Research on Evaluating the Effects of Digital Construction in Comprehensive Museums: A Collaborative Evaluation Approach Based on Cultural Cycle Theory and Grounded Theory

Abstract

At present, the digital construction of museums has created a novel cultural ecosystem that integrates digital preservation of cultural heritage, intelligent management, immersive experiences, and cloud-based services. However, insufficient synergistic integration of technological applications constrains the comprehensive release of the digital construction’s efficacy, while the absence of cultural assessment dimensions hinders the effective articulation of mechanisms whereby digital technology empowers cultural innovation. These concerns collectively constitute the primary impediments hindering museums from attaining sustainable development. The effectiveness of museum digital construction is fully clarified by combining grounded theory qualitative research methods with cultural cycle theory in this study. The Analytic Network Process (ANP) is used to manage interdependent relationships between factors, and cloud models are used to clarify indicator ambiguity, which allows for accurate assessment of digital construction results, consequently bolstering the sustainability of museum digitalization initiatives. The developed ‘qualitative–quantitative’ collaborative evaluation methodology for museum digital construction includes three sub-objectives: technology embedding, value co-creation, and institutional adaptation, as well as five primary indicators and ten secondary indicators. An empirical analysis of the ‘Smart Jiangxi Museum’ digital construction initiative at the Jiangxi Provincial Museum in China indicates that the project has achieved an ‘excellent’ standard. The findings of a previous qualitative study are effectively supported by this conclusion. This study presents a systematic approach for museum evaluation and gives decision-making guidance for museums to attain sustainable use of cultural resources, promote social knowledge transmission, and facilitate green, low-carbon transformation of operational models in the digital era.

1. Introduction

Comprehensive museums are essential elements of social culture because they preserve historical and cultural heritage, provide educational enlightenment, promote cultural interaction, and foster technological innovation [1]. In accordance with the “Opinions on Promoting the Implementation of the National Cultural Digitization Strategy” jointly released by the General Office of the CPC Central Committee and the General Office of the State Council, public cultural institutions, including museums, are mandated to enhance the development of digital cultural resources and collaboratively further the establishment of the national cultural big data system. This policy guidance has led comprehensive museums to evolve their digital practices from conventional physical exhibitions to virtual displays, online interactions, and digital collection management, thereby creating a digital ecosystem that encompasses educational, research, and recreational functions.

Currently, due to the swift evolution of information technology, the digital development of extensive museums has transitioned from ideation to extensive execution. A complex ecosystem consisting of collection digitization, intelligent administration, immersive experiences, and cloud services is being constantly expanded [2]. Nonetheless, there have been other concerns and challenges that have arisen during this practical process [3,4,5]. The use of technology is not coordinated. Technology installation is often performed in a fragmented manner, without systematic planning. This tendency to concentrate on individual components instead of the overall context hinders evaluation systems from transcending one-dimensional constraints, thereby impeding the assessment of overall efficacy. In addition, there is a lack of cultural perspective. The holistic relationship between technology and culture in domains such as production, representation, consumption, and recognition is disrupted when evaluations disproportionately emphasize technology implementation. A unilateral evaluative perspective is created by this omission, which hampers the elucidation of the many mechanisms through which digital technology fosters cultural revitalization.

This paper utilizes the theory of cultural cycle as its theoretical framework and grounded theory as its practical instrument to systematically perform qualitative research. It develops a multidimensional, multi-perspective evaluation indicator system to effectively tackle practical issues in the digital development of comprehensive museums. The introduction of the Analytic Network Process (ANP) and cloud model for quantitative assessment transcends the linear constraints of conventional hierarchical models, clarifies indicator ambiguities, and facilitates a visual representation of evaluation results. The proposed ‘qualitative–quantitative’ collaboration framework in this study offers strong theoretical foundations and practical direction for the ongoing high-quality advancement of the museum industry. Simultaneously, it creates a systematic analytical framework for conducting evaluations of cultural digitization projects.

2. Literature Review

2.1. Comprehensive Museums

In August 2022, the International Council of Museums (ICOM) proclaimed a revised definition of museums: “Permanent, non-profit institutions that benefit society by researching, collecting, conserving, interpreting, and exhibiting both tangible and intangible heritage.” Museums have different functional purposes depending on the cultural context. In Western museology, museums are often categorized into four types: art, science, history, and comprehensive museums [6]. In China, museums were classified into three categories: specialized, commemorative, and comprehensive [7]. China’s museum authorities and specialists categorize Chinese museums into four categories: historical, art, science and technology, and comprehensive, using internationally recognized classifications that take into account domestic conditions. Data from the Ministry of Culture and Tourism indicates that comprehensive museums represent the largest segment of museums nationwide, constituting 36.28%, and providing the foundation of China’s museum sector. Furthermore, large museums display a diverse array of local culture, history, and art, presenting a multifaceted perspective that enables the public to systematically comprehend regional civilizations.

2.2. Digital Construction of Museums

Digital construction refers to the transformation of conventional business activities, information systems, and services into digital formats to improve efficiency, decrease costs, optimize customer experience, and create new business prospects [8]. In this context, the digital construction of museums represents a particular implementation of this concept, wherein museums incorporate contemporary digital technologies into their collection, conservation, research, exhibition, and dissemination activities to improve the efficiency and quality of museum operations [9].

Modern museums are increasingly adopting and incorporating different new technologies as society and technology progress. Wang examined the transformative impact of digital modeling technologies on the design, production, and distribution frameworks of museum cultural and creative products, thereby enhancing the dynamic transmission of cultural resources and commercial innovation [10]. Hu et al. Employed augmented reality and additional technologies to establish a digital museum, illustrating that these platforms significantly boost user interactivity, generate considerable visitor engagement for virtual tours, and foster cultural dissemination and economic development [11]. The notion of museum digitalization is primarily represented by the technological applications as a concrete implementation.

2.3. Grounded Theory

Anselm Strauss and Barney Glaser, two Columbia University academics, developed Grounded Theory as a research methodology. This is a qualitative research methodology that uses systematic techniques to inductively construct a grounded theoretical framework for comprehending a particular occurrence [12]. Grounded theory requires empirical evidence; yet, its primary characteristic is not in its empirical basis but in its ability to derive new concepts and ideas from empirical data [13].

Grounded theory is categorized into three schools: original grounded theory, procedural grounded theory, and constructive grounded theory [14,15,16]. Based on the integration of the grounded theory from three schools, several academics have consolidated the coding process into three stages: open coding, spindle coding, and selective coding, also referred to as main coding, secondary coding, and tertiary coding [17]. To ascertain the validity and efficacy of the established theoretical model, it is essential to evaluate the theoretical saturation of the results derived from coding.

2.4. Cultural Cycle Theory

In the 1980s, British academic Johnson concentrated on the examination of production and consumption cultures, reformulating the conventional linear model of ‘production-distribution-consumption’ into a circular framework. This created a cyclical structure with four interrelated elements: the production of social meaning, texts, reading, and living culture, thus creating the theory of the cultural cycle [18]. In the 1990s, Hall utilized Marx’s concept of the ‘production-circulation-consumption-reproduction’ cycle to enhance Johnson’s cultural cycle model. He transformed the unidirectional transmission relationship into an interactive paradigm, establishing a cyclical system of “production-representation-consumption- identity-rules [19].” In the ‘cultural cycle’ system, production pertains to the creation and fabrication of cultural products or phenomena; representation concerns the conveyance of cultural meanings and values through diverse symbols, emblems, and forms of expression; consumption highlights the audience’s process of receiving and utilizing cultural products or services, acting as the final stage for value realization; and identity signifies the collective value orientation regarding meaning shared between producers of cultural products and their audience. Rules encompass the institutions, norms, standards, and rules of conduct adhered to in cultural creation, consumption, and associated processes. While ‘rules’ frequently occupy a preeminent status, they are not immutable. They address developmental demands by adapting and, therefore, generating new ‘rules’ [20].

As a pioneer in cultural studies, Hall’s ‘cultural cycle’ is a significant theoretical framework within the field. This idea posits that culture is not only a reflection of economic or political processes, but rather an essential element of societal reality, equivalent to economic or political advancement. This perspective has altered individuals’ comprehension of ‘culture,’ framing it as a pragmatic social practice or suggesting that all social practices may be analyzed through a cultural lens [21]. This theory is currently being used in different fields, often to examine the operational mechanisms of significant cultural occurrences or to assess the patterns that govern traditional cultural systems [22,23,24]. It emphasizes detailed examinations of particular segments alongside thorough analyses of the cyclical processes involved in the production, representation, consumption, identity, and rules of cultural products, thereby creating a comprehensive analytical framework that encompasses cultural production, consumption, and policy regulation [25,26,27,28].

3. Theoretical Framework and Research Methods

3.1. Construction of an Evaluation Indicator System Based on Dual-Directional Analysis of Grounded Theory and Cultural Cycle Theory

3.1.1. Open Coding

Open coding represents the initial phase of grounded theory, involving a meticulous examination of raw data to formulate concepts. By persistently comparing and contrasting these notions, logical relationships are found, and analogous or identical concepts are aggregated into categories [29]. In the process of naming concepts and categories using open coding, original assertions from the textual data are prioritized whenever feasible to maintain the authentic meaning of the content.

3.1.2. Spindle Coding and the Cultural Cycle Theory

Spindle coding is the second phase of grounded theory, where initial categories are refined and broader themes are synthesized [30]. Cultural cycle theory is used in coding to enable a top-down analysis of basic categories, which enhances the bottom-up refining of grounded theory. By integrating bottom-up empirical induction with top-down theoretical deduction, this bidirectional analytical framework provides innovative analytical insights for this investigation.

Grounded theory, a prevalent method in qualitative research, entails the analysis and processing of textual material with NVivo software, representing a form of human–computer interaction. In the spindle coding phase, NVivo’s inherent clustering feature is utilized to categorize the initial categories into distinct main categories. This study introduces cultural cycle theory to explore the cultural interpretation of digital technology empowering museum construction. Based on the connotation of the five stages of the cultural cycle theory, the initial categories derived from open coding are encompassed. The main categories for the spindle coding step were discerned by comparing and organizing the outcomes of these two analytical approaches.

3.1.3. Selective Coding

Selective coding constitutes the third phase of grounded theory, involving additional analysis and induction of main categories to derive core categories and subsequently construct interconnections between them [31]. In selective coding, it is essential to integrate all major categories into the core categories. The use of this approach is crucial in grounded theory research, as it ensures that the study can extract a theoretically significant framework from specific data.

3.1.4. Theoretical Saturation Testing

The final phase of grounded theory involves theoretical saturation testing, which involves acquiring additional data beyond the initial study sample and analyzing it through the first three stages. If there are no new concepts or categories, it means that theoretical saturation has been accomplished, resulting in a theoretical model that encompasses initial categories, main categories, and core categories [32]. To determine whether to continue collecting additional data for grounded analysis, grounded theory research frequently uses this strategy.

3.1.5. Indicator System Construction

The validity of research conclusions is directly influenced by the quality of indicator selection, which is why developing an assessment indicator system in empirical research is essential. Utilizing grounded theory to identify evaluation indicators guarantees data traceability and dependability, reveals interconnections within categories, and delineates conceptual substance, while also exposing multi-stakeholder collaborative processes, thus enabling multidimensional evaluation [33]. This study will create a suitable evaluation indicator system based on the established categories. The building model is depicted in Figure 1.

3.2. Indicator Weight Calculation Based on ANP

ANP, introduced by Professor Satty, is a decision-making methodology that considers multiple criteria when dealing with hierarchical frameworks with autonomous components. It aims to address interdependencies and feedback relationships among components in a complex decision-making issue [34,35,36]. The indicator system established here exhibits nonlinear interactions between the indicators. Conventional hierarchical models do not adequately represent such intricate relationships. ANP facilitates the systematic quantification of indicator weights by building indicator networks and performing hypermatrices computations, resulting in decision outputs that are more consistent with practical realities.

3.2.1. Construction of the ANP Network Structure

There are two layers in ANP: the control layer and the network layer. The control layer encompasses decision objectives and criteria, all of which are regarded as mutually independent and exclusively determined by objective aspects. A control factor may be devoid of decision criteria, but it must have at least one purpose. The network layer is composed of interdependent elements that are regulated by the control layer, making it a network structure. This reciprocal influence does not only exist among distinct element groups, but it may also occur among elements within the same group.

3.2.2. ANP Matrix Calculation

The judgment matrix is created by evaluating the relative importance of elements using Professor Satty’s 1–9 scale approach. This matrix undergoes consistency checks, and if discrepancies are found, more modifications are required. Following verification, the unweighted hypermatrix, weighted hypermatrix, and limit-weighted hypermatrix can be computed sequentially to provide the weight values for each indicator.

Taking a specific criterion $Bi$as the primary criterion and the elements $Cij$within $Bi$it as secondary criteria, an indirect dominance comparison is conducted sequentially for each element $Bn(n=\mathrm{1,2},\cdots ,n_i,n\ne i)$ under its influence $Cij$. This establishes the decision matrix based on the dual criteria. After passing consistency tests, the normalized eigenvectors ${\omega }_n^{ij}={\left({\omega }_{n1}^{ij},{\omega }_{n2}^{ij},\cdots ,{\omega }_{n{k}_n}^{ij}\right)}^T$can be calculated using the eigenvalue method. This leads to the creation of the indirect dominance matrix $W_{ni}$, which reflects the dominance of each element in the criterion $Bi$over each element in the criterion $Bn$. Repeating this comparative analysis process, traversing all criteria $Bi$and sub-criteria $Cij$, enables the construction of the unweighted hypermatrix $Ws$.

$$W_{ni}=\left[\begin{array}{cccc}{\omega }_{n1}^{i1}& {\omega }_{n1}^{i2}& \cdots & {\omega }_{n1}^{i{k}_i}\\ {\omega }_{n2}^{i1}& {\omega }_{n2}^{i2}& \cdots & {\omega }_{n2}^{i{k}_i}\\ ⋮& ⋮& ⋮& ⋮\\ {\omega }_{n{k}_n}^{i1}& {\omega }_{n{k}_n}^{i2}& \cdots & {\omega }_{n{k}_n}^{i{k}_i}\end{array}\right]$$

(1)

$$W_s=\left[\begin{array}{c}{W}_{11}\\ W_{21}\\ ⋮\\ W_{51}\end{array}\begin{array}{c}{W}_{12}\\ W_{22}\\ ⋮\\ W_{52}\end{array}\begin{array}{c}\cdots \\ \cdots \\ ⋮\\ \cdots \end{array}\begin{array}{c}{W}_{15}\\ W_{25}\\ ⋮\\ W_{55}\end{array}\right]$$

(2)

Taking the effectiveness of digital construction for comprehensive museums as the primary criterion and a specific criterion $Bi$ as the secondary criterion, pairwise comparisons are conducted sequentially to establish a judgment matrix. The normalized eigenvector ${\left(a_{1i},a_{2i},\cdots ,a_{5i}\right)}^T$ is calculated, and traversing the criteria $Bi$ yields the weighted matrix $As$. By applying weights to the unweighted matrix $Ws$and subsequently normalizing it, the weighted hypermatrix $W_s^{\omega }=A_s\times W_s$ is obtained.

$$A_s=\left[\begin{array}{c}{a}_{11}\\ a_{21}\\ ⋮\\ a_{51}\end{array}\begin{array}{c}{a}_{12}\\ a_{22}\\ ⋮\\ a_{52}\end{array}\begin{array}{c}\cdots \\ \cdots \\ ⋮\\ \cdots \end{array}\begin{array}{c}{a}_{15}\\ a_{25}\\ ⋮\\ a_{55}\end{array}\right]$$

(3)

$$W_s^{\omega }=\left[\begin{array}{cccc}\overline{W_{11}}& \overline{W_{12}}& \cdots & \overline{W_{15}}\\ \overline{W_{21}}& \overline{W_{22}}& \cdots & \overline{W_{25}}\\ ⋮& ⋮& ⋮& ⋮\\ \overline{W_{51}}& \overline{W_{52}}& \cdots & \overline{W_{55}}\end{array}\right]$$

(4)

The weighted hypermatrix $W_s^{\omega }$ represents the one-step advantage between elements. Considering mutual dependencies and influences, iterative processing yields the $m$-step advantage degree, denoted as $W^m$. When $m\to \infty , \mathit{lim}{W}^m$ it exists, the limit-weighted hypermatrix $W^{\infty }$ is obtained, where the elements represent the weights of the respective indicators.

$$W^{\infty }=\left[{\begin{array}{c}\overline{W_{11}}\\ {\overline{W_{21}}}^{\infty }\\ ⋮\\ {\overline{W_{51}}}^{\infty }\end{array}}^{\infty }{\begin{array}{c}\overline{W_{12}}\\ {\overline{W_{22}}}^{\infty }\\ ⋮\\ {\overline{W_{52}}}^{\infty }\end{array}}^{\infty }\begin{array}{c}\cdots \\ \cdots \\ ⋮\\ \cdots \end{array}{\begin{array}{c}\overline{W_{15}}\\ {\overline{W_{25}}}^{\infty }\\ ⋮\\ {\overline{W_{55}}}^{\infty }\end{array}}^{\infty }\right]$$

(5)

3.3. Comprehensive Evaluation Based on the Cloud Model

The cloud model, proposed by Professor Li Deyi, an academician of the Chinese Academy of Engineering, is a framework designed to manage uncertainty in transitions between qualitative concepts and quantitative descriptions. It is based on stochastic mathematics, probability theory, and fuzzy mathematics. The numerical characteristics of it are characterized by expectation, entropy, and hyper entropy [37] Evaluation metrics often involve fuzziness and uncertainty because certain aspects of the digitalization outcomes of comprehensive museums are difficult to quantify precisely. A cloud model is introduced in this paper to facilitate the fuzzy quantification of evaluation indicators and the visualization of comprehensive assessments [38]

3.3.1. Determining the Evaluation Grades

Firstly, based on expert consultation and linguistic conventions, this paper utilizes the linguistic values ‘poor,’ ‘below general,’ ‘general,’ ‘good,’ and ‘excellent’ to describe evaluation grades, assigning numerical intervals to each level. Secondly, using the bilateral constraint method as illustrated in Equations (6)–(8), the numerical characteristics for each evaluation grade are determined, with all values rounded to two decimal places. Refer to

Table 1. Evaluation Grades.

Evaluation Grade	Grade Interval	Characteristic Parameters of the Cloud Model
Poor	[0.00, 25.00)	(12.50, 4.17, 0.50)
Below General	[25.00, 50.00)	(37.50, 4.17, 0.50)
General	[50.00, 75.00)	(62.50, 4.17, 0.50)
Good	[75.00, 90.00)	(82.50, 2.50, 0.50)
Excellent	[90.00, 100.00]	(95.00, 1.67, 0.50)

. In the equations, $Q_{max}$ and $Q_{min}$ denote the upper and lower bounds of the evaluation grade intervals, respectively, with $k$ being a constant fixed at 0.5 in this study.

$$E_x=\left(Q_{\max }+Q_{\min }\right)/2$$

(6)

$$E_n=\left(Q_{\max }-Q_{\min }\right)/6$$

(7)

$$H_e=k$$

(8)

3.3.2. Confirm the Evaluation Indicator Cloud

Initially, using the Delphi method, specialists from the museum digitization field were invited to evaluate each indicator on a 100-point scale, with 0 representing the lowest performance and 100 representing the highest. An expert rating matrix was developed to compute the expected value, entropy, and super-entropy for each secondary indicator using the methods presented in Equations (9)–(11). Secondly, in conjunction with the secondary indicator weights established by the ANP approach, the cloud digital characteristics of the primary indicators are computed using the formulas for cloud weight combinations shown in Equations (12)–(14). The digital characteristics of the cloud objective layer can be calculated using the same methods.

$$E_x=\overline{X}={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n\left(x_i\right)}$$

(9)

$$E_n=\sqrt{{\displaystyle \frac{\pi }{2}}}{\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n\left|x_i-E_x\right|}$$

(10)

$$H_e=\sqrt{\left|\left.{\displaystyle \frac{1}{n-1}}{\displaystyle \sum _{i=1}^n{\left(x_i-E_x\right)}^2-{E_n}^2}\right|\right.}$$

(11)

$$E_x={\displaystyle \frac{{\displaystyle \sum _{i=1}^n{E}_{x_i}{\omega }_i}}{{\displaystyle \sum _{i=1}^n{\omega }_i}}}$$

(12)

$$E_n={\displaystyle \frac{{\displaystyle \sum _{i=1}^n{\omega }_i^2{E}_{n_i}}}{{\displaystyle \sum _{i=1}^n{\omega }_i^2}}}$$

(13)

$$H_e={\displaystyle \frac{{\displaystyle \sum _{i=1}^n{\omega }_i^2{H}_{e_i}}}{{\displaystyle \sum _{i=1}^n{\omega }_i^2}}}$$

(14)

3.3.3. Establishing the Comprehensive Evaluation Cloud

The comprehensive cloud is a cloud model developed using fuzzy synthesis to evaluate cloud and indicator weights. After calculating the numerical attributes of the comprehensive cloud, these values are entered into the forward cloud generator to create the comprehensive evaluation diagnostic cloud map. The evaluation criteria for the museum’s digital construction results are established by assessing the comprehensive cloud against standard clouds. There are two methods of comparison that exist. One method aligns the positions and shapes of the comprehensive cloud and the standard cloud within a unified coordinate system, with the standard cloud nearest to the comprehensive cloud dictating the evaluation grade. The other method computes the similarity between the comprehensive cloud and standard clouds of each grade utilizing a similarity formula, where the grade yielding the highest value represents the evaluation outcome [39] The present study utilizes both comparative approaches for grading evaluation, preserving the visual intuitiveness of cloud model assessment while augmenting the objectivity of results through mathematical modeling, thus significantly strengthening the accuracy and scientific rigor of grading outcomes. The similarity between indicator clouds and standard clouds can be computed using the same methodology.

4. Empirical Analysis: A Case Study of “Smart Jiangxi Museum”

4.1. Sample Selection

The Jiangxi Provincial Museum is a perfect example of a complete museum because it exhibits Jiangxi’s cultural traits, natural history, and artistic creations in great detail. Its esteemed reputation and significant impact, along with its extensive collections and varied exhibitions, position it as an essential conduit for the region’s culture [40]. The ‘Smart Jiangxi Museum’ program, recognized as one of the ‘Top Ten Digital Innovation Practices in Culture and Tourism for 2022′, has thoroughly surpassed the constraints of conventional exhibitions. It has achieved notable outcomes in improving visitor experience, optimizing exhibition content and arrangement, and bolstering cultural propagation, showcasing substantial exemplary effects and reference value. This study utilizes the ‘Smart Jiangxi Museum’ project from the Jiangxi Provincial Museum as the principal source for grounded theory analysis.

4.2. Data Sources

This study utilizes a multi-source data integration methodology, amalgamating policy documents, media coverage, expert interview transcripts, academic publications, and more resources pertinent to the ‘Smart Jiangxi Museum’ effort. Ultimately, it consolidates 10 publications that contain many perspectives, including official, popular, and expert viewpoints, as illustrated in

Table 2. Grounded Research Object.

No.	Data Name
1	Notice from the General Office of the Ministry of Culture and Tourism on the Release of 2022 Digital Innovation Initiatives in the Culture and Tourism Sector
2	Media Emphasis: Highlights of CCTV’s “News Live Room” program featuring the “Smart Jiangxi Museum” service system from our museum
3	Seventy Years of Glory: Tracing the Past, Embracing the Present|③ Innovative Breakthroughs in the Gan Treasure Box-A Dual Journey of Mutual Progress in the New Era
4	Digital Innovation Case Study|“Smart Jiangxi Museum”: Strengthening the Museum’s “Brain” to Energize Management and Services
5	Good News|“Smart Jiangxi Museum” Selected as One of the Top Ten Digital Innovation Practice Cases by the Ministry of Culture and Tourism
6	National Top Ten Cases! Development of the “Smart Jiangxi Museum” Comprehensive Management Service System
7	Team Practice|Nanchang Team: Focusing on Deep Interaction to Make Museum Culture More Accessible
8	Digital Innovation Case Study|Digital Technology Innovation Drives the Development of Smart Museums
9	Expert Interview Transcript
10	Relevant Academic Journals

. Initially, major public documents on the digital advancement of the Jiangxi Provincial Museum were gathered via web channels, utilizing official advertising identifiers like ‘Smart Jiangxi Museum’ as central keywords. Secondly, semi-structured expert interviews were designed to acquire comprehensive insights. Furthermore, pertinent academic material was obtained using the phrase ‘Jiangxi Provincial Museum Digitalization,’ thereby amalgamating several sources to guarantee diversity and comprehensiveness of data origins.

4.3. Data Analysis

Subsequent to the organization and summarization of the gathered textual data, this study loaded it into NVivo software for processing and analysis. The processing procedure involves a systematic, iterative interaction between humans and machines, directed by researchers, rather than being automated. Initially, establish a project and import the textual data. Subsequently, employ NVivo’s auto-coding functionality to analyze the textual data systematically, examining it paragraph by paragraph and line by line. Subsequent to labeling and conceptualizing, synthesize and improve the initial concepts to establish a set of ‘free nodes’ that denote the initial categories. Subsequently, designate ‘tree nodes’ as the main categories, then classify the ‘free nodes’ produced using open coding under the relevant ‘tree nodes’ according to their semantic linkages. Subsequently, through selective coding, the core category that encompasses additional categories is determined, facilitating the construction of a systematic theoretical model. Ultimately, replicate the three-tier coding process on the designated academic journals to ascertain if new categories and links arise, thereby conducting a theoretical saturation test. The precise coding procedure is as follows.

4.3.1. Establish the Initial Categories

The NVivo 12 software was used to import the first nine textual materials from Table 2 for a comprehensive analysis and deconstruction. The information labeled from deconstruction was further abstracted and refined into concepts during this process. After eliminating irrelevant, duplicate, and extremely low-frequency concepts, a total of 28 initial concepts were obtained. After further refinement to eliminate overlaps, the concepts were consolidated into 10 initial categories.

Table 3. Open Coding Results.

Initial Categories	Initial Concepts	Original Text Data (Excerpt)
Digital Twin of Cultural Relics	3D Scanning and Modeling	Utilizing high-precision 3D scanning and modeling techniques, three-dimensional and lifelike virtual models have been created for hundreds of cultural relics.
	High-Definition Planar Scanning	High-definition flatbed scanning enables paintings, calligraphy, and rare ancient texts to be presented in their entirety for public viewing.
Digital Resource Conversion	Exhibition Planning	Exhibitions are curated using digital research findings, delving deeply into the stories and cultural significance behind the artifacts.
	Publication of Papers	Publish academic papers exploring the application of digital technologies in cultural heritage conservation, exhibition planning, and visitor services, focusing on the practice, theory, and case studies of museum digitalization.
Cross-platform Representation	Official Website	The official website features a dedicated digital collection gallery, presenting 360-degree panoramic views of cultural relics.
	WeChat Public Number	Regularly publishes short stories about cultural relics on the official account, accompanied by high-quality images of artifacts and engaging animations.
	Weibo/TikTok/Xiaohongshu	Regularly publish short videos and engaging educational content about our collections on Weibo, TikTok, and Xiaohongshu.
Cross-media Representation	Touchscreen Equipment	Numerous interactive touchscreen devices are installed throughout the museum, allowing visitors to rotate artifacts 360 degrees with a simple tap.
	VR/AR Equipment	Immersive scenes are created using VR and AR technology. Visitors wearing the equipment can ‘travel back in time’ to ancient Jiangxi and interact with historical figures.
	Holographic Projection Equipment	At the Jiangxi Provincial Museum, visitors are immersing themselves in the millennia-long cultural evolution of Jiangxi through digital 3D holographic projections.
	Artistic Devices	The Jiangxi Provincial Museum employs a comprehensive approach, integrating graphic displays, replicas, sculptures, and other artworks with supplementary exhibits to deliver a multifaceted, vivid, and contextual narrative.
Audience Demand Response	Interactive Experience Equipment	Children desire more engaging interactive experiences, such as artifact jigsaw puzzles and educational quiz games.
	Knowledge Acquisition Needs	Parents desire more detailed and accessible explanations of artifacts to facilitate knowledge transfer to their children.
	Convenient Visiting Needs	Visitors desire faster loading speeds and smoother viewing for online exhibitions, enabling effortless museum exploration anytime, anywhere.
Diverse Service Formats	Online Service Functions	Online exhibition halls enable visitors to view 3D models of museum artifacts from home via mobile devices or computers, with 360-degree rotation capabilities.
	Offline Service Optimization	Multiple guided tour modes enhance convenience and personalization, with scheduled commentary and innovative navigation catering to visitor needs.
Cultural Education Inheritance	School Education Cooperation	Collaborating with schools to develop guided tours tailored for school curricula, incorporating technologies such as AR to enhance student comprehension.
	Social Education Outreach	Hosting diverse thematic lectures and cultural training activities, such as research seminars on Han Dynasty culture and ceramic heritage, to engage public participation.
Cultural Identity Acceptance	Audience’s Interest and Love of Culture	Given that over 90% of visitors expressed interest or strong interest in museum digitization, it is evident that attendees hold a supportive stance towards museum culture.
	Audience’s Interactive Experience of History	Through interactive artifact displays, VR/AR applications, and other technologies, audiences can comprehensively and meticulously examine cultural relics, converse with virtual characters, and gain deeper insights into historical narratives.
Digital Basic Management Specification	Personnel Management Rules	Staff information integration encompasses basic records, academic qualifications, employment history, and job transfer records, all of which are entered into a unified system.
	Collection Management Rules	For digitalized collection management, a detailed archive is established for each item, recording prototype information, digital acquisition dates, and methods.
Digital Operation Management Specification	Online Booking and Access Mechanism	Visitors can book appointments through the Jiangxi Provincial Museum’s official website or its WeChat public account, “Jiangxi Provincial Museum”. Upon accessing the booking system, select the visit date, time slot, and number of attendees. Complete the personal details form and submit the order to finalize the booking.
	Specification for the Use of Digital Equipment	Visitors using equipment within the museum, such as audio guides, must adhere to the relevant rental and usage regulations.
	Data Privacy and Security Notices	To standardize data collection on audience behavior, our library has formulated the ‘Digital Service Data Collection Rules’. It is clearly stipulated that only anonymous data related to exhibition services (such as the length of stay of exhibits and the heat map of the guide route) should be collected, and sensitive data, including facial features and biometric information, should not be collected.

displays the coding materials.

4.3.2. Establish the Main Categories

The spindle coding phase was guided by the inherent principles of cultural cycle theory and included systematically integrating and synthesizing the preliminary categories established during the open coding stage. The specific procedure is shown in Figure 2. This procedure resulted in five main categories: Digital Content Production, Cultural Digital Representation, Digital Consumption Services, Cultural Identity Perception, and Institutional Rules Regulation.

Table 4. Spindle Coding Results.

Main Categories	Initial Categories	Connotation
Digital Content Production	Digital Twin of Cultural Relics	Employing digital technology to create high-precision replicas and simulations of cultural artifacts, thereby generating virtual models corresponding to physical objects.
	Digital Resource Conversion	Applications of digital technology in cultural heritage conservation, exhibition planning, and visitor services.
Cultural Digital Representation	Cross-platform Representation	Museums publish information across various platforms from time to time.
	Cross-media Representation	Displaying information across different devices.
Digital Consumption Services	Audience Demand Response	Level of responsiveness to audience needs.
	Diverse Service Formats	Includes both online and offline services.
Cultural Identity Perception	Cultural Education Inheritance	Cultural heritage is perpetuated through educational means.
	Cultural Identity Acceptance	Audience affirmation and acceptance of culture.
Institutional Rules Regulation	Digital Basic Management Specification	Including personnel management and collection management.
	Digital Operation Management Specification	Including online booking and access mechanisms, digital equipment usage specifications, and data privacy and security guidelines.

displays the coding outcomes.

4.3.3. Establish Core Categories

This work synthesizes the meanings of five primary categories into three fundamental domains: technical embedding, value co-creation, and institutional adaptation. Three sub-objectives have been defined to assess the effectiveness of digital construction in comprehensive museums.

The connection between digital content production and cultural digital representation is inherent to digital technology. The former uses digital resources in all aspects of museum operations to create innovative content, which includes virtual replicas of tangible artifacts and online exhibitions. The latter utilizes various platforms and media to convey information, with digital technology functioning as a fundamental instrument in the communication process. Thus, the concept of technological embedding encompasses both. Digital consumption services serve as the ultimate realm for value realization, enabling audiences to engage in immersive cultural experiences that enhance their comprehension of historical settings and the craftsmanship of artifacts. Institutions must improve the availability of digital content due to visitors’ personalized expectations expressed during their engagement with museum services. This facilitates the conversion of cultural heritage into ‘living culture’, integrating cultural significance into modern life. Cultural identity is an inherent value that arises from audiences’ recognition and endorsement of cultural importance based on their interpretation of heritage during consumption. The concept of value co-creation is established collaboratively between the domain of value realization and the concept of value. The regulation of institutional rules encompasses both digital basic management specifications and digital operational management specifications. This primary domain is classified specifically as the core category of institutional adaptation. By implementing a regulatory framework encompassing staff management, collection stewardship, service operations, technical standards, and data security, museums may guarantee high-quality development within a compliance structure during their digital transformation. The regulatory framework is constantly changing. It addresses the developmental requirements of technological embedding and value co-creation through iterative optimization, resulting in the adaptive reconstruction of the regulatory framework.

4.3.4. Validating Theoretical Models Through Academic Journals

This study performed a complete review of four academic journal papers to confirm the rigor and scientific validity of the model for elements impacting the efficacy of digital building in comprehensive museums [41,42,43,44]. The results indicate that all extracted conceptual categories are within the ten pre-established categories, and no new links or categories were found. This signifies that the model has reached theoretical saturation.

4.3.5. Establish an Effectiveness Evaluation Indicator System

Three sub-objectives, five primary indicators, and ten secondary indicators are established through the use of qualitative research from grounded theory and theoretical inference from cultural cycle theory in this study. The paper establishes an evaluative framework for measuring the effectiveness of digital development in comprehensive museums, as shown in

Table 5. Evaluation Index System.

Sub-Objectives	Primary Indicators	Secondary Indicators
Technology Embedding	Digital Content Production (B1)	Digital Twin of Cultural Artifacts (C11)
		Digital Resource Conversion (C12)
	Cultural Digital Representation (B2)	Cross-platform Representation (C21)
		Cross-media Representation (C22)
Value Co-creation	Digital Consumption Services (B3)	Audience Demand Response (C31)
		Diverse Service Formats (C32)
	Cultural Identity Perception (B4)	Cultural Education Inheritance (C41)
		Cultural Identity Acceptance (C42)
Institutional Adaptation	Digital Rules Regulation (B5)	Digital Basic Management Specification (C51)
		Digital Operation Management Specification (C52)

.

4.4. Evaluation of Digital Construction Outcomes for Comprehensive Museums Based on the ANP-Cloud Model

4.4.1. Construction of the ANP Network Architecture

A network model is established based on the influence relationships between indicators as illustrated in Figure 3. Unidirectional arrows denote a dominant relationship from the tail to the head of the arrow, bidirectional arrows indicate mutual dominance between the two elements, and curved arrows represent reciprocal dominance relationships within a group of elements.

4.4.2. Determining Indicator Weights

The relative importance of each element was determined using a 1–9 scale. The data was obtained from questionnaire responses, using the rational weighting of indicator importance assigned by ten experts who specialize in museum digitalization. This generated the comparative judgment matrix necessary for evaluation. The foundational data was inputted into Super Decision3.2.0 (SD) software, and the final weights and rankings for each evaluation indicator are presented in

Table 6. Weights of Each Evaluation Indicator.

Sub-Objective	Primary Indicator	Secondary Indicator	Weight	Ranking
Technology Embedding (0.45)	Digital Content Production (0.23)	Digital Twin of Cultural Artifacts	0.10	5
		Digital Resource Conversion	0.13	4
	Cultural Digital Representation (0.22)	Cross-platform Representation	0.17	1
		Cross-media Representation	0.05	9
Value Co-creation (0.38)	Digital Consumption Services (0.23)	Audience Demand Response	0.08	6
		Diverse Service Formats	0.15	2
	Cultural Identity Perception (0.15)	Cultural Education Inheritance	0.08	7
		Cultural Identity Acceptance	0.07	8
Institutional Adaptation (0.17)	Institutional Rule Regulation (0.17)	Digital Basic Management Specification	0.03	10
		Digital Operation Management Specification	0.14	3

.

4.4.3. Determining Indicator Cloud

The ten experts from the Effect Evaluation Group were reconvened to score each indicator against the evaluation grading standards in Table 5, based on the actual circumstances of the ‘Smart Jiangxi Museum’ initiative. An expert scoring matrix was constructed based on the scores obtained. The inverse cloud generator was used to calculate the digital characteristics of the secondary indicator cloud model. The ANP indicator weights were integrated to compute the digital characteristics of the primary indicators and target layer.

Table 7. Cloud Digital Characteristics of Indicators.

Comprehensive Cloud Digital Characteristics	Sub-Objective Cloud Digital Characteristics	Primary Indicator Cloud Digital Characteristics	Secondary Indicator Cloud Digital Characteristics
(90.16, 1.89, 0.75)	(92.33, 1.80, 0.76)	(92.02, 1.60, 0.85)	(94.56, 1.32, 0.76)
			(90.12, 1.76, 0.90)
		(92.66, 2.03, 0.66)	(92.85, 2.04, 0.67)
			(92.04, 1.90, 0.61)
	(88.70, 2.11, 0.81)	(90.16, 1.95, 0.75)	(88.10, 2.93, 1.13)
			(91.35, 1.62, 0.63)
		(86.39, 2.51, 0.97)	(87.16, 2.40, 1.03)
			(85.49, 2.64, 0.89)
	(87.53, 1.56, 0.32)	(87.53, 1.56, 0.32)	(90.41, 1.78, 0.64)
			(86.86, 1.55, 0.31)

presents the results.

4.4.4. Evaluation Results Analysis

The cloud digital characteristics of the target layer were fed into the forward cloud generator, resulting in a comprehensive cloud chart and three sub-target cloud charts, as illustrated in Figure 4. The similarity between each target cloud and the standard cloud was computed using MATLAB R2018a software, with results displayed in

Table 8. Similarity Degree Between Target Cloud and Standard Cloud.

Target Layer	Excellent	Good	General	Below General	Poor	Grade
Digitalization Outcomes	0.60	0.56	0.39	0.19	0.00	Excellent
Technology Embedding	0.72	0.53	0.37	0.18	0.00	Excellent
Value Co-creation	0.57	0.60	0.39	0.19	0.00	Good
Institutional adaptation	0.54	0.64	0.40	0.20	0.00	Good

.

Figure 4a illustrates that the comprehensive cloud is positioned between the ‘Good’ and ‘Excellent’ classifications, with a tendency towards the ‘Excellent’ grade. The similarity between the comprehensive cloud and the “Good” standard cloud is 0.56, and its similarity with the ‘Excellent’ standard cloud is 0.60. Consequently, the building result of the ‘Smart Jiangxi Museum’ project is classified as ‘Excellent’, aligning with the findings from the first data collection.

Cloud chart results and similarity scores were used to analyze sub-objectives. The sub-objective ‘Technology Embedding’ received an ‘Excellent’ rating, indicating substantial investment and effective outcomes in digital technology for the ‘Smart Jiangxi Museum’ project. The sub-objective ‘Value Co-creation’ received a ‘Good’ rating, indicating room for improvement to achieve an ‘Excellent’ rating. This suggests decision-makers should focus not only on technological investment but also on value transformation, such as enhancing responsiveness to visitor needs, diversifying service formats, disseminating historical and cultural education, and fostering audience recognition of cultural significance, to achieve synergistic development for museums in the context of digital empowerment. The sub-objective ‘Institutional Adjustment’ received a ‘Good’ rating, performing slightly less effectively than ‘Value Co-creation’. This indicates that there is room for optimization in the refined implementation and operational efficiency of the management mechanisms of the ‘Smart Jiangxi Museum’ project. The fact that existing policies and systems may not fully meet the demands of digitalization in museums is reflected. Institutional frameworks must continuously iterate to adapt to evolving environments and requirements in the context of digital technology driving cultural development.

The analysis indicates that the ‘Smart Jiangxi Museum’ project has achieved significant results in its digital development. The primary factor behind its overall ‘Excellent’ rating is the substantial investment made in technological integration. The synergy between value co-creation and institutional adaptation is still lacking. A certain contradiction can be seen in the contrast between the leading nature of technological investment and the relative lag in value co-creation and institutional adaptation. Digital construction requires a balanced investment in technology, value transformation, and institutional innovation to achieve sustainable progress in museum digitization.

5. Conclusions and Limitations

This study surpasses the constraints of conventional viewpoints on museum digitization research by employing a synergistic ‘qualitative–quantitative’ research method. This framework integrates cultural cycle theory with grounded theory, utilizing ANP and cloud model to develop a theoretical analytical framework and quantitative model that includes technological, cultural, and institutional components. Empirical evidence from the Jiangxi Provincial Museum closely corresponds with existing research, strongly affirming the reliability of this evaluation model. The proposed model and implementation pathway offer explicit direction and inspiration for the sustainable development of museum digitalization. Its contributions are distinctly evident in the following three fundamental dimensions:

Initially, at the technical embedding level, advocate for the sustainable use of cultural resources. Managers ought to promote creative methodologies in digital content creation and underscore the multiplicity of cultural digital representations. This serves not only to augment attractiveness but also as an essential strategy for converting static cultural resources into dynamic digital assets that may be perpetually created and utilized.

Secondly, at the level of value co-creation, facilitate the social transmission of information. Managers must prioritize audience input regarding service experiences and concentrate on visitors’ comprehension and connection with cultural material. The primary objective is to surpass temporal and spatial limitations. Museums can transform from static ‘treasuries of knowledge’ into dynamic ‘social classrooms’ through immersive, interactive digital storytelling, facilitating the efficient transmission and worldwide sharing of knowledge across age and geographical boundaries.

Thirdly, at the level of institutional adaptation, direct the green and low-carbon transformation of operational models. Managers must create a thorough regulatory framework and diligently monitor advancements in technology embedding and value co-creation, swiftly amending and refining regulations as necessary. During this process, green and low-carbon ideas must be deliberately integrated into institutional design. Utilizing intelligent management systems to attain energy conservation and consumption reduction in buildings will facilitate the advancement of museum digitization towards environmentally friendly development within a compliant and organized framework.

Nevertheless, this study has certain limitations, including a relatively limited sample scope and data sources that are primarily reliant on existing materials. To improve objectivity and validity, future research may increase sample sizes. Moreover, given the evolution of digital technologies, future research should focus on the value generated by emerging technologies in the use of museum digitalization. This approach prevents an overemphasis on technological investment at the expense of value output, thus refining the practical framework for comprehensive museum digitalization to achieve sustainable development in this field.