Relationship between Crowdsourcing and Crowdservicing

國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

55

secondary case study. One of the equivalent practical examples of crowdsourcing and crowdservicing is provided by the interviewees for such validating procedure.

4.3.1. Relationship between Crowdsourcing and Crowdservicing

From the description and definition of crowdsourcing and crowdservicing, the interviewees shared a similar perspective on the relationship between the characteristics of crowdsourcing and crowdservicing. Richard Brown believes that the practice of crowdsourcing may require an active platform that periodically initiates the interaction between the “crowd” and the platform; however, the practice of crowdservicing should be able to sustain on a passive platform once the interaction mechanism is constructed. He also comments that crowdservicing is supported on passive platform with strong feedback and iteration mechanism. In other words, Richard Brown suggests that a crowdsourcing practice may not require a feedback mechanism that allows the participating agents further develop based on the feedback. Brian Chang also shared this perspective and believes that crowdservicing is built from root-up initiatives instead of top-down distribution. He suggests that crowdservicing as a reflection of co-service should be collaborative efforts built on that the core concept of developing user for user, content for user and user for content relationships. Brian Chang also noted that the practice of crowdservicing envisions the ideas of independent service provider within a decentralized environment, thus, contribution to distributed services efforts from a centralized institution without collaboration between the participating agents cannot be regarded as a practice of crowdservicing. For instance, Richard Brown indicated that Amazon’s Mechanical Turk distribute services from service demanders as a task to service providers which would then imply a lack of collaborative mindset. Therefore, even though Amazon’s Mechanical Turk allows independent service providers to contribute to a demanded service, since collaboration on such platforms are not as apparent, it is regarded as a crowdsourcing platform that functions based on short-termed cooperative initiatives, i.e. the contributor/users of Amazon Mechanical Turk cooperate with the

‧ 國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

56

platform for monetary values. Brian Chang also agrees to the notion that such contribution to distributed services should be regarded as cooperative crowdsourcing initiatives. The interviewees also regarded crowdservicing as the next possible evolutionary practice after crowdsourcing much like the relationship between co-creation and co-service. For instance, Carson Chen and Brian Chang suggest that the relationship between crowdsourcing and crowdservicing is analogous to the relationship between co-creation and co-service.

The interviewees suggested that crowdsourcing and crowdservicing have different relationship with cognition. Carson Chen and Shang-Sheng Jeng suggest that crowdsourcing not necessary will enhance cognition but it is dependent on cognition since it can be a cooperative and/or collaborative practice. They have also noted that when crowdsourcing is of the collaborative nature, knowledge is collected onto the platform hosting the crowdsourcing practice and may enhance cognition. However, Brian Chang indicated that crowdservicing is more likely to enhance cognition than crowdsourcing since the crowdservicing requires a closer collaboration effort compared to crowdsourcing. Carson Chen and Shang-Sheng Jeng also indicated that since the practice of crowdservicing functions under an environment with Semantics technology, the cognition pattern would be different compared to the crowdsourcing environment without Semantics technology.

Specifically, they both suggest that Semantics technology can assign meaning to collected data, thus implying that the data can be more closely connected via meaning instead of loosely connected via labels or tags.

The interviewees suggested that crowdsourcing and crowdservicing have similar relationship with communication. For instance, Shang-Sheng Jeng indicated that both crowdsourcing and crowdservicing is enabled and enhanced by the Web’s enhancement of connecting users and machines together. Carson Chen and Brian Chang also noted that the practice of crowdsourcing and crowdservicing both enable a more connected communication between the participating agents, due to the nature of cooperative and/or collaborative practices.

‧

The interviewees have suggested that crowdsourcing and crowdservicing have different relationship with cooperation. They believe that some application of crowdsourcing may focus solely on cooperation. For instance, Richard Brown, Brian Chang and Carson Chen all commented that many application of crowdsourcing is in fact distribution of task to the crowd with no apparent signs of collaboration, i.e. the participating agents only cooperate on the platform to complete a task. Although they have noted that crowdsourcing may utilize the mechanisms for cooperation as a part to complete a collaboration practice. The interviewees have view the relationship between crowdservicing and cooperation differently. For instance, Brian Chang implies through his emphasis that crowdservicing is an enhanced version of crowdsourcing that, cooperation occurs as a part of the collaboration practice in crowdservicing. He further implies that crowdservicing cannot function solely on cooperation, since cooperation cannot elaborate and facilitate the user for user, user for content, and content for user relationships due to its goal-completion-only nature.

The interviewees have suggested that crowdsourcing and crowdservicing have different relationship with collaboration. Brian Chang and Carson Chen believe that crowdsourcing implies a collective collaboration effort such that the collaboration occurs as the contents and participating agents are being collected onto the platform.

Although they have suggested that the connective mode of collaboration may be present, but the collective mode of collaboration appears to be the dominant mode of collaboration. Wikipedia as a crowdsourcing collective collaboration effort is the key examples that both interviewees have suggested. However, they believe that crowdservicing shares a different relationship with collaboration. For instance, both of these interviewees have suggested that collaboration through crowdservicing functions under complex iterative interactions between the collective and connective mode but with the emphasis on the connective mode. Both interviewees indicated that for the complex iterative interactions to occur, the platform must collective collaborates on the platform to attract a suitable amount of content and participating agents. After an adequate platform is developed, the collaboration

‧ 國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

58

practice will focus on making connections and enabling connective collaboration between the content and participating agents on the platform to enable services and demand for services, or essentially facilitating the user-for-user, user-for-content and content-for-user relationships. Brian Chang adds that collective collaboration will occur as a part in the connective collaboration in order to “collect” collaboration efforts that can facilitate and bring resource to the connective collaboration practices.