Understanding Green Consumption: A Literature Review Based on Factor Analysis and Bibliometric Method

Abstract

In recent years, research on green consumption has grown at an exponential rate. Because this field of study has seen such rapid growth, research hotspots have been constantly changing, making it difficult for scholars to keep track of the most current hotspots and trends related to the topic of green consumption. In this study, we employed Citespace, COOC1.9, and SPSS 20 to map knowledge in the field of green consumption and to identify current research preferences, cooperative networks among countries and institutions, and collaborative networks among authors. A total of 2194 papers from the period of 2016–2022, sourced from the Web of Science, were collected as our data sample. The results show that the topic of green consumption has caught the attention of researchers around the world, particularly in some countries with high levels of economic development, for instance, in China, USA, and England. In addition, although there are numerous scholars who have focused on the study of green consumption, currently, there are few efficient and productive authors. Collaborative networks among authors, and cooperative networks among institutions and countries, are all still immature and need to be further strengthened. A principal component analysis (PCA) showed that the existing literature focuses on the following three topics: (1) consumer green behavior, (2) corporate green production, and (3) green marketing in social media. In addition, we conducted a multidimensional scaling (MDS) analysis to verify our results. Finally, we offer some suggestions intended to inform and enrich the field for future researchers.

1. Introduction

Despite substantial social development and the economic prosperity resulting from technological development, our planet’s environment is contaminated and damaged [1]. Furthermore, the environment is progressively deteriorating as individual spending power increases [2]. In this context, the quiet rise of green consumption offers an effective solution [3]. According to Machova’s study, information about endangered animal species and warnings about deforestation have strongly influenced consumers to change their purchasing habits. Currently, if the production process does not have a negative impact on the environment, many consumers prioritize green products when making decisions, even if the costs are higher [4]. Additionally, many corporations show a preference for the marketing of green products and the promotion of green consumption [5].

Research on green consumption has increased significantly in recent years [6]. As countries continue to issue environmental policies, our awareness of environmental issues has become more profound [7]. Therefore, research on green consumption has been further promoted. Prior to 2016, few relevant studies were indexed in the Web of Science database, whereas in 2016, there were over two hundred papers published. Since then, the number of papers published each year has increased significantly, sometimes doubling, with over seven hundred papers published in 2021. The number of highly cited papers has also exceeded sixty. This trend shows that green consumption has become a hot research topic.

However, although there is a great deal of current research on green consumption, the research hotspots are constantly changing, thus many scholars find it difficult to stay aware of the current research topics and tendencies related to the topic [6]. Through preliminary literature search, we find that there is a lack of bibliometric analysis in the field of green consumption. Bibliometric analysis can help researches obtain a quick, accurate, and comprehensive understanding of research hotspots, author information, and other necessary information related to a particular field [8].

In the present study, we aim to conduct a systematic review of research on green consumption through a factor analysis and bibliometric method, and to map out the networks of partners so scholars are able to achieve better collaboration. In addition, by conducting a principal component analysis (PCA) and using multidimentional scaling (MDS), we summarize current research priorities and provide some directions worthy of further research in the area of green consumption. Keywords are a distillation of the core ideas of a text. Keywords that appear more frequently can reflect the focus of a field of study [9]. For example, Woocheol and Feroz [10] used a keyword analysis to identify topics and central themes in the field of employee engagement; Chiang Chang-Tang [11] also used a keyword analysis to determine current research priorities in the hotel and tourism industry and suggested some future research directions for the industry. Others, such as Weismayer [12], Keramatfar [13], Qikai [14], and Sangsung and Sunghae [15] have also clearly illustrated the importance of keyword analyses of hot topics and research trends in different fields. Here, we also use a keyword analysis to identify cutting-edge hotspots and further research in the area of green consumption. Unlike the existing literature, in this study, we innovatively used a PCA to reduce the dimensionality of keywords and tested with MDS, which has been shown to be significantly effective in reducing the dimensionality of textual information [16].

2. Materials and Methods

2.1. Data Collection and Analysis

To find relevant papers in the literature, we searched the Web of Science following a three-step systematic literature review process [17]. First, we set individual keywords to select papers from journals, including “green consumption”, “sustainable consumption”, “green product consumption”, and “green purchase”. Then, for a more complete search, we combined the keywords, including “green behavior” and “consumption”, “sustainable behavior” and “consumption”, and “green production” and “consumption”. We took into consideration the relevance and mass of each study retrieved, as well as the validity and typicality of keywords. To eliminate interruption or interference, we verified the genre of the texts and excluded book evaluations and opinion letters. Finally, 2194 papers were retained. These articles were published from January 2016 to February 2022. This time frame was chosen because few relevant studies were indexed in the Web of Science database prior to 2016. Additionally, we focused our search on journal articles because the academic community considers journal literature to represent the most up-to-date source of knowledge in a given field [18].

2.2. Principal Component Analysis

Traditional manual text classification can encounter significant difficulties in scenarios where there is a large amount of text. Manual text classification requires a great deal of manpower in the form of field experts and knowledge engineers, and such reliance on humans makes it hard to guarantee the accuracy and identity of rules, whereas automatic text classification can address these issues.

In statistics, in order to solve problems objectively, various influential factors have to be comprehensively considered and fully analyzed. These factors form high-dimensional vectors which reflect specific information for studying data to different degrees. When there is a certain level of correlation between two space vectors, it can be assumed that the two vectors are classified with overlapping information. A principal component analysis (PCA) is a mathematical method for converting high-dimensional vectors with correlation into a group of linear, uncorrelated, low-dimensional vectors using orthogonal transformation [19]. Zhang, Li, and Zong [20] proved that principal component analysis could significantly reduce the dimensionality of text data.

2.3. Multidimensional Scaling

Multidimensional Scaling (MDS) is a method that transforms a high-dimensional dataset into a low-dimensional representation while preserving an intrinsic message. It uses similarities (or dissimilarities) between objects to generate maps [21]. The result of an MDS is a spatial map that provides information spatially about the relationships among different objects, where objects that are similar are close together, while dissimilar objects are far apart [22]. For example, applying MDS to similarities between color blocks results in red being close to orange and far from green, and so on [23]. Compared with other dimensionality reduction techniques, MDS is popular due to its simplicity and wide range of applications, and because it provides valuable maps that simplify complex datasets into more understandable visual relationships [24]. In this study, we demonstrate the relevance of keywords in the form of a two-dimensional graph.

2.4. Analytical Tools Used

To obtain the literature, we used the WoS analysis results tool. To perform a collaborative network analysis among authors, institutions, and countries, we used the Citespace software. To obtain a dual keyword co-occurrence matrix, we used the COOC 1.9 software. To perform principal component analysis and multidimensional scaling analysis, we used the SPSS software.

Citespace is one of the most widely used knowledge mapping tools. It has been specifically developed to aid in the visualization of analytic processes, and it can generate collaborative networks [25]. Despite the popularity of Citespace [8], to the best of our knowledge, few scholars have used Citespace to analyze green consumption studies; however, we used it to undertake a collaborative network analysis.

3. Results

3.1. Collaborative Network of Authors

Authors in the area of green consumption are visualized by using the Citespace software. This software identified the collaborative network among productive authors in this area of research. There are 2194 retrieved papers that are exported and converted to plain text format, as required by Citespace. These papers are then imported for analysis. The result is shown in Figure 1: there are 235 nodes and 179 connective links.

A node represents an author, and the size of the node represents the number of pieces of published journal literature. According to Figure 1, there are many nodes, but they are not large. These results confirmed that there were a relatively large number of international scholars engaged in green consumption research, but not many highly productive authors. Highly productive authors are the main force of research work in a field [26]. In the book “Little Science, Big Science”, the famous scientist and historian Price pointed out that 50% of all the papers on the same subject are written by highly productive authors. The number of highly productive authors who have written half of all the papers in the field of green consumption can be obtained by formula m = 0.749(n_max)^0.5 [27], where n_max = 10 refers to the number of papers published by these authors from 2016 to 2022. The value of n_max is equivalent to 10, after calculation. Statistically, m can be worked out to be 3. Therefore, the highly productive authors in the field of green consumption were those who had published more than 3 papers. The results showed that 37 authors published 165 papers, accounting for 7% of the total samples, revealing a large gap between 7% and the 50% that had been expected. This means that a stable core author group has not been formed in this research field [28]. According to the frequency of published authors, as

Table 1. Frequency of published authors.

No.	Author	Frequency	No.	Author	Frequency
1	Minglang Tseng	10	6	Antonio Lobo	5
2	Genovaite Liobikiene	7	7	Jihad Mohammad	5
3	Ming K. Lim	6	8	Aiste Dovaliene	5
4	Ingo Balderjahn	5	9	Daniel Fischer	5
5	Ulf Schrader	5	10	Farzana Quoquab	5

shows, the top ten most efficient and productive authors in this field were: Minglang Tseng, Genovaite Liobikiene, Ming K. Lim, Ingo Balderjahn, Ulf Schrader, Antonio Lobo, Jihad Mohammad, Aiste Dovaliene, Daniel Fischer, and Farzana Quoquab.

In terms of author collaboration, the connection line represents cooperation, and the thickness of the line represents the amount of cooperation output. According to Figure 1, we can see that the number of connections between authors is small and thin. It shows that there was little collaboration among authors in this field and that they had not yet formed a close collaborative network. Most of the existing author collaborations were two-authorship collaborations, and there was less collaboration among three or more authors. The scope of collaborations was relatively small. In addition, most of the existing collaborations were between authors with similar research directions. For example, two authors, Minglang Tseng and Ming K. Lim, had the closest cooperation in the field of green consumption, and their research directions were also very similar, focusing on green supply chain management and circular economy.

3.2. Cooperative Network of Institutions

To further identify the highly productive institutions and inter-institutional cooperative networks in this field, we also employed the Citespace software to obtain a distribution and collaboration map of international institutions, as shown in Figure 2, with 308 nodes and 321 links. According to

Table 2. Frequency of institutions.

No.	Institutions	Frequency	No.	Institutions	Frequency
1	Lund Univ	29	6	Chinese Acad Sci	17
2	Tech Univ Berlin	25	7	Leuphana Univ Luneburg	17
3	Univ Manchester	24	8	Bucharest Univ Econ Studies	16
4	Univ Leeds	18	9	Aarhus Univ	14
5	Delft Univ Technol	18	10	Arizona State Univ	13

, we can see that there were six research institutions or units with more than 15 papers in this field, namely Lund University, Berlin Institute of Technology, University of Manchester, University of Leeds, Delft University of Technology, Chinese Academy of Science, Leuphana University of Luneburg, and Bucharest University of Economic Studies. And the top ten institutions include nine universities and only one scientific institution (Chinese Academy of Social Sciences), a ranking that indicates that universities were the core strength of green consumption research [29].

In terms of institutional cooperation in publishing, Figure 2 shows that there were more collaborations between institutions with the largest number of publications, while those with fewer publications are basically distributed in a dotted pattern. These results show that there was still a lack of inter-institutional cooperation internationally, and there were few collaborative research efforts and cooperative outputs.

3.3. Cooperative Network of Countries

The level of scholarship on the issue of green consumption in separate nations can be determined through a cooperative network analysis [30,31]. The node type was set as the country and other settings were kept unchanged to obtain a graph of the number of published papers and cooperation of different countries in the area, as illustrated in Figure 3, with 66 nodes and 456 connecting lines. As shown in

Table 3. Frequency of countries.

No.	Countries	Frequency	No.	Countries	Frequency
1	People’s R. China	365	6	Italy	103
2	USA	254	7	Australia	103
3	England	223	8	Spain	96
4	Germany	170	9	Netherlands	88
5	India	104	10	Sweden	87

, there were seven countries with more than 100 published papers in this field, namely China, USA, England, Germany, India, Italy, and Australia.

The number of nodes reflects the fact that green consumption research has drawn the attention of scholars all over the world. However, in regard to the quantity of published papers and cooperation between countries, research on green consumption has still been mainly concentrated in a few major countries such as China, USA, and England. Particularly, China had a significant effect in the sphere of green consumerism, and cooperated closely with other countries. In countries with low levels of economic development, there were very few research results in this area, and they were mostly carried out in cooperation with nations such as China and England. The result demonstrates that green consumption was more linked to a country’s economic level than traditional consumption

3.4. Hotspot Analysis in Green Consumption

3.4.1. Factor Analysis

Keywords are a distillation of the core ideas of a text. Keywords that appear more frequently can reflect the hot topics in a field of study [9]. In this study, we conducted a PCA for dimensionality reduction and clustering of keywords to reveal the research hotspots in the area of green consumption.

First, we used the COOC 1.9 software, which is a bibliometric analysis software from the GitHub (San Francisco, CA, USA), to build the keyword co-occurrence matrix D, which was a 6324 × 6324 matrix, as shown in

Table 4. Frequency of co-occurrence keywords.

No.	Keyword	Frequency	No.	Keyword	Frequency
1	Sustainable consumption	662	59	Consumers	15
2	Sustainability	250	60	Supply chain management	14
3	Green consumption	122	61	Perceived value	14
4	Circular economy	95	62	Fashion	14
5	Sustainable development	76	63	Sustainable	14
6	Consumer behavior	74	64	Eco-innovation	14
7	Sharing economy	56	65	Environmental behavior	14
8	Consumption	54	66	Social media	13
9	Sustainable consumption and production	54	67	Literature review	13
10	Green marketing	47	68	Social influence	13
11	Purchase intention	47	69	Perceived consumer effectiveness	13
12	Consumer behavior	47	70	Pls-sem	13
13	Climate change	46	71	Sustainable fashion	13
14	Green products	45	72	Energy consumption	13
15	Theory of planned behavior	44	73	Green public procurement	13
16	China	44	74	Social norms	12
17	Organic food	44	75	Energy	12
18	Environmental concern	43	76	Responsible consumption	12
19	food waste	34	77	Sustainable lifestyles	12
20	Pro-environmental behavior	32	78	Green purchase behavior	12
21	Environment	32	79	Well-being	12
22	Theory of planned behaviour	31	80	Sustainable tourism	12
23	Carbon footprint	31	81	Gender	12
24	Sustainable development goals	31	82	Segmentation	11
25	Sustainable production	28	83	Remanufacturing	11
26	Collaborative consumption	28	84	Cluster analysis	11
27	Green procurement	27	85	Brazil	11
28	Practice theory	26	86	Culture	11
29	Willingness to pay	25	87	Motivation	11
30	Ethical consumption	24	88	Environmental policy	11
31	Food	23	89	Happiness	11
32	Barriers	23	90	Young consumers	11
33	Environmental Knowledge	23	91	Green purchase behavior	11
34	Values	22	92	Developing countries	11
35	Green purchase intention	22	93	Green consumption behavior	11
36	Recycling	22	94	Social practice theory	11
37	Environmental sustainability	22	95	Cleaner production	11
38	Life cycle assessment	22	96	Sustainable marketing	10
39	Sustainable consumption behavior	21	97	Green consumers	10
40	Attitude	21	98	Local food	10
41	Sufficiency	21	99	Malaysia	10
42	Consumer	19	100	Energy efficiency	10
43	Pro-environmental behavior	19	101	Behavior	10
44	COVID-19	19	102	Sustainable behavior	10
45	Environmental awareness	17	103	Households	10
46	Structural equation modeling	17	104	Green purchasing	10
47	Green behavior	16	105	Environmental impact	10
48	Materialism	16	106	nudging	10
49	Sustainable food consumption	16	107	Environmental attitude	10
50	Marketing	16	108	Agriculture	10
51	Attitudes	16	109	Millennials	10
52	Survey	16	110	Industrial ecology	10
53	India	15	111	Economic growth	10
54	Mindfulness	15	112	Trust	10
55	Voluntary simplicity	15	113	Supply chain	10
56	Behavior change	15	114	Environmental consciousness	10
57	Green product	15	115	Renewable energy	10
58	Corporate social responsibility	15

. This represented the 6324 keywords we obtained from 2194 papers, and each matrix indicated the frequency of the keywords corresponding to the ranks and columns appearing together in the same document. Since keywords with low co-occurrence frequencies are not representative, we selected 115 keywords with co-occurrence frequencies of ten or more (as shown in Table 4) to build the original keyword co-occurrence matrix (see an example (fragment) in

Table 5. Keywords co-occurrence matrix D (fragment).

Keywords	1	2	3	4	5	6
1. Sustainable consumption	0	53	9	32	19	29
2. Sustainability	53	0	8	12	9	14
3. Green consumption	9	8	0	0	5	0
4. Circular economy	32	12	0	0	5	3
5. Sustainable development	19	9	5	5	0	3
6. Consumer behavior	29	14	0	3	3	0

). Then, the original matrix was transformed to a Pearson correlation coefficient matrix using SPSS. The degree of resemblance between two papers was represented by a correlation: the stronger a positive relationship, the higher the level of perceptual similarity between the papers. [32]. Since the data may be normalized and the number of zeros minimized, correlation coefficients are better than co-occurrence frequencies for statistical analysis [33]. The correlation matrix was used as input for the multivariate analysis of the data and to explain the conclusions employed. In this study, we employed three multivariate methodologies to examine the hot regions in green consumption, including PCA and MDS analysis, which are in line with the prior literature [34].

We began our research by mapping and describing the selected green consumption keywords. To cluster the keywords, we initially used factor analysis. A factor analysis was used to group published papers into related groups or factors, depending on their degree of similarity [34]. The factor analysis enabled us to uncover some of the new study areas in green consumption research by looking at how keywords fit together.

The number of extracted factors was determined using Kaiser criteria and scree tests, using PCA as the extraction method and varimax rotation of the extracted factors as the interpretation method. Factor loadings show the relationship between a paper and a factor, as well as how much of the paper belongs to the set [34]. The analysis resulted in three factors which explained 79.7% of the variance (as shown in

Table 6. Factor labels and number of keywords.

Factor	Label	Number of Keywords
Factor 1	Consumer green behavior	70
Factor 2	Corporate green production	23
Factor 3	Green marketing in social media	22

).

To explain the three factors, we examined each set of keywords, looking for common themes. The final interpretation was summarized by the authors, and the three final research hotspots identified included: research on consumer green behavior (Factor 1), research on corporate green production (Factor 2), and research on green marketing in social media (Factor 3).

Factor 1 contained the most keywords among the three factors, and it also explained the majority of the variance (51.5%). Regarding Factor 1, at first glance, it focused on examining consumer behavior, and more specifically, considered the representativeness of each keyword in the factor. The closer a positive value of the keyword loadings is to one, the more representative the keyword is. The most representative keywords in Factor 1 are “consumer behavior”, “trust”, “happiness”, “culture”, “values”, “pro-environmental behavior”, and “well-being”. By combining the papers in which the keywords were found, we found that the most popular research on green consumption focused on factors influencing consumers’ green consumption behaviors. The keywords “trust” and “value” represent consumers’ perceptions of green products. Whether a green product is trustworthy and the level of trust consumers have in the product are very important factors influencing consumers’ green consumption behaviors [35], and trust also performs a key role in consumers’ opinions toward the value of a product. [36]. “Happiness” and “culture” are based on the study of consumers’ own attributes. Consumers’ own core values, happiness, and cultural influences also play important roles in green consumer behavior, and are an important area for research [37]. The next most popular factors are “theory of planned behavior”, “attitude”, “willingness to pay”, and “green consumption behavior”. The loadings of the keywords were also greater than 0.7, indicating that research on consumers’ attitudes, intentions, and behaviors was also a hot topic. Consumers’ intentions are not equivalent to consumers’ behaviors, and it is very important that consumers’ intentions become behaviors. Research in this direction is also essential.

With regards to Factor 2, the most important keywords are “supply chain management”, “green procurement”, “sustainable consumption and production”, “corporate social responsibility”, and “sustainable production”. The fact that these keywords stood out shows that the scholars mainly focused on the green production of corporations and the social responsibility of corporations in their green production. However, less research has been done on after-sale services and PR for products. For green products, after-sale services and PR are very important aspects, affecting consumers’ sales experiences and their evaluation of the corporations [38]. Therefore, scholars should devote more effort to research on after-sale services and public relations of corporations.

Finally, Factor 3 focuses on green marketing in social media. The most important keywords associated with Factor 3 are “green marketing”, “social media”, “sustainable marketing”, and “marketing”. The loadings above 0.7 showed that these keywords are important. Social media has a huge impact on both consumers and corporations [2]. Corporations can use social media to gain information about potential consumers and directly recommend green products [39]. It is also mainly through social media that consumers obtain information about products, making decisions based on that information [40]. In the traditional consumer sector, research on social media is well established. In the area of green consumption, green marketing has consistently demonstrated positive customer response, but there are still many areas for further research [41]. Especially considering the emergence of new concepts such as metaverse technology, social media marketing is set to change even more profoundly and generate more research opportunities [42].

Overall, the above three factors identified three current research hotspots. Factor 1 focused on consumers’ green behaviors and factors that transform consumers’ attitudes, intentions, and behaviors; Factor 2 focused on the green production of corporations, while Factor 3 focused on green marketing in social media (as shown in

Table 7. Clustering results (fragment).

Factor 1	Factor 2	Factor 3
Consumer behavior	Green procurement	Marketing
Culture	Corporate social responsibility	Green marketing
Values	Supply chain management	Social media
Trust	Sustainable production	Sustainable marketing

).

3.4.2. Multidimensional Scaling

For the purposes of this subsection, MDS is used to test the soundness and robustness of the performed PCA [43]. Using a Pearson correlation coefficient matrix, the MDS analysis generates a two-dimensional graph in which the position of each keyword depends on the distance between keywords; the closer the keywords are relative to each other, the more similar the concepts between them, and the more internal consistency there is. We usually used RSQ to test whether the result was accepted or not. RSQ represents the fraction of total variance that can be interpreted by distance relative to space [22]. The MDS results show that RSQ equals 0.97, which is very close to 1. Therefore, the multidimensional scale fitting of the distances between keywords is quite good and the data are acceptable.

The MDS map is shown in Figure 4. It is evident that it is compatible with the factor analysis results. In fact, the examined keywords tended to cluster visually in three main sets, similar to the three factors, and occupied specific places; we accentuated these factors to enable scholars to make comparisons between MDS and factor analyses. Factor 1 (consumer green behavior) is concentrated on the map on the left. Factor 2 (corporate green production) is concentrated on the map on the right, while Factor 3 (green marketing in social media) is mainly distributed in the middle of Factors 1 and 2.

Additionally, as illustrated in Figure 4, the majority of keywords are spread across the right-hand side of the chart, indicating that the overwhelming bulk of research has been focused on consumers. This is not difficult to understand, as the consumer is the most important subject in green consumption.

4. Discussion

The current study uses a bibliometric approach and factor analysis to examine the overall evolution of green consumption research since 2016, in the context of growing literature in terms of volume, geographical distribution, authors, and topic areas. This section is based on the above analysis results, making some recommendations for further research, and presenting some of the study’s shortcomings.

4.1. Recommendations for Future Research

The study of green consumption involves a very wide range of disciplines, including Management, Economics, Environmental Science, Business, Humanities, and so on. Scholars’ research directions may cover multiple fields, but scholars cannot be proficient in every field. Collaborating with other scholars allows them to complement each other’s strengths and accomplish more in-depth research. In addition, cultural and policy factors vary greatly among institutions and countries. Our understanding of the published journal literature of each institution and country shows that their research is largely based on their own national or regional contexts, meaning many of the findings are not well generalized to other countries and regions. Another point is that the focus of research on green consumption is not entirely the same across institutions and countries. Therefore, we also need to encourage collaboration among scholars from different institutions and countries to make the findings more generalizable.

Through the analysis of the keywords above, we can understand that the current research on green consumption is mainly focused on three themes, namely consumer green behavior, corporate green production, and green marketing in social media. Among them, the research on consumers is the most popular, but the research on corporate and social media is lacking. However, corporations and social media are also an integral part of green consumption, and without corporate green production, consumers cannot conduct green consumer behavior. Therefore, it is also important to research how to promote and enhance the green production of enterprises. Social media is also a very important channel for connecting consumers and enterprises. According to a study by Dedeoğlu et al. (2020), consumers often trust the content of social media more than the content of corporations. Consumers consider social media content to be more unbiased and reliable than corporate content [44]. Therefore, research on social media is essential. How to make fuller and more effective use of social media is a very important area of research for the future.

4.2. Limitations

While this study provides a thorough examination of the collaborative and cooperative networks and research hotspots in the field of green consumption, it also has certain shortcomings.

Firstly, the selected papers are written in English only, which, although dominant in the field, does not cover all the research networks and hotspots in the field. There are many other relevant papers written in Chinese or Spanish that should also be included in a sound geographical or authorship analysis. In addition, the papers in this study were selected from the Web of Science, which does not guarantee coverage of all papers in English. Moreover, despite the fact that we tried to perform our manual screening with as much detail as possible, there may be a few published papers in our collection that were irrelevant, which is unavoidable in a big sample, thus we could only try to reduce the presence of irrelevant papers. As a result, future research may investigate incorporating published papers written in other languages or using different databases to round out the data.

Secondly, despite the fact that the search query was properly constructed and included a wide range of keywords, the danger of missing relevant phrases exists, especially given changes in keywords over time and the presence of acronyms or synonyms. Furthermore, in this study, we did not examine the quality of the selected papers, because this process would entail the creation of criteria that may not have been relevant to all related areas. Because we attempted to provide a comprehensive study, the precise inclusion and exclusion criteria were judged to be appropriate for the proper selection of the final body of papers from the literature.

Finally, in the present study we employed keyword analysis to establish the research trends in the area, and the co-occurring keywords captured the core ideas as well as trends in a domain of research, thus showcasing research hotspots in the field [45]. Therefore, future studies should use a combination of a co-citation analysis of the literature and a keyword analysis to corroborate each other and to improve the conclusions. In addition, in this study, we used high-frequency co-occurring keywords for the analysis. In contrast, there are new and meaningful research directions with keywords that may be used very infrequently but are nevertheless important. Therefore, future studies should also screen and rank low-frequency keywords to prevent the oversight of potentially important studies.

5. Conclusions

We chose 2194 papers on green consumption from the Web of Science between January 2016 and February 2022. Based on the selected papers, we used Citespace to map the collaboration networks among authors and cooperative networks among institutions and countries. For the factor analysis, we extracted terms with a co-occurrence frequency larger than ten, and summarized three factors that represented the current research hotspots in the field, i.e., consumer green behavior, corporate green production, and green marketing in social media. We should not only focus on the current research hotspots, but also pay attention to some important, but currently less researched, aspects.

In conclusion, we hope that our efforts will enable more scholars to make sense of emerging topics in this field of research, which is becoming increasingly important. In this regard, we submit that our summary contributes to the deepening of research on green consumption.