Keywords

1 Introduction

Advances in information and communication technologies (ICT) and the emergence of IoT (Internet of Thing) have enabled people to redefine the boundaries of collaboration. This trend provides the possibility for large scale groups of distributed humans to join into mass collective projects and harness their potential joint power to deal with multi-faceted problems in social, economic, and environmental contexts. The emergence of the mass collaboration paradigm and its application to different domains is now reshaping the landscape of a wide variety of tasks, both locally and globally. Evidences clearly show that mass collaboration, by exploiting the capabilities of thousands of people, can create a kind of agile problem-solving system which is almost superior to any type of intelligent artefact that is made to serve similar purpose [1].

Mass collaboration brings together multitudes of individuals that may have not had the opportunity to work together before and may remain anonymous. It brings the opportunity to utilize the brainpower of participants in a collective effort and orchestrate their attempts in order to reach a common goal. In this context, Internet and ICT have a facilitating role to play. In such collective action participants can efficiently and quickly contribute in developing an idea, plan, action, process, project, or artefact, to help solving a grand challenge [2].

There are many interesting applications of mass collaboration. For instance, its application in social learning occurs at a wider scale than the individual or group learning. In this case a large number of interested people capitalize on one another’s resources, skills, and knowledge aiming to learn something new, and create lasting impact together. Mass collaborative learning, indeed, refers to a method of learning that can take place at community level where thousands of participants collectively and proactively engage in the process of knowledge acquisition, building, sharing, and developing, and where they can add their own contributions or even revise others’ contributions. As opposed to traditional and formal learning methods delivered by instructors and utilizing systematic learning approaches within educational settings, mass collaborative learning stands upon the contribution of decentralized and self-directed participants who produce knowledge in an informal way [3].

In this case, knowledge creation and sharing can be considered as the core of learning that relies on the participation of a variety of people in learning networks helping to reach the community objectives. In addition, the ability to manage such knowledge is key to community success, which secures its competitive advantage and capability to achieve a sustainable superior performance. In this regard, it is significant to promote knowledge building and sharing that drives communities to create and/or add more value, thus engaging in effective innovation [4].

There are a number of factors that differentiate small organizations and communities from large entities, such as the type of organizational structure. An organizational structure determines how power, roles, and duties can be defined, controlled, and coordinated toward reaching community goals. It also specifies the way in which knowledge, information or data flow across different layers of the organization. Every organization or community certainly needs a structure (even if self-organizing) in order to survive, take actions, and grow [5]. Every community should select its structure based on its requirements and priorities. The type of organizational structure implicitly indicates in which ways internal works can be carried out.

In the past, the structures of communities were mostly designed for effectiveness and efficiency although they are nowadays designed for agility, speed, and adaptability to be able to compete and win in today’s global competitive environments. As organizations or communities are becoming more and more digital-based and there is a transformation towards performing projects collaboratively, they are also facing with an imperative to redesign their structures in order to learn more rapidly, quickly respond to demands, and adapt to the characteristics of new workforces and workplaces. While the business environment, customer needs, technology capabilities and the nature of work in organizations and communities are likely to change, the organizational structure needs to reshape as well in a deliberate and strategic way. As such, the design of structures for adaptability is a shift away from traditional organizational structures like the hierarchical, centralized and bureaucratic models, towards unconventional models where projects are fulfilled collectively by network participants [6].

However, thus far there have been very few attempts to report on the role of organizational structures in the context of mass collaboration and learning. Furthermore, there are no clear evidences in the literature that show how mass collaborative projects can define, design, implement, and develop appropriate structures. Therefore, gaining some insight on what kinds of organizational structures have more chances for being adopted in mass collaborative learning projects is the foremost motivation for conducting this research work. Thus, a key research question that emerges is:

What kind of organizational structure within a community should be established to help developing learning through mass collaboration?

The proposed hypothesis to address this research question is:

Community learning through mass collaboration could be helped if existing models of organizational structures for long-term strategic networks are extended to allow more fluid borders and new roles, incentives and internal subgroups are defined to focus on learning and knowledge generation.

For this study, in order to search, choose, and review relevant papers, databases such as, SCOPUS, IEEE Xplore, Web of Science and Google scholar were used, being the goal to identify relevant examples and evaluate their organizational structures.

2 Relationship to Innovation in Industrial and Service Systems

From an organizational perspective, knowledge communities can speed knowledge creation, transfer, and utilization on an ongoing basis, as well as facilitate knowledge mobilization (for example, through providing suitable spaces for discussion in order to narrow the gaps between research and practice) [7].

Knowledge communities are often found to introduce changes to a system, and promote the culture of innovation. Such kind of communities can be called communities of innovation which are dedicated to support innovation. Communities of innovation are creative and dynamic entities that pursue innovative solutions to societal challenges. Communities of innovation are not only responsible for a growing number of innovations, but can also provide a common ground for learning. In this subject, they can freely and efficiently impart information and knowledge to the wider public. The literature shows that some examples of communities of innovation [8, 9] have successfully influenced the learning process.

Furthermore, “online innovation communities have an ability to learn in a dispersed setting without any formal involvement, their learning capability is actually very remarkable, making it even more striking that we lack academic insight how these learning competences come about” [9]. Mass collaboration through a large online community can be applied in various domains and fields of study. For instance, it can foster learning and optimization of the innovation portfolio through:

  • Increasing the flow of new ideas, knowledge, or information generation,

  • Boosting the chance of association between ideas, knowledge, or information,

  • Improving the quality of ideas, knowledge, or information,

  • Speeding up the collaborative feedbacks,

  • Developing connection between members,

  • Reaping the power of collective intelligence,

  • Etc.

However, it is vital in this new context to be able to evaluate how much trustworthy the acquired knowledge is, because knowledge is power, and it serves as a basis for making choices and decisions in communities. One fundamental step in the process of learning, particularly in an online environment, is to ensure that the created and shared knowledge or information is reliable, as well as the accuracy and credibility of the materials that people encounter with, are high.

It is largely evidenced that in World Wide Web, and specifically in networked-collaborative activities neither all delivered materials are reliable nor will all stay stable. In addition, on one hand, the quality and value of various types of Internet sources (that are available in different formats) are not all high, and on the other hand not all Internet users are able to accurately evaluate the appropriateness of all types of online sources [10]. Thus, along with informal learning in such communities, it is essential to assess the quality and reliability of knowledge or information created in whatever format, particularly in mass collaborative projects. To cope with this challenge, approaches such as machine learning [11], digital audio and video output [12], BS detector [13], and linguistic and network-based approaches [14] can play a relevant role.

3 Analysis of Selected Cases

In this study, in order to gain a clear understanding of the organizational structure of mass collaboration, and propose an appropriate structure for mass collaborative learning projects, 14 relevant examples of mass collaboration in different domains were selected from the literature including, (1) Wikipedia (a well-known case of a web-based encyclopedia that is written collaboratively by its users), (2) Digg (a social networking website that aggregates interesting online news, pictures, and videos), (3) Yahoo! Answer (a community-driven question-and-answer website that allows users to ask questions and answer questions), (4) SETI@home (a computing project and scientific experiment that benefits of Internet-connected computers in the search for signs of Extraterrestrial Intelligence), (5) Scratch (an online community that enables children to program and share interactive media with other people), (6) Galaxyzoo (a crowdsourced and on-line astronomy project which classifies the morphology of galaxies, and then analyze their pictures and rate them), (7) Foldit (an online puzzle video game that uses the power of distributed computing to create and design the primary structure of chosen proteins), (8) Applications of the Delphi method (a structured communication technique based on the results of several rounds of questionnaires sent to a panel of experts), (9) Climate Colab (an open problem-solving platform where a community of experts on climate change evaluate plans to reach global climate change goals), (10) Assignment Zero (an experiment in crowd-sourced journalism, allowing collaboration between lots of people to work on a publishable story, with many parts), (11) DonationCoder (a community of programmers who develop and finance their own free software), (12) Experts Exchange (a trusted global online community offering millions of verified solutions from industry experts), (13) Waze (a navigation app that runs on smartphones and tablet computers, through which users help each other to find directions and avoid traffic jams), and (14) Makerspaces (which are physical or digital spaces for open collaboration, where people have access to resources for developing projects with the aim of creating products or services).

In Tables 1 and 2, the organizational structure and main characteristics of two of the above-mentioned examples are summarized as an instance.

Table 1. Organizational structure of Wikipedia
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Table 2. Organizational structure of Digg
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In summary, elements for a typical organizational structure for mass collaborative learning projects, as derived from the 14 studied examples, are displayed in Fig. 1.

Fig. 1.
figure 1

Elements for an organizational structure for mass collaborative learning

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Having critically analyzed the organizational structure of 14 studied examples of mass collaboration and also reviewed related papers, it is concluded that each organizational structure stands upon some building blocks and fundamental elements. In this sense, it is therefore suggested that the organizational structure of mass collaborative learning (for creating, developing, and servicing) should take into account at least four core elements and three supplementary elements. Core elements including, (A) the required mechanism for members to join the community, (B) the roles that can be taken and played by members, (C) the methods of governing the community, and (D) the way that possessed knowledge or information can be managed properly and efficiently. Supplementary element consists of, (a) the ways and levels that members can engage in different activities, (b) the ways that different roles can built and involved in interrelationships, and (c) the power, rights, and responsibilities that members can take. In this structure the role of supplementary elements is augmenting, clarifying, and facilitating core elements. This organizational structure depends on the specific situations and conditions of application. It is expected that the proposed structure in this work can contribute to the development of this field of study, and enrich the understanding of the complex organizational structures for mass collaboration and learning.

4 Conclusions and Future Work

Mass collaborative learning provides greater opportunities for distributed contributors to engage in virtual global learning and take advantage of powerful social communities of experts and peers to develop innovative solutions to major challenges. Despite of successful outcomes that mass collaborative learning already gained, we still need to clarify our understanding about the required organizational structures for this emerging phenomenon.

Evidences demonstrate that collaboration and innovation are not mutually exclusive; on the contrary, they feed and build upon each other. That is, collaboration brings and drives innovation, and innovation happens through collaboration [15,16,17,18]. Considering that fact, in this study, in order to identify appropriate organizational structures for mass collaborative learning projects, the organizational structure of 14 real examples of mass collaboration are reviewed.

This work is still ongoing, but it is expected that the preliminary findings of this review and the proposed organizational structure can provide communities and learners with helpful guidelines and directions for achieving effective mass collaborative learning.