Digital Heritage from a Socio-Technical Systems Perspective: Integrated Case Analysis and Framework Development

Abstract

Digital heritage (DH) research serves as a bridge between technological applications and broader cultural, social, and policy issues. A comprehensive understanding of DH requires the integration of multiple fields. To address this, this work applies a socio-technical systems (STS) perspective to DH as a strategy to bridge the technological and social aspects. It first examines how DH functions as STSs, analyses the dynamic interactions between technological and social subsystems, and explains the need to achieve joint optimisation to tackle the complexity of DH research. Second, a comparative analysis of six STS models is conducted, using the Venice Time Machine project as a representative case, to explore both the potential and limitations of STSs as a theoretical framework for DH. Third, STS theory is applied to emphasise that the approach needs to incorporate cultural expression, technological feasibility, diverse stakeholder interests, and long-term adaptability in order to address the complexity of current DH challenges. Finally, an STS-DH framework is proposed to guide the design, implementation and evaluation of DH projects using the elements identified through the present analysis. This work extends STS theory applications to cultural heritage digitisation; provides stakeholders with new practical tools; recognises the lack of empirical research in this field and highlights the need for further research.

1. Introduction

Cultural heritage is a social construct that evolves over time and space in response, among other factors, to social, economic and cultural processes [1]. With the advent of the digital age, the cultural heritage that is created, used, and disseminated is undergoing profound transformation. Notably, UNESCO regards digitisation as a key strategy for protecting world cultural heritage [2]. This strategy involves early-stage digital archive construction, mid-term 3D modelling and virtual reconstruction, rapidly developing immersive interactive experiences, and AI-driven intelligent restoration. The use of advanced technologies has increased rapidly, influencing how communities worldwide develop and experience heritage [3]. Alongside this transformation, new terms have emerged from mass digitisation, among which is digital heritage (DH) [4]. For clarity, this study follows UNESCO’s [5] definition, which describes DH as unique resources of human knowledge and expression that are either created in digital form or converted from existing analogue sources. DH is a broad field of research and practice, in which the use of digital technologies to transform cultural heritage is its core approach [6]. Despite the growing attention to digital technologies, the digitisation of cultural heritage should be understood as more than a purely technical matter. Discussions in the cultural sector and academia have centred not only on the potential of digital technologies [7], as DH is a multidimensional system, involving technology, society, environment, culture, the economy and politics. Hence, researchers and practitioners in the field must explore advanced technologies while paying close attention to the complex interactions among cultural values, social structures, policies, regulations and relevant stakeholders.

Since it was first proposed, socio-technical systems (STS) theory has enjoyed around 70 years of international development and application by researchers and practitioners [8]. STS theory emphasises the inseparability of technology and society, and advocates that both should be taken into account in the design and application of solutions [9,10,11]. Its core philosophy emphasises the joint optimisation of social and technical subsystems and, as an open-system design framework, it proposes that environmental factors will influence the way the overall system behaves [12]. The STS perspective recognises that DH faces complex challenges that go beyond purely technical solutions, i.e., they involve humans, organisations and broader social systems [13]. More specifically, cultural heritage involves local contexts, historical milestones and diverse cultural traditions that shape the environmental subsystem [14]. From this perspective, digital heritage can be understood as a socio-technical system, with digitisation technologies being the technical subsystem, and cultural heritage institutions and related practices being the social subsystem, all of which are influenced by the external environment, for example, governmental policies, funding structures and public values.

While the use of technology is becoming increasingly important in the field of cultural heritage, DH research still faces several challenges. Cultural heritage takes many forms, both tangible and intangible, each with its own set of digitisation requirements. Concurrently, technological evolution, such as 3D representations, extended reality, and cloud computing, continues to shape DH practices, the nature and potential of which vary depending on heritage projects. Furthermore, DH involves a wide range of stakeholders at various levels, from governments and cultural institutions to communities and content creators, all of whom have different interests, capacities, and expectations. DH projects must also address sustainability, ethical concerns, and social dimensions, recognising that cultural heritage digitisation is more than just a technical process; it is also about cultural memory and identity. These factors, taken together, highlight the complexity of DH research, as well as its fragmented assessment methods and lack of uniform standards. While STS approaches offer many solutions to complex problems in various fields, few studies have applied them in the DH field. These challenges highlight opportunities to achieve a more balanced integration of technical and social systems in DH, particularly through deeper engagement with STS theory. To bridge this gap, this study expands the applicability of STS theory to DH research. From a theoretical perspective, STS theory has been applied mainly in the fields of industrial organisation, system design, and information systems [10,15,16]. Applying it to DH will not only help to explain the complexity of the digitisation process; it will also provide a testing platform for the adaptability of STS theory to emerging social issues. From a practical perspective, the framework proposed in this study may facilitate the design, implementation and evaluation of DH projects. STS theory has moved on since its inception. There has been a call for a contemporary analysis, given technological advances, specifically from the perspective of socio-technical sustainability [17]. In the context of the rapid development of digital technologies and the increasing importance of cultural sustainability, this theoretical framework may provide more possibilities for DH research.

2. Research Aim

This study has three main aims. First, it aims to integrate previous research on DH and STS theory, focusing on connections between the two. Second, a case study explores DH from an STS perspective, with the aim of demonstrating that STS shares characteristics with DH. Third, it aims to create a framework that combines DH and STS theory as an exploratory research direction, serving as a practical reference for the digitisation processes of DH projects. Based on these aims, the following research questions are posed.

Q1: What are the key connections between DH and STS theory?

Q2: How do these connections demonstrate that STS can explain the characteristics of DH?

Q3: Building on these connections, what are the key elements that should be present in a socio-technical framework for DH based on STS theory?

To address these questions, the remainder of the work is structured as follows. Section 3 describes the research methodology in detail. Section 4 reviews the theoretical foundations of DH and STS and explores the applicability of STS theory to DH projects. Section 5 takes the Venice Time Machine as a typical case and, using a matrix analysis, assesses the strengths and weaknesses of existing STS models. Section 6 discusses the research results, with an emphasis on developing an STS-DH framework. Section 7 concludes with a discussion of the research implications of the study and suggestions for future work.

3. Methodology

This study adopts a qualitative approach to conduct a theoretical review of previous research works in the DH and STS fields, identifying the key characteristics of these fields. It then explores STS theory and its alignment with DH, highlighting both its applicability to and its limitations in terms of DH research. This two-stage, analytical approach provides a solid theoretical foundation for the work.

A single-case multi-model analysis strategy, the most important methodological component of the study, was adopted. Relevant case study guidelines were followed to design the study and select a specific example that would be representative and typical of DH research [18]. Additionally, we drew on secondary public data and conducted a systematic analysis (desktop-only, with no direct contact with the project) [19]. The study then followed a theoretical triangulation strategy [20]. Six STS theoretical models were applied, in turn, to the analysis of the case. Matrix analysis has been used in various academic and practical applications [21]. This form of analysis is very suitable for comparatively evaluating the advantages and limitations of different theoretical models, in this case DH-related, and lays the foundation for building an inclusive framework.

Finally, the framework development method was applied to create a conceptual basis for the socio-technical digital heritage system. This framework aims to undertake groundbreaking work, apply STS theory, and establish a new model based on DH characteristics, which can be continuously improved in future research.

4. A Review of Digital Heritage and Socio-Technical Systems Theory

4.1. Current Digital Heritage Research

Research into DH has been extensive in the past few decades, with numerous projects undertaken worldwide to establish standards, complement best practices, and propose new theories. However, the digitisation of cultural heritage is a complicated, difficult, and controversial task [22]. DH research involves a variety of disciplinary perspectives, such as technical preservation in information science [23], intellectual property concerns in legal studies [24], and user experience considerations in human-computer interaction [25]. The digitisation process also involves multiple institutions, including memory institutions, technology labs, and cultural policy makers, whose concerns differ significantly [4]. This is also reflected in related reports and documents from international organisations such as WIPO, ICOM, and UNESCO, which contain material that is complex and at times contradictory, requiring consideration of a wide range of commercial, cultural, ethical, historical, moral, religious, or spiritual issues [4]. This dispersion leads to a lack of standardisation of research methods and theoretical frameworks, hindering the overall development of the field.

4.2. Multidimensional Complexity in Digital Heritage

4.2.1. Diversity of Heritage Types

The first challenge for DH research is to address the various types of cultural heritage and their respective digitisation requirements. Ahmad [26] divided cultural heritage into tangible and intangible heritage. The digital preservation of tangible heritage focuses primarily on capturing its physical characteristics and facilitating its dissemination. The rapid development of digital technologies such as remote sensing, laser scanning, cloud computing, and virtual reality creates opportunities to preserve and share tangible cultural heritage [27]. The digital protection of intangible heritage is more complex. According to Giaccardi’s research [28], intangible heritage digitisation requires the development of new recording approaches that combine audiovisual technologies, interactive narratives, and community participation.

4.2.2. The Transformative Role of Technological Tools

The second complexity challenge in DH is that the evolution of technological tools shapes its practices. Advances in cultural heritage digitisation practice have been driven by technological breakthroughs [29]. Advanced digitisation, digital preservation, and accessibility requirements have transformed conservation and scientific research methods in the cultural heritage field [30]. Technological advancements such as extended reality, social media, 3D representations, aerial scanning, personalisation, mesh networking, IoT, and automated guidance, among others, are expected to shape the next two decades [31]. Similarly, as previously discussed, cultural heritage has multiple characteristics and is not a conventional data source; therefore, the digitisation of various types of heritage requires careful consideration [32]. DH projects need to use specific digital technologies, either in isolation or in combination, depending on their objectives. The nature and potential of digitisation therefore vary from programme to programme and project to project, even within institutions [33]. There remains complexity in terms of the most appropriate investigative, documenting, and planning approaches [34].

4.2.3. Multi-Level Subjects in Different Cultural Organisational Contexts

The further complexity of DH is reflected in the interactions between different cultural organisations. Castañer and Campos [35] argued that the abilities of a heritage organisation can be analysed from a macro-, meso- or micro-perspective. At the macro-level, Borowiecki and Navarrete [36] revealed that governmental policies, rules, strategic planning and guidelines shape digital practices. Countries have varying cultural traditions and political systems, which drive how they define and manage DH. At the meso-level, different types of heritage organisations, such as archives, libraries and museums, have distinct professional traditions, working methods and goals [37]. In addition, their decision-making is, naturally, influenced by disparities in funding, staffing and available technologies. At the micro-level, the participation of users, interest groups and content creators further increase complexity. Institutions have sought more active participation from local communities, with a growing focus on marginalised groups [38]. Nonetheless, there are significant inter-community differences, rooted in historical, cultural contexts and social relationships, in attitudes towards, and levels of engagement with, DH. Above all, these three levels are not linear, but form an interconnected network, and alignment among policies, institutional capacity and community expectations is key to the success of DH initiatives.

In general, the complexity faced by DH stems not only from these three levels. Among other factors, DH needs also to address the challenges related to the sustainability and continuity of cultural heritage over time [39]; to consider complex ethical challenges in order to balance the relationship between preservation, sharing and respect for cultural rights [40]; and to take a people-centred approach that recognises that cultural heritage digitisation is not only a technical issue but also a process of social memory and identity construction [41]. This complexity, interdisciplinarity and multidimensionality present both challenges and opportunities, offering a rich context for the application of STS theory.

4.3. Overview of Socio-Technical Systems Theory

4.3.1. Development History

STS theory was first proposed by Trist and Bamforth [10], at the Tavistock Institute, as a means of analysing the relationship between technical and social systems in the context of the mechanisation of British coal mines. Subsequently, Emery and Trist [9] proposed the principle of ‘joint optimisation’, arguing that system effectiveness depends on the synergistic optimisation of both technical and social subsystems, rather than the optimisation of a single dimension. STS theory is built on the foundations of open systems theory [42], i.e., that systems maintain continuous interaction with the external environment, and their boundaries are dynamic rather than static [43]. Leavitt [44] developed a model for the interaction of tasks, personnel, structures and technical elements in organisations, which provided an underlying framework for STS theory. Cherns [45] later summarised nine principles for designing STSs, which established a foundation for their application. Since the 1980s, the application scope of STS theory has expanded to the field of information systems. Bostrom and Heinen [46] applied the socio-technical perspective to the study of management information systems and analysed the socio-technical reasons for the failure of information systems. Entering the 21st century, the multi-level perspective proposed by Geels [47] applied STS theory to technological innovation and social change. Baxter and Sommerville [48] introduced the theory into the field of software engineering and developed socio-technical software engineering methodology. In the past decade, STS theory has continued to evolve to adapt to the challenges of intelligent, complex systems. DesignX aims to address the challenges of designing complex socio-technical systems, with its emphasis on systematic thinking, iterative design and interdisciplinary collaboration [49]. Today, the theory’s application scope has expanded to many fields, such as healthcare [50], urban design [51] and museum-based augmented reality [52].

4.3.2. Major Models

STSs, as interdisciplinary frameworks, involve broad cooperation between sociology, psychology, cognitive models, computer science, information science and engineering, with no discipline being more important than another [53]. Researchers, based on the foundational theories of STS, have developed several models to explain the concept.

Leavitt’s Diamond Model

Leavitt [44] introduced the Diamond Model in 1964 (Figure 1). Originally, the framework served as a cornerstone for understanding the dynamics of organisational change. The model views organisational systems as multivariate systems of four interacting and aligned components—task, structure, technology and people/actors. The model demonstrates the virtues of good classification: it is simple, comprehensive, sufficiently well-defined, and grounded in existing theory. It can be easily adapted by adding new categories as needed, wherever the nature of a specific problem requires [54].

Nograšek & Vintar’s Extended Model

Nograšek and Vintar’s [55] model is an upgrade of Leavitt’s model (Figure 2). Researchers often use it to analyse the influence of technologies upon organisational change. The model added the element of organisational culture to Leavitt’s framework and reformatted it into a five-element system (organisational culture, processes, structure, people and technology).

Davis et al.’s Hexagonal Socio-Technical Model

Davis et al. [8] extended, through action research and case study analysis, Leavitt’s model [44], proposing a hexagonal socio-technical model (Figure 3) featuring an organisational system with six subsystems—goals, people, processes/procedures, buildings/infrastructure, culture and technology [56]. Furthermore, the system exists with an external environment characterised by financial/economic circumstances, regulatory frameworks and stakeholders. However, Davis et al. did not provide much detail on the subsystems in their model. Some researchers use this lack of explanation to develop their own subsystems based on the context and aim of their research [57].

Bostrom & Heinen’s Socio-Technical Model

Bostrom and Heinen’s [46] socio-technical model (Figure 4) synthesises STS research. It is used mainly to analyse the interactions between technology and society in information systems. The model represents organisational systems as multivariate systems of social systems and technical systems. The proposal is that the two systems are interrelated and that they must continuously adapt to the external environment to maintain a state of equilibrium.

Geels’ Multi-Level Perspective Framework

Geels [47] proposed a multi-level perspective framework (Figure 5) that constitutes an important research method in socio-technical transition theory. In the framework, different levels of aggregation are identified—landscape development (macro-level), socio-technical regimes (meso-level), and technological niches (micro-level). The multi-level perspective argues that a technical transition is non-linear. It is a result of the interplay of endogenous and exogenous dynamics at three levels [58].

Whitworth’s Socio-Technical System Levels Model

The socio-technical systems levels model (Figure 6) features four different socio-technical system levels, namely hardware, software, human-computer interaction, and socio-technical [59]. Whitworth [59] argued that an STS is a social system built upon a technical base, with ‘technical’ beings the hardware-software combination, and ‘social’ being people, their interrelations, company policies, and norms. Each level evolves from the one below it and, in turn, reshapes the entire system. Thus, disciplines such as engineering, computing, psychology, and sociology represent different perspectives of the same complex system [60].

4.4. Analysis of the Applicability of STS Theory to DH

Before conducting the research, it was necessary to explore the consistency between the characteristics of STS and DH. This approach has been taken in other domains to explore the compatibility of systems approaches before applying systems methods [51].

4.4.1. Open System Complexity

Emery and Trist [9] originally coined the term STS to describe open systems that involve complex interactions between humans, machines and the environmental aspects of work systems. These interactions feature partly linear relationships, such as cause and effect, and partly non-linear relationships, which can be complex, unpredictable and unexpected [61]. DH combines the complexity of cultural heritage with the additional challenges set by digitisation. As such, DH is inherently more intricate than traditional heritage approaches. Previous DH-focused studies have demonstrated its complexity [62,63,64]. In particular, Limp et al. [62] proposed that the acquisition and creation of digital representations of heritage must involve a comprehensive digital ecosystem that fully takes account of all its elements and their interconnectedness. STS theory, which is based on open systems, provides an appropriate framework for analysing the complexity of DH.

4.4.2. Interaction Between Technical and Social Subsystems

The core of STS theory lies in the interaction between the technical system and the social system, while the digitisation of cultural heritage sits at the intersection of technology and cultural heritage. In the early 21st century, Russo and Watkins [65] examined how new media technologies connected cultural institutions with new audiences through community co-creation initiatives, showing that digital cultural communication needs more than just convergent technology infrastructure; the cultural institution must also consider the audience’s familiarity with the new literacy, and supply and demand within the target cultural market. This interaction between technical and social systems makes the DH system a typical STS.

4.4.3. Joint Optimisation

Joint optimisation is a key factor in STS theory, and the DH field requires joint optimisation thinking. On the one hand, technological innovations and applications determine the key trends in cultural heritage user practices [66]. On the other hand, technology projects that do not employ heritage experts often oversimplify cultural elements, compromising preservation goals [67]. These studies emphasise the importance of maintaining a balance between technological applications and cultural aspects to ensure that technologies truly aid in the preservation and popularisation of cultural heritage.

4.4.4. Multi-Level Perspective

Based on STS theory, Geels [68], taking a multi-level perspective, examined the dynamics of socio-technical transitions in the context of sustainability, emphasising that systems can be analysed at three levels, micro-, meso- and macro-. As discussed in Section 4.2.3., DH also exhibits multi-level characteristics. Although few studies have explicitly employed a multi-level perspective to study DH, some have addressed the integration of heritage sites into urban areas [69], interactions between heritage actors at local, national, and international levels [70], and landscape protection and planning [71]. It is anticipated that, as research into DH progresses, multi-level, perspective-based studies will significantly feature.

4.4.5. Limitations in Theoretical Fit

In this study it has been shown that DH has STS characteristics. The theoretical linkage between them can be understood as having four perspectives: (1) open system complexity; (2) interactions between technical systems and social systems; (3) joint optimisation; and (4) multi-level perspectives. However, due to the specific nature of cultural heritage, this theoretical fit has limitations that current STS theory cannot overcome. First, STS theory focuses on functionality and efficiency but lacks cultural dimensions. How to express heritage values through digital technologies is not explored in STS theory [72]. Second, the theory’s explanation of technical elements seems incomplete, and whether it can specifically explain DH technologies is an open question. In addition, the following question arises: can STS models, when applied to DH, explain that cultural heritage involves the creation of identities, social interactions and interpersonal relationships (based on their cognitive, imaginative and emotional elements) [28]? Finally, heritage is built over an exceptionally long time [73]. How can STS theory explain this complex, temporal dynamic change?

5. Case Study: Venice Time Machine Project

5.1. Case Selection

The Venice Time Machine project, one of Europe’s largest transnational DH projects, aims to create a historical equivalent of a ‘Google Map’ by reconstructing 2D and 3D representations of Venice [74]. The project entails interdisciplinary collaboration, cooperation across multiple institutions, and the use of various technologies, as well as a diverse network of stakeholders that reflects the complexity of DH. Its emphasis on openness, interoperability, and long-term accessibility, along with its sustained operation, provides a temporal perspective for examining the system’s development. In addition, the project provides publicly available data, including academic publications, project reports, and media coverage, allowing for systematic analysis without direct project access [19]. In this context, the Venice Time Machine Project provides an appropriate case for addressing the ‘how’ and ‘why’ questions of complex phenomena [75], which are central to investigating DH from STS theory. Its scale, ambition, and academic visibility, together with its alignment with the research objectives, motivated its selection as the single representative case study [76]. The authors have no personal or institutional affiliation with the project, nor any financial or professional interest in its outcomes; its inclusion was solely based on academic relevance.

5.2. Project Background

The Venice Time Machine is a pioneering international digital humanities scientific programme; it was launched by the EPFL (École Polytechnique Fédérale de Lausanne) and the University Ca’ Foscari of Venice in 2012 (Figure 7). An ‘ambitious project to digitise 10 centuries of the Venetian state’s archives uses big data and AI to reconstruct the history of Venice in virtual form’ [77]. In the project, the aim is to scan documents including maps, monographs, manuscripts and sheet music. It promises not only to open reams of hidden history to scholars, but also to enable researchers, thanks to advances in machine-learning technologies, to search and cross-reference information [78]. The Venice Time Machine is part of the Time Machine Europe project, which uses modern technologies to create Europe-focused historical spatiotemporal 4D reconstructions [79]. The project is now in its second phase, with activities being undertaken under its 2020 to 2028 plans [80]. In 2019, disputes arose between its partners due to data usage rights issues, resulting in the project being suspended [81]. The project resumed in 2020 with a new governance structure that places greater emphasis on multi-party participation and a balanced distribution of rights and interests.

5.3. Analysis of Venice Time Machine from an STS Perspective

This section discusses the application of six STS models (Section 4.3.2.) to the Venice Time Machine project. The primary source drawn on in this section is the proposal submitted to the European Commission by project leader Frédéric Kaplan [82].

5.3.1. Analysis Using Leavitt’s Diamond Model

Leavitt’s diamond model emphasises the interdependence of four organisational elements [44]. In the Task element, the project aims to digitise, transcribe and index multiple historical archives to create a semantically linked data graph of Venice’s history. In the Structure element, the project adopts an international cooperation framework, jointly led by Swiss and Italian institutions. Taking advantage of the synergies between multiple EU-funded projects, the plan is to establish business incubators and education centres to form a complete innovation ecosystem. In the Technology element, the project has developed a variety of relevant innovative technologies, including scanning technologies, distributed storage systems, deep learning architectures, semantic graphs, historical simulators and augmented reality interfaces, all driven by task requirements. In the People element, the project brings together more than 300 researchers and students in a multi-disciplinary team, including computer scientists, historians and architects.

5.3.2. Analysis Using Nograšek & Vintar’s Extended Model

Nograšek and Vintar expanded Leavitt’s model, reducing the Task element but adding Process and Culture elements, to provide a more comprehensive organisational change analysis framework [55]. In the Process element, the project has established a systematic workflow, from archive digitisation to deep learning analysis, through to the creation of a linked data network. Moreover, the project addresses the challenge of ensuring intellectual accountability in its reconstructions by establishing transparent processes that link historical records with computer-based visualisations. In the Culture element, the project brings together participants from different countries and disciplines (including basic sciences, engineering, computer science, architecture, history and history of art) to break traditional disciplinary boundaries, promote a culture of interdisciplinary cooperation and shift the paradigm of humanities research.

5.3.3. Analysis Using Davis et al.’s Hexagonal Socio-Technical Model

Davis et al.’s hexagonal socio-technical model expands on Levitt, adding Cultural, Goal and Infrastructure elements, emphasising the influence of external environmental factors [8]. The model provides a more comprehensive analysis of the Venice Time Machine project. In the Infrastructure element, the project developed digital storage systems, high-performance computing environments, data management systems and semantic network platforms involving a variety of physical spaces, especially integrating educational spaces to support students’ participation in research. As for external environmental factors, the project benefits from multiple funding sources, and plans are in place to make the centre self-financing to ensure its long-term sustainability in the cause of promoting cultural entrepreneurship. In terms of its regulatory framework, the project is being developed in coordination with the relevant EU framework and must comply with local regulations on cultural heritage protection. The project features a multi-tiered network of stakeholders across academia, cultural institutions, policy and funding bodies, technology providers, and end-user communities.

5.3.4. Analysis Using Bostrom & Heinen’s Socio-Technical Model

Bostrom and Heinen’s model divides information systems into technical and social systems [46]. Elements of the two subsystems are consistent with those in the models featured above, but the specific concept of joint optimisation between the two subsystems is worth discussing. In this project, the technical system provides scholars, research teams, and cultural institutions with unprecedented data processing capabilities. It is noteworthy that, mid-project, the institutional cooperation (Structure) in the social subsystem was interrupted for a time, causing the entire project to stagnate: this shows the consequences of an imbalance between technical and social systems.

5.3.5. Analysis Using Geels’ Multi-Level Perspective Framework

From the perspective of the multi-level perspective framework [47], the Venice Time Machine project can be analysed as follows. At the macro-level, global trends towards digitalisation and recognition of the importance of cultural heritage are increasingly prominent. These aspects are supported by EU policies and funding, which naturally create favourable conditions for progress in the area. At the meso-level, the project, often based on these EU policies and funding, operates under international interdisciplinary and cross-institutional mechanisms. At the micro-level, the project creates space for the development of new methods, which has resulted in several technological, educational, and business model innovations. The project has thus progressed through distinct stages. This temporal progression makes Geel’s framework, with its inherent time variable, an appropriate analytical framework for analysing and advancing the project.

5.3.6. Analysis Using Whitworth’s Socio-Technical System Levels Model

Whitworth [59] proposed a levels-based structure for STS, progressing from hardware, software, human-computer interactions to social-technology. We now analyse the project from physical infrastructure to social-technology organisation. In terms of hardware, the Time Machine project deployed advanced physical equipment, including new scanning technologies, distributed storage facilities, and high-performance computing infrastructure. In terms of software, the project developed complex data processing and analysis functions, including deep-learning approaches adapted to image analysis, large-scale semantic graphs with metahistorical coding and inference engines, and a 4D multiscale geohistorical simulator. To aid in the human-computer interaction area, immersive and augmented reality interfaces and a dedicated historical data search engine were designed. Plans are also underway to establish a virtual archive platform enabling students to directly participate in research projects. Furthermore, the socio-technical nexus constitutes the most critical level of the project, with one of its primary objectives being the creation of ‘a collective digital information system mapping the European economic, social, cultural, and geographical evolution across time’ [79]. The project has received multi-funding support and multi-national policy support, integrated multi-institutional resources, organised multi-disciplinary research teams, and planned a business incubation ecosystem. It is worth noting that the model’s framework emphasises the interdependence between the lower and upper parts of each level: this interaction is particularly evident in the Venice Time Machine project.

5.4. Model Comparison Matrix Analysis

Based on Averill [20], for the purposes of the study, the authors designed an evaluation matrix to (1) systematically compare the explanatory power of the six STS models discussed above in DH applications and (2) analyse whether a model has DH characteristics. This matrix is based on key DH-related points (discussed in Section 4.4.5.) that cannot be directly addressed through theoretical analysis. It includes four parameters for evaluating the explanatory power and applicability of each model:

(1)

Cultural Expression, the most important evaluation parameter. It specifically assesses whether the STS model can address what Smith [83] termed ‘authorised heritage discourse’, while reflecting cultural values and local ethical inclusion.

(2)

Technical Feasibility, this parameter assesses each model’s ability to analyse the different levels of DH technologies, such as, at a lower level, XR, laser scanning, and digital twin, and at a higher level, format migration and technological evolution [84].

(3)

Social Stakeholders, the task complexity in heritage conservation is beyond the knowledge of any single person, which reinforces the need for collaboration and turns teamwork into a key success factor [85]. This key parameter examines how the model accounts for the social interaction and emotional needs of the social stakeholders in DH (indigenous communities, heritage professionals and technicians).

(4)

Sustainability, cultural heritage, as a well-positioned development sector, operates under sustainability principles [86], which the digitisation process should maintain. This parameter addresses the long-term viability of DH, and the analysis examines whether the model has the capacity to balance the progressive nature of technological developments with the static nature of cultural heritage.

This study borrows a dimension of system analysis proposed by Buchanan [87]: What is systematised? This question addresses the core components within a system (such as people, technology, practices and institutions) and how they are identified, organised and incorporated into the system’s structure. Reflecting this thinking, this study regards a DH project as a complete system and employs four analysis parameters (Figure 8). This approach helps determine whether different STS models are logically sound and coherent enough to effectively support the development of a practical DH system.

5.5. Results

The matrix analysis is shown at

Table 1. STS Model Applicability Matrix for Digital Heritage.

Model	Cultural Expression	Technological Feasibility	Social Stakeholders	Sustainability
Leavitt’s Diamond Model	●○○○○ Lacks the ability to analyse this parameter.	●●●○○ Although the model incorporates the technological level, it cannot fully analyse this parameter in the heritage context.	●●●○○ Although the model incorporates the social level, it cannot fully analyse this parameter in the heritage context.	●●○○○ Lacks a time dimension, limited explanation of the long-term co-evolution of technology and society.
Nograšek & Vintar Model	●●○○○ Although the model incorporates the organisational culture level, it offers little explanatory power in the heritage context.	●●●○○ Although the model incorporates the technological level, it cannot fully analyse this parameter in the heritage context.	●●●○○ Although the model incorporates the social level, it cannot fully analyse this parameter in the heritage context.	●●○○○ Lacks a time dimension, limited explanation of long-term co-evolution of technology and society.
Davis et al.’s Hexagonal Socio-Technical Model	●●○○○ Although the model incorporates the culture level, it offers little explanatory power in the heritage context.	●●●●○ The model can analyse this parameter, as it takes into account the influence of external environmental conditions on technology.	●●●●○ The model can analyse this parameter, as it takes into account stakeholder influences in the external environment.	●●●○○ Lacks a time dimension, but includes a more comprehensive analysis of multi-factor interactions and environmental factors.
Bostrom & Heinen’s Socio-Technical Model	●○○○○ Lacks the ability to analyse this parameter.	●●●○○ Although the model incorporates the technological level, it cannot fully analyse this parameter in the heritage context.	●●●○○ Recognises that people are a key component, but has limited explanatory power in the heritage context.	●●○○○ Lacks a time dimension, limited explanation of long-term co-evolution of technology and society.
Geels’ Multi-Level Perspective	●●○○○ Although the model incorporates the culture, symbolic meaning level, it offers little explanatory power in the heritage context.	●●●●○ The model can analyse this parameter, as it takes into account, based on a multilevel perspective, the influence of technological dynamics over time.	●●●●○ The model can analyse this parameter; its multi-level structure explains wider cultural stakeholder issues.	●●●●● The model can effectively analyse this parameter, as it employs time as a variable.
Whitworth’s Socio-Technical System Levels	●●●○○ Although the model incorporates community requirements parameters in its socio-technical level dimension, it cannot fully analyse this parameter in the heritage context.	●●●●● The model can effectively analyse this parameter as it focuses strongly on the technological aspects of STS (hardware, software and human-computer interaction).	●●●●○ The model can analyse this parameter, as it takes into account specific social requirements.	●●●○○ Lack of a time dimension, but level structure effectively addresses the sustainable relationship between technical and cultural needs.

. The vertical axis of the matrix shows the six STS models, and the horizontal axis the key evaluation parameters. Each model is scored on a scale of 1–5: 1 = Very limited explanatory power; 2 = Limited explanatory power; 3 = Moderate explanatory power; 4 = Strong explanatory power; 5 = Excellent explanatory power.

Based on the four evaluation parameters, an analysis of the STS Model Applicability Matrix (Table 1) shows that:

(1)

Cultural expression is the most challenging parameter in the models, with no model achieving excellent explanatory power. The STS framework for DH should pay attention to cultural heritage characteristics, but the models seem to have limited explanatory power for cultural expression.

(2)

The models had strong technical feasibility performance, especially the Whitworth. Of course, whether the models can fully address the unique technical preservation challenges of DH requires further research.

(3)

For social stakeholders, most of the models showed moderate to strong explanatory power. However, these models lack the explanatory power and accuracy to address the specific social dynamics and interdisciplinary complexity of DH.

(4)

The Sustainability parameter measures the dynamic and time-based differences among the six models. Geels’ multi-level perspective achieves excellent explanatory power by explicitly focusing on changes over time. Other models show limited explanatory power, and lack the time dimension necessary to explain the long-term synergistic development of technology and cultural heritage that is critical to DH.

Aside from cultural expression, Whitworth’s STS model demonstrates the greatest technological feasibility. Davis et al.’s hexagonal model is the most comprehensive in terms of stakeholder elements, whereas Geels’ multi-level perspective provides the strongest explanation for sustainable development. This study confirms that, while existing STS models can explain the core elements of DH to varying degrees and provide valuable frameworks, they still have significant limitations. The results show the lack of clear cultural heritage elements, the limitations of a single STS theory, and the need for the combined application of multiple STS models. More targeted models are needed to address the unique characteristics of DH within STS theory.

6. Discussion: Towards a Socio-Technical Digital Heritage System (STS-DH)

In previous sections, we reviewed the inherent complexity of DH research and provided an overview of STS theory, identifying the key connections between the two. We then examined STS theory-based models using a case study, demonstrating that existing STS models can explain DH projects to some degree. However, these models have limitations, indicating the need for a more appropriate approach to explaining the elements of DH. This section discusses how the research findings address the three research questions posed in Section 2. The specific answers to each question are then examined in detail.

6.1. Digital Heritage Is a Complex Socio-Technical System

Based on a review of DH and STS theory, as well as a cross-analysis of the two concepts, there is evidence that DH shares key characteristics with STS, indicating that digital heritage (including the actions, processes, and outcomes of cultural heritage digitisation) is a typical socio-technical system. DH demonstrates open system complexity, interactions between technical and social subsystems, joint optimisation requirements, and multi-level dynamics. Furthermore, DH poses challenges that extend beyond the current scope of STS theory and introduces additional complexity as a result of its cultural heritage specificities, making it a more intricate system than ordinary STS.

6.2. STS Meets DH Characteristics Despite Limitations

The Venice Time Machine project case study examined STS models based on STS theory. These models differ in their elements and forms, but they share common principles and have been shown, to some extent, to address the multidimensional needs of DH projects, confirming that STS is a useful theoretical tool for explaining current and guiding future DH projects. However, the models vary in their explanatory power regarding technological aspects, stakeholder considerations, and sustainability, and share limitations in terms of cultural expression. This highlights the importance of developing a framework that integrates these elements to more specifically account for the unique characteristics of the cultural heritage digitisation context.

6.3. It Is Necessary to Develop a Framework for Open Research

While current research is still limited by a lack of empirical studies, it is critical to conduct additional research in this field. The research potential of STS in DH, as revealed in this study, supports the need for a new, concrete, and exploratory STS-DH framework (Socio-Technical Systems in Digital Heritage), building upon STS theory and existing models. As shown in the STS Model Applicability Matrix (Table 1), current STS models cannot fully support DH projects. Therefore, the study’s results suggest that, to provide that support, future STS-DH frameworks should strengthen the following: ensure the centrality of the cultural dimension, consider stakeholders’ multidimensionality, provide technology-specific levels of analysis, and adopt a multi-level sustainability perspective.

(1)

Centrality of the cultural dimension. Unlike generic STS models, the STS-DH framework should place cultural considerations at its core, rather than seeing them as secondary.

(2)

Multidimensional system elements. To achieve joint optimisation, the framework needs more specific DH elements.

Cultural elements (such as cultural values, cultural inclusiveness and ethical codes)

Technical elements (such as tasks, hardware and software)

Social elements (such as people, structure and policy)

Environmental elements (such as regulatory frameworks, financial conditions and stakeholders)

(3)

Technology-specific level. Following the approach of Whitworth [59], the framework should distinguish between hardware infrastructure, software systems, human-computer interactions and higher-level socio-technical concerns, recognising that meeting the challenges faced at each level requires different approaches.

(4)

Sustainability multi-level perspective. The framework should be able to undertake time-based analyses, drawing on Geels’ [47] model. The Geels’ multi-level perspective has been shown to be appropriate for modelling the multi-level interactions seen in DH projects.

The current focus of STS-DH is shown in the figure below (Figure 9):

To summarise, the STS-DH framework shares foundational principles with existing STS models (Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6), while also incorporating DH-specific elements. It explicitly incorporates cultural centrality, combines DH-specific multiple stakeholder elements, integrates both the multidimensionality of technological aspects and multi-level sustainability. Although this framework is still in its early stages and needs to be refined, it represents a step forward towards a more specialised and applicable model for concrete DH applications.

7. Conclusions

This study examines the applicability of DH within the framework of STS theory, and identifies the needs of DH projects in terms of the STS model. In addition, based on the research results obtained, an open development framework for the integration of STS and DH is proposed. As a result, this study has addressed the three research questions by demonstrating the following: (1) DH itself can be understood as an STS, with STS theory explaining DH as a complex system shaped by the joint optimisation of technology and society; (2) through case studies, STS has been confirmed as a suitable theoretical tool for addressing current and future DH projects, and the elements of the STS model have been shown to have both strengths and limitations when applied to the DH context; (3) the combined STS–DH framework points to an inspiring research direction; a preliminary framework has been proposed, incorporating the principles of STS theory and the specific element needs of DH, laying the groundwork for future research. In terms of theoretical contribution, this study has introduced STS theory into the field of DH, filling a gap in existing research that frequently focusses on a single disciplinary or dimensional perspective in isolation, without incorporating a socio-technical perspective. In terms of practice, the proposed STS-DH framework serves as a new theoretical tool for guiding the design, implementation, and evaluation of future DH research.

Despite its contributions, this study has some limitations. In essence, it is exploratory in nature, with the goal of providing a socio-technical perspective as a theoretical foundation for future DH research. The analysis is based on a case study, but it focuses on a single instance which may limit the generalisation of the theoretical framework. Second, there are limitations in the selection of the STS models that may have led to more appropriate models being ignored. Moreover, as an initial attempt to establish a new STS-DH framework, the study relies on secondary data and does not include qualitative or quantitative data collection and analysis.

Nonetheless, the STS-DH framework serves as a conceptual and practical reference, emphasising an innovative approach for bridging STS theory and DH practice. Its limitations encourage us to conduct additional research aimed at laying the groundwork for future practical applications, such as guiding studies on specific types of technologies and diverse cultural settings. Extended reality technologies are becoming increasingly popular in the field of virtual heritage, with widespread adoption in museums, heritage sites, and archaeological sites around the world [88]. At the same time, the importance of DH for social and economic development in diverse cultural contexts is becoming increasingly clear. Further research will explore strategies for using extended reality technologies in cultural heritage preservation, as well as using STS theory as the primary theoretical framework to deepen cross-cultural understanding of heritage digitisation. Once completed, the framework will be validated and promoted through empirical research conducted in ongoing practical projects. Future research is expected to refine the STS-DH framework to better address the complexity of DH projects, enabling it to flexibly accommodate different cultural contexts, social changes, emerging technologies, and temporal dimensions, while remaining consistent with key theoretical principles.