Understanding Crowdsourcing Contest Fitness Strategic Decision Factors and Performance: An Expectation-Confirmation Theory Perspective

Contest-based intermediary crowdsourcing represents a powerful new business model for generating ideas or solutions by engaging the crowd through an online competition. Prior research has examined motivating factors such as increased monetary reward or demotivating factors such as project requirement ambiguity. However, problematic issues related to crowd contest fitness have received little attention, particularly with regard to crowd strategic decision-making and contest outcomes that are critical for success of crowdsourcing platforms as well as implementation of crowdsourcing models in organizations. Using Expectation-Confirmation Theory (ECT), we take a different approach that focuses on contest level outcomes by developing a model to explain contest duration and performance. We postulate these contest outcomes are a function of managing crowdsourcing participant contest-fitness expectations and disconfirmation, particularly during the bidding process. Our empirical results show that contest fitness expectations and disconfirmation have an overall positive effect on contest performance. This study contributes to theory by demonstrating the adaptability of ECT literature to the online crowdsourcing domain at the level of the project contest. For practice, important insights regarding strategic decision making and understanding how crowd contest-fitness are observed for enhancing outcomes related to platform viability and successful organizational implementation

Who Gets the Job? Synthesis of Literature Findings on Provider Success in Crowdsourcing Marketplaces

Background: Over the past decade, crowdsourcing marketplaces — online exchange platforms which facilitate commercial outsourcing of services — have witnessed a dramatic growth in the number of participants (service providers and customers) and the value of outsourced services. Deciding about the most appropriate provider is a key challenge for customers in crowdsourcing marketplaces because available information about providers may be incomplete and sometimes irrelevant for customer decisions. Ineffective information impedes many service providers to develop long-term relationships with customers, obtain projects on a regular basis and survive on crowdsourcing marketplaces. Previous studies have investigated the impact of a range of factors on customers’ choice decisions and providers’ success, given the important role of customer–provider relationship development for long-term success on crowdsourcing marketplaces. Method: This paper reviews the literature of crowdsourcing marketplaces with the aim of developing a comprehensive list of factors that influence customers’ choice decisions and providers’ success. Results: We found 31 conceptually distinct profile information components/factors that determine customers’ choices and providers’ business outcomes on crowdsourcing marketplaces. Conclusion: We classified these 31 factors into five major categories: 1) prior relationship between a customer and a provider or a customer’s invitation, 2) providers’ bidding behavior, 3) crowdsourcing marketplace or auction characteristics, 4) providers’ profile information, and 5) customer characteristics. The main factors in each category, associated considerations, related literature gaps and avenues for future research are discussed in detail

An Empirical Analysis of User Participation on Crowdsourcing Platform: A Two-sided Network Market Perspective

Crowdsourcing has recently emerged as a new platform for matching the demand and supply between professionals and businesses who seek external expertise for business task execution. Driven by the unique features of the two-sided crowdsourcing markets (such as auction-style competition on quality by professionals), this study seeks to examine how the dynamics of the two-sided crowdsourcing platform affect customers and professionals’ strategic behaviors and market outcomes. Using longitudinal transaction data from a crowdsourcing websites, we plan to empirically examine how the participation of professionals and customers, task reward and task completion rate are affected by the characteristics of the professionals such as distribution of the winning professionals and their reputation. The results of our study are expected to contribute to the growing literature on crowdsourcing and provide important insights on the design and assessment of the sustainability and profitability of the crowdsourcing business model

Exploring Human Resource Management in Crowdsourcing Platforms

The correct execution of process activities is usually responsibility of the employees (i.e., human resources) of an organisation. In the last years, notable support has been developed to make resource management in business processes more efficient and customisable. Recently, a new way of working has emerged and caught significant attention in the market: crowdsourcing. Crowdsourcing consists of outsourcing activities in the form of an open call to an undefined network of people, i.e., the crowd. While in traditional resource management in business processes resources are known and task assignment is usually controlled, the workers in crowdsourcing platforms are unknown and are allowed to select the tasks they want to perform. These and other di↵erences between resource management in business processes and in crowdsourcing platforms have not been explicitly investigated so far. Taking as reference the existing mature work on resource management in business processes, this paper presents the results of a study on the existing support for resource management in crowdsourcing platforms.Austrian Research Promotion Agency (FFG) 845638 (SHAPE

Modeling, enacting, and integrating custom crowdsourcing processes

Crowdsourcing (CS) is the outsourcing of a unit of work to a crowd of people via an open call for contributions. Thanks to the availability of online CS platforms, such as Amazon Mechanical Turk or CrowdFlower, the practice has experienced a tremendous growth over the past few years and demonstrated its viability in a variety of fields, such as data collection and analysis or human computation. Yet it is also increasingly struggling with the inherent limitations of these platforms: each platform has its own logic of how to crowdsource work (e.g., marketplace or contest), there is only very little support for structured work (work that requires the coordination of multiple tasks), and it is hard to integrate crowdsourced tasks into stateof-the-art business process management (BPM) or information systems. We attack these three shortcomings by (1) developing a flexible CS platform (we call it Crowd Computer, or CC) that allows one to program custom CS logics for individual and structured tasks, (2) devising a BPMN-based modeling language that allows one to program CC intuitively, (3) equipping the language with a dedicated visual editor, and (4) implementing CC on top of standard BPM technology that can easily be integrated into existing software and processes. We demonstrate the effectiveness of the approach with a case study on the crowd-based mining of mashup model patterns

INFORMATION TRANSPARENCY AND USER BEHAVIOR IN EMERGING ONLINE MARKETPLACES: EMPIRICAL STUDIES OF SOCIAL MEDIA AND OPEN INNOVATION MARKETS

Web 2.0 and social media have significantly increased the amount of information available to users not only about firms and their offerings, but also about the activities of other individuals in their networks and markets. It is widely acknowledged that this increased availability of information is likely to influence a user's behavior and choices. However, there are very few systematic studies of how such increased information transparency influences user behavior in emerging marketplaces. My dissertation seeks to examine the impact of increased information transparency - particularly, information about other individuals - in two emerging platforms. The first essay in my dissertation compares online "social" marketing on Facebook with "non-social" marketing and examines their relative impacts on the likelihood of adoption, usage and diffusion of an "App". While social marketing - wherein a user gets to see which of her other friends have also "liked" the product being marketed- is one of the fastest growing online marketing formats, there are hardly any studies that have examined the value of the social aspect of such marketing. I find that social marketing is associated with increased app adoption, usage, and diffusion as compared to non-social marketing. The study also uncovers interesting tradeoffs between the effects of different types of "social" information on user behavior outcomes. The second essay examines the behavior of contestants in an open innovation design marketplace, wherein firms seek solutions from a crowd through an online contest. The study examines how the availability of information about other contestants as well as the availability of feedback information provided to others by the contest holder, impacts a focal contestant's behavior and outcomes. I find that contestants adopt different strategic behaviors that increase their odds of winning the contest under the different information-transparency regimes. The findings have interesting implications for the design of online contests and crowdsourcing markets. Overall, my dissertation provides a deeper understanding of how the visibility of different types of information in online platforms impacts individual behaviors and outcomes

Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models

Methodological challenges of research on crowdsourcing

Crowdsourcing jest pojęciem stosunkowo nowym i pomimo zainteresowania badaczy nadal niewiele o nim wiadomo. Obserwuje się jednocześnie trudności natury poznawczej i praktycznej. Stało się to przesłanką do podjęcia refleksji na temat metodologii badań nad tym pojęciem. Przedmiotem artykułu jest identyfikacja dotychczasowych procedur badania crowdsourcingu, ze szczególnym uwzględnieniem wyzwań metodologicznych, jakie mogą pojawić się przed badaczami tego pojęcia. Artykuł powstał w oparciu o systematyczny przegląd literatury. Jego wyniki pozwoliły sformułować pewne wskazówki metodologiczne dla dalszych badań. Badania powinny być prowadzone z uwzględnieniem trzech poziomów crowdsourcingu: organizacja, technologia, and uczestnictwo. Dodatkowo podejście ilościowo-jakościowe pozwoli na poszerzenie wiedzy o crowdsourcingu.Crowdsourcing is a relatively new concept and, despite the interest of researchers, still little is known about it. At the same time, one observes difficulties of a cognitive and practical nature. This has become a premise for a reflection on the methodology of research on this subject. The subject of the article is the identification of the existing procedures of studying crowdsourcing, with particular inclusion of the methodological challenges that researchers of this concept may face. The article was written based on a systematic literature review. Its results enabled the formulation of some methodological guidelines for further research. Research should be conducted taking into account three levels of crowdsourcing: organization, technology, and participant. In addition, a quantitative and qualitative approach will enable the expansion of knowledge about crowdsourcing