Next Article in Journal
Livestream Scheme Selection in the E-Commerce Supply Chain: Under Agency and Resale Sales Modes
Previous Article in Journal
A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University Campus
Previous Article in Special Issue
Digital Economy, Government Innovation Preferences, and Regional Innovation Capacity: Analysis Using PVAR Model
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Systems
Volume 13
Issue 5
10.3390/systems13050396
systems-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Unlocking Digital Potential—The Impact of Innovation and Self-Determined Learning

by
Sandra Starke
1,* and
Iveta Ludviga
2
1
Banku Augstskola School of Business and Finance, University of Latvia, LV-1013 Riga, Latvia
2
Faculty of Business and Economics, Riseba University, LV-1048 Riga, Latvia
*
Author to whom correspondence should be addressed.
Systems 2025, 13(5), 396; https://doi.org/10.3390/systems13050396
Submission received: 15 April 2025 / Revised: 9 May 2025 / Accepted: 19 May 2025 / Published: 21 May 2025
(This article belongs to the Special Issue Organizational Digital Innovation and Transformation in Enterprise and Government Strategies)
Browse Figures
Versions Notes

Abstract

:
In an era of rapid digital transformation, organisations must cultivate dynamic capabilities that promote innovation and continuous learning. This study examines how self-determined motivation and innovation adoption are crucial enablers in developing the digital competencies essential for employees to navigate digital transformation. Grounded in Self-Determination Theory and the Diffusion of Innovation framework, our research underscores the systemic role of individual agency, technological advancements, and organisational structures in facilitating workforce adaptation. Employing a quantitative approach with 152 survey participants, our findings reveal that self-determined motivation alone is inadequate, while adopting innovation significantly influences digital competence. We demonstrate that human-centred factors must align with systemic digital transformation efforts. Moreover, we highlight the necessity of integrating employee capabilities into broader enterprise and government digital innovation strategies. The implications of this study are both theoretical and practical. We stress the need for organisations to design change processes that support digital knowledge acquisition and adaptability in evolving workplaces. Our research offers a systemic perspective on digital transformation, reinforcing that successful organisational innovation requires structured learning environments that empower employees. By fostering an ecosystem where digital competencies are nurtured, organisations can enhance agility, resilience, and sustained competitiveness in the digital age.

1. Introduction

Digital transformation is profoundly reshaping workplaces, job roles, and tasks [1]. As open systems, organisations are composed of interdependent subsystems—individuals, technologies, structures, and cultures—that must align dynamically to cope with external technological pressures and internal learning demands [2,3]. This perspective recognises that digital transformation is not merely a technical upgrade but a socio-technical process involving feedback loops between innovation, individual competence, and organisational change. Governments and industries anticipate adopting new technologies and digital offerings to benefit both companies and citizens; digital technologies are opening new potentials for organisations to innovate and create new opportunities [4]. The systemic process leverages digital technologies to fundamentally alter services and operations within organisations and societies [5]. Within this broader system, the digital potential as a capacity of individuals and organisations to harness digital technologies effectively, develop and apply relevant competencies, and adapt to continuous innovation and change becomes increasingly relevant [6,7]. Therefore, the willingness to learn and adapt [8] warrants further investigation. Previous research has explored the use of new technologies and organisational change, emphasising the significance of external conditions on one hand and the importance of personal attributes, such as self-efficacy and the capacity to organise and reflect on one’s work, on the other [9,10]. Based on the premise that the desired benefits of the transformational process can only be realised when individuals are willing to adopt it [11], the focus on individual factors is accentuated.
The ongoing disruptive changes necessitate fundamentally transforming individual capabilities and adaptation readiness. New competency profiles must be developed and tailored to meet evolving work requirements [12]. Higher education is responsible for preparing students and cultivating the necessary competencies. Hartmann et al. [13] aimed to identify and categorise what is termed “future skills”, defined as competencies needed to confront challenges in an uncertain and changing environment [9]. Thus, a deeper exploration of new competencies for “learning 4.0” in workplaces [6,14] is essential. The transformational process impacts the current workforce due to job specifications and task changes. It is inadequate to acquire knowledge of specific technologies or work processes alone. The founding community for German research has published a discussion paper in collaboration with McKinsey, highlighting essential competencies for a changing world, which are divided into four categories: technological, digital, classic, and transformative competencies [15]. The classic competencies encompass creativity and problem-solving abilities, which are enhanced through self-reflection and autonomous problem-solving. Innovative teaching concepts encourage independently acquired knowledge, such as design thinking or learning videos [13].
Applying this to a new work context emphasises the need for the active participation of employees in digital workplaces. For this purpose, this study investigates two fundamental antecedents of individual competence and learning in digital workplaces: (1) self-determined motivation and (2) innovation adoption. To address the existing knowledge gap about the prerequisites of individual digital competencies, we integrate research streams on innovation adoption and motivation alongside studies in education and learning, focusing on developing future learning concepts for the workforce and adaptability for transformation [9].
As an empirical implication, we created a conceptual model and tested the presumed effect of self-determined motivation and innovation adoption on individuals’ digital competencies and learning. Understanding the mechanisms that enable employees to succeed in new work environments allows organisations to act effectively and accelerate digital transformation. Given that digital competence and learning are critical factors for individuals, organisations, and nations, deeper insights are necessary to establish appropriate framing conditions or provide targeted support.

2. Literature Review and Hypothesis Development

As a socio-technical process, digital transformation impacts not only technologies but also individual skills, organisational culture, and governance structures [16]. Recent studies have emphasised that successful digital transformation is contingent upon developing digital competencies across all levels of an organisation [17]. Organisations are increasingly adopting advanced technologies such as Artificial Intelligence (AI), big data analytics, and the Internet of Things (IoT) to enhance innovation and lead to sustainable and efficient operation [16]. In this context, the need to foster the development of individual competencies is highlighted in recent studies [15]. Goldin and Katz highlighted the relationship between economic growth, technology, and education concerning the U.S. labour market [18]. They argue that human capital is a significant driver of economic development, emphasising the need for investments in knowledge of new technologies. Contemporary workplaces are characterised by uncertainty and disruptive changes due to digital transformation. As there is no standard definition of digital transformation, this paper adopts the summary proposed by Vial [5], which defines digital transformation as a process aimed at enhancing services or processes by instigating significant changes through the implementation and use of technology. Developing digital competence, knowledge management, and deployment presents a new challenge. Learning and knowledge are essential capabilities that foster continuous innovation and facilitate digital transformation [7]. Previous research points to sustained learning effects through the self-determined acquisition of relevant skills and knowledge [19,20]. This research emphasises personal responsibility and self-determination as essential for integrating digital technologies into everyday routine processes and developing sustained knowledge and learning. This paper investigates the relationship between self-determined motivation, innovation adoption, digital competence, and learning to better understand the mechanisms and leverage involved.

2.1. Dependent Variable: Learning

The current literature displays learning as a fundamental factor in performing within digital environments through knowledge and competence. However, there is no unified concept of learning in digital transformation. Crossan et al. [21] highlighted the complexity and varying viewpoints on individual, group, and organisational levels, as well as cognitive and behavioural factors. Learning enables individuals and organisations to perform and innovate under rapidly changing conditions [22]. Fiol et al. defined organisational learning as improving actions through better knowledge and understanding [22]. Argyris and Schön underscored individuals’ needs and their effect on well-being and motivation to enhance learning [23]. This adaptability and continuous learning loops [24] need to be established to perform in changing environments under uncertain conditions [25]. Previous research suggests the positive influence of organisational learning as a contextual factor on innovation adaptation [26] and digital competencies [27]. In contrast, learning at the individual level is a process involving modifications in response to stimuli due to environmental interactions [28]. This paper measured and tested learning on an individual level to better understand the mechanisms impacting the targeted transformation process [29].

2.2. Independent Variables

2.2.1. Self-Determined Motivation

The Self-Determination Theory (SDT) has been studied across various fields to explain motivation, particularly in the workplace [30]. It posits that employees’ performance and well-being are influenced by their motivation for job-related activities [31]. Deci and Ryan argue that all employees have fundamental needs for autonomy, competence, and relatedness. The level of satisfaction and type of motivation drive this effect. Autonomy refers to the perceived control over one’s behaviour, allowing for voluntary choices in action [32]. Digital transformation is a disruptive phenomenon. The workforce increasingly faces digital technology and processes in both professional and personal contexts. Some individuals experience a loss of control as a result. When applying this theory to new digital workplaces, we assume that employees may struggle to meet the new digital requirements [6], which could explain the lagging transformational process. Individuals desire to maintain their identity by making an ambitious contribution to their work (intrinsic motivation). However, they may be unable to do so due to a lack of competence, potentially leading to frustration, resignation, or even burnout [33]. SDT emphasises autonomy as a basic human need that, when supported, results in more effective and sustained behavioural regulation [32]. Even if employees initially lack intrinsic motivation to engage in digital transformation, it is possible to design external factors, such as a supportive work environment or an organisational learning culture that promotes freedom of choice, to foster autonomous motivation [34]. We consider autonomy to have a significant positive impact on intrinsic motivation for developing digital competencies. Competence is the sense of being practical and contributing effectively with one’s capabilities to achieve desired outcomes [32]. Interactions with the environment lead to the development of specific competencies through adaptation. Consequently, the need to acquire competencies motivates individuals to engage in learning [31]. For successful digital transformation, new competencies are required. According to the age and duration of the job, these competencies were probably not necessary, or not needed to the same extent, during employees’ studies or job training. We argue that fulfilling their basic need to feel competent enhances employees’ intrinsic motivation to learn and acquire new digital competencies. Relatedness involves feeling respected and knowing that others are close, caring, and understanding [32]. Social factors significantly influence employees’ motivation to contribute to their work and behaviour to benefit the environment [34], creating a motivating and supportive atmosphere for digital transformation. Employees not intrinsically motivated to engage autonomously in digital transformation may internalise extrinsic motivation through collective interests. SDT was developed to understand intrinsic motivation and why individuals engage in activities out of genuine interest [35]. Applying this theory to digitally transformed workplaces illustrates that motivation is a fundamental mechanism enabling employees to cope with changing situations. Based on this, we posit that employees are more motivated to acquire digital competencies and learn to apply digital technologies when their basic needs are satisfied through empowerment, strengthening, and connection [30].
Hypothesis 1. (H1):
An individual’s self-determined motivation positively affects their digital competencies in the context of digital transformation.
Hypothesis 2. (H2):
An individual’s self-determined motivation positively affects learning in the context of digital transformation.

2.2.2. Innovation Adoption

The Diffusion of Innovation theory (DOI) is a social science theory that explains how innovation spreads over time as a process [36]. Individuals and their perceptions of technology and processes play a crucial role. According to the DOI, the stages of adoption depend on five perceived attributes: relative advantage, compatibility, complexity, trialability, and observability. Relative advantage refers to the degree to which an innovation is viewed as better than previous processes, products, or technology [28]. This perception encourages the early adoption of innovations. Compatibility indicates how much an innovation aligns with an individual’s values, needs, and past experiences [10]. Innovations that conflict with existing norms and practices tend to be adopted more slowly. Complexity measures how easy or difficult an innovation is perceived in terms of understanding and usage [36]. Thus, this attribute is synonymous with “ease of use”. Innovations that are simple to use will likely be adopted sooner. Trialability assesses the degree to which an innovation can be tested and explored over a limited period [37]. Innovations will be adopted more quickly if individuals can familiarise themselves with them and experiment in a safe environment. Observability refers to how visible an innovation is, allowing individuals to see its results and reducing uncertainty [38].
An innovation will be adopted more quickly if it can be seen and discussed with others who have already adopted it. DOI was developed to describe the stages of how an innovation spreads over time, diffusing as a process. These aspects are antecedents to categorise users and their tendency to adopt innovation. The categorised groups (innovators, early adopters, early majority, late majority, and laggards) are essential for organisations to differentiate and develop strategies for how these personalities can be targeted [38]. As far as this study wants to shed light on the effect of individual self-determined motivation and innovation adoption to accelerate digital transformation, it focuses on the individual perception of the five mentioned attributes. Individual innovation adoption is the personal attitude to accept an innovation (an idea, product, process, or service perceived as new) [39]. We assume that the perceived attributes of innovation are relevant antecedents for the willingness of individuals to acquire digital competencies to work with the latest technologies and digital processes.
Hypothesis 3. (H3):
Individual innovation adoption positively influences digital competencies in the context of digital transformation.
Hypothesis 4. (H4):
Individual innovation adoption positively impacts learning in the context of digital transformation.

2.3. Mediating Variable: Digital Competence

Knowledge and competence affect employees’ confidence in digital technology [15,40]. Digital competencies enable employees to participate actively in a digitalised environment [33]. Organisations must identify their employees’ existing and required competencies and develop solutions to transform their human capital, adapting to changing technologies [41]. Recommendations and initiatives from the European Union emphasise the significance and scope of key competencies for all citizens, applicable in the private sphere, the labour market, and the economy. In their council recommendations, the EU suggests that member states promote these key competencies, defined as “a combination of knowledge, skills, and attitudes” necessary for lifelong learning [42]. The European Commission developed a digital competence framework for citizens (DigComp), encompassing competence areas such as communication, networking, digital literacy, security, and content development [43], outlining knowledge, skills, and attitudes for each competence. The terms “competencies” and “skills” are often used interchangeably, yet they differ in specificity. The European Commission defines competencies in its “Recommendations for Lifelong Learning” as knowledge, skills, and attitudes necessary for various aspects of life, such as personal fulfilment, active participation, and employment [42]. Stofkova et al. [44] describe digital skills as formal learning that contributes to the knowledge needed to handle digital assets in the economy. In this context, skills represent a more detailed application of specific use cases within the broader competence framework. Research in HR development and education aims to identify the key competencies required for the labour market, especially in personnel development and education [6,33,45,46]. This research paper adopts the DigComp framework to focus on digital competencies and define digital competencies as the combination of knowledge, skill and attitudes required to use digital technologies effectively and safely in work.
Previous research indicates that interaction with the environment and adaptation lead to learning over time [31]. Suppose individuals possess the behavioural intention to adopt innovation and acquire self-directed knowledge. In that case, digital competence—such as the ability to operate digital learning platforms, search for suitable learning content, or use digital tools to design their learning paths [42]— is expected to impact learning effectiveness. Therefore, we hypothesise a mediating effect of digital competence.
Hypothesis 5. (H5):
Digital competencies mediate the effect of self-determined motivation on learning.
Hypothesis 6. (H6):
Digital competencies mediate the effect of innovation adoption on learning.
The constructed model is presented in Figure 1, based on the literature review and the proposed relationships.

3. Materials and Methods

The research design involves a quantitative data analysis. The sample was gathered using a combination of purposive and convenience sampling methods, including professional networks via LinkedIn and the Prolific survey platform. The constructs were measured based on existing research and theories, and a conceptual model was developed. A questionnaire was designed to test this model according to the items and measurements utilised in previous studies [47,48,49]. The hypothesis and measures were tested using a questionnaire on LinkedIn and Prolific platforms. Different scales were employed for the psychological separation to avoid common method bias: a 5-point Likert scale for digital competencies and a 7-point Likert scale for other items [50]. The statistical analysis was conducted in the Smart PLS software with standardised data.

3.1. Variables

Items to measure the dependent variable of learning were adopted by Arranz et al. [48] and Lee et al. [51], assessing knowledge resources for behavioural and cognitive learning factors. These two subdimensions, each with three items, are adequate for measuring the desired outcome, as learning results from individual behaviours combined with their ability to explore, detect and solve problems, change established routines, and perform in new digital environments [52]. The independent variables are based on established scales. Self-determination motivation items were developed based on the Basic Needs Scale [31], which has been adopted in various studies [53,54]. We measured three items in each of the three dimensions of autonomy, competence, and relatedness. To measure innovation adoption, the items were developed based on Rogers’ Diffusion of Innovation theory [36], which has been widely applied in this research field to test innovation adoption [55,56]. Three items measure each of the five perceived attributes of relative advantage, complexity, compatibility, trialability, and observability, which are recognised in research as technological antecedents for innovation adoption at the individual level [57]. The proposed mediating variable, digital competence, comprises three items each in the five dimensions [17] based on the competence areas developed by the DigComp framework (Figure 2) as an appropriate measurement in the human-centred approach [46]. This framework was chosen due to its multidimensional structure and adaptability across industries. Its integration ensures comparability since this framework is established in learning and education research [46,58].
For each of these five areas, the self-assessed current level and willingness to increase this level are measured. This is adapted from the O*net program [59], which aims to investigate and provide information about the competencies relevant to the labour market and its impact on the U.S. economy. This database and these measures are established and widely applied to analyse the needs of organisations and employees [60].
Sample questions for all measures are displayed in Table 1.

3.2. Survey Design

To prevent common method bias, the measurements of dependent and independent variables were methodologically separated using various scales and intentionally divided by placing socio-demographic questions in between [50]. The questionnaire consisted of 52 questions in English and was translated into German, based on existing scales used in previous research. The survey was distributed in both languages via “LinkedIn” to reach an appropriate sample of employees with basic digital capabilities, and extended with participants completing the questionnaire in English were collected using the online platform Survey Monkey.
Participants were asked to share the survey link on social networks or directly, using purposive sampling, a commonly used approach in research [62]. To expand the range of responses, the survey was also conducted via the Prolific platform. Prolific is a marketplace for online survey research, which is also applied in other research on digital transformation in healthcare [63]. The tools SmartPLS 4 and Jamovi 2.4.7 were used to analyse the gathered data.

4. Results

The survey received responses from 152 participants, 126 in English and 26 in German. Furthermore, 52% of the respondents are female, 47% are male, and 1% were divers. Most participants were employees (25%) or seniors/experts (27%), while 23% held management positions. In addition, 40% were aged 41 and above, and the majority of the participants (47%) were between the ages of 25 and 40. The software Jamovi and Smart-PLS were used to analyse the data. Harman’s single-factor test (HSF) was employed as a preliminary measure. This analysis assumes that the presence of a single dominant factor points to common method bias. All items are summarised into one single factor, and the percentage of variance in the total of this single factor is compared against the threshold of 0.5 [64]. The variance was tested using the principal component analysis in Jamovi. The resulting 0.25 was below the threshold of 0.5, indicating that the item characteristics differed [65]. Based on this result, common method bias was not prevalent in this study. Next, the data distribution was checked using the Shapiro-Wilk test. Since p was lower than 0.05 for all items, the Shapiro-Wilk test was significant, and the data were not normally distributed. Because of this, Smart PLS 4 was used to test and analyse the results, applying PLS-SEM since normal distribution was not a precondition. PLS-SEM is an appropriate tool for multivariate analysis, which is widely applied in business research [66].

4.1. Outer Model Results

Confirmatory factor analysis (CFA) was conducted in Jamovi to assess the model fit. An RMSEA of less than 0.05 indicates a good fit, 0.08 indicates a reasonable fit, and over 0.1 indicates a poor fit. The chi-square statistic represents the difference between the expected and observed data; a lower chi-square value signifies a better model fit. The results are presented in Table 2, where the chi-square test suggests no exact fit, while the RMSEA indicates a reasonable model fit.
However, these model fit indicators may be overly sensitive for constructs with many items [67]. Therefore, the model was constructed and further tested using Smart PLS software, evaluating the outer model for reliability. Items with weaker loadings that do not meet the recommended threshold for indicator reliability should only be deleted to enhance composite reliability or internal consistency [66]. Based on the results of the p-value test for statistical significance, four indicators with factor loadings below the threshold of 0.708 were removed (AUT2, COMP1 from the construct self-determined motivation; CPX2, CPX3 from the construct innovation adoption). After removing these items, Cronbach’s alpha for all variables is greater than 0.7, and the items of each construct are related to each other. For convergent validity, the average variance extracted (AVE) should exceed 50%, which is also achieved after this elimination, thus ensuring internal consistency and item reliability [68]. The results are displayed in Table 3.
Next, discriminant validity was assessed using the heterotrait-monotrait (HTMT) ratio of correlation to detect validity issues [69]. Each indicator’s factor loading is greater than that of all other constructs’ loadings (Table 4), except for self-determined motivation/innovation adoption, which is acceptable since both constructs are conceptually similar [70]. This suggests that the items assess distinct constructs [63].
The variance inflation factor (VIF) was assessed for all items concerning collinearity statistics. Since the VIF is lower than 3 for all items, multicollinearity is absent [71].

4.2. Inner Model Results

R2 measures the degree to which the independent variables predict the dependent variable. Values above 0.75 are classified as substantial, 0.5 as moderate, and 0.25 as weak [66]. The inner model results are displayed in Table 5.
Digital competence can be predicted to be 53%, based on both independent variables: self-determined motivation and innovation adoption. The constructed model has good predictive power for the outcome variable learning (58%). The path coefficient of 0.597 supports the hypothesised relationship between innovation adoption and digital competencies (H3) and is statistically significant, with a p-value of 0.000. The relationship between self-determined motivation and digital competencies (H1) is weaker (0.158) and not statistically significant (p-value 0.090).
The mediation effect was calculated using Baron and Kenny’s steps [72] for mediation. In contrast to self-determined motivation, which is dismissed, the adoption of innovation significantly impacts the dependent variable of learning (Table 6).
The path coefficient of the independent variable to the hypothesised mediating variable, digital competence, demonstrates a significant connection (Table 7).
Adding the mediating variable significantly decreases the effect between the independent and dependent variables, as presented in Table 8 [73].
Based on these steps, the proportion of variance in a dependent variable explained by a mediation relationship (Variance Accounted For = VAF) is calculated (Table 9):
VAF = indirect effect = a × b
total effect a × b + c’
The following applies:
  • a = path coefficient from IV to mediator
  • b = path coefficient from mediator to DV
  • c’ = direct effect from IV to DV in the presence of the mediator
According to Hair et al. [74], a partial mediation of digital competence is observed in the relationship between innovation adoption and learning, confirming hypothesis H6.
The hypothesised positive effect on learning, moderated by digital competencies, is only supported for innovation adoption. Self-determined learning shows no impact on either digital competence or learning. Therefore, the supposed mediating effect of digital competence between self-determined motivation and learning (H5) is rejected. Table 10 provides the conclusion of the investigated hypothesis.

5. Discussion

Digital transformation significantly impacts the labour market [75]. Maazmi et al. pointed out that an educated and adaptable workforce is a critical success factor for effective digital transformation [76]. In recent papers, knowledge, skills, and competencies have often been used synonymously [8,17,77,78], lacking clear definitions or distinctions. Competence and learning are highlighted as essential prerequisites for digital transformation, yet concrete concepts and measurements are rare [79,80]. Therefore, this study helps close this gap by operationalising the developed concepts.
The provided measures are internally consistent and reliable, as demonstrated by the analysis of the outer model. The inner model analysis highlights the assumed relationship between innovation adoption, digital competencies, and learning. The results align with other studies [6,17,81]. De Vries et al. created a heuristic innovation framework for the public sector. Similar to the measured items of the innovation construct in this study, innovation characteristics were supplemented by individual factors [26]. Individual participation must be fostered to establish learning and the effective use of new technological innovations [81]. Talwar et al. [82] emphasise the importance of engaging all stakeholders, as digitalisation strategies may fail due to resistance to innovation adoption. Companies must reconfigure digital competencies [83] and encourage employees to innovate [84].
We examined self-determined motivation and innovation adoption as fundamental theories and discussed them, confirming the hypothesised impact of innovation adoption on digital competencies. The independent variable innovation adoption shows a significant connection to digital competence (path coefficient 0.600/p-value 0.000) and learning (path coefficient 0.601/p-value 0.000); however, the anticipated positive effect of self-determined motivation could not be confirmed (0.159/p-value 0.086 for digital competence, 0.140/p-value 0.111 for learning). This absence of a statistically significant effect on digital competence and learning outcomes challenges the assumption derived from Self-Determination Theory (SDT) that motivation drives workplace engagement and competence acquisition [31]. Despite this theoretical grounding, our findings reveal that self-determined motivation alone cannot facilitate digital upskilling or promote learning in digital transformation contexts. This result contributes to the ongoing discussion since recent literature suggests that autonomy and intrinsic motivation must be embedded within a broader system of enablers (e.g., organisational support and structured workplace training offerings) [35,85,86,87]. In fast-paced digital environments, where employees face continuous disruptions and novel technologies, motivation can be undermined by a lack of perceived support; where individuals are provided with opportunities to participate and learn safely, Gagné postulated that motivation might not translate into performance if individuals perceive their abilities or external resources as insufficient [35].

5.1. Theoretical Implications

This study contributes to theory in three ways. First, it enhances knowledge by testing the Self-Determination Theory (SDT) in digital work environments. Contrary to theoretical assumptions, the findings indicate that self-determined motivation is insufficient to foster digital competence development and learning. External factors, such as organisational requirements and environmental conditions, appear more influential. Second, the study extends the Diffusion of Innovation Theory by introducing digital competencies as a key mediating variable in innovation adoption, emphasising that competence acquisition is critical for successful digital transformation. Third, it contributes to learning theory by proposing that organisations must understand the paths to establish sustained learning as a significant aspect of organisational adaptability to navigate digital transformation effectively.

5.2. Practical and Managerial Implications

Our results indicate that individuals will likely develop the necessary competencies and establish learning if they adopt innovation. Beyond innovation, other external factors appear to be more influential in developing digital competence than individual motivation. Employees’ ability to integrate new digital competencies is not solely reliant on their intrinsic motivation, indicating the influence of external system-based enablers, such as organisational support, infrastructure, and training initiatives.
We emphasise the need for a change management system that embeds digital learning frameworks and facilitates structured workforce adaptation. Organisations must support innovation adoption through targeted initiatives such as testing options to boost trialability and observability, which align with on-the-job training and upskilling programmes. Tailored digital competence development should integrate employees’ previous experiences and be embedded in continuous learning systems. Managers should treat digital competence as a dynamic capacity that evolves through exposure to innovation and organisational learning environments.
From a systems perspective, organisations function as open, adaptive entities that continuously evolve in response to technological advancements and workforce development needs. Our findings demonstrate that individual adoption of innovation is crucial for acquiring digital competencies, reinforcing the necessity of early engagement with emerging technologies.
Fostering a culture of experimentation and curiosity can unlock both individual and organisational digital potential. Corporate initiatives aligned with frameworks such as the DigComp can ensure coherence and adaptability in digitally transformed environments.

5.3. Limitations and Future Research

While the study provides valuable insights into the effects of self-determined motivation and innovation adoption on individual competence and learning, several limitations must be acknowledged. The sample size of 152 respondents restricts the generalisability of the findings. Although the predictive power, with a resulting R2 of 0.5, is considered moderate, it may limit the generalisability of the findings. Digital competence and learning are multifaceted constructs likely influenced by additional contextual and organisational variables not captured in this model.
Future studies should broaden the sample in terms of sector, region, and culture and conduct multi-group analyses based on firm characteristics or test alternative models. We also encourage longitudinal designs to investigate how digital competence and learning evolve. The results might be biased due to the author’s social network, which mainly comprises individuals working in educational contexts or healthcare. This is acceptable since the aim was to test the developed measures and construct, which was placed on social media for convenient access.

6. Conclusions

Digital transformation is a systemic process where organisations must coordinate employees, technologies, structures, and knowledge to remain adaptive. Our findings affirm that innovation adoption plays a central role in building digital competence and learning, serving as an enabler to activate learning pathways. Contrary to theoretical assumptions derived from Self-Determination Theory, this study reveals that intrinsic motivation alone is insufficient to drive digital competence development or learning. This finding suggests that unless supported by organisational resources and innovation opportunities, individual intent may not translate into learning outcomes.
Based on our results, we reflect on the current transitional state of digital work environments, where the uncertainty of new technologies challenges motivation as an antecedent for learning in organisational contexts. Learning emerges when individual-level attitudes function within broader, enabling structures. The results underscore the need for organisations to invest in digital enablement and expose the workforce to innovation. A change that embeds structured learning opportunities and support mechanisms, urging practitioners to reconsider learning strategies and shift to a holistic model that integrates individual, organisational, and technological factors, ensures employability at the national workforce level. It is indispensable to transform current human capital towards new knowledge and mindsets through sustained learning.
The results of this study serve as a starting point for further research. There is a need for more nuanced investigation into the interplay between intrinsic and extrinsic drivers in competence development.
By enhancing theoretical understanding and providing practical insights, this research contributes to developing digital learning strategies in evolving workplace environments. Future research should expand on these findings using diverse samples, longitudinal data, and alternative model structures to explore the mechanisms between innovation, motivation, and learning in digital transformation.

Author Contributions

Conceptualisation, methodology, data collection, and analysis, writing—original draft preparation: S.S.; writing—review and editing, supervision: I.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Internal and External consolidation of the University of Latvia, No. 5.2.1.1.i.0/2/24/I/CFLA/007, grant number 71-20/386.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

References

  1. Howcroft, D.; Taylor, P. ‘Plus ca change, plus la meme chose?’-researching and theorising the ‘new’ new technologies. New Technol. Work Employ. 2014, 29, 1–8. [Google Scholar] [CrossRef]
  2. Lang, K.; Hasler, D.; Hackl, T.; Ehmig-Klassen, K. A systems theory-based conceptual framework for holistic digital transformation. In Proceedings of the ISPIM Innovation Conference—Innovating Our Common Future, Berlin, Germany, 20–23 June 2021; pp. 1–24. [Google Scholar]
  3. Valentinov, V.; Van Assche, K.; Hermans, F. Toward a digital transformation of the theory of the firm: Emergence as framework for organizational sustainability. Can. J. Adm. Sci./Rev. Can. Des Sci. De l’Administration 2023, 40, 270–282. [Google Scholar] [CrossRef]
  4. Rippa, P.; Secundo, G. Digital academic entrepreneurship: The potential of digital technologies on academic entrepreneurship. Technol. Forecast. Soc. Change 2019, 146, 900–911. [Google Scholar] [CrossRef]
  5. Vial, G. Understanding digital transformation: A review and a research agenda. J. Strateg. Inf. Syst. 2019, 28, 118–144. [Google Scholar] [CrossRef]
  6. van Laar, E.; van Deursen, A.J.A.M.; van Dijk, J.A.G.M.; de Haan, J. Determinants of 21st-Century Skills and 21st-Century Digital Skills for Workers: A Systematic Literature Review. SAGE Open 2020, 10, 2158244019900176. [Google Scholar] [CrossRef]
  7. Ellström, D.; Holtström, J.; Berg, E.; Josefsson, C. Dynamic capabilities for digital transformation. J. Strategy Manag. 2022, 15, 272–286. [Google Scholar] [CrossRef]
  8. Ohlert, C.; Giering, O.; Kirchner, S. Who is leading the digital transformation? Understanding the adoption of digital technologies in Germany. New Technol. Work Employ. 2022, 37, 445–468. [Google Scholar] [CrossRef]
  9. Ehlers, U.-D. Future Skills; Springer Fachmedien: Wiesbaden, Germany, 2020. [Google Scholar]
  10. Venkatesh, V.; Morris, G.M.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425. [Google Scholar] [CrossRef]
  11. Dirsehan, T.; Can, C. Examination of trust and sustainability concerns in autonomous vehicle adoption. Technol. Soc. 2020, 63, 101361. [Google Scholar] [CrossRef]
  12. Oberländer, M.; Beinicke, A.; Bipp, T. Digital competencies: A review of the literature and applications in the workplace. Comput. Educ. 2020, 146, 103752. [Google Scholar] [CrossRef]
  13. Hartmann, J.; Heckner, M.; Plach, U. Future Skills bei Studierenden—Messung und Einflussfaktoren. Die Neue Hochsch. 2023, 65, 24–27. [Google Scholar]
  14. Carretero, S.; Vuorikari, R.; Punie, Y. The Digital Competence Framework for Citizens; Publications Office of the European Union: Luxembourg, 2017. [Google Scholar]
  15. Suessenbach, F.; Winde, M.; Klier, J.; Kirchherr, J. Future Skills 2021; McKinsey & Company: New York, NY, USA, 2021. [Google Scholar]
  16. Vărzaru, A.A.; Bocean, C.G. Digital transformation and innovation: The influence of digital technologies on turnover from innovation activities and types of innovation. Systems 2024, 12, 359. [Google Scholar] [CrossRef]
  17. Bikse, V.; Lusena-Ezera, I.; Rivza, P.; Rivza, B. The Development of Digital Transformation and Relevant Competencies for Employees in the Context of the Impact of the COVID-19 Pandemic in Latvia. Sustainability 2021, 13, 9233. [Google Scholar] [CrossRef]
  18. Goldin, C.; Katz, L.F. The Race Between Education and Technology; Harvard University press: Cambridge, MA, USA, 2009. [Google Scholar]
  19. Kang, H.Y.; Kim, H.R. Impact of blended learning on learning outcomes in the public healthcare education course: A review of flipped classroom with team-based learning. BMC Med. Educ. 2021, 21, 78. [Google Scholar] [CrossRef]
  20. Wintgen, M.; Krehl, A.; Heß, M. Nachhaltiges Lernen durch Lehr- und Forschungsprojekt an der Hochschule Niederrhein. Die Neue Hochschule. 2023, 6, 28–31. [Google Scholar]
  21. Crossan, M.M.; Lane, H.W.; White, R.E.; Djurfeldt, L. Organizational Learning: Dimensions for a Theory. Int. J. Organ. Anal. 1995, 3, 337–360. [Google Scholar] [CrossRef]
  22. Fiol, C.M.; Lyles, M.A. Organizational Learning. Acad. Manag. Rev. 1985, 10, 803–813. [Google Scholar] [CrossRef]
  23. Golembiewski, R.T. Organizational Leaming: A Theory of Action Perspective. J. Appl. Behav. Sci. 1979, 15, 542–548. [Google Scholar] [CrossRef]
  24. Argyris, C. Learning and Teaching: A Theory of Action Perspective. J. Manag. Educ. 1997, 21, 9–26. [Google Scholar] [CrossRef]
  25. Starbuck, W.H.; Greve, A.; Hedberg, B.L.T. Responding to Crises. J. Bus. Adm. 1978, 9, 111–137. [Google Scholar]
  26. De Vries, H.; Bekkers, V.; Tummers, L. Innovation in the Public Sector: A Systematic Review and Future Research Agenda. Public Adm. 2016, 94, 146–166. [Google Scholar] [CrossRef]
  27. Ivaldi, S.; Scaratti, G.; Fregnan, E. Dwelling within the fourth industrial revolution: Organizational learning for new competences, processes and work cultures. J. Workplace Learn. 2022, 34, 1–26. [Google Scholar] [CrossRef]
  28. Lachman, S.J. Learning is a Process: Toward an Improved Definition of Learning. J. Psychol. 1997, 131, 477–480. [Google Scholar] [CrossRef]
  29. Ivory, C.; Sherratt, F.; Casey, R.; Watson, K. Getting caught between discourse(s): Hybrid choices in technology use at work. New Technol. Work Employ. 2020, 35, 80–96. [Google Scholar] [CrossRef]
  30. Schaufeli, W. Engaging Leadership: How to Promote Work Engagement? Front. Psychol. 2021, 12, 754556. [Google Scholar] [CrossRef] [PubMed]
  31. Deci, E.L.; Ryan, R.M. Intrinsic Motivation and Self-Determination in Human Behavior; Springer: Boston, MA, USA, 1985. [Google Scholar]
  32. Ng, J.Y.Y.; Ntoumanis, N.; Thøgersen-Ntoumani, C.; Deci, E.L.; Ryan, R.M.; Duda, J.L.; Williams, G.C. Self-Determination Theory Applied to Health Contexts. Perspect. Psychol. Sci. 2012, 7, 325–340. [Google Scholar] [CrossRef]
  33. Ehlers, U.-D. Future Skills-The Key to Changing Higher Education. Project Next Skills. 2020. Available online: www.NextSkills.org (accessed on 17 December 2023).
  34. Deci, E.L.; Olafsen, A.H.; Ryan, R.M. Self-Determination Theory in Work Organizations: The State of a Science. Annu. Rev. Organ. Psychol. Organ. Behav. 2017, 4, 19–43. [Google Scholar] [CrossRef]
  35. Gagné, M. The Oxford Handbook of Work Engagement, Motivation, and Self-Determination Theory; Oxford University Press: New York, NY, USA, 2014. [Google Scholar]
  36. Rogers, E.M. Diffusion of Innovations; Free Press: New York, NY, USA, 1962. [Google Scholar]
  37. Mary, C.; Robert, M.; Institute for the Future; California HealthCare Foundation. Diffusion of Innovation in Health Care; California Healthcare Foundation: Oakland, CA, USA, 2002. [Google Scholar]
  38. Robinson, L. A Summary of Diffusion of Innovations; EdTech Hub: Victoria, UK, 2009. [Google Scholar]
  39. Varadarajan, R. Innovation, Innovation Strategy, and Strategic Innovation. In Innovation and Strategy; Emerald Publishing Limited: Leeds, UK, 2018; pp. 143–166. [Google Scholar]
  40. Ullrich, A.; Reißig, M.; Niehoff, S.; Beier, G. Employee involvement and participation in digital transformation: A combined analysis of literature and practitioners’ expertise. J. Organ. Change Manag. 2023, 36, 29–48. [Google Scholar] [CrossRef]
  41. Augier, M.; Teece, D.J. Dynamic Capabilities and the Role of Managers in Business Strategy and Economic Performance. Organ. Sci. 2009, 20, 410–421. [Google Scholar] [CrossRef]
  42. Council of the European Union. Council Recommendation of 22 May 2018 on Key Competences for Lifelong Learning; Council of the European Union: Brussels, Belgium, 2018. [Google Scholar]
  43. European Commission; Centre, J.R.; Vuorikari, R.; Kluzer, S.; Punie, Y. DigComp 2.2, The Digital Competence Framework for Citizens—With New Examples of Knowledge, Skills and Attitudes; Publications Office of the European Union: Luxembourg, 2022. [Google Scholar]
  44. Stofkova, J.; Poliakova, A.; Stofkova, K.R.; Malega, P.; Krejnus, M.; Binasova, V.; Daneshjo, N. Digital Skills as a Significant Factor of Human Resources Development. Sustainability 2022, 14, 13117. [Google Scholar] [CrossRef]
  45. Goulart, V.G.; Liboni, L.B.; Cezarino, L.O. Balancing skills in the digital transformation era: The future of jobs and the role of higher education. Ind. High. Educ. 2022, 36, 118–127. [Google Scholar] [CrossRef]
  46. Biggins, D.; Holley, D.; Evangelinos, G.; Zezulkova, M. Digital Competence and Capability Frameworks in the Context of Learning, Self-Development and HE Pedagogy. In Proceedings of the E-Learning, E-Education, and Online Training: Third International Conference, eLEOT 2016, Dublin, Ireland, 31 August–2 September 2016; Springer International Publishing: Cham, Switzerland, 2017; pp. 46–53. [Google Scholar]
  47. Dwivedi, Y.K.; Balakrishnan, J.; Das, R.; Dutot, V. Resistance to innovation: A dynamic capability model based enquiry into retailers’ resistance to blockchain adaptation. J. Bus. Res. 2023, 157, 113632. [Google Scholar] [CrossRef]
  48. Arranz, N.; Arroyabe, M.F.; Li, J.; de Arroyabe, J.C.F. An integrated model of organisational innovation and firm performance: Generation, persistence and complementarity. J Bus. Res. 2019, 105, 270–282. [Google Scholar] [CrossRef]
  49. Audretsch, D.B.; Belitski, M. Knowledge complexity and firm performance: Evidence from the European SMEs. J. Knowl. Manag. 2021, 25, 693–713. [Google Scholar] [CrossRef]
  50. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
  51. Lee, J.; Song, H.-D.; Hong, A. Exploring Factors, and Indicators for Measuring Students’ Sustainable Engagement in e-Learning. Sustainability 2019, 11, 985. [Google Scholar] [CrossRef]
  52. Dörner, O.; Rundel, S. Organizational Learning and Digital Transformation: A Theoretical Framework. In Digital Transformation of Learning Organizations; Springer International Publishing: Cham, Switzerland, 2021; pp. 61–75. [Google Scholar]
  53. Meske, C.; Junglas, I. Investigating the elicitation of employees’ support towards digital workplace transformation. Behav. Inf. Technol. 2021, 40, 1120–1136. [Google Scholar] [CrossRef]
  54. Roca, J.C.; Gagné, M. Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Comput. Human Behav. 2008, 24, 1585–1604. [Google Scholar] [CrossRef]
  55. Raman, R.; B, S.; G, V.; Vachharajani, H.; Nedungadi, P. Adoption of online proctored examinations by university students during COVID-19, Innovation diffusion study. Educ. Inf. Technol. 2021, 26, 7339–7358. [Google Scholar] [CrossRef]
  56. Call, D.R.; Herber, D.R. Applicability of the diffusion of innovation theory to accelerate model-based systems engineering adoption. Syst. Eng. 2022, 25, 574–583. [Google Scholar] [CrossRef]
  57. Othman, A.; Al Mutawaa, A.; Al Tamimi, A.; Al Mansouri, M. Assessing the Readiness of Government and Semi-Government Institutions in Qatar for Inclusive and Sustainable ICT Accessibility: Introducing the MARSAD Tool. Sustainability 2023, 15, 3853. [Google Scholar] [CrossRef]
  58. Mattar, J.; Santos, C.C.; Cuque, L.M. Analysis and comparison of international digital competence frameworks for education. Educ. Sci. 2022, 12, 932. [Google Scholar] [CrossRef]
  59. National Center for O*NET Development by the U.S. Department of Labor E and TA. O*Net Resource Center. 2023. Available online: https://www.onetcenter.org/overview.html (accessed on 17 December 2023).
  60. Roemer, L.; Lewis, P.; Rounds, J. The German O*NET Interest Profiler Short Form. Psychol. Test Adapt. Dev. 2023, 4, 156–167. [Google Scholar] [CrossRef]
  61. Nguyen, H.T.T.; Pham, H.S.T.; Freeman, S. Dynamic capabilities in tourism businesses: Antecedents and outcomes. Rev. Manag. Sci. 2023, 17, 1645–1680. [Google Scholar] [CrossRef]
  62. Mergel, I.; Edelmann, N.; Haug, N. Defining digital transformation: Results from expert interviews. Gov. Inf. Q. 2019, 36, 101385. [Google Scholar] [CrossRef]
  63. Iyanna, S.; Kaur, P.; Ractham, P.; Talwar, S.; Najmul Islam, A.K.M. Digital transformation of healthcare sector. What is impeding adoption and continued usage of technology-driven innovations by end-users? J. Bus. Res. 2022, 153, 150–161. [Google Scholar] [CrossRef]
  64. Podsakoff, P.M.; Podsakoff, N.P.; Williams, L.J.; Huang, C.; Yang, J. Common Method Bias: It’s Bad, It’s Complex, It’s Widespread, and It’s Not Easy to Fix. Annu. Rev. Organ. Psychol. Organ. Behav. 2024, 11, 17–61. [Google Scholar] [CrossRef]
  65. Navarro, D.; Foxcroft, D. Learning Statistics with Jamovi: A Tutorial for Psychology Students and Other Beginners; Open Book Publishers: Cambridge, UK, 2019. [Google Scholar]
  66. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a Silver Bullet. J. Mark. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
  67. Montoya, A.K.; Edwards, M.C. The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation. Educ. Psychol. Meas. 2021, 81, 413–440. [Google Scholar] [CrossRef]
  68. Alojairi, A.; Akhtar, N.; Ali, H.M.; Basiouni, A.F. Assessing Canadian business IT capabilities for online selling adoption: A Net-Enabled Business Innovation Cycle (NEBIC) perspective. Sustainability 2019, 11, 3662. [Google Scholar] [CrossRef]
  69. Ab Hamid, M.R.; Sami, W.; Mohmad Sidek, M.H. Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. J. Phys. Conf. Ser. 2017, 890, 012163. [Google Scholar] [CrossRef]
  70. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M.; Danks, N.P.; Ray, S. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using, R.; Springer International Publishing: Cham, Switzerland, 2021. [Google Scholar]
  71. Akinwande, M.O.; Dikko, H.G.; Samson, A. Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. Open J. Stat. 2015, 5, 754–767. [Google Scholar] [CrossRef]
  72. Baron, R.M.; Kenny, D.A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 1986, 51, 1173. [Google Scholar] [CrossRef]
  73. Sidhu, A.; Bhalla, P.; Zafar, S. Mediating effect and review of its statistical measures. Empir. Econ. Lett. 2021, 20, 29–40. [Google Scholar]
  74. Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage: Thousand Oaks, CA, USA, 2014. [Google Scholar]
  75. Dengler, K.; Matthes, B. The impacts of digital transformation on the labour market: Substitution potentials of occupations in Germany. Technol. Forecast. Soc. Change 2018, 137, 304–316. [Google Scholar] [CrossRef]
  76. Al Maazmi, A.; Piya, S.; Araci, Z.C. Exploring the Critical Success Factors Influencing the Outcome of Digital Transformation Initiatives in Government Organizations. Systems 2024, 12, 524. [Google Scholar] [CrossRef]
  77. Troise, C.; Tani, M.; Matricano, D.; Ferrara, E. Guest editorial: Digital transformation, strategic management and entrepreneurial process: Dynamics, challenges and opportunities. J. Strategy Manag. 2022, 15, 329–334. [Google Scholar] [CrossRef]
  78. Tjin, A.; Tsoi, S.L.N.M.; de Boer, A.; Croiset, G.; Koster, A.S.; van der Burgt, S.; Kusurkar, R.A. How basic psychological needs and motivation affect vitality and lifelong learning adaptability of pharmacists: A structural equation model. Adv. Health Sci. Educ. 2018, 23, 549–566. [Google Scholar] [CrossRef]
  79. Aggestam, L.; Svensson, A. How digital applications can facilitate knowledge sharing in health care. Learn. Organ. 2025, 32, 58–74. [Google Scholar] [CrossRef]
  80. Banerjee, P.; Sharma, N. Digital transformation and talent management in industry 4.0: A systematic literature review and the future directions. Learn. Organ. 2024, ahead-of-print. [Google Scholar] [CrossRef]
  81. Wiśniewska, S.; Wiśniewski, K.; Szydło, R. The Relationship between Organizational Learning at the Individual Level and Perceived Employability: A Model-Based Approach. Sustainability 2021, 13, 7561. [Google Scholar] [CrossRef]
  82. Talwar, S.; Talwar, M.; Kaur, P.; Dhir, A. Consumers’ resistance to digital innovations: A systematic review and framework development. Australas. Mark. J. 2020, 28, 286–299. [Google Scholar] [CrossRef]
  83. Shakina, E.; Parshakov, P.; Alsufiev, A. Rethinking the corporate digital divide: The complementarity of technologies and the demand for digital skills. Technol. Forecast. Soc. Change 2021, 162, 120405. [Google Scholar] [CrossRef]
  84. Nylén, D.; Holmström, J. Digital innovation strategy: A framework for diagnosing and improving digital product and service innovation. Bus. Horiz. 2015, 58, 57–67. [Google Scholar] [CrossRef]
  85. Kreuder, A.; Frick, U.; Rakoczy, K.; Schlittmeier, S.J. Digital competence in adolescents and young adults: A critical analysis of concomitant variables, methodologies and intervention strategies. Humanit. Soc. Sci. Commun. 2024, 11, 48. [Google Scholar] [CrossRef]
  86. Alnasrallah, W.; Saleem, F. Determinants of the Digitalization of Accounting in an Emerging Market: The Roles of Organizational Support and Job Relevance. Sustainability 2022, 14, 6483. [Google Scholar] [CrossRef]
  87. Harteis, C.; Goller, M.; Caruso, C. Conceptual Change in the Face of Digitalization: Challenges for Workplaces and Workplace Learning. Front. Educ. 2020, 5, 1. [Google Scholar] [CrossRef]
Systems 13 00396 g001
Figure 1. Constructed model.
Figure 1. Constructed model.
Systems 13 00396 g001
Systems 13 00396 g002
Figure 2. Digital competence areas; own illustration based on the DigComp framework.
Figure 2. Digital competence areas; own illustration based on the DigComp framework.
Systems 13 00396 g002
Table 1. Excerpt of sample survey questions.
Table 1. Excerpt of sample survey questions.
ConstructSample QuestionSource
Self-determined motivation
9 items		I feel like I am free to decide for myself to build digital skills.
I am confident at using technology in my workplace.
I enjoy interacting with people who are open to new innovative technology.		[31,53,54]
Innovation adoption
15 items		New digital technology makes it easier to accomplish my tasks.
New digital technology is compatible with the way I do my work.
New digital technology is easy to understand.
Being able to try out digital technology was important in my decision to use it.
I will use digital technologies after seeing my colleagues using them.		[36,55,56]
Digital competence
15 items		What level of skill do you think you have in searching for information online and working with this data?
What level of skill do you think you have using digital communication tools and collaboration platforms?
What level of skill do you think you have creating and editing digital content?
What level of skill do you think you have knowing how to protect devices and data from dangers in the digital environment?
What level of skill do you think you have in recognising technical problems and finding appropriate solutions?		[46,59,60]
Learning
6 items		The company I am currently working for motivates the employees for continuous education and learning.
I manage my own learning and plan my schedule to acquire knowledge independently.		[48,52,61]
Table 2. Model fit.
Table 2. Model fit.
Test for Exact FitFit Measure
X2dfpRMSEA
1700881<0.0010.0782
Table 3. Outer model results.
Table 3. Outer model results.
Cronbach’s
Alpha		Average
Variance
Extracted (AVE)		Composite
Reliability
(rho_a)		Composite
Reliability (rho_c)
Self-determined motivation
7 items		0.8000.4580.8110.854
Innovation
adoption
13 items		0.8700.4040.8870.894
Digital comp.
15 items		0.9000.4200.9070.915
Learning
6 items		0.7880.4760.8200.842
Table 4. Matrix of heterotrait-monotrait (HTMT) ratio.
Table 4. Matrix of heterotrait-monotrait (HTMT) ratio.
Digital Comp.Innovation AdoptionLearningMotivation
Digital comp.
Innovation
adoption		0.797
Learning0.7790.819
Self-determined motivation0.7330.9320.762
Table 5. Inner model results.
Table 5. Inner model results.
ConstructDigital CompetenceLearning
Path Coeff.p-ValuePath Coeff.p-Value
R20.532 0.583
Self-determined
motivation		0.1580.0900.0880.304
Innovation adoption0.5970.0000.3530.000
Digital comp. 0.3950.000
Table 6. Step 1: Independent variable → Dependent variable.
Table 6. Step 1: Independent variable → Dependent variable.
ConstructLearning
Path Coeff.p-Value
Self-determined
motivation		0.1400.111
Innovation adoption0.6010.000
Table 7. Step 2: Independent variable → Mediator.
Table 7. Step 2: Independent variable → Mediator.
ConstructDigital Competence
Path Coeff.p-Value
Self-determined
motivation		0.1590.086
Innovation adoption0.6000.000
Table 8. Step 3: Independent variable → Dependent in the presence of the mediator.
Table 8. Step 3: Independent variable → Dependent in the presence of the mediator.
ConstructLearning
Path Coeff.p-Value
Self-determined
motivation		0.0880.304
Innovation adoption0.3530.000
Table 9. Results of the mediation analysis of digital competence.
Table 9. Results of the mediation analysis of digital competence.
Independent VariableDirect EffectIndirect EffectTotal EffectVAFMediation
Self-determined motivation0.140 *0.159 * × 0.395 ** = 0.0630.4170.151No
mediation
Innovation
adoption		0.601 **0.600 ** × 0.395 ** = 0.2370.8270.287Partial
mediation
* p > 0.05, ** p < 0.001.
Table 10. Hypothesis Results.
Table 10. Hypothesis Results.
HypothesisResultConclusion
H1: Self-determined motivation → Digital comp.Not supportedThe fulfilment of employees’ basic needs does not affect their digital competence.
H2: Self-determined motivation → LearningNot supportedThe fulfilment of employees’ basic needs does not affect the resulting learning.
H3: Innovation adoption → Digital comp.SupportedEmployees’ openness to adopting innovation positively affects their digital competence.
H4: Innovation adoption → LearningSupportedEmployees’ openness to adopting innovation positively affects the learning effectiveness.
H5: Self-determined motivation → Digital comp. → LearningNot supportedThere is no mediating effect between employees’ self-determined motivation and learning.
H6: Innovation adoption → Digital comp. → LearningSupportedDigital competence mediates the positive effect of employees’ innovation adoption on learning effectiveness.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Starke, S.; Ludviga, I. Unlocking Digital Potential—The Impact of Innovation and Self-Determined Learning. Systems 2025, 13, 396. https://doi.org/10.3390/systems13050396

AMA Style

Starke S, Ludviga I. Unlocking Digital Potential—The Impact of Innovation and Self-Determined Learning. Systems. 2025; 13(5):396. https://doi.org/10.3390/systems13050396

Chicago/Turabian Style

Starke, Sandra, and Iveta Ludviga. 2025. "Unlocking Digital Potential—The Impact of Innovation and Self-Determined Learning" Systems 13, no. 5: 396. https://doi.org/10.3390/systems13050396

APA Style

Starke, S., & Ludviga, I. (2025). Unlocking Digital Potential—The Impact of Innovation and Self-Determined Learning. Systems, 13(5), 396. https://doi.org/10.3390/systems13050396

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop