Mapping Entrepreneurial Collaborative Economy Landscape: A Systematic Literature Review with Textometric Analysis

Abstract

The collaborative economy is experiencing a remarkable surge, offering vast potential for growth. Consequently, this burgeoning movement has become a focal point of interest in the realm of entrepreneurship. However, numerous unexplored or inadequately addressed research gaps persist, leaving us without a well-defined paradigm for what we can term the entrepreneurial collaborative economy. In light of these challenges, this study embarks on a quest to bridge these gaps through a comprehensive systematic literature review. Two research objectives guided our endeavor: (1) mapping the literature related to the collaborative economy in the field of entrepreneurship to propose a research taxonomy, and (2) analyzing areas in this field that warrant further research. Our literature review, conducted using the PRISMA methodology, yielded 407 studies. Employing advanced textometric techniques, we uncovered a research taxonomy consisting of three distinct clusters within entrepreneurial collaborative economy studies. In particular, our investigation has unveiled that the entrepreneurial collaborative economy paradigm remains in a state of emergence within the academic literature. The paper concludes with thought-provoking discussions and key insights.

1. Introduction

Collaborative economy (CE), also known as the sharing economy or peer-to-peer economy (Palátová et al., 2023), has emerged as a dynamic socio-economic phenomenon in which individuals and organizations directly share resources, goods, services, or information, primarily through digital platforms (Akande et al., 2020; Dabbous & Tarhini, 2021; Hellwig et al., 2015). It offers a compelling alternative to traditional centralized models of consumption and production, emphasizing decentralized and community-driven interactions, all without the need for intermediaries (Mont et al., 2020; Wirtz et al., 2019).

Although the roots of the CE phenomenon extend beyond the present, the rapid proliferation of social networks and mobile devices has accelerated its growth (Pouri & Hilty, 2021). The term “collaborative consumption” was first coined in a 1978 study (Felson & Spaeth, 1978), defining it as “the efforts by people to engage in joint activities with others” (Menéndez-Caravaca et al., 2021). Subsequently, influential movements such as the free and open-source software movement focused on collaborative software design (Gamalielsson & Lundell, 2014).

Today, many digital collaborative platforms and apps spanning diverse industries facilitate peer-to-peer transactions in innovative ways (Cheng et al., 2021). These platforms engender trust among participants, offering the mechanisms needed for seamless interaction and business transactions (Ert et al., 2016). Prominent examples include: (1) Airbnb, which enables individuals to rent their homes or rooms to travelers, (2) Lyft or Uber, ride-hailing apps connecting drivers with passengers, (3) BlaBlaCar a popular European app connecting drivers with empty seats to passengers traveling in the same direction, and (4) Turo, a car rental platform allowing owners to rent their vehicles when not in use. Such platforms and apps reduce the need for ownership and lower environmental footprints (Belk, 2014; Ertz & Boily, 2019; Y. Wan et al., 2022), promoting more sustainable and efficient resource utilization (Govindan et al., 2020; Koul et al., 2022; Toni et al., 2018).

In this dynamic milieu, entrepreneurial initiatives have begun to flourish. Entrepreneurship, characterized by the identification, creation, and pursuit of opportunities to develop new products, services, or businesses in the CE, plays a pivotal role (Honig, 1998; T. Zhang et al., 2019). It acts as a catalyst for growth and innovation in CE (Ince et al., 2023; Yun et al., 2019), stimulating creativity, uncovering new opportunities, and contributing to job creation and economic development (Karami & Read, 2021). In addition, the CE offers an array of uncharted territories ripe for exploration, where entrepreneurs can provide innovative solutions. In this context, the concept of entrepreneurial orientation has emerged as a means to infuse entrepreneurial behavior into existing firms, encompassing traits such as innovativeness, proactiveness, competitive aggressiveness, risk-taking, and autonomy (Pearce et al., 2010; Tuan, 2017). In fact, CE, entrepreneurship, and entrepreneurial orientation are naturally connected, as they share a common foundation in innovation, value creation, and the pursuit of opportunities. In turn, entrepreneurial orientation enhances the ability to identify and seize those opportunities within the collaborative ecosystem. In this way, entrepreneurs contribute to the CE by promoting open international competition where customers can freely access and exchange goods and services, thereby enhancing individual and collective well-being (Papaoikonomou et al., 2022).

Despite the potential of entrepreneurship in the CE, no comprehensive paradigm exists that can be termed “entrepreneurial collaborative economy”. The current research landscape in this domain remains fragmented and challenging to navigate. However, in this study, we do not intend to introduce a new theoretical paradigm, but rather to adopt this expression as an analytical lens to examine how entrepreneurial principles operate within CE contexts. In this notion, we aim to better understand how opportunity recognition, innovation, and value creation emerge when entrepreneurial actors engage with CE platforms and practices.

In this manner, on the one hand, digital platforms play a central role, not only by matching supply and demand efficiently but also by enabling new forms of governance, scalability, and innovation in resource utilization. On the other hand, entrepreneurship encompasses the processes by which individuals or organizations identify opportunities, mobilize resources, and take calculated risks to create and capture value. When applied to CE contexts, entrepreneurial activity becomes a key driver for platform creation, innovative business models, and the diversification of collaborative practices beyond consumption to include production, financing, and service provision. Thus, the intersection of these two domains warrants systematic analysis, as it reveals how entrepreneurial mindsets and behaviors can shape, expand, and sustain CE ecosystems.

Consequently, this study aims to fill this gap by conducting a systematic literature review, with a twofold purpose: (1) mapping the literature related to the CE in the field of entrepreneurship to propose a research taxonomy, and (2) analyzing in this field the areas that warrant further research. This systematic literature review, conducted using the PRISMA methodology, identified 407 relevant studies. In addition, textometric analysis, facilitated by the IRAMUTEQ software (Version 0.7 Alpha 2), enhanced our understanding of the CE’s rapid expansion. Taken together, these lenses not only provided a well-structured understanding of the CE’s evolution but also contributed a comprehensive foundation for identifying approaches and guiding future research in this domain.

This paper is structured as follows. Section 2 provides the background of the CE, establishing the groundwork for defining the scope of the entrepreneurial collaborative economy. Section 3 presents the PRISMA methodology and outlines the research design. Section 4 presents the results of the textometric analysis. Section 5 delves into the discussions of the results, while Section 6 concludes with implications and directions for future research.

2. Collaborative Economy Background

The CE has directly impacted the redefinition of strategies of a large number of companies (Chuah et al., 2021). Companies of diverse sizes and backgrounds have embraced digital platform-based models to join the CE, although not all with the same degree of success (Hazée et al., 2020). In general, CE-connected enterprises harness digital platforms to establish connections with individuals or entities, offering more efficient ways for acquiring goods and services (Weber, 2016). Consequently, CE opens up new opportunities for individuals and companies (Thornton, 2024).

The CE is considered a heterogeneous phenomenon encompassing a wide array of economic activities (Gruszka, 2017; Gurău & Ranchhod, 2020). It amalgamates different modes of consumption, production, and labor, including collaborative consumption, collaborative production, collaborative finance, collaborative knowledge, collaborative value co-creation, gig economy, and collaborative governance (Bogatyreva et al., 2021; Menéndez-Caravaca et al., 2021; Mouazen & Hernández-Lara, 2023; Ravenelle, 2019). In this domain, value co-creation is typically mediated by digital platforms that enable peer-to-peer interactions, facilitate trust through transparent information systems, and allow for flexible resource allocation across diverse participants. Moreover, governance mechanisms in CE platforms often distribute decision-making power, granting users an active role not only as consumers or providers but also as co-designers of services and experiences. By framing value co-creation within the CE, we emphasize these dynamics, which integrate technological intermediation, shared governance, and network effects as key enablers of sustained value creation.

2.1. Collaborative Consumption

Collaborative consumption stands out as one of the most well-known phenomena of the CE (Hallem et al., 2020). This socio-economic model redefines ownership by encouraging individuals and organizations to share access to goods and services, facilitated by community-based online platforms (Anwar, 2023; Hamari et al., 2016). Recent technological advancements, evolving consumption patterns, heightened consumer demand, and shifting consumer preferences have fueled the significant growth of this movement (Barnes & Mattsson, 2017; de Rivera et al., 2017; Dreyer et al., 2017; Minami et al., 2021; Perera et al., 2023). Collaborative consumption relies on the principle that underutilized assets, such as cars, homes, tools, clothes, and skills, can be efficiently shared or rented, resulting in enhanced efficiency, reduced waste, cost savings, all while reducing the environmental footprint (Hamari et al., 2016). This disruption has challenged conventional industries, fundamentally altering the notions of ownership and access to goods and services, with implications spanning society, the economy, and the environment (Guillen-Royo, 2023). Collaborative consumption emerges as a sustainable alternative to conventional consumption (Belk, 2014), manifesting notable in the growth of digital platforms for clothing rental and second-hand resale in the fashion industry (Arrigo, 2021) and the emergence of ride-sharing in the transportation sector (V. M. de Oliveira et al., 2022).

2.2. Collaborative Governance

Collaborative governance has witnessed remarkable growth in diverse CE domains, such as environmental protection (Ge et al., 2023; Shao et al., 2023), emergency (Tangney et al., 2023), transportation (Tiglao et al., 2023), food (Lelieveldt, 2023; Sundqvist-Andberg & Åkerman, 2022), and sustainability (Allal-Chérif et al., 2022; Glass et al., 2023). It embodies the practice of collaborating with various stakeholders to collectively make decisions, striving for optimal utilization of knowledge and resources (Bendell et al., 2011; Noh & Yashaiya, 2019; Sun, 2015). The main objective of collaborative governance is to achieve better use of knowledge and resources in a more efficient way, combining the diversity of perspectives of stakeholders, such as government, organizations, companies, social community groups or individuals, to develop solutions that satisfy all groups or are best for the common well-being (Lou et al., 2022; Saraswati et al., 2023). Collaborative governance hinges on fostering negotiation processes where participants collectively make decisions, set objectives, and formulate policies and strategies (Paliokaitė & Sadauskaitė, 2023).

2.3. Collaborative Production

Prior research has identified collaborative production in areas as diverse as agriculture (Alimohammad et al., 2022), manufacturing (Ding et al., 2021), transport (Tiglao et al., 2023), robotics (Malik & Brem, 2021), online communication (Dwyer, 2011), and food (Maheshwari et al., 2022). It comes to life when multiple individuals or organizations work together to create a product or develop a service (Zang et al., 2004), thereby adding value to customers (Selim et al., 2008). Each participant contributes their specific expertise to the project, with a coordinator ensuring efficient production processes (Bouaziz et al., 2022), informed decision making, and cost-effective outcomes (Vargas et al., 2016).

2.4. Collaborative Finance

Collaborative finance, also known as peer-to-peer finance, has grown significantly in the last decade (Menéndez-Caravaca et al., 2021), notably driven by the proliferation of crowdfunding (Ansart & Monvoisin, 2017). This form of finance brings together individuals or companies to raise funds from a multitude of contributors, each providing modest sums (Berné-Martínez et al., 2021). Collaborative finance is frequently used to develop projects with social or environmental goals (Bárcena-Ruiz & Sagasta, 2021; Jin et al., 2019; Moraux et al., 2023). Platforms like Kickstarter, Indiegogo, and GoFundMe offer innovative financing alternatives, challenging traditional financial institutions (Panjwani & Xiong, 2023; Tian et al., 2021; Yu & Xiao, 2023). Yet, collaborative finance platforms face challenges related to regulation, trust, and sustainability, where blockchain stands as a potent solution (X. Wan et al., 2023), facilitating decentralized value generation (Ren et al., 2023; Viano et al., 2023).

2.5. Collaborative Knowledge

Collaborative knowledge refers to the activities developed jointly by a group of individuals or organizations to build a collective and shared understanding, information, or expertise (Wang, 2011). It encompasses a myriad of approaches, including meetings, discussions, shared documents, or digital collaborative tools, serving as a crucible for problem-solving, decision-making, and innovation (Beaudoin et al., 2022). This collaborative movement transcends geographical boundaries and time zones, enabling seamless cooperation (Saleh Al-Omoush et al., 2021). Prior research has explored collaborative knowledge’s impact from diverse perspectives on areas such as cooperative learning environment (Tan & Chen, 2022), supply chain agility, and corporate sustainability, as well as employee productivity (Dong et al., 2023; Heerwagen et al., 2004; Hu et al., 2020; Mensah & Enu-Kwesi, 2018).

2.6. Collaborative Value Co-Creation

Collaborative value co-creation emphasizes the collaborative processes through which customers, companies, and other stakeholders collaboratively generate and develop economic and social value (Ostertag et al., 2021). Placing customers at the center of the value creation process results in products and services tailored to individual needs (Lee et al., 2023; Song et al., 2023). This movement recognizes that customers possess unique needs, preferences, and expectations, empowering them to actively shape the value derived from products and services (Jia et al., 2023). This collaboration unfolds through diverse avenues, including social media, online communities, feedback mechanisms, and face-to-face interactions (Li & Tuunanen, 2022). Collaborative value co-creation offers companies new business models based on innovations, relying on continuous feedback, advocacy, information sharing, and learning (Guo et al., 2023). Gathering insights and data from customer interactions plays a pivotal role in enhancing offerings and bolstering the value co-creation process (Cao et al., 2022), making it an essential factor in attracting financing and boosting project success rates (Su et al., 2021).

2.7. Gig Economy

The CE exerts a substantial influence on the work landscape (Arriagada et al., 2023), exemplified by the emergence of the gig economy. Characterized by short-term, freelance work arrangements (Mouazen & Hernández-Lara, 2023), this phenomenon has been fueled by technological advancements (Bögenhold et al., 2017). Individuals increasingly engage in project-based work as independent contractors rather than being permanent employees of a single company (Adams et al., 2018; Bogatyreva et al., 2021; Ravenelle, 2019). The gig economy provides them the flexibility to choose when or where they work (T. Zhang et al., 2019), supplementing their income (Honig, 1998), offering opportunities to those facing traditional employment barriers (Pedersen & Netter, 2015), and nurturing entrepreneurship (T. C. Zhang et al., 2018).

3. Literature Review Methodology

In this section, we delve into the methodology employed for our literature review, specifically adopting a systematic approach. It is crucial to distinguish between a narrative literature review and a systematic literature review, as highlighted by (Parums, 2021). Unlike a narrative review, which selects the evidence for synthesis in a non-reproducible manner without the need for an exhaustive search (Ferrari, 2015), a systematic literature review “aims to comprehensively locate and synthesize research that bears on a particular question, using organized, transparent, and replicable procedures at each step in the process” (Littell et al., 2008). This systematic approach contributes to promoting transparent reporting of research (Sarkis-Onofre et al., 2021) while ensuring the consistency and comprehensiveness of our research (Kamali Saraji & Streimikiene, 2023).

To carry out this systematic literature review, we adopted the PRISMA methodology. PRISMA is renowned for its robustness when conducting systematic literature reviews, as acknowledged by (Moher et al., 2009, 2010). Although initially proposed for the field of medicine, PRISMA has found widespread application in various fields conducting systematic literature reviews (Panic et al., 2013; Peixoto et al., 2021; Rethlefsen et al., 2021). PRISMA was proposed in 2009 (Liberati et al., 2009), and since then, it has evolved to align with the latest advancements in knowledge. In fact, the most recent protocols for the implementation of this technique were published in 2020 (Page et al., 2021). In 2021, the PRISMA guideline was incorporated to enhance bibliographic searches (Rethlefsen et al., 2021).

PRISMA guides the systematic literature reviews through a checklist of 27 items spanning four phases: (1) identification, (2) screening, (3) eligibility, and (4) inclusion (Liberati et al., 2009). This structured approach enables the transparent justification of the inclusion and exclusion of studies within the literature review (Zhao & Taib, 2022). In our study, the peer-reviewed articles that served as the primary source of information for our systematic review came from the following databases: (1) Emerald Insight, (2) ScienceDirect, (3) Springer, (4) Taylor & Francis, (5) Sage, (6) Scopus, (7) the Web of Science, and (8) Google Scholar. Figure 1 summarizes the number of papers incorporated or excluded in each phase, as well as the count of studies finally included in our analysis. It is worth noting that all PRISMA protocols were strictly adhered to.

In this context, to ensure a comprehensive coverage of the literature on the intersection between the CE and entrepreneurship, we selected these multiple databases spanning different disciplinary areas, including social sciences, business and management, and information technology. This diversity of sources minimized the risk of omitting relevant studies, given the interdisciplinary nature of the topic. Also, the application of strict inclusion and exclusion criteria ensured that only relevant and high-quality publications were retained for analysis. The initial search results were imported into reference management software, where duplicates were automatically identified and removed. A subsequent manual review was conducted to confirm the elimination of all duplicate entries, ensuring the integrity of the final dataset.

Building upon the conceptual framework developed in the preceding section of this article, our search strategy encompassed terms related to CE and entrepreneurship, embracing all economic activities associated with this phenomenon. These terms included: (1) Collaborative Economy, (2) Collaborative Consumption, (3) Collaborative Knowledge, (4) Collaborative Finance, (5) Collaborative Governance, (6) Value Co-creation, (7) Collaborative Production, (8) Gig Economy, (9) Sharing Economy, (10) Peer-to-peer Economy, and (11) Entrepreneurship. Given the breadth of these terms, the incorporation of search operators was key to effectively identifying relevant papers. Consequently, we employed the search operators such as AND, OR, and “*”, in combination with the symbols [ ] and “”, to construct the following search query:

[“Collaborative Economy” OR “Collaborative Consumption” OR “Collaborative Knowledge” OR “Collaborative Finance” OR “Collaborative Governance” OR “Value Co-creation” OR “Collaborative Production” OR “Gig Economy” OR “Shar* Economy” OR “Peer-to-peer Economy”] AND [Entrepreneur*].

This meticulously crafted search string reflects the breadth of terms associated with the CE, as well as the diversity of economic activities linked to this phenomenon. As a result, the initial phase of our search identified 1812 articles, with 403 duplicates identified and excluded before starting the screening phase. Subsequently, during the screening phase, 621 articles were eliminated as they did not align with our research objectives. This phase involved a comprehensive analysis of titles, abstracts, keywords, and introduction sections. In the eligibility phase, we conducted a more rigorous examination, leading to the removal of 367 articles that did not jointly explore the topics of CE and entrepreneurship, along with 14 articles not written in English. Ultimately, our analysis encompassed 407 articles.

4. Results

In this section, we present the outcomes of our analysis, which was conducted using the Iramuteq (Interface for Multidimensional Text Analysis and Questionnaires) software(Version 0.7 Alpha 2). Iramuteq, a Python-based tool (Version 3.11) built on the foundations of the R statistical software (Version 4.2.3) package and distributed under the GNU license, serves as our primary textometric analytical instrument. Leveraging the chi-square test (χ2), this software allows us to delve into the multidimensional aspects of our textual data. In starting our analysis, we followed the guidelines provided by (Sousa, 2021), particularly employing the Alceste method (Marpsat, 2010). To prepare the corpus for analysis, we meticulously constructed a source file in UTF-8 encoding. In this file, each of the 407 articles included in our systematic literature review must be separated into distinct lines, with each article preceded by **** * (for example, **** *Article1). Our textometric analysis incorporated the following: (1) title, (2) abstract, and (3) keywords. Our analyses encompassed three key facets: (1) lexicometric analysis, (2) similarity analysis, and (3) clustering analysis.

4.1. Lexicometric Analysis

The lexicometric analysis serves as the starting point of the analysis, enabling us to gain insights into the dataset’s (words) structural composition. Iramuteq allows distinguishing between active words, such as nouns, adjectives, and verbs, that appear multiple times in the corpus, and supplementary words, like prepositions and connectors. Achieving this distinction requires a lemmatization process, where we identify the root of a word, eliminating the inflections (Mendes et al., 2019). This lemmatization process is crucial for subsequent clustering analysis.

Table 1. Preliminary analysis after lemmatization.

Concept	Total Number
Number of occurrences	87,785
Number of lexical forms	5379
Number of hapax legomena	2158	2.46% of occurrences
		40.12% of forms
Active forms	4831
Supplementary forms	548

shows a summary of this lemmatization process, and

Table 2. The 100 most frequently used active words.

Order	Active Forms	Freq.	Order	Active Forms	Freq.	Order	Active Forms	Freq.
1	Economy	1157	34	knowledge	152	67	experience	90
2	Share	1067	35	governance	151	68	benefit	90
3	Value	687	36	implication	150	69	structure	89
4	Social	558	37	Case	150	70	collaboration	89
5	Collaborative	527	38	System	144	71	airbnb	88
6	Business	471	39	Future	144	72	university	87
7	Creation	413	40	Review	143	73	strategy	84
8	Platform	385	41	entrepreneur	142	74	urban	82
9	Innovation	384	42	Peer	139	75	methodology	82
10	Entrepreneurship	354	43	Tourism	132	76	education	82
11	Service	329	44	enterprise	130	77	product	81
12	Consumption	295	45	stakeholder	129	78	online	80
13	Analysis	277	46	develop	128	79	institutional	80
14	Base	262	47	Role	127	80	smart	77
15	Sustainable	257	48	public	127	81	organization	77
16	Digital	239	49	community	126	82	growth	76
17	Market	236	50	datum	124	83	dynamic	76
18	Literature	230	51	management	118	84	interaction	75
19	Development	205	52	Firm	116	85	field	75
20	Consumer	205	53	customer	116	86	company	75
21	City	190	54	relationship	115	87	issue	74
22	Economic	182	55	sector	114	88	term	72
23	Entrepreneurial	181	56	opportunity	112	89	support	72
24	Practice	179	57	local	112	90	information	72
25	Process	176	58	actor	111	91	user	71
26	Ecosystem	174	59	factor	109	92	logic	71
27	Sustainability	173	60	policy	108	93	environmental	71
28	Impact	173	61	industry	108	94	key	70
29	Understand	171	62	level	102	95	government	70
30	Network	166	63	activity	102	96	exchange	69
31	Technology	158	64	Gig	99	97	challenge	69
32	Resource	156	65	Open	94	98	empirical	65
33	Theory	154	66	circular	93	99	provider	63
						100	project	63

incorporates the 100 most frequently occurring words in the corpus, ranked by frequency.

4.2. Similarity Analysis

Similarity analysis offers a graphical representation of the conceptual organization within our textual corpus, depicting it as a network of active forms (Sousa, 2021). This analysis focuses on the co-occurrence of active forms across the entire textual corpus (Salviati, 2017). In particular, we employ a graph-based approach, where the nodes and their connections are determined by their co-occurrence in the same text, assessed through the chi-square of association test (Degeneve et al., 2022). Figure 2 illustrates the output developed by Iramuteq for this similarity analysis, emphasizing words with a frequency greater than 100 for clarity.

For proper interpretation of this graph, it is essential to consider both font size and line thickness (Bueno et al., 2021). Larger font sizes indicate stronger co-occurrences, while thicker lines represent more intense relationships. Specifically, Figure 2 showcases “economy” as the central active form in our textual corpus, closely linked to the lemmatized form “share” with “value” and “creation” standing out prominently among the rest of the incorporated words.

4.3. Clustering Analysis

This analysis aims to achieve a dual clustering: one for words and another for articles. Our clustering analysis leverages the Descending Hierarchical Classification technique based on Reinert’s method (Reinert, 1990), an approach that enhances internal similarity of active forms within topical clusters while highlighting the deviations between such clusters (R. P. de Oliveira et al., 2022). To ensure the robustness of our results, we adopted an algorithm’s stopping criterion, mitigating issues of reliability and validity in text analysis (Mondragon et al., 2022).

Executing this method with Iramuteq entails following established values recommendations. We consider three crucial premises (Apaolaza-Llorente et al., 2023): first, setting the expected value of the active form greater than 3; second, conducting a chi-square value test against the cluster, with χ2 ≥ 3.89 (p = 0.05) and df = 1; and third, ensuring a minimum threshold of 50% for the frequency of an active form within a cluster. This process involves five key steps (Mondragon et al., 2022): fragmenting the textual corpus into segments, identifying the presence of complete active forms in each segments, constructing a contingency table to distribute vocabulary per segment, creating a squared distances matrix from this table based on shared active forms, and adopting a descending hierarchical cluster solution that best distinguishes vocabulary segments. The result of this analysis is shown in Figure 3, which presents a dendrogram revealing three distinct clusters of active forms. For a deeper understanding of each cluster’s composition,

Table 3. Cluster Composition. The 30 most impacted words per class (excluding verbs and basic words).

	CLUSTER 1					CLUSTER 2					CLUSTER 3
N	Fr.s.t.	Freq.	%	Chi2	Forms	Fr.s.t.	Freq.	%	Chi2	Forms	Fr.s.t.	Freq.	%	Chi2	Forms
1	337	446	5.56	135.96	Value	164	205	80.00	187.56	consumption	86	125	68.80	355.97	Review
2	254	317	0.13	126.85	Creation	381	661	57.64	184.62	share	108	210	51.43	293.86	literature
3	139	157	8.54	96.32	Process	201	290	69.31	158.07	platform	42	45	93.33	257.01	systematic
4	118	127	2.91	95.69	Ecosystem	65	72	90.28	94.50	peer	109	232	46.98	256.14	analysis
5	210	272	7.21	86.41	Innovation	372	763	48.75	78.26	economy	41	46	89.13	235.99	bibliometric
6	196	252	7.78	82.76	Entrepreneurship	60	72	83.33	71.83	gig	28	28	100.00	186.27	citation
7	79	87	0.80	58.13	Enterprise	98	151	64.90	58.01	consumer	138	414	33.33	177.48	research
8	59	61	96.72	53.06	Education	32	33	96.97	53.76	mobility	25	25	100.00	166.10	publication
9	50	54	92.59	38.81	University	53	68	77.94	53.14	airbnb	16	16	100.00	105.90	database
10	47	50	4.00	38.32	Learn	31	32	96.88	51.95	person	17	18	94.44	104.46	journal
11	41	42	7.62	37.66	Student	32	34	94.12	50.34	car	36	71	50.70	89.82	Field
12	100	132	5.76	35.13	Network	51	67	76.12	47.88	online	26	42	61.90	88.50	Topic
13	89	115	7.39	34.46	Stakeholder	36	45	80.00	38.33	worker	51	126	40.48	86.38	identify
14	54	62	7.10	33.75	Institutional	55	80	68.75	38.27	form	15	17	88.24	84.12	document
15	48	54	88.89	32.25	Logic	23	24	95.83	37.51	rental	25	41	60.98	83.09	content
16	77	98	78.57	31.78	Actor	22	23	95.65	35.71	transport	23	37	62.16	78.62	Theme
17	41	46	89.13	27.72	Capability	33	41	80.49	35.64	alternative	14	18	77.78	66.00	technique
18	49	58	84.48	27.15	Start	41	56	73.21	34.26	provider	12	15	80.00	58.77	publish
19	231	366	3.11	26.71	Social	36	47	76.60	34.09	access	23	47	48.94	53.43	academic
20	73	97	5.26	24.41	Firm	29	35	82.86	33.67	intention	28	68	41.18	47.80	method
21	56	71	8.87	23.25	Collaboration	38	51	74.51	33.34	accommodation	19	37	51.35	47.73	cluster
22	40	47	85.11	22.71	Engagement	130	247	52.63	32.69	service	13	20	65.00	47.22	scientific
23	53	67	79.10	22.26	Interaction	36	49	73.47	30.30	good	42	127	33.07	46.23	Future
24	86	120	71.67	22.24	Role	16	16	100.00	28.52	ridesharing	7	7	100.00	46.15	medicine
25	59	77	76.62	21.40	Strategy	25	31	80.65	27.02	ownership	8	9	88.89	45.16	bibliometrics
26	32	36	8.89	21.33	Foster	16	17	94.12	24.99	transaction	16	29	55.17	45.13	science
27	337	446	5.56	135.96	Value	14	14	100.00	24.93	meal	10	14	71.43	41.66	keyword
28	254	317	0.13	126.85	Creation	23	29	79.31	23.77	internet	30	81	37.04	41.57	structure
29	139	157	8.54	96.32	Process	17	19	89.47	23.66	Uber	15	28	53.57	40.29	domain
30	118	127	2.91	95.69	Ecosystem	178	381	46.72	22.21	Collaborative	9	12	75.00	40.19	Intellectual

highlights the most significant active forms (p < 0.001) within each group.

In addition to the clustering analysis, we complemented our investigation with a factorial correspondence analysis (FCA). This graphical representation further enhances our comprehension of the previous results. As depicted in Figure 4, FCA identifies the relationships between clusters, providing an insightful perspective on our clustering analysis results (Hirschfeld, 1935). It is important to interpret this chart by considering the axes in opposition (Salviati, 2017). Specifically, Cluster 1 is represented in red, Cluster 2 is in green, and Cluster 3 is in blue.

5. Discussions

The textometric analysis undertaken in this study revealed the growing interest in the subject of entrepreneurial collaborative economy. Despite the challenge of conducting a systematic literature review on this topic due to the absence of a clear paradigm, we have successfully categorized the studies on this phenomenon into three distinct clusters, representing a conglomerate of diverse economic activities. These clusters, which we will delve into next, offer valuable insights into the entrepreneurial collaborative economy.

5.1. Cluster 1: Innovative Ecosystems for Value Co-Creation

The largest of the three clusters, Cluster 1, encompasses 50.7% of the active words and is associated with 42.8% of the articles analyzed. It prominently features the following: (1) value, (2) creation, (3) process, (4) ecosystem, (5) innovation, and (6) entrepreneurship. We aptly name this cluster “innovative ecosystems for value co-creation”.

In essence, this cluster underscores the impact of the CE as an ecosystem for co-creating value through innovative endeavors. Co-creation, as defined by Prahalad and Ramaswamy (2004), involves the collaborative co-creation of new products or services by various stakeholders, including customers, managers, and employees. Widjojo et al. (2019) further highlight the development of collaboration networks with external agents as drivers of value co-creation and innovative deployment.

Co-creation in the context of CE has been examined from multiple perspectives and across various fields. For instance, Simone et al. (2017) provide evidence of peer-to-peer as an emerging ecosystem amplifying value co-creation through service logics. Moreover, studies have explored co-creation’s impact on customer satisfaction dimensions (Díaz-Méndez & Gummesson, 2012; Nájera-Sánchez et al., 2022; Shin, 2016). Factors influencing customer satisfaction in collaborative consumption, such as platform reliability, platform responsiveness, vendor competence, vendor empathy, and co-sharer’s empathy, have been identified (Lin et al., 2020). Nadeem et al. (2021) suggest that value co-creation results from the effect of customer satisfaction on consumer engagement in this type of commerce. (Valdez-Juárez et al., 2021) highlight the significant role of customer satisfaction in electronic commerce in open innovation environments.

From an entrepreneurial perspective, Ratten (2020) highlights the rise of social policies aimed at entrepreneurial solutions for overcoming crises like COVID-19, fostering value co-creation. Tuan (2017) corroborates entrepreneurship’s role in boosting customer value co-creation. Studies related to the gig economy, prominently included in this cluster (Chandna, 2022; Massi et al., 2020), underline the gig economy’s contribution to entrepreneurship (Ravenelle, 2019). Barrios et al. (2022) affirm the incorporation of CE-linked business platforms in the local economy in the United States, triggering interest in new entrepreneurial businesses related to the gig economy. Mouazen and Hernández-Lara (2023) demonstrate the positive impact of the gig economy ecosystem on women’s entrepreneurship. Additionally, Bogatyreva et al. (2021) emphasize the gig economy’s significant and positive impact on entrepreneurial intentions.

The formation of stakeholder relationships, as highlighted by Shams and Kaufmann (2016), plays a pivotal role in structuring entrepreneurial co-creation. Abhari et al. (2019) concur, affirming that entrepreneurship enhances opportunities for idea sharing. Leick et al. (2022) go further, asserting that individual attributes are linked to entrepreneurial activities within the CE, with competencies enhanced in socialization, cooperation, and innovation transfer (Wala & Salmen, 2022).

Cluster 1 also incorporates studies detailing entrepreneurial activities within the CE, especially in emerging economies. Wu et al. (2022) provide evidence of inclusive entrepreneurial activities in China reducing poverty through the shared economy. Boafo et al. (2022) assert that entrepreneurial activities related to the sharing economy in Sub-Saharan Africa promote exports and internationalization. Laakkonen et al. (2019) observe a collaborative business network between forest entrepreneurs, facilitating the integration of intangible resources, enabling the development of novel forest services. Lastly, Widjojo et al. (2019) reveal the presence of collaboration networks in an Indonesian organic community, driving resource integration, forming value co-creation platforms, and leading to innovation in product, process, marketing, and organization.

5.2. Cluster 2: Sharing Economy Platforms in Entrepreneurial Contexts

Cluster 2 is the second largest cluster, accounting for 36.1% of the active forms and linked to 38.46% of the analyzed articles. Dominant terms in this cluster include: (1) consumption, (2) share, (3) platform, (4) peer, and (5) economy, leading us to designate it as “sharing economy platforms in entrepreneurial contexts”.

Given the digital nature of most CE activities, the sharing economy serves as a strategic avenue for global entrepreneurial opportunities (Anwar, 2023). Sahut et al. (2021) assert that digital entrepreneurship should be analyzed in association with digital business models, the digital entrepreneurship process, and the creation of digital startups, digital platforms, and ecosystems. Baranauskas and Raišienė (2022) argue that accelerated digitalization of the economy and digital entrepreneurship have promoted a transition of traditional business models into networked and integrated digital platform models.

Sharing economy platforms, in general, are expected to provide new entrepreneurship opportunities (Gössling & Michael Hall, 2019). Bonamigo et al. (2022) offer valuable guidance for entrepreneurs on selecting collaborators to compose value co-creation platforms, enhancing startup performance. Abhari et al. (2019) underline the platform design’s significance in attracting diverse contributors for collaborative innovation. Koul et al. (2022) highlight the role of new digital platforms for peer–peer models, enabling collaborative utilization of transportation resources with important implications for value co-creation by entrepreneurs.

Grinevich et al. (2019) confirm that sharing platforms can grow by flexibly incorporating green, economic, and social logics. D. T. de Oliveira and Cortimiglia (2017) propose a conceptual framework for value co-creation in web-based multisided platforms, enhancing our understanding of the co-creation phenomenon. Atsız and Cifci (2022) identify two motives and eight dimensions to join the sharing economy: (1) social and cultural motives (the gratification of hosting, altruism, source of cultural capital, and social interaction) and (2) economic motives (monetary, facilitators, network, and independence).

Collaborative consumption entrepreneurs, as identified by Barnes and Mattsson (2016), build tribal communities by matching supply and demand in an innovative way, mainly using social media. Leick et al. (2022) analyze individual–contextual determinants of entrepreneurial service provision within platform-based CE, exploring regional and national influences on urbanization. Roh (2016) suggests that innovative and proactive sharing economy platforms can enable new business models for social entrepreneurship, even bringing in women entrepreneurs (Johnson & Mehta, 2022). In a similar manner, Mouazen and Hernández-Lara (2023) provide evidence of the positive influence of the entrepreneurial ecosystem and gig economy on women’s entrepreneurship.

Some studies within this cluster focus on crowdfunding platforms, a topic immediately related to entrepreneurship. Fehrer and Nenonen (2020) note the rapid growth of crowdfunding platforms and their ability to connect entrepreneurs, investors, and customers. Rey-Martí et al. (2019) emphasize crowdfunding platforms as a means to legitimize intermediaries promoting social entrepreneurship projects. Chandna (2022) discusses digital platforms for crowdfunding, breaking barriers related to legitimacy issues and the lack of traditional funding sources.

Nevertheless, these platforms are not without criticism. Ahsan (2020) contends that “the sharing economy reflects the intensification of an ongoing neoliberal trend that misuses the concept of entrepreneurship in order to justify certain forms of employment practices, and make a case for regulatory oversight”. Josserand and Kaine (2019) introduce the term ‘sub-entrepreneur’ to describe gig platform workers as a “type of independent contractor who experiences less freedom than those with true entrepreneurial scope and autonomy in their work”. Ravenelle (2017) suggests that gig economy platforms may render entrepreneurs vulnerable.

5.3. Cluster 3: Studies of Collaborative Economy Literature Review

The smallest of the three clusters, Cluster 3, encompasses 13.2% of the active forms and 18.7% of the papers. Dominant terms within this cluster include: (1) review, (2) literature, (3) systematic, (4) analysis, and (5) bibliometric, promoting the designation “studies of collaborative economy literature review”.

Notably, there are relatively few studies that undertake comprehensive reviews of the literature related to CE, many of which are either partial or lack systematic analysis. This underscores the significance of studies like the present one. Saragih (2019), for instance, conducted a systematic literature review on co-creation in the music business, concluding that value co-creation can be harnessed by entrepreneurs to gain monetary, experiential, or social value through virtual and physical co-creative platforms. Shin (2016) proposes a conceptual approach to the relationships between the social economy, social welfare, and social innovation based on a literature review. Braun (2010) conducted a literature review to propose a skilling framework for women entrepreneurs in the knowledge economy, highlighting the need to enhance opportunities for female entrepreneurs in this realm.

Furthermore, some literature reviews have sought to classify studies related to this phenomenon. Silva and Moreira (2022) conducted a bibliometric analysis of entrepreneurship and the gig economy, identifying five main clusters of studies: (1) self-employment and social economy, (2) sharing economy and sustainable development, (3) entrepreneurship and innovation, (4) gig economy and platform economy, and (5) digitalization. Klarin and Suseno (2021) developed a State-of-the-Art review of the sharing economy, categorizing inter-related concepts into four clusters: (1) freelance work and its implications, (2) transportation and solutions for the sustainable development of the sharing economy, (3) user experience and collaborative consumption, and (4) the sharing economy in the context of hospitality and tourism.

6. Conclusions

In the pursuit of a deeper understanding of the entrepreneurial collaborative economy, this research has undertaken a systematic literature review using the PRISMA methodology. Additionally, we have conducted a textometric analysis employing Iramuteq software. This study had two objectives: (1) mapping the literature related to the CE within entrepreneurship with the aim of proposing a research taxonomy, and (2) analyzing unexplored research within this field. The findings confirmed that our chosen methodology has achieved both objectives.

To begin, our investigation has unveiled that the entrepreneurial collaborative economy paradigm remains in a state of emergence within the academic literature. The current research landscape in this area has proven to be somewhat elusive. Through a comprehensive mapping exercise, our results have shed light on this very gap. To be specific, our research on the entrepreneurial collaborative economy has led to its classification into three overarching clusters: (1) innovative ecosystems for value co-creation, (2) sharing economy platforms in entrepreneurial contexts, and (3) studies of collaborative economy literature review.

The first cluster underscores the prevailing perception within the literature that the entrepreneurial collaborative economy functions as an innovative ecosystem, fostering the co-creation of value. Several studies have focused on the role of co-creation as a mechanism for creating new products and services through the collaboration of a wide range of stakeholders. The second cluster highlights the potential of sharing economy platforms as a viable avenue for entrepreneurial activities. These platforms, it appears, present fresh opportunities for entrepreneurs, with existing research emphasizing their pivotal role in promoting collaborative innovations. Finally, the third cluster shows the paucity of studies that have scrutinized the existing literature in this area, thus underscoring the compelling necessity to embark on such investigations.

Turning to our second objective, it is evident that the literature exhibits a noticeable bias towards two specific facets of the CE: (1) collaborative value co-creation and (2) the gig economy. Consequently, scholarly attention has predominantly concentrated on analyzing entrepreneurship activities in these two areas of the CE, while somehow overlooking the remaining five dimensions. Therefore, there is a palpable need for further research delving into the intricate relationships between entrepreneurship and: (1) collaborative consumption, (2) collaborative production, (3) collaborative governance, (4) collaborative finance, and (5) collaborative knowledge. A particularly pressing endeavor would be to dissect the barriers, tensions, and critical success factors within each of these CE movements, as these aspects have been relatively underexplored within the research landscape.

In addition, the overrepresentation of collaborative value co-creation and the gig economy in the literature can be attributed to several factors. First, both areas resonate strongly with dominant research streams in business, management, and innovation studies, where concepts such as customer engagement or co-production are already well-established. Second, the gig economy has received considerable attention from policymakers, media, and the public due to its implications for labor markets, workers’ rights, and regulatory frameworks. Finally, the scalability and global reach of prominent gig and co-creation platforms make them attractive case studies for understanding platform dynamics and user interactions.

In summary, our findings have contributed to a better understanding of the entrepreneurial collaborative economy, a subject that is gradually gaining scholarly attention. It is foreseeable that in the ensuing years, research related to this topic will experience substantial growth. Nevertheless, it is essential to acknowledge that this study is not without its limitations derived from the methodology and technique used. Although we conducted thorough searches for articles in major publishers and databases, others that could have incorporated a greater number of papers into the analysis were excluded. Furthermore, the nature of textometric techniques may have led to the inadvertent exclusion of certain words from our analysis, rendering them unaccounted for. This research, however, has laid the groundwork for a more profound exploration of the entrepreneurial collaborative economy, signifying its growing significance in academia. As we move forward, it is imperative that future studies take into consideration the potential enhancements and refinements of our approach.