Crowdsourcing and Digital Information: Looking for a Future Research Agenda

Abstract

Crowdsourcing has become increasingly relevant in academic research due to its role in the evolving digital landscape, where information is a key driver of organizational performance. In a context dominated by emerging technologies and digital platforms, organizations are turning to external sources for data and idea generation. This paper offers a comprehensive review of the literature on crowdsourcing and digital information, using bibliometric techniques and qualitative analysis to identify major trends. The findings reveal several shifts in focus: from conceptual frameworks to practical applications; from customer participation to broader stakeholder involvement; and from general technological and managerial approaches to specific technologies and emerging perspectives in entrepreneurship and finance. The primary contributing disciplines are Computer Science, Engineering, and Information Science. Recent research (post 2023) emphasizes constructs such as “crowdfunding”, “digital platforms”, and “machine learning”, moving beyond earlier focuses like “citizen science” and “social media.” This review also reveals growing interest in managerial, medical, and cultural heritage applications, alongside a decline in research related to geography and crisis management over the past two years. This study enhances our understanding of current research directions and practical implications in crowdsourcing and digital information, offering valuable insights for both academics and practitioners.

1. Introduction

Crowdsourcing has emerged as a critical phenomenon in the digital information era, playing a key role in idea generation, problem-solving, and the transformation of organizational models. Since Howe [1] introduced the concept, it has gained increasing attention from both scholars and practitioners, driven by rapid technological advancements.

Crowdsourcing—also referred to as “massive outsourcing” or “voluntary outsourcing” [2,3]—was originally defined by Howe [1] (p. 1) as “taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call”. Garrigos-Simon & Narangajavana-Kaosiri [4] (p. 2) define crowdsourcing as “the action of taking a specific task or job, whether or not previously performed by an employee of an organization or by a designated agent (such as a contractor, an external worker or a supplier), and subcontracting it, through an “open call” to a large group of people (inside or outside the organization), a community or the general public via the Internet, for compensation that does not have to be financial”.

The concept of crowdsourcing encompasses several defining characteristics. First, it relies on collective intelligence and co-creation, drawing on the contributions of individuals rather than organizations as primary sources of innovation and resources. This redefines organizational boundaries, evolving traditional outsourcing into more open and flexible structures.

The “crowd” is typically large, diverse, and composed of both external and internal stakeholders. Participation may be broad or selectively targeted and can involve humans as well as non-human agents. Contributors provide various resources—such as labor, funding, knowledge, or expertise—often mediated through digital platforms and new technologies [5].

Crowdsourcing applies across tasks of varying complexity and modularity within the value chain, including activities not previously undertaken by organizations or formerly handled by employees [6]. It can be initiated by individuals, firms, nonprofits, or public institutions and is relevant across sectors and disciplines.

Importantly, the model generates mutual benefits: while organizations gain valuable input, participants may receive compensation not only in monetary form but also through social recognition, personal development, or skill acquisition—signaling a shift in traditional labor dynamics. Ethical considerations, particularly regarding fair treatment and recognition of contributors, are essential to its responsible implementation [7,8].

Crowdsourcing has become a prominent and expanding topic in academic research, attracting attention across a wide range of disciplines. Its widespread adoption across various domains reflects its growing significance [9]. In particular, managing information across digital platforms has become a central aspect of crowdsourcing in the current technological context [10]. From a practical perspective, it enhances firm competitiveness by offering cost-effective, high-quality, and rapid solutions [1,11]. It also generates positive network externalities, reduces dependence on traditional suppliers, mitigates information asymmetries, accelerates data collection, and fosters innovation through voluntary participation and broad public engagement [12,13,14].

Lebraty and Lobre-Lebraty [15] identify three primary sources of value creation in crowdsourcing: cost reduction, innovation, and authenticity. Furthermore, crowd participation can promote market orientation, organizational change, and increased technological exposure, which may positively influence performance [3]. Moreover, crowdsourcing can indirectly contribute by developing distinctive capabilities, driving innovation, and reshaping organizational and customer behaviors [4,16].

However, despite its exponential growth and practical relevance, research on crowdsourcing remains limited [17]. Although Garrigós-Simón and Narangajavana [4] provide an in-depth review of the relevant literature, the existing literature reviews in this area are reduced, require updating, and have not specifically addressed particular aspects related to crowdsourcing and other specific issues.

Even more scarce is the literature exploring its intersection with digital information (the aim of this paper), with a notable lack of comprehensive reviews on the current state and future research directions. This constitutes a significant gap, especially considering the pivotal role of information—commonly recognized as the most critical asset for modern organizations. In the context of an increasingly complex and digitalized technological environment, the ability to manage digital information effectively is not merely beneficial but essential to organizational resilience and innovation. Within this framework, crowdsourcing emerges as a vital tool, offering a dynamic and participatory approach to information management that enables organizations to harness collective intelligence, enhance data processing capabilities, and adapt more effectively to evolving informational demands.

This article presents a bibliographic and integrative review of the literature on crowdsourcing and digital information (CDI). It identifies emerging trends, key applications, and future research directions in the field. This study contributes to the academic literature in four key ways. First, it presents the first comprehensive bibliometric analysis of crowdsourcing and digital information (CDI) research, offering novel insights into the field. Second, it provides a detailed examination of the literature’s development by analyzing core references, sources, authors, and especially keywords, thereby identifying key patterns, networks, and emerging trends. Third, this study introduces a methodological innovation that facilitates the comparison of different periods and enhances the visualization of research evolution. Finally, the findings offer practical insights for professionals and propose solid foundations and directions for future scholarly work.

2. Materials and Methods

To examine this research landscape, we employ bibliometric analysis, complemented by visual mapping techniques. Bibliometrics is an interdisciplinary method that applies mathematical and statistical tools to quantitatively assess scholarly knowledge [17]. Originating in 1917 [18], it is now widely used to trace the development of academic fields. Its capacity to generate objective, concise, and interpretable insights into specific research areas highlights its methodological value [17].

The data for this study were obtained from the Web of Science (WoS) Core Collection, the most widely used and recognized source in bibliometric research [19]. While both the WoS and Scopus are commonly employed, this study focuses exclusively on WoS due to its higher selectivity and its inclusion of only the most reputable journals [20]. The WoS is also regarded as the most neutral and representative database in the field [21].

The study retrieved all publications related to the keywords “crowdsourcing” (or “crowd sourcing”), “digital,” and “information” up to 31 March 2025, with data collection conducted in April 2025. The dataset includes full records and cited references. From an initial set of 963 documents, only articles, reviews, letters, and notes were selected to focus on the core literature, following the criteria of Garrigós-Simón et al. [21]. This resulted in a final sample of 679 documents, representing 70.5% of the original dataset.

For the co-occurrence analysis, two distinct periods were considered, 2009–2022 and 2023–2025, with a focus on author keywords. Although the post-2023 sample accounts for approximately one-third of the total and the unequal period lengths may influence the results, this approach was adopted to highlight the most recent trends in the literature.

The bibliometric indicators used are the most common in the literature: the number of papers published in the area to analyze productivity; the number of citations to measure the most influential papers and authors [20]; the h-index to determine the quality of the papers [22]; and the WoS impact factor to see the dissemination power of the journals [23].

The paper also utilized the VOSviewer 1.6.20 software [24] for data mapping, providing a clearer illustration of structures, networks, and clusters in the analysis. The analyses conducted included a co-occurrence analysis of authors’ keywords and a co-citation analysis of references, journals and authors [25]. The work also develops a hierarchical analysis of clusters or groups of related works and develops a bibliographic coupling of authors [26]. These analyses are the most used in the bibliometric literature [21,23].

Our research introduces novel visual innovations by employing color coding to facilitate the comparative analysis of authors’ keyword evolution across different periods. This approach aims to enhance the visualization of thematic networks and their development, contributing new methodological tools to academic research.

3. Results

The results are structured around five main analyses. First, this section outlines the current state, trends, and developments in the literature on crowdsourcing and digital information, including an examination of citation patterns. Second, it identifies and reviews the most frequently cited and co-cited papers in the field. Third, the paper analyzes the primary publication sources through citation and co-citation analyses. Fourth, the study explores the co-occurrence of author keywords, tracing their evolution over distinct phases. Finally, it examines the most influential authors using co-citation and bibliographic coupling techniques.

3.1. The Status and Evolution of Crowdsourcing and Digital Information in the Literature

The first article on crowdsourcing in a digital context indexed in Web of Science was published by Trainor in 2009 [27], highlighting the role of open-source software and crowdsourcing in enhancing library services. Shortly thereafter, academic interest in the topic surged. By 2012, the number of publications (articles, reviews, and letters) had grown to 12, reaching 62 in 2017 and peaking at 89 in 2023. This growth in publications was mirrored by a sharp rise in citations: over 100 annual citations were recorded by 2014, 576 in 2017, and more than 1000 citations annually since 2019. In 2024, citations peaked at 2680. Figure 1 presents the annual trends in publications and citations.

Table 1. General structure of citations in crowdsourcing and digital information.

Number of Citations	Number of Articles	Cumulative n. of Articles	% Articles	% Accumulated Articles
≥200	8	8	1.18	1.18
≥150	5	13	0.74	1.91
≥100	10	23	1.47	3.39
≥50	37	60	5.45	8.84
≥25	63	123	9.28	18.11
≥10	154	277	22.68	40.80
<10	402	679	59.20	100.00
Total	679

Source: Own elaboration based on WoS 2025.

presents the citation structure in the field of CDI. According to the Web of Science, one article has received over 500 citations, and eight have surpassed 200 (1.18% of the total). Additionally, 40.8% of the publications have received at least 10 citations. The h-index for the field is 56, indicating that 56 papers have received at least 56 citations each, which reflects the overall impact of the area [23]. In total, the field has accumulated 13,937 citations, with an average of 20.53 citations per article.

3.2. Analysis of Citation and Co-Citation of Papers

3.2.1. Most Cited Papers in CDI

Table 2. The top 20 papers with the most citations in CDI.

Papers with the Most Citations in QT
R	Journal	TC	Article	Authors	Year	CY
1	TCS	550	“The Psychology of Fake News”	Pennycook, G and Rand, DG	2021	13.50
2	CAD	456	“Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation”	Wu, DZ; Rosen, DW; (...); Schaefer, D	2015	45.60
3	MQ	307	“Computer-Aided Design”	Steelman, ZR; Hammer, BI and Limayem, M	2014	27.91
4	JMIR	296	“Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation”	Tangcharoensathien, V; Calleja, N; (...); Briand, S	2020	59.20
5	IATN	268	“Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones”	Yang, DJ; Xue, GL; (...); Tang, J	2016	29.78
6	EAPD	230	“Automated identification and characterization of parcels with OpenStreetMap and points of interest”	Liu, XJ and Long, Y	2016	25.56
7	ITPAM	213	“Demographic Estimation from Face Images: Human vs. Machine Performance”	Han, H; Otto, C; (...); Jain, AK	2015	21.30
8	WRR	204	“The NorWeST Summer Stream Temperature Model and Scenarios for the Western US: A Crowd-Sourced Database and New Geospatial Tools Foster a User Community and Predict Broad Climate Warming of Rivers and Streams”	Isaak, DJ; Wenger, SJ; (...); Parkes-Payne, S	2017	25.50
9	PNAS	196	“Next-generation Digital Earth”	Goodchild, MF; Guo, HD; (...); Woodgate, P	2012	15.08
10	ITAC	191	“Building Naturalistic Emotionally Balanced Speech Corpus by Retrieving Emotional Speech from Existing Podcast Recordings”	Lotfian, R and Busso, C	2019	31.83
11	NTWE	186	“Amazon Mechanical Turk and the commodification of labour”	Bergvall-Kåreborn, B and Howcroft, D	2014	16.91
12	ARPH	176	“Social Media- and Internet-Based Disease Surveillance for Public Health”	Aiello, AE; Renson, A and Zivich, PN	2020	35.20
13	B	161	“Structured crowdsourcing enables convolutional segmentation of histology images”	Amgad, M; Elfandy, H; (...); Cooper, LAD	2019	26.83
14	RS	145	“Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case”	Chini, M; Pelich, R; (...); Matgen, P	2019	24.17
15	JMIR	132	“Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions”	Morris, RR; Kouddous, K; (...); Schueller, SM	2018	18.86
16	SD	131	“The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms”	Orlandic, L; Teijeiro, T and Atienza, D	2021	32.75
17	RDM	128	“Digital transformation, for better or worse: a critical multi-level research agenda”	Dabrowska, J; Almpanopoulou, A; (...); Ritala, P	2022	42.67
18	AMCC	128	“Noninvasive Fetal ECG: the PhysioNet/Computing in Cardiology Challenge 2013”	Silva, I; Behar, J; (...); Moody, GB	2013	10.67
19	AAAG	125	“Citizen Science in the Age of Neogeography: Utilizing Volunteered Geographic Information for Environmental Monitoring”	Connors, JP; Lei, SF and Kelly, M	2012	9.62
20	JBR	124	“Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies”	Mariani, MM and Wamba, SF	2020	24.80

Source: Own elaboration based on WoS 2025. R: ranking; TC: total citations; CY: citations per year. TCS: Trends in Cognitive Sciences; CAD: Computer-Aided Design; MS: MIS Quarterly; JMIR: Journal of Medical internet Research; IATN: IEEE-ACM Transactions on Networking; EAPD: Environment and Planning B-Planning & Design; ITPAM: IEEE Transactions on Pattern Analysis and Machine Intelligence; WRR: Water Resources Research; PNAS: Proceedings of the National Academy of Sciences of the United States of America; ITAC: IEEE Transactions on Affective Computing; NTWE: New Technology Work and Employment; ARPH: Annual Review of Public Health; B: Bioinformatics; RS: Remote Sensing; SD: Scientific Data; RDM: R&D Management; AMCC: 40th Annual Meeting on Computing in Cardiology; AAAG: Annals of the Association of American Geographers; JBR: Journal of Business Research.

lists the most frequently cited articles on CDI within our sample, based on citation counts from the Web of Science. Citation count serves as a key indicator of an article’s quality, influence, and academic relevance [17,23].

The most cited article in the sample is Pennycook and Rand [28], with 550 citations and the highest average citations per year (137.5). This work, situated in psychology and behavioral sciences, explores why individuals believe and share misinformation online, highlighting the role of crowdsourced veracity ratings.

Wu et al. [29] ranks second with 456 citations and third in citations per year (45.6). Positioned at the intersection of computer science and management, it analyzes cloud-based design and manufacturing within a digital framework.

Steelman et al. [30], published in MIS Quarterly, ranks third in total citations (307), though it lags in annual citation rate. The study examines the use of digital crowdsourcing platforms, such as Amazon Mechanical Turk, for data collection in the digital age.

Tangcharoensathien et al. [31], published in the Journal of Medical Internet Research, is fourth in total citations (296) but ranks second in citations per year (59.2). The article proposes crowdsourced strategies for managing the COVID-19 infodemic through stakeholder collaboration

3.2.2. References Co-Citation Analysis

The co-citation analysis—based on the network of documents co-cited by the 679 selected publications—reveals the intellectual structure, thematic connections, and evolution of the field of CDI. In this network, nodes represent co-cited documents, highlighting core research themes.

Figure 2 shows that Howe [1], Goodchild [32], and Estellés-Arolas & González-Ladrón-De-Guevara [33] lead in both total link strength and citation frequency. Howe (2006), cited by 49 of the 679 documents (after merging duplicates), is foundational for introducing and defining the term “crowdsourcing.” Although highly cited, this article is not part of the sample itself as it was published in a magazine not indexed by the Web of Science.

Goodchild [32], with 40 citations from the sample, is the second most co-cited work. It examines volunteered geographic information (VGI), compares it with citizen science, and explores issues such as motivation, privacy, and data accuracy.

Estellés-Arolas and González-Ladrón-De-Guevara [33] complete the top three. Their study offers a comprehensive review of crowdsourcing definitions and proposes a validated, integrated definition widely adopted in the field.

Figure 2 displays six co-citation clusters, each representing thematic concentrations within the CDI literature.

The largest cluster (red, 30 items) is led by Goodchild [32] and centers on volunteered geographic information and citizen science. It includes highly cited works such as Zook [34], Bonney et al. [35], Goodchild et al. [36], and Haklay [37].

The second-largest cluster (green, 29 items) is led by Afuah and Tucci [38] and includes Fornell and Larcker [39] and Brabham [40]. This group focuses on crowdsourcing from a managerial and methodological perspective.

The third cluster (dark blue, 23 items), led by Howe [1] and Estellés-Arolas & González-Ladrón-De-Guevara [33], emphasizes conceptual and theoretical analyses of crowdsourcing. It also includes Brabham [12,41], Howe [42], and Braun & Clarke [43].

The fourth cluster (yellow, 15 items) focuses on crowdfunding, a financial dimension of crowdsourcing. It includes Mollick [44] and Belleflamme et al. [45], ranked fifth and seventh in citations, respectively.

The fifth cluster (violet, three items), led by Gao et al. [46], approaches crowdsourcing from a digital and computing perspective.

The sixth and smallest cluster (light blue, three items) is marginal, with no articles among the top 50 most cited.

3.3. Analysis of Citation and Co-Citation of Sources

3.3.1. Leading CDI Areas and Journals

The 679 documents on CDI were published across 577 journals and span 128 subject categories. The dominant fields were Computer Science (453 articles, 66.62%), Engineering (26.07%), Information Science & Library Science (22.98%), Communication (21.94%), and Business & Economics (20.47%).

Two journals—ISPRS International Journal of Geo-Information and Sensors—each published 10 papers on the topic. Thirteen journals, each contributing five or more articles, accounted for ninety documents (13.25% of the total) (see

Table 3. The top 13 journals with CDI publications.

R	Journal	APC	H-C	TAP	TCC	ACC	PCC	%APC	IF	≥200	≥100	≥50	≥20
1	IIJG	10	6	5291	124	124	12.40	0.19	2.8				2
2	S	10	6	67,796	173	173	17.30	0.01	3.4			1	2
3	IA	8	3	92,961	36	36	4.50	0.01	3.4
4	JMIR	8	5	8708	520	520	65.00	0.09	5.8	1	2	2	3
5	TFSC	8	6	6208	120	119	15.00	0.13	12.9			1	1
6	SU	7	5	90,218	85	85	12.14	0.01	3.3				2
7	AJCCH	6	2	478	26	26	4.33	1.26	2.1
8	IITJ	6	4	11,183	26	26	4.33	0.05	8.2
9	PO	6	6	304,806	207	207	34.50	0.00	2.9			1	4
10	SD	6	4	5597	177	177	29.50	0.11	5.8		1	1	2
11	JPHS	5	4	1443	181	180	36.20	0.35	3.5		1	1	3
12	JAIST	5	4	1758	61	61	12.20	0.28	2.8				1
13	RS	5	4	36,955	256	253	51.20	0.01	4.2		1	2	3

Source: Own elaboration based on WoS 2025. R: ranking; H-C: the H index in the area of crowdsourcing and digital information; APC: articles published in CDI.; TAP: total articles published (2009–2025); TCC: total citations in CDI: ACC: articles cited in CDI; PCC: average of cites by articles in CDO; %APC: percentage of articles published in CDI (APC/TAP); IF: impact factor; ≥200, ≥100, ≥50 and ≥20: articles with more of 200, 100, 50 and 20 citations; IIJG: ISPRS International Journal of Geo-Information; S:Sensors; IA: IEEE Access; JMIR: Journal of Medical Internet Research; TFSC: Technological Forecasting and Social Change; SU: Sustainability; AJCCH: ACM Journal on Computing and Cultural Heritage; IITJ: IEEE Internet of Things Journal; PO: Plos One; SD: Scientific Data; JPHS: JMIR Public Health and Surveillance; JAIST: Journal of the Association for Information Science and Technology; RS: Remote Sensing.

). Among them, the highest h-index values (6) were reported by ISPRS International Journal of Geo-Information, Sensors, Technological Forecasting and Social Change, and PLOS One.

In terms of journal specialization, ACM Journal on Computing and Cultural Heritage had the highest share of its total output (1.26%) dedicated to the field, followed by JMIR Public Health and Surveillance (0.35%) and Journal of the Association for Information Science and Technology (0.28%).

Regarding impact, measured by average citations per article, Journal of Medical Internet Research led with 65 citations per article, followed by Remote Sensing (51.2) and JMIR Public Health and Surveillance (36.2)

3.3.2. Journal Co-Citation Analysis

This co-citation analysis examines the frequency with which two journals are cited together in a third source, revealing intellectual linkages across disciplines [21]. As shown in Figure 3, three primary clusters emerge.

The first cluster (red, 44 items) is led by Lecture Notes in Computer Science—the most cited journal (2307 citations) but 28th in link strength (42,641). It also includes PLOS One (third in citations, 1865) and Science (eighth). This group is primarily associated with computer science, engineering, and related fields such as medicine, geography, and environmental sciences.

The second cluster (green, 24 items) is led by MIS Quarterly (second in citations with 245, but first in link strength: 8835), Computers in Human Behavior (144 citations, 2818 link strength), and Technological Forecasting and Social Change (137 citations, 4395 link strength). Journals in this group focus on information science and the application of computing in management and marketing.

The third cluster (blue, 17 items) includes Academy of Management Journal (151 citations; second in link strength: 6304), Management Science (151 citations, 5302), Research Policy (147 citations, 5440), and Organization Science (143 citations; third in link strength: 5736). This cluster is primarily oriented toward management and organizational studies.

3.4. Co-Occurrence Analysis of “Author Keywords”

This section analyzes publications on CDI across two periods: 2009–2022 and 2023–2025.

Figure 4a focuses on the first period (2009–2022). Excluding crowdsourcing (unifying variants like crowd-sourcing and crow sourcing), the most frequent keyword is citizen science, followed by social media.

The red cluster (11 items), the largest, includes keywords such as digital humanities, innovation (6th and 7th in frequency), sustainability (13th), and collaboration (16th). The green cluster, also with 11 items and led by social media (3rd), includes emergency management and digital humanitarianism (both ranked 13th and 16th).

The dark blue cluster (10 items) includes COVID-19 and internet (both 8th), as well as surveillance (16th). The yellow cluster (nine items) features Internet of Things and privacy (8th and 16th).

The violet cluster (seven items) is led by OpenStreetMap and cloud computing, though it lacks terms among the top 10. The light blue cluster (six items), is led by citizen science and VGI (Volunteered Geographic Information, fourth; we consolidated multiple variants of this term).

The orange cluster (five items) includes big data and machine learning (fourth and eighth). The brown cluster (four items) contains crowdsourcing, and open innovation (eighth).

The pink cluster (four items) is led by smartphone (16th), while the remaining four clusters contain only one or two keywords each, all in marginal positions.

Figure 4b presents the keyword co-occurrence network for the 2023–2025 period. While crowdsourcing remains the dominant term, new leading keywords include crowdfunding, digital platforms, machine learning, and blockchain.

The red cluster (nine items) is led by crowdfunding and digital platforms—now the second and third most frequent terms—and includes digital transformation (15th). The green cluster (eight items), headed by blockchain and digital twin (both ranked 5th), also features task analysis and privacy (8th and 15th, respectively).

The dark blue cluster (seven items) is led by citizen science (9th) and includes digital health (11th), artificial intelligence, and photogrammetry (both 14th). The yellow cluster, also with seven items, is centered on crowdsourcing and includes machine learning (4th), deep learning (9th), and crowdwork (14th).

The violet cluster (five items) is led by cultural heritage (7th) and includes online communities and social media (both 11th). Lastly, smartphone, though outside the top 19 keywords, is the sole member of the light blue cluster

The results reveal a significant shift in keyword rankings, highlighting evolving research trends. As shown in

Table 4. The top author keyword co-occurrence of CDI-related publications.

R	Keyword (2009–2022)	Oc	Co	Keyword (2023–2025)	Oc	Co
1	crowdsourcing	187	192	crowdsourcing	63	56
2	citizen science	24	35	crowdfunding	17	11
3	social media	22	44	digital platforms	11	10
4	big data	15	36	machine learning	10	9
5	Vgi	15	22	blockchain	9	20
6	digital humanities	14	20	digital twin	9	10
7	innovation	9	13	cultural heritage	8	8
8	COVID-19	7	13	task analysis	7	20
9	internet	7	6	citizen science	6	6
10	internet of things	7	9	deep learning	6	7
11	machine learning	7	6	digital health	5	3
12	open innovation	7	11	online communities	5	6
13	emergency management	6	17	social media	5	4
14	openstreetmap	6	9	artificial intelligence	4	5
15	sustainability	6	8	crowdwork	4	3
16	cloud computing	5	8	digital transformation	4	4
17	collaboration	5	8	photogrammetry	4	3
18	digital humanitarianism	5	9	privacy	4	12
19	privacy	5	5	affordances	3	3
20	smartphone	5	8	authentication	3	7
21	surveillance	5	9	built environment	3	1
22	artificial intelligence	4	13	case study	3	5
23	co-creation	4	6	citizen participation	3	0
24	crisis informatics	4	17	COVID-19	3	1
25	critical cartography	4	6	digital entrepreneurship	3	2
26	crowdsensing	4	4	digital heritage	3	2
27	data collection	4	6	entrepreneurship	3	5
28	digital health	4	8	fairness	3	5
29	digital libraries	4	10	information sharing	3	7
30	gamification	4	4	misinformation	3	5
31	metadata	4	6	Mturk	3	3
32	online communities	4	8	reinforcement learning	3	5
33	participatory sensing	4	7	reliability	3	7
34	research libraries	4	6	sensors	3	9
36	Twitter	4	7	sharing economy	3	1
				smart manufacturing	3	5
				smartphone	3	2
				surveillance	3	6

Source: Own elaboration based on WoS 2025. R: rank; Oc: author keyword occurrences; Co: author keyword co-occurrence link. In keyword columns—in yellow color: the keywords that enter the ranking (there are not in the first period but are in the second); in green: the ones that increase in the second period; in orange: the ones that decrease in frequency; in red: the ones that disappear from the ranking in the second period. In the column of occurrences (second period), the colors indicate the various clusters.

, crowdsourcing remains the only consistent term, while the rest of the top keywords undergo substantial changes.

First, the new crowdsourcing cluster incorporates emerging terms such as deep learning, crowdwork, authentication, misinformation, and MTurk, with machine learning rising to fourth place. This reflects both a technological evolution and a shift in theoretical focus—from client-centered co-creation to the broader involvement of diverse stakeholders through crowdwork.

Second, all components of the main post-2023 cluster (red) are newly ranked, led by crowdfunding and digital platforms (second and third, respectively). This underscores the increasing emphasis on the financial and managerial applications of crowdsourcing and digital information. Similarly, the second cluster (green), now focused on blockchain, digital twin, and task analysis—all within the top eight—signals a move from general innovation-focused terms (e.g., innovation, open innovation) to more specific entrepreneurship-related concepts (digital entrepreneurship, entrepreneurship).

This trend is also reflected in the replacement of broad technological terms such as big data, internet, IoT, cloud computing, and data collection with more targeted tools and concerns such as privacy and fairness. The prominence of smartphone also declines.

Moreover, the importance of citizen science diminishes, dropping from second to ninth place, and the literature related to geographic and emergency applications—volunteered geographic information, emergency management, OpenStreetMap, and critical cartography—disappears from the top rankings. In contrast, new technologies like artificial intelligence and photogrammetry, as well as digital health, gain prominence.

Heritage-related research also rises, with cultural heritage and digital heritage entering the ranking at seventh and nineteenth, respectively. Meanwhile, online communities and social media occupy the eleventh position, though the latter has declined from its previous third-place standing.

Finally, several previously prominent keywords, including digital humanities and digital humanitarianism, have vanished from the current ranking, underscoring a broader thematic reorientation in the field.

These changes reflect a broader evolution in the literature—from an initial phase focused on defining crowdsourcing to a new phase emphasizing practical and methodological concerns. This shift explains the emergence of keywords such as task analysis (now ranked eighth) and case study, as well as the growing role of advanced technological tools that increasingly serve as research methodologies.

A detailed analysis reveals shifting disciplinary emphases within the literature. While perspectives from Economics (e.g., sharing economy) and Planning/Environmental Sciences (e.g., built environment) show emerging relevance—despite the disappearance of sustainability—there is a notable decline in contributions from Documentation, Psychology, and Marketing, as evidenced by the absence of keywords such as digital libraries, research libraries, and gamification. In contrast, terms associated with Law and Ethics (privacy) and, particularly, Finance (crowdfunding, now the second most prominent keyword) have gained prominence.

The findings also underscore a paradigm shift in the management and information systems literature, transitioning toward the vision of Web 3.0, where digital technologies enable enhanced human collaboration and adaptive web services [2]. This evolution aligns with the rise in ubiquitous computing and pervasive digital environments and the increasing automation of data-driven decision-making in business intelligence. It also reflects the emergence of Marketing 5.0 and 6.0, grounded in next-generation technologies.

This transformation is particularly evident in the post-2023 ranking, with the ascent of machine learning, the introduction of blockchain and deep learning, and the continued rise in artificial intelligence—all of them signifying the growing centrality of cutting-edge technologies in current research

3.5. Authors Co-Citation Network and Bibliographic Coupling of Authors

Figure 5 presents the author co-citation network, revealing five distinct clusters. The largest, in red (23 items), is led by Howe and Brabham—the most and third-most cited authors with 97 and 68 citations, respectively—alongside Estellés-Arolas (34) and Deng (24). This group is primarily oriented toward management and organizational studies.

The second cluster, in green (also 23 items), is headed by Zhang, the World Health Organization, Liu, and Wang (with 33, 24, 23, and 22 citations, respectively). This cluster reflects an interdisciplinary focus, integrating computational, health-related, and social network research.

The blue cluster (29 items) is led by Nambisan (42 citations), Majchrzak (24), and Boudreau (22), and centers on digital and strategic innovation and entrepreneurship.

The yellow cluster (16 items) focuses on crowdfunding from a financial perspective and is led by Mollick (37 citations), Belleflamme (28), and Burtch (22).

Finally, the violet cluster (14 items) is dominated by Goodchild, the second-most cited author (88 citations), along with Haklay (65) and Elwood (36). This group specializes in volunteered geographic information, citizen science, and environmental data.

Figure 6 illustrates the bibliographic coupling of authors, highlighting five main clusters with a total of eighteen interconnected authors. The ranking is led by Tucker (361 link strength, five documents), followed by Tang (327 l.s., four doc.), Clifford (287 l.s., four doc.), and Joshi and Taylor (both with 259 l.s., 3 doc.).

The largest cluster, in red (seven authors), includes Joshi, Taylor, Steinke, Van Etten, and the five lowest-ranked authors. This group focuses on management information systems and citizen science.

The green cluster (four authors) includes Clifford, Behar, Zhu, and Sameni (ranked seventh to ninth), with a shared emphasis on physiological and biomedical computing and medical applications.

The light blue cluster (11 authors) encompasses the top-ranked authors—Tucker, Tang, and Wu (sixth)—and is oriented toward health services and medical research.

The yellow cluster (two authors), Liu and Zhang (ranked 10th), is centered on computer science topics such as the Internet of Things and intelligent big data management.

The violet cluster (two authors), López-Pérez and Molina (ranked 14th), is linked to computational methods and software development in biomedical contexts.

4. Discussion and Conclusions

This study has examined the scholarly literature on crowdsourcing and digital information using bibliometric analysis. Through this methodology, this research traced the evolution of the field, offering a structured overview of its intellectual development. The findings enabled the identification, organization, and visualization of key knowledge domains; the assessment of the field’s current state and historical trajectory; the exploration of prevailing theoretical perspectives; and the recognition of practical applications and emerging trends. Furthermore, this study provides insights to enhance implementation strategies and mitigate potential challenges associated with the use of crowdsourcing in digital information management

This work offers several key findings on the evolving field of crowdsourcing and its intersection with digital information. Crowdsourcing is identified as a multifaceted and interdisciplinary phenomenon, characterized by ongoing debates, evolving discourses, and critical perspectives since its inception [12,33]. In this work, crowdsourcing is considered as “the action of taking a specific task or job, whether or not previously performed by an employee of an organization or by a designated agent (such as a contractor, an external worker or a supplier), and subcontracting it, through an “open call” to a large group of people (inside or outside the organization), a community or the general public via the Internet, for compensation that does not have to be financial” [1,24,33] (p. 2). Our analysis underscores the complexity of the crowdsourcing process and elaborates on its key characteristics and interdisciplinary nature by focusing on its intersection with digital information. Furthermore, the analysis highlights crowdsourcing’s strategic value in enhancing organizational competitiveness and efficiency.

Crowdsourcing has emerged as a pivotal tool for organizations and a dynamic area of scholarly inquiry. As a form of digital innovation, it facilitates the decentralization of knowledge production and task execution, enabling organizations to leverage dispersed expertise, creativity, and labor across global networks [47]. Its transformative impact is particularly salient in the reconfiguration of e-commerce models and organizational processes, representing a paradigm shift in how tasks are conceptualized and operationalized in the digital age [48].

Moreover, and specifically, the study underscores the centrality of information as the most strategic resource for contemporary organizations. It argues that the effective generation, dissemination, and management of digital information—facilitated by crowdsourcing platforms—is critical for organizational adaptability and competitiveness in increasingly complex technological environments [49,50].

Although academic research on crowdsourcing remains relatively nascent, especially at the intersection with digital information, the convergence of globalization, digitalization, and rapid technological evolution has created a fertile context for its development and institutionalization [51]. The bibliometric analysis conducted in this study reveals a marked increase in scholarly output and influence, as evidenced by publication volume, citation rates, and the emergence of leading contributors to the field. These trends reinforce both the theoretical relevance and practical viability of crowdsourcing as a cornerstone of digital strategy.

In addition to mapping the evolution of the field on CDI, in a pioneering way, this study offers critical insights for future research trajectories and provides actionable guidance for practitioners seeking to harness crowdsourcing for innovation, efficiency, and strategic growth in the digital economy.

4.1. Theorical Implications and Research Agenda

The rapid emergence and exponential growth of the literature on crowdsourcing and digital information underscore its significant implications and substantial potential for future research. However, advancing this research requires a comprehensive understanding of the current state of the literature, its primary domains, key themes, and emerging opportunities.

Our findings suggest that CDI research is undergoing a notable transformation.

There is a shift from broad conceptual and theoretical discussions—often centered on defining crowdsourcing—toward more focused analyses of its concrete applications, organizational implementation, and management practices.

The prominence of conceptual research is evident, as the first and third most co-cited papers—Howe [1] and Estellés-Arolas & González-Ladrón-De-Guevara [33]—are foundational works focused on defining crowdsourcing. Similarly, the third-largest co-citation cluster centers on theoretical analyses.

Empirical research is primarily focused on case studies and investigations related to emerging technological developments, which increasingly serve as methodological tools (see Table 4). Methodological contributions also play a significant role; notably, the second-largest co-citation cluster encompasses both managerial and methodological perspectives.

Despite these advances, there remains a notable gap in the application of classical quantitative methodologies, such as structural equation modeling and other statistical techniques, which continue to be secondary (see Table 4 and Figure 2). This gap reflects both the scarcity and limitations of available data, which constrain more rigorous, data-driven investigations of CDI processes. Moreover, the findings highlight the need to develop novel indicators to effectively assess the benefits, challenges, and organizational impacts of crowdsourcing

The results identify the main fields engaged with CDI, trace their evolution, and reveal connections across areas of study and application. This highlights emerging analytical perspectives that may open new avenues for research. While traditional disciplines such as Computer Science, Engineering, and Information Science remain central, fields like Communication and Business–Economics are increasingly relevant and offer significant potential for future exploration. Let us concentrate on the main areas observed.

4.1.1. Computer Science

The dominant role of Computer Science in the CDI field is evident, as two of the top three most-cited papers focus on cloud-based design [29] and computer-aided design [30]. Additionally, the fifth-largest cluster in the reference co-citation analysis reflects a strong digital and computing orientation. Sensors emerges as the most prolific and influential journal—sharing the highest h-index with Technological Forecasting and Social Change—while Lecture Notes in Computer Science leads the largest journal co-citation cluster, a network closely tied to computer science and engineering disciplines.

The second-largest author co-citation cluster shows an interdisciplinary orientation, combining computational approaches with social network analysis and other domains. The bibliographic coupling of authors reveals two key clusters within this area: one focused on computer science topics like the Internet of Things and intelligent big data management and another on computational methods and software development within biomedical contexts.

An analysis of the evolution of author keyword co-occurrence networks indicates a shift from broad, traditional technological terms (e.g., big data, IoT, cloud computing, data collection) toward more specialized constructs aligned with advances in ubiquitous computing and artificial intelligence (e.g., machine learning, deep learning, photogrammetry). Meanwhile, terms such as digital humanities and digital humanitarianism have declined in prominence, suggesting the growing influence of “non-human” contributors [52] and a shift toward technology-driven innovation in organizational management.

Future research should further explore the expanding roles of image and multimedia analysis [53,54], sensor technologies, and mobile applications in the CDI landscape.

4.1.2. Engineering and Information Science and Library Science

Engineering, the second most prominent field in CDI research, is closely intertwined with Computer Science. This overlap is evident in shared publication venues and citation patterns—Sensors and PLOS One, for example, are among the most prolific and highly cited sources in both areas. The largest cluster in the journal co-citation analysis reflects this integration, encompassing journals from computer science, engineering, and related disciplines, including PLOS One and Science.

Information Science and Library Science also hold a significant position in CDI research. The Journal of the Association for Information Science and Technology stands out for having a high concentration of publications in the field. However, the evolution of the author keyword co-occurrence network reveals a decline in this area’s influence, reflected in the decreasing prominence of constructs such as digital libraries and research libraries.

Further research could focus on the individual evolution of these disciplines.

4.1.3. Management

The managerial perspective—particularly within the field of Information Systems—emerges as a key dimension in CDI research, despite being formally ranked in the fourth most prominent area. A deeper analysis reveals strong connections: the second most-cited paper [29] addresses manufacturing, while the third [30], published in MIS Quarterly, focuses on digital platforms. The second-largest reference co-citation cluster adopts a managerial orientation, as do the second and third journal co-citation clusters, led by MIS Quarterly and Academy of Management Journal, respectively. Similarly, the largest author co-citation cluster includes scholars with a managerial focus.

Several important trends emerge from the analysis. First, a theoretical shift is observed—from a client-centered co-creation model to broader stakeholder engagement through crowdwork, as reflected in the co-occurrence of author keywords. Second, growing interest in specific managerial applications is evident, particularly in areas such as blockchain, digital twins, and task analysis (second largest cluster). Third, there is a transition from general innovation-related terms (e.g., open innovation, social media) (or the focus on the social media or smartphones) toward concepts linked to digital entrepreneurship and the implementation of crowdsourcing in organizational processes (keywords such as digital entrepreneurship, entrepreneurship, and task analysis, or the third author co-citation cluster).

A notable development is the rise in the financial dimension of crowdsourcing, especially crowdfunding. This is evident in the fourth-largest reference co-citation cluster, centered on crowdfunding, as well as in the keyword co-occurrence analysis, where crowdfunding ranks as the second most important term for the 2023–2025 period, leading the largest keyword cluster alongside digital platforms. The author co-citation network also identifies a distinct cluster with a financial focus.

Future research should further investigate the application of CDI across various value chain activities [16] and explore incentive mechanisms beyond extrinsic motivations. Additionally, emphasis should be placed on ‘fair play’ principles and the effective management of human capital.

4.1.4. Psychology, Law, Ethics Marketing, Economics

Additional emerging trends can be identified in the CDI field. One notable development is the growing emphasis on human capital, both in the development and organizational implementation of CDI. This is reflected in the rising relevance of keywords related to human resource management, digital transformation, and the broader management of the ubiquitous web. Related disciplines such as Psychology, Law, Ethics, Marketing and Economics are also increasingly represented.

Psychological and behavioral sciences show particular promise. The most cited paper in the field [28] originates from this area, and journals like Computers in Human Behavior play a prominent role. Although the evolution of author keyword co-occurrence does not indicate a significant rise in contributions from psychology overall, notable activity is observed in studies using Amazon Mechanical Turk (MTurk), which appears frequently as a keyword.

Future research could explore underdeveloped yet promising directions, including cognitive psychology (e.g., learning processes such as reinforcement learning) and reputation analysis—both of which are emerging as potential areas of growth for CDI.

The growing importance of the individual perspective in CDI is reflected in the increased prominence of terms related to Law and Ethics (e.g., privacy, fairness, affordances), and Marketing (e.g., authentication, misinformation) (observe the keywords co-occurrence analysis)—area represented in the journal co-citation analysis. Ethical concerns, particularly around privacy and fairness, remain essential for ensuring crowd participation and trust [11,55]. Marketing also presents significant potential, as well as integrating interdisciplinary approaches to crowdsourcing management.

In addition, perspectives from Economics (e.g., sharing economy) are gaining relevance. Keyword co-occurrence analysis highlights its emerging role, indicating its growing contribution to CDI research. Further research should build upon these trends, with a particular focus on the co-creation process and the circular economy facilitated by CDI.

4.1.5. Geography and Citizen Science

Geography and Citizen Science play a prominent role in CDI research. Goodchild [32], a foundational work on volunteered geographic information (VGI), is the second most co-cited paper, and this field represents the largest reference co-citation clusters. The ISPRS International Journal of Geo-Information stands out as the leading publication in terms of volume and h-index. Geographic sources also appear prominently in the largest journal co-citation cluster.

The author co-citation network reveals a distinct cluster centered on VGI, citizen science, and environmental data, while the main cluster in the bibliographic coupling of authors reflects a blend of management information systems and citizen science. Keyword co-occurrence analysis shows that until 2023, citizen science and VGI ranked among the top four keywords in the CDI literature. However, recent trends indicate a decline in their prominence, with geographic and emergency-related constructs (e.g., emergency management) disappearing from top rankings (although some new resources such as digital twins are extensively used nowadays in related areas).

Conversely, cultural heritage is gaining importance. The ACM Journal on Computing and Cultural Heritage has the highest proportion of its output dedicated to CDI, and cultural heritage leads the fifth-largest keyword co-occurrence cluster for the 2023–2025 period. It now ranks seventh overall, alongside the emergence of digital heritage among the top keywords.

Future research could explore related geographical domains such as transport and tourism or digital twins of the cities [53], which show potential as emerging areas within the CDI field.

4.1.6. Medicine

Medical perspectives are increasingly significant in CDI research. The fourth most-cited paper—Tangcharoensathien et al. [31], ranked second in citations per year—focuses on crowdsourcing strategies for managing the COVID-19 infodemic. JMIR Public Health and Surveillance has the second-highest share of its total output dedicated to CDI, while it and the Journal of Medical Internet Research rank third and first in average citations per article.

Medicine also features prominently in the largest journal co-citation cluster. The keyword digital health rose to 11th place in the 2023–2025 co-occurrence ranking, indicating increased relevance. The second-largest author co-citation cluster includes scholars integrating computational and health-related research. Similarly, in the bibliographic coupling of authors, the second cluster focuses on biomedical computing and medical applications, the third on health services and medical research, and the fifth on computing in medical contexts.

Future research should explore the integrated use of crowdsourcing, computing, and medicine to advance physiological computing and enhance health services and applications

4.1.7. Others

CDI research spans a wide range of disciplines and should be examined through diverse methodological and disciplinary lenses. Environmental sciences stand out, with constructs like built environment gaining prominence in keyword co-occurrence analysis and environmental journals appearing in the main journal co-citation cluster. Qualitative evidence also highlights CDI’s relevance in communication, biology, telecommunications, mathematics, and arts humanities, as well as in emerging or underexplored fields such as education, physics, linguistics, or chemistry—some of them supported by publication and co-citation patterns in multidisciplinary journals like PLOS One, IEEE journals, and Science.

Overall, the dispersion of CDI-related work across scientific domains, coupled with the expansion of the literature in computing, management, and geography, and the growing diversity of relevant keywords, points to strong potential for future development. Additionally, the presence of “trendy” topics may shape the field’s trajectory, as citation-driven publication incentives influence both authors and journals.

4.2. Practical Implications

Professionals across sectors face the challenge of integrating new policies and processes within their organizations. The diverse disciplinary perspectives on CDI contribute to a more comprehensive understanding, fostering innovation. Crowdsourcing is applicable across industries and domains [11], offering opportunities both for developing new business models [12,56] or information models [53] and for enhancing organizational management.

CDI encompasses various crowdsourcing schemes—such as crowdvoting, creative crowdsourcing, microwork, crowd workforce management [16], and crowdfunding [44,57]—and can be employed throughout multiple stages of the value chain [2,3,6,16,58], including in tasks traditionally overlooked by firms [52], or for marketing purposes.

However, the success of CDI initiatives depends on effective task analysis and design, contributor selection, and attention to considerations such as privacy. Engagement and workers’ motivation also depends on the evolution of online communities [59] and social media. Poorly structured processes or inadequate crowd management (crowdwork) can significantly limit the potential impact of crowdsourcing efforts.

The implementation of CDI especially raises critical ethical considerations, particularly regarding fair remuneration [7], fairness, affordance, misinformation, and the risks associated with the “dark side” of crowdsourcing [8]. Managers must ensure the sustainability of initiatives [21], promote social value, and integrate ethical practices across social, environmental, cultural, and economic dimensions [60] or consider heritage and the digital health.

Emerging CDI frameworks should also account for non-human participation [52], enabled by Web 3.0 technologies and the pervasive web [2], or for the development of the Industry 4.0 [58]. Practitioners need to assess the evolving role of digital platforms [57], new planning and management tools, open innovation solutions [59,61], and the digital transformation around artificial intelligence [54,56], blockchain [10,62], digital twin [53,54,56,62] and especially machine learning and deep learning [54].

Future CDI should be conceived as a dynamic, multilevel process shaped by advances in computer science, management, psychology, geography, and medicine. This evolution must continuously redefine its challenges and applications to address complex economic, social, environmental, technological, and ethical issues—particularly those linked to participant motivation and fair engagement [55,59].

4.3. Limitations and Future Research

The scope of this study highlights certain limitations that offer opportunities for further research. The emerging relevance and rapid expansion of CDI, alongside limited scholarly output in key scientific domains, underscore important research gaps. Future studies could build on the trends identified here, adopting more specialized disciplinary lenses—such as Computer Science, Management, or Geography.

In particular, inside the management domain, greater attention is needed on areas such as marketing, finance, social media management, intelligent technologies, knowledge management, intellectual capital, open innovation, entrepreneurship, ethics, and corporate social responsibility.

Additionally, the literature shows signs of increasing maturity, with a shift toward empirical research. However, the development of robust measurement indicators remains an important challenge for advancing the field.

The bibliometric approach employed in this study presents inherent limitations if not complemented by qualitative analysis, which we have sought to address through detailed data interpretation. The exclusive use of the WoS Core Collection, focusing only on articles, reviews, letters, and notes, also constrains the scope. Future research could expand on this by incorporating additional document types, alternative databases (e.g., Scopus, Google Scholar), gray literature (e.g., theses), or non-English sources. Moreover, the use of diverse analytical tools and software could enrich the findings. Further refinement through an in-depth examination of specific clusters or thematic areas is also recommended.