3

Overview of the emerged theory

This study generated a grounded theory titled Progressive Digital Adaptation, which explains the process through which nurses progressively adapt to digital transformation in healthcare settings. The theory identifies the conditions that facilitate or hinder progression through adaptation stages, and the strategies nurses employ to navigate digital transformation. The theory is structured around five major themes: (1) resistance to technology—a recurring experience, (2) learning and skill acquisition—an ongoing, iterative process, (3) integration into clinical practice, (4) contextual factors shaping adaptation, and (5) mechanisms of sustained adaptation and transformation. Each theme consists of several categories grounded in participant narratives and supported by sample quotations. See Table 2 for themes and categories. The core category—progressive digital adaptation—represents a developmental shift in nurses’ digital engagement, influenced by organizational structures, peer dynamics, personal beliefs, and perceived efficacy. For more quotations see Supplementary File 3.

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Table 2.

Emerged themes and categories.

Theme	Categories	Key concepts
Theme 1: Resistance to Technology-A Recurring Experience	1.1 Fear of Making Mistakes	Professional identity threat, error anxiety, self-doubt, sustaining vs. Struggling trajectories
	1.2 Lack of Confidence in Technology Use
	1.3 Digital Culture Shock
	1.4 Variation in Resistance Duration and Intensity
Theme 2: Learning and Skill Acquisition-An Ongoing, Iterative Process	2.1 Formal Training Program	Training quality variability, social learning, generational tensions, time constraints on learning
	2.2 Peer Collaboration/Non-Collaboration and Negative Peer Dynamics
	2.3 Self-Directed Learning
	2.4 Divergent Learning Trajectories
Theme 3: Integration into Clinical Practice	3.1 Workflow Integration (Sustaining Trajectory) 3.2 Trial-and-Error Learning	Routine embedding, system as second nature, fragile equilibrium, paper backup maintenance
	3.3 Partial Integration and Fragile Adaptation (struggling trajectory)
Theme 4: Contextual Factors Shaping Progressive Digital Adaptation	4.1 Technical System Characteristics	System reliability, time structures, managerial facilitation, social learning environments
	4.2 Organizational Resource Allocation
	4.3 Leadership Engagement
	4.4 Peer Networks and Collaborative Cultures
Theme 5: Mechanisms of Sustained Adaptation and Professional Transformation	5.1 Managerial Support	Leadership visibility, continuous education, IT responsiveness, identity transformation
	5.2 Ongoing Training
	5.3 Responsive Technical Support Infrastructure

Critically, nurses’ experiences of digital adaptation followed a non-linear and cyclical trajectory. While some moved from resistance to integration, others experienced setbacks, stalled progress, or recurring challenges. Analysis revealed two distinct pathways: a sustaining trajectory characterized by relatively rapid progression toward confident digital use, and a struggling trajectory characterized by prolonged resistance, stalled learning, partial adoption, or regression to earlier stages. Strategies employed by nurses included formal training participation, peer collaboration, self-directed learning, trial-and-error experimentation, and seeking managerial support. Facilitating conditions included technical system reliability, protected time for learning, responsive leadership, and collaborative peer networks. Hindering conditions included system instability, time constraints, managerial disengagement, and absence of peer support. Approximately 15-20% of participants (n=5-6) experienced sustained resistance that never fully resolved.

Theme 1: Resistance to technology—a recurring experience

Many nurses in this study initially resisted digital systems, expressing fear of making mistakes and limited confidence in their technical abilities. These expressions of insecurity were intrinsically connected to professional identity. For nurses, professional identity is fundamentally constructed around their capacity to provide competent, safe patient care through clinical judgment and therapeutic relationships. As presented in the Introduction, unfamiliar digital workflows were perceived as threats to safe practice; in the Results, this concern emerged as a recurring experiential driver of resistance. The fear of making mistakes thus represented a deeper concern about maintaining professional credibility as competent caregivers. This identity threat was further compounded when digital systems were perceived as prioritizing documentation over direct patient interaction, creating tension between relational care values and technical demands of digital documentation.

Theme 2: Learning and skill acquisition—an ongoing, iterative process

As nurses moved beyond initial resistance, they entered an active learning phase. The transition was neither automatic nor universal, depending on training quality, support availability, and practice time. Learning recurred cyclically with each system update or workflow modification.

This period was marked by a gradual shift from apprehension to engagement, as nurses developed familiarity and confidence with the technology. Learning was not a one-dimensional process—it involved a combination of formal training, peer collaboration, and self-directed strategies. Descriptive words such as “training,” “familiarity,” “peer,” and “self-learning” were repeatedly used by participants to describe their experiences in this phase. However, the pace and success of skill acquisition diverged sharply between nurses following sustaining versus struggling trajectories. For nurses on sustaining trajectories, the learning phase represented genuine progression—building on reduced resistance to achieve functional competence within 2-3 months. For nurses on struggling trajectories, the learning phase was characterized by repeated setbacks, stalled progress, and in some cases, regression back to active resistance.

Theme 3: Integration into clinical practice

As nurses developed digital competencies, they began integrating technology into clinical routines. However, integration was neither stable nor irreversible—it represented a fragile equilibrium disrupted by technical, organizational, or contextual changes, causing nurses to cycle back into resistance or intensive learning. Some achieved integration, lost it following disruptions, and rebuilt it multiple times. Others oscillated between integrated use and minimal engagement based on workload pressures. Integration was therefore not an endpoint but a temporary state requiring continuous organizational support, technical reliability, and protected practice time.

Integration experiences diverged dramatically between sustaining and struggling trajectories. Nurses on sustaining trajectories achieved relatively smooth embedding of digital tools, describing them as becoming “second nature” within 4-6 months. Nurses on struggling trajectories experienced fragile, incomplete integration characterized by selective use, parallel paper documentation, and regression when pressures intensified. For many, integration represented a transformation where technology became normalized in care delivery, reshaping workflows and reinforcing professional identity.

Theme 4: Contextual factors shaping progressive digital adaptation

Throughout the adaptation journey, nurses’ experiences were profoundly shaped by contextual factors—organizational, technical, and social—that either facilitated or hindered their progression through the stages of digital adaptation. These factors did not emerge only at specific points in the trajectory but operated continuously, influencing initial resistance, learning pace, integration depth, and sustained engagement in complex and interconnected ways. Understanding these contextual dimensions is essential for explaining why nurses followed divergent pathways despite encountering the same digital transformation initiative.

Theme 5: Mechanisms of sustained adaptation and professional transformation

Nurses who successfully navigated adaptation attributed sustained transformation to specific organizational mechanisms maintaining conditions for ongoing digital engagement. This represents the culmination of the sustaining trajectory, where digital competence became embedded in professional identity and practice.

Three mechanisms enabled sustained digital engagement: ongoing training, responsive technical support, and sustained leadership engagement. These stabilizing forces buffered against setbacks, reinforced skill development, and fostered long-term confidence. Critically, these supports were most effective when provided continuously rather than only during initial implementation. For nurses experiencing these mechanisms consistently, technology use evolved from a competency to master into an internalized dimension of professional identity.

Dual adaptation trajectories

This grounded theory reveals dual adaptation trajectories: a sustaining trajectory where nurses gradually integrate digital tools with increasing confidence, and a struggling trajectory marked by recurring resistance, avoidance, or stalled progression. These coexisting paths underscore the non-linear nature of digital adaptation, shaped by individual, organizational, and contextual factors. Some nurses progressed smoothly, while others cycled back or disengaged, depending on access to support systems, confidence, and digital familiarity.

Adaptation stages and transition mechanisms

The adaptation journey typically begins with Initial Resistance to Technology, where nurses described fear, uncertainty, and lack of confidence. This stage reflected the emotional labor required to reconcile professional identity with new technological expectations. Critically, resistance was not confined to this initial encounter but recurred cyclically throughout the journey—re-emerging when technical failures, system updates, or high workload periods undermined previously achieved confidence. Resistance was therefore not a stage to be overcome once but a recurring state that nurses moved in and out of repeatedly. Mechanism enabling transition from resistance to learning: Psychological safety creation—when managers normalize errors and provide protected practice time, fear-based avoidance shifts to exploratory engagement, freeing cognitive resources previously consumed by threat monitoring.

As nurses moved into Learning and Skill Acquisition, they shifted from passive resistance to active engagement. Competence was built through formal training programs, peer collaboration, and self-directed learning. This phase was marked by situated learning and collective problem-solving, where social interaction played a central role in reducing anxiety and fostering confidence. Mechanism enabling transition from learning to integration: Procedural automatization through graduated practice—beginning with low-acuity scenarios allows skills to become automatic before facing high-complexity situations. Without protected practice time, skills remain effortful and conscious, never achieving the automaticity required for seamless workflow integration.

In Integration into Clinical Practice, nurses began embedding digital tools into their daily workflows. This process involved workflow adaptation and trial-and-error learning, where digital systems evolved from being perceived as external impositions to becoming natural extensions of care delivery. However, the pace and depth of integration varied depending on system usability, peer support, and institutional facilitation. Mechanism triggering regression from integration: Cognitive overload during high-acuity periods or major system updates forces reversion to deeply ingrained paper-based routines. This regression occurs not due to skill loss but because extreme pressure forces reallocation of cognitive resources to patient care, abandoning recently acquired but still-effortful digital practices.

Adaptation as a non-linear, cyclical process

The theoretical coding revealed three distinct patterns illustrating the cyclical nature of Progressive Digital Adaptation:

Pattern 1: Regression Following Technical Disruptions. Nurses who had achieved confident integration sometimes reverted to earlier stages when confronted with major technical failures. For instance, P23 described how a system-wide update “felt like starting over”—forcing her to re-learn interfaces and rebuild efficiency. P9 reported that after repeated system crashes, she lost trust in the technology and began maintaining parallel paper documentation, effectively regressing from integration back to partial resistance.

Pattern 2: Stalled Progression and Partial Adoption. Some participants became stalled at intermediate stages, achieving only partial adoption. P29 explicitly stated: “I have three years until retirement. I’ll do what I must, but I’m not going to master this system.” P16 remained in partial integration, using the system “for the basics” but lacking time to explore additional features.

Pattern 3: Cyclical Movement Between Stages. Several nurses described oscillating between stages depending on contextual factors. P8 reported periods of growing confidence punctuated by returns to anxiety when workflow pressures increased, reverting to minimal documentation strategies during high-acuity shifts then returning to comprehensive digital engagement during less demanding periods.

Finally, Sustained Adaptation and Transformation emerged when support systems—particularly consistent managerial engagement and ongoing training—enabled nurses to maintain confidence and internalize digital practices as part of their professional identity.

Mechanism enabling sustained transformation: Continuous organizational reinforcement through visible managerial engagement maintains expectancy-value beliefs—nurses remain certain they can perform digital tasks effectively and convinced the organization values these efforts. When managers disengage post-implementation, these beliefs erode, triggering gradual regression even after months of successful integration.

Theoretical distinctiveness

Progressive Digital Adaptation extends existing theories in three key ways. First, unlike Rogers’ Diffusion of Innovation, ²² which describes adoption as a relatively stable outcome following the decision stage, and TAM/UTAUT^34,35 frameworks that predict initial acceptance based on perceived usefulness, our theory reveals that healthcare technology integration is reversible—nurses who achieved confident use regressed to avoidance following disruptions. This addresses a critical gap identified by De Leeuw et al. ¹⁹ regarding post-adoption abandonment in healthcare settings.

Second, while Normalization Process Theory ³⁶ describes the implementation “work” required for embedding practices, it does not fully account for individual-level variation within standardized implementations. May et al. ³⁶ focus on collective action and organizational processes; our theory explains why divergent trajectories emerge even when implementation work is adequately performed—specifying enabling and obstructing mechanisms operating at the individual-context interface.

Third, while stage models describe phases, Progressive Digital Adaptation explicates how transitions occur and why they fail. This builds on Corbin and Strauss’s ²⁶ process-oriented approach by specifying mechanisms through which contextual conditions shape individual trajectories.

Initial resistance to technology

Many nurses in this study expressed initial resistance to digital systems due to fear of making mistakes and limited confidence. This resistance reflected more than simple technical insecurity—it represented a threat to professional identity, which for nurses is fundamentally rooted in their ability to provide competent, safe patient care.^24,25 Similar findings were reported by Ross et al. ³⁷ who clarified that unfamiliar technology often triggers anxiety among healthcare professionals, and Tawfik et al. ³⁸ who found that frustration with complex systems links to emotional exhaustion in high-pressure clinical settings. Technical problems encountered during initial system use amplified resistance by confirming nurses’ fears that systems were unreliable. Glitches and crashes during early attempts validated avoidance behaviors—nurses reasoned “why invest effort learning something that doesn’t work?” This created self-reinforcing cycles where technical failures prevented the early success experiences necessary for self-efficacy building.

Arnetz et al. ³⁹ highlighted that sociotechnical barriers—inadequate training, lack of intuitive design, and minimal user involvement—exacerbated nurses’ apprehension during COVID-19. Mumtaz et al. ⁴⁰ emphasized that resistance is rooted in deeper concerns about professional credibility, patient safety, and disruption to established workflows. Burgess and Honey ⁴¹ found that without structured support, nurses perceived new technology as burden rather than facilitative tool.

Transition mechanism: The shift from resistance to active learning was facilitated when organizations created psychologically safe environments where errors were normalized and support was immediately available. Conversely, when early technical failures occurred without peer support to buffer their impact, self-efficacy beliefs became fixed around “I cannot do this,” preventing progression regardless of subsequent training.

Critical caveat: Unresolved resistance

While Progressive Digital Adaptation describes a trajectory toward integration, not all nurses followed this pathway. Some participants’ initial resistance never fully resolved. These nurses continued experiencing persistent anxiety, avoided advanced features, and relied heavily on colleagues even months after implementation. Factors associated with sustained resistance included proximity to retirement, minimal baseline digital exposure, repeated technical failures during early encounters, and absence of mandatory competency requirements. Future research should examine factors differentiating sustained resistance from progressive adaptation.

Learning and skill acquisition

Following initial resistance, nurses turned to various learning strategies. Formal training sessions provided foundational technical skills and helped reduce anxiety. This concurs with Walzer et al. ⁴² who highlight the critical role of needs-based support programs in developing digital competency. Peer collaboration emerged as a powerful facilitator. Participants described how support from experienced colleagues built confidence and fostered mutual problem-solving. This aligns with Coffetti et al. ⁴³ who found that interdisciplinary collaboration and end-user involvement significantly enhance nurses’ acceptance of digital tools. Also, self-directed learning was evident among nurses seeking independent mastery. This resonates with Mlambo et al.'s metasynthesis ⁴⁴ underscoring the value nurses place on continuing professional development as integral to professionalism and lifelong learning.

Transition mechanism: Progression from learning to integration occurred when nurses could practice under graduated pressure—beginning with low-acuity scenarios that allowed procedural automaticity to develop before facing high-complexity situations.^42,44 Without protected time for graduated practice, skills remained effortful and conscious, never achieving automaticity required for seamless workflow integration. ⁴³ This explains why understaffed units showed prolonged learning phases—cognitive load remained high, blocking transition.

Structural barriers to learning

Skill acquisition was significantly constrained by time scarcity and workload pressure. Chronic understaffing and lack of protected training hours limited participation in formal learning opportunities.^45,46 Some nurses felt pressure to self-learn during unpaid personal hours, causing frustration and resentment.^47–50 This pattern represents both an ethical concern and practical barrier—when self-directed learning becomes an expectation outside compensated hours, it functions as unpaid labor contributing to burnout. Organizations must ensure that time for digital skill development is explicitly allocated within working hours rather than implicitly expected during nurses’ personal time. The absence of such protected time not only undermines fairness but serves as a demotivator prolonging resistance to digital transformation.^3,4

The dark side of peer dynamics

While peer collaboration facilitated learning, our findings also revealed significant instances of non-collaboration. Some digitally proficient nurses actively withheld assistance, viewing it as additional unpaid labor. Generational tensions created barriers—older nurses reported feeling judged while younger nurses perceived older colleagues as resistant. These negative peer dynamics had substantial consequences: nurses experiencing non-collaboration reported prolonged learning phases, increased anxiety, and in some cases, complete disengagement.

This finding extends Coffetti et al., ⁴³ who identified team-level influences on technology adoption but did not examine the consequences of negative peer interactions. However, it is important to acknowledge that while peer mentoring is valuable, over-reliance on informal peer support may create additional burden for nurses who are frequently sought out for assistance, potentially disrupting their own clinical responsibilities. 4 ⁴³ To address this concern, organizations should consider establishing dedicated digital support roles or ‘superuser’ positions with protected time specifically allocated for supporting colleagues during digital transitions. This approach ensures that support remains accessible while preventing the overburdening of individual nurses who possess strong digital skills. ⁴¹

For nursing practice

The findings emphasize the importance of structured, ongoing capacity-building for nurses to ensure that digital tools become an integrated part of clinical workflows rather than an added burden. Early resistance—driven by fear of errors, lack of confidence, and digital culture shock—can be mitigated through targeted orientation programs, peer mentoring, and simulation-based practice that occurs before nurses face high-acuity clinical situations.

Embedding digital competencies into continuous professional development frameworks helps maintain confidence and prevent skill decay. Additionally, fostering a psychologically safe environment where nurses can ask questions, experiment, and learn without fear of judgment accelerates adaptation and improves the quality of patient care. Organizations should recognize that adaptation is not a one-time achievement but requires ongoing support to maintain, particularly during system updates or periods of high workload pressure.

For nursing management

Managerial leadership plays a pivotal role in sustaining digital transformation. Leaders should allocate protected time for training within working hours—this investment is essential for several reasons. First, it demonstrates organizational commitment to digital competency as a valued professional skill. Second, it prevents the ethically problematic expectation that nurses learn complex systems during unpaid personal time. Third, it enables graduated practice that allows procedural automaticity to develop before nurses face high-complexity situations.

Specifically, organizations should consider the following: dedicated training time during orientation to new systems with reduced patient care assignments; protected time for practice and exploration beyond initial training sessions; scheduled refresher sessions when systems are updated; and recognition that digital competency development is part of professional paid work, not an additional activity that needs to be done during one’s free time.

Leaders should also provide accessible technical support, recognize staff achievements in digital competence to reinforce motivation, and ensure that system usability aligns with clinical needs. Incorporating principles from the Progressive Digital Adaptation model into change management strategies can help managers anticipate and address the cyclical nature of adaptation—acknowledging that progress may be disrupted by technical failures, workflow pressures, or staff changes. Management should establish feedback loops to address operational challenges quickly and prevent the erosion of expectancy-value beliefs that triggers regression.

For future research

This study highlights the need for further investigation into how contextual factors—such as educational background, gender dynamics, and healthcare infrastructure—influence the pace and depth of digital adaptation. Quantitative studies could examine causal pathways between adaptation stages, leadership behaviors, and patient outcomes. For example, research could test whether protected practice time (e.g., ≥2 hours/week) predicts faster resistance-to-learning transitions, or whether technical reliability thresholds (e.g., >95% system uptime) correlate with sustained integration.

Comparative cross-country studies would be valuable to explore how the Progressive Digital Adaptation model applies across different technological maturity levels and cultural contexts. Longitudinal research could assess sustainability of adaptation over time and the influence of evolving technologies on professional identity and clinical practice. Additionally, studies should examine factors differentiating sustained resistance from progressive adaptation, exploring whether alternative support mechanisms could facilitate adaptation for nurses currently excluded from the progressive trajectory.

Future research could be strengthened by integrating the Medical Research Council (MRC) Guidance for process evaluations of complex interventions, ⁵⁹ which shares conceptual similarities with Progressive Digital Adaptation including attention to contextual factors, implementation mechanisms, and mechanisms of impact. Applying the MRC framework could support systematic validation of the theory through: investigation of dose-response relationships (e.g., sufficient protected time); identification of critical contextual moderators; and specification of implementation fidelity criteria. Additionally, intervention studies testing organizational strategies to facilitate stage transitions—and comparative research across different digital technologies (electronic health records, telehealth, clinical decision support systems)—would enhance both theoretical robustness and practical utility of the theory.