Abstract
The aim of the paper is a holistic analysis of the technological, organizational and human capabilities for exploiting the potential of digital technologies for farming. The conceptual background results from maturity models. So far, these have been less pursued in the context of digitalization in agriculture. The paper is therefore dedicated to (1) the development of a digital maturity model for farms based on the state of the art in methodological standards for maturity model development, and (2) a quantitative analysis the current situation of farms (n=123) in Germany, including an evaluation of this developed model. The developed model comprises nine dimensions and is designed as a diagnostic tool for strategic farm management. When applying the tool to the sample, it becomes obvious that the digital maturity of farms is more advanced in the dimensions describing technology and human factors, while organizational dimensions are less developed. The paper ends with implications and limitations of the presented research.
1. Introduction
Digital transformation is an overall change process including technological, organizational and human factors affecting the agricultural sector (Lutz, 2017). Work processes in agriculture are becoming increasingly digital and interconnected (Schick, 2014) leading to a change of farmers’ activities from manual work to monitoring and controlling activities (Goller et al., 2020). While the sector is characterized as low digitized in global comparisons of industries (Pierpaoli et al., 2013), in Western countries there is a high level of automation, e.g. in animal husbandry or crop production (Pfeiffer et al., 2021). Scholars describe a shift from traditional methods in agriculture to sophisticated technologies (Rose et al., 2022). Porter and Heppelmann (2014) go beyond a pure technology-based perspective and treat agriculture as a use case for network-based business model development. Meanwhile, the concept of Agriculture 4.0 gains attention (Krombholz, 2019). For farmers, the use of Big Data, artificial intelligence, or even robotics can lead to a more resource-efficient and sustainable management of their farm (Fountas et al., 2020). This perspective includes capabilities of technology, organization and human actors.
Certain challenges need to be overcome to take advantage of the opportunities of Agriculture 4.0 (Abbasi et al., 2022). Common challenges include missing skills to use the technologies, a lack of trust, high investment costs, or even difficulties related to the interoperability of technologies (Rose et al., 2022). Farms also have concerns about the loss of privacy or firm specific knowledge because of Big Data (Sykuta, 2016). This is accompanied by the question of public acceptance of digitized agriculture (Pfeiffer et al., 2021).
The management literature indicates that organizations need to co-create the changes, so they don´t get hung up without other organizations doing so in parallel (Fitzgerald et al., 2013). In order to deal with these challenges, a digital maturity model has been proposed as a valuable and helpful tool for organizations to create the digital transformation (Schenk and Schneider, 2019). Even though there is an established research community elaborating these models in the context of the industrial sector (e.g. Schumacher et al., 2016; Teichert, 2016), they have not sufficiently been adapted to agriculture (e.g. Büyük et al., 2021; Kutnjak et al., 2020).
Only a few maturity models are tailored to the context of agriculture (Büyük et al., 2021; Kutnjak et al., 2020). The existing models (e.g. Büyük et al., 2021; Safiullin et al., 2021; Zhang et al., 2019) take an ecosystem perspective as a helpful approach for industry monitoring. Due to the broad focus, the instruments cannot easily be applied for managing a single farm as focal organization (e.g. Moore, 1993). Another challenge is that existing outlines stress technology infrastructure and software components when evaluating digital maturity (e.g. Büyük et al., 2021), but neglect further characteristics of the user context. From industrial management research on digitalization in SME´s (e.g. Herzog et al., 2022), it can be deduced that a socio-technical system approach including technological features, human users, and organizational context factors (Ulich, 2011) allows to address transformation challenges. A socio-technical approach enables a holistic perspective on people, technology and organization. In the sense of the socio-technical system concept (Strohm and Ulrich, 1997), these categories are entangled (Orlikowski, 1992), human-machine interaction takes place in a higher-level organizational framework. Technical and social framework conditions need to be taken into account when the research interest goes beyond facilities but aims to understand dynamic capabilities of organizational development.
The aim of the paper is to advance the maturity model development in agriculture with a specific entrepreneurial focus on farms. A tool tailored to farm management can be used regardless of the size or shape of the farm business and support farmers in their organizational transformation. A socio-technical foundation of the model is important to address options of strategic farm management development. In the next step, we outline characteristics of a holistic digital maturity model for farms as the focal organizations based on a literature review and a qualitative and quantitative field analysis. Going one step further, we apply this model to farms (n=123) in Germany and evaluate the digital maturity level.
2. Theoretical outline
2.1 Digital maturity of organizations
Digital maturity refers to the process of digital transformation of an organization and gives an indication of how far an organization is integrating digital technologies (Fitzgerald et al., 2013; Kane et al., 2019). Thordsen et al. (2020: p. 358) outline that “to evaluate the status quo of a company´s [organization´s] digitalization and to provide guidance for future investments, latest IS literature has established the term digital maturity.” Eremina et al. (2019: p. 2) further highlight that digital maturity can be defined as “willingness and ability of the company [organization] to change and apply innovative technologies, depending on the trends, in order to remain competitive in the market.” Digital maturity, therefore, is an indicator of how well organizations can master the digital transformation (Rossmann, 2018). Benchmarking the digital maturity of these capabilities supports strategic decision-making in organizations and enables comparison of the internal speed and dynamics of organizations with external developments (Teichert, 2019). If the digital maturity level is known, targeted measures for further transformation can be implemented (Kutnjak et al., 2020) and competitive advantages can be secured (Thordsen et al., 2020).
Weritz et al. (2020) suggest that there is evidence in the literature that many organizations do not yet have digital maturity. There are two different approaches in research to measure the digital maturity of an organization: (1) empirical digital maturity scales (Rossmann, 2018) and (2) digital maturity models (de Carolis et al., 2017). Within the framework of maturity models, maturity is assessed based on various criteria at different levels (de Bruin et al., 2005). Williams et al. (2019) highlight that maturity models are a popular tool for determining maturity levels of organizations. Using this model organizations can assess their current state of development in the context of digital transformation (Thordsen et al., 2020). Maturity models serve as monitoring instruments to benchmark the competitiveness of organizations according to the underlying criteria. At the same time, the models support subsequent development processes in the course of transformation activities. Since digital maturity encompasses both technological and managerial factors (Teichert, 2019), this construct takes a holistic approach and can be understood as a strategic management tool in organizations. Research indicates that the digital maturity of an organization is positively related to business performance (Çallı and Çallı, 2021; Irimiás and Mitev, 2020). The topic of digital maturity has a high practical relevance (Thordsen et al., 2020), especially against the background of constant changes and innovations due to digital transformation (Weritz et al., 2020) and their effect on organizations.
In recent years, the development of maturity models has increased and is important for SMEs in determining digital maturity (Schallmo et al., 2021). In many cases, models with an industrial/information system were developed (Sandor and Guban, 2021; Thordsen et al., 2020). Sandor and Guban (2021) base their proposition on a literature review on digital maturity models in SMEs. As an outcome, there is a focus on technological advancement of an organization to determine its digital maturity. They go one step further and take the technical architecture as well as the software components into account that represent organizational and people-related dimensions. Their model comprises technical solutions, hardware, software also including orgware, online presence and peopleware. In terms of socio-technical system design (Strohm and Ulich, 1997), there is a lack of a holistic perspective, where the interaction between human, organization and technology is central when it comes to overcoming digital transformation challenges (e.g. Herzog et al., 2022). Furthermore, Thordsen et al. (2020) conducted a literature review to analyze existing digital maturity models and stated that the majority of these models were developed by practitioners and that the development based on scientific criteria is not always recognizable.
When developing a maturity model, both a theoretical as well as a methodological basis should be implemented rather than just a pure project perspective (García-Mireles et al., 2012). For the generalization of maturity models, transparent development and the use of a methodological basis, as well as evaluation and validation, are important (Ifenthaler and Egloffstein, 2020). However, this is partly lacking in the general research (García-Mireles et al., 2012).
Becker et al. (2009) as well as de Bruin et al. (2005) developed transparent methods to create a maturity model. The method developed by Becker et al. (2009) is based on the seven phases of the design science process (comparison with existing maturity models, iterative procedure, evaluation, multi-methodological procedure, identification of problem relevance, problem definition and targeted publication of results). De Bruin et al. (2005) highlight six phases in the development of a maturity model (scope, design, populate, test, deploy and maintain). For the use case of agriculture in this paper, the method of de Bruin et al. (2005) is particularly suitable. Agricultural research in the context of digital maturity shows little evidence on which to build. At the same time, de Bruin’s et al. (2005) method offers more openness in developing a maturity model.
De Bruin et al. (2005) emphasize that the use of a maturity model can have three different objectives: (1) descriptive, (2) prescriptive, and (3) comparative. Descriptive means that the maturity model represents the current situation. It does not include the derivation of measures to develop maturity. However, the prescriptive view would include this perspective of development measures. In this respect, the comparative perspective allows a benchmark with organizations in the same industry on the basis of a maturity model. In general, it is important to align the dimensions (Teichert, 2019) and maturity levels (Pöppelbuß and Röglinger, 2011) of the model.
Based on these explanations, it is possible to derive various key aspects for the development of a maturity model:
(1) Defining a goal of the maturity model: only if it is clearly defined what the model is to be used for it can fulfil its task and provide strategic added value for the organization.
(2) Depending on the goal, the approach to the maturity levels should be designed: if the goal is to derive concrete further development measures, the translation of the maturity level should also make this possible.
(3) A systematic, methodical approach to the development of the model should be chosen if possible.
(4) A socio-technical perspective (Strohm and Ulich, 1997) enables the development of a holistic maturity model.
Given these criteria, a transparent and validated model can be developed, which is the aim of this article. For the use case of farms in this paper, the focus on maturity models is particularly suitable. This approach is advantageous for farmers, because they can directly compare their own farm to others. Furthermore, they can derive needs for targeted development opportunities.
2.2 Digital maturity models in agriculture
Only a few maturity models are tailored to the context of agriculture (Büyük et al., 2021; Kutnjak et al., 2020). For example, Utami et al. (2020) use an industry-unspecific maturity model to analyze the maturity of business processes of agribusinesses. To this end, Ronaghi (2021) developed a maturity model to assess the maturity of an agricultural blog chain or Mendes et al. (2021) focus on the maturity from agricultural start-ups. Maturity models with a focus on digital transformation or Agriculture 4.0 are less common in the agricultural literature (Büyük et al., 2021; Mendes et al., 2021). Thereby, the survey of the digital maturity of agricultural enterprises would be of particular interest against the background of the transformation to a digital agriculture (see Fountas et al., 2020). At the same time, it remains to be noted that some questions related to the digitalization of agriculture are still open, for example, new collaborative arrangements, impacts of digital agriculture or enabling use of data and technologies (Ingram et al., 2022). It is also evident that research on digitization in agriculture from a natural and technical science perspective is already more advanced than the consideration of this topic from a social science perspective (Klerkx et al., 2019).
In the agricultural science literature, three models tailored to digital maturity in the agricultural context can be found and are summarized in Table 1 below.
Overview of maturity models in the agricultural context
Citation: International Food and Agribusiness Management Review 2025; 10.22434/ifamr.1027
At first glance, it is clear that these digital maturity models focus less on farms and tend to be more dedicated to the digital maturity of the agricultural industry. Differences between the models become apparent primarily with regard to the approach taken in developing the model, the focused dimensions and the definition of maturity levels.
García-Mireles et al. (2012) made it clear that existing maturity models often lack a theoretical and methodological foundation. In the case of the agricultural digital maturity models, two models (Safiullin et al., 2021; Zhang et al., 2019) are based on a literature review, and for the model of Büyük et al. (2021) a best-worst method was used to.
Teichert (2019) highlights that digital maturity is a holistic construct that encompasses both technological and managerial aspects. This implication aligns with the human-technology-organization concept (Strohm and Ulich, 1997), which originates from sociotechnical system design. However, it is clear from the models that the focus is primarily on technological aspects. Less attention is paid to the interaction between the three levels of human, technology and organization. All three models (Büyük et al., 2021; Safiullin et al., 2021; Zhang et al., 2019) cover this holistic perspective. Büyük et al. (2021) also consider specific dimensions of agriculture, while the dimensions of the other models are more general. Even though the survey of digital maturity is an important topic, especially for SMEs across industries, there is no uniform approach to the dimensions used (Schallmo et al., 2020).
At this point, we take a more detailed look at the definition of maturity levels across existing models. Büyük et al. (2021) and Safiullin et al. (2021) developed a digital maturity model for agriculture based on a mathematical approach of maturity levels. While Büyük et al. (2021) used the simple weights average method, Safiullin et al. (2021) preferred an addition of values that were related to the target maximum indicator of digital maturity. Both approaches have in common that a digital maturity level is calculated across all dimensions. If we relate this approach to the aim systematization of the maturity models of de Bruin et al. (2005), the approach of Büyük et al. (2021) and Safiullin et al. (2021) tends to cover the descriptive form. Calculating a digital maturity level for an entire organization across all dimensions makes it difficult to identify barriers and areas requiring transformative actions. The digital agricultural maturity model of Zhang et al. (2019) is structured differently. Zhang et al. (2019) show a value for each dimension of digital maturity. Therefore, it can be assumed that the prescriptive approach is fulfilled.
2.3 Summary
From these theoretical considerations, it is apparent that two requirements should be considered when developing a digital maturity model for farms:
(1) A digital maturity model is to be developed from which farms can both derive transformation activities and carry out benchmarking. Maturity models can be used to specifically manage transformation processes in organizations (Schenk and Schneider, 2019). Managers not only have the opportunity to steer transformation processes with this tool, but also to make relevant design fields for transformation visible (Berghaus and Back, 2016). A roadmap for transformation activities can be derived from the maturity model (Teichert, 2019).
(2) Furthermore, it should pursue the scientific claim of a transparent development and use a relevant method. Thordsen et al. (2020) highlight that the majority of maturity models have a practical background, and the scientific framework criteria are less considered. Taking the perspective of García-Mireles et al. (2012), it takes a systematic approach to develop maturity models. Furthermore, a generalizable maturity model needs transparency in its development, a methodological basis, evaluation and validity testing (Ifenthaler and Egloffstein, 2020). It can be concluded that a systematic, methodical approach should be used to develop a maturity model.
In this regard, looking at existing agricultural digital maturity models already developed, it becomes clear that Zhang et al. (2019), for example, have chosen an approach for calculating maturity levels that allows the derivation of concrete transformation activities, but the dimension development is based on a pure literature study. In this regard, Büyük et al. (2021), for example, developed a maturity model that uses a descriptive form of maturity levels and different methods for dimension development.
There seems to be a gap in the research regarding maturity models that fulfill both requirements. The aim of this paper is, therefore, to develop a digital maturity model from a socio-technical perspective tailored for the specific target group of farms and following a scientific model development.
3. Methodology: development of the maturity model for agricultural farms
The aim is to develop a maturity model that allows to identify farm-specific development needs in the context of digital transformation in order to support strategic decision making in farms. For this reason, a field study and data analysis took place within a research project in Germany founded by the federal ministry of food and agriculture.
In the context of this paper, the maturity model for farmers was developed in accordance with the method of de Bruin et al. (2005). This method included six phases. The method begins with the scope phase, stating that the target group of the model are farms. Thereby, just as in Schumacher et al. (2016), the maturity model has a scientific as well as a practical purpose. On the one hand, the maturity model is used to collect data for research and to provide a picture of the digital maturity of farms. On the other hand, farmers can assess their digital maturity based on relevant factors and derive strategies for developing their digital maturity. As part of this phase, it was determined that the model had both a prescriptive and comparative scope.
In the next phase, the design of the model was determined. The model should consider the holistic construct of digital maturity (Teichert, 2019) and is therefore based on the theoretical human-technology-organization model (Strohm and Ulich, 1997).
The third phase of the method, populate, focuses on the content of the dimensions and the operationalization of the maturity measurement. For the development of the dimensions an iterative approach was chosen, which includes several different methods. Based on a literature review of relevant current issues in agriculture, 15 semi-structured expert interviews on digital change in agriculture were conducted from February to March 2020, including an image-laying technique that provided insights into the agricultural value network. A pre-test was carried out beforehand to evaluate the interview guide. The interviews focused on the current changes in agriculture, but also on the challenges and potential associated with digitalization in agriculture and how working methods on farms have changed. The participants were representatives of various stakeholders in German agriculture including chambers, associations, research institutions, service providers, financial institutions and farms from all over Germany. The interviews lasted 90 minutes on average, which were then transcribed and analyzed using a structured content analysis according to Mayring (2000). Looking at the human-technology-organization model according to Strohm and Ulich (1997), it becomes clear in the analysis of the expert interviews that there are different understandings of the concept of digitalization at the human level. It is often associated with support in office work, such as “digitization means that things that used to be written on paper are now written on the computer” (Interview 15, farmer). A different perspective on digitalization is, “in principle, I hope that digitized agriculture will make things easier, because digitalization can’t mean that the processes I do manually or on paper today will be done digitally tomorrow because I convert a sheet of paper into a PDF. That’s not digital for me. For me, digitalization is helpful and useful when it simplifies entire processes” (Interview 10, bank adviser). Although digitalization is a new subject area for farmers, they have comparatively high digital competencies as they benefit from their affinity with technology, because “farmers are tech-savvy and enjoy tinkering around and are not at all afraid of technology” (Interview 14, chamber of agriculture). From a technological perspective, digital technology is only invested in when the benefits are recognizable and, above all, quantifiable for farmers. One expert sums it up as follows: “Digitalization must bring the advantage that I can work more efficiently and thus save money and time. It has to do that, of course. And if that is then recognized or something, then we also have a win-win situation” (Interview 1, service provider). It is also clear from the interviews that fundamental strategic changes at the farm are only implemented “with the succession of generations” (Interview 14, chamber of agriculture). At the same time, farms are currently facing various challenges that they need to overcome to survive on the market, like economically, it is the generation of profit, ecologically it is the protection of the climate and the environment, socially it is the view of society and politically it is the management of data (Interview 3, researcher).
Based on the interviews results, a quantitative questionnaire was developed to survey farmers in Germany. This approach was chosen to gain an insight into the needs of farms. The survey focused on competencies of farmers, use of digital technologies and dealing with and overcoming current challenges as farms. Items for the online survey were derived from the core results of the expert interviews based on the responses of the interview participants and supplemented by the literature. The items are based on a seven-point response Likert-scala ranging from strongly disagree to strongly agree. To validate the questionnaire, a pre-test was carried out with representatives of the German agricultural sector. The only prerequisite for participation in the survey was that the participants were farmers from Germany. The link to the survey was distributed within the project network and via other distribution lists, for example, published in farmers’ magazines, sent to customers of an agricultural bank, or shared via farmers’ networks. The link to the online survey was accessed a total of 359 times between September 2020 and February 2021, and 123 farmers from across Germany participated.
The sample (n=123) has following characteristics: 87% of the participants were male and 47,2% of the sample were academics. With 91,1% mainly farmers with one farm took part in the survey from different regions of Germany. The majority of the farmers (91,0%) had a conventional farm. The random sample included different sizes of farms, from smaller to larger farms (measured in terms of cultivated area) and 80.3% of the farmers run the farm mainly as a main occupation.
Exploratory factor analyzes were carried out to analyze the data obtained with a view to developing a maturity model and creating dimensions. This procedure enables the analysis of item loadings and their interrelated effect on factors (Bühner, 2011). In order to analyze the item structure of the insights gained from the interviews, an explorative factor analysis was carried out for each item battery (extraction method main axis analysis with promax rotation technique) except for the competences of farmers. With regard to the competences of farmers, the focus is not on individual competence dimensions, but on competences in general. This approach is also reinforced by the fact that a closer analysis of competence profiles led to the conclusion that no competence profiles have crystallized among farmers.
The final step of the development is the operationalization of the maturity level. The purpose of the model is to enable farmers to derive transformation activities themselves and to provide them with a benchmark. Therefore, a maturity determination on a mathematical basis was used in alignment to current research. In the context of agriculture this approach has already been established by Safiullin et al. (2021) or Büyük et al. (2021). We adapted the approach in the following manner: We measured maturity levels as dispersion across percentiles. This approach provides farmers with direct information on their maturity compared to other farms.
4. Key findings
4.1 Dimensions of a digital maturity model for farms
The analyzes performed led to the following dimensions of the digital maturity model. “Farmer competencies” consist of one factor and includes 25 items. In addition to this human category, the understanding of digitalization was also analyzed in more detail. During the factor analysis (KMO=0.803, Bartlett test=0.000), two factors crystallized: “exploration mindset” (3 items; eigenvalue=1.189) and “exploitation mindset” (4 items; eigenvalue=3.278). Together, the factors explain 63.813% of the variance. From a technological perspective, the focus was on a battery of items based on the objectives on the basis of which farmers use digital technologies on their farms. The factor analysis (KMO=0.820, Bartlett test=0.000) crystallized the following factors: “establish a better relationship with consumer/customer” (2 items; eigenvalue=1.510), “improve production and processes” (4 items; eigenvalue=1.785) and “network with other actors and security in decision-making” (7 items; eigenvalue=5.595). The explained variance of these factors is 68.375%. The organization category comprises two item batteries for assessing current challenges. On the one hand, these are assessed with regard to in how far farmers are able to deal with them and, on the other hand, farmers assess how they can overcome them. The explorative factor analysis (KMO=0.836, Bartlett test=0.000) for the items on dealing with current challenges shows a four-factor solution: dealing with specifications, guidelines and the society´s view (5 items; eigenvalue=5.351), dealing with changes in terms of technology and infrastructure (5 items; eigenvalue=2.022), dealing with the market and competition (3 items; eigenvalue=1.219) and dealing with labor shortages and personnel management (2 items; eigenvalue=1.112). Together, the factors explain 64.695% of the variance. Also, the explorative factor analysis (KMO=0.791, Bartlett test=0.000) for the assessment of how farmers can overcome the challenges shows a four-factor solution: coping with internal issues (8 items; eigenvalue=5.579), coping with changes in the market (4 items; eigenvalue=2.305), coping with specifications and guidelines (3 items; eigenvalue=1.541) and coping with societal issues (2 items; eigenvalue=1.183). The explained variance of these factors is 62.404%. The results of the two factor analyzes show clear parallels in the factor formation, which is not surprising because ultimately the same challenges were assessed from two different perspectives. For this reason, the factors from the two analyzes were combined. The three factors dealing with specifications, guidelines and the view of society, coping with specifications and guidelines and coping with societal issues formed the dimension “coping and dealing with other external and societal factors” (10 items). The two factors dealing with market and competition and coping with changes in the market formed the dimension “coping and dealing with changes in the market” (7 items). The third dimension “coping and dealing with internal factors” (15 items) is formed from the three factors dealing with changes in terms of technology and infrastructure, dealing with labor shortages and personnel management and coping with internal issues.
In conclusion, it can be summarized that a total of nine dimensions have emerged from the investigations. Each category is reflected by three dimensions. Table 2 provides an overview of the dimensions developed and their definition. An overview of the items can be found in Table A1 in the Appendix.
Overview of dimensions of the developed maturity model for farms
Citation: International Food and Agribusiness Management Review 2025; 10.22434/ifamr.1027
The human issues category comprises the dimensions farmer competencies (e.g. digital competencies), exploration mindset and exploitation mindset to define the digital transformation of agriculture. The technology category includes dimensions aimed at the goal of using digital technologies such as establish a better relationship with the consumer/customer, improve production and processes and network with other actors and security in decision-making. The third category of organization incorporates dimensions related to both coping and dealing with current challenges. The three dimensions are coping and dealing with internal factors (leadership, technology, infrastructure), coping and dealing with changes in the market, and coping and dealing with other external and societal factors (specifications, guidelines, society). Figure 1 shows the developed digital maturity model for farms.
Digital maturity model for farms (Herzog et al., 2022).
Citation: International Food and Agribusiness Management Review 2025; 10.22434/ifamr.1027
Also highlighted in Figure 1 the three maturity levels are the 25% percentile, the 50% percentile, and the 75% percentile. These percentiles indicate the degree of dispersion. Accordingly, the 25% percentile implies that 25% of the farmers’ responses are below this level.
4.2 Findings: digital maturity level of German farms
In addition to the content structure of the maturity model, Figure 1 also illustrates the results of using the maturity model as a diagnostic tool with farmers in Germany. The percentiles are based on the information provided by 123 German farmers, who participated in the farmer digital survey in 2020–2021. This diagnostic tool for farms shows in its application that farms tend to focus on the existing rather than taking a future perspective with regard to digital transformation. This means, that they associate digital transformation with the digitization of the office, rather than the possible future potential that comes with it. This is evident, for example, in the exploration/exploitation mindset. The terms exploration and exploitation go back to March (1991) and mean the focus on the existing as opposed to the focus on renewal. Farms tend to have an exploitation mindset (focuses more on digital transformation as a support for office work and relates more to the known and existing processes/procedures), rather than an exploration mindset (takes a future perspective in the direction of developing a new business model or acquiring new customers). This is also evident in the context of technology. Farmers’ perspective on digital technologies is on exploiting their potential for already established activities but not related to new options. Here again, there is a stronger focus on the existing (improving production and processes) and less on future topics (establishing a better relationship with the consumer/customer).
An overarching analysis of the three categories shows that farms have a comparatively higher level of digital maturity in the category human issues and technology than in the category organization. Although, as just mentioned, transformation activities building up digital maturity are also evident in human issues and technology. It is clear that there is more potential for transformation in the category organization.
The measurement of farm characteristics enables an assessment of the digital maturity level according to various farm characteristics (e.g. farm size, farm type, region, etc.). In order to carry out more detailed analyzes, a larger sample is required in a next step. With regard to the characteristic of farm size, however, initial tendencies can already be seen that smaller farms have a lower level of digital maturity compared to larger farms. This can be seen across all three categories analyzed.
5. Discussion
5.1 Summary of the findings
The first step in this study was to develop a digital maturity model for farms. This should enable farmers to independently assess the digital maturity of their farm and at the same time to compare it to other farms. In the second step, the study explored the use of this maturity model as a diagnostic tool for the maturity of farms in Germany.
Based on the analysis of existing maturity models in the context of digital transformation in agriculture, a maturity model for analyzing digital maturity was developed specifically for farms. The contribution of this paper lies in having developed a model that (1) is specifically tailored to farms from a socio-technical perspective and based on the latest methodological standards for the development of maturity models, (2) enables a comparison with other farms, (3) derives practical implications for the development of digital maturity of farms and that (4) has already been applied in practice. In many cases, the literature (e.g., García-Mireles et al., 2012) has been critical of the development of a model from a practical context. This was taken into account in this paper by basing the development of the maturity model on the method of de Bruin et al. (2005). In addition, the model provides a holistic view. According to Strohm and Ulich (1997), the model is based on the perspectives of human, technology and organization. These were tailored to farms by means of the definition of dimensions. In terms of socio-technical system design, these are analyzed individually within the framework of the model but interpreted collectively. The perspectives are interdependent. Particularly in times of digital transformation, the individual skills of farmers are needed in conjunction with technological prerequisites within the framework of organizational conditions. This also becomes clear in the analysis of the 123 farms and brings added value in the derivation of implications for the development of digital maturity. Not only from the farms’ own results, but also through benchmarking across maturity levels as a measure of dispersion, farmers can derive value for transformation processes.
With respect to the analyzed farms when using the maturity model as a diagnostic tool, it became apparent that they were already well positioned in the areas human and technology. However, it became clear that the perspective of the farmers is on the immediate present (exploitation). The spotlight lies on current processes and procedures, and optimization is sought through the use of digital technologies, for example. There is less focus on a future perspective with regard to the potential of digital transformation for the farm itself (exploration). By using digital technologies, for example, fundamental processes or procedures can be designed differently, or new/different business models can be developed. Farms seem to be good at linking human and technological components. The highly developed digital competencies support the use of digital technologies in this regard. The job profile of farmers is more technically oriented. At the same time, however, it is evident in the data that the step into the organizational dimension is still left out. This has already become clear in other contexts, but the organizational dimension remains outside (e.g., Hohagen et al., 2021). Farmers are aware of the potential of technologies, but the measures are not integrated into the organization and the farm management. The job description of farmers is undergoing a transformation, from working in the field and in the barn to more work in the office. Brooks (2021), for example, talks about a digital farmer. Technological advances are making an impact on the industry of farming from traditional farming practices to more automated and data-intensive practices (Fountas et al., 2020). Ultimately, this also leads to a role transformation of farmers into farm managers with supervisory and monitoring responsibilities. Digital transformation enables farmers to monitor farm operations remotely (Fountas et al., 2020). Ultimately, the role of farm management is also accompanied by strategic and management tasks aimed at combining individual capabilities with technological prerequisites with reference back to the organizational framework conditions.
5.2 Theoretical implications for the research and practical implications for farm managers
The literature shows that only a few digital maturity models with reference to the agricultural context were developed (e.g. Büyük et al., 2021; Kutnjak et al., 2020). Based on that, a model was developed with a specific focus on farms. Farms play a key role in the agricultural sector and bear the responsibility of feeding the citizens of Germany. Farms are subject to structural change, which is reflected in the fact that fewer farms are cultivating larger and larger areas. Because of this change in combination with the digital transformation it becomes clear that it is important to develop a model that supports farmers in strategic decision-making. Farmers can now use a model that is based on scientific criteria development and also enables comparison with other farms. There is a great added value in that. As part of the development, it is shown how a model can be developed that allows the comparison of farms. The developed model goes well beyond the existing models in the agricultural literature. This is particularly evident when referring to the summary of existing maturity models in agriculture in table 1. Table 3 shows the comparison between the maturity model developed in this paper and the models already existing in the literature. It also becomes clear that the development approaches greatly differ.
Comparison of the developed maturity model with existing models from literature
Citation: International Food and Agribusiness Management Review 2025; 10.22434/ifamr.1027
An added value of the contribution also lies in the theoretical anchoring of the model in the socio-technical system concept according to Strohm and Ulich (1997). In addition, a holistic approach is taken to analyzing the digital maturity of an organization. In contrast to the existing models (see Table 1), the developed model focuses even more strongly on the human and organizational level. While, for example, Büyük et al. (2021), Safiullin et al. (2021) and Zhang et al. (2019) look at more technological individual aspects with a view to assessing the digital maturity of an organization, the developed model focuses on an integrative perspective of human, technology and organization. In comparison to the existing models, less of a purely technological perspective is taken with a view to the use of the latest technological innovations, data management or the digital competencies of employees. Ultimately, the digital maturity of a farm also includes dealing with political framework conditions or other external factors such as competition. In addition, the developed model also focuses more on the farmers themselves as managers of the farm. The models already available in research focus less on managers and more on employees. In addition, the model was not only developed theoretically but was used directly in the context of a quantitative study and was subjected to an initial evaluation in this context.
Practical implications for farms can be derived from the use of the maturity model as a diagnostic tool. Using this tool enables farmers to analyze the digital maturity of their farm based on scientifically sound criteria. The tool is therefore suitable as a basis for decision-making with a view to the future strategic digital orientation of the farm and can be understood as a kind of checklist for farmers with criteria that are important for promoting digital maturity. The integration of this findings in other strategic tools like a SWOT analysis (see e.g. Müller-Stewens and Lechner, 2016) enable the determination where the farm currently stands. From this a digital organizational strategy could be developed. This provides farmers with a concrete framework for action that supports them in their strategic work. As farmers increasingly become managers of their farm, there are ultimately a variety of requirements that need to be met. Tools such as the digital maturity model offer the potential to support farmers in their strategic work and ultimately reduce the demands placed on them. Farmers can use this tool to take an initial inventory of their digital maturity and then repeat the measurement at regular intervals to evaluate the measures taken to promote maturity.
The goal in using this diagnostic tool should not be based on developing a farm’s digital maturity to the highest level in all dimensions. Although, higher digital maturity is associated with higher competitiveness (Thordsen et al., 2020). Farms can also be competitive in the market, even if a few dimensions are not highly developed. Therefore, the focus should be on a dimension that is suitable for the farm. This is also because the context factors of the farms must be taken into account when deriving measures to support the transformation process on the farm. Contextual factors such as the positioning of the farm in a niche area or the availability of fewer resources (financial, material, human) can influence the level of digital maturity but also its ability to develop.
Practical implications for the development of digital maturity can be drawn from the results of the 123 farms. This can be done at different points. With reference to the human perspective, a concrete starting point would be the competence development of farmers through, for example, learning in the process of work via informal learning (Elsholz and Gillen, 2012). The ability of farmers to act across situations can be promoted through targeted measures. In the work context, digital competencies can be promoted, which are of central importance due to the digital transformation and the accompanying changes in the demands placed on farmers. In addition, it became clear in the results that farmers are less likely to set their sights on digital transformation in the direction of future potentials. According to March (1991), a distinction is made between maintaining what exists as opposed to activities of renewal. Here, specifically, the ambidextrous focus could be highlighted, which is built on the balance between existing and renewal. First of all, farmers must be sensitized to the future prospects of digital transformation. Reflecting on the potential that the transformation offers can help them develop their digital maturity. At the same time, farmers can position their farm more competitively for the future and thus make long-term strategic decisions. Digital transformation can have positive influences in a variety of areas, and in the long term, it can help to attract new customers or change business models. This can also be accompanied by a different perspective on the use of technologies. Here, the potentials of (digital) technologies should be made clear to farmers. Focusing on the benefits that can be achieved using digital technologies can counteract the barriers to their use (Gabriel et al., 2021). Finally, a management perspective should be supported with recommendations for action. The results make clear that farmers need more support in the strategic positioning of their farm to be able to develop successfully in the future and to be competitive in the market. If farmers are supported in their farm management, the digital maturity of their farms can be increased. One starting point would be the development of courses, that cover aspects of strategic business management as well as specific tools for market positioning (see e.g. Müller-Stewens and Lechner, 2016). However, the commercial competencies of the management or the use of digital technologies also play a role here. Additionally, the results show that activities are still geared more towards single-loop learning and less toward double-loop learning (Argyris, 1976). However, at this point lies strategic potential for the development of farms, which is becoming increasingly important in times of change. Double-loop learning takes more of a future perspective in this context.
It becomes clear that the development of a farm’s digital maturity can start with different aspects. At the same time, it is also visible that different measures can interact with each other and that a holistic approach is more effective for the sustainable development of maturity. The fact that farmers could compare their farm with other farms that also correspond to their business sector or size, for example, means that they can achieve greater added value for the derivation of targeted measures.
Organizations can be particularly influenced by external factors or unforeseen crises (Duchek, 2020). Farmers should therefore be sensitized to external influences that can have an impact on farms’ operations or processes. Ultimately, a holistic view of a farm can help farmers to better position themselves in the face of crises and adversity. The development of digital maturity can go hand in hand with the promotion of organizational resilience to develop the resilience of the farms (Robertson et al., 2022).
6. Limitations and outlook
The aim of this work was to develop a digital maturity model for farms that would identify farm-specific development needs in the context of digital transformation and from which concrete recommendations for action could be derived. In addition, the goal was to apply the model and gain an initial insight into the current situation of German farms. Still, some limitations emerged by the development of the digital maturity model, which will be discussed in the following. First, it has to be mentioned that the validation only includes data from German farmers. Thus, the dimensions as well as the operationalizations are specific to this target region. When applying the model in other countries that have a significantly lower overall level of digital infrastructure, a classification of the results or an adjustment of the operationalization must be made. With respect to future research questions, the application of the maturity model within an international context to validate the proposed scales would be fruitful. A further limitation of the study is that the sample is currently too small to make statements regarding the digital maturity of different farms clustered by farm size, farm type or region. However, these findings would be more interesting from both a research and practical perspective in order to put together, among other things, target-specific development offers for farms, but also comparisons between the farms could also provide even more insights. These limitations open up a wide range of possibilities for further research. In view of the results of the diagnostic tool on the digital maturity of German farms, the next step should be to expand the sample. This would allow more detailed analyzes of farms according to various farm characteristics. At the same time, an extension of the sample could also be pursued beyond Germany and throughout Europe. This could provide insights into the level of digital maturity of farms beyond Germany. To take this step, however, the developed tool needs to be evaluated in other regions and other development contexts of digitalization. The tool is currently very much tailored to farms in Germany, and it is difficult to generalize the results beyond German farms. Other regions could use this tool to gain an initial insight into their digital maturity and thus make initial trends visible. To use the tool beyond Germany’s borders and implemented in operational practice, further evaluation is required. In this context, however, longitudinal analyzes could be carried out using the example of German agriculture in order to make details of the development perspectives visible. This regular monitoring could also make it clear which measures are fruitful in developing digital maturity of farms. To pursue this path of expanding the data, the maturity model will be implemented in a computer-aided manner in the future, but also to facilitate self-assessment, as has already been done by Büyük et al. (2021). In this way, farmers will be given the opportunity to use this tool independently and at the same time receive their results from the system. From a researchers’ point of view, a software-based implementation of the maturity model can also drive the expansion of the database.
Furthermore, it is clear that not all phases of the development of a model by de Bruin et al. (2005) were equally considered in the development of the digital farm maturity model. This is another limitation of the paper. The model presented here has not yet been conclusively evaluated and the model needs further application fields to be conclusively evaluated. According to de Bruin et al. (2005), when developing a maturity model, the testing of the quality criteria as well as the testing of the generalizability are of central importance. This is exactly what is lacking in some studies (García-Mireles et al., 2012). However, by extending and applying the maturity model, these important points can be addressed.
The digital collection of data, but also the implementation of the maturity model, is also accompanied by a limitation of the study. There is a possibility that only farmers who have a certain degree of digital affinity will use the maturity model. Farmers who have less contact with digital surveys, for example, could stay away from data collection.
Another limitation is that the measures to promote digital maturity cannot be developed independently of the contextual factors of the farm. In order to develop effective measures, they need to be adapted to the circumstances of the farm. This includes the consideration of various resources, such as financial or human resources.
This topic is not only highly relevant from a research perspective, but also from a practical perspective. Particularly in the light of unforeseen situations, it would be beneficial for farmers to be able to present their farms competitively on the market, which could benefit from giving an insight into the digital maturity of the farm. The first step is to make farmers aware of this topic and to embed the model in agricultural training as a strategic management tool. Farmers can use this diagnostic tool to visualize strategic development opportunities on their farm and thus make targeted decisions. At the same time, it can also be used to prepare for the changes brought about by Agriculture 5.0, which includes for example machine learning (Martos et al., 2021).
Acknowledgements
The authors declare that they have no conflict of interest. This work took place within the BMEL funded project “Experimentierfeld Agro-Nordwest” (Funding Code: 28DE103D22).
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Appendix
Overview of the items
Citation: International Food and Agribusiness Management Review 2025; 10.22434/ifamr.1027
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