Literature Review 

This dissertation is about the case study of Amazon Mechanical Turk (AMT). A research question that is adopted for the study is “What factors influence workers’ acceptance of a digital work platform? The case study of Amazon Mechanical Turk (AMT)”

In the following sections, this research has been segregated into three separate parts for discussing the factors that are impacting the acceptance of the workers to work on the digital platform. These three fields include the introduction of the gig economy and crowdsourcing industry and how are they influencing the AMT platform. After that, the Technology Acceptance Model known as the TAM model will be dealt with separately as this model would be the tool for answering the research question (Brawley & Pury, 2016). It is claimed by the TAM model that two important factors such as perceived ease of use and perceived usefulness can influence the acceptance of people, which might then influence the actual behaviors of people towards the new technology. However, these factors are known to be related to the AMT platform, hence it can be used for analyzing the acceptance of workers towards the AMT platform specifically. 

The gig economy and crowdsourcing

The gig economy has been found to be growing exponentially in numbers and gaining importance in recent years, but at the same time, it is having an impact on labor rights which is being overlooked largely. The working farm in the gig economy will include the crowd work and work on demand through apps above which the supply and demand of the activities of working are matched with online or through mobile apps. As per Coyle, Outsourcing and freelancing work became the keywords for organizational performance in a gig economy wherein work is converted into a commodity (Coyle, 2017). The challenges involved in the dominance of the nature of work performed by freelancers and its contribution to the economy have invited potential concerns for studies pertaining to job satisfaction, turnover, and organizational attraction. The ambiguities pertaining to definitions of freelancing have been resolved with explicit illustration of short-term employment characterized with remunerations on the basis of performance in projects. In a gig economy freelancing becomes a source for alternative employment thereby validating the sustainability of the economy. While it can be imperatively observed that entrepreneurs and freelancers have the capability of providing the best services in the specific sector, a gig economy is contradicted by activists and politicians on the grounds of perceiving freelancers as vulnerable workers rather than as a contribution to the economy. Freelancers can be identified as profound issues by the conventional labor force practices and principles which tend to create notable conflicts pertaining primarily to the legal dimensions of crowdsourcing (De Stefano, 2015). The majority of academic studies which have been conducted in the context of human resource management and practice depict a formal interpretation of the abilities of organizations to acquire professional undertakings from developed countries. The implications of the technological revolution have also created the necessity for effective training of the workforce in order to sustain a competitive advantage of economic significance.  According to Felstiner, the provision of freelancing jobs can be perceived as a cognizable improvement in the attitudes and experiences associated with conventional job experiences. The growth of a gig economy enables the opportunities for realizing improvements in the access routes for additional income other than the conventional job (Felstiner, 2011). The common perception among different people related to crowdsourcing and self-employment is vested in the acquisition of significant income from works obtained from online platforms. The proliferation of the gig economy is recognized for the introduction of changes on the individual level as well as social level emphasizing the nature of an independent worker and the tasks associated with their job. Crowdsourcing can be profoundly related to the outsourcing of a task or function by a designated agent to an unprecedented network of laborers. The implementation of crowdsourcing has diversified into different phenomena such as social engagement, user-generated content, co-creation, prediction, knowledge aggregation, and open innovation. Some of the essential factors influencing the realization of crowdsourcing facilities include the provision of monetary remuneration or the design of the jobs (Graham, Hjorth & Lehdonvirta, 2017).  A work of this form will be able to provide a better match for the job opportunities and flexible working schedules are allowed. Although the way to a severe commodification of work can be paved off of by them. In this literature review, the inferences of this commodification and supporters of the complete acknowledgment of these activities in the gig economy at work are discussed.  It even shows how the gig economy is not considered as a separate feature of the economy and is part of wide-ranging phenomena like formalization and casualization of work and the extent of the non-standard forms of employment.  Later it will analyze the risks that are associated with these activities with respect to the basic principles and rights that are present at work as these are defined by the International Labor Organization, where it will be addressing the problem of misclassification in the gig economy about the employment status of workers. As per Kittur et al, the present trends that are relevant are hence being examined in which the emergence of the forms of the organization of workers in self is included. Finally, some of the policy proposals are analyzed critically so that there would be a possibility of creating the transitional category between employee and the independent contractor of a worker for classifying the work in the gig economy (Kittur et al., 2013). Along with this other uncertain proposals are also put forth such as an extension of the basic rights of labor to all the workers without considering the employment status and credit of the role of social partners in this aspect at the same time the hastened deregulations are avoided.

However, a profound challenge has been observed in the case of digital platforms and the required work in a gig economy is the lack of data. The external opinions of workers are transferred from one location to another in the form of anecdotes and arguments thereby depicting formal indications towards profound disparities. The concerns against crowdsourcing initiatives and the prominence of a gig economy are observed profoundly for ambiguities pertaining to the minimum wage in different countries (Kaufmann, Schulze & Veit, 2011). Prominent examples of conflicts in the crowdsourcing industry could be perceived in the market conditions in the United States wherein the minimum wage is considerably lower alongside poor conditions of alternate jobs and limited provision of public facilities such as welfare and healthcare. 

These factors are also complemented with the concerns of legal rulings and ethical obligations pertaining to work on a digital platform depicting the setbacks noticed in terms of employee recognition and policies for employee retirement. Political pressure pertaining to the role of the gig economy is destroying the real economy could lead to limitations such as tariff barriers thereby indicating the challenges for crowdsourcing. The online platform models for executing the crowdsourcing initiatives are perceived as profound challenges for the existing infrastructure of consumer and employee protection since they are designed with the conventional organizations in mind. On the other hand, it cannot be commented that the most suitable response in terms of policies in the digital platform of services could be obsolete. The regulations and policies pertaining to digital platforms helped in the particular services of monitoring the performance of the digital platform models alongside ensuring conformity with legal implications (Kuhn, 2016). The other concerns that have been noticed in academic research related to the application of crowdsourcing, as well as its implications in the context of contemporary labor practices, refer to the overlooking of other dimensions while perceiving crowdsourcing as a medium for the transaction of jobs. The scale of crowdsourcing initiatives and the flexibility of accessing as well as completing crowdsourcing projects also pose considerable deviations from the conventional labor market practices which involved the significance of documented academic qualification criteria (Martin et al., 2014). Therefore the understanding of practical applications of crowdsourcing and the reflection on formal labor market concerns for other segments which have been unemployed for a considerable period of time.   

As per Parigi & Ma, It is quoted by the CEO of Amazon who owns the Amazon Mechanical Turk (AMT) is one of the most prominent and highly used crowd work sourcing platforms as this kind of practice will be given the accessibility to humans as a service (Parigi & Ma, 2016). Irrespective of this kind of quotation which refers only to crowdsourcing they even hold factual for the work which is on-demand through apps. The best explanation is given by them in answering the query why serious attention is needed to them by labor researchers and institutions, society, and government on the whole.  The quote humans as a service will convey the idea of great forms of the commodification of people perfectly. However, the terms commodification and re-commodification are not confined to the gig economy as a much wider part of the labor market is a concern to them. However, some features of the gig economy have worsened significantly the effects of this commodification for various reasons ((Ross et al., 2010).

Technology Acceptance Model (TAM)

Online crowdsourcing markets nowadays are becoming more in popularity in the form of sources for the collection of data. In this literature review, we would be examining the reliability of the survey that is resulting from the samples of the student, consumer panels, and the online crowdsourcing markets, particularly Amazon’s Mechanical Turk. For, exploring the potential differences in demographics, estimates of the structural model, measurement invariances, and psychometric survey is conducted by examining the technology acceptance model (Saxton, Oh & Kishore, 2013).

The digital technology of Amazon Mechanical Turk is an effective technology that has no pre-designed structure of knowledge; rather it is a highly flexible, user-centric, and interactive system that accommodates the changing activity and knowledge of humans readily. For instance, the social bookmarking website is the best example. With no categories being predefined the emergence of knowledge categorization is dynamic and its structure changes as people start interacting with the system. Over time it becomes a delicious platform for the individuals with a multitude of unidentified individuals who will be working in contrast to the digital economy that has been established which will have no upfront knowledge taxonomy that is defined earlier by the professionals or system analysts. 

The crowdsourcing web service of Amazon and Amazon Mechanical Turk will be illustrating the nature of crowdsourcing. The mTurk crowdsourcing service of Amazon is described as the artificial intelligence service which will be performing the human intelligence tasks with its technology acceptance model so that it cannot be duplicated easily or cannot be replaced by the machines but can be handled easily by the human intelligence (Webster, 2016). This service can be applied simply to a real person’s skills and intelligence and also via an artificial environment of varied computing networks for finding solutions for the problems that are difficult for machines but not for humans. It actually means that the crowdsourcing platform is a technology that is networked with virtual production space so that people can have interaction with each other and can perform their economic activities for their own advantage. 

Apprehending the impact of employee satisfaction with the practices of crowdsourcing in Amazon’s Mechanical Turk (MTurk) platforms could be derived from the technology acceptance model (TAM) which depends on the analysis of employees’ perception of new technology. The application of technologies to the domain of crowdsourcing is reflective of large-scale changes in the organization of work in the context of personal as well as organizational levels. According to Martin et al, the application of TAM tests is accountable for perceiving the applicability of the technological platform by users and the perception of the effectiveness of the platforms (Martin et al., 2014). The growth of research in the domain of crowdsourcing refers to the application of knowledge for promising the development of interpersonal and long-term relationships within the work environment. Crowdsourcing enables the completion of tasks that could be possible through human endeavor only such as surveys and image classification. The request for the task involves the description of compensation offered by the delegating agency and the complete description of the work to be completed by the worker. The existences of a varied assortment of other notable channels through which crowdsourcing and its practical implications can be derived refer to the comprehensiveness of opportunities for information sharing. The organization of a system that facilitates a considerable degree of the behavior of the delegating agents as well as the decision-making approaches of the individual undertaking the project could be identified as a factor that determines the outcomes of TAM tests (Kittur et al., 2013). 

The implementation of a Technology Acceptance Model is profoundly observed in the outcomes that relate to the perception of effectiveness i.e. the effectiveness of the model from the perspective of the user and the perception of applicability of the model. The application of the concerned platform in a wide range of ventures required by the user could be also accounted as prominent characteristics of the acceptance of technology by users. Therefore crowdsourcing and the gig economy’s dependence on the introduction of new technologies can be catered only through a prolific understanding of the compatibility which users are able to accomplish while operating in the crowdsourcing platforms. As per Graham, Hjorth & Lehdonvirta, the consideration of various factors that can be impactful on the efficiency of crowdsourcing platforms has been related to the consideration of psychological factors and the increasing prominence of ethical concerns and informed consent implications create further ambiguities. The process objectives of a TAM could be collated with practical work experiences with crowdsourcing platforms thereby presenting a feasible impression of the relationships among peers and supervisors (Graham, Hjorth & Lehdonvirta, 2017). The effectiveness of the crowdsourcing platforms such as MTurk could be largely based on the privileges and flexibility accessed by workers in terms of the time and choice of work. The decisions on projects are quite demarcated from the conventional implications of decision-making in employment settings. The variation in different alternative forms of work such as contracting, teleworking, contingent, and temporary work has been revisited by the crowdsourcing platforms with unconventional benefits such as completion of task without any direct communication between delegating agent and the task performer. 

An apprehension of the hazards associated with working on crowdsourcing as well as practical work experiences can facilitate a wide-ranging impression of the industry. The concerns for developing worker visibility as well as the development of frameworks to apprehend the needs of workers through feedback as well as the illustration of requester needs through the element of quality control must be ascertained precisely in a crowdsourcing environment. The concerns of these factors are relevantly associated with indications towards addressing the future state of events in the domain of crowdsourced work (Martin et al., 2014). The acceptance of crowdsourcing by general workers could be apprehended cognizably on the grounds of diverse work experiences thereby leading to the existence of various motivations for the effective performance of employees. The differences that can be observed among workers on crowdsourcing platforms have been largely attributed to the geographic demarcations, experience, and underlying disparities. 

 A comprehensive study of the prolific dimensions of crowdsourcing has led to the interpretation of a substantial rise in popularity among requesters and workers which could also be associated with the growth of revenues from the crowdsourcing platforms since 2009 (Saxton, Oh & Kishore, 2013). The favourable nature of the crowdsourcing platforms can be validated on the grounds of access to comprehensive sources of work as well as inexpensive human computation services which include individuals with varying locations, cognitive abilities, and physical abilities. The acceptance of the technologies of crowdsourcing can be validated on the grounds of observing job satisfaction as a functional predictor of turnover (Graham, Hjorth & Lehdonvirta, 2017). It has been imperatively perceived that users could interpret the effectiveness of technology from the implications of situational characteristics and individual differences resulting in the apprehension of the comprehensive categories of job satisfaction indicators. Some of the notable approaches which could validate the concerns of job satisfaction refer to situational, dispositional, and dispositional-situational interactive factors. The predictors in the individual approaches have been validated on the grounds of research in academic literature pertaining to job satisfaction in traditional contexts of employment. The dispositional approach suggests that the satisfaction of an individual with a new technology or job is dependent on personality traits which are guided by the elements of negative affectivity and trait positive; core self-evaluations and big five personality traits. Hence the impact of the individual differences for determining the job effectiveness among the workers on crowdsourcing platforms has been considered as a major subject of emphasis in the literature related to crowdsourcing studies (De Stefano, 2015). 

The following research activity aims to draw profound reflections on the effectiveness of crowdsourcing platforms such as Amazon Mechanical Turk services to accomplish the desired outcomes of a gig economy. It is imperative to understand the research objective which is directed towards the apprehension of the basic details of the crowdsourcing industry and gig economy thereby contributing to the identification of the value of technologies to identify the feasibility of new crowdsourcing initiatives implemented in a contemporary environment. The review of literature is reflective of the different components of the gig economy wherein the common analysis could be ambiguous and the arguments have to depict the treatment of the arguments jointly (Brawley & Pury, 2016). The considerable implications for worker and requester consideration could be observed in the potential future implications such as motivation, pay, and feedback which could facilitate better interaction, better reputation mechanisms, and increase motivation. The considerations of requesters who put out jobs are also significant elements observed in the literature which could assist in attempting the research question emphasizing on recognition of the influence of task decomposition, quality control, and coordination as well as workflow mechanisms involving electronically mediated collaboration. The comprehensive review of literature projects crowdsourcing with considerable advantages as well as pitfalls in specific areas especially in terms of information technology management (Saxton, Oh & Kishore, 2013). Crowdsourcing has been prominently associated with the implications of new opportunities for social mobility and income development in global jurisdictions that could be characterized by pitfalls of stagnant local economies and a lack of investment in local government infrastructures. While the research assumes to present a generic impression of the crowdsourcing industry and its involvement with the development of the gig economy, the research also presents notable indications from the perspective of the practical examples of Amazon Mechanical Turk which is a significant crowdsourcing platform presently.      

Reference 

Brawley, A.M. and Pury, C.L., 2016. Work experiences on MTurk: Job satisfaction, turnover, and information sharing. Computers in Human Behavior, 54, pp.531-546.

Coyle, D., 2017. Precarious and Productive Work in the Digital Economy. National Institute Economic Review, 240(1), pp.R5-R14.

De Stefano, V., 2015. The rise of the just-in-time workforce’: On-demand work, crowd work and labor protection in the gig economy.

Felstiner, A., 2011. Working for the crowd: employment and labor law in the crowdsourcing industry.

Graham, M., Hjorth, I. and Lehdonvirta, V., 2017. Digital labour and development: impacts of global digital labor platforms and the gig economy on worker livelihoods. Transfer: European Review of Labour and Research, 23(2), pp.135-162.

Kittur, A., Nickerson, J.V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., Lease, M. and Horton, J., 2013, February. The future of crowd work. In Proceedings of the 2013 conference on Computer supported cooperative work (pp. 1301-1318). ACM.

Kaufmann, N., Schulze, T. and Veit, D., 2011, August. More than fun and money. Worker Motivation in Crowdsourcing-A Study on Mechanical Turk. In AMCIS (Vol. 11, No. 2011, pp. 1-11).

Kuhn, K.M., 2016. The Rise of the “Gig Economy” and Implications for Understanding Work and Workers. Industrial and Organizational Psychology, 9(1), pp.157-162.

Martin, D., Hanrahan, B.V., O’Neill, J. and Gupta, N., 2014, February. Being a turker. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (pp. 224-235). ACM.

Parigi, P. and Ma, X., 2016. The gig economy. XRDS: Crossroads, The ACM Magazine for Students, 23(2), pp.38-41.

Ross, J., Irani, L., Silberman, M., Zaldivar, A., and Tomlinson, B., 2010, April. Who are the crowdworkers?: shifting demographics in mechanical turk. In CHI’10 extended abstracts on Human factors in computing systems (pp. 2863-2872). ACM.

Saxton, G.D., Oh, O. and Kishore, R., 2013. Rules of crowdsourcing: Models, issues, and systems of control. Information Systems Management, 30(1), pp.2-20.

Webster, J., 2016, September. Microworkers of the Gig Economy: Separate and Precarious. In New Labor Forum (Vol. 25, No. 3, pp. 56-64). Sage CA: Los Angeles, CA: SAGE Publications.