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• DOI: 10.55041/ijsrem26979
• Corpus ID: 265488889

Inventory Management Systems: A Comprehensive Review and Analysis

• Kirti Pandey , Aadyant Tripathi , +2 authors Jatin Abrol
• Published in INTERANTIONAL JOURNAL OF… 1 November 2023

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Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature

• Review Article
• Published: 07 February 2023
• Volume 30 , pages 2605–2625, ( 2023 )

Cite this article

• Özge Albayrak Ünal   ORCID: orcid.org/0000-0001-7798-8799 1 ,
• Burak Erkayman   ORCID: orcid.org/0000-0002-9551-2679 1 &
• Bilal Usanmaz   ORCID: orcid.org/0000-0003-0531-4618 2  

6928 Accesses

15 Citations

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Today, companies that want to keep up with technological development and globalization must be able to effectively manage their supply chains to achieve high quality, increased efficiency, and low costs. Diversified customer needs, global competitors, and market competition have led companies to pay more attention to inventory management. This article provides a comprehensive and up-to-date review of Artificial Intelligence (AI) applications used in inventory management through a systematic literature review. As a result of this analysis, which focused on research articles in two scientific databases published between 2012 and 2022 for detailed study, 59 articles were identified. Furthermore, the current situation is summarized and possible future aspects of inventory management are identified. The results show that the interest in AI methods has increased in recent years and machine learning algorithms are the most commonly used methods. This study is meticulously and comprehensively conducted so it will probably make significant contributions to the further studies in this field.

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Benefits, challenges, and limitations of inventory control using machine learning algorithms: literature review

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Özge Albayrak Ünal & Burak Erkayman

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Albayrak Ünal, Ö., Erkayman, B. & Usanmaz, B. Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature. Arch Computat Methods Eng 30 , 2605–2625 (2023). https://doi.org/10.1007/s11831-022-09879-5

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Received : 13 August 2022

Accepted : 23 December 2022

Published : 07 February 2023

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DOI : https://doi.org/10.1007/s11831-022-09879-5

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Please note you do not have access to teaching notes, a review of inventory management research in major logistics journals: themes and future directions.

The International Journal of Logistics Management

ISSN : 0957-4093

Article publication date: 15 August 2008

The purpose of this paper is to provide a review of inventory management articles published in major logistics outlets, identify themes from the literature and provide future direction for inventory management research to be published in logistics journals.

Design/methodology/approach

Articles published in major logistics articles, beginning in 1976, which contribute to the inventory management literature are reviewed and cataloged. The articles are segmented based on major themes extracted from the literature as well as key assumptions made by the particular inventory management model.

Two major themes are found to emerge from logistics research focused on inventory management. First, logistics researchers have focused considerable attention on integrating traditional logistics decisions, such as transportation and warehousing, with inventory management decisions, using traditional inventory control models. Second, logistics researchers have more recently focused on examining inventory management through collaborative models.

Originality/value

This paper catalogs the inventory management articles published in the major logistics journals, facilitates the awareness and appreciation of such work, and stands to guide future inventory management research by highlighting gaps and unexplored topics in the extant literature.

• Inventory management
• Supply chain management

Williams, B.D. and Tokar, T. (2008), "A review of inventory management research in major logistics journals: Themes and future directions", The International Journal of Logistics Management , Vol. 19 No. 2, pp. 212-232. https://doi.org/10.1108/09574090810895960

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Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

Roles Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Xi’an Fanyi University, Xi’an City, China

• Published: November 3, 2021
• https://doi.org/10.1371/journal.pone.0259284
• Reader Comments

The PLOS ONE Editors retract this article [ 1 ] because it was identified as one of a series of submissions for which we have concerns about peer review integrity and similarities across articles. These concerns call into question the validity and provenance of the reported results. We regret that the issues were not identified prior to the article’s publication.

The author either did not respond directly or could not be reached.

6 Sep 2023: The PLOS ONE Editors (2023) Retraction: Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era. PLOS ONE 18(9): e0291318. https://doi.org/10.1371/journal.pone.0291318 View retraction

The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.

Citation: Ran H (2021) Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era. PLoS ONE 16(11): e0259284. https://doi.org/10.1371/journal.pone.0259284

Editor: Haibin Lv, Ministry of Natural Resources North Sea Bureau, CHINA

Received: September 12, 2021; Accepted: October 17, 2021; Published: November 3, 2021

Copyright: © 2021 Hailan Ran. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This work was supported by Xi’an Fanyi University Research team (No. XF17KYTD202).

Competing interests: The authors have declared that no competing interests exist.

Introduction

With the rapid development of science and technology, the combination of manufacturing processes, industrial IoT (Internet of Things), advanced computing, and other technologies has become increasingly close. Meanwhile, the manufacturing mode has changed from a product-centric mode to a user-centric mode [ 1 , 2 ]. Due to the complexity of business processes in large manufacturing plants, it is necessary to coordinate the relationship between people, information system, and physical system, which causes the traditional imbalance between resource allocation and task planning [ 3 ]. Moreover, concepts such as the intelligent plant, intelligent transportation and smart city have emerged as AI (artificial intelligence) and computer technology develop fast. Lv et al. (2018) designed a new government service platform by using 3D (three-dimensional) geographic information system and cloud computing to effectively manage and use urban data. In addition, they achieved the 3D analysis and visualization of urban information through the smart city platform, which made the life of the masses more convenient [ 4 ]. This proves that the application of computer and AI technology has become a hot research topic.

The development of China’s industry in the next decade will shift from labor-intensive production to technology-intensive production, which will bring great progress in advanced technology. Correspondingly, domestic enterprises have begun to explore the transformation approach to adapt to market changes and meet government needs. The fast-growing IoT applications can produce enormous amounts of data at the network edge, effectively promoting the generation and development of edge computing. Edge computing is one of the crucial technologies to realize intelligent industry. In large manufacturing workshops, sensors, instruments, and intelligent devices can collect mass of machine data [ 5 ]. These kinds of data are the main sources of industrial big data. Moreover, it is difficult to effectively master and forecast market demands. To reduce the dependence on the accuracy of market demand forecasting and improve the efficiency of supply chain inventory management, it is necessary to improve inventory management efficiency to adapt to the changes in market demand. Besides, it is essential to use management methods to compensate for many negative impacts of market uncertainty [ 6 ]. In this case, the upstream and downstream enterprises of the supply chain must create a constant speed supply chain based on the network platform to reduce the inventory cost of the supply chain and meet the needs of customers in real time. Industrial big data is considered as a necessary means to further enlarge product profit margin. At present, industrial data platform is the paramount component of data storage, calculation and analysis for intelligent factories. With the increase in smart devices in smart factories, a large number of data such as RFID (radio frequency identification) is obtained, providing a rich data set for the manufacturing industry. As IoT applications develop rapidly, mass of data is generated at the edge of the network, effectively facilitating the emergence and development of edge computing. Consequently, in large manufacturing workshops, sensors, instruments, intelligent terminals, and other devices can collect a large amount of machine data, as the main source of industrial big data. Under the background of increasingly socialized mass production and global economic integration, all links of the supply chain, such as raw material supply, production, logistics, consumption, processing, distribution, and retail must cooperate closely. Nevertheless, the coordination and management in all links, including inventory management, are still relatively closed, significantly reducing the comprehensive benefits of the overall supply chain.

The industrial production data is investigated here based on the analysis of the related concepts and production modes of supply chain and cloud manufacturing. Then, the demand prediction method for different types of industrial spare parts and the inventory management system are proposed via cloud-edge collaborative computing. The purpose of this work is to optimize inventory management and utilization efficiency by predicting the demand for vulnerable spare parts, and improve the performance of inventory management system with the advantage of cloud-edge collaboration computing. Moreover, cloud computing and IoT technology are utilized to explore the implementation method of refining the traditional inventory management of the supply chain. The innovation of this study is that corresponding demand prediction methods are studied separately according to three demand modes of vulnerable spare parts, namely periodic demand, stationary demand, and trend demand. Specifically, the simple exponential smoothing method is used to predict demand of stationary spare parts. The quadratic exponential smoothing method is selected to predict the linear demand, and the feature synthesis method is proposed for forecasting the spare parts with periodic demand mode. On this basis, edge computing is employed to develop a cloud-edge collaborative computing architecture, to optimize the spare parts prediction algorithm and improve inventory management efficiency and pertinence.

Related theories and research methods

Overview and status of supply chain inventory management.

IoT technology is the combination of intelligent recognition technology, wireless sensor technology, ubiquitous computing technology, and network technology. The global IoT network is still in the stage of concept, demonstration, and test, many key technologies need to be further studied, and standardization norms need to be further developed. However, it has triggered the third wave of information industry development in the world after computers and the Internet, which is an impactful upgrade of the application of information technology to human production and life. Supply chain is a kind of complete and functional network chain consisting of suppliers, manufacturers, distributors, retailers and end users centering on the business center enterprise and formed through controlling feed-forward information flow and the feedback of material flow and information flow [ 7 ]. There are diversified research works about supply chain inventory management. Bornkamp (2019) emphasized the importance of supply chain in his research. The author believed that the renegotiation of the UK-EU relationship would most likely take several years, but European distributors had to assess their current inventory management to mitigate future disruptions. Moreover, with the political pattern continuing to change, the growing e-commerce market would bring trade growth, so managing availability and distribution of inventory was critical to reducing overall costs, improving cash flows and increasing flexibility in supply chain operations, in order to effectively serve the European market [ 8 ]. Aaha et al. [ 9 ] analyzed six professional education courses offered by THE Council of Supply Chain Management Professionals, including senior certified professional forecaster, certified production and inventory management professionals, certified supply management professionals, and supply chain professionals. They took personal interests and organizational interests as the two main standards, and took the professional education plan as an alternative [ 9 ]. Evidently, people have gradually realized the significance of supply chain, and delved into supply chain deeply and professionally. Fig 1 reveals the basic structure of the supply chain.

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https://doi.org/10.1371/journal.pone.0259284.g001

The supply chain not only aggerates the logistics information and funds of suppliers and users, but also forms its own value. In the distribution link of the supply chain, the appreciation in products has been achieved through packaging, processing, transportation, and delivery. Supply chain inventory management is the process of defining the overall goal of supply chain inventory management and reviewing the inventory strategy of enterprises on supply chain nodes. The supply chain inventory management aims to sustain the optimal overall supply chain inventory and reduce the total inventory to respond to changing market demands. The improvement of the cost of supply chain inventory and supply chain can enhance the rapid response of inventory to the market.

Introduction to concepts related to cloud-edge collaboration for logistics management

Cloud manufacturing includes cloud-edge collaboration technology, AI service technology, container-based platform service technology, digital twins service technology, data security, and other related technologies. It is a novel type of digital, intelligent, and smart networked manufacturing with Chinese characteristics. Fig 2 reveals the overall schematic of the system of cloud manufacturing technology.

https://doi.org/10.1371/journal.pone.0259284.g002

The foundation of intelligent cloud manufacturing is a ubiquitous and human-centered network, which integrates digital technology such as information manufacturing technology and intelligent technology comprehensively [ 10 ]. The cloud manufacturing system enables users to obtain manufacturing resources and capabilities according to their own needs anytime and anywhere through the cloud-based manufacturing service platform, and intelligently perform various activities throughout the life cycle.

In the industrial field, the IoT proactively identifies and remotely controls all physical devices in the cloud manufacturing scenario of existing network infrastructure, and obtains content in the physical world (real space) in the information world (cyberspace). The data reflects the whole life cycle of the corresponding physical equipment, and realizes the digital twins [ 11 , 12 ].

Internet technology facilitates the active and independent analysis of industrial product manufacturing process, generates intelligent perception and active prediction of the outside world, and forms a closed-loop process of automatic repair and complete feedback. With the emergence of intelligent control, industrial IoT can optimize all aspects of industrial systems, including intelligent manufacturing and business systems, real-time monitoring, supply chain collaboration, value-added services, and other business needs. The wide application of industrial IoT technology makes the production process more active and intelligent, which can accurately predict and effectively solve the potential obstacles, to effectively increase corporate profits [ 13 , 14 ].

The continuous development of the mobile Internet has brought new convenience for people’s life and production, as well as more needs and challenges, such as higher requirement of timeliness, security, and reliability. Hence, edge computing is needed to improve cloud computing ability. Fig 3 illustrates the typical architecture of edge computing for intelligent plants.

https://doi.org/10.1371/journal.pone.0259284.g003

Many problems such as single-point faults may occur in industrial applications. In addition to the unified control of the cloud, the edge nodes have the computing ability to independently make decisions and solve problems, which can improve factory productivity, while avoiding equipment failure. In IoT scenarios, edge computing focuses on solving problems of lightweight data size closer to the user’s by transferring computing operation [ 15 ]. Therefore, it cannot completely replace cloud computing, but assists cloud computing to improve work efficiency. With the deepening of industry research and academic research, cloud collaboration is widely used in numerous fields such as medical treatment, industry, and finance. Cloud-edge collaborative architecture can balance the load and reduce the hardware requirements of edge devices, making the peripheral equipment more convenient while maintaining the capacity [ 16 , 17 ]. Fig 4 provides a manufacturing factory example based on cloud-edge collaborative computing architecture.

https://doi.org/10.1371/journal.pone.0259284.g004

Demand prediction of vulnerable spare parts in IoT supply chain environment

In the cloud manufacturing scenario, the amount of data sent by the terminal equipment deployed in each plant is different for various plant equipment and actual business needs. Therefore, it is necessary to design a scheme for the edge server equipped in different plants to effectively reduce the procurement funds of enterprises and avoid the waste of limited resources. Based on this consideration, a demand prediction method is proposed for vulnerable spare parts, and it is combined with the cloud-edge cooperative inventory management system to improve the efficiency and quality of inventory management.

Timely maintenance and supply of spare parts are two important components of the after-sales service system provided by large equipment manufacturers in the service network [ 18 ]. Among them, the efficiency of spare parts inventory management determines whether spare parts can reach the demand in time, which directly affects the market competitiveness of service systems and manufacturers. In the IoT era, many consumption data and consumption behaviors based on IoT provide sufficient data basis for market demand prediction. Shen et al. (2020) extracted knowledge from user generated content and depicted the differences between IT service companies’ use of social media and users’ expectations based on daily interaction between suppliers with customers [ 19 ]. The data analysis method is also adopted here to forecast the spare parts demand.

The purpose of inventory management is to deal with various changes and uncertainties in spare part supply to ensure the normal operation of spare part supply. According to the function and direction of spare parts, they can be divided into two categories: maintenance spare parts and service spare parts.

The function of maintenance spare parts is to ensure the normal operation of production equipment, while the function of service spare parts is to ensure the after-sales service of products. Different types of spare parts have diverse inventory management purposes and management methods [ 20 ]. In summary, in the case of low total cost of spare parts inventory, it is very practical to study how to optimize the inventory management system according to the actual situation of enterprises to achieve a significant improvement in service level. The spare parts inventory management strategy includes spare parts classification, spare parts demand analysis, spare parts shortage management, spare parts inventory mode, and inventory strategy.

The common vulnerable parts of pump trucks in industrial production are taken as the research object here to predict their needs, including conveying cylinder, concrete piston, and cutting ring, usually with relatively large demands. Through the analysis of the sales volume of concrete piston in different regions, the demand is classified into the following three categories: periodic demand time series, demand time series with rising trend, and stable demand time series [ 21 ].

The prediction based on periodic demand time series is first discussed. Spare parts with periodic changes in demand patterns include random components and periodic components in the past demand time series [ 22 , 23 ]. The proposed prediction method calculates the cycle length according to the time series of spare parts demand in the past, calculates the demand data of original spare parts according to the cycle length, and divides each segment and performs polynomial fitting. The polynomial function of each cycle is integrated to obtain a new polynomial function to extract periodic ports and remove random factors, which is used as a prediction model to predict the demand of the next period.

In Eq (1) , T is the target time series, S denotes the time series needed for similarity measurement, and n represents the length of two time series. Besides, t i or s i refers to a factor at a time in a time series.

In Eq (2) , P TS refers to the correlation coefficient of time series. Meanwhile, d ( T , S ) stands for the Euclidean distance of two time series data, and f ( T , S ) is the similarity measure function.

In Eq (4) , the value of a is 1, and the value of b is n/2. α signifies the threshold.

There are several methods to predict the continuous demand of spare parts for the non-periodic demand time series, such as the exponential smoothing method and the weighted moving average method. The exponential smoothing method is an improvement of moving average method characterized by simple form, easy implementation, and high precision, which can accurately reflect the changes in demand data and is widely used in practice. Therefore, the exponential smoothing method is selected as the spare parts demand prediction method based on aperiodic demand time series here.

Among Eqs ( 7 ) and ( 8 ), a denotes the smooth constant, and S t + 1 stands for the smooth value of (t+1) period.

For the prediction of intermittent time series, the intermittent demand time of spare parts has two characteristics. (1) There is less demand. In other words, there is no demand during this period. (2) There is great volatility in demand value. These two characteristics cause a large prediction error of intermittent time series [ 27 ]. Furthermore, the time aggregation prediction method is used to predict the demand of intermittent time series.

Inventory management system based on cloud-edge collaboration

Core competitiveness is crucial to large equipment manufacturers, because efficient management of spare parts inventory can effectively reduce costs and improve service levels. The engineering machinery and equipment usually have a complex structure and various components and parts. However, the existing spare parts inventory management is still cumbersome and unsystematic, which determines inventory according to personal experience and plans demands according to inventory proportion, bringing great pressure to the production department and other related departments. The solution of traditional cloud computing architecture is to download the sensor data of various factory equipment, and use the data analysis technique of big data. Meanwhile, it transits the downloaded data to remote cloud servers through the data acquisition module, to improve work efficiency and competitiveness [ 28 , 29 ]. Here, the cloud-edge collaborative computing in industrial IoT is proposed to solve the rapid response problem of real-time control and data fast processing in large-scale manufacturing plants. Fig 5 provides the architecture of cloud-edge collaborative computing.

https://doi.org/10.1371/journal.pone.0259284.g005

The deployment of industrial IoT in intelligent manufacturing environment mainly contains the equipment perception layer, data resource layer, service application layer, and operation and maintenance management layer, which work together to maintain all data links [ 30 , 31 ]. From the specific business point of view, the cloud components are mainly responsible for the formation of the model of the collected data, and the peripheral components are basically responsible for obtaining the model from the data dictionary, providing timely services for factory equipment in real time. Reducing the training time of models and networks can shorten the response time of the closed-loop system and improve the overall production quality of the plant equipment. OpenStpack and Starling X enable companies to build their own cloud- edge collaborative computing services using the most advanced open-source cloud computing platform and the latest distributed cloud computing platform, respectively.

The solution of traditional cloud computing architecture is to upload all kinds of sensor data from factory equipment, such as vibration, pressure, and temperature, to the cloud remote server through data acquisition module. Besides, it utilizes the popular big data analysis technology to establish the mathematical model of index data and factory equipment performance, to enhance the production quality, work efficiency, and market competitiveness of factory equipment. Taking the coal industry as an example, the mine is generally located in a remote location where it is difficult to implement network communication. Due to the characteristics of large scale, numerous varieties, low value density, and fast update and processing requirements of coal mine data, the traditional cloud computing architecture is inadequate, because it is easy to produce problems of single point faults and slow closed-loop response. Based on the above analysis, the cloud-edge collaborative computing architecture is selected for the industrial IoT to cope with the problems of fast real-time control response and fast data calculation in large manufacturing workshops. Fig 6 illustrates the workflow of cloud-edge collaborative computing architecture, where various data acquisition devices and user requests are collectively referred to as collectors. The smart endpoint simply pre-processes information from the collectors and sends it to the computing node in the edge server cluster [ 32 ]. Then, the I/O intensive virtual machine on the computing node receives the information and stores it in the database on the storage node.

https://doi.org/10.1371/journal.pone.0259284.g006

The following is the specific processing of the edge server:

• the intelligent terminal sends the collected data to the edge data storage module;
• data processing module retrieves the corresponding data from the edge data storage module according to the user’s request;
• data processing module carries out lightweight big data analysis according to the model parameters provided by the data dictionary module. Besides, the edge data dictionary module is analyzed and synchronized;
• the decision module outputs the processing results of the data processing module to the intelligent equipment and checks them accordingly.

The procedure of the remote centralized server is as follows:

• the edge server and remote centralized data storage module synchronize incremental data;
• data processing module retrieves data from the remote centralized data storage module according to user needs;
• the data processing module conducts large-scale big data analysis according to the model parameters provided by the data dictionary module.

The analysis and synchronization of the remote data dictionary module are presented as follows:

• edge server synchronizes incremental data with the remote centralized data storage module;
• the data processing module retrieves data from remote centralized data storage module according to user needs;
• the data processing module conducts large-scale big data analysis according to the model parameters provided by the data dictionary module. Meanwhile, analysis and synchronization are performed on the remote data dictionary module;
• the remote data dictionary module synchronizes data processing with edge data dictionary module according to specific requirements.

Edge servers and remote centralized servers regularly analyze and use stored data, and the data dictionary is updated to ensure the correctness of the decision message.

Simulation and experimental design

Three time series prediction methods are provided for the demand prediction of vulnerable parts based on spare parts. The demand data of high-strength circular chains in the mining industry is used here for verification. The circular chain is also a spare part of construction machinery, and the experimental data comes from the network. In the simulation experiment, the genetic algorithm is introduced as a comparative algorithm to verify the performance of the inventory management system based on cloud-edge collaborative computing architecture. Table 1 indicates the task parameters under different configurations in this experiment.

https://doi.org/10.1371/journal.pone.0259284.t001

Analysis of demand prediction results and the performance verification of inventory management system

Comparison results of the prediction method based on demand of vulnerable spare parts..

After the prediction model is established, the predicted value of spare parts demand is calculated to be compared with the true value. The polynomial is established and fitted according to the period length. Fig 7 illustrates the relationship between fitting times and prediction errors.

https://doi.org/10.1371/journal.pone.0259284.g007

Fig 7 shows that the prediction error decreases first and then increases with the increase of fitting times. When the fitting time of the polynomial reaches 10, the prediction error begins to stabilize. After 13 times of fitting of the polynomial, the prediction error reaches a minimum of 11.7%, and then begins to increase. Based on this result, in the following simulation experiment, 13 times of fitting are used to obtain the fitting polynomial of each section when the polynomial regression model is used to fit the demand data of spare parts.

The eigenvalues and the weighted fitting process of each cycle are shown in Fig 8 .

Eigenvalues and the weighted fitting processes ((a): eigenvalues of each cycle; (b): weighted fitting processes of each cycle).

https://doi.org/10.1371/journal.pone.0259284.g008

Fig 8 displays the intermediate process of eigenvalue fitting. As can be seen from Fig 8B , the prediction error of monthly demand after weighted fitting is smaller. The mean value of the sum of eigenvalues and the weighted sum of eigenvalues shown in Fig 8A is used to synthesize new feature sets. In other words, the determination of values of a n , b n , and c n is similar to the determination of polynomial degree. Through experimental analysis, the prediction accuracy is the highest when w = 0.1, w = 0.1, and w = 0.8.

Fig 9A illustrates the mean value of the sum of eigenvalues and the weighted sum of eigenvalues shown in Fig 8A . Fig 9 provides the prediction results of spare parts demand based on the weighted synthesized eigenvalues.

Prediction results of spare parts demand based on weighted eigenvalues ((a): weighted eigenvalues; (b): prediction results based on weighted eigenvalues; (c): error comparison of two processing methods).

https://doi.org/10.1371/journal.pone.0259284.g009

According to Fig 9b , from a macro perspective, the prediction result based on the weighted sum is closer to the real value than the prediction based on the mean value of sum of the eigenvalues. From the perspective of error value, the highest prediction error based on the weighted eigenvalues is 34.9%, and the lowest is 2.2%. Through the comparison of error in Fig 9C , the average relative error based on the weighted fitting is lower than that based on the mean value of the sum of eigenvalues, the former is 11.7%, and the latter is 18.4%. Therefore, the prediction accuracy of the prediction model established by the weighted fitting method is higher. To sum up, the prediction method based on weighted fitting of eigenvalues has the smallest error and the best fitting effect in the demand prediction of machine spare parts.

Verification results of the perdition method based on vulnerable spare parts demand. The simulation experiment adopts the moving average period coefficient prediction of the prediction method based on weighted eigenvalues as a comparison with the true value. The specific results are presented in Fig 10 .

Weighted eigenvalues and prediction results ((a): weighted eigenvalues; (b): fitting processing of weighted eigenvalues; (c): comparison between prediction results and true results).

https://doi.org/10.1371/journal.pone.0259284.g010

In Fig 10A , a n refers to the first set of eigenvalues, b n denotes the second set of eigenvalues, c n represents the eigenvalues after fitting, the value range of cycle length is 1 ~ 13, and the threshold is 10% of the mean value. Polynomial fitting is carried out for the first two data segments to obtain the periodic term of the data segment, which is used to predict the true value of the third cycle segment. When n = 10, the prediction error is the smallest, so the degree of the fitting polynomial is n = 10. The fitting polynomial function of each segment is obtained. From Fig 10 , the average relative error between the actual value of spare parts demand and the predicted value is 9.4%. When the moving period coefficient method is used to predict the demand for spare parts, the average relative error between the predicted value and the actual value is 13.0%.

The proposed prediction method is also used to predict the demand of the circular chain, and the results are compared with those of the moving average period coefficient method, to further verify the advantages of this method. The comparison results are shown in Fig 11 .

https://doi.org/10.1371/journal.pone.0259284.g011

From Fig 11 , the average absolute error of the actual value and predicted value of spare parts demand based on the moving average period coefficient method is 286.8, and the average relative error is 12.8%. The average absolute error of the polynomial fitting model is 250.7, and the average relative error is 11.7%. Therefore, the proposed prediction mode has a better prediction effect.

The prediction results of exponential smoothing method and quadratic exponential smoothing method are shown in Fig 12 .

The prediction results of exponential smoothing method and quadratic exponential smoothing method ((a): the prediction results of exponential smoothing method; (b): the prediction results of quadratic exponential smoothing method).

https://doi.org/10.1371/journal.pone.0259284.g012

The simple smoothing index prediction method is used to investigate the spare parts demand data with the nonlinear trend. According to Fig 12A , the predicted value of spare parts demand by smoothing index prediction method is close to the actual value, and the average relative error is 18.0%. The quadratic smoothing index prediction method is aimed at the spare parts demand data with linear trend. From the results in Fig 12B , the predicted value of demand of the quadratic exponential smoothing method is close to the actual value, and the average relative error is 11.3%. In conclusion, the exponential smoothing method and quadratic exponential smoothing method both have high prediction accuracy in spare parts demand.

To sum up, the cycle length detection method based on similarity is adopted to calculate the cycle length. Then, the data is divided into several segments according to its cycle length, and polynomials are used to fit the data in the cycle segment. Moreover, the polynomials are synthesized to obtain a new polynomial function, which is used as the prediction model to predict the demand in the next cycle. The experimental results demonstrate that this prediction method can achieve high prediction accuracy.

Performance verification results of inventory management system based on cloud-edge collaborative computing. The algorithm of the inventory management system optimizes the resource allocation for virtual machines from the impact of virtual machines on the performance of physical machines and the impact of different configurations of virtual machines on task execution time. Table 2 and Fig 13 signify the resource allocation scheme for the best virtual machine performance obtained by the two algorithms and the comparison of the results after 100 executions of six tasks at the same time, respectively.

Performance comparison between two algorithms ((a)-(f) represent the experimental results from Task 1 to Task 6).

https://doi.org/10.1371/journal.pone.0259284.g013

https://doi.org/10.1371/journal.pone.0259284.t002

Fig 13 illustrates the performance comparison between the proposed algorithm and the genetic algorithm. Specifically, the average completion time of the six tasks executed by the genetic algorithm is 15.64 seconds, 14.92 seconds, 21.55 seconds, 21.34 seconds, 24.03 seconds, and 23.95 seconds, respectively. The average completion time of the six tasks by the proposed algorithm is 15.10 seconds, 15.00 seconds, 20.35 seconds, 20.60 seconds, 23.66 seconds, and 23.59 seconds. From the completion time point of view, the proposed virtual machine performance algorithm has shorter processing time and higher efficiency than genetic algorithm. In terms of stability, the genetic algorithm fluctuates greatly, so the proposed algorithm has higher stability.

In conclusion, in the prediction of spare parts demand with strong periodicity, the prediction method based on weighted fitting of eigenvalues has the smallest error and the optimal fitting effect in the prediction of machine spare parts demand, and the lowest error after fitting is only 2.2%. For spare parts with non-periodic linear demand and spare parts with nonlinear demand, exponential smoothing method and quadratic exponential smoothing method are used for prediction respectively, and the prediction results are close to the actual value. The spare parts demand prediction method proposed here can well complete the prediction for three different types of time series of demand data of spare parts, and the relative error of prediction is maintained at about 10%. The prediction effect can meet the basic requirements of spare parts demand prediction, and the prediction accuracy is higher than that of periodic prediction method. Compared with genetic algorithm, the cloud-edge collaborative computing algorithm for inventory management system takes less processing time and has higher efficiency. In terms of stability, genetic algorithm fluctuates greatly, but the algorithm reported here is much more stable.

Conclusions

Efficient spare parts inventory management can reduce inventory costs, improve service level, and bring huge benefits to large equipment manufacturing enterprises. There are a variety of spare parts for large-scale equipment as well as many uncertain factors in the supply process. Therefore, it is essential to continuously update relevant technologies for higher efficiency of spare parts inventory management, to save inventory costs. Based on the supply chain background, the critical role of inventory management plan and spare parts demand relationship in improving the core competitiveness of enterprises. Secondly, according to different types of spare parts demand prediction data, different spare parts and demand prediction methods for vulnerable parts are proposed. In addition, the efficiency of inventory management is improved by predicting the demand for industrial vulnerable parts. For the three spare demand models of vulnerable parts, including periodic model, stationary model, and trend model, the corresponding demand forecasting methods are studied respectively. The simple exponential smoothing method is used to predict the spare parts with stable demands, while the quadratic exponential smoothing method is used to predict the demand for spare parts with linear trend. Meanwhile, the prediction method based on weighted fitting of eigenvalues is adopted to predict the periodical demand of machine spare parts. Finally, an inventory management system based on cloud-edge collaborative computing is proposed to reasonably allocate inventory resources and improve the utilization of inventory resources. The prediction method based on weighted fitting of eigenvalues proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the lowest error after fitting is only 2.2%. Exponential smoothing method and quadratic exponential smoothing method are used for spare parts with non-periodic linear demands and spare parts with nonlinear demands, respectively, and the prediction results are close to the actual values. In terms of completion time, the virtual machine performance algorithm reported here realizes shorter processing time and higher efficiency than genetic algorithm. In terms of stability, this research algorithm is much more stable than the genetic algorithm. Despite particular outcomes achieved in this work, due to the limitations of research level and some objective factors, there are still some deficiencies. On the one hand, there remains space for improvement in the relative error of the prediction method for vulnerable spare parts proposed here. It is expected to further improve the accuracy and efficiency of prediction by introducing the deep learning algorithm in future. On the other hand, there lacks the combination of the prediction method based on vulnerable spare parts and the inventory management system based on cloud-edge collaborative computing reported here. The follow-up work will make efforts to integrate spare parts demand forecasting and inventory resource management into one intelligent system.

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Quality of Inventory Management System: Case Study of BARMM-Ministry of Public Works-Basilan District Engineering Office

14 Pages Posted: 22 Aug 2023

Lesley Ann Atilano-Tang

Scintilla Juris-Philippines

Kurt Damsani

Ministry of public works - basilan district engineering office.

Date Written: April 20, 2023

This capstone examines the quality of the inventory management system of the BARMM-Ministry of Public Works-Basilan District Engineering Office in Basilan Province, Philippines. The capstone utilizes a case study research design and employs a mixed-methods approach to gather data from both primary and secondary sources. The theoretical framework of this study is based on the principles of Public Administration, particularly the concept of organizational effectiveness. The results of the study show that the current inventory management system of the engineering office has several weaknesses, including inadequate documentation, lack of standardization, and poor tracking of inventory levels. These issues can lead to inefficiencies in the management of the office's resources and can have a negative impact on the delivery of public services. The study recommends several strategies to improve the quality of the inventory management system, such as implementing a centralized inventory system, developing standard operating procedures for inventory management, and investing in training and development programs for staff. The findings of this study have significant implications for the BARMM-Ministry of Public Works-Basilan District Engineering Office and other public organizations in the Philippines. By implementing the recommended strategies, these organizations can enhance their organizational effectiveness and improve their ability to provide efficient and effective public services to their constituents. The study contributes to the existing literature on inventory management systems and organizational effectiveness in the context of Public Administration.

Keywords: inventory management system, quality, case study, BARMM, Ministry of Public Works, Basilan District Engineering Office, public administration, efficiency, effectiveness, procurement, supply chain management

Suggested Citation: Suggested Citation

Lesley Ann Atilano-Tang (Contact Author)

Scintilla juris-philippines ( email ).

CPADS, Normal Road, Baliwasan, Zamboanga City, 7000 Philippines Zamboanga City, Zamboanga del Sur 7000 Philippines +639207256273 (Phone)

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• Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

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This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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Xianru Shang, Zijian Liu, Chen Gong, Zhigang Hu & Yuexuan Wu

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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