Introduction

The sharing economy (SE) has been highlighted as the cutting edge of most emerging business models (Eckhardt et al. 2019; Kuhzady et al. 2021). Underpinned by information technology, artificial intelligence (AI), mobile technologies, advanced analytics, and big data, accompanied by changing customers' preferences, business platforms have been revolutionized, and a new one has arisen (Caldieraro et al. 2018). Digital and sharing platforms have disrupted and transformed various industries, enabling individuals and businesses to connect directly, creating new business models, and offering more choices and convenience to consumers. Uber and Lyft are two common examples. They have transformed the traditional taxi industry by providing a digital platform that connects consumers and drivers directly (Wirtz et al. 2019). Also, Airbnb has challenged the traditional accommodation industry by providing a platform that allows individuals to rent out their homes or spare rooms to travelers. However, additional sectors have been disrupted, for example, Amazon and Alibaba have transformed the retail industry by establishing online marketplaces that connect buyers and sellers. Similarly, Kickstarter and Indiegogo have revolutionized how entrepreneurs and artists fund their projects. Upwork, Freelancer, and Fiverr have established global marketplaces for freelancers to offer their services to clients (Kuhzady et al. 2021; Wirtz et al. 2019).

This study defines SE as a technologically enabled socioeconomic platform that facilitates the sharing of underutilized assets among a network of actors. The essence of marketing is based on exchange (Bagozzi 1974), which permits the permanent transfer of ownership. However, a sharing-enabled platform empowered by the digital revolution grants users a temporary exchange of goods and services without the transfer of ownership via online platforms. Sharing platforms have resulted in widespread usage among customers and service providers in a wide range of industries, including transportation (e.g., Airbnb and Lyft), hospitality (e.g., Airbnb), financial services (e.g., Transferwise), shared office space (e.g., WeWork), fashion rentals (e.g., Rent the Runway), and food services (e.g. Deliveroo; Xu et al. 2021a, b).

The recent advances in new technologies and proliferation of sharing services are considered as a global phenomenon, which has received remarkable attention within and beyond the marketing field (e.g., Eckhardt et al. 2019; Hazée et al. 2020; Rojanakit et al. 2022; Täuscher and Kietzmann 2017; Tussyadiah and Pesonen 2018), for example, highlighting the new business opportunities of SE (van Welsum 2016), classifying dynamic forms of SE platforms (Wirtz et al. 2019), providing tactics to manage service providers in SE (von Richthofen and von Wangenheim 2021), investigating the impact of big data governance and analytical algorithms on sharing platforms performance (Basukie et al. 2020), reconceptualizing SE as socioeconomic system of actors (Eckhardt et al. 2019), understanding the dynamics of service platform ecosystems (Xu et al. 2021a, b), suggesting pathways for sustainable development of the SE ecosystem (Leung et al. 2019), examining customers' purchasing intention and behaviors in the SE (Lo et al. 2020), and investigating deviant sharing behaviors (Hou et al. 2022).

Despite varied studies on SE, the marketing literature lacks a comprehensive and state-of-the-art review for understanding and mapping the evolution of SE scholarship and providing guideposts for future SE research (Klarin and Suseno 2021; Rojanakit et al. 2022). There is a need to map and analyze the SE literature in the marketing domain and to provide frontiers for future research. SE phenomena have received scattered and fragmented investigation in different domains including management (e.g., Burtch et al. 2018; Constantiou et al. 2017), tourism, hospitality (e.g., Boateng et al. 2019; Leung et al. 2019; Tussyadiah and Pesonen 2018), and information systems (Han et al. 2021; Yin et al. 2018). A comprehensive and reflective review of sharing economy is required to identify knowledge gaps and research output for scholars, countries, and journal outlets. Thus, the current work aims to examine and analyze the predominant topics of SE, identify landmark publications, influential authors, and networks for keywords and among authors, to provide a holistic view for sharing economy knowledge base.

In terms of methods, performance analysis and science mapping are adopted to examine the evolution of the field and analyze the knowledge. First, performance analysis is used to examine the contributions of research entities (e.g., authors, journals, countries, and institutions). Second, science mapping is employed to map dominant topics and to outline the developments of SE field over time (Donthu et al. 2021; Klarin and Suseno 2021).

This study makes several contributions. First, this work challenges traditional literature reviews and adopts AI-based machine learning (ML) algorithms, natural language processing, data analytics techniques, and science mapping tools to provide a comprehensive outlook and evolution for the research field (Klarin and Suseno 2021; Ordenes et al. 2014). Second, by leveraging topic modeling, the key aspects of SE are uncovered, providing a holistic outlook on implicit trends and relationships (Moro et al. 2019). Third, performance analysis and science mapping techniques help identify how different disciplines in this SE are structured and interconnected. This enriches our understanding of SE and offers a synthesis of domain knowledge dictionaries. Fourth, the study provides a comprehensive framework for future research avenues for SE, which deepens the understanding of service platform actors, including platform providers, peer service providers, and customer-related factors.

The rest of the paper is structured as follows. The paper begins by explaining the methodological approach adopted. Afterward, we present the results of the performance analysis. The science mapping analysis is then presented. A review of the salient topics of SE in marketing follows. Finally, the paper discusses the results and provides a future research agenda for examining the role and influence of a triangle of SE actors.

Methodology

This study utilizes bibliometric analysis following prior studies (e.g., Caputo and Kargina 2022; Ghorbani et al. 2022) and topic modeling to conduct a systematic literature review of SE in marketing. The bibliometric approach involves the application of quantitative techniques to conduct performance analysis and the science mapping of research knowledge in the research field. This approach is considered to be more objective than other review techniques that are subjective in nature (e.g., content analysis, thematic reviews using qualitative analysis; Chandra et al. 2022; Donthu et al. 2021; Ertz and Leblanc-Proulx 2019). Furthermore, bibliometric analysis follows a systematic, rigorous, and transparent review protocol (Iacobucci et al. 2019; Chandra et al. 2022). Therefore, the steps and procedures in data collection and analysis are guided by scientific rationale (Mosaad et al. 2023; Chandra et al. 2022).

This study adopts two bibliometric techniques and topic modeling. First, performance analysis is a descriptive analysis technique that examines the contribution of research entities, including authors, journals, countries, and institutions in the field (Donthu et al. 2020, 2021). Second, science mapping is adopted to map the intellectual intercorrelation and structural connections among research entities in the field by deploying citation analysis, co-citation analysis, co-word analysis, journal, and author co-citation techniques (Arora and Chakraborty 2021). Finally, the study adopts topic modeling to establish and analyze the underpinnings and dominant themes of SE in the marketing domain (Mustak et al. 2021). Latent Dirichlet Allocation (LDA) is utilized to develop topic modeling, which autonomously ranks the assigned topics, determines the co-occurrence of keywords, and reveals latent information about the research.

Data collection

The data collection, identification, screening, and inclusion decisions and procedures are guided by the PRISMA protocol for developing a transparent and rigorous scientific review (Moher et al. 2009). Specifically, the PRISMA protocol includes four main stages: identification, screening, eligibility, and inclusion (see Fig. 1).

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Review procedure PRISMA protocol.

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In the identification stage, a total of 64,722 records were collected from 2000 to 2022 by using documents indexed in the Web of Science (WoS) database (Cheng 2016; Mustak et al. 2021). To account for the most recent developments in SE, WoS was chosen as the search engine. Using the Boolean WoS search, the search terms for the study were systematically adopted using a combination of “sharing economy AND marketing.” The top-cited papers were downloaded and examined to extract terms and word combinations to utilize the same search words or closely related ones in our study (Mustak et al. 2021). SE can be defined in different domains, but the current study focuses on the dominant topics of SE in marketing. The keywords used for the search were chosen to reflect a marketing perspective. The study adopted other keywords related to SE, such as “Uber” and “Airbnb” (Cheng 2016). Table 1 summarizes the database search details of the study. The results returned a total of 64,722 bibliometric records of journal articles, conference papers, and books.

Table 1 Database search details of the study
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In the screening stage, the articles were filtered according to the source type. For the best focus of our review, these records have been refined to include only peer-reviewed and refereed articles from academic journals (Cheng 2016) because journal articles are a premium-based channel for academic publication that utilizes co-citation analysis for references, which strengthens the results' reliability (Cheng 2016). Because non-journal articles are not subject to rigorous peer-review processes, conference papers, books, and book chapters were excluded from the search terms (Paul et al. 2021; Fahimnia et al. 2015). For further refinement to exclude journals published in other fields, all articles published in the marketing field were included in our analysis. Out of 2,682 articles, 1,961 were excluded because they did not meet the inclusion criterion of being published in marketing or consumer research journals. In total, 721 articles met the screening criteria, and their full texts were returned.

In the eligibility stage, the 721 articles were evaluated on the basis of journal quality and source type. The titles, abstracts, introductions, and conclusions of the articles were carefully read. Only articles that focused on SE and had a clearly described method remained. Afterward, the remaining articles were evaluated for quality. Articles that met two selection criteria for quality were considered for publication quality, including the Academic Journal Guide by the Chartered Association of Business Schools (CABS., 2021) and the Herzing Journal Quality List (Harzing 2020). In total, 396 articles were excluded from the review in the next stage. Moreover, further journals that were not indexed by Harzing (2020) or CABS (2021) (e.g., Harvard Business Review, MIT Sloan Management Review, Technological Forecasting, and Social Change) were included in our review since they offer an insightful contribution to SE knowledge. In this process, 325 peer-reviewed articles published in top-tier journals were included in the next phase.

In the inclusion stage, our review included 325 articles published in journals on the quality list. Full information for the articles was downloaded, including author name, titles, keywords, abstracts, countries and regions, number of citations, and author-level metrics.

Data analysis

Bibliometric analysis is used to perform knowledge mapping for a research topic to uncover objective latent patterns (Donthu et al. 2021; Wang et al. 2021). Biblioshiny in R was utilized in the performance analysis of the SE literature in marketing. Thus, the performance of the most prolific constituents can be summarized (e.g., authors, journals, and countries). Based on the collected articles, the evolution and characteristics of the SE field have been analyzed based on several academically recognized indicators for evaluating the impact and productivity of the literature (Chandra et al. 2022; Xu et al. 2021a, b).

To summarize the bibliometric structure and intellectual knowledge of SE in marketing and science mapping, two software programs have been adopted: CiteSpace v5.8 and VOSviewer v1.6.18. CiteSpace was adopted to map knowledge in the SE field and to generate co-citation clusters. VOSviewer was used to generate a keyword co-occurrence network and density visualization.

Author influence and affiliation explore the frequency of the occurrence of a text in a wide spectrum of domains, as well as the contribution of organizations concerning their geographic area (Fahimnia et al. 2015). For the keyword co-occurrence network, the study identified the interconnectedness of concepts and keywords based on their frequency in the bibliometric data. Co-citation analysis visualizes leading researchers in the field and the co-occurrence of articles in other scientific works. Density visualization maps the most prominent words and concepts and provides a presentation for variable distribution to identify the theoretical base in the specific field. It was used to visualize the interrelationships between keywords and concepts to identify generated clusters that focus on a specific research area (Donthu et al. 2021; Mustak et al. 2021).

LDA, an automated text-mining tool, was adopted to generate topic modeling. It is a generative probabilistic modeling approach and a powerful state-of-the-art modeling technique (Foulds et al. 2013) that explores semantic structures for discrete data to provide an explicit representation of a large dataset of documents (Campbell et al. 2015). Building upon the rules of Nikolenko et al. (2017), in the LDA's model, articles donated with D = {d1, d2,…,dM} were a collection of M documents, with T topics expressed with W different words, where each document d ∈ D of length Nd is modeled as a discrete distribution θ(d) over the set of topics from a symmetric Dirichlet (zj = t) = (d)t, where z is a discrete variable that indicates the topic for each word instance j ∈ d.

In turn, each topic corresponds to a multinomial distribution over the words, p(w|zj = t = Φw(t). The Dirichlet priors α can be assigned to the distribution of topic vectors θ, θ ~ Dir(α), similar to β for the distributions of words in topic, ϕ ~ Dir(β). The user is required to set the value of T for the number of topics because the number of topics is critical for generating insightful and meaningful results. It is common to evaluate the LDA model's goodness-of-fit through the evaluation index of perplexity. Low perplexity levels indicate a better goodness-of-fit model for the entities collected (Vu et al. 2019). Empowered by the Jupyter Notebook platform, Python v.3 was utilized to conduct topic modeling using LDA and data visualization.

The steps of Pravakaran (2018) were followed to build topic modeling for SE in marketing. Native LdaModel in Genism Python library was used to build topic modeling, and then data visualization was created using the power of matplotlib plots. The packages and datasets have been imported. Then, sentences were tokenized to split large sentences into small words, and the data were cleaned to remove stop words and redundant words from the abstract (aim, methodology, and conclusion). Bigram and Trigram models were created to link separate words, such as “sharing” and “economy” as “sharing economy.” SpaCy library lemmatizes each word to its root, which retains only verbs, nouns, adverbs, and adjectives. Finally, the LdaModel package was adopted to generate topic modeling.

Performance analysis of sharing economy research

The analysis of the core collection revealed that marketing scholars have recently paid attention to the SE field. The publishing timescale showed that research work in this emerging field was low and fragmented from 2001 to 2009 (Fig. 2). The number of published papers has grown over the following years, reaching double digits in 2016. A noticeable upsurge can be seen by four-five times; fifty articles were published in 2017 and 86 articles in 2021, reflecting a remarkable interest from scholars in the field, which was driven by emergent business models and growing use of sharing platforms in marketing.

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Publishing trend in sharing economy filed

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It should be noted that this trend was led by a wide spectrum of journals in business research, including marketing, general management, and even specialized journals, wherein studying SE ranges from conceptualization of the concept to exploring collaboration patterns, and analysis of the intellectual structure of the field. As shown in Table 2, the analysis of published papers by top publication outlets elucidates that the highest number of articles were published in the International Journal of Contemporary Hospitality Management (44), followed by the Journal of Business Research (40), Technological Forecasting and Social Change (39), and Tourism Management (31). The leading dominance of consumer-oriented journals, such as Psychology and Marketing and Journal of Retailing and Consumer Services, demonstrates the interest of scholars in consumer behavior in SE. At the same time, a considerable number of articles have appeared in service-specialized journals, such as Journal of Service Research, Journal of Service Management, and Journal of Services Marketing, which present service-based considerations for SE platforms.

Table 2 The top 10 publication journals in the field of sharing economy in marketing
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Statistics on countries' contributions to SE research output clarify that the predominant scholarly work in the field is concentrated in North America, Europe, and East Asia (see Fig. 3). Three countries generate the majority of research papers on SE: the United States (126), England (67), and China (42). For affiliation analysis and university level, the most striking feature is the dominance of American universities in research output in SE (see Fig. 4). Boston University is the main contributor, with 17 articles, followed by California State University (13), Florida State University, Texas A&M University, University of London (11 articles per each), Denver University, California State Polytechnic University, University of North Carolina (9 articles each), Hong Kong Polytechnic University (8), and Bournemouth University (7).

Fig. 3
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Top ten countries in terms of research output on sharing economy in marketing

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Fig. 4
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Visualization tree map of top 10 universities research output

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Moving to the document citation level (see Table 2), Belk (2014) produced the most seminal paper in the field, citing 1,024 articles, followed by Eckhardt (2012), Zervas et al. (2017), and (Ert et al. 2016). The majority of published papers are the output of international cooperation between different scholars, reflecting the growing and cross-border interest in SE topics (Table 3).

Table 3 The top 10 most-cited articles in sharing economy
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Science mapping analysis of sharing economy research

Empowered by AI-based statistical algorithms, science mapping aims to provide a comprehensive outlook and taxonomy for studies on SE by mapping the data available in SE research.

Co-citation network

Co-citation clusters are extensively utilized in science mapping to study the intellectual structure of an academic field in different scientific areas (Cheng 2016). It is a strong grouping mechanism that visualizes the connections among scientific documents, providing an emergent pattern and outlook for the semantic similarities in the studies (Chen et al. 2010). Co-citation is a document-coupling technique that measures the semantic relationship among scientific entities to determine the intellectual structure among documents; therefore, documents are semantically related when they receive more co-citations (Small 1973; Shiau et al. 2017). A co-citation matrix that quantifies the association between studies was developed; thus, the documents were classified based on the similarities of the schemes to verify the dominant topics and themes of SE (Shiau et al. 2017).

Using a spectral clustering algorithm, CiteSpace was utilized to extract label words from each citation cluster. For co-citation analysis, as shown in Fig. 5, each polygonal box represents a cluster with similar themes. The silhouette value of each cluster was used to measure cluster homogeneity. A higher value of S ensures high homogeneity (high correlation within the cluster), and more nodes are revealed (higher number of articles) in the cluster (Wu et al. 2019). The results showed that the S value for nine clusters was closer to 1, which indicated that the clusters were classified precisely and had high correlation and aggregation. Figure 5 shows that the most frequent and extensive literature's dominant theme is SE. It takes a central position and is frequently cited by collaborative consumption, sharing services, and social support customers. On the contrary, the studies used empirical analysis in SE and occupied a lonely position in the charts; they tended to cite among themselves, occupying an isolated position in the chart.

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Co-citation network of sharing economy literature in marketing

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Significant publications

The study adopted the betweenness centrality metric Freeman (1978) to determine landmark studies of SE in the marketing field and to quantify the importance of a node's position in a network. This metric indicates a node's ability to carry information between unconnected clusters of nodes. It measures the extent to which the nodes in the network exert influence over the network based on the number of shortest paths (δv,w(u)) and divides it by the total number of shortest paths in the entire network (δv,w) (Donthu et al. 2021). A node with high betweenness centrality inclines to connect different clusters and has high influence because information passes through it (Chen 2016). The centrality scores allowed us to find pivotal points between tripping points in the network. The nodes in the network can take relative scores; high-scoring nodes contribute to connecting different clusters than low-scoring nodes.

The current work seeks to visualize the salient visual attributes of the network and identify the documents based on co-citation analysis. Such a graph-theoretical method reduces network-wide operations; hence, it improves the interpretability of the network. The Pathfinder algorithm was utilized to find routes between nodes to measure how two articles are cited by a third article and to extract the most salient patterns from a network, in other words, how paired articles share commonalities of knowledge. As noted, landmark publications are seminal articles that report new ideas or insights; thus, analyzing such papers enables us to determine the importance awarded to the topic of SE worldwide. Table 4 provides the ranking order for the top 10 articles in terms of centrality scores. The results indicated that the largest cluster (#0) was labeled SE with a silhouette value of 0.75, followed by the collaborative consumption cluster with 99 members, and finally, the hotel performance cluster with a silhouette value of 0.818.

Table 4 Landmark publications on sharing economy based on centrality metric
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Visualization map

The VOSviewer mapping technique was employed to generate a co-occurrence network of keywords and density visualization because it is known for its high-quality visual representation (Sinkovics 2016). Analysis of the co-occurrence of keywords is central because it visualizes trending research themes and research directions in the field (Xu et al. 2021a, b). A threshold criterion was established for inclusion in the visualization map at five occurrences. The results of the keyword co-occurrence map generated 140 keywords that met the threshold value. As shown in Fig. 6, they were divided into six clusters with different colors. Map analysis indicated the top keywords with the highest link strength, including SE (1,075), Airbnb (559), collaborative consumption (335), satisfaction (324), innovation (214), and quality (114). These results indicate that prominent topics and hot spots in the SE domain are SE, Airbnb, collaborative consumption, customer satisfaction, service innovation, and service quality.

As shown in Fig. 6a, six main clusters were generated and depicted with different colors. Cluster 1 (red) has the dominant themes of “collaborative consumption” and “consumption.” Cluster 2 (green) has the core themes of “innovation” and “technology.” Cluster 3 (blue) has “sharing economy” and “trust” as core themes. Cluster 4 (yellow) has “satisfaction” and “tourism” as core themes. Cluster 5 (purple) has “Airbnb” and “experience” as prominent themes. Cluster 6 (baby blue) has “quality” and “determinants” as prominent themes. Figure 6b depicts density visualization, which reveals and visualizes SE's trending keywords and concepts. In analyzing the density map in the top-right direction, results indicate that articles with an organizational perspective tend to emphasize performance, governance, strategy, and ecosystem. While on the top left of the map, there are studies that examine customer satisfaction, brand loyalty, and repurchase intentions. Moving to the middle of the map, it is possible to note that a number of dense areas in SE are devoted to customer-centered research and the keywords customer attitude, intention, acceptance, perception, participation, and experience. At the bottom are studies that address topics related to hospitality, peer-to-peer (P2P) accommodation, hosts, and guests. The research focus was scattered between 2018 and 2020, and the “sharing economy” served as a research center for platform economy studies.

Fig. 6
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Visualization map of the keyword co-occurrence network

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Topic modeling

Topic modeling is a topic summarization technique in which documents are distrusted into topics in which the works and their relevant frequencies are collected into an organized structure to find correlations between terms and documents (Moro et al. 2019; Blei 2012). Because the processing and analysis of current information are beyond human capabilities, topic modeling is a valuable tool for identifying, classifying, and uncovering latent topics in a large amount of literature using ML algorithms (Mustak et al. 2021; Campbell et al. 2015). Topic modeling was adopted to summarize the results from the large terms into a few topics by condensing the sharing literature, inferring top trends in the area, and helping to uncover research gaps in SE. The latent topics of the 325 articles on SE were modeled using LDA. The word cloud for dominant keywords in articles was generated using Python algorithms. A term frequently appearing in the text appears more prominent in the cloud. As shown (see Fig. 7), the world cloud provides a comprehensive outlook for keywords and common terms in the SE literature.

Fig. 7
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Word cloud for dominant keywords in sharing economy literature

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The topic modeling analysis outlines the key salient terms in research on SE in marketing. As shown in Table 5, these themes can be categorized into two categories: customer-related research (topics 1, 2, 4, 5, and 7) and digital platform-related research (topics 3, 6, and 8). The categorization was based on each cluster's connectedness, relatedness, and relevance.

Table 5 Dominant topics in research on sharing economy
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Customer-related research

This cluster includes five topics related to customer-related research. As shown in Fig. 8, topic numbers were used to identify the modeled topics, which are auto-assigned by Python algorithms, including analyzing customer ratings and reviews (Topic 1), understanding customer experience (Topic 2), understanding P2P accommodation services (Topic 4), mapping customer relationships in SE (Topic 5), and analyzing user loyalty and engagement (Topic 7).

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Inter-topic distance map of the modeled topics

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Topic 1-related research focuses on analyzing customer ratings and reviews by leveraging big datasets of online reviews to conduct an in-depth examination of online customer experience, attitude, and recommendations (Ai et al. 2019; Berg et al. 2020; Cheng and Jin 2019; Yi et al. 2021). The reviews and sentiment ratings of customer experience were found to significantly predict customer recommendations (Luo et al. 2021; Zhu et al. 2021). By identifying the determinants of the customer of review rating, hospitality operators (e.g., Airbnb) can predict customer satisfaction in P2P rentals (Zhu et al. 2019). They can also have profound effects on room sales. The analysis indicates that hotel booking decisions are governed by online customer reviews (Ai et al. 2019). Peer reviews and evaluations are critical sources of information. In a study of 245,455 reviews, Cheng and Jin (2019) indicated that guests' sentiment ratings regarding hosts significantly predict guests' satisfaction and actual recommendations.

The mapping for this topic uncovered another theme related to review bias in P2P platforms. Several studies highlight the negative effects of review bias on the trustworthiness and credibility of SE platforms. For example, Berg et al. (2020) indicated that complaint bias contributes to overestimated positive ratings and lower reliability in the P2P platform. Several studies demonstrated that Airbnb's rating system operates based on positivity bias in the online rating. It includes fewer negative reviews, making it difficult for guests to differentiate between high-quality and low-quality listings (Zhu et al. 2019). Furthermore, Osman et al. (2019) showed that the positivity bias in consumer reviews affects the reputation of SE platforms for peer‐to‐peer rented accommodation.

Quantitative studies and big data analytics tend to be more dominant in comparison with other clusters, with key terms such as data mining, text analysis, thematic analysis, sentiment analysis, ordinary least squares (OLS), ordered logit model (OLM), hierarchical regression, poisson regression, and logistic regression. Interestingly, the themes in this topic fall within the domain of marketing, hospitality, and tourism management. Theoretical lenses are rooted in signaling theory, expectation-disconfirmation theory, information integration theory, Bayesian theory, and economics of information theory to estimate customer evaluations and sentiments (Ai et al. 2019; Zhu et al. 2019).

Topic 2 focuses on understanding and evaluating the customer experience in SE. The existing studies on this topic mainly focus on exploring the drivers and barriers of customer participation in SE (Benoit et al. 2017; Hazée et al. 2020; Lee & Kim 2018; Mahadevan 2018). Some studies highlighted the economic, social, and hedonic values as antecedents for customer participation in SE (Hamari et al. 2016; De Canio et al. 2020; Lee & Kim 2018). For instance, De Canio et al. (2020) emphasized sustainability, social interaction, and social esteem motives for participating in P2P accommodation. Others focused on the effects of hedonic and utilitarian values associated with users' experiences on users' satisfaction and loyalty (Lee and Kim 2018).

Another stream of studies examined the dark side of customer experience. They predominately investigate the barriers that hinder users' participation in SE and aspects of negative experiences. For example, Hazée et al. (2020) disclosed users' experience concerns towards digital platforms, including functional concerns (e.g., complexity, value) and psychological concerns (e.g., compatibility, contamination) that negatively affect users' experience and participation in sharing services. Also, Buhalis et al. (2020) point out that disruptive host behaviors due to false descriptions, fraud, dishonesty, and other disruptions lead to negative experience outcomes and value co-destruction for users. This topic offers critical managerial implications for platforms. Accommodation-sharing platforms should provide users with accurate information and tips, offering novelty, and create authentic service experience. Quantitative inquiry seems to be prevalent in this topic including terms such as structure equation modeling (SEM), partial least square (PLS-SEM), regression, survey, survey data, and fuzzy-set qualitative comparative analysis (fsQCA). The key focus on this topic is to focus on customer experience, studies focused mainly on the enablers and hinders of customer participation in SE.

Studies on Topic 4 aim to understand P2P hospitality services such as Airbnb, homestays, Couchsurfing, and other accommodation platforms. The mapping of this cluster underlines that a notable part of studies of SE examined P2P service in hospitality management and tourism research. One stream of research investigated the positive effect of P2P accommodations on the restaurant industry (Dogru et al. 2020a, b; Dogru et al. 2020a, b). For example, Belarmino et al. (2021) showed that increased demand for Airbnb services contributes to the restaurant industry's employment, revenues, and employee earnings.

Another research theme within this topic pertains to the effects of Airbnb listings on hotel performance in the international context (Dogru et al. 2020a, b), indicating the negative impact of the increase in Airbnb listings by 1% cloud decease hotel revenues by 0.016% and 0.031% per available room. Along the same line, the rise of Airbnb supply negatively affects the performance matrices of hotel industry in U.S. hotel markets. Some studies investigated the factors associated with occupancy rates and hosts' competitive productivity in P2P accommodation (Leoni et al. 2020; Kim et al. 2021). For example, Kim et al. (2021) addressed the driving forces for hosts' competitive productivity maximization and how monopolistic competition sustains selling of listings (Leoni et al. 2020).

Research on Topic 5 pertains to maintaining and enhancing customer relationships in SE. Studies within this topic emphasize building and maintaining a sustainable relationship with digital SE platform customers (Chark 2019; Gruen 2017; Papaoikonomou and Valor 2016). For instance, Nadeem et al. (2020) highlighted the importance of social support and relationship quality on customers' value co-creation in SE and how relationship benefits (e.g., social, special treatment benefits) significantly contribute to customer loyalty in SE services. Other studies demonstrated that explored the significant role of relationship norms on customer behaviors toward SE brands and price-fairness perception (Chark 2019).

The second stream within this topic identifies and understands the impacts of affinity-seeking strategies on host–guests' social relationships (Qiu et al. 2022). While true sharing could create a sense of belonging and shared values, a sense of community, and maintain long-term relationships between actors (Dreyer et al. 2017). The contribution of this topic tends to be inductive research, reflected in terms such as semantic network, thematic analysis, inductive, semi-structured interviews, theory building, and qualitative methodology. Critical theories within this topic include stakeholder theory, social cognition theory, social support theory, relationship quality theory, grounded theory, and practice theory.

Finally, Topic 7 analyzes user loyalty and engagement in the SE context. The first theme within this topic predominately focuses on analyzing user loyalty (Akhmedova et al. 2020; Mody et al. 2019). For instance, Pino et al. (2022) focused on the interlinked relationship between customer–service provider identification and customer behavioral loyalty. Other studies examined interaction effects of social and spatial distances on guest loyalty toward peer-to-peer accommodation (So et al. 2019), offering three winning strategies for authentic consumption experience (Mody et al. 2019) that enhance customer loyalty in the SE (Akhmedova et al. 2020).

Another theme in this topic pertains to customer engagement with sharing service regarding motives, dynamics, and consequences (Baker et al. 2021; Breidbach and Brodie 2017; Milanova and Maas 2017). For example, Breidbach and Brodie (2017) explored how engagement platforms in the SE contribute to value co-creation and actor engagement. Some work indicated that individual and collective psychological ownership (Schivinski et al. 2020) and financial benefits are crucial preconditions for engaging users in SE (Milanova & Maas 2017). This topic offers important critical implications for SE managers on implementing and using engagement platforms more effectively to facilitate user engagement and boost loyalty.

Platform-related research

The platform-reseated research category includes three main topics (3, 6, and 8), which are auto-numbered by LDA algorithms and outlined in Fig. 8.

The studies on Topic 3 refer to developing a marketing strategy to improve the relationships with users. For example, Rong et al. (2021a, b) indicated that co-creating shared values by integrating local culture and moral virtues can assist SE platforms in maintaining long-term relationships with users and retaining sustainable innovation. Studies showed that coordination of domains of digital marketing channels (e.g., email marketing, social media marketing) could improve consumer relationships and contribute to the growth of SE companies (Key 2017). Other research streams examined camouflage strategies' effects on consumer trust, indicating that platform providers should be transparent and honest with their customers to preserve and foster trust with the platform users (Venkateswaran et al. 2021). It is imperative for platform providers to adopt regulation strategies to prevent undesirable customer behavior and protect vulnerable customers (Hofmann et al. 2017) to sustain consumer–provider relationships.

Topic 6 addresses the issue related to platform openness—the extent to which platforms have fewer restrictions on participation, development, or use across their distinct roles of developer or end user (Broekhuizen et al. 2021). Research on this topic has examined the effects of ecosystem openness in the platform on performance, indicating that the role conflict of platform operators (as leaders and platform followers) induces stress and negatively affects venture performance (Nambisan and Baron 2021). The research outlined the drivers and mechanisms of platform openness, providing clues as to which circumstances platforms are more likely to use an open or closed strategy (Broekhuizen et al. 2021).

Some other studies showed platform openness is an essential pillar of a successful digital orientation; therefore, digital platform companies should adopt an open marketing strategy to enable more effective access and use of complementary resources beyond the company boundaries (Quinton et al. 2018). This topic offers insightful implications for practitioners to embed a culture of experimentation and risk-taking and to be open to collaborating with external partners and leveraging external resources.

Finally, Topic 8 related to platform-related research pertains to platform ecosystem actors. For example, Wirtz et al. (2019) mapped the motivation, behaviors, and roles of actors involved in the SE platform ecosystem, including platform providers, service providers, competitors, customers, society, and policymakers. The research also outlined an evolutionary framework for understanding the dynamics of service platform ecosystems. The model identifies three key constituents of the platform ecosystem: diverse types of actors, cooperative and competitive interactions within and among actors, and environmental space and resources (Xu et al. 2021a, b). SE platforms should co-create shared economic values with key ecosystem partners by embedding a local consumption culture to maintain a competitive advantage (Rong et al. 2021a, b).

The research also outlined an evolutionary framework for understanding the dynamics of service platform ecosystems. The model identifies three key constituents of the platform ecosystem: diverse types of actors, cooperative and competitive interactions within and among actors, and environmental space and resources (Xu et al. 2021a, b). Studies on this topic tend to adopt qualitative methods, such as content analysis, qualitative longitudinal, inductive method, and conceptual approach frequently highlighted. The reason for that is due to the nature of research on the topic, which is still in its early stages.

Discussion

General discussion

This study adopted performance analysis and science mapping to provide a comprehensive review of the literature on SE and its intellectual structure in the marketing area. Performance analysis indicated a notable rise in the number of articles and citations on SE research in marketing. A significant amount of international cooperation between scholars and countries has been established in SE research. Most published articles were generated in the United States, England, and China. The American universities were leading the trend and topped the list of publications in SE: Boston University, California State University, Florida State University, and Texas A&M University.

This study involved the creation of keyword co-occurrence maps, clusters of co-citations, landmark publications, and visualization mapping of interrelationships and interconnectedness between concepts that symbolized the study focus. The study provides fruitful insights for researchers in the SE field by mapping intellectual knowledge, research avenues, significant authors and institutions, and leading journals in the field. For example, the analysis of leading publication journals, which exert the most influence on the scientific community and circles, revealed that the Journal of Business Research was the dominant and most significant publishing outlet in SE, followed by the Journal of Marketing, and the International Journal of Hospitality Management.

This study provides key theoretical contributions in several ways. Firstly, this research is the first, to our knowledge, to map and synthesize SE concepts and main themes in marketing from which the extant literature on SE in marketing has been organized. This study makes a contribution to the theory by determining the main streams of research on SE in marketing, namely analyzing customer ratings and sentiments, understanding and evaluating customer experience, customer orientation towards peer-to-peer accommodation services, mapping customer relationships in the sharing economy, analysis of user loyalty, development of marketing strategies to enhance platforms, identification of platform openness, and actors within the digital platform ecosystem. A comprehensive understanding of the main streams of research is integral for understanding and developing theoretical underpinnings and positioning of research. The study contributes to the theory by delineating different types of the dark side of SE, such as negative customer reviews, negative customer experience, and value co-destruction. By analyzing platform openness and digital platform ecosystem actors, this study better explains how different actors interact in a platform environment and how collaboration and innovation can be fostered. Finally, the study indicated that despite the growing interest in area SE, there are many future research avenues that need to be developed to clarify the processes, boundary conditions, and consequences on service platform performance and actors' well-being.

Future research agenda for marketing scholarships in the sharing economy

Based on the state-of-the-art analysis of SE literature, the study provides a future research agenda within marketing (Benoit et al. 2017), addressing three interrelated research streams: (1) customer/consumer-oriented research, (2) platform provider-oriented research, and (3) service provider-oriented research (see Fig. 9).

Fig. 9
figure 9

Future research agenda framework on sharing economy

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Customer/consumer-oriented research

Scholars and theorists have extensively examined the bright side of SE, including customers' acceptance, attitudes, trust, e-WOM, and loyalty toward sharing (Tian et al. 2022; Ert et al. 2016; Yang et al. 2017; Pitt et al. 2020; Hamari et al. 2016). More theoretical and empirical-based evidence is needed regarding the situation where collaborative-based services fail to fulfill their promises to promote customer welfare. New methodological approaches, such as ethnography, netography, and eye-tracking techniques, are required to measure the effects of negative service incidents and outcomes. Prior research highlights that individuals' misbehaviors could be construed differently as a function of culture (Severance et al. 2013). It would be interesting for future research to conduct a cross-culture study to examine how customers' misbehavior (e.g., lying and sexual harassment) influences providers' psychological and cognitive states in Western vs. Eastern countries. Building upon qualitative methods, scholars may examine the typologies of customer dysfunctional behavior in the sharing economy, and how they affect the overall customer experience in SE. Future empirical research is needed to address the following questions: what are the psychological and cognitive drivers and consequences of customer discrimination in the sharing economy in home-sharing (e.g., Airbnb) vs. ridesharing (e.g., Uber) contexts, and how can these adverse outcomes be mitigated? Furthermore, scholars can examine various customer responses (e.g., underlying conditions favoring customer voice responses vs. existing ones). How do the implemented policies against customer inequality behaviors lead to a reduction of incidents and sustain customer trust in the platform?

Previous research has narrowly focused on traditional measures and metrics of service experience, such as satisfaction (e.g., Mahadevan 2018), loyalty (e.g., Lee and Kim 2019), trust, and (Venkateswaran et al. 2021); however, they did not account for a comprehensive outlook for how customer experience with sharing platforms affects customers' well-being. Therefore, researchers should examine how customer experience with sharing platforms (e.g., Uber, Airbnb, Lyft) impacts customers' subjective, emotional, financial, and physical well-being. Future research may examine how well-being priorities differ across different industries, such as transportation (e.g., Lyft) vs. food service (e.g., Uber Eats), for SE actors (customer vs. platform provider vs. peer service provider).

A huge and massive amount of customer-generated content (reviews, locations, prices, dates, and customer profiles) is unstructured and outside organizations' structure (Mustak et al. 2021). Scholars need to examine how AI-enabled techniques, such as AI prediction tools, analytics, and classification algorithms, must be utilized to manage customer relationships and improve customer behaviors and satisfaction in the sharing economy. Scholars can develop customer analytics to facilitate the management of customers' relationships and predict customer behaviors and satisfaction with SE online platforms.

Peer service provider-oriented research

Most SE service providers are part-timers; therefore, financial incentives and control are insufficient to motivate them to deliver the promised branded service experience (Sundararajan 2014; Habibi et al. 2016). Platform operators should provide sufficient knowledge and education for service providers to deliver customer experiences. Future research may empirically examine how service providers' management strategies (e.g., peer control, platform provider control, actor screening) influence the well-being of peer service providers and their ability to deliver a distinctive customer experience in the sharing economy.

Despite the notable growth of SE platforms, many companies have failed to build a two-sided business model with mass supply and demand for their services (Täuscher and Kietzmann 2017). The current academic literature on peer service providers has mainly focused on the drivers that promote actors' participation in SE and has shown that peer service providers engage in SE because of economic benefits, entrepreneurial freedom, and social motives (Benoit et al. 2017). To the best of our knowledge, few studies have offered conceptual insights into the factors that hinder peer service providers from participating in SE (e.g., Hazée et al. 2020; Tussyadiah and Pesonen 2018). Future research should empirically address the following question: what are the barriers (affective, cognitive, behavioral factors) that hinder peer service providers from participating in the sharing economy, and how can these barriers be addressed and overcome? Scholars may examine how personality traits of service providers (e.g., neuroticism and agreeableness) moderate the effects of relationships between barriers and actual participation.

Platform provider-oriented research

Unlike traditional service episodes, the typical service encounter for sharing services, such as Uber, includes two different entities (e.g., Uber as a platform provider and driver as a peer service provider) representing the service delivery network. All these entities work together to provide an extraordinary and seamless customer experience (Suri et al. 2019). This complex sharing business network needs to focus attention on service failures in sharing services. There is a need to understand how platform providers respond to and recover from service failures committed by both the customers and peer service providers in SE. Future research may examine how service-enabling organizations support peer service providers in acting as troubleshooters to handle service failure. Moreover, marketing scholars may examine how platform providers employ resolution strategies (e.g., refund, apology, compensation) across sharing-based services (e.g., Uber, Airbnb) to influence the firm's image.

Recent business news and cases have highlighted the increasing number of deviant behaviors committed by customers and peer service providers (Pagones 2021; Paul 2021). In the last two years, for example, 6,000 sexual assaults took place in Uber cars, leading the company to lose just over $1 billion of its market cap (Guynn 2019). Scholars of business management are urged to examine how platform providers mitigate the negative consequences of customers' and peer providers' misbehavior in online vs. offline settings. Future research may investigate how customer characteristics (e.g., rudeness, aggression) and peer service provider characteristics (lack of training, incivility), organizational factors (e.g., service environment stressors) affect the adoption of misbehavior mitigation strategy (Lages et al. 2023). Specifically, questions should be asked regarding how AI-enabled technologies (i.e., sensors and cameras) could be utilized to detect and curb such incidents.

The COVID-19 pandemic significantly affected the labor market and demand for SE services, leading to job loss for peer service providers. Business management scholars are encouraged to examine how platform providers tolerate misbehavior (i.e., by customers and peer service providers) in the post-COVID-19 environment, where concerns about socioeconomic instability and unemployment mean that employees may feel pressured to accept deviant behaviors.

Implications for marketing practices in the sharing economy

This work also contributes to marketing practices and SE firms to improve their business models. The discovered topics of SE have significant key implications for marketing practice. The listing of salient SE topics indicates that studies on SE take an outside-in perspective. An enormous amount of customer reviews, usage dates, and experience content exists out of organizations' hands. Therefore, SE companies should invest heavily in collecting data and deploying AI-based algorithms to extract managerial and impactful insights. Airbnb's rating system has been criticized for operating based on a positivity bias in online reviews (Zhu et al. 2019). It has been suggested that SE platforms should implement measures to prevent review bias, such as using algorithms to detect and remove fake reviews and provide detailed and informative responses to negative reviews.

Customers and peer service providers do not participate in SE because of a complex set of multidimensional functional and psychological barriers (Hazée et al. 2020). Therefore, platform providers should focus on strategies that encourage both the supply and demand sides of the platform to grow simultaneously. This could include initiatives such as targeted marketing campaigns, incentives for users to join and participate, and improving the user experience on the platform. Sharing practices may result in social, environmental, or economic unintended consequences for actors. Government should legislate policies and management schemes to regulate and balance the interests and responsibilities of all stakeholders to mitigate negative aspects. Platform Openness decision is critical for platform success, which is crucial to the firm's value creation. Accordingly, platform providers should consider the tradeoffs of openness decisions and conduct proper quality checks because irrational openness can diminish control over suppliers. To build and maintain long-term and sustainable relationships, SE platforms should proactively create their industry ecosystem, embed themselves within connected industry ecosystems, and co-create shared values to sustain innovation. Given the lack of a regulatory and institutional framework for SE, policymakers should develop policies to protect actors' rights (i.e., service enablers, employees, and customers). Finally, from a macroeconomic point of view, this paper highlights the current knowledge of SE as an economic growth model with high growth potential.

Limitations

There are some limitations related to the methods adopted in this study. The study employed a quantitative analysis of existing SE research; qualitative research may provide a deep outlook on SE phenomena. The study's analysis was limited to a sample from the WoS databases, and the study's findings were restricted to the selected terms and keywords, which were developed from the SE literature. Another limitation is that the study focused on peer-reviewed published or in-press articles in the marketing field. The study can be extended to other domains, such as psychology, management, or economics, to provide a holistic view of the phenomenon. Despite these limitations, this study contributes to SE literature by mapping the intellectual structure and evolution of SE scholarship and providing research avenues for future investigation.