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Article

Impeding Digital Transformation by Establishing a Continuous Process of Competence Reconfiguration: Developing a New Construct and Measurements for Sustained Learning

by
Sandra Starke
1,* and
Iveta Ludviga
2
1
Banku Augstskola School of Business and Finance, Latvian University Business School, LV-1013 Riga, Latvia
2
Faculty of Business and Economics, Riseba University, LV-1048 Riga, Latvia
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(23), 10218; https://doi.org/10.3390/su162310218
Submission received: 6 September 2024 / Revised: 14 November 2024 / Accepted: 20 November 2024 / Published: 22 November 2024
(This article belongs to the Section Sustainable Management)
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Abstract

:
Organisations need dynamic capabilities in the ongoing digital transformation to reconfigure knowledge and learning. There is a need to define new concepts and explain mechanisms of relevant factors to build dynamic capabilities. Organisations acting in healthcare experience a dilemmatic situation. New digital processes and business models are promising benefits for cost-containment measures, improved patient-centric care, and digital services. However, investments are needed to benefit. The critical question is the following: How can individual actors in healthcare be motivated to engage in this transformational process to build and reconfigure relevant competences and establish new learning routines? Founded on the essence of the existing literature, we assume sustained learning to be a relevant dynamic capability to seize and sense competences and reconfigure human capital. This paper answers the call for deeper investigations into the mechanisms in new digitally transformed environments and sectors focussing less on performance and competitive advantages, like public administration or the healthcare sector. Based on previous research, validated in qualitative interviews and quantitative testing, we define the new construct of sustained learning with its subdimensions. By providing measures, we build the grounds for further quantitative research.

Graphical Abstract

1. Introduction

Digital transformation with new technologies and processes impacts almost every part of life and turns workplaces upside down. Organisations must reconfigure relevant capabilities to stay on track and fulfil mandatory obligations or customer needs. There is a need to develop new concepts to identify, build, and extend relevant competences of employees. For a better understanding of these urgent requirements, let us take the healthcare sector as an example. New digital processes and business models are promising benefits for cost-containment measures with opportunities to improve patient-centric care and offer digital services [1,2]. However, the transformational process needs to catch up to expectations. A highly varied stakeholder structure characterises the healthcare sector, with different capabilities [3]. Hospitals may embrace new technological opportunities, but the investments required in expensive technological infrastructure could present a barrier [1,4]. Patients may benefit from digital services, but a significant proportion, mostly older people, do not have the technological equipment or lack the competences to use these services [5,6]. Medical doctors or other healthcare providers can be the linchpin in supporting patients and guiding them through the digital offering [7]. However, the openness to integrate new technologies, learning, and gaining necessary competences are prerequisites for this [8,9].
These examples highlight the need for dynamic capabilities and new routines to cope with rapidly changing environments and challenges. There is a large body of research on dynamic capabilities. However, the focus is mainly on the relationship between dynamic capabilities and organisational performance. Albert-Morant et al. [10] indicate that the recent literature concerns issues like technology or managerial aspects. Only a few researchers are drawing a connection between dynamic capabilities and learning.
Nevertheless, even in these works, there are fragmented viewpoints. Research focuses on knowledge management, managerial capabilities, and organisational learning [10,11,12,13,14]. Zollo and Winter [15] presented in their work a possible solution for the ongoing discussion, pointing out that learning itself could be considered a dynamic capability to shape operational capabilities [11]. Learning is investigated as a process of renewal to create (explore) and use (exploit) knowledge [16].
Assuming learning is a dynamic capability, we aimed to investigate learning not as a process but as the desired outcome, including individual mindsets and behavioural aspects. Overall, there is a need for more quantitative research investigating the mechanisms in new digitally transformed environments and sectors with less focus on performance and competitive advantages, like public administration or the healthcare sector [17,18]. With this study, we want to shed light on new concepts of dynamic capabilities in those less competitive sectors, building the grounds for quantitative research. We argue that in the context of the contemporary dynamic digital transformation of workplaces, there is a need for a new concept at the employee level—sustained learning. For employees, it is not enough to be ready to embrace learning and acquire competences from time to time; this should be a continuous, sustained process. We claim this sustained learning as a new micro-foundation of organisational dynamic capabilities since this could ensure that digitalisation is a continuous process and organisational dynamic capabilities are sustained over time. Therefore, this paper defines sustained learning, integrates the concept of learning, including knowledge management, builds or reconfigures relevant competences in the digital transformation process, and provides measures at the individual level for further empirical testing.
Our research has a theoretical contribution, extending dynamic capabilities to mandatory digital offerings in a regulated setting with restricted competition and defining sustained learning as the new dynamic capability for digital transformation in healthcare. We advise managers and legislators on developing individual measures to accelerate the healthcare sector’s digital transformation. We also provide a methodological novelty, building the grounds for quantitative research with the newly developed construct and measures.
The rest of this paper is structured as follows. First, we build the theoretical foundation, introducing the essential elements of dynamic capabilities theory and its current shortcomings in integrating continuous learning processes, resulting in sustained learning capabilities. We developed the new construct and items to measure it in the first qualitative part of this study. As a basis for this, we investigated similar concepts of learning goal orientation, growth mindset, and organisational learning theory in regard to their suitability to explain this micro-foundation. We evaluated the definitions of these concepts to help develop that of the new construct. Next, we describe the construct definition and the initial question pool in the methodological section. Then, we provide the results of the qualitative interviews performed to sharpen the definition and items for measuring, as well as two quantitative tests: the distinctiveness of similar constructs, and the underlying dimensionality of our new construct. We discuss the potential of the construct to explain sustained learning capabilities and provide measurements. Finally, we present our conclusions and suggestions for future research.

2. Literature Review and Analysis

When seeking to gain a deeper insight into digital transformation in the healthcare sector, the need to focus on more than technology is evident, as humans are also decisive. The provision of healthcare services includes the interaction between people, technology, and databases [19]. The knowledge and competences of individuals shape capabilities, as Goldin and Katz [20] highlighted in their work, examining the relationship between growth, technology, and education in the case of the U.S. labour market. They argue that human capital is a significant driver of economic development, with the need for investments in human capital, especially in knowledge of new technologies.
A primary challenge is the uncertainty due to the disruptive changes caused by digital transformation. To succeed in this change, organisations need to develop dynamic capabilities. Dynamic capabilities are the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments [21].
There are two different schools of thought in dynamic capabilities research grounded in evolutionary elements [22]. Eisenhardt and Martin [23] argue that dynamic capabilities consist of specific processes; therefore, best practices can be identified at an industry or firm level [23]. On the contrary, Teece, Pisano, and Shuen [21] focus on the individual firm level with the role of routines, individual characteristics, and behaviours in achieving variation and adoption as outcomes in disruptive and rapidly changing environments [22]. Despite the large body of research examining dynamic capabilities, there are some critiques due to the vagueness of the fundamental concepts and the low level of empirical testing [24]. Agarwal and Selen [25] note that most studies investigating dynamic capabilities focus on the structures and origins, but quantifiable concepts are needed to understand and influence the effects. This shortcoming is particularly evident in the discussion about the role of learning, in which some see learning as a process shaped by dynamic capabilities. In contrast, others argue that learning mechanisms control the development of dynamic capabilities [11].
Developing digital competences, knowledge management, and deployment [26] is a new challenge, leading to a never-ending learning process. Continuous learning and knowledge improvement are arising as a dynamic capability that enables innovation and digital transformation [27,28]. Therefore, learning is an essential dynamic capability of individuals and firms, and it should be a continuous process that should be sustained over time. Digital competences are the first building block for this desired outcome of sustained learning in this disruptively changed environment. First, we investigated the following existing concepts and whether they can explain sustained learning as a new micro-foundation of organisational dynamic capabilities.

2.1. Learning Goal Orientation

Learning goal orientation is part of a multidimensional construct developed by Vandewalle [29] to distinguish between prove and avoid dimensions for problem-solving tasks with adults as the target population in the field of social sciences. Learning goal orientation is the desire to develop the self by acquiring new skills, mastering new situations, and improving one’s competence [29]. The other two dimensions of the overall construct of goal orientation measurement are to prove (performance) goal orientation and avoid (performance) goal orientation. This concept only captures some of the nuances of contemporary learning processes in organisations. It thus differs from sustained learning since it was developed long ago and is mid-specific for significant life domains such as academics, work, and athletics. Learning goal orientation is based on the work of Carol Dweck about problem-solving tasks and mastery patterns as a unidimensional construct using single-item instruments with young children or adolescents as the target population [29].

2.2. Growth Mindset

The second concept is the growth mindset. It postulates that intelligence is malleable and can be changed. Failures are an opportunity to learn and grow with the belief in the importance of effort [30]. The construct is unidimensional. In her later work, Dweck [31] gives an overview of the three decades of her research and offers a variety of examples and domains from sports and relationships. However, the growth mindset concept is widely applied in the domain of education within the field of psychology. The distinction between growth or fixed mindsets and sustained learning lies within the invested effort, depending on the prospect of success. Behavioural, psychological experiments according to the learning outcomes of children or students are the focus of growth mindset research. We must integrate other aspects in the contemporary era of digitally transformed workplaces. However, the motivation and self-efficacy of individuals [32] are decisive for learning outcomes. Therefore, our newly developed sustained learning integrates the aspect of open mindsets, leading to behavioural changes and implementation to adapt to changing environments in workplaces.

2.3. Organisational Learning

The third concept is organisational learning, investigated from various viewpoints and levels; hence, it is also investigated in the context of digital transformation and is a relevant basis for developing the new construct. There is no standard definition of organisational learning [33]. In digital transformation, dynamic capabilities are relevant to reconfiguring human resources. Organisational learning is multidisciplinary, including organisational behaviour, cognitive and social sciences, and strategic management [34]. The definition provided by Crossan et al. [16] as a dynamic process of learning and renewal in organisations on individual, group, and organisational levels, therefore, was adopted for this study. Organisational learning is complex due to the variety of factors impacting these institutional learning processes in an organisation. Therefore, the newly developed concept of sustained learning differs from organisational learning theory. It is based on the individual aspects affecting the organisation’s capability to reconfigure and adjust human resources in change processes for digital workplaces. The construct of sustained learning is multidimensional, with organisations as targeted populations in the work domain. We did not integrate organisational artefacts, values, or culture, which are not targeted in our concept.

3. Materials and Methods

This study aimed to define and create a valid instrument to measure the new construct of sustained learning and develop questions suitable to measure the investigated construct [35]. A mixed-methods approach is applied (Figure 1). Based on the previous literature, the construct was determined in the first step where we identified reference scales [36]. Based on this, questions were developed and discussed with experts in interviews, as qualitative research [37,38]. A survey was applied to validate the resulting questions and assess the extent to which these questions match the construct of sustained learning compared to similar constructs, as the quantitative part [39].
Accordingly, a literature review was conducted in February 2024 to develop and define the concept. We structured the gathered data and identified similar constructs and measures. A search in the database Scopus with the search terms “sustain*” AND “learning” within article titles, abstracts, and keywords between 2004 and 2024 and in the English language produced 48,093 documents. The subjects were limited to social sciences, business, management, and accounting. After being limited to open-access publishing, 9066 documents remained. Within these resulting documents, we conducted an additional search, limited to the keywords “learning”, “sustainable development”, “education”, “innovation”, and “knowledge”, limiting the results to 3160 documents.
These documents were searched for quantitative articles using the keywords “quantitative”, “questionnaire”, “survey”, and “item”, resulting in 744 papers. We searched the abstracts of this residual database for matching constructs, leaving 704 articles, and selected 40 for a deeper investigation of the entire paper. This literature review was performed in iterative steps, and the authors discussed and revised it. This formed the foundation for developing the initial broad question pool, since adopting existing measures is recommended to address issues of clarity and content validity in operationalising a new construct [40].
We established a group of experts to discuss the definition and the initial pool of questions [35]; twenty experts from five different countries were asked to participate, chosen due to their professional experience, expertise, or specific knowledge, who are part of the activity field [38]. The experts were selected based on the authors’ network and a search for suitable expertise in the research portal of the state of Rhineland-Palatinate “Sciport”, which offers web access to scientists, research activities, and publications. This purposive sampling is commonly used for expert interviews [37]. Nine participants agreed to participate in the expert group, covering different viewpoints and expertise. The questions were enhanced in comprehensibility and ranked to choose the most suitable questions to measure the construct. The qualitative interviews were performed in person or online using M.S. Teams in March 2024 in English and German to refine and validate the questions’ definitions, subdimensions, and quality. For the German translation of documents, we used the tool DeepL and discussed the quality of translations with the German participants. These experts rated the question pool using a 7-point Likert scale to choose the final question setting. Then, the mean was calculated and ranked.
Validity is fundamental to prove that the instrument is measuring what it is about to measure [35]. Since content validity is widely applied to prove that the items are suitable to measure the defined construct [41], we evaluated this. We calculated each item’s Content Validity Index (CVI) [42]. As quality criteria, the mean value for every item was calculated to keep only the questions best suited to measure the construct [43]. Based on this, we kept only items judged as appropriate to measure the defined construct, and the Hinkin Tracey Correspondence Index (HTC) was calculated in addition to ensuring content validity [44].
The narrowed question pool was tested in iterative quantitative surveys and set up on Prolific in the next step. The samples comprised part- or full-time employed Germans. The extent to which the questions measure the defined construct compared to provided definitions of similar constructs was rated [39]. Prolific is a marketplace for online survey research. This tool has already been applied in other research on digital transformation in healthcare [7].

4. Results

The developed concept is grounded on existing research and further expanded in the context under consideration. We assume individuals are crucial in achieving digital progress through new learning processes in an agile working culture [8]. According to the research of C. Dweck [31], a growth mindset leads to developing the full potential of people and pushes organisations into a transformative change. As a result, we initially defined sustained learning as a new pattern of behaviour with a fundamental attitude towards learning that enables organisations to develop or continuously reconfigure competences (knowledge and skills). Behaviours that include the ability to explore, recognise, and solve problems support sustained learning and the willingness to acquire relevant competencies autonomously and improve them continuously. Therefore, there are three dimensions: mindset, behaviour, and improvement. This initial definition, the dimensions, and the developed question pool based on the literature review are provided in Appendix A.
Grounded on this concept, sustained learning as a dynamic capability is the desired outcome to support digital transformation in healthcare or other similar sectors where the offered services are highly demanded or not interchangeable. The constant progress and improvement of employees’ learning produce unimagined outcomes and conditions for success [30]. This enables companies to digitally transform based on their ability to identify or reconfigure capabilities and adapt innovations [45].
We conducted expert interviews (Table 1) to discuss and validate the construct and item clarity and evaluate congruence [44], with experts selected from an academic background to give a qualified rating of suitability and item development. Secondly, expertise in digital change was required; one participant worked as an I.T. manager in a commercial enterprise before returning to academia. Managers from companies were additionally asked to participate to cover a broader background. This purposive sampling covered relevant knowledge in the field of interest, avoiding a biased selection.
As recommended by Lunn, a minimum of three experts were needed. For the total number of interviews, we followed the principle of “theoretical saturation” [46]. The experts were first asked to evaluate the definition and the dimensions. It was discussed whether the definition is understandable in the context and whether the derived dimensions are comprehensible based on this provided definition. We made improvements and adjustments based on the results, and discussed them again in the following interview.
After four interviews, there were only minimal findings; after nine interviews, there were no further improvements, so we determined we had reached saturation. The developed question pool based on the literature review was also discussed. As a qualitative aspect, we asked the experts to evaluate if the questions matched the dimensions and were comprehensible. To measure content validity, the experts ranked all 19 questions on a 7-point Likert scale from 1 (the question is poorly suited to measure the construct) to 7 (the question is exceptionally well suited to measure the construct), where 4 represents the median (the question is acceptable to measure the construct). For all items, the mean value and Content Validity Index (CVI) [42] were calculated, and these are displayed in Table 2. The CVI is widely applied to measure content validity, where experts judge the relevance of items and agree on their suitability [42].
According to Colquitt’s work, the initial question pool was narrowed down based on the results of the expert ratings [39]. The first evaluation criterion was the number of matches with which the experts rated the question well-suited, measured using the CVI. This critical value of the CVI depends on the number of experts. According to Almanasreh et al. [47], content validity is prevalent when the proportion of experts (among 5–10 experts in total) rating the item as relevant meets the threshold of 0.78, which was applied in the previous work [47,48,49]. Next, additional items were sorted out for simplicity and clarity, and experts judged only questions as being very good or highly well-suited to measure the construct [43]. According to that, we narrowed down the initial pool to three items for each dimension. As a result of the expert interviews, we define sustained learning as an attitude of individuals that combines new patterns of behaviour with a continuous willingness to question routines or habits and learn new things. Sustained learning includes the motivation to explore new things, recognise and solve problems, and be willing to acquire relevant knowledge and competences autonomously. This individual attitude and behavioural pattern enable organisations to develop or reconfigure resources continuously during digital transformation. The experts discussed these initial three dimensions critically. We decided to keep them in the first step and re-evaluate them after further tests. The descriptions and finalised questions are provided in Appendix B.
For these selected nine questions, the Hinkin Tracey Correspondence Index (HTC) as an additional consensus index was calculated (Table 3). This index was developed by Colquitt et al. [39], analysing 112 scales published in high-ranked journals. In this calculation, the average score is divided by the number of options in the scale as universally applicable evaluation criteria [44]. A perfect match is represented by the value 1, very strong ≥ 0.91, strong 0.87 to 0.90, moderate 0.84 to 0.86, weak 0.60 to 0.83, and lack of content validity ≤ 0.59. According to this, the resulting HTC value of 0.87 represents a high match. Therefore, the items correspond to the construct definition. Content validity is a necessary precondition for establishing construct validity [50].
In the next step, we applied additional quantitative research to test the distinctiveness of the newly developed construct. The shorter number of questions from the expert discussion was rated compared to similar constructs. Then, we provided a questionnaire in Prolific (which can be provided by the authors on demand) according to the recommendations by Colquitt [39]. The survey was intended to evaluate to what extent the questions were adequate at measuring the new sustained learning construct and provide evidence of its distinction from similar constructs [39]. In total, 212 participants joined the online distributed survey on Prolific, of which 11 did not finish the questionnaire. So, in total, 201 respondents rated the questions for each provided construct. The results of this survey are displayed in Table 4.
The items measuring sustained learning were rated as well-suited to measure the construct (value five and above), except question number 9. The questions in the third dimension (improvement) were all rated as slightly more suitable for measuring the construct of learning goal orientation. With these results in mind, we placed a nine-question survey measuring the three newly defined dimensions of the construct of sustained learning in Prolific for a first test and a better understanding of the underlying dimensionality. The sample consisted of part- or full-time employed persons in Germany, where 205 responses were received.
An exploratory factor analysis (EFA) using the software Jamovi Version 2.4.8.0 was conducted for evaluation. The EFA resulted in two main factors (Figure 2). The items measuring the third dimension, “improvement”, were closely related to the items measuring the dimension of behavioural patterns.
The Root Mean Square Error of Approximation (RMSEA) was measured to assess the quality of the model fit. The resulting value of 0.0630 indicates a good model fit between the hypothesised and observed data [51].
In addition, we assessed chi-square (χ2), p-value (p), and chi-square divided by the degrees of freedom (df) as measures of model fit (Table 5). According to the rule of thumb provided as a reference by Schermelleh-Engel, Moosbrugger, and Müller (2003) [52], all fit measures showed a good model fit.
We calculated Cronbach’s alpha for each dimension and the whole scale to assess reliability. Cronbach’s alpha reliability coefficient for the overall sustained learning construct was 0.859, for the dimension mindset 0.797, and for behaviour 0.712; therefore, reliability was found. Only the third dimension, continuous improvement, did not meet the threshold.
The authors discussed the questions and found matches between the measuring behaviour and improvement items. The items measuring behavioural aspects (subdimension 2) were designed to measure individuals’ activity, which also includes some extent of improvement, causing the overlap with the items meant to measure the third subdimension. Based on these results, we conclude that sustained learning is a two-dimensional construct. The respective survey items to measure the construct subdimensions are shown in Table 6.

5. Discussion

In this study, the authors sought to develop a new construct of sustained learning and provide suitable measures for its subdimensions. This construct measures a continuous process of competence reconfiguration essential for digital transformation. The expert discussions in the qualitative part enabled the development of the concept and a question pool. We addressed how individuals can be engaged in digital transformation by providing insights into the essential elements of sustained learning as a dynamic capability. We highlighted the fundamental importance of knowledge and establishing new learning routines, extending the existing research [11,17].
The theoretical foundation of a large body of the latest research on digital transformation is grounded on the theory of dynamic capabilities, explaining the firm’s ability to react in uncertain and dynamic environments by sensing, seizing, and configuring internal and external resources [21]. Learning is frequently mentioned as a relevant factor, and possible opportunities to sustain learning are investigated [53,54]. However, there needs to be better knowledge about competence generation and the relationship between learning and dynamic capabilities, as Zollo et al. [15] highlight in their work. Based on the existing literature, we created the initial definition of sustained learning. We first revealed three dimensions of the construct: the essential mindset to be open to something new, the behavioural aspect to act and reflect, and the implementation of continuous improvement and learning loops. Our findings align with the work of Augier et al. [13], which points out the relevance of integrating behavioural aspects in elaborating concepts of technological evolution and innovation with the role of learning routines.
When testing the distinctiveness versus similar constructs, the questions measuring the first two dimensions exposed a clear differentiation from established constructs. Only the third dimension, measuring continuous improvement, indicated a different result. These questions are more related to learning goal orientation. The discussed meaning of this subdimension is to measure the outcome as continuous improvement of competences. Vandewalle [29] developed an instrument to measure goal orientation in work domains. They based the learning goal orientation subdimension on the Work and Family Orientation (WoFo) Questionnaire. The learning goal orientation focuses on striving to achieve goals in sports or careers. The subdimension of continuous improvement in our construct represents the implementation of new behavioural patterns resulting from an open mindset, leading to the fact that the items measuring these dimensions are closely related to the concept of learning goal orientation. The items measuring the subdimension continuous improvement have already received mixed reviews in the discussions from the experts. Some believe that dimensions two and three belong together and argue that a behaviour change requires some implementation. As there are also opposing views, the authors initially decided to keep the three dimensions separate and to test the underlying dimensionality in a questionnaire.
When evaluating the exploratory factor analysis according to the responses to this questionnaire (Appendix B), the arguments of experts against the third dimension were confirmed, resulting in only two subdimensions. As already displayed, the dynamic capabilities of organisations include routines, enabling organisations to act in a planned manner to unexpected changes [27]. Winter [55] argues that environmental conditions often force changes, and dynamic capabilities can be distinguished from regular capabilities by the reaction to the change. Reacting and ad hoc problem-solving are described as a regular capability. Problem-solving may lead to a learning process through adjustment but not to developing dynamic capabilities characterised by routines, clear patterns of behaviour, and evolutionary development [55]. Based on that, the two dimensions of the construct, mindset and behaviour, best match the intention to measure sustained learning as a dynamic capability, supported by the previous literature [15,22,27,56]. We decided to exclude the critically discussed third dimension, which better suits measuring the learning orientation. Since the construct measures sustained learning as a capability, this exclusion can be justified by referring to Dougherty [57], stating that a “capability” refers to the potential to complete an action, whether it is performed or not.
The results suggest that the developed items are valid, and the model evaluation provided a good fit. Our study supports the arguments of other researchers, postulating a connection between dynamic capabilities and learning [11,15] and linking both streams to the new concept of sustained learning. We emphasise that sustained learning integrates the development of new mindsets and behavioural patterns at the individual level, specifically targeting an open mindset towards new technologies, a habit of questioning routines, and a commitment to continual improvement. These qualities are foundational for creating a culture where professionals actively learn and adapt to digital advancements, thus driving organisational transformation.

6. Conclusions

Research on dynamic capabilities is one of the most prolific streams in digital transformation [10], with the essential elements to sense, seize, and reconfigure resources to build dynamic capabilities and adapt to transformed workplaces based on routines and behavioural patterns [21]. Another prominent research stream in the digital transformation space investigates knowledge management, competences, and learning [58,59,60,61]. Addressing how individuals can be engaged in digital transformation, we propose that by promoting sustained learning, digital transformation can effectively be supported by developing an adaptable workforce. This, in turn, enables organisations to reconfigure resources and integrate new technologies more effectively. This approach shifts the focus from occasional training to a sustained, self-directed learning process that can help overcome healthcare’s digital transformation challenges, such as resource limitations and the diverse technological proficiency among stakeholders.
Within the present study, we developed a new concept linking the research streams of dynamic capabilities with learning. We also provide measures to assess sustained learning as a desired outcome in less competitive sectors like healthcare or public administration. We defined the concept of sustained learning, identified and validated two subdimensions, and tested them compared them to similar constructs.
Our research makes a theoretical contribution by extending dynamic capabilities theory and providing the new construct of sustained learning as the desired outcome for success in digital transformation in healthcare.
The conceived measurement on the individual level makes it easier for organisations to measure this concept and apply it to their employees. Therefore, the managerial implication of our research is to enable organisations to gain new insights into the sources of their dynamic capabilities.
It also provides a methodological novelty by developing a new scale to measure sustained learning as a grounds for quantitative testing. The initial results are auspicious but need to be validated by further examination. We will address two issues in future research.
First, the dimensions of sustained learning should be refined with additional qualitative exploration of the third dimension of continuous improvement. Interviews or a case study can be conducted for deeper insights. Second, quantitative research is needed to test the factors impacting sustained learning as a dynamic capability in digital transformation. A model, including the improved questionnaire, could be developed and tested.
Of course, this study has its limitations since it was limited to employees in Germany for the first validation. The newly developed concept, with its subdimensions, should be tested in sectors with less need for competitive advantage or performance as a desired outcome. Future research could also apply this survey to other countries to investigate the effects on other cultural backgrounds or different advancement stages of digital transformation.

Author Contributions

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

Funding

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

Institutional Review Board Statement

Ethical review and approval were not required for this study because it was non-interventional, with no risks. The participants were fully informed about our research aims and the use of the data with guaranteed anonymity.

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

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 this study, the collection, analysis, or interpretation of data, the writing of the manuscript, or the decision to publish the results.

Appendix A

Initial Definition of Sustained learning: A new pattern of behaviour with a basic attitude towards learning that enables organisations to continuously develop or reconfigure competences (knowledge and skills). Sustained learning is supported by behaviours that include the ability to explore, recognise, and solve problems and the willingness to continuously and autonomously acquire relevant competences.
Based on this definition developed from the literature review, the dimensions of the construct of sustained learning were determined as follows:
  • Open mindset to explore new things.
  • Question habits and routines.
  • Continuous improvement of own competences.
Table A1. Initial question pool.
Table A1. Initial question pool.
ItemQuestionSource
MindsetI would never stop learning as there is a risk of not keeping up to date and missing out on opportunities.[62]
MindsetNew experiences with digital technologies or tools are learning opportunities for me.[63]
MindsetI regularly join or listen to conversations and discussions about new technologies.[64]
MindsetI can derive new ideas from things I have learned.[65]
MindsetIf there are new technologies or tools, which make things easier for me, I want to know more about it.New
MindsetI incorporate feedback to make changes in my behaviour[66]
BehaviourI am interested in new topics, try to interact, and informed myself about occurring new technologies.[65,67,68]
BehaviourI value original ideas and constant innovation.[69]
BehaviourI am watching explanation videos (e.g., on YouTube or other platforms) and/or read additional instructions to improve my knowledge.[67]
BehaviourI am not afraid to critically reflect my underlying assumptions.[64,69]
BehaviourIf I learn something new about digital technologies, I think about how I could transfer this into my daily routines.[67]
BehaviourIf I learn something new about digital technologies, I rethink how I did things before and try to make it better based on my new knowledge.[67]
BehaviourI continuously judge my decisions and activities towards using digital technologies.[64]
BehaviourOne of my basic values is to include learning as a key to improvement.[69]
ImprovementI try to integrate digital technologies, even if I need to think about doing things in a different way I did before.[63,67]
ImprovementI view the ability to learn as the key to improvement.[62]
ImprovementI long for learning to improve myself.[62]
ImprovementI perceive learning as an investment, not an expense.[62]
ImprovementI can transfer the knowledge I already have to adapt other similar technology[68]

Appendix B

Refined Definition after Expert Interviews on Sustained Learning: An attitude of individuals that combines new patterns of behaviour with a continuous willingness to question routines or habits and learn new things. Sustained learning includes the motivation to explore new things and recognise and solve problems, as well as the willingness to autonomously acquire relevant knowledge and competences. This individual attitude and behavioural pattern enable organisations to continuously develop or reconfigure resources in digital transformation.
After discussion, the three dimensions were revised in terms of content and kept for testing:
  • Individual attitude: Open mindset to explore new things—Accept.
  • Behavioural pattern: Questioning habits and routines—Act and Reflect.
  • Outcome: Continuous improvement of competences—Implementation and Learning Loops.
Table A2. Refined question pool after expert interviews.
Table A2. Refined question pool after expert interviews.
ItemQuestion
MindsetIf there are conversations and discussions about new technologies in my environment, I am interested in them.
MindsetIf someone tells me about new ideas to use technology, I am open to them
MindsetIf there are new technologies or tools that make my work easier, I want to know more about it.
BehaviourI watch explanation videos (e.g., on YouTube or other platforms) and/or read additional instructions to improve my knowledge.
BehaviourWhen I learn something new about digital technologies, I rethink my previous actions and try to develop them further with the new knowledge.
BehaviourIf situations are changing, I adapt my decisions and activities regarding the use of digital technologies.
ImprovementI acquire new knowledge and skills independently.
ImprovementI am willing to invest time and money to integrate new technologies or processes in my daily live.
ImprovementThe knowledge I already have helps me to use other technologies

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Sustainability 16 10218 g001
Figure 1. Steps to develop a new construct; authors’ illustration based on Straub [35].
Figure 1. Steps to develop a new construct; authors’ illustration based on Straub [35].
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Figure 2. Exploratory factor analysis. Source: Authors’ data, analysed with Jamovi.
Figure 2. Exploratory factor analysis. Source: Authors’ data, analysed with Jamovi.
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Table 1. Expert interviews.
Table 1. Expert interviews.
ExpertCountryExpertise
1USAUniversity (Professor): Knowledge management, motivation
2GermanyUniversity (Professor): Digital management, innovation management
3LatviaUniversity (PhD, Lecturer): Employee engagement and motivation, H.R.
4GermanyUniversity (Assistant Professor): Innovation, digital learning
5GermanyFreelancer (PhD): Education, German translation
6GermanyHealthcare (Division Management): H.R. development, digital competences
7GermanyUniversity (Professor): Social sciences, employability skills
8GermanyHealthcare (Department Management): Design thinking, innovation
9GermanyUniversity (Researcher): Future skills, education and lifelong learning
Source: Authors’ data.
Table 2. Results of expert question ratings.
Table 2. Results of expert question ratings.
QuestionExpert RatingCalculated Values
Dimension1234567Mean *CVI
1Mindset 123215.000.67
2Mindset 1 17 5.560.89
3Mindset 1 1345.890.89
4Mindset 2346.221.00
5Mindset 1 176.560.89
6Mindset 21 1235.000.67
7Behaviour 126 5.560.89
8Behaviour 2 12135.000.67
9Behaviour 1 446.220.89
10Behaviour 213214.890.67
11Behaviour 1 2425.670.89
12Behaviour 2436.111.00
13Behaviour 1 1435.890.89
14Behaviour12 1234.780.67
15Improvement 31325.440.67
16Improvement 1 536.000.89
17Improvement 1 2335.670.89
18Improvement 2526.001.00
19Improvement 1 715.780.89
Source: Authors’ data (* 7-point Likert scale).
Table 3. Final question pool rated with correspondence index.
Table 3. Final question pool rated with correspondence index.
Dimension1234567Mean *CVIHTC
Mindset 1 1345.890.890.84
Mindset 2346.221.000.89
Mindset 1 176.560.890.94
Behaviour 1 446.220.890.89
Behaviour 2436.111.000.87
Behaviour 1 1435.890.890.84
Improvement 1 536.000.890.86
Improvement 2526.001.000.86
Improvement 1 715.780.890.83
Average 0.930.87
Source: Authors’ data (* 7-point Likert scale).
Table 4. Survey distinctiveness results (based on survey n = 201).
Table 4. Survey distinctiveness results (based on survey n = 201).
DimensionSustained Learning *Learning Goal Orientation *Growth Mindset *Organisational Learning *
1Mindset5.51 **5.144.615.09
2Mindset5.47 **5.324.935.11
3Mindset5.72 **5.574.914.98
4Behaviour5.485.77 **4.964.30
5Behaviour5.71 **5.365.544.51
6Behaviour5.345.215.40 **4.71
7Improvement5.485.74 **4.884.27
8Improvement5.135.31 **4.594.30
9Improvement4.464.47 **4.424.05
Source: Authors’ data. * Mean values calculated from a 7-point Likert scale. ** All items with the highest rank are marked in the green colour.
Table 5. Model evaluation.
Table 5. Model evaluation.
Fit MeasureResultAcceptable FitGood Fit
χ234.22df < χ2 ≤ 3df0 ≤ χ2 ≤ 2df
0 ≤ 34.2 ≤ 38
p0.0180.01 ≤ p ≤ 0.05
0.01 ≤ 0.018 ≤ 0.05		0.05 < p ≤ 1.00
df19
χ2/df1.82 < χ2/df ≤ 30 ≤ χ2/df ≤ 2
0 ≤ 1.8 ≤ 2
Source: Authors’ data and reference scales [52].
Table 6. Final construct subdimensions and survey items.
Table 6. Final construct subdimensions and survey items.
SubdimensionSurvey Item
Individual attitude: Open mindset to explore new things—Accept.If there are conversations and discussions about new technologies in my environment, I am interested in them.
If someone tells me about new ideas to use technology, I am open to them.
If there are new technologies or tools that make my work easier, I want to know more about them.
Behavioural pattern: Questioning habits and routines—Act and Reflect.I watch explanation videos (e.g., on YouTube or other platforms) and/or read additional instructions to improve my knowledge.
When I learn something new about digital technologies, I rethink my previous actions and try to develop them further with the new knowledge.
If situations are changing, I adapt my decisions and activities regarding the use of digital technologies.
Source: Authors’ data.
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Starke, S.; Ludviga, I. Impeding Digital Transformation by Establishing a Continuous Process of Competence Reconfiguration: Developing a New Construct and Measurements for Sustained Learning. Sustainability 2024, 16, 10218. https://doi.org/10.3390/su162310218

AMA Style

Starke S, Ludviga I. Impeding Digital Transformation by Establishing a Continuous Process of Competence Reconfiguration: Developing a New Construct and Measurements for Sustained Learning. Sustainability. 2024; 16(23):10218. https://doi.org/10.3390/su162310218

Chicago/Turabian Style

Starke, Sandra, and Iveta Ludviga. 2024. "Impeding Digital Transformation by Establishing a Continuous Process of Competence Reconfiguration: Developing a New Construct and Measurements for Sustained Learning" Sustainability 16, no. 23: 10218. https://doi.org/10.3390/su162310218

APA Style

Starke, S., & Ludviga, I. (2024). Impeding Digital Transformation by Establishing a Continuous Process of Competence Reconfiguration: Developing a New Construct and Measurements for Sustained Learning. Sustainability, 16(23), 10218. https://doi.org/10.3390/su162310218

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