Bibliometrics is a measurement method used to describe and analyze the situation and progress of a discipline or research field (Van Raan, 2019). According to Noyons et al. (1999), performance analysis and knowledge maps are the two major procedures in bibliometric analysis. Performance analysis is the performance and scientific productivity of authors, countries, and institutions that publish papers (Thelwall, 2008). In addition, the average citations per paper is a crucial index in performance analysis, which represents the influence of papers, authors, journals, institutions, and countries. Therefore, this quantitative index was added to the performance analysis in this study (Ding and Yang, 2020).

Knowledge maps are another valuable tool of bibliometric analysis which are often used to explore the framework structure and evolutionary path of key research fields (Cobo et al., 2011). With the help of modern computer technology, visual knowledge maps can clearly present the relationships among various elements in one research field (Merigó et al., 2015).

Knowledge maps can be divided into co-occurrence and evolution analyses. Keyword co-occurrence describes the situation in which two keywords appear in the same article; the more frequent the co-occurrence, the closer the connection of keywords, and so is the relationship. Through co-occurrence analysis of keywords, hotspots in a research field can be identified. Through evolutionary analysis, the development process and evolution trend of a research field can be learned (Ding and Yang, 2020; Baminiwatta and Solangaarachchi, 2021).

The two types of software VOSviewer (Version:1.6.17, developed by Van Eck and Waltman at the Centre for Science and Technology Studies) and CiteSpace (Version:5.8.R3, developed by Chen C. at Drexel University) were applied to draw a knowledge map. These two types of software are popular among many scholars in knowledge mapping. Through the analysis of 481 articles in bibliometric journals, Pan et al. (2018) found that CiteSpace is the most frequently used bibliometric software, although VOSviewer has been used often in recent years. Since the two types of software are based on distinct data analysis methods, they have different advantages. VOSviewer adopts a data normalization method based on probability theory. It can be used to construct a variety of visual maps of keywords, co-organizations, and co-authors. VOSviewer is simple to use, and its maps are clear (Van Eck and Waltman, 2010). In this study, VOSviewer was used to analyze keyword co-occurrence. CiteSpace adopts a data normalization method based on set theory to measure the similarity of data units from which the time zone view is drawn. Thus, the process of evolution and replacement of research hotspots in the dimension of time is evident (Chen, 2006). Therefore, researchers can learn the development process and trend of this field to conduct the evolution analysis of the research field explored in this paper.

Bibliometrics has been widely used in literature analyses since its independence as a discipline in 1969. This method, which reviews and investigates the existing literature in each field through quantitative methods, helps to generate a convincing conclusion (Mayr and Scharnhorst, 2015). In recent years, owing to the progress of different techniques (including but not limited to computer science, database management, and statistics), bibliometrics has been considered scientific and effective by an increasing number of scholars. Undoubtedly, bibliometrics has become a strong discipline for analyzing literature.

This study chose the SSCI from the Web of Science (core collection) as the data source. The main reasons for this are as follows:

The retrieval strategy this study adapted is TS = [(“volunteer” OR “volunteering”) AND (“motivation” OR “motives”)]. The period was from January 2000 to December 2021, and the deadline was December 31, 2021. Regarding the type of literature, we selected articles. A total of 472 journal papers were selected in the first step; a summary of the preliminary data is presented in Table 1.

If the information obtained from the database is used without careful screening, there may be problems such as repetition, deficiency, or inconsistency with the subject. Therefore, it is necessary to screen and standardize the data before analysis to avoid affecting the results and reducing inaccuracies owing to the quality of the data itself. According to the data standardization process proposed by Taskin and Al (2019), the steps of the data review and screening process in this study were as follows:

The 162 papers analyzed in this study were written by 451 authors from 240 institutions in 40 countries, published in 77 journals, and cited 5,498 references from 2,724 publications (see Table 2).

Figure 2 reveals the distribution of publication times in the field of volunteer motivation. Overall, the number of studies on volunteer motivation is increasing and can be divided into two stages bounded by 2012. The first stage lasted from 2000 to 2012. Few articles were published every year, but the number of articles increased with continuous fluctuations. The second stage began in 2013 with a significant increase in the number of published papers. From 2013 to 2021, the average number of published articles exceeded 10. In 2021, this number reached 19, indicating that volunteer motivation has become more popular among scholars, and this field has ushered in vigorous development.

These theories are of great significance in guiding research. According to the statistics of the 162 papers, 15 different theories were used. The number of studies in which each theory was adopted are presented in Table 3. Moreover, of the 162 studies, 15 used more than one theory, whereas 72 did not specify any theory. Of all these theories, functional theory and functional approach were those most frequently adopted by researchers to analyze volunteers' participation motivation (the two were applied in 45 studies), followed by self-determination theory (Deci and Ryan, 1985) and theory of planned behavior (Ajzen, 1985), which were applied in 25 and 16 studies, respectively. Other theories are role identity theory (Finkelstein and Brannick, 2007; Finkelstein, 2008, 2009; Marta et al., 2014), socioemotional selectivity theory (Okun and Schultz, 2003; Aydinli et al., 2015; Kulik, 2017; Yamashita et al., 2019), social cognitive theory (Wang et al., 2011; Hallmann and Zehrer, 2016), social exchange theory (Bang et al., 2019), and others (see Table 3).

Research methods refer to the tools and methods used to discover new phenomena and new things or to propose new theories and new points to reveal the internal laws of things. Among the 162 empirical research papers analyzed in this study, 17 studies used qualitative research methods, while 148 studies used quantitative research methods, 3 of which used both quantitative and qualitative methods (Pajo and Lee, 2011; Hyde and Knowles, 2013; Van Schie et al., 2015).

Interviews are the most commonly used method in qualitative research. Many interview studies use grounded theory to encode and analyze the information collected (MacNeela and Gannon, 2014; Williamson et al., 2018). Regression analysis and structural equation model (SEM) are the most frequently used methods in quantitative analysis. SEM includes factor analysis and path analysis. To clearly show the statistical results, this study stipulates that the research method be a single-row structural equation model and that the statistics not be repeated in the factor analysis and path analysis. Factor analysis (including exploratory factor analysis and confirmatory factor analysis) and correlation analysis (including t-tests and analysis of variance) are also widely used. Simple path analysis, which was used in 10 studies, was not popular. Four studies only made use of the descriptive statistics of the data (Gerstein et al., 2004; Pajo and Lee, 2011; Hyde et al., 2016; Dyson et al., 2021). The frequencies of the methods used are listed in Table 4.

Through the analysis of the number of papers published by each author, we can identify the representative scholars and the core research strength in the field of volunteer motivation. Price (1963), a scholar in the field of bibliometrics, indicated that for a research topic, half of the papers were written by a group of productive authors, the number of which is equal to the square root of the total number of authors. That is: ∑m+1In(x)=N where n(x) represents the number of authors who have written papers. I = n_max is the number of papers by the most productive author in this field (according to the statistics by VOSviewer, n_max = 8). N is the number of authors and m is the minimum number of papers published by the core author. According to Price's law, m = 0.749*nmax ≈ 2.12. Therefore, authors with more than two articles (excluding two) were considered core authors in this field (Price, 1963). As a result, there were a total of 12 core authors. Table 5 presents the relevant information of all the core authors in this field, including their names, number of documents, and average citations per paper.

The research focus of different scholars varies significantly. Wehner has been the most productive author in the field of volunteer motivation in the past 20 years. He has published eight papers. He focused on empirical research using a structural equation model under the guidance of a functional approach, self-determination theory, or the theory of planned behavior. Okun is a renowned scholar of volunteer research. In the past 20 years, he has published four papers on volunteer motivation, which have been cited 125.25 times per paper on average. His main research subjects were senior-citizen volunteers. He focuses on the relationship between the change of ages and volunteer motivation (Okun and Schultz, 2003; Okun and Michel, 2006). In addition, he focuses on the impact of religious beliefs on volunteer motivation (Okun et al., 2015).

To identify the countries that have contributed the most in the field of volunteer motivation, this study analyzed the number of published papers from 40 countries. Table 6 lists the top ten countries in this field. According to the data in Table 6, the United States is the most influential country, with 49 papers (2,222 citations), accounting for 30% of the total. China and Australia ranked second and third, with 28 (353 citations) and 16 papers (626 citations), respectively. From the analysis of geographical distribution, it was found that most of the top ten countries are in North America and Europe, except China, Australia, and South Korea.

We further analyzed the publishing organizations. Table 7 lists the top ten institutions with the number of published papers. Among them, Arizona State University (the United States) and the Swiss Federal Institute of Technology (Switzerland) have the most papers in the field of volunteer motivation research, each has seven published articles. In addition, among the top 10, five institutions are in America, and their citations per paper are over 30. Arizona State University not only has the highest scientific productivity but additionally ranks the highest in terms of citations per paper. To some extent, these data reflect those institutions in America are in a leading position in this field.

Journals are important vehicles for publishing high-quality papers. Some scholars believe that there are two critical factors in evaluating journals' impact in certain fields: the number of papers published in the journals and the number of papers cited. The more published the articles, the more frequently their papers are cited, and the greater their impact in a certain field (Dzikowski, 2018). Therefore, our research analyzed ten journals on volunteer motivation, with the largest number of published papers. Simultaneously, we calculated the average citations per paper (Table 8).

Table 8 reveals that Voluntas (24 documents) and Non-profit and Voluntary Sector Quarterly (22 documents) ranked highest in the number of papers. This shows that the two journals focused on volunteer motivation. Other journals that published more than five pieces were Sustainability and Journal of Social Service Research. Journal of Social Service Research is a representative journal in the era of open access. In recent years, the rapid development of open-access journals has promoted research in this field. Although scholars have diverged on the best approach to open the source, the idea of sharing research achievements has reached a global consensus (Huang et al., 2020).

With further analysis of the average citations per paper in Table 8, Personality and Individual Differences are cited most frequently with 87.75 times per paper. The second was Social Behavior and Personality with 55.75 times per paper. Another journal with more than 30 citations is Non-profit and Voluntary Sector Quarterly (32.27 times per paper). This indicates that the documents published in the above 3 journals have gained more attention in research on volunteer motivation.

The keywords of a paper represent the focus of a study, while the keywords with high frequency illustrate the hotspots in a certain field. Cluster analysis of high-frequency keywords can show the macro situation of one research field. Wei et al. (2019) proposed that Price's Law can be applied not simply to analyze core authors in a certain field, but also to locate high-frequency keywords. According to the formula, M = 0.749* Nmax (N_max is the keyword with the highest frequency. According to VOSviewer, N_max = 112), M ≈ 7.92. Therefore, keywords with an occurrence frequency ≥7 are high-frequency keywords in this field.

The results of the visual analysis of the high-frequency keywords are shown in Figure 3. In the figure of the co-occurrence of keywords, the size of the node represents the occurrence frequency of keywords. The larger the node, the more frequently one keyword appears. Node colors represent different clusters or research topics: the line of nodes represents various levels of correlation intention, and the thicker the line, the more times the two appear together in the same literature.

Figure 3 depicts that yellow and blue clusters are closely related, whereas green and red clusters are relatively independent. To understand the correlation between keywords, our study sorted the vocabulary of the four clusters, as shown in Table 9.

The mainstream research spots in one field can be obtained through the analysis of high-frequency keyword clusters, which helps researchers understand more details within each focus. Next, the four clusters were analyzed sequentially.

The green cluster (cluster #1) of keywords centers on the functional approach (an approach to analyzing the structure and function of things and phenomena). The Volunteer Function Inventory (VFI) is devised under the guidance of functionalist theory (Clary et al., 1998). Most studies closely related to the green cluster of keywords choose research variables and organize a research framework based on VFI (Clary and Snyder, 1999; Allison et al., 2002; Finkelstein et al., 2005; Houle et al., 2005; Liao-Troth, 2005; Mowen and Sujan, 2005; Kim et al., 2010; Willems et al., 2012; Bang et al., 2013; Oostlander et al., 2014; Alexander et al., 2015; Khalemsky et al., 2020). Moreover, papers related to the green cluster of keywords combine functionalist theory and role identity theory to analyze the service motivation of volunteers. This indicates that the important service motivation is the role identity volunteers gain in social relationships in the organization (Penner, 2002).

The yellow cluster (cluster #2) of keywords is centered on the Self-Determination Theory (SDT). SDT emphasizes the subjective impact of people's egos on the formation of motivation. It identified that behaviors are formed based on psychological satisfaction (Deci and Ryan, 1985). The motivations for SDT fall into self-determined(intrinsic) and non-self-determined(extrinsic). Thus, there is egoistic and altruistic motivation. Papers related to the keywords in the yellow cluster were mostly written under the guidance of SDT. For instance, Finkelstein (2009) studied self-determination in volunteer services and found that intrinsic motivation is only one of the important aspects of self-determination, whereas extrinsic motivation is equally important. Meanwhile, in voluntary service, doing something meaningful is also one of the principal factors of volunteer motivation. Güntert and Wehner (2015) used empirical research methods to analyze the relationship between self-determination, controlling motivation, and volunteer identities. A significant causal relationship was found between self-determination motivation and volunteer identity. Van Schie et al. (2015) distinguished two focuses of self-determination motivation: general and organization-centered. Through empirical research, it was found that general self-determination motivation is related to the motivational potential of the task, whereas value consistency explains organization-centered self-determination motivation.

The keywords of the blue cluster (cluster #3) center on the theory of planned behavior (TPB). It focuses on the relationships among the variables of perceived behavioral control, subjective norms, attitude, volunteer motivation, and behavioral intentions (Kao et al., 2019). TPB was first proposed by Ajzen in 1985 and after its introduction into the field of volunteer research, immediately became a common theory to explain the motivation and intention of volunteers. For example, Warburton and Terry (2000) found that voluntary service motivation is jointly determined by social norms, perceived behavioral control, and moral obligations; Greenslade and White (2005) concluded that TPB could reasonably explain the high participation of the elderly in voluntary activities in Australia; Hyde and Knowles (2013) found through empirical research that TPB explains most of the variance in Australian college students' voluntary service intention. The study suggests that perceptual control and moral obligation should be used to encourage students to participate in voluntary activities. In addition, TPB is used to study the willingness of volunteers to stay on and predict the possibility of participation in future volunteer activities. Several studies have provided reasonable answers to volunteers' lasting motivation from the perspective of TPB (White et al., 2017; Almas et al., 2020).

The red cluster (cluster #4) focused on satisfaction. Relevant articles have primarily analyzed the relationship between volunteer motivation and satisfaction, such as the relationship between volunteer experience and future motivation for voluntary service (Bang et al., 2019; Kim et al., 2019). Other research studies the differences in volunteers' experiences motivated by several factors and found that volunteers with autonomous motivation have lower burnout than other volunteers (Ramos et al., 2016; Morse et al., 2020). Volunteer burnout is also an important keyword in the red cluster. After its proposal (Haski-Leventhal and Bargal, 2008) during the analysis of volunteer service stages of Israeli volunteers in 2009, volunteer burnout has been studied as a factor that influences volunteer motivation and leads to the loss of volunteers (Willems et al., 2012).

In conclusion, through cluster analysis, it is evident that theory is of great significance to the study of volunteer motivation, and an excellent research cannot succeed without corresponding theoretical guidance. Based on the clusters of keywords, we can further learn the core theories in volunteer motivation research and the focuses under the guidance of these theories. At the same time, based on Table 3, there are also some theories applied to volunteer motivation for less time. The purpose of using theories is often to find a reasonable explanation of volunteer service motivation and willingness from different perspectives. The continuous influx of different theories provides new visual aspects for volunteer motivation research, being an important boost to promote the development of this field.

To identify the changes in research topics in different periods, which is a dynamic process, this study divides the timeline into two periods (the first from January 2001 to December 2010 and the second from January 2011 to December 2021). We analyze the keyword networks of the two periods to study the evolution of volunteer motivation research topics.

As shown in Figure 4, most articles about volunteer motivation from 2000 to 2010 are closely related to Cluster #1 (see Figure 3). The close relation between keywords, such as “community,” “role identity,” “personality,” and Cluster #1 shows that most of the research at this time was guided by a functional approach (Allison et al., 2002; Finkelstein et al., 2005; Houle et al., 2005; Liao-Troth, 2005; Mowen and Sujan, 2005; Kim et al., 2010). Meanwhile, keywords such as “helping behavior,” “service,” and “performance” show that research at this time mainly focused on voluntary activities and motivation without much expansion. Besides, the keyword “satisfaction” indicates that the research at this stage was closely related to Cluster #4 (see Figure 3), which reflects that research on volunteer motivation had paid attention to the important variable of satisfaction (Davis et al., 2003; Finkelstein, 2009).

As shown in Figure 5, the research topics from 2011 to 2021 show a diversified trend. The presence of keywords such as “planned behavior” and “altruism” indicates that at this time, the theories for the study of volunteer motivation began to gradually increase, and research guided by self-determination theory and planned behavior theory began to appear. The research at this stage began to show a close association between Clusters #2 and #3 (see Figure 3). Although the functional approach was still an important keyword at this stage, its popularity had decreased significantly compared to the proportion of published papers. This research has many subdivisions. For example, some studies began to focus on the influence of past volunteer experience and people's age on volunteer motivation (Kulik, 2017), while others focused on the relationship between volunteer motivation and volunteer management. The keywords “engagement” and “employee” reflect that research on volunteer motivation has expanded to the recruitment and employment of volunteers by relevant organizations (Meneghini et al., 2018). In 2019, after some scholars proposed that past volunteer experience may influence volunteer motivation, volunteer experience gradually became a new focus of volunteer motivation research (Bang et al., 2019).

By comparing Figures 4, 5, it can be seen that although there are some changes in the topics of volunteer motivation research, the focus has not changed greatly: Research is always centered on “motivation,” “satisfaction,” “personality,” “functional approach,” and “intention,” which shows that the main research theme in this field was basically determined before 2010. Most research after 2010 supplements the original research framework and expands it based on original theoretical research (Wicker, 2017).

The sudden emergence and transformation of keywords can reflect the changes in hotspots in a single research field. Therefore, the burst detection function provided by CiteSpace was used to detect the explosive words with the top 10 explosive intensities (Figure 6).

As shown in Figure 6, in the early twenty-first century, most studies of voluntary motivation regarded volunteer behavior as a service activity or organized citizenship behavior (which appeared as a burst keyword during 2007–2008). Many studies have adopted the functional approach proposed by Clary et al. (1998), the keyword of which showed sustained popularity from 2002 to 2011 (Allison et al., 2002; Finkelstein et al., 2005; Houle et al., 2005; Liao-Troth, 2005; Mowen and Sujan, 2005; Kim et al., 2010). During this period, theory and practice were intertwined. Theory guides many empirical studies, whereas practical research promotes the development of theory.

From 2008 to 2013, volunteer motivation in the community context became a hot topic, and altruism and prosocial behavior attracted the attention of some researchers during this period (Snyder and Omoto, 2008; Hyde and Knowles, 2013). From 2015 to 2017, many studies were conducted on the influence of age on volunteer motivation (Kulik, 2017). These burst keywords reflect that the research topic of volunteer motivation deepened and expanded as the variables of volunteer motivation also increased.

Among the explosive words, there have been two focuses in recent years: the engagement of volunteers in volunteer activities (Meneghini et al., 2018) and volunteers in tourism activities (Paraskevaidis and Andriotis, 2017). Hersberger-Langloh et al. (2021) and other scholars found that volunteers in cultural events tend to engage in a volunteer activity for a longer time. Volunteer tourism activities are rich in forms such as volunteer teaching tourism with Chinese characteristics (Wu et al., 2018) and volunteer motivation and retention research in scenic spots (Ngah et al., 2021).

The cultural background has been verified as a crucial background element affecting volunteer motivation (Aydinli et al., 2016). To learn the influence of Eastern and Western cultures represented by China and the United States on volunteer motivation, 25 Chinese literature, and 38 American literature were selected for analysis. First, we created a high-frequency keyword figure.

Table 10 reveals that there are some differences in the research hotspots of volunteers from diverse cultural backgrounds in China and America. Research on volunteers in China focuses more on prosocial motivation (Aydinli et al., 2016), with an emphasis on the contribution of voluntary behavior to society or the community. Research on volunteers in America pays more attention to the personality of volunteers (Finkelstein, 2010), emphasizing their role identity. We used CiteSpace to conduct a visual analysis to better understand the differences and evolutionary trends of volunteer research under diverse cultural backgrounds. The results are shown in Figures 7, 8, respectively.

A comprehensive analysis of Figures 7, 8 show that the study on volunteer motivation derived around 2006 in China, slightly later than that in the United States; in terms of research subjects, college student volunteers are the common focus of volunteer research in China and the United States. The American volunteer study also focused on seniors (Kahana et al., 2013; Okun et al., 2015), but less attention has been paid to China, and only a few studies have been conducted on senior volunteers in Hong Kong (Cheung et al., 2006). From the perspective of the research background, beliefs, and other related keywords are more concerned with religious factors in the study of volunteers in the United States. Since China lacks a broad and unified religious belief, differences in religious culture lead to great disparities in volunteer motivation (Mencken and Fitz, 2013; Cnaan et al., 2017); and with regard to the adopted theory in research, the common occurrence of the keyword in time zone, functional approach, indicates that functional theory has been widely used by Chinese and American researchers with reference to VFI. However, the application of functional theory in the United States is much wider than in China. Through a literature search, 18 of the 38 papers with Americans as the research objects were guided by functional theory, while only 3 of the 25 articles with Chinese as the research objects used functional theory (Lai et al., 2013; Aydinli-Karakulak et al., 2016; Butt et al., 2017). Regarding the time when the theory was used, research on the motivation of Chinese volunteers under the guidance of functional theory began in 2013, while American volunteers were studied earlier. Since Clary initiated the theory in 1998, scholars have applied it successively (Omoto et al., 2000; Allison et al., 2002), with an increase in its application around 2005 (Finkelstein et al., 2005; Houle et al., 2005; Liao-Troth, 2005).

Referring to the variables, keywords such as the community in Figure 7 indicate that the motivation of Chinese volunteers is influenced by collectivism. Value expression, patriotism, pride, and identity are variables observed by researchers (Wang and Wu, 2014) and prosocial motivation is usually prominent (Aydinli et al., 2016). An empirical study of Chinese volunteers highlighted that a stronger national identity often corresponds to more significant volunteer motivation, while national identity significantly influences people's intention to participate in voluntary activities (Lai et al., 2013). Keywords such as personality in Figure 8 indicate that the United States is deeply influenced by the trend of liberalism and focuses on the motivation behind individualism (Finkelstein, 2010). Nevertheless, while concentrating on the motivation for individualism, researchers still focus on collectivism. As can be seen from the figure, although the frequency of keywords such as community service is lower than that of personality, some scholars continue to focus on it (Finkelstein, 2009).

Compared with China, the United States has formed a complete system for the study of volunteer motivation, especially in the study of religious culture. These studies explain the behavior of American volunteers during voluntary service (Ozorak, 2003; Johnston, 2013). Although China has the same high level of citizen volunteer participation as the United States (Rochester et al., 2010), research on volunteer motivation is slightly behind that of the United States in terms of time and is influenced by its culture. Moreover, past studies have often neglected some motivations of individualism, and from the development trend shown in the time zone map, research on Chinese scholars is progressing with the research development trend. It could be perceived from the two keywords, engagement, and management, that recently, China and the US have tended to share similar hotspots.

Volunteer motivation, as a topic that evolves with the times, still enjoys popularity in various disciplines such as psychology, management, and sociology. This study analyzes relative studies on volunteer motivation from 2000 to 2021 with VOSviewer and CiteSpace, and systematically reviews its development trend using a bibliometric method. Meanwhile, this study discusses and analyzes the core authors, national institutions with high productivity, key journals, and keyword clusters, draws knowledge maps, and summarizes the existing theoretical and empirical research on volunteer motivation. Based on the bibliometric analysis, the following conclusions were drawn.

This study clarified the quantization of published articles on volunteer motivation through bibliometric analysis. Based on this conclusion, core articles were selected to read, analyze, and review, which shows the development overview, research focus, and evolution trend of this field. They provide theoretical support for the formulation of volunteer recruitment strategies (Wicker, 2017). The practical value of this study is as follows:

This study had some limitations owing to objective factors. First, bibliometric analysis software has a high data requirement. Therefore, this study only selected articles in SSCI from the core collection of the Web of Science to ensure the quality and integrity of the collected papers. We excluded other databases, conference papers, and comments; therefore, we may have neglected some scientific research and unique opinions. Although this study obtained objective quantitative data with professional software for bibliometric analysis, the analysis and interpretation of data would inevitably be subjective. Therefore, the impact of subjectivity on the data analysis is unlikely to be completely avoided.

In sum, future studies will widen the scope of articles and understand research trends and frontier hotspots. Furthermore, we intend to actively communicate with scholars who are experts in the field of volunteer motivation to acquire objective and frontier views to minimize the negative impact of personal subjectivity on research analysis.

This study applied systematic and scientific research methods to summarize the main research topics in volunteer motivation research. It was found that changes in research theories have played an important role in promoting research on volunteer motivation. Therefore, it can be reasonably predicted that more research theories will be applied to boost its prosperity and development. However, because of the limited space, there are still some aspects of this study remaining to be explored. For example, we didn't classify and quantify the types of volunteer activities, which should be refined to gain more comprehensive quantitative information, so as to get more rigorous conclusions. Meanwhile, although this study made a comparative analysis of volunteer research in China and in the US, the coverage is still not wide enough. We suggest that future studies should cover the differences in volunteer motivation in various countries and regions, and analyze its mechanism to reveal the influence of different cultures on people's volunteer motivation behavior.