Relevance

For knowledge-thirsty web users around the world, there are now ample opportunities to acquire educational resources previously unavailable to them. Massive open online course (MOOC) projects such as Coursera and Khan Academy are changing the face

of education by offering free courses from top-tier universities. On Coursera, for example, a single course on Introduction to Finance had over 100,000 students enrolled. Language barriers, however, threaten to undermine learning effectiveness for non-English speakers, creating a need for translated lecture videos. The success of online crowdsourcing projects hinges upon the careful design of tasks and incentives tailored to attract the most effective participants (Lohr, 2009). As TED is a platform that offers equally educational resources in the form of lecture-like speeches, the results of this paper can hopefully shed lights on how organizations can engage volunteers for content translation efforts.

Chapter 2. Literature review 2.1 Crowdsourcing

Web users contribute all sorts of valuable resources online every day, sometimes without them knowing it. YouTube, for example, functions fully on user-generated content for its service, and every time people type the letters of character recognition CAPTCHAs to pass website security systems, they are facilitating the digitalization of books and newspaper archives (von Ahn, Maurer, McMillen, Abraham & Blum, 2008). Based on existing examples, online contributions can fall into two simple classifications: Active contributions consist of the intentional sharing of opinions and ratings, software codes, expertise or goods and services; passive contributions refer to passively sharing information and resources such as purchasing behavioral data that helped Amazon better make product recommendations, web-linking behaviors that form the basis of Google Search’s algorithm and idle computing capacity leveraged by Skpye for its VoIP services (Cook, 2008). Many companies were quick to tap into the potential of online user contributions by establishing a system that can aggregate these external resources for product improvement, cost reduction and a diversity of purposes (ibid). Such system, as Wikipedia demonstrates, can be an open platform that encourages volunteers to work toward a common goal (“building a free, accurate, global encyclopedia”) while having a volunteer-based maintenance taskforce in place for quality control¹. This present study focuses mainly on voluntary contributions made online through “crowdsourcing”—the new buzzword that denotes the emerging activity of outsourcing to the crowd (Howe, 2006).

2.1.1 Definitions

The term “crowdsourcing” came into public awareness following Jeff Howe’s (2006) publication of his Wired Magazine article. Drawing from the rise of non-professional photographers in the stock photography industry, he defined the phenomenon as “the act of taking a job traditionally performed by employees and outsourcing it to an undefined, generally large group of people in the form of an open call” (ibid).

Brabham (2008) later described it as an “online, distributed problem-solving and production model”.

1http://en.wikipedia.org/wiki/Wikipedia:Maintenance

In its broadest sense, crowdsourcing can be applied to all activities that leverage the contribution of a large, distributed group of people. It is often used interchangeably with “open innovation” and “collective intelligence”, and can include Open Source Initiatives, in which software codes are shared for program development and refinement (Borst, 2010). Crowdsourcing is in fact not a new concept. Many

organizations have benefited from the contributions of the general public for activities such as content creation, idea generation and solution submission. The consumer goods company, Procter & Gamble, for example, posted its research and development questions on open innovation websites, offered a reward for the best solution and kept the intellectual property (Howe, 2008, p.9-10). The popularity of this idea in recent years is driven partly by the greater complexity of the business environment and organizations’ need to look for external expertise (Chesbrough, 2003), and partly by the wider accessibility of tools and technology that even amateurs can master (Howe, 2008). The growing interest in crowdsourcing has also led to an accumulating amount of studies trying to make sense of it (Bayus, 2012; Lakhani & Wolf, 2005; von Krogh

& von Hippel, 2006). After reviewing varying definitions and characteristics of crowdsourcing, Estellés-Arolas and González-Ladrón-de-Guevara (2012) attempted to present an exhaustive description of this activity:

Crowdsourcing is a type of participative online activity in which an individual, an institution, a non-profit organization, or company proposes to a group of

individuals of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a task. The undertaking of the task, of variable complexity and modularity, and in which the crowd should participate bringing their work, money, knowledge and/or experience, always entails mutual benefit.

The user will receive the satisfaction of a given type of need, be it economic, social recognition, self-esteem, or the development of individual skills, while the

crowdsourcer will obtain and utilize to their advantage that what the user has brought to the venture, whose form will depend on the type of activity undertaken.

The definition covers the three key elements of crowdsourcing activities: the crowd, the initiator and the process, with an emphasis on the benefits for both the crowd and the initiating organization.

The crowd refers to participants of crowdsourcing projects. They are not employees

or workers along a company’s value chain; rather, they are a large group of

“distributed, plural, collaborative” individuals (Brabham, 2008). The so-called

“crowd” is usually regarded as non-experts or amateurs, but previous surveys showed that its composition often include people with professional credentials (Estellés-Arolas & González-Ladrón-de-Guevara, 2012). They can participate in

crowdsourcing projects as an individual, but in many cases they collaborate with each other. Franke & Sonali (2003) for example observed that a lot of product innovation happens within voluntarily assembled communities of end-users. As contributors are not constrained by contractual obligations, they choose whether or not to offer their support at free will, driven either by intrinsic or extrinsic needs (Lakhani & Wolf, 2005).

The initiator is an organization or an individual who puts up a task and asks for the crowd’s voluntary support. It can be for-profit or not-for-profit by nature, but it is often assumed that the key benefit of using online voluntary resources is cost reduction, since contributors generally receive small or no financial rewards (Borst, 2010). However, as the experience of Facebook and Cisco shows, hosting

crowdsourcing projects may require considerable resources, sometimes more than business as usual (Hosaka, 2008; Jouret, 2009). Yet with the help of volunteers, initiators enjoy the many advantages of having external talents and knowledge, such as increased efficiency, improved product quality and a more engaged and satisfied customer base (Borst, 2010; Howe, 2008).

The process of crowdsourcing can be seen as a model of problem solving, production, open innovation, business strategy, customer engagement or labor management, depending on the underlying purpose of the organizing firm. Despite the differences in how each organization orchestrates such activities, the Internet is almost present in all crowdsourcing processes. As Malone, Laubacher & Dellarocas (2010) pointed out, wider accessibility of tools and communication technologies has enabled communities to connect and collaborate, creating a virtual world of collective intelligence. The distributed nature of the Internet also allows crowdsourcing projects to scale up easily, without necessitating a broad and deep contribution (Cook, 2008). For instance, only about one in every 1,000 Wikipedia users contributes to the creation of its content, and active contributors only have to complete small tasks each time (ibid).

Crowdsourcing is capitalizing on the ability of the network to “divvy up an

overwhelming task—such as the writing of an exhaustive encyclopedia—into small enough chunks that completing it becomes not only feasible, but fun” (Howe, 2008, p.11).

2.1.2 Key success factors of crowdsourcing projects

Though the benefits of crowdsourcing seem appealing, organizations wishing to take advantage of the collective wisdom need to make an equally compelling case to attract public participation.

Malone et al. (2010) collected around 250 cases of crowdsourcing applications across a diverse range of areas and identified the “genes,” or building blocks of collective intelligence. The authors suggest that all organizations trying to initiate

crowdsourcing projects answer the following four fundamental questions:

 What is being done? The organization should decide if they wish to engage the crowd to create something new, or to make a decision. For example, they can ask the crowd to produce new videos, or to vote on the best videos.

 Who is doing it? The organization should consider if the crowd, instead of traditional management, might have the abilities required to perform the tasks more efficiently and effectively.

 Why are they doing it? The organization should make the strategic decision whether to appeal to money, love or glory to motivate the crowd.

 How is it being done? Once the organization decides to engage the crowd for content creation, they should also evaluate if the activities at hand can be divided into smaller tasks and worked on in a distributed manner. If not, they need to have a mechanism in place to manage the interdependencies of collaborated work. On the other hand, if the crowd is called upon for decision-making, the organization will have to determine whether group decision or individual decisions are required.

After the design decisions are made, the organization can pull together the appropriate elements to develop their own initiatives. In a similar attempt to conceptualize the phenomenon, Sharma’s (2010) study drew on outsourcing literature as well as case examples to propose a success model for crowdsourcing (Figure 2.1), where success is defined as the ability to mobilize a critical mass to join the effort. The model

consists of six key attributes: (1) Vision and Strategy of the initiative, which is valuable, coherent, flexible and is accepted by the crowd; (2) Human Capital, referring both to the number and quality of the crowd. Training may be required to help develop necessary skills; (3) Infrastructure, such as access to and adoption of the technology; (4) Linkages and Trust formed between individuals and workgroups, which enable the sharing of knowledge and resources. It is argued that crowdsourcing is in essence a people-oriented system, and such linkage is important to sustain the projects; (5) External Environment befitting the objectives of the projects; (6) Motive Alignment, which lies at the core of this model, refers to the extent to which the crowd’s motivation is in line with the purpose of the initiative.

Figure 2.1 Critical Success Factors Model of Crowdsourcing Source: Sharma (2010)

Both Malone et al.’s building blocks and Sharma’s success model help to guide organizations through the rollout process of crowdsourcing projects. Focusing on the

“crowd”, this present study explores and provides deeper insights into the contributors’

expectations for crowdsourcing initiatives based on an empirical investigation of volunteer experience.

2.1.3 Motivation of the contributing crowd

In addition to the organization process, one research area regarding crowdsourcing initiatives that has emerged revolves around the motivations behind crowdsourcing participation (von Krogh & von Hippel, 2006). Howe (2008) observed two shared attributes across crowdsourcing projects: Participants are generally not driven by financial rewards, and they are donating their leisure time—or excess capacity—to the cause. He went on to say that “people derive enormous pleasure from cultivating

Motive

their talents and from passing on what they’ve learned to others. Collaboration, in the context of crowdsourcing, is its own reward” (ibid, p.15). Howe’s observation is supported by exploratory researches, in which enjoyment-based intrinsic motivation is a recurring theme. Lakhani & Wolf’s (2005) study examining voluntary contribution to Open Source Software development found that skilled programmers enjoy the sheer pleasure and intellectual stimulation derived from doing what they are good at.

In Cámara and Sebastián’s (2013) survey of TED Open Translation Project participants, values, reciprocity, enjoying free time and learning were the highest ranked motivations. Borst (2010), on the other hand, studied the motivation of top performers in four different crowdsourcing projects and argued that depending on the nature of the project, they are driven by an interplay between pure pleasure and the desire for external rewards.

2.1.4 Crowdsourcing translation

As illustrated in the previous sections, one of the prominent features of crowdsourcing is the use of volunteers collaborating in a community setting. Unlike community interpreting, where the practice of untrained native speakers acting as language liaisons is a well-established field of study, crowdsourcing translation, or community translation as a social activity is a relatively untouched area (O’Hagan, 2011). Indeed, traditional translation studies are predominantly professional-focused, and amateur translation has triggered concerns over the negative impact on the profession (Pérez-González & Susam-Saraeva, 2012). However, as volunteer translation activities gradually move towards the center of the socioeconomic scene as a result of the democratization of technology, they have drawn the attention of scholars even in the translation industry. Calling the activity “wiki-translation,” Cronin (2012) for

example observed that while the new, interactive medium for content generation and consumption in Web 2.0 is driving up more translation needs, it will continue to reshape the translation profession, turning translation consumers into translation producers. Some practitioners are in fact encouraging professional translators to contribute to this new development by playing process and project management or quality assurance roles (Zetzsche, 2010).

The elements of crowdsourcing are in fact not entirely new to the translation community. Instances of collaborative translation where professional and

non-professional translators work together can be found historically in the translation of books, and communities of practice where members exchange knowledge are not uncommon among translators (Risku & Dickinson, 2009). Meanwhile, fans of Japanese animations have long devoted their time to “fansubbing,” creating and syncing translated subtitles for their favorite cartoon series (Díaz-Cintas, 2006;

Dwyer, 2012). What is unique about crowdsourcing translation, though, is the existence of organizations that systematically use free or relatively cheap labor to fulfill their content localization needs.

However, not all participants of crowdsourcing translation are non-professionals, and not all are unpaid. For this reason, Pym (2011) suggested using “volunteer translators”

as the opposite term for professional translators, and O’Hagan (2011) opted for

“community translators.” Regardless of their translation training backgrounds,

volunteers may be drawn to crowdsourcing projects by a shared desire to contribute to a greater cause (Pérez-González, 2012). As Baker (2009) indicated, activist translators and interpreters may choose to volunteer their time and effort to support certain political cause or to support linguistic diversity. Volunteer translators have also proved to be valuable in times of emergency and in war zones, such as in the

aftermath of the earthquake in Haiti, when they helped translate emergency reporting messages from Kreole to English for the relief workers to respond in time (Munro, 2010).

Crowdsourced translation initiatives can be divided into three types: outsourcing-driven (projects initiated by for-profit companies in similar ways they would outsource a task), product-driven (localization of free software for common usage), and cause-driven (projects initiated by not-for-profit organizations for a cause) (McDonough, 2012). While outsourcing-driven projects involve for-profit companies such as Facebook calling for users to localize its interface into different languages, usually to boost user engagement, product-driven initiatives allow translators to make computer programs or open-source software available to a broader user base.

Initiatives such as Wikipedia and the TED Open Translation Project (OTP), on the other hand, would fall under the cause-driven category, with clear mission statements to spread knowledge further. McDonough’s (2012) survey on volunteer translators of Wikipedia revealed that making information accessible to other language users and

helping the organization they volunteer for are the top reasons for volunteering (ibid).

Similarly, two recent interdisciplinary researches about volunteer motivation of TED OTP also agreed that volunteers participate mainly because they identify with TED’s philosophy and that they want to be involved in TED’s mission of spreading ideas (Cámara & Sebastián, 2013; Olohan, 2013).

Along with Wikipedia and Facebook, the TED Open Translation Project has appeared as a representative example for the “wiki-translation” phenomenon. While the

previous two studies focused on volunteer motivation, this research takes a step further and seeks to investigate the main sources of volunteer satisfaction and dissatisfaction. As volunteer satisfaction and motivation are intricately related, this research can be regarded as an extension to previous studies.

2.1.5 TED Open Translation Project

The TED Open Translation Project is an initiative launched by the TED Conferences that allows talks available on its website to be translated into different languages by volunteer translators. As a nonprofit organization running under the slogan “Ideas worth spreading,” TED began as a one-off conference in 1984 that aimed to bring people from three areas: Technology, Entertainment and Design. Now covering a wide range of topics delivered in the form of 18-minute presentations by leading practitioners of their fields, TED hosts two conferences annually and makes the talks available on its website for public viewing. In 2009, to bring TEDTalks to a wider audience, the organization collaborated with dotSUB, a browser-based subtitling platform, to create an online translation system for volunteers to translate TEDTalks’

English subtitles under a Creative Commons license. The translation platform was later moved to another subtitling platform, Amara, in 2012. For TED translators, subtitling works in three stages: translation, review and approval, with the first two tasks done by volunteer translators, and the last action completed by Language Coordinators (management roles assigned by TED to volunteers who are active in their local communities). To ensure accuracy of the transcript, English subtitles are generated professionally by TED, and TED Translators can then request to work on a translation or a review task. For each published work, volunteer translators are credited by name. With more than 40,000 talks translated into over 100 languages, TED Open Translation Project is one of the first and most comprehensive translation

crowdsourcing projects done by a media platform².

2.2 Volunteer and work satisfaction

There is little agreement over which theoretical framework is best suited for volunteering studies (Hustinx, Cnaan & Handy, 2010). Moreover, limited research has been done in the sphere of volunteer satisfaction (Galindo-Kuhn & Guzley, 2001).

Past literature shows that it is mainly understood as “job satisfaction in the non-paid setting” through long-standing conceptual models derived from physiological or behavioral studies, often applied to explain job satisfaction (Gidron, 1983). Such approach is reasonable, given the similarities between paid and non-paid work

experience: A volunteer can undertake the same task under the same context as a fully paid employee.

Yet the differences cannot be neglected. Some scholars argue that volunteers should be regarded as customers of the organization. Since volunteers are not obliged to stick to one organization, they can opt in and opt out anytime (Boezeman & Ellemers, 2009), selecting the most preferred organization to pursue their own goals and objectives (Wisner et al., 2004). Without the lever of pay and punishment,

organizations can only seek to address volunteer satisfaction with the job in order to ensure service sustainability (ibid). In this respect, volunteers are analogous to

customers “buying the benefits they seek, paying with their time and skills which they can almost invariably take elsewhere” (Wilson & Pimm, 1996, p.37).

To understand volunteer expectations and experience, this research incorporates job satisfaction literature in both paid and non-paid settings, with references to marketing studies. In particular, it utilizes the Kano’s model of satisfaction, which has been widely used in both job satisfaction and customer satisfaction studies.

To understand volunteer expectations and experience, this research incorporates job satisfaction literature in both paid and non-paid settings, with references to marketing studies. In particular, it utilizes the Kano’s model of satisfaction, which has been widely used in both job satisfaction and customer satisfaction studies.