Content area
Purpose
Smart libraries are the result of the application of smart technologies in the era of digital intelligence. The establishment and improvement of its service evaluation system serve as indicators for evaluating the growth of smart libraries.
Design/methodology/approach
This study introduces and improves the capability maturity model (CMM), creatively constructs a service maturity model specifically designed for smart libraries and combines the Delphi method with the analytic hierarchy process (AHP) to establish a service maturity evaluation system for smart libraries while calculating indicator weights. Finally, two representative smart libraries are selected as case studies, and an empirical application is conducted using the fuzzy comprehensive evaluation method.
Findings
The empirical study shows that the developed smart libraries service maturity evaluation system holds significant theoretical and practical value in evaluating smart libraries.
Originality/value
Enhances the CMM and creatively constructs a service maturity model for smart libraries. Combines the Delphi method with AHP to establish a service maturity evaluation system while calculating indicator weights. Uses a fuzzy comprehensive evaluation method to evaluate two representative smart libraries. Demonstrates that the smart library services maturity evaluation system holds significant theoretical and practical value.
1. Introduction
With the advancement of smart technology in the field of library services, the connotation and scope of smart libraries are also constantly evolving and changing. The concept of smart library was first proposed by Aittola et al. (2003) and they pointed out that smart libraries are an online mapping service that can help readers quickly find the location of collections. From the research perspective of information technology application, Chinese scholar Yan (2010) defined smart libraries as modern libraries based on the application of emerging information technology, which is a combination of library, Internet of Things, cloud computing and smart devices. As the core element of smart library development, the establishment and improvement of smart services evaluation system is an important indicator for the healthy development of smart libraries, which has research value for the construction of smart libraries. At present, there are not many studies on the construction of a smart library services maturity evaluation system, and they mostly stop at the construction of an indicator system, with few empirical application examples. Therefore, this paper starts with the capability maturity model (CMM) theory, which is used to transform the related smart library services maturity model, combines the analytic hierarchy process (AHP) and the Delphi methods to formulate the evaluation indicators and assign them, constructs the smart library services maturity evaluation system, and, finally, conducts the empirical application through the fuzzy comprehensive evaluation method. In terms of theory, this paper introduces a new theoretical model to the research field of smart library evaluation system construction, which has a certain significance of theoretical innovation and application. In practice, the new scientific smart library services maturity evaluation system constructed in this paper can, to a certain extent, provide evaluation bases for the government, universities, libraries and other stakeholders, which can help to find, supervise and improve problems, and has a certain practical value.
2. Literature review
The concept of smart libraries first appeared in Europe and the USA. Specifically, the University Library of Oulu in Finland first introduced a new service called smart libraries with the Rotuaari project in 2003, stating that smart libraries refer to a mobile library service that is not limited by space and can be perceived by users (Aittola et al., 2003). In 2010, the American Library Association (ALA) set up the column “Smarter Libraries Through Technology” in the magazine Smart Libraries Newsletter, which continuously focuses on and explores the issues related to smart libraries from the aspects of library technology, services and practices, and puts forward strategic suggestions for guiding the construction of smart libraries (Breeding, 2010). In addition, scholars from South Korea, Japan, Australia and other countries have gradually paid attention to the research and exploration in the field of smart libraries. It is clear that the development and construction of smart libraries have become a common direction of countries around the world.
In terms of the related research on a smart library evaluation system, the evaluation item of a “New Evaluation Plan” proposed by the ALA in 1999 (Wu et al., 2013), which is suitable for the service evaluation of smart libraries, included three key phrases: service environment, information control and service impact. The smart library services of Western libraries show a highly diversified form, including not only rich and colorful topic services and smart personalized recommendation services but also ubiquitous mobile library services, perfect learning space services and considerate special group services. Many kinds of research focus on the innovative application services of new technologies such as big data, Internet of Things, cloud computing, distributed construction and mobile internet. They emphasize the applicability of smart applications, pay attention to smart and convenient IT service quality and generally tend in the direction of technology application.
In terms of theoretical perspectives and models, scholars’ choices are very diverse and innovative. The perspectives of high-frequency theories include D-S evidence theory, entropy theory and “total evaluation” theory, and the high-frequency models include the SERVQUAL evaluation model, Kano model and service quality gap model. In general, scholars construct evaluation systems for different types of libraries, such as smart libraries, university libraries and public libraries, on the one hand, and different modules of the same type of libraries, such as people, equipment, space and service dimensions, on the other hand. However, the existing researches mainly focus on system construction, theoretical perspective innovation and summary suggestions, and most of them stop at the construction of an evaluation system and lack empirical verification. If the empirical analysis of systematic science is added, the scientificity and persuasiveness of the constructed smart library evaluation system will be greatly improved. In addition, the existing evaluation system also faces the practical problem of insufficient application.
In terms of related research on the CMM, many academic teams have made attempts and explorations. After Crowston and Qin (2010) first proposed the CMM, Peng et al. (2015) constructed the data management CMM from the important dimensions of privacy, accessibility, feasibility, sustainability, data quality assurance, data quality monitoring and data quality evaluation. At the same time, Qin et al. (2017) completed the development of the data management CMM to evaluate and improve research data management practices. The CMM model was originally used as a standard for evaluating and improving the software development process, enabling organizations to systematically identify problems and bottlenecks in software development. It has since then been adapted for use in manufacturing, project management, human resource management and other industries. Based on the CMM model, researchers have developed the project management maturity model, the human resource capability maturity model and the business process management maturity model, tailoring maturity assessment models to industry-specific characteristics. CMM continues to play a critical role in improving operational efficiency and market competitiveness in these industries. While a single maturity evaluation model has not yet gained widespread acceptance in library services, it is possible to build a tailored maturity evaluation model using the concepts and methods of the CMM. Cox et al. (2017) paid more attention to experience-based, especially library digital service research, and assessed the level of library research data service according to the tertiary levels of “basic application,” “well developed” and “extensive application,” and then improved the library digital service maturity model by comparing the survey data. In addition, the Australian National Data Service project constructed a similar library maturity evaluation framework from the five dimensions of policy processes, supporting services, IT infrastructure, metadata and research data (Cox et al., 2019).
In conclusion, the research literature on smart libraries, evaluation systems and maturity models is slowly growing. There are few theoretical innovation research results in the area of smart library evaluation systems. There are many research results on technology applications, and the research on CMM is relatively mature, but the research results on smart library services evaluation are still relatively scarce. The related research on smart library evaluation systems mainly focuses on system construction, theoretical perspective innovation and summary suggestions. Most of them stop at the construction of the evaluation system, and they lack empirical tests. Therefore, this study will focus on three main research questions:
How to construct the service maturity model of smart libraries in the digital intelligence era?
How to construct the service maturity evaluation system of smart libraries?
How to apply the constructed evaluation system to verify its feasibility and value?
3. The construction of smart library services maturity model based on capability maturity model
3.1 The capability maturity model
The CMM is an incremental improvement model proposed by the Carnegie Mellon Software Engineering Institute of the US Department of Defense in 1987 to assess software development capability. The CMM is based on the concept of process management, which assumes that capability improvement is a gradual and incremental process that can be divided into several distinct stages. The key process areas are identified by decomposing and analyzing the key process factors in each stage. Only when all of the key process areas included in that level meet the corresponding maturity goals can they be upgraded to the next level. This is a step-by-step process for improving capability maturity (Chen, 2018).
The specific structure of the CMM is shown in Figure 1, which can be divided into two main parts: the maturity level and the internal structure. These two parts clearly show the three issues of the level of each capability maturity development stage, the key content in the process of improving capability maturity and the effective way to achieve capability improvement. The five levels correspond to different levels of capability maturity. In addition to the initial level, each level includes the key process areas required to achieve that level of maturity. When the objectives of the key process areas are achieved at the appropriate level, the maturity level is considered to have been achieved. Each key process area includes key practices that describe effective organizational activities, and the common characteristics indicate the characteristics of effective key process areas and key practices.
The use of CMM should meet the following two prerequisites. First, the CMM represents a gradual improvement and upgrading process, so it must ensure the cyclicity of the process. Second, each stage level of the CMM contains the key areas that should be prioritized, so the process must be divided. The improvement of smart library services maturity is a spiral process, and each gradually improved service is the result of the continuous improvement of the service capability maturity. In addition, improving the service maturity of smart libraries is the result of multiple activities. Therefore, it is feasible to construct the service maturity model of smart libraries by using CMM to meet the above requirements (Chen, 2018).
3.2 The applicability design of capability maturity model in smart library services maturity evaluation
3.2.1 Analysis of demand-driven models of smart library services.
To study the maturity of smart library services, it is necessary to first sort out the development model of smart library services, to clarify, “What are the development drivers of smart library services?”, “How to drive the development progress?”, “How to reach a spiral state?” and “How to reach a spiral state?” As shown in Figure 2, first, the development of smart library services begins with the demand-driven library users; the user’s growing demand for hardware and software needs, environmental needs, resource needs, psychological needs and other types of needs is the root cause of the continuous improvement of library services; second, the constant demand for various types of resource inputs, such as funds, books, equipment and so on, the library managers use various types of resources to improve the quality of library services. Library administrators use various resources to improve the quality of library services at all levels to meet the needs of library users. Finally, the continuous iterative updating of smart library services generates a new round of demand in the benign interaction with users, forming a spiral demand upgrading mode. Under such a demand-driven model, the service quality of smart libraries continues to improve, and the maturity of the services also increases step-by-step.
3.2.2 Applicable reconstruction of the smart library services maturity evaluation model.
Based on the demand-driven model of smart library services in the previous section, this study modifies and innovatively designs the CMM model to adapt to the research topic by focusing on the maturity of smart services in the library that is the subject of the study and referring to the relevant existing research literature. As shown in Table 1 and Figure 3, the model mainly includes four stages, one demand-driven mode and one system.
Four stages: The model is divided into the initial stage, the development stage, the maturity stage and the optimization and innovation stage. The four different stages correspond to different service capability elements. In the initial stage, there was only a relatively simple smart service, and the concept was not clear. In the development stage, the types, functions and channels of services were gradually diversified, and the related system structure was also developed simultaneously. In the mature stage, the related services and institutions form a mature system. In the optimization and innovation stage, smart library services have entered a deeper optimization and innovation stage, and the rapidly changing artificial intelligence technology has further strengthened the optimization and innovation of smart services.
One demand-driven model: User demand drives the upgrading of resource investment to promote the continuous upgrading of library services and the continuous improvement of maturity. After reaching a new stage of development, it drives the generation of new user needs and so on.
One system: The demand-driven model of library service includes user demand, resource support, scope and smart service, which is a spiral demand upgrading model. Referring to the underlying logic, combined with relevant literature, policy documents and field visits to understand the information obtained, the evaluation system was finally constructed. The service maturity development level of library cases was evaluated according to the evaluation system.
4. The construction of smart library services maturity evaluation system based on analytic hierarchy process
Based on the smart library services maturity evaluation model constructed above, a standard scientific maturity indicator system needs to be constructed to judge the maturity level of the library. Therefore, this section will further refer to relevant information to select key indicators, first formulate the indicator system, and conduct the first round of expert consultation to improve and repair the indicators at all levels. The second round of expert consultation will be conducted and the weights will be calculated by using the AHP method to build a smart library services maturity indicator system.
4.1 Construction of evaluation indicator system and first round of expert consultation
The construction of a smart library services maturity evaluation system should be generated in the update iteration of theory and practice, and the design of its evaluation indicator should strictly follow the principle of scientific decision-making. In the process of constructing the evaluation system, this paper followed the principles of scientificity, objectivity, comprehensiveness, feasibility and foresight. The indicators were selected according to the policy documents on the construction of smart libraries in China (Table 2), the evaluation system of the existing research literature (Table 3) and the field survey (Table 4). The research in the field of a smart libraries indicator system is deepening and integrating in two main directions, the most representative of which is the construction of evaluation indicator system with two types of public libraries and university libraries as the object, so this study selects a case that meets the requirements of representativeness and accessibility in each of public smart libraries and university smart libraries, namely, the library of a provincial capital city in eastern China (Library A) and the library of a university in eastern China (Library B). The field research revealed that both libraries are typically smart. Library A is a smart-themed library branch built by the District Culture and Radio, Television, Tourism and Sports Bureau under the guidance of the Zhejiang Library and other provincial and municipal city business authorities. It is open to the public free of charge, the library in its region on the basis of the deep cultural heritage but also can be seen everywhere in the wisdom of the elements: smart bookshelves, smart lending cabinets, face recognition equipment, smart robots, big data system screen, VR machine, e-book waterfall, self-service lending and returning machines, self-service disinfection cabinets and other equipment are readily available. Library B is a typical university smart library under development to support the innovation and transformation of library services and promote the construction of modern smart libraries. The university library is constantly adding smart equipment and advanced management systems such as an interactive information display center system, big data display system, library-wide navigation system, “dynamic” shared space management system, ubiquitous robotic system plus window lending function, and so forth, and projects, such as smart inventory and three-dimensional virtual library construction, have also been included in the construction plan.
Through the field survey of the two libraries, key indicators for evaluating smart libraries were selected, and a first version of the evaluation indicator system was developed. Then, the Delphi method was used to consult experts in the field of smart libraries. The Delphi method, also known as the expert survey method, is basically an anonymous feedback correspondence method. Its general process is that after obtaining the opinions of experts on the problem to be predicted, it is organized, summarized, counted, and then anonymously fed back to the various experts, and then again solicited, and then centralized, and then fed back again, until the unanimous opinion is obtained (Feng, 2006). An expert team of 12 experts and academics in the field, as well as front-line managers of smart libraries practice institutions, was selected to evaluate the preliminary indicators and make reasonable and valuable suggestions. A total of 12 questionnaires were distributed in the first round of expert consultation, and ten valid questionnaires were received.
This study takes into account the overall scientific, operational and forward-oriented nature of the indicator system, combined with the questionnaire data and experts guidance and optimized the indicator system through a series of additions, deletions, mergers and modifications. The optimized evaluation system is presented in Table 5 and includes 4 primary indicators, 11 secondary indicators and 36 tertiary indicators.
4.2 Calculation of evaluation indicator weight
4.2.1 Construction of hierarchical structure model.
The AHP is a multi-objective decision-making method combining qualitative and quantitative, which was proposed by Svaty (Wang and Xu, 1990). The application of the AHP can lead to the transformation of the smart library services maturity indicator system from an unstructured state to a structured state in the digital intelligence era (Liu, 2013). When applying the AHP, the first step is to construct a hierarchical structural model. The goal level is the overall aim of the whole evaluation system, the criterion level is the various attributes or factors that make up the goal; each criterion can better express the various aspects of the goal, and the scheme level includes various measures and decision programs to achieve the goal, so it is also called the measure level or indicator level. As shown in Figure 4.
Based on the above principles, this study decomposes the objective layer, the criteria layer and the scheme layer. The objective layer is the service maturity indicator system of smart libraries in the smart age. The criteria layer consisted of four primary indicators, including S1 smart technology and equipment services, S2 smart management services, S3 smart resource services and S4 smart librarian services. The four indicators cover the four main elements involved in achieving the goal. The 11 secondary indicators under the criteria layer are the middle layer, namely, the subcriteria layer (S1-1 to S4-3), which contains the intermediate links involved in achieving the goals. The lowest level is the scheme level (A1–A36), which consists of 36 tertiary indicators.
4.2.2 Construction of the judgment matrix and second round of expert consultation.
After completing the construction of the hierarchical structure model, it is necessary to construct the judgment matrix of pairwise comparison importance degree with multiple indicators of the same level based on the AHP 1–9 scaling method. The second round of expert questionnaires is designed, and ten experts are invited to form an expert group to complete the questionnaire information. The ten experts in Rounds 1 and 2 are the same people.
As shown in Table 6, S12 in the judgment matrix represents the relative importance scale between S1 and S2 elements for the target layer. The whole indicator system is divided into 4 levels, with a total of 51 indicators and a total of 59 pairwise judgments are required.
The plan is divided into nine levels, from one to nine, taking into account the importance of the influencing factors to the same level as the measurement scale. Experts are invited to compare and score the listed indicators according to their importance, and the scoring needs to meet the principle of logical consistency. If S1 > S2 and S2 > S3 in the scoring, S1 > S3, otherwise the questionnaire is invalid. The meaning of the numerical scale is shown in Table 7.
4.2.3 Weight calculation and consistency test.
Ten questionnaires are collected in the second round of expert consultation. After downloading the data, the sum product method is used to calculate the weight of each indicator in each matrix and verify its consistency. The detailed steps are as follows:
Use equation (1) to normalize each column of the judgment matrix A = (aij)n×n;
Calculate the row vectors using equation (2);
The eigenvectors (3) were obtained by normalizing them using equation (4);
Equation (5) was used to calculate the maximum eigenvalue; and
Equation (6) was used to obtain the consistency indicator.
The value of consistency ratio CR obtained by using equation (7) can be obtained by referring to the consistency test table.
Taking the judgment matrix of four primary indicators as an example, after determining that the individual judgment matrix of ten experts meets the consistency test, the geometric mean of each group of data judged by the experts is taken for the next calculation.
As shown in Table 8, for a total of four indicators, S1 smart technology and equipment services, S2 smart management services, S3 smart resource services and S4 smart librarian services, a four-order judgment matrix is constructed to conduct AHP method research. The calculation method is: the feature vector (0.978, 0.744, 1.373 and 0.905) and the corresponding weight values (24.459%, 18.589%, 34.332%, 22.620%, respectively).
In addition, the maximum eigenvalue calculated by combining the eigenvectors is 4.013, and the CI value is 0.004 [CI = (the maximum eigenvalue −n)/(n − 1)], which could be used for the consistency test. According to Table 9, the RI value is 0.890, which can be used for the following consistency test calculation.
In general, the lower the CR value, the more reasonable the consistency of the judgment matrix. If the CR value is less than 0.1, the judgment matrix passes the consistency test. If the CR value is greater than 0.1, the consistency is poor, and it is not logical. As shown in Table 10, the CI value obtained by the fourth-order judgment matrix of the criterion layer is 0.004, the RI value is 0.890 and the CR value is 0.005 < 0.1, indicating that the scoring results of this judgment matrix meet the consistency test.
After summarizing the subsequent 15 judgment matrices, the weights are calculated in turn, as shown in Table 11.
The combination weight of each layer to the dependency layer is calculated hierarchically from top to bottom, and the overall consistency test is conducted to determine the weight distribution of indicators at all levels and the construction of the smart library services maturity evaluation system in the digital intelligence era is completed, as shown in Table 12.
5. Empirical application
To verify the feasibility of the evaluation system and show its application process, this study selects two different types of smart libraries cases in the previous field survey (namely, a provincial capital city, Library A, in East China and a university Library B, in East China). The empirical evaluation is conducted using the fuzzy comprehensive evaluation method and questionnaire survey method. The fuzzy comprehensive evaluation method is based on the concept of fuzzy mathematics and realizes the comprehensive evaluation of fuzzy objects with multiple factors and multiple levels. The basic principle of this method is to evaluate the fuzzy objects in the section as a whole by quantitative means based on user evaluation. This method is suitable for solving fuzzy and subjective evaluation problems, which can transform the qualitative assessment criteria in the evaluation process into quantitative evaluation data, and the fuzzy objects are expressed by intuitive data, which has a very good evaluation effect for complex problems (Wang and Yu, 2021).
This study selects one case that meets the requirements of representativeness and accessibility in each of the public smart libraries and university smart libraries, which will be referred to later as Library A and Library B, due to the requirement from the library side that the paper should be anonymized in the writing of the paper. The selection of cases is based on the basic principles of representativeness and data availability (Liu et al., 2021). In terms of representativeness, the case study should have more significant characteristics of smart libraries and have a certain audience size and a certain degree of influence. In terms of data availability, the service maturity index information of the research case is mainly obtained from the following channels: first, external research: questionnaire research on library users, librarians and experts in the field, collect qualitative index data, combine with the actual statistical data and apply the index system in the model and the maturity measurement standard to judge; second, external inquiry: through the library website, the official WeChat public website of the library, the official contact phone number, e-mail consultation with relevant staff, official contact phone number, e-mail consultation with relevant staff and query relevant public information on the internet to obtain relevant information and obtain part of the quantitative indicator data; third, internal research: through visits, research staff and other channels to understand the internal information and obtain part of the quantitative indicator data. To measure and calculate the fuzzy objects more accurately, this study divided the 36 tertiary indicators into 32 qualitative indicators and 4 quantitative indicators. First, the quantitative indicators include D28, D29, D30 and D33. Due to the large scale gap between different libraries, the data magnitude of the same indicator may vary greatly in different libraries. To filter out the influence of data magnitude, the collected data must be processed. Therefore, expert opinions are consulted to classify the order of magnitude of each indicator, and the objective data are assigned (1 point → 5 points). Then, 15 senior library users, two librarians and three experts were invited to form a group, and they were asked to rate each indicator on a Likert scale according to their personal experience and accumulated industry experience. Finally, the fuzzy comprehensive evaluation method is used to convert the above data into an affiliation matrix, and the grade of the comprehensive evaluation results is calculated and determined.
The quantitative indicator part is assigned according to the grades divided by the experts, and the case-related data were obtained through public information surveys, visiting research, consultation with library officials or experts and other channels. The data obtained are presented in Table 13.
In the qualitative indicators part, the group of 20 people rated each indicator of the two case smart libraries according to their experience. (1 = very low value, 2 = low value, 3 = general value, 4 = high value and 5 = very high value).
The statistical results of the questionnaire data and the constructed weight judgment matrix R are shown in Table 14.
Combined with the data obtained in Table 14, the scores obtained from the quantitative indicators are added to the weight judgment matrix R, and the weighted average type M(*,+) operator is used for fuzzy comprehensive evaluation calculation by SPSS tools. The affiliation degrees of the five comment sets of the Library A case are {0.032, 0.242, 0.242, 0.242, 0.242}, respectively. The weight of the “general” comment in the five comment sets is the largest, and the maximum affiliation degree rule of the set could be obtained, and the final comprehensive evaluation result is “general.” In the case of Library B, the affiliation degrees of the five comment sets are {0.116, 0.274, 0.274, 0.26, 0.076}, respectively. The weight of the “lower” comment in the five comment sets is the largest, and the result of the final comprehensive evaluation is “lower.”
As shown in Tables 15 and 16, when the assessment values (1, 2, 3, 4 and 5) are added to the five comments (very low, low, general, high and very high), the total score of Library A is 3.419, and the total score of Library B is 2.906.
According to the evaluation model of smart library services maturity based on CMM constructed above, the service maturity of smart libraries is divided into five levels: initial stage, development stage, maturity stage, optimization and innovation stage. The expert advisory group determine the corresponding relationship between the comprehensive score of the comprehensive fuzzy evaluation and the maturity level as follows:
A score of 0 to 2 is the initial stage: smart library services are still in the initial stage, only relatively simple smart services and the concept is not clear.
A score of 2.1 to 3.0 is the development stage: the smart library services has been gradually developed, the service types, functions and channels have been gradually diversified and the related system construction has also been synchronously developed.
A score of 3.1 to 4.0 is the maturity stage: the development of smart library services tends to be mature and stereotyped, and the related services and systems form a mature system.
A score of 4.1 to 5.0 is the optimization and innovation stage: smart library services move toward a deeper innovation and development stage, and the rapidly changing artificial intelligence technology continues to enable the optimization and innovation of smart services.
According to the calculation results in Tables 17 and 18, the comprehensive score of Library A is 3.419, and the evaluation result of service maturity is in the mature stage. The comprehensive score of Library B is 2.906, and the evaluation result of service maturity is the development stage.
To verify the scientific nature of the research results obtained in this empirical stage, the research results of this stage are specially brought to the expert group teachers for evaluation opinions. The experts say that the comprehensive scores of the two case libraries obtained in this empirical application are roughly consistent with the maturity of their smart service development, which proves that the evaluation system and its application method constructed in this study are feasible and scientific.
6. Discussion
To further expand the application value of the study, this study proposes strategies for improving the maturity of smart library services from the perspective of the 4S evaluation system, and from the four levels of smart technology and equipment services, smart management services, smart resource services and smart librarian services, so as to provide a reference for the constructive development of other smart library services. The study is intended to provide a reference for the constructive development of other smart library services.
6.1 Smart technology and equipment services level
The use of smart technology to construct a smart core and consolidate the smart foundation to build a wisdom shell is both the driving force of the top-level design of the wisdom society and the supply chain to support the normalization of the library’s smart management. In the previous expert scoring assignment stage, smart technology and equipment services in the dimension indicator level get 24.46% weight assignment, indicating that this indicator is crucial for the construction and development of smart libraries, in which the proportion of smart technology is slightly higher than that of smart equipment, and the D1-D9 plate scores of Library B still have more room for improvement.
In terms of smart technology, communication technology represented by 5G and wifi technology is the substrate of library informatization and wisdom upgrading, which guarantees the communication technology network with a high speed rate, low latency and full coverage, which can not only provide smart library users with a smoother and faster usage experience but also can provide a more complete communication network support for the application of emerging technologies, such as big data, cloud computing and meta-universe. On the other hand, with the continuous iteration and application of new generation information technologies, such as big data, artificial intelligence, cloud computing and so on, the related smart technologies are used in conjunction with the service scenarios, and the resources and services are data-driven, which can be further used to create more interconnected, perceptive and smart applications, and can be applied to the construction of the smart library system, regular services and personalized and characteristic services. In addition, the two cases of different types of smart libraries selected for empirical analysis in this study have low scores in the index item of the meta-universe technology application level. The application of meta-universe technology in the improvement of smart library services is an important direction for the future development of smart libraries. The smart library services derived from the concept of “meta-universe” can make the library services management migrate in the direction of comprehensive awareness, interconnection and efficient operation.
What is meant in terms of smart library equipment is to build a smart shell of hardware, providing real-time maintenance and iterative upgrading, and also fully reserving the development of network services space and user-friendly interfaces. In addition, smart equipment can achieve a high degree of integration with smart buildings: based on a digital building entity, smart technologies, such as the Internet of Things, are embedded in the library’s buildings, equipment and resources, using cloud computing and big data platforms to provide logic, using AI algorithms, to provide the basis for decision-making, and achieve the integration and interoperability between the building, the equipment, the resources and the services.
6.2 Smart management services level
The rapid development of smart technology promotes the transformation of library resource management, the establishment of an online and offline linkage of smart services management system. Improving the level of smart management of resources has become one of the key initiatives in the construction of smart library resources. Combined with the previous data can be obtained, the level of smart management services, the weight of smart operation management is higher, accounting for 8.17%, followed by smart book management and smart space management, accounting for 5.44% and 4.79%, respectively, of which it is worth paying attention to the level of smart analysis of user behavior accounted for 40.32% of the weight of the S2-2 indicator layer. The D10–D18 data performance of Library A is relatively more prominent.
In terms of smart book management, it can be combined with online and offline systems to form a common smart management network. Smart libraries should build a new generation of library smart services and management platforms, realize the integrated management of paper, electronic and digital resources, and improve the level of smart management of online e-book resources. It is also necessary to strengthen the construction of smart management of an offline collection of books and newspapers, to realize smart warehousing through book inventory robots and to build a smart operation system of collecting, editing and streaming based on big data, to realize meaningful borrowing, precise positioning navigation and real-time reference and consultation services and so on (Liu et al., 2019).
In terms of smart operation and management, it is necessary to make good use of resources and user data. On the one hand, librarians should deepen the value of the library’s collection resources, accelerate knowledge innovation, promote knowledge circulation and encourage knowledge creation. On the other hand, user behavior, collection resources, space management and other massive and fragmented data traces are valuable digital resources generated in the process of library operation, but can also use a data-driven thinking root system, its analysis and transformation, and become a lever to improve the level of library operation and management.
In terms of smart space management, on the one hand, smart libraries should strengthen the construction and management of the physical space of offline entities, focusing on the functional reengineering and service expansion of various types of smart physical space, shifting from simple literature reading and preservation space to dynamic, open and professional wisdom and innovation space, so as to better promote knowledge circulation and user community exchange, and enhance the expressiveness, hierarchy and plurality of the resources in the physical space (Yang and Deng, 2020). On the other hand, it should strengthen the construction and management of online virtual space, generalize the wisdom service, take root in the online reader community according to the reading characteristics of user fragmentation and socialization, and shift from “attracting” users to “recommending” the service to the virtual space of libraries.
6.3 Smart resource services level
Smart resources are the “source of living water” of smart libraries, and in the previous weighting stage, the weight of smart resource services in the first-level indicators accounts is 34.33%, the importance of which is obvious. At the level of smart resource services, smart collection resources, smart platform resources and smart consulting resources complement each other, and in the refined indicators, the weight coefficients of the electronic construction of collection resources and the construction of new media platforms account are higher, respectively, 5.59% and 4.88%, indicating that the construction of resource databases and the platform publicity are particularly important, and in the empirical data, it can also be seen that the two case libraries pay a lot of attention to these two items. The content of the board is highly valued, and the data performance is also relatively good, but the think tank services receive less attention.
In terms of smart collection resources, the utilization rate of rich online resources of current smart libraries, especially professional educational resources and academic value, is not high enough, which indicates that the open sharing of its resources is low, so the construction of digital resources should be strengthened and the resource sharing system should be built. The main way of resource opening in the library sector is cross-border cooperation, such as the resource cooperation between Peking University, Wuhan University Library and Baidu Academic. And the current digital technology environment is conducive to the integration of librarians, technology, resources and services into each other, optimizing the promotion of personalized reading and deepening the integration of information technology and knowledge services.
In terms of smart platform resources, it is possible to promote the construction of websites, clients and small programs, expand the channels of users, and realize 24-h service for massive resources. For example, Tsinghua University Library and Xiamen University Library have independently developed digital library apps to provide readers with a variety of services, such as borrowing and querying, reader services, bibliographic search, and so forth, and provide users with cloud-based digital reading services that can be enjoyed at any time. It is also necessary to build a new media platform matrix, establish and operate accounts on social media platforms, attract different types of user groups and strengthen communication power and influence.
In terms of smart consulting resources, the professionalism of artificial consulting and smart robot consulting will be continuously strengthened, and human-machine collaboration will provide users with more comprehensive and accurate online consulting services and also apply natural language processing tools similar to ChatGPT, accumulate user Q&A data to expand the corpus to train AI models, so as to make smart libraries smart consulting services capability level higher. In addition, a variety of channels can also be used to provide smart consulting services, for example, university libraries can disseminate decision support products through offline channels (conferences, lectures, etc.) but also through new media channels (two micro-miniature and one jittery, etc.), to develop wisdom consulting services and enhance their impact.
6.4 Smart librarian services level
No matter how the forms of smart libraries develop and change, librarians are always the original driving force for its development and progress, and also the most powerful and subjective initiative of the core elements. From the analysis of the previous data, we can see that the weight coefficients of the three indicators of the comprehensive quality of smart librarians, the training of smart librarian talents and the structure of librarian talent are 9.07%, 7.82% and 5.73%, respectively, of which the coefficient of the third level indicator of the D34 core business capacity (including information retrieval, professional consultation, scientific and technological research and new skills) is as high as 4.12%, so it can be seen that the business capacity of librarians is important for the improvement of the librarian services. The empirical analysis found that the data performance of university libraries in this area is better than that of public libraries.
Talent recruitment is an important way to maintain the long-term vitality of the library talent team (Wang, 2020). On the one hand, libraries can continuously optimize the talent recruitment program to promote the positive interaction between supply and demand. On the other hand, libraries can optimize talent allocation in many different dimensions around business needs and smart development needs. For example, to improve the number of professional information technology librarians, specialized subject librarians, master and doctoral high quality talent librarians in the number of all librarians in the proportion. In the training of smart librarians, it is also necessary to improve the construction of smart librarian assessment and incentive systems, increase the investment of training funds from the professional discipline literacy, professional and technical skills, and user service level of the three aspects to improve the comprehensive quality of smart librarians. For example, Harvard University Library encourages their librarians to conduct research; they established the Douglas Bryant Scholarship program to support their librarians to complete all kinds of academic research projects, providing intellectual support for innovation in the field of library services (Hu and Liu, 2009).
7. Conclusion
As an important institution for urban talent training, scientific research support and cultural inheritance, the library is one of the cornerstones of human social and cultural development. Smart libraries are an inevitable development trend for libraries to seize the opportunity to enhance their value in the era of digital intelligence, and it is also an inevitable choice for the development of new smart cities (Hu and Zou, 2022). Smart library services are the core element of smart libraries, and the establishment and improvement of their evaluation system are indicators of the healthy development of smart libraries. First, this study innovatively constructed the service maturity level model of smart libraries based on the CMM. Second, two rounds of Delphi expert consultation were conducted, and the AHP was combined to construct the service maturity indicator system of the smart libraries in the digital intelligence era. Finally, in the empirical application, the university smart libraries and public smart libraries were selected for a fuzzy comprehensive evaluation, which verified the feasibility and rationality of the evaluation system. It is hoped that the empirical scale can be expanded in the future to improve the application scope of the service maturity evaluation system of smart libraries in this paper and to continuously optimize the service maturity evaluation system of smart libraries.
The authors declare that they have no financial or personal relationship with other person or organizations that could inappropriately influence the work and that they have no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position expressed in the manuscript or the review of the manuscript.
Funding: National Social Science Fund Youth Project of China [No. 20CTQ010].
Schematic diagram of the capability maturity model
Demand-driven model of smart library services
Schematic diagram of the maturity evaluation model of smart library services
Schematic diagram of the hierarchical structural model
Classification of smart library services maturity evaluation model
| Stages | Hierarchical division | Service capability elements/indications |
|---|---|---|
| A | Initial stage | Smart library services are still in the initial stage, and there are only relatively simple smart services with unclear concepts |
| B | Development stage | Smart library services are gradually developing, and the service types, functions and channels are gradually diversified, and the related system construction is also developing synchronously |
| C | Maturity stage | The development of smart services in smart libraries tends to be mature and stereotypic, and the relevant services and systems have formed a mature system |
| D | Optimizing and |
Smart library services have entered a deeper stage of innovation and development, and various cutting-edge technologies continue to empower the optimization and innovation of smart services |
Source: Authors’ own work
Compilation of policy documents related to the construction of smart libraries
| Policy documents | Key indicators | Year | Organization |
|---|---|---|---|
| Open Integration, Connecting Everything: Zhejiang Public Libraries “Internet Plus” Action Plan | “Internet + lending process,” “Internet + digital reading,” “Internet + knowledge services” and “Internet + lifelong education” | 2015 | Zhejiang Library |
| Indicator System for Smart libraries in the Sino-Singapore Tianjin Eco-city | Smart buildings, data interconnection, online services, smart circulation and smart management | 2020 | China-Singapore Friendship Library |
| Guidelines for the Construction of Smart Libraries (Rooms) in Primary and Secondary Schools in Anhui Province | Environment, equipment, management and assessment | 2020 | Anhui Education Department |
| Outline of the 14th Five-Year Plan for the Development of the Chinese Library Association (2021–2025) | Academic research, decision-making consultation, popular science reading, compilation and publication and international exchange | 2021 | China Library Association |
| National Library’s 14th Five-Year Development Plan | Digitalization; networking, intelligence/smart transformation, quality improvement and enhanced cooperation | 2021 | National Library of China |
| Opinions on Advancing the Implementation of the National Cultural Digitization Strategy | Modernization, digitalization; intelligence | 2022 | General Office of the Central Committee of the Communist Party of China and the State Council |
Source: Authors’ own work
Organized evaluation system of existing research literature
| Literatures | Key indicators | Reference |
|---|---|---|
| An empirical study on the evaluation system of service quality of college libraries in China under the new information environment | Service evidence, service reliability, service trust, service responsiveness and service empathy | Fan et al. (2015) |
| A study of librarians’ core competencies in a smart libraries environment | Cognitive and adaptive skills, service and action skills, collaboration and communication skills and development and innovation skills | Zheng and Bao (2017) |
| A study on the context-aware service model and evaluation of “smart libraries” | Contextual data resources, system technology, user experience and management performance | Zhou et al. (2017) |
| A study on measuring and evaluating the intelligence level of smart libraries | Smart sensing, smart management, smart service and smart decision-making | Liu and Zhang (2018) |
| Research on the construction of a national library service quality evaluation indicator system based on LibQUAL + TM system | Service perception, information control and library environment | Yang and Yang (2019) |
| Construction and analysis of evaluation index system of smart libraries construction | Librarians, infrastructure, management and services | Duan et al. (2021) |
| Construction of the index system for evaluating the effectiveness of public library services | Resource conversion rate, activity promotion degree, librarian service power, user impact degree and culture cultivation degree | Chang and Zhang (2021) |
| Research on the evaluation system of smart libraries under the perspective of user satisfaction | Space, resources and services | Liu (2022) |
| A study on the evaluation of library reading promotion activities based on the “total evaluation” analytical framework | Formal evaluation; content evaluation; utility evaluation | Zhang and Hou (2022) |
| Construction of the index system for evaluating the effectiveness of public library services | Resource conversion rate, activity promotion degree, librarian service power, user impact degree and culture cultivation degree | Chang and Zhang (2021) |
Source: Authors’ own work
Selection of indicators in the field survey of smart library services
| Library A | Library B | ||
|---|---|---|---|
| Facilities | Smart bookshelf, smart lending cabinet, face recognition equipment, smart robot, big data system screen, VR all-in-one machine, e-book waterfall, self-service lending and returning machine, self-service sterilizer cabinet, etc. | Facilities | Self-lending machine, interactive information display center system, big data display system, library-wide functional area navigation system, “smart” shared space management system and ubiquitous robot system |
| Space | Reading space, smart living room, star theatre, audiobook wall and VR experience area | Space | Multimedia integrated laboratory and broadcasting equipment exhibition area |
| Resources | Abundant digital resources and personalized resource push | Resources | China Association for Literature and Information Security in Higher Education (CALIS) and China Center for Humanities and Social Sciences Literature in Higher Education (CASHL) |
| Platforms | Xinyue, Book Search, Linli Youyao and Linli Youyao Book Lending | Platforms | Reader’s Star Reading Promotion, Online Bookstore, Ruta Shopping and Learning Access |
| Consulting | Public Consulting Services | Consulting | Online Consulting Services |
Source: Authors’ own work
Evaluation system of smart library services maturity (optimized version)
| Primary indicators | Secondary indicators | Tertiary indicators |
|---|---|---|
| S1 Smart technology and equipment services | S1-1 Smart technology | D1 Application level of big data analysis and presentation technology |
| D2 Application level of cloud computing technology | ||
| D3 Application level of artificial intelligence technology | ||
| D4 Application level of metaverse technology | ||
| D5 Communication technology support (such as 5G and wifi) | ||
| S1-2 Smart devices | D6 Basic smart devices (such as self-service borrowing and returning machines, big data display walls, smart robots, book retrieval machines and smart disinfection cabinets) | |
| D7 Smart sensing devices (such as RFID, NFC and Bluetooth devices) | ||
| D8 Virtual experience devices (such as VR devices and AR devices) | ||
| D9 Smart monitoring equipment (such as smart access control equipment and video surveillance system) | ||
| S2 Smart management services | S2-1 Smart book management | D10 Degree of smart management of collection books and newspapers |
| D11 Degree of smart management of e-book resources | ||
| D12 Degree of integration of RFID positioning system | ||
| S2-2 Smart operation management | D13 Smart level of resource utilization analysis | |
| D14 Smart management level of visitor system | ||
| D15 Smart level of user behavior analysis | ||
| S2-3 Smart space management | D16 Learning seminar room construction management | |
| D17 Shared space construction management | ||
| D18 Virtual space construction management | ||
| S3 Smart resource services | S3-1 Smart collection resources | D19 Construction of electronic collection resources |
| D20 Cloud platform database construction | ||
| D21 Construction of personalized reading promotion services | ||
| S3-2 Smart platform resources | D22 Official website construction | |
| D23 Client/applet construction | ||
| D24 New media platform account construction | ||
| S3-3 Smart consulting resources | D25 Manual online consultation services | |
| D26 Smart robot consulting services | ||
| D27 Think tank consulting services | ||
| S4 Smart librarian services | S4-1 Librarian talent structure | D28 Proportion of professionals in information technology |
| D29 Proportion of specialized subject librarians | ||
| D30 Proportion of master and doctoral high-quality librarians | ||
| S4-2 Training of smart librarians | D31 Maturity of smart librarian team construction | |
| D32 Maturity of smart librarian evaluation system construction | ||
| D33 Proportion of training funds for smart librarians | ||
| S4-3 Comprehensive quality of smart librarians | D34 Core business competence (including information retrieval, professional consultation, science and technology novelty search ability) | |
| D35 Professional technical competence (including professional software operation ability, mastering ability of emerging smart devices and data analysis and processing ability) | ||
| D36 User service competence (including answering user inquiries, providing personalized recommendations and meeting user needs) |
Source: Authors’ own work
Pairwise judgment matrix
| Elements | S1 | S2 | S3 | … | Sn |
|---|---|---|---|---|---|
| S1 | S11 | S12 | S13 | … | S1n |
| S2 | S21 | S22 | S23 | … | S2n |
| S3 | S31 | S31 | S33 | … | S3n |
| … | … | … | … | … | … |
| Sn | Sn1 | Sn2 | Sn3 | … | Snn |
Source: Authors’ own work
Judgment matrix scale and its meaning
| 1 | The two factors are equally important |
| 3 | The former factor is slightly more important than the latter |
| 5 | The former factor is obviously more important than the latter |
| 7 | The former factor is strongly more important than the latter |
| 9 | The former factor is extremely more important than the latter |
| 1/3 | The latter factor is slightly more important than the former |
| 1/5 | The latter factor is obviously more important than the former |
| 1/7 | The latter factor is strongly more important than the former |
| 1/9 | The latter factor is extremely more important than the former |
Source: Authors’ own work
Summary table of the AHP results of primary indicators
| Item | Eigenvector | Weight |
Maximum eigenvalue | CI value |
|---|---|---|---|---|
| S1 Smart technology and equipment services | 0.978 | 24.459 | 4.013 | 0.004 |
| S2 Smart management service | 0.744 | 18.589 | ||
| S3 Smart resource services | 1.373 | 34.332 | ||
| S4 Smart librarian services | 0.905 | 22.620 |
Notes:RI = 0. 023; CR = 0.015 < 0.1
Source: Authors’ own work
RI table of random consistency for primary indicators
| Matrix order n | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RI value | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 | 1.41 | 1.46 | 1.49 | 1.52 | 1.54 | 1.56 | 1.58 | 1.59 | 1.59 |
| Matrix order n | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| RI value | 1.60 | 1.61 | 1.62 | 1.62 | 1.63 | 1.64 | 1.64 | 1.64 | 1.65 | 1.65 | 1.66 | 1.66 | 1.66 | 1.67 |
Source: Authors’ own work
Summary of consistency test results
| Maximum eigenvalue | CI value | RI value | CR value | Consistency test results |
|---|---|---|---|---|
| 4.013 | 0.004 | 0.890 | 0.005 | Pass |
Source: Authors’ own work
Summary table of the AHP results of indicators
| Item | Eigenvector | Weight value (%) | Maximum eigenvalue | CI value | RI value | CR value |
|---|---|---|---|---|---|---|
| S2-1 Smart book management | 0.878 | 29.268 | 3.008 | 0.004 | 0. 520 | 0.008 |
| S2-2 Smart operations management | 1.319 | 43.971 | ||||
| S2-3 Smart space management | 0.803 | 26.761 | ||||
| S3-1 Smart collection resources | 1.063 | 35.425 | 3.009 | 0.005 | 0. 520 | 0.009 |
| S3-2 Smart platform resources | 1.287 | 42.886 | ||||
| S3-3 Wisdom consulting resources | 0.651 | 21.689 | ||||
| S4-1 Librarian talent structure | 0.760 | 25.330 | 3.000 | 0.000 | 0. 520 | 0.000 |
| S4-2 Training of smart librarians | 1.038 | 34.591 | ||||
| S4-3 Comprehensive quality of smart librarians | 1.202 | 40.079 | ||||
| D1 Application level of big data analysis and presentation technology | 1.069 | 21.385 | 5.059 | 0.015 | 1.120 | 0.013 |
| D2 Application level of cloud computing technology | 0.953 | 19.065 | ||||
| D3 Application level of artificial intelligence technology | 0.936 | 18.728 | ||||
| D4 Application level of metaverse technology | 0.732 | 14.644 | ||||
| D5 Communication technology support (e.g. 5G, wifi, etc.) | 1.309 | 26.178 | ||||
| D6 Basic smart devices | 1.481 | 37.022 | 4.029 | 0.010 | 0. 890 | 0.011 |
| D7 Smart sensing devices | 0.908 | 22.706 | ||||
| D8 Virtual experience devices | 0.807 | 20.171 | ||||
| D9 Smart monitoring devices | 0.804 | 20.101 | ||||
| D10 Degree of smart management of collection books & newspapers | 1.022 | 34.055 | 3.000 | 0.000 | 0. 520 | 0.000 |
| D11 Degree of smart management of e-book resources | 1.368 | 45.596 | ||||
| D12 Degree of integration of RFID positioning system | 0.610 | 20.349 | ||||
| D13 Smart level of resource utilization analysis | 1.078 | 35.945 | 3.000 | 0.000 | 0. 520 | 0.000 |
| D14 Smart management level of visitor system | 0.712 | 23.740 | ||||
| D15 Smart level of user behavior analysis | 1.209 | 40.315 | ||||
| D16 Learning seminar room construction management | 1.125 | 37.508 | 3.000 | 0.000 | 0. 520 | 0.000 |
| D17 Shared space construction management | 0.995 | 33.166 | ||||
| D18 Virtual space construction management | 0.880 | 29.326 | ||||
| D19 Construction of electronic collection resources | 1.204 | 40.118 | 3.013 | 0.006 | 0. 520 | 0.012 |
| D20 Cloud platform database construction | 0.900 | 30.003 | ||||
| D21 Construction of personalized reading promotion services | 0.896 | 29.879 | ||||
| D22 Official website construction | 0.904 | 30.139 | 3.002 | 0.001 | 0. 520 | 0.002 |
| D23 Client/applet construction | 0.957 | 31.894 | ||||
| D24 New media platform account construction | 1.139 | 37.966 | ||||
| D25 Manual online consulting services | 1.060 | 35.329 | 3.017 | 0.009 | 0. 520 | 0.017 |
| D26 Smart robot consulting services | 1.033 | 34.439 | ||||
| D27 Think tank consulting services | 0.907 | 30.232 | ||||
| D28 Proportion of professionals in information technology | 1.193 | 39.759 | 3.023 | 0.011 | 0. 520 | 0.022 |
| D29 Proportion of specialized subject librarians | 1.189 | 39.648 | ||||
| D30 Proportion of master and doctoral high-quality librarians | 0.618 | 20.593 | ||||
| D31 Maturity of smart librarian team construction | 1.309 | 43.646 | 3.000 | 0.000 | 0. 520 | 0.000 |
| D32 Maturity of smart librarian evaluation system construction | 0.854 | 28.480 | ||||
| D33 Proportion of training funds for smart librarians | 0.836 | 27.874 | ||||
| D34 Core business competence | 1.363 | 45.429 | 3.002 | 0.001 | 0. 520 | 0.001 |
| D35 Professional technical competence | 0.851 | 28.356 | ||||
| D36 User service competence | 0.786 | 26.215 |
Source: Authors’ own work
Evaluation system of smart library services maturity (full version)
| Primary indicator | Weights (%) | Secondary metrics | Weights (%) | Combination weights (%) | Tertiary indicators | Weights (%) | Combined |
|---|---|---|---|---|---|---|---|
| S1 Smart technology and equipment services | 24.46 | S1-1 Smart technology | 60.00 | 14.68 | D1 Application level of big data analysis and presentation technology | 21.39 | 3.14 |
| D2 Application level of cloud computing technology | 19.07 | 2.80 | |||||
| D3 Application level of artificial intelligence technology | 18.73 | 2.75 | |||||
| D4 Application level of metaverse technology application | 14.64 | 2.15 | |||||
| D5 Communication technology support (such as 5G and wifi) | 26.18 | 3.84 | |||||
| S1-2 Smart devices | 40.00 | 9.78 | D6 Basic smart devices (such as self-service borrowing machine, big data display wall, smart robot, book retrieval machine and smart disinfection cabinet) | 37.02 | 3.62 | ||
| D7 Smart sensing devices (such as RFID, NFC and Bluetooth devices) | 22.71 | 2.22 | |||||
| D8 Virtual experience equipment (such as VR equipment and AR equipment) | 20.17 | 1.97 | |||||
| D9 Smart monitoring equipment (such as smart access control equipment and video surveillance system) | 20.10 | 1.97 | |||||
| S2 Smart management services | 18.59 | S2-1 Smart book management | 29.27 | 5.44 | D10 Degree of smart management of collection books and newspapers | 34.06 | 1.85 |
| D11 Degree of smart management of e-book resources | 45.60 | 2.48 | |||||
| D12 Degree of integration of RFID positioning system | 20.35 | 1.11 | |||||
| S2-2 Smart operations management | 43.97 | 8.17 | D13 Smart level of resource utilization analysis | 35.95 | 2.94 | ||
| D14 Smart management level of visitor system | 23.74 | 1.94 | |||||
| D15 Smart level of user behavior analysis | 40.32 | 3.30 | |||||
| S2-3 Smart space management | 26.76 | 4.97 | D16 Learning seminar room construction management | 37.51 | 1.87 | ||
| D17 Shared space construction management | 33.17 | 1.65 | |||||
| D18 Virtual space construction management | 29.33 | 1.46 | |||||
| S3 Smart resource services | 34.33 | S3-1 Smart collection resources | 35.43 | 12.16 | D19 Construction of electronic collection resources | 40.12 | 4.88 |
| D20 Cloud platform database construction | 30.00 | 3.65 | |||||
| D21 Construction of personalized reading promotion services | 29.88 | 3.63 | |||||
| S3-2 Smart platform resources | 42.89 | 14.72 | D22 Official website construction | 30.14 | 4.44 | ||
| D23 Client/applet construction | 31.89 | 4.70 | |||||
| D24 New media platform account construction | 37.97 | 5.59 | |||||
| S3-3 Smart consulting resources | 21.69 | 7.45 | D25 Manual online consultation services | 35.33 | 2.63 | ||
| D26 Smart robot consulting services | 34.44 | 2.56 | |||||
| D27 Think tank consulting Services | 30.23 | 2.25 | |||||
| S4 Smart librarian services | 22.62 | S4-1 Structure of librarian talent | 25.33 | 5.73 | D28 Proportion of professionals in information technology | 39.76 | 2.28 |
| D29 Proportion of specialized subject librarians | 39.65 | 2.27 | |||||
| D30 Proportion of master and doctoral high-quality librarians | 20.59 | 1.18 | |||||
| S4-2 Training of smart librarians | 34.59 | 7.82 | D31 Maturity of smart librarian team construction | 43.65 | 3.42 | ||
| D32 Maturity of smart librarian evaluation system construction | 28.48 | 2.23 | |||||
| D33 Proportion of training funds for smart librarians | 27.87 | 2.18 | |||||
| S4-3 Comprehensive quality of smart librarians | 40.08 | 9.07 | D34 Core business competence (including information retrieval, professional consultation, scientific and technological novelty search ability) | 45.43 | 4.12 | ||
| D35 Professional technical competence (including professional software operation ability, mastering ability of emerging smart devices, data analysis and processing ability) | 28.36 | 2.57 | |||||
| D36 User service competence (including answering user inquiries, providing personalized recommendations and meeting user needs) | 26.22 | 2.38 |
Source: Authors’ own work
Expert scoring and case scoring table of different orders of magnitude
| Data | D28 Proportion of |
D29 Proportion |
D30 Proportion of |
D33 Proportion of |
|---|---|---|---|---|
| Table of expert assignments for different orders of magnitude | ||||
| 1 point (%) | 0–10 | 0–20 | 0–10 | 0–10 |
| 2 points (%) | 11–20 | 21–40 | 11–30 | 11–20 |
| 3 points (%) | 21–30 | 41–60 | 31–50 | 21–30 |
| 4 points (%) | 31–40 | 61–80 | 51–70 | 31–40 |
| 5 points (%) | 41–100 | 81–100 | 71–100 | 41–100 |
| Library A | ||||
| Data (%) | 37.50 | 75.00 | 12.50 | 35 |
| Points assigned | 4 | 4 | 2 | 4 |
| Library B | ||||
| Data (%) | 22.50 | 82.50 | 85 | 17 |
| Points assigned | 3 | 5 | 5 | 2 |
Source: Authors’ own work
Weight judgment matrix R
| Evaluation factors | Weight |
Number of reviews each | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| A Library | B Library | ||||||||||
| V1 | V2 | V3 | V4 | V5 | V1 | V2 | V3 | V4 | V5 | ||
| D1 Application level of Big data analysis and presentation technology | 3.14 | 0 | 0 | 2 | 13 | 5 | 0 | 4 | 11 | 5 | 0 |
| D2 Application level of cloud computing technology | 2.80 | 0 | 1 | 2 | 10 | 7 | 1 | 6 | 8 | 5 | 0 |
| D3 Application level of artificial intelligence technology | 2.75 | 0 | 0 | 5 | 9 | 6 | 0 | 6 | 11 | 3 | 0 |
| D4 Application level of metaverse technology application | 2.15 | 1 | 3 | 11 | 4 | 1 | 5 | 11 | 3 | 1 | 0 |
| D5 Communications technical support | 3.84 | 0 | 1 | 2 | 11 | 6 | 0 | 1 | 11 | 8 | 0 |
| D6 Basic smart devices | 3.62 | 0 | 0 | 2 | 9 | 9 | 1 | 2 | 7 | 8 | 2 |
| D7 Smart sensing devices | 2.22 | 0 | 0 | 8 | 8 | 4 | 1 | 6 | 8 | 4 | 1 |
| D8 Virtual experience equipment | 1.97 | 0 | 0 | 2 | 10 | 8 | 4 | 7 | 8 | 1 | 0 |
| D9 Smart monitoring equipment | 1.97 | 0 | 1 | 3 | 12 | 4 | 0 | 3 | 9 | 8 | 0 |
| D10 Degree of smart management of collection books and newspapers | 1.85 | 0 | 1 | 2 | 13 | 4 | 0 | 3 | 8 | 8 | 1 |
| D11 Degree of smart management of e-book resources | 2.48 | 0 | 1 | 3 | 11 | 5 | 0 | 4 | 9 | 7 | 0 |
| D12 Degree of integration of RFID positioning system | 1.11 | 0 | 2 | 4 | 9 | 5 | 1 | 6 | 9 | 4 | 0 |
| D13 Smart level of resource utilization analysis | 2.94 | 0 | 1 | 2 | 11 | 6 | 0 | 6 | 7 | 7 | 0 |
| D14 Smart management level of visitor system | 1.94 | 0 | 2 | 6 | 11 | 1 | 0 | 5 | 7 | 8 | 0 |
| D15 Smart level of user behavior analysis | 3.30 | 0 | 2 | 6 | 11 | 1 | 1 | 6 | 10 | 3 | 0 |
| D16 Study seminar room construction management | 1.87 | 0 | 2 | 6 | 11 | 1 | 0 | 6 | 8 | 6 | 0 |
| D17 Shared space construction management | 1.65 | 0 | 2 | 5 | 7 | 6 | 0 | 5 | 8 | 6 | 1 |
| D18 Virtual space construction management | 1.46 | 0 | 2 | 4 | 14 | 0 | 4 | 8 | 7 | 1 | 0 |
| D19 Construction of electronic collection resources | 4.88 | 0 | 1 | 5 | 10 | 4 | 0 | 2 | 9 | 8 | 1 |
| D20 Cloud platform database construction | 3.65 | 0 | 3 | 6 | 8 | 3 | 0 | 5 | 10 | 5 | 0 |
| D21 Construction of personalized reading promotion services | 3.63 | 0 | 2 | 5 | 9 | 4 | 3 | 2 | 9 | 6 | 0 |
| D22 Official website construction | 4.44 | 1 | 2 | 10 | 7 | 0 | 0 | 2 | 8 | 8 | 2 |
| D23 Client/applet construction | 4.70 | 0 | 3 | 9 | 8 | 0 | 1 | 3 | 10 | 6 | 0 |
| D24 New media platform account construction | 5.59 | 0 | 1 | 7 | 12 | 0 | 0 | 2 | 9 | 9 | 0 |
| D25 Manual online consulting services | 2.63 | 0 | 1 | 7 | 10 | 2 | 2 | 3 | 9 | 6 | 0 |
| D26 Smart robot consulting services | 2.56 | 0 | 3 | 11 | 5 | 1 | 1 | 9 | 10 | 0 | 0 |
| D27 Think tank consulting Services | 2.25 | 3 | 4 | 9 | 3 | 1 | 2 | 5 | 9 | 4 | 0 |
| D31 Maturity of smart librarian team construction | 3.42 | 0 | 0 | 6 | 12 | 2 | 1 | 5 | 9 | 5 | 0 |
| D32 Maturity of smart librarian evaluation system construction | 2.23 | 0 | 0 | 8 | 11 | 1 | 1 | 6 | 10 | 3 | 0 |
| D34 Core business competence | 4.12 | 0 | 0 | 4 | 13 | 3 | 0 | 3 | 3 | 13 | 1 |
| D35 Professional technical competence | 2.57 | 0 | 1 | 4 | 14 | 1 | 0 | 5 | 10 | 5 | 0 |
| D36 User service competence | 2.38 | 0 | 0 | 6 | 10 | 4 | 0 | 3 | 8 | 9 | 0 |
Source: Authors’ own work
Calculation results of affiliation matrix of Library A
| Results | Very low | Low | Average | Higher | Very high |
|---|---|---|---|---|---|
| Degree of affiliation | 0.13340 | 1 | 1 | 1 | 1 |
| Affiliation degree normalization (weight) | 0.032 | 0.242 | 0.242 | 0.242 | 0.242 |
Source: Authors’ own work
Calculation results of affiliation matrix of Library B
| Results | Very low | Low | Average | Higher | Very high |
|---|---|---|---|---|---|
| Degree of affiliation | 0.40044 | 0.94367 | 0.94270 | 0.89524 | 0.26233 |
| Affiliation degree normalization (Weight) | 0.116 | 0.274 | 0.274 | 0.26 | 0.076 |
Source: Authors’ own work
Calculation results of comprehensive score value of Library A
| Variables | Coefficients | Comments assign points |
|---|---|---|
| Very low | 0.032 | 1 |
| Lower | 0.242 | 1 |
| General | 0.242 | 3 |
| Higher | 0.242 | 4 |
| Very high | 0.242 | 5 |
| Composite score value | 3.419 | |
Source: Authors’ own work
Calculation results of comprehensive score value of Library B
| Variables | Coefficients | Comments assign points |
|---|---|---|
| Very low | 0.116 | 1 |
| Lower | 0.274 | 2 |
| In general | 0.274 | 3 |
| Higher | 0.260 | 4 |
| Very high | 0.076 | 5 |
| Composite score value | 2.906 | |
Source: Authors’ own work
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