Developing and empirically testing a sustainable knowledge model: using Alkire-Foster construction logic

Research in recent years shows that four-fifths of European organisations consider knowledge a strategic asset. Knowledge management has increasingly become a tool for enhancing organizational and national economic competitiveness through the conscious and systemic management of knowledge (Al-Emran et al., 2018; Kianto et al., 2019). The possibility of organizational knowledge management and utilization can be realized through knowledge management system building. If organisational culture and managerial behaviour do not support one of the most critical steps in the knowledge management process, (knowledge sharing), and the climate of trust that favours it, knowledge loss will be faced over time (Novitasari et al., 2021; Kim, 2019; Dey and Mukhopadhyay, 2018). The problem of knowledge loss has been a problem for centuries, even millennia, (both at societal and organisational levels), but research into its significance is not a preferred area (Massingham, 2018; Levallet and Chan, 2019). Problems at the organisational level, such as staff turnover, inadequate knowledge-sharing culture, lack of knowledge storage systems, lack of time, inadequate skills, etc., all cause knowledge loss and knowledge retention. Massingham (2018), in a case study of an Australian Department of Defence organisation, suggests that lost human capital can reduce organisational performance and productivity; lost social capital can reduce organisational memory; lost structural capital can reduce organisational learning; and lost relational capital can result in interrupted external knowledge flows. The value of these losses is difficult to express in monetary terms, but they are a demonstrable shortfall in organisational performance. Further studies (Galan, 2023; Lorenz et al., 2023; Daghfous et al., 2023) and our own research experience urge us to find solutions that prevent knowledge loss and support knowledge retention. Developing a solution brings with it the need for long-term sustainability of knowledge, which influences future organisational performance. To ensure synergy between organisational knowledge and long-term organisational functioning for knowledge sustainability, a specific set of tools is needed. (Massingham, 2018; Mariano et al., 2020; Di Vaio et al., 2021). This toolbox is not yet well developed in the literature.

The links between sustainability and knowledge are explored through the role of knowledge management in supporting sustainability (Weina and Yanling, 2022; Alkathiri et al., 2024; Arduini et al., 2024). Backgrounding questions that would answer the conditions for the long-term sustainability of knowledge itself. The above-mentioned problem of knowledge loss raises an increasingly urgent need to develop a methodology to solve the problem of knowledge sustainability. Defining the conditions for knowledge sustainability requires further areas to be explored, such as exploring the context of an organisational culture based on trust, the importance of digitalisation and the impact of technostress. Although the literature and related practical research have identified several gaps that are vital for knowledge retention and sustainability, the way to address them is only vaguely outlined in the studies. (Hubert and Lopez, 2013; Asrar-ul-Haq and Anwar, 2016; Contreras-Medina et al., 2023; Martínez-Falcó et al., 2023; Yeboah, 2023). Therefore, the research questions are: (1) can a model be constructed that supports knowledge sustainability and provides a solution to address the problems raised; (2) what conditions are needed to theoretically develop and practically test the model; (3) what role does trust play in ensuring the model works; (4) what logic/model can provide a framework for the concept of knowledge sustainability?

The research aims to develop a theoretical model for knowledge sustainability and test the developed model through a practical example. The framework for the model to be developed is the “GNH of Business” logic, which includes the rating of sustainability criteria. This is complemented by consideration of the impact of digitalisation/artificial intelligence on trust. In this way, all the correlations that have been identified as gaps in the literature can be provided in the model to be developed.

It represents a theoretical contribution of the research:

1. Identifying the organisational domains/characteristics that influence knowledge sustainability

2. Developing a framework of relationships between the elements/characteristics that ensure knowledge sustainability

3. Identifying the logical framework for knowledge sustainability based on the relationships

4. Developing a theoretical model of knowledge sustainability

The practical contribution of the research:

1. To provide managers with a tool to review the conditions and relationships of knowledge sustainability,

2. Using the model, management can respond promptly and in a targeted manner to knowledge sustainability problems in the organisation

3. The conclusions drawn from the results of theoretical model testing can be applied in any organisation.

In the following chapters, the literature review will present the most important elements necessary to build the theoretical model (trust, “GNH of Business”, digitalisation/technostress, the importance of knowledge management, sustainable knowledge). The theoretical model will then be tested in practice. The discussion is followed by conclusions, exploitation of the results and future research directions.

The literature review has identified several concepts that need to be conceptualised to ensure that the contexts described below are understood in the same way by readers (See Table 1).

The study of trust in the workplace dates back to the early 1960s and has become increasingly popular in academic research (Kim, 2019; Novitasari et al., 2021; Sharif et al., 2021). An organisational culture based on trust can significantly support or hinder successful knowledge management initiatives (Hadas, 2020). According to a study by Fantazy and Tipu (2019) competitive organisational culture and knowledge development interact and have a positive impact on organisational performance.

Trust is the currency of successful organisations and is strengthened by the cooperation of team members who trust each other and who, building on trust, are more productive, creative and resilient, which increases the overall effectiveness of the organisation (Sharif et al., 2021; Jarjabka et al., 2024; Minerova et al., 2021). As well as emphasising trust, its opposite, mistrust, is also worth noting. According to participants in a study by Twaronite (2016), the top five reasons for employee distrust of an organisation are: unfair appraisals, unequal opportunities for pay and promotion, lack of strong leadership, high turnover, and lack of collaborative teams. Fischer et al. (2020) analysed the multidimensionality of trust through aspects of organisational behaviour, commitment and leadership. The results of all studies support that organizational trust and its impact on organizational performance is closely related to trust in the leader (Zsigmond et al., 2024). Further research that has examined the relationship of trust with learning, knowledge and talent development falls under the topic of knowledge management, which will be returned to in a later subsection Fisher et al., (2020), Iscandarov (2018), Liu (2021), Mujtaba and Mubarik (2022).

It follows from the scientific literature that trust is closely related to all organisational characteristics that influence successful performance (Zampetakis, 2022; Katou, 2022). It is difficult to find a method or analytical model that is capable of combining these factors, building on a holistic approach, and capable of qualifying and addressing gaps. The logic of the most suitable solution is briefly presented in the next chapter, following a review of the relevant literature.

In recent decades, criticisms of GDP have led to the development of several indicators (World Happiness Report, Human Development Index, Happy Planet Index, etc.), that attempt to measure the so-called soft elements of the performance of individual economies (Stiglitz, 2020; Sachs, 2019; Bencsik, 2022a). All of these indicators have also come in for a lot of criticism, mainly because their measurement tends to focus on psychological states and almost completely ignores people’s physical states and needs (Bondarchik et al., 2016; Amendola et al., 2023; Marian et al., 2023).

In addition to the numerous indicators and their recommendations/criticisms, a measurement model based on the Buddhist religion has been developed in Asian countries, which has become known as GNH (Gross National Happiness). (The logic is well-known in alternative economics, but its dissemination is hampered by several prejudices.) (Daga, 2014; McClosky, 2012). It is designed to more accurately and deeply reflect the “happiness” and overall well-being of the population/organisational members than monetary measures. Under the heading of “happiness”, both traditional and non-traditional domains are measured as parameters that qualify human well-being. The logic developed for the business domain shows values that apply to employees in organisations (Alkire et al., 2015). It takes a holistic approach to measuring people’s happiness and well-being, allowing both objective and subjective parameters to be assessed (Ura, 2005). At the same time, the provision of organisational conditions is essential for sustainable organisational functioning based on individual happiness.

The GNH concept aims at a balanced development at the societal and organisational level, a systems approach that eliminates short-term thinking and contributes to achieving stability. This in itself serves the goals of sustainability. It is organised around four pillars (sustainable development, environmental protection, cultural preservation and good governance) that identify the areas and goals that are most important for the economy to achieve happiness (Ura, 2005). The four pillars include a measurement of sustainability criteria. The four pillars can be further broken down into nine areas: psychological well-being, health, time use, education, quality of life, good governance, cultural diversity, community vitality, and ecological diversity. The 9 domains are further divided into 33 measurable parameters, which are rated through standard questionnaire questions. Although the original logic was developed at the societal level, in recent years (Zangmo et al., 2017) an organisational-level application of this has been developed (revised in 2018) under the name “GNH of Business”, which provides a similarly complex assessment tool for economic operators/organisations (Zangmo et al., 2018). The principle and the measurement represent the same values, approached from two perspectives: employee happiness and organisational readiness. Accordingly, the questionnaire used to measure was divided into two parts. At both the societal and the organisational level, the measurement of individual happiness and satisfaction is used to calculate the GNH happiness index (Sebastian, 2018; Yangka et al., 2018).

In the model, each pillar represents a value of sustainability and in rating the domains, trust is included in several domains, with different approaches. Thus, its use is justified, which also establishes the link between the elements of the theoretical model to be developed. The logic of the “GNH of Business” provides the opportunity to analyse the successful functioning of an organisation based on sustainable knowledge from both the individual and the organisational perspective. Identified problem areas can be corrected by the decision of the organisational management and the possibility for balanced development for sustainable knowledge is given (Bencsik, 2023). The model (by its basic concept) only implicitly includes the technological effect that critically affects organisational functioning, which is the phenomenon of technostress (Martin et al., 2022; Panda, 2022). This problem occurs at the individual level and affects happiness, satisfaction and, through this, the functioning of the knowledge management process and performance. At the same time, the level of technical readiness of the organisation has an impact on this individual feeling, affecting the organisational success. It is therefore of particular importance in the forthcoming model. Based on our personal experience and previous research, we assume:

In the next section, we will look at the reasons behind the phenomenon of technostress.

Throughout history, there have been concerns that automation, mechanisation, computing and, more recently, artificial intelligence (AI) and robotics, are taking over jobs that were intended for human resources and causing irreversible damage to the labour market (Vrontis et al., 2022; Chowdhury et al., 2023).

Stress is a frequently mentioned negative phenomenon in today’s organisational practice, resulting from the imbalance between the demands of a given situation and the ability to meet them (Rasool et al., 2022; Sanchez-Gomez et al., 2021). Stress situations can manifest themselves in different forms, one of which, which is increasingly demanding attention, is technostress, which is the consequence of the forced use of information systems (Salazar-Concha et al., 2021; Nastjuk et al., 2023). It is a complex phenomenon, the extent and significance of which is increasing in parallel with the advance of advanced information technology and digitalisation. Several stress factors related to IT can be identified that inhibit successful performance, affect commitment, trust and compromise personal fulfilment and learning, creating adverse situations in both work and private life (Bondanini et al., 2020; Grummeck-Braamt et al., 2021; Bencsik and Juhasz, 2023). In many cases, there is a problem of work-life imbalance, a constant forced presence, a lack of the necessary skills or the need to adapt constantly. Of course, the organisation’s operational purpose, technical readiness and many other parameters influence the feeling of technostress.

Taking all this into account, it is worth reflecting on the impact of digitalisation/artificial intelligence, the use of ICT tools on the level of trust within the organisation, on its development or destruction, and on the possibilities of implementing knowledge management actions (Buschmeyer et al., 2023). Building on the research reviewed, we hypothesise:

Based on the above, the relationship between technostress and the above-mentioned factors should be reflected in the relationship framework of the model to be developed. The next chapter will also address these links.

Over the past decades of knowledge management research, no generally accepted definition has emerged. There are many approaches in the literature, from the classical formulation (Zhou and Fink, 2003; Dalkir, 2017; Hislop et al., 2018; Camila and Denilson, 2019; Bolisani and Bratianu, 2018) to new metaphors. Among the classical approaches, one of the best-known formulations is that knowledge management is a business model that uses knowledge as an asset of the organisation to achieve competitive advantage. It is a management tool that supports an integrated approach to identifying, valuing, exploiting, creating, enhancing, protecting, sharing and applying an organisation’s intellectual capital (Zhou and Fink, 2003).

This line of thinking confirms that investment in intellectual capital is a key to competitiveness. The need to build knowledge management systems and to integrate them into organisational operations is decades old in Western societies, while in less successful economies, such as in Central and Eastern Europe, it is a less preferred business model (Csath, 2020; Marquardt et al., 2023). The need to build systems is often expressed at the level of management and strategy, but very few companies reach the level of operational implementation.

Fully meeting the objectives of knowledge management systems (KMS) depends on the development of high-level ICT systems that also support knowledge sustainability, and on the functioning of organisational memory (Walsh and Ungson, 1991). Both conditions require a culture of trust.

In recent years, there have been many research and publications discussing the results on the relationship between knowledge sharing and trust (Alsharo et al., 2017; Kipkosgei et al., 2020). All without exception confirm that trust influences the occurrence, quality and depth of knowledge sharing, thus it is closely related to the construction and functioning of organizational knowledge management systems. Trust in this case is built on personal trust based on direct relationships. The above-mentioned technology, ICT systems and the ever-increasing challenges of digitalisation raise the issue of impersonal trust (Ayadi et al., 2020). Impersonal trust, expressed in the use of technical tools, software, and other intelligent systems, greatly affects the feasibility of the steps in the knowledge management process.

How under what conditions and with what tool set these conditions can be achieved at the systemic level for knowledge sustainability is a little-researched question. In the next chapter, the importance of sustainable knowledge will be explored in some depth.

Incorporating sustainable knowledge and knowledge management systems into organisational operations goes beyond the typical expectations of sustainability (such as the preservation of the environment, the need to use renewable energy sources, etc.). It thinks on a scale that seeks not only to create the physical conditions and theoretical possibilities but also to imagine the conditions for an innovative future while at the same time achieving organisational well-being (happiness) (Alkathiri et al., 2024; Arduini et al., 2024). (This is another reason why a holistic approach is justified). In discussing the sustainability of knowledge, the research logic that characterises sustainability in general is reversed. It is not a question of how knowledge management helps an organisation to operate sustainably. The question is what organisational conditions and operations are needed to preserve and maintain common organisational knowledge in the long term. In formulating the answer, it is necessary to explore the interrelationship between the following factors (from individual happiness to sustainability), the consideration of the technostress phenomenon (its role in influencing knowledge sharing, capture, collaboration, trust building, etc.) and the trust-based organisational culture that underpins the feasibility of knowledge management processes become clear (Fantazy and Tipu, 2019). These factors require a holistic approach and a complex mindset. In summary, based on the essential characteristics briefly reviewed above, the GNH of Business model can provide a logical framework and quantification for assessing the sustainability of knowledge (in the context of a trust-based organisational culture), provided that the steps of the knowledge management process and the influence of artificial intelligence are taken into account (Greyling and Rossouw, 2024). In addition to quantification, the model allows for the clear and accurate identification of detectable problems, thus providing the basis for sound management decisions (Contreras-Medina et al., 2023). Based on our previous research and our personal experience, our assumption:

In the next chapter, the logic of the model is presented.

The logic of the model design can be deduced from the literature summarised above. According to this, not only the relationship between knowledge management and trust has been the focus of research in previous years, but also the issue of artificial intelligence (AI) and trust has been addressed in several publications (Shneiderman, 2020; Textor et al., 2022). AI supporting knowledge management systems in organisational operations is not an issue in today’s digitalised world (Jallow et al., 2020; Sandhya and Balaji, 2022). The extent varies, but supporting “smart” systems can be found in all organisations. However, it is true that if there is a lack of trust in people to share knowledge, to co-develop and a lack of trust in the application and use of AI tools, then organisational functioning faces serious problems (Jarrahi et al., 2023; Buschmeyer et al., 2023). The question is how to identify and address the organisational problems that arise. To answer the question, we will apply a model built on the theoretical background knowledge presented above.

Trust is the link between the elements studied in this research and the development, innovation, knowledge sharing, development and retention (Tan and Lim, 2009; Carmeli et al., 2009; Fait and Sakka, 2021).

The social/organizational determination of culture and values, responsibility for people and the environment, and overall sustainable organizational functioning based on individual happiness and well-being can be measured by the logic that has been successfully applied for several years in some countries (Singapore, Bhutan, Japan, Brazil, Spain, etc.) (Alkire, 2008) The characteristics of national culture are reflected in the values of the organisational culture, and the religious background is a key element of this (Jarjabka et al., 2024). Therefore, it was necessary to investigate whether adapting the logic developed in the conditions of Buddhist culture, there are parameters that are biased by religious preference and/or are not suitable for measurement in the conditions of Christian culture. The previous research results (Bencsik and Juhasz, 2023) have shown that all questions in all the areas studied are suitable (regardless of religious background) for application in the context of Christian culture (in some cases, some correction of wording or unit of measurement was necessary).

The relationship between trust at the organisational level, which underpins the functioning of knowledge management systems supported by IT/AI, can be mapped in a simple diagram. (See Figure 1.)

Based on the results of our research (Sundaresan and Zhang, 2022; Bencsik, 2022) the model shown in Figure 2 can be drawn, which represents the relationships presented above. In addition to quantifying the happiness index of the organisation, it allows for the precise identification of problem areas where intervention is needed to ensure the sustainability of knowledge (The 8-step process developed by Probst (1998) can be used to identify the steps in the knowledge management process (Bencsik and Juhasz, 2023). Each element of the cube represents 1–1 operational characteristic. The axes can be directly explored by identifying the “GHN of Business” areas of concern for the knowledge management process steps and technostress elements at the organisational level. When a problem arises, it is not necessary to scan the whole organisation, a comprehensive problem identification, because the critical element can be immediately and directly identified as an element of the cube. (E.g. if there is a problem at the organisational level (y axis) in the “GNH of Business” Time use indicator (z axis) and in the knowledge sharing area of the knowledge management process (x axis), the results of the questionnaire survey will identify that the problem in knowledge sharing is due to a lack of time.) In this way, problems in the relationship between the sustainability spatial indicators and the steps of the knowledge management process can be accurately identified and immediate decisions can be taken to solve them. This will ensure the smooth functioning of the knowledge management process and, through it, the sustainability of knowledge. In the next chapter, the practical applicability of the model is discussed. The model is shown in Figure 2 below.

As the aim of the research was to develop a new model and test it in practice, it was necessary to build on both inductive and deductive logic. Inductive research based on literature is an accepted methodological approach in the social sciences (Locke, 2007; Sauce and Matzel, 2017). It is particularly used when the use of literature to provide a basis for analysis helps to develop the research question or to define an initial framework (Cooper et al., 2012; Barrett and Younas, 2023). Exploratory research and data collection have been carried out in an earlier phase of this research and the results have been published. Thus, the typically accepted literature research findings, (which are case studies, my own and others' quantitative research findings) can legitimately be used as the data collection phase of inductive research. In the spirit of the inductive approach, we have found patterns and regularities by building on previous observations and experiences, our own and literature research findings, and by detecting patterns and regularities. The research questions were formulated, and the theoretical model and the hypotheses were drawn up by narrowing down the correlations identified. Building on deductive logic, the theoretical relationships were tested through a study of a selected organisation, where the validity of the hypotheses was tested.

The research investigated a field where the phenomenon under investigation is relatively new and unexplored. This is to justify and test in practice the need for sustainable knowledge. The type of research is partly basic research as it is about the development of scientific theories. Generating a theory that is not immediately applicable to all organisations. However, it is a basis for development in various management fields and stimulates new ways of thinking that can improve the way professionals deal with problems. On the other hand, it is applied research, since it involves the practical testing of the developed model by applying existing theory. The research was structured through the following phases:

1. Definition of the research problem, (see the research aim)

2. Formulation of the research questions, (see the Introduction section)

3. Exploring the theory related to the research questions and building on this to develop the research model, (theoretical part)

4. Designing a practical study, (based on theoretical model)

5. Gathering the basic data needed to test the model and analysing them based on the logic of the model, (practical research to check the theoretical model)

6. Answering research questions, comparing research results with other similar results and generalising the results, (see the discussion section)

7. Concluding.

The paper does not aim to derive the full research and computational methodology, but only to illustrate the results with a simplified example to demonstrate the applicability of the model.

As an example, we present the case of one of the SMEs involved in the study, which was engaged in manufacturing activities, is presented. The company itself requested the analysis. A full sample was taken, all employees (170) completed the questionnaire and were eligible for analysis. Two senior managers responded (according to the rules for the application of the method, one manager’s response is sufficient to assess the organisational conditions in a given organisation).

The calculation procedure is based on the logic of the Alkire-Foster (AF) method (Alkire et al., 2015). In the first step, the analysis was based on the questions included in the original questionnaire (Zangmo et al., 2017, 2018).

In the second step, the results were evaluated together with the responses to the problems caused by digitalization/artificial intelligence, and the increasing demands of technology. It was clearly shown that employees had lower satisfaction scores in all investigated areas (see Table 2). This difference was also reflected in the overall happiness index score. This implies that employees are disturbed (hindered or caused difficulties/distrust) by the ICT tools both in their personal lives and at work. These problems can be accurately identified by the model, showing the area where the problem is occurring, along with the level of trust.

According to the logic of the calculation, in both tables, the proportion of each area is weighted equally (∑100%). In each case, the weight of the indicators is given by the area weight divided by the number of indicators examined. The threshold for each indicator is determined according to the original logic. (See Table 2.) (To help identify the problems, more than 100 questions in the table reveal the value judgements of staff and managers behind the areas and indicators.)

The hypotheses on the factors influencing the model’s connectivity are formulated at the end of the subsections and reviewed in the literature review. Our hypotheses relate to the characteristics of each domain constituting the connectivity of the theoretical model and the relationships that can be detected between them.

As a result of the investigation, the areas where the most urgent action is needed have become clear. The results in the table show that there are problems in all areas.

Immediate visibility of the critical values to be tested will significantly simplify management decisions and resulting interventions. The link between problems at the individual and organisational level can also be directly demonstrated. Individual mistrust or unpreparedness, or any other problem, underlying problems in organisational functioning can be deduced from the characteristics of relationships. The problems are reflected in the questionnaire responses at the individual level, but the consequences are also reflected at the organisational level in the aggregate calculation results and reality. The impact of problems on work behaviour distorts the results of other organisational processes such as knowledge sharing or knowledge capture, teamwork, etc. The results in Table 2 suggest that the first hypothesis is plausible.

It was clear to see that staff had lower satisfaction scores in all the areas surveyed (see Table 2). This difference was also reflected in the overall happiness index score. This means that employees are disturbed (hindered or have difficulties/uncertainty) by the IT tools both in their personal lives and at work. These problems can be accurately identified by the model, showing the area where the problem occurs, along with the level of trust. Based on the results, the second first hypothesis is accepted.

When assessing organisational conditions, the questionnaires completed by managers do not include questions that examine individual behaviour and satisfaction. Therefore, the influence of digitalisation/artificial intelligence is not valid here, the calculated values have not changed in this case. The complexity of the model and the indicators measured allow for the assessment of sustainability characteristics, so their evaluation is an important feature. The results are summarised in Table 3.

Based on the values in the table, Hypothesis 3 is also accepted.

According to the calculation logic, the sum of the two questionnaire results with equal weighting gives the final index value. Ignoring the influence of artificial intelligence (1) and taking into account (2), the weighted averages are:

Although the difference may not seem large, the influence of technostress is evident and should not be ignored. The value obtained can be considered average compared to the standards.

Although the questionnaires and the calculation method used are validated, the reliability of the results was checked by means of Cronbach’s alpha (α). Measurement reliability typically shows that there are no measurement errors in the test. The calculated value is considered to be the accuracy of the measurement (Cronbach, 1951; Wessa, 2021). The result is acceptable if its value is above 0.7. Based on the analysis of the questions and main groups of the employee questionnaire, the Cronbach’s Alpha coefficient indicates that the results meet the reliability requirements. For all items Cronbach Alpha is: 0.7556; Std. Alpha: 0.8556; G6(smc): 0.7574.

The Cronbach’s alpha is also higher than the acceptability level for the management questionnaires. For all items, Cronbach Alpha is: 0.7649; Std. Alpha: 0.7650; G6(smc): 0.6425.

The questionnaire survey allows for the collection of such detailed data that the underlying causes and their consequences can be easily identified. This allows management decisions to focus on the problem at hand and not to look elsewhere for the underlying causes. The higher the value of the computational method based on the model, the more likely it is to be characterised by cooperation based on organisational trust, proper functioning of the steps in the knowledge management process, and employee and organisational happiness. The model includes a rating of organisational conditions through the assessment of sustainability characteristics. Overall, through holistic contexts, it is possible to assess the sustainability of the knowledge specific to the organisation.

The research has produced a holistic model that is a niche in the field of sustainable knowledge research. The model frames the qualification of several organisational characteristics and processes, the interconnection of which creates the possibility of knowledge sustainability. Previous research at the theoretical level has assessed the influencing effect of a single attribute, but the assessment of the interrelationship of all these attributes is missing in the literature. When looking at these research results separately, the findings are the same except in one area. This research builds on the results of previous studies on trust (Novitasari et al., 2021; Bencsik and Juhász, 2023), including the impact of impersonal trust on organizational success (Vanhala and Tzafrir, 2021; Nosi et al., 2022). Many studies on the impact of an organisational culture based on trust make similar findings (Kim, 2019; Novitasari et al., 2021; Sharif et al., 2021). It is essential for knowledge sharing and teamwork, which is the basis for successful organisational functioning. Knowledge management system building has been a focus of research for many years, with particular attention to the problem of knowledge sharing. The findings in this area are confirmed by the present research (Al-Emran et al., 2018; Kianto et al., 2019; Rivera et al., 2021; Men et al., 2020). Similarly to the research on the impact of technostress, studies with different purposes give similar results. This implies that technostress affects trust between employees and between employees and IT/AI tools, which also determines trust at the organizational level and, through this, knowledge sustainability (Berke et al., 2021; Al Danaf and Berke, 2021). An important difference to the literature is the use of the “GNH of Business” logic, which provides the basis and framework for the theoretical model developed, The previous literature has argued that this logic is not applicable in business practice because it was developed within the framework of Buddhist culture (Daga, 2014). This position has been refuted by some previous studies, but these have not led to a breakthrough change in thinking (Sengupta, 2007; McCloskey, 2012; Greyling and Rossouw, 2024). The present research has shown that for all the parameters of the questionnaire used, the factor under investigation can be adapted under the same conditions in the context of Christian culture (Bencsik, 2023). The logical approach of the model, which combines employee satisfaction and organisational happiness with sustainability criteria, is particularly useful. The importance of the logic is supported by the emphasis on the preference for sustainability in the calculations (Bencsik, 2022). This thinking provides the opportunity to provide the framework for the theoretical model in the present research (Brooks, 2013). Thus, by combining the parameters highlighted in the research (trust, KM, IT/technostress), it provides an opportunity to build on previous research to provide the conditions for knowledge sustainability in the form of a holistic model. These results also provide an opportunity to answer the research questions: (1) A theoretical framework/model can be developed with a set of relationships that can be used to ensure knowledge sustainability; (2) The development and practical testing of the model requires the identification of the influencing effects of the relevant organisational characteristics. (3) In the meantime, the crucial role of organisational trust (in its personal and impersonal forms) has been demonstrated. (4) The model provides a logical framework for the thinking that underpins the synergy of conditions that ensure knowledge sustainability. In addition to answering the research questions, the hypotheses were confirmed. In this spirit, it was found to be true that the phenomenon of technostress negatively affects employee satisfaction, and hence organisational happiness, in all the areas studied. Of the areas studied that rated employee happiness, the highest level of dissatisfaction was found for the quality of life. In the area of management decisions and actions related to sustainability, there is a lag compared to the expected standard.

The aim of the research was achieved. A theoretical model has been developed that can ensure the smooth functioning of knowledge management systems in a trust-based organisational culture, based on a holistic approach that leads to the long-term sustainability of knowledge. The viability of the knowledge sustainability model (the cube of trust) has been demonstrated by the field study. The model developed offers a new approach and solution for business organisations. The 3-dimensional solution ensures that problems at the individual and/or organisational level can be solved without excessive disruption to the environment, rather than costly and lengthy organisational interventions at the point and time of problem occurrence. The model clarifies and speeds up managerial decisions and provides the conditions for balanced organisational functioning. It provides the basis for a continuous flow of knowledge within the organisation, which is both a prerequisite for the sustainability of knowledge and a guarantee for the sustainability of the environment and organisational operations (Nizamidou and Vouzas, 2021; Katou, 2022; Cesario et al., 2023).

The literature on sustainability is growing rapidly, with the results of studies of different aspects of organisational areas flooding the pages of journals (Contreras-Medina et al., 2023; Martínez-Falcó et al., 2023; Yeboah, 2023). Yet there are gaps without which any challenge to organisational sustainability will remain unanswered (Daghfous et al., 2023). These include sustainable knowledge, which is fundamental to all aspects of sustainability (Bencsik, 2022; Alkathiri et al., 2024). The present research seeks to fill this gap by developing a holistic approach. The theoretical research has identified the organisational characteristics that influence the sustainability of knowledge. Although these factors have been the focus of separate studies in previous research, (Alkathiri et al., 2024; Arduini et al., 2024) the exploration of their combined impact, interrelationships and relationship with knowledge sustainability represents a new approach. Exploring the links is a necessary but not a sufficient condition. A novel contribution to current thinking is the demonstration that the GNH way of thinking (Bencsik, 2023) provides a suitable framework for the full and continuous functioning of the elements of the knowledge management process. It also supports the sustainability of knowledge by addressing the negative effects of technostress in a culture of trust (Bondanini et al., 2020). Overall, a theoretical model has been developed that can support the sustainability of processes in any organisation by ensuring the sustainability of knowledge.

It is a natural phenomenon in the functioning of an organisation that problems arise, which management needs to address as best and as quickly as possible. The professional background to the decisions taken to solve these problems is not always available, and the decisions and their consequences are often questionable. The model developed allows the problems in the areas studied (which are sufficiently broadly defined thanks to the structure of the questionnaires) to be precisely identified. This will allow targeted decisions to be taken that can cost-effectively address the problems without disrupting the organisation as a whole. The model provides an opportunity to understand the interconnectedness of sustainable knowledge needed to support sustainable organisational functioning and to build on this to formulate strategic goals that will drive organisational success. Popular and nowadays fashionable sustainability initiatives without sustainable knowledge will provide a yield of a lifetime. If management is willing to accept the view that the sustainability of knowledge requires a culture of trust, a knowledge management system and a technostress-free organisational climate, then the model’s relationship system and framework is suitable for the long-term preservation of organisational knowledge. Not all elements of knowledge need to be retained. It is the responsibility of management to ensure that knowledge is passed on to successors that will ensure long-term success through economic, social and natural values, as defined by sustainability. This organisational position and mindset automatically bring with it the principles of sustainable management.

The limitations of the research include the neglect of the methodology (used in the research) used in the study. The number of studies based on the Alkire-Foster logic is significantly lower than the methodological solutions of studies with similar objectives. Therefore, a different approach and methodology may generate controversy. It is a question of dealing with a subject which, on the one hand, belongs to the field of alternative economics and, on the other hand, of focusing on several missing or little-used concepts. A precise and generally accepted definition of these concepts remains to be developed. There are serious gaps in the development of knowledge management linkages in business practice so the issue of sustainable knowledge is not even being addressed. The concept is completely new, both in the field of sustainability and in management science. Further confusing and unclear terms (happiness, satisfaction, flow, and well-being) are often used together in studies as synonyms. The model’s multifaceted approach can be difficult to follow for non-specialists. When testing a research model, the responses received may, due to the sensitivity of the topic, subjectively influence the results. Although the theoretical model has been tested, the novelty of the topic means that no firm statements can be made about its generalisability. The research was only able to cover a slice of the relevant literature. The relationship between the disciplines involved in the research has not been investigated before, so no results of control studies or theoretical comparisons can be presented. We could not find any studies in the literature that followed a similar logic, so we cannot provide supporting references in this respect. Thus, the paper is based on the original author’s ideas and professional value judgements, which have been validated in practice. The reluctance to respond, which is common to questionnaire-based research, may also be a problem in further phases of model testing (due to the complexity of the questionnaire).

In the next phase of the research, the results of a real managerial decision will be examined to solve a real problem. This phase of the research will demonstrate (through real-time management and successful outcomes of real problems) the validity of a new approach to ensure an organisation that can fully support the knowledge management process. Building on trust and targeting the well-being of employees, it will create the potential for full knowledge flow and sustainability (a prerequisite for ensuring a holistic approach is a sustainable management organisation and a sustainable environment around it). The impact assessment of practical application should also be kept in mind. Further options such as testing the model with an international sample are also possible directions. The extension of the model is also a future possibility, notably through digital solutions, network learning and the sustainability of AI-enhanced knowledge. Including additional factors (role of digital learning platforms, role of online communities) in the development of sustainable knowledge is also a possible research direction. Such research requires studies that clarify confusing concepts, fill in missing definitions and support the importance of knowledge in the design of sustainability strategies, and the role of ESG, in preference to the SDGs.

The research is supported by the Research Centre at Faculty of Business and Economics (No PE-GTK-GSKK A095000000-4) of University of Pannonia (Veszprem, Hungary).