What are the similarities and differences between crowdsourcing and sharing economy? What factors influence their use in developing countries? In light of recent developments in the use of IT-mediated technologies, such as crowdsourcing and the sharing economy, this manuscript examines their similarities and differences, and the challenges regarding their effective use in developing countries. We first examine each individually and highlight different forms of each IT-mediated technology. Given that crowdsourcing and sharing economy share aspects such as the use of IT, a reliance on crowds, monetary exchange, and the use of reputation systems, we systematically compare the similarities and differences of different types of crowdsourcing with the sharing economy, thus addressing a gap in the current literature. Using this knowledge, we examine the different challenges faced by developing countries when using crowdsourcing and the sharing economy, and highlight the differences in the applicability of these IT-mediated technologies when faced with specific development issues.
Mayor and Executive Board of the Municipality of Amsterdam have agreed on the Action Plan on Sharing Economy and herewith gives space to the opportunities the sharing (or collaborative) economy offers to the city. Sharing economy is a broad concept, amongst other things it is about making more efficient use of goods, services and skills. By using online platforms, people can for example exchange, rent and borrow stuff from each other more easily. The consumer is at the centre and gets more affordable and easier access to services and goods. The Mayor and Executive Board want to stimulate the sharing economy where possible without losing sight of any excesses. Risks include an uneven playing field or a lack of social security. Thus the sharing economy is not a question of ban or authorize, but of monitor and seize opportunities where possible (March 2016).
"Collaboration in Cities: From Sharing to ‘Sharing Economy’". World Economic...eraser Juan José Calderón
White Paper del World Economic Forum de Diciembre de 2017 In collaboration with PwC del titulado: "Collaboration in Cities: From Sharing to ‘Sharing Economy’"
The Fundamentals of Policy CrowdsourcingAraz Taeihagh
What is the state of the research on crowdsourcing for policymaking? This article begins to answer this question by collecting, categorizing, and situating an extensive body of the extant research investigating policy crowdsourcing, within a new framework built on fundamental typologies from each field. We first define seven universal characteristics of the three general crowdsourcing techniques (virtual labor markets, tournament crowdsourcing, open collaboration), to examine the relative trade-offs of each modality. We then compare these three types of crowdsourcing to the different stages of the policy cycle, in order to situate the literature spanning both domains. We finally discuss research trends in crowdsourcing for public policy and highlight the research gaps and overlaps in the literature.
Examination of crowdsourcing as a tool for policy makingAraz Taeihagh
Crowdsourcing is rapidly evolving and applied in situations where ideas, labour, opinion or expertise of large groups of people are used. Crowdsourcing is now used in various policy making initiatives; however, this use has usually been focused on open collaboration platforms and specific stages of the policy process such as agenda-setting and policy evaluations. Moreover, other forms of crowdsourcing have been neglected in policy making with a few exceptions. This article examines crowdsourcing as a tool for policy making and explores the nuances of the technology and its use and implications for different stages of the policy process. The article addresses questions around the role of crowdsourcing and whether it can be considered as a policy tool or a technology enabler.
Call for papers - ICPP6 T13P05 - PLATFORM GOVERNANCE IN TURBULENT TIMES.docxAraz Taeihagh
CALL FOR PAPERS
T13P05 - PLATFORM GOVERNANCE IN TURBULENT TIMES
https://www.ippapublicpolicy.org/conference/icpp6-toronto-2023/panel-list/17/panel/platform-governance-in-turbulent-times/1428
Abstract submission deadline: 31 January 2023
GENERAL OBJECTIVES, RESEARCH QUESTIONS AND SCIENTIFIC RELEVANCE
Platforms significantly increase the ease of interactions and transactions in our societies. Crowdsourcing and sharing economy platforms, for instance, enable interactions between various groups ranging from casual exchanges among friends and colleagues to the provision of goods, services, and employment opportunities (Taeihagh 2017a). Platforms can also facilitate civic engagements and allow public agencies to derive insights from a critical mass of citizens (Prpić et al. 2015; Taeihagh 2017b). More recently, governments have experimented with blockchain-enabled platforms in areas such as e-voting, digital identity and storing public records (Kshetri and Voas, 2018; Taş & Tanrıöver, 2020; Sullivan and Burger, 2019; Das et al., 2022).
How platforms are implemented and managed can introduce various risks. Platforms can diminish accountability, reduce individual job security, widen the digital divide and inequality, undermine privacy, and be manipulated (Taeihagh 2017a; Loukis et al. 2017; Hautamäki & Oksanen 2018; Ng and Taeihagh 2021). Data collected by platforms, how platforms conduct themselves, and the level of oversight they provide on the activities conducted within them by users, service providers, producers, employers, and advertisers have significant consequences ranging from privacy and ethical concerns to affecting outcomes of elections. Fake news on social media platforms has become a contentious public issue as social media platforms offer third parties various digital tools and strategies that allow them to spread disinformation to achieve self-serving economic and political interests and distort and polarise public opinion (Ng and Taeihagh 2021). The risks and threats of AI-curated and generated content, such as a Generative Pre-Trained Transformer (GPT-3) (Brown et al., 2020) and generative adversarial networks (GANs) are also on the rise (Goodfellow et al., 2014) while there are new emerging risks due to the adoption of blockchain technology such as security vulnerabilities, privacy concerns (Trump et al. 2018; Mattila & Seppälä 2018; Das et al. 2022).
The adoption of platforms was further accelerated by COVID-19, highlighting their governance challenges.
The sharing economy: How economic activity is shifting to, and being enhanced...Andrea Silvello
The term sharing economy is widely perceived as a synonym of “collaborative economy” or “on demand economy”, but it actually represents a very wide concept which lacks a common definition.
Rachel Botsman defines the collaborative economy as “a system that activates the untapped value of all kinds of assets through models and marketplaces that enable greater efficiency and access ”. The concept behind the sharing economy is indeed very simple: anything that is not being used can be rented out. This framework includes services such as renting, bartering, loaning, gifting, and swapping of underutilized material or immaterial possessions. These idle resources are useful to create an efficient circular system by reallocating or trading them with people who want or need them. Recycling, upcycling and sharing the lifecycle of products are common features of the sharing economy. “Waste” is the result of a misallocation of resources: today technology often allows us to easily correct that misallocation, by redistributing or trading a great variety of “sleeping” assets and resources (table 1). For instance, Uber and AirBnb platforms allow customers to share cars and homes, while TaskRabbit connects people with free time with people who need someone to perform small tasks.
Mayor and Executive Board of the Municipality of Amsterdam have agreed on the Action Plan on Sharing Economy and herewith gives space to the opportunities the sharing (or collaborative) economy offers to the city. Sharing economy is a broad concept, amongst other things it is about making more efficient use of goods, services and skills. By using online platforms, people can for example exchange, rent and borrow stuff from each other more easily. The consumer is at the centre and gets more affordable and easier access to services and goods. The Mayor and Executive Board want to stimulate the sharing economy where possible without losing sight of any excesses. Risks include an uneven playing field or a lack of social security. Thus the sharing economy is not a question of ban or authorize, but of monitor and seize opportunities where possible (March 2016).
"Collaboration in Cities: From Sharing to ‘Sharing Economy’". World Economic...eraser Juan José Calderón
White Paper del World Economic Forum de Diciembre de 2017 In collaboration with PwC del titulado: "Collaboration in Cities: From Sharing to ‘Sharing Economy’"
The Fundamentals of Policy CrowdsourcingAraz Taeihagh
What is the state of the research on crowdsourcing for policymaking? This article begins to answer this question by collecting, categorizing, and situating an extensive body of the extant research investigating policy crowdsourcing, within a new framework built on fundamental typologies from each field. We first define seven universal characteristics of the three general crowdsourcing techniques (virtual labor markets, tournament crowdsourcing, open collaboration), to examine the relative trade-offs of each modality. We then compare these three types of crowdsourcing to the different stages of the policy cycle, in order to situate the literature spanning both domains. We finally discuss research trends in crowdsourcing for public policy and highlight the research gaps and overlaps in the literature.
Examination of crowdsourcing as a tool for policy makingAraz Taeihagh
Crowdsourcing is rapidly evolving and applied in situations where ideas, labour, opinion or expertise of large groups of people are used. Crowdsourcing is now used in various policy making initiatives; however, this use has usually been focused on open collaboration platforms and specific stages of the policy process such as agenda-setting and policy evaluations. Moreover, other forms of crowdsourcing have been neglected in policy making with a few exceptions. This article examines crowdsourcing as a tool for policy making and explores the nuances of the technology and its use and implications for different stages of the policy process. The article addresses questions around the role of crowdsourcing and whether it can be considered as a policy tool or a technology enabler.
Call for papers - ICPP6 T13P05 - PLATFORM GOVERNANCE IN TURBULENT TIMES.docxAraz Taeihagh
CALL FOR PAPERS
T13P05 - PLATFORM GOVERNANCE IN TURBULENT TIMES
https://www.ippapublicpolicy.org/conference/icpp6-toronto-2023/panel-list/17/panel/platform-governance-in-turbulent-times/1428
Abstract submission deadline: 31 January 2023
GENERAL OBJECTIVES, RESEARCH QUESTIONS AND SCIENTIFIC RELEVANCE
Platforms significantly increase the ease of interactions and transactions in our societies. Crowdsourcing and sharing economy platforms, for instance, enable interactions between various groups ranging from casual exchanges among friends and colleagues to the provision of goods, services, and employment opportunities (Taeihagh 2017a). Platforms can also facilitate civic engagements and allow public agencies to derive insights from a critical mass of citizens (Prpić et al. 2015; Taeihagh 2017b). More recently, governments have experimented with blockchain-enabled platforms in areas such as e-voting, digital identity and storing public records (Kshetri and Voas, 2018; Taş & Tanrıöver, 2020; Sullivan and Burger, 2019; Das et al., 2022).
How platforms are implemented and managed can introduce various risks. Platforms can diminish accountability, reduce individual job security, widen the digital divide and inequality, undermine privacy, and be manipulated (Taeihagh 2017a; Loukis et al. 2017; Hautamäki & Oksanen 2018; Ng and Taeihagh 2021). Data collected by platforms, how platforms conduct themselves, and the level of oversight they provide on the activities conducted within them by users, service providers, producers, employers, and advertisers have significant consequences ranging from privacy and ethical concerns to affecting outcomes of elections. Fake news on social media platforms has become a contentious public issue as social media platforms offer third parties various digital tools and strategies that allow them to spread disinformation to achieve self-serving economic and political interests and distort and polarise public opinion (Ng and Taeihagh 2021). The risks and threats of AI-curated and generated content, such as a Generative Pre-Trained Transformer (GPT-3) (Brown et al., 2020) and generative adversarial networks (GANs) are also on the rise (Goodfellow et al., 2014) while there are new emerging risks due to the adoption of blockchain technology such as security vulnerabilities, privacy concerns (Trump et al. 2018; Mattila & Seppälä 2018; Das et al. 2022).
The adoption of platforms was further accelerated by COVID-19, highlighting their governance challenges.
The sharing economy: How economic activity is shifting to, and being enhanced...Andrea Silvello
The term sharing economy is widely perceived as a synonym of “collaborative economy” or “on demand economy”, but it actually represents a very wide concept which lacks a common definition.
Rachel Botsman defines the collaborative economy as “a system that activates the untapped value of all kinds of assets through models and marketplaces that enable greater efficiency and access ”. The concept behind the sharing economy is indeed very simple: anything that is not being used can be rented out. This framework includes services such as renting, bartering, loaning, gifting, and swapping of underutilized material or immaterial possessions. These idle resources are useful to create an efficient circular system by reallocating or trading them with people who want or need them. Recycling, upcycling and sharing the lifecycle of products are common features of the sharing economy. “Waste” is the result of a misallocation of resources: today technology often allows us to easily correct that misallocation, by redistributing or trading a great variety of “sleeping” assets and resources (table 1). For instance, Uber and AirBnb platforms allow customers to share cars and homes, while TaskRabbit connects people with free time with people who need someone to perform small tasks.
The digital labour market uder debate: Platforms, workers, rights and WorkertechAlbert Canigueral
"The digital labour market uder debate: Platforms, workers, rights and Workertech" is a study about the future of work and the future of workers. The report has been comissioned to Ouishare by Cotec Foundation with the suport of Malt.
A Framework for Policy Crowdsourcing - Oxford IPP 2014Araz Taeihagh
Can Crowdsourcing be used for policy? Previous work posits that the three types of Crowdsourcing have different levels of potential usefulness when applied to the various stages of the policy cycle. In this paper, we build upon this exploratory work by categorizing the extant research with respect to Crowdsourcing for the policy cycle. Premised upon our analysis, we thereafter discuss the trends, highlight the gaps, and suggest some approaches to empirically validate the application of Crowdsourcing to the policy cycle.
Sharing economy platforms and applications are already being used widely in the B2C markets, such as Über and Airbnb, but sharing economy solutions for the B2B markets still includes a lot of potential. The principles of sharing economy are being utilized in the B2B markets for instance by sharing machinery in agriculture and forestry. In addition, other tangible assets, such as equipment, raw materials, office space and warehouses can be shared between companies. Intangible shared assets include for instance companies' brainpower, knowledge and intellectual capital.
Antenna for Social Innovation. We Share. Who Wins: unravelling the controvers...ESADE
In this fourth edition of the Antenna for Social Innovation, we discuss one of the most fascinating and controversial economic transformations: the growth of the collaborative economy. This transformation has been accompanied by a series of events that is destined to revolutionise our societies – namely, the expansion of the Internet, as well as the rise of smartphones, social networks, advances in artificial intelligence, and the capacity to instantly process huge amounts of information at a tiny cost. We talk about societies in a broad sense because the new wave of developments in the digital economy will transform the economic sphere of our lives – as well as the workplace, tax system, educational models, consumption patterns, and communications.
Introduction to Society Chapter Thirteen Weekly Assignments TMargaritoWhitt221
Introduction to Society
Chapter Thirteen Weekly Assignments
The Functions of Government
1. List five primary functions of government
2. Identify three contrasting views of government
3. Explain the liberal, conservative, radical, reactionary, and anarchist philosophies of government
4. Distinguish a democracy from an autocracy
5. List some distinguishing characteristics of a democracy
6. Explain the democratic concept of the individual
7. List the common justifications for an autocracy
8. List four characteristics of autocracy
9. Summarize the three views of the nature of government
10. List the seven exaggerated characterizations on how the role of government is viewed
11. Draw a diagram illustrating the continuum of autocracies
The digital entrepreneurial ecosystem
Fiona Sussan & Zoltan J. Acs
Accepted: 21 March 2017 /Published online: 11 May 2017
# Springer Science+Business Media New York 2017
Abstract A significant gap exists in the conceptualiza-
tion of entrepreneurship in the digital age. This paper
introduces a conceptual framework for studying entre-
preneurship in the digital age by integrating two well-
established concepts: the digital ecosystem and the
entrepreneurial ecosystem. The integration of these
two ecosystems helps us better understand the interac-
tions of agents and users that incorporate insights of
consumers’ individual and social behavior. The Digital
Entrepreneurial Ecosystem framework consists of four
concepts: digital infrastructure governance, digital user
citizenship, digital entrepreneurship, and digital market-
place. The paper develops propositions for each of the
four concepts and provides a theoretical framework of
multisided platforms to better understand the digital
entrepreneurial ecosystem. Finally, it outlines a new
research agenda to fill the gap in our understanding of
entrepreneurship in the digital age.
Keywords Entrepreneurship . Ecosystem .
Matchmakers . Digital infrastructure . Digital
governance . Digital citizenship . Multisided platforms .
Information technologies
JEL classification L26 . 011 . P40 . P00
1 Introduction
As the Economist magazine went to press the lead story
was about reinventing the company.1 This new compa-
ny type is at the heart of a growing debate on how to
understand the digital economy. Ever since the launch of
Uber, Snapchat, and AirBnB and the earlier success of
Google, Amazon, and Facebook, a new breed of
company has emerged that uses digital technology,
entrepreneurship, and innovation to upend industries
on a global scale (Stone 2017).2 Most of these compa-
nies are matchmakers (Evans and Schmalensee 2016,
p.1).3 What these companies have in common is that
they all connect members of one group with another
group. The core competencies of these companies are
their ability to match one group of customers with
another group of customers by reducing the transaction
cost of a match (Coase 1937). These multisided plat-
forms would not exis ...
The aim of this study is to figure out an overview on the literature and related studies on the
awareness of digital labor in the economic system and how production of the capitalist system affects labor
employment and shaped the increase in the socio-economic inequality, which has benefited the capital in the
last 20 years,
Crowdsourcing: a new tool for policy-making?Araz Taeihagh
Crowdsourcing is rapidly evolving and applied in situations where ideas, labour, opinion or expertise of large groups of people are used. Crowdsourcing is now used in various policy-making initiatives; however, this use has usually focused on open collaboration platforms and specific stages of the policy process, such as agenda-setting and policy evaluations. Other forms of crowdsourcing have been neglected in policymaking, with a few exceptions. This article examines crowdsourcing as a tool for policymaking, and explores the nuances of the technology and its use and implications for different stages of the policy process. The article addresses questions surrounding the role of crowdsourcing and whether it can be considered as a policy tool or as a technological enabler and investigates the current trends and future directions of crowdsourcing.
Future of the sharing economy An emerging view 30 March 2017Future Agenda
Humans have always shared. More recently, enabled by technology, new forms of sharing and access have begun to transform industries as well as the way we live our lives, creating financial return and social reward for participants. From AirBnB (爱彼迎 ) to Uber and Didi Chuxing, the sharing economy has rapidly moved from niche to mainstream in a number of categories, most notably accommodation and transportation.
But where next? Building on insights from the wider Future Agenda programme with recent research and interviews with a number of industry leaders and experts, we’re delighted to share an emerging view of the Future of the Sharing Economy.
Over the next few weeks we are asking for feedback and opinion from around the world. We’d really welcome your perspective, comments, challenge and additional insights to co-create an enriched informed future view for all. We will then update and share.
As with all Future Agenda output, this is being published under creative commons (share alike non commercial) so you are free to share and quote as suits.
Electronic government (e-government) has been attracting the attention of the world for the past two decades, and specifically, upon the advent of the internet. Governments worldwide have spent billions of dollars to date to transform themselves into e-government. However, their efforts and large investments resulted mainly in online portals and scattered electronic services. Various studies indicate that e-government initiatives are failing to meet citizens' expectations for convenient service delivery systems. Nonetheless, the rapid pace at which technology is innovatively evolving and its disruptive nature is forcing new realities to be accepted in e-government domain. The new forms of mobility made possible by the transforming technologies are not only changing how people live their lives today, but also redefining business models, employee productivity, customer relationship, and even how governments are structured. The growing usage of smartphones and tablets have significant impact on all industries, but at large how government services are delivered. This study attempts to provide some qualitative input to the existing body of knowledge. It sheds light on some trends that have high impact to disrupt existing technological-based channels of interaction between governments and citizens, and ultimately on service delivery. It also sheds light on the role of modern identity management infrastructure in enabling higher levels of trust and confidence in mobile transactions.
Experiments on Crowdsourcing Policy Assessment - Oxford IPP 2014Araz Taeihagh
Can non-experts Crowds perform as well as experts in the assessment of policy measures? To what degree does geographical location relevant to the policy context alter the performance of non-experts in the assessment of policy measures? This research in progress seeks to answer these questions by outlining experiments designed to replicate expert policy assessments with non-expert Crowds. We use a set of ninety-eight policy measures previously evaluated by experts, as our control condition, and conduct the experiments using two discrete sets of non-expert Crowds recruited from a Virtual Labor Market (VLM). We vary the composition of our non-expert Crowds along two conditions; participants recruited from a geographical location relevant to the policy context, and participants recruited at-large. In each case we recruit a sample of 100 participants for each Crowd. Each experiment is then repeated at the VLM with completely new participants in each group to assess the reliability of our results. We will present results on the performance of all four groups of non-experts jointly and severally in comparison to the expert assessments, and discuss the ramifications of our findings for the use of non-expert Crowds and VLM’s for policy design. Our experimental design applies climate change adaptation policy measures.
International Journal of Marketing Studies; Vol. 6, No. 5; 201TatianaMajor22
International Journal of Marketing Studies; Vol. 6, No. 5; 2014
ISSN 1918-719X E-ISSN 1918-7203
Published by Canadian Center of Science and Education
21
Digital Strategies of Consumer Involvement and Innovation Dynamics:
A Cross-Sector Explorative Study
Eleonora Paolocci1
1 IULM University, Milan, Italy
Correspondence: Eleonora Paolocci, IULM University, Milan, Italy. E-mail: [email protected]
Received: May 24, 2014 Accepted: June 26, 2014 Online Published: September 28, 2014
doi:10.5539/ijms.v6n5p21 URL: http://dx.doi.org/10.5539/ijms.v6n5p21
Abstract
The study aims at exploring the collaborative dynamics between firms and consumers through Web tools. At
present, there is limited empirical research aimed at investigating if and how the involvement of consumers in
the implementation of open approaches, mediated by digital technologies, is actually implemented. The study
presents a recent multifactorial investigation of the topic where literature lacks in. Through the Web-analysis of
practices of a sample group of 180 companies operating in different market sectors, the author wants to explore
spread and type profiles of collaborative strategies, investigating the existence of a possible correlation with the
served markets and other moderator variables. Findings, identifying a ‘spectrum’ of engagement and co-creation
mechanisms, suggest forms of aggregation and profiling in the approach followed by the firms and illustrate how
the characteristics of virtual spaces allow them to explore new frontiers in the implementation of open
approaches, with different degrees of involvement.
Keywords: co-creation, consumer insight, empirical research, open innovation (OI), virtual integration
1. Introduction
Existing academic literature suggests a significant potential of collaboration with consumers in the process of
market value creation through ICTs (von Hippel, 2001; von Hippel & Katz, 2002; Sawhney, Verona, & Prandelli,
2005; Prandelli, Verona, & Raccagni, 2006; Bilgram, Brem, & Voigt, 2008; Füller & von Hippel, 2008;
Prandelli, Sawhney, & Verona, 2008; Füller, Muhlbacher, Matzler, & Jawecki, 2009; Morgan & Wang, 2010).
Considerable attention has been given to the benefits offered by the advent of digital technologies: low-cost
interaction; increase in the speed and duration of the engagement process; easier sharing processes if compared
to what can be done offline, where dynamics are limited to contexts of physical closeness (Dahan & Hauser,
2002; Afuah, 2003). The importance of collaborating with consumers in the development of innovative products
and services has been recognised for many years and there has been a steady proliferation of studies on this topic
(von Hippel, 1976, 1978, 1986, 1988; Grönross, 1990; Day, 1991; Bruce, Leverick, Littler, & Wilson, 1995;
Gales & Mansour-Cole, 1995; Prahalad & Ramaswamy, 2004a; Vargo & Lusch, 2004). However, it is only
recently that the attention g ...
The Sovereign Digital Platform - A Strategic Option for Societal DevelopmentFrancis D'Silva
This is a Short Paper presented at the ECIS 2018 Workshop on Public Sector Platforms (www.platformization.org)
http://www.platformization.org/Articles/dSilva_Sovereign%20digital%20platforms-final%20-%20ECIS%202018.pdf
Digitalisation of the public sector has emerged as a separate field, focusing on effective government and the provision of universal services. In this paper, building on the platform literature, we extend this perspective, suggesting that a particular class of platforms, which we call Sovereign Digital Platforms, can serve the needs of the public sector, but also contribute to efficiency and growth in the private sector.
Our empirical evidence is Altinn, a Norwegian public-sector platform, which was established in 2003. Altinn is more than a technical platform; it is also the core of a government-business ecosystem of innovation and participation, enabled by trust emerging from key public registers and their institutional custodians. We use the unique experience of Altinn to develop some key concepts of the Sovereign Digital Platform, and to discuss the implications for digitalisation policies.
Unmasking deepfakes: A systematic review of deepfake detection and generation...Araz Taeihagh
Due to the fast spread of data through digital media, individuals and societies must assess the reliability of information. Deepfakes are not a novel idea but they are now a widespread phenomenon. The impact of deepfakes and disinformation can range from infuriating individuals to affecting and misleading entire societies and even nations. There are several ways to detect and generate deepfakes online. By conducting a systematic literature analysis, in this study we explore automatic key detection and generation methods, frameworks, algorithms, and tools for identifying deepfakes (audio, images, and videos), and how these approaches can be employed within different situations to counter the spread of deepfakes and the generation of disinformation. Moreover, we explore state-of-the-art frameworks related to deepfakes to understand how emerging machine learning and deep learning approaches affect online disinformation. We also highlight practical challenges and trends in implementing policies to counter deepfakes. Finally, we provide policy recommendations based on analyzing how emerging artificial intelligence (AI) techniques can be employed to detect and generate deepfakes online. This study benefits the community and readers by providing a better understanding of recent developments in deepfake detection and generation frameworks. The study also sheds a light on the potential of AI in relation to deepfakes.
A governance perspective on user acceptance of autonomous systems in SingaporeAraz Taeihagh
Autonomous systems that operate without human intervention by utilising artificial intelligence are a significant feature of the fourth industrial revolution. Various autonomous systems, such as driverless cars, unmanned drones and robots, are being tested in ongoing trials and have even been adopted in some countries. While there has been a discussion of the benefits and risks of specific autonomous systems, more needs to be known about user acceptance of these systems. The reactions of the public, especially regarding novel technologies, can help policymakers better understand people's perspectives and needs, and involve them in decision-making for governance and regulation of autonomous systems. This study has examined the factors that influence the acceptance of autonomous systems by the public in Singapore, which is a forerunner in the adoption of autonomous systems. The Unified Technology Adoption and Use Theory (UTAUT) is modified by introducing the role of government and perceived risk in using the systems. Using structural equation modelling to analyse data from an online survey (n = 500) in Singapore, we find that performance expectancy, effort expectancy, social influence, and trust in government to govern autonomous systems significantly and positively impact the behavioural intention to use autonomous systems. Perceived risk has a negative relationship with user acceptance of autonomous systems. This study contributes to the literature by identifying latent variables that affect behavioural intention to use autonomous systems, especially by introducing the factor of trust in government to manage risks from the use of these systems and filling the gap by studying the entire domain of autonomous systems instead of a narrow focus on one application. The findings will enable policymakers to understand the perceptions of the public in regard to adoption and regulation, and designers and manufacturers to improve user experience.
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The digital labour market uder debate: Platforms, workers, rights and WorkertechAlbert Canigueral
"The digital labour market uder debate: Platforms, workers, rights and Workertech" is a study about the future of work and the future of workers. The report has been comissioned to Ouishare by Cotec Foundation with the suport of Malt.
A Framework for Policy Crowdsourcing - Oxford IPP 2014Araz Taeihagh
Can Crowdsourcing be used for policy? Previous work posits that the three types of Crowdsourcing have different levels of potential usefulness when applied to the various stages of the policy cycle. In this paper, we build upon this exploratory work by categorizing the extant research with respect to Crowdsourcing for the policy cycle. Premised upon our analysis, we thereafter discuss the trends, highlight the gaps, and suggest some approaches to empirically validate the application of Crowdsourcing to the policy cycle.
Sharing economy platforms and applications are already being used widely in the B2C markets, such as Über and Airbnb, but sharing economy solutions for the B2B markets still includes a lot of potential. The principles of sharing economy are being utilized in the B2B markets for instance by sharing machinery in agriculture and forestry. In addition, other tangible assets, such as equipment, raw materials, office space and warehouses can be shared between companies. Intangible shared assets include for instance companies' brainpower, knowledge and intellectual capital.
Antenna for Social Innovation. We Share. Who Wins: unravelling the controvers...ESADE
In this fourth edition of the Antenna for Social Innovation, we discuss one of the most fascinating and controversial economic transformations: the growth of the collaborative economy. This transformation has been accompanied by a series of events that is destined to revolutionise our societies – namely, the expansion of the Internet, as well as the rise of smartphones, social networks, advances in artificial intelligence, and the capacity to instantly process huge amounts of information at a tiny cost. We talk about societies in a broad sense because the new wave of developments in the digital economy will transform the economic sphere of our lives – as well as the workplace, tax system, educational models, consumption patterns, and communications.
Introduction to Society Chapter Thirteen Weekly Assignments TMargaritoWhitt221
Introduction to Society
Chapter Thirteen Weekly Assignments
The Functions of Government
1. List five primary functions of government
2. Identify three contrasting views of government
3. Explain the liberal, conservative, radical, reactionary, and anarchist philosophies of government
4. Distinguish a democracy from an autocracy
5. List some distinguishing characteristics of a democracy
6. Explain the democratic concept of the individual
7. List the common justifications for an autocracy
8. List four characteristics of autocracy
9. Summarize the three views of the nature of government
10. List the seven exaggerated characterizations on how the role of government is viewed
11. Draw a diagram illustrating the continuum of autocracies
The digital entrepreneurial ecosystem
Fiona Sussan & Zoltan J. Acs
Accepted: 21 March 2017 /Published online: 11 May 2017
# Springer Science+Business Media New York 2017
Abstract A significant gap exists in the conceptualiza-
tion of entrepreneurship in the digital age. This paper
introduces a conceptual framework for studying entre-
preneurship in the digital age by integrating two well-
established concepts: the digital ecosystem and the
entrepreneurial ecosystem. The integration of these
two ecosystems helps us better understand the interac-
tions of agents and users that incorporate insights of
consumers’ individual and social behavior. The Digital
Entrepreneurial Ecosystem framework consists of four
concepts: digital infrastructure governance, digital user
citizenship, digital entrepreneurship, and digital market-
place. The paper develops propositions for each of the
four concepts and provides a theoretical framework of
multisided platforms to better understand the digital
entrepreneurial ecosystem. Finally, it outlines a new
research agenda to fill the gap in our understanding of
entrepreneurship in the digital age.
Keywords Entrepreneurship . Ecosystem .
Matchmakers . Digital infrastructure . Digital
governance . Digital citizenship . Multisided platforms .
Information technologies
JEL classification L26 . 011 . P40 . P00
1 Introduction
As the Economist magazine went to press the lead story
was about reinventing the company.1 This new compa-
ny type is at the heart of a growing debate on how to
understand the digital economy. Ever since the launch of
Uber, Snapchat, and AirBnB and the earlier success of
Google, Amazon, and Facebook, a new breed of
company has emerged that uses digital technology,
entrepreneurship, and innovation to upend industries
on a global scale (Stone 2017).2 Most of these compa-
nies are matchmakers (Evans and Schmalensee 2016,
p.1).3 What these companies have in common is that
they all connect members of one group with another
group. The core competencies of these companies are
their ability to match one group of customers with
another group of customers by reducing the transaction
cost of a match (Coase 1937). These multisided plat-
forms would not exis ...
The aim of this study is to figure out an overview on the literature and related studies on the
awareness of digital labor in the economic system and how production of the capitalist system affects labor
employment and shaped the increase in the socio-economic inequality, which has benefited the capital in the
last 20 years,
Crowdsourcing: a new tool for policy-making?Araz Taeihagh
Crowdsourcing is rapidly evolving and applied in situations where ideas, labour, opinion or expertise of large groups of people are used. Crowdsourcing is now used in various policy-making initiatives; however, this use has usually focused on open collaboration platforms and specific stages of the policy process, such as agenda-setting and policy evaluations. Other forms of crowdsourcing have been neglected in policymaking, with a few exceptions. This article examines crowdsourcing as a tool for policymaking, and explores the nuances of the technology and its use and implications for different stages of the policy process. The article addresses questions surrounding the role of crowdsourcing and whether it can be considered as a policy tool or as a technological enabler and investigates the current trends and future directions of crowdsourcing.
Future of the sharing economy An emerging view 30 March 2017Future Agenda
Humans have always shared. More recently, enabled by technology, new forms of sharing and access have begun to transform industries as well as the way we live our lives, creating financial return and social reward for participants. From AirBnB (爱彼迎 ) to Uber and Didi Chuxing, the sharing economy has rapidly moved from niche to mainstream in a number of categories, most notably accommodation and transportation.
But where next? Building on insights from the wider Future Agenda programme with recent research and interviews with a number of industry leaders and experts, we’re delighted to share an emerging view of the Future of the Sharing Economy.
Over the next few weeks we are asking for feedback and opinion from around the world. We’d really welcome your perspective, comments, challenge and additional insights to co-create an enriched informed future view for all. We will then update and share.
As with all Future Agenda output, this is being published under creative commons (share alike non commercial) so you are free to share and quote as suits.
Electronic government (e-government) has been attracting the attention of the world for the past two decades, and specifically, upon the advent of the internet. Governments worldwide have spent billions of dollars to date to transform themselves into e-government. However, their efforts and large investments resulted mainly in online portals and scattered electronic services. Various studies indicate that e-government initiatives are failing to meet citizens' expectations for convenient service delivery systems. Nonetheless, the rapid pace at which technology is innovatively evolving and its disruptive nature is forcing new realities to be accepted in e-government domain. The new forms of mobility made possible by the transforming technologies are not only changing how people live their lives today, but also redefining business models, employee productivity, customer relationship, and even how governments are structured. The growing usage of smartphones and tablets have significant impact on all industries, but at large how government services are delivered. This study attempts to provide some qualitative input to the existing body of knowledge. It sheds light on some trends that have high impact to disrupt existing technological-based channels of interaction between governments and citizens, and ultimately on service delivery. It also sheds light on the role of modern identity management infrastructure in enabling higher levels of trust and confidence in mobile transactions.
Experiments on Crowdsourcing Policy Assessment - Oxford IPP 2014Araz Taeihagh
Can non-experts Crowds perform as well as experts in the assessment of policy measures? To what degree does geographical location relevant to the policy context alter the performance of non-experts in the assessment of policy measures? This research in progress seeks to answer these questions by outlining experiments designed to replicate expert policy assessments with non-expert Crowds. We use a set of ninety-eight policy measures previously evaluated by experts, as our control condition, and conduct the experiments using two discrete sets of non-expert Crowds recruited from a Virtual Labor Market (VLM). We vary the composition of our non-expert Crowds along two conditions; participants recruited from a geographical location relevant to the policy context, and participants recruited at-large. In each case we recruit a sample of 100 participants for each Crowd. Each experiment is then repeated at the VLM with completely new participants in each group to assess the reliability of our results. We will present results on the performance of all four groups of non-experts jointly and severally in comparison to the expert assessments, and discuss the ramifications of our findings for the use of non-expert Crowds and VLM’s for policy design. Our experimental design applies climate change adaptation policy measures.
International Journal of Marketing Studies; Vol. 6, No. 5; 201TatianaMajor22
International Journal of Marketing Studies; Vol. 6, No. 5; 2014
ISSN 1918-719X E-ISSN 1918-7203
Published by Canadian Center of Science and Education
21
Digital Strategies of Consumer Involvement and Innovation Dynamics:
A Cross-Sector Explorative Study
Eleonora Paolocci1
1 IULM University, Milan, Italy
Correspondence: Eleonora Paolocci, IULM University, Milan, Italy. E-mail: [email protected]
Received: May 24, 2014 Accepted: June 26, 2014 Online Published: September 28, 2014
doi:10.5539/ijms.v6n5p21 URL: http://dx.doi.org/10.5539/ijms.v6n5p21
Abstract
The study aims at exploring the collaborative dynamics between firms and consumers through Web tools. At
present, there is limited empirical research aimed at investigating if and how the involvement of consumers in
the implementation of open approaches, mediated by digital technologies, is actually implemented. The study
presents a recent multifactorial investigation of the topic where literature lacks in. Through the Web-analysis of
practices of a sample group of 180 companies operating in different market sectors, the author wants to explore
spread and type profiles of collaborative strategies, investigating the existence of a possible correlation with the
served markets and other moderator variables. Findings, identifying a ‘spectrum’ of engagement and co-creation
mechanisms, suggest forms of aggregation and profiling in the approach followed by the firms and illustrate how
the characteristics of virtual spaces allow them to explore new frontiers in the implementation of open
approaches, with different degrees of involvement.
Keywords: co-creation, consumer insight, empirical research, open innovation (OI), virtual integration
1. Introduction
Existing academic literature suggests a significant potential of collaboration with consumers in the process of
market value creation through ICTs (von Hippel, 2001; von Hippel & Katz, 2002; Sawhney, Verona, & Prandelli,
2005; Prandelli, Verona, & Raccagni, 2006; Bilgram, Brem, & Voigt, 2008; Füller & von Hippel, 2008;
Prandelli, Sawhney, & Verona, 2008; Füller, Muhlbacher, Matzler, & Jawecki, 2009; Morgan & Wang, 2010).
Considerable attention has been given to the benefits offered by the advent of digital technologies: low-cost
interaction; increase in the speed and duration of the engagement process; easier sharing processes if compared
to what can be done offline, where dynamics are limited to contexts of physical closeness (Dahan & Hauser,
2002; Afuah, 2003). The importance of collaborating with consumers in the development of innovative products
and services has been recognised for many years and there has been a steady proliferation of studies on this topic
(von Hippel, 1976, 1978, 1986, 1988; Grönross, 1990; Day, 1991; Bruce, Leverick, Littler, & Wilson, 1995;
Gales & Mansour-Cole, 1995; Prahalad & Ramaswamy, 2004a; Vargo & Lusch, 2004). However, it is only
recently that the attention g ...
The Sovereign Digital Platform - A Strategic Option for Societal DevelopmentFrancis D'Silva
This is a Short Paper presented at the ECIS 2018 Workshop on Public Sector Platforms (www.platformization.org)
http://www.platformization.org/Articles/dSilva_Sovereign%20digital%20platforms-final%20-%20ECIS%202018.pdf
Digitalisation of the public sector has emerged as a separate field, focusing on effective government and the provision of universal services. In this paper, building on the platform literature, we extend this perspective, suggesting that a particular class of platforms, which we call Sovereign Digital Platforms, can serve the needs of the public sector, but also contribute to efficiency and growth in the private sector.
Our empirical evidence is Altinn, a Norwegian public-sector platform, which was established in 2003. Altinn is more than a technical platform; it is also the core of a government-business ecosystem of innovation and participation, enabled by trust emerging from key public registers and their institutional custodians. We use the unique experience of Altinn to develop some key concepts of the Sovereign Digital Platform, and to discuss the implications for digitalisation policies.
Unmasking deepfakes: A systematic review of deepfake detection and generation...Araz Taeihagh
Due to the fast spread of data through digital media, individuals and societies must assess the reliability of information. Deepfakes are not a novel idea but they are now a widespread phenomenon. The impact of deepfakes and disinformation can range from infuriating individuals to affecting and misleading entire societies and even nations. There are several ways to detect and generate deepfakes online. By conducting a systematic literature analysis, in this study we explore automatic key detection and generation methods, frameworks, algorithms, and tools for identifying deepfakes (audio, images, and videos), and how these approaches can be employed within different situations to counter the spread of deepfakes and the generation of disinformation. Moreover, we explore state-of-the-art frameworks related to deepfakes to understand how emerging machine learning and deep learning approaches affect online disinformation. We also highlight practical challenges and trends in implementing policies to counter deepfakes. Finally, we provide policy recommendations based on analyzing how emerging artificial intelligence (AI) techniques can be employed to detect and generate deepfakes online. This study benefits the community and readers by providing a better understanding of recent developments in deepfake detection and generation frameworks. The study also sheds a light on the potential of AI in relation to deepfakes.
A governance perspective on user acceptance of autonomous systems in SingaporeAraz Taeihagh
Autonomous systems that operate without human intervention by utilising artificial intelligence are a significant feature of the fourth industrial revolution. Various autonomous systems, such as driverless cars, unmanned drones and robots, are being tested in ongoing trials and have even been adopted in some countries. While there has been a discussion of the benefits and risks of specific autonomous systems, more needs to be known about user acceptance of these systems. The reactions of the public, especially regarding novel technologies, can help policymakers better understand people's perspectives and needs, and involve them in decision-making for governance and regulation of autonomous systems. This study has examined the factors that influence the acceptance of autonomous systems by the public in Singapore, which is a forerunner in the adoption of autonomous systems. The Unified Technology Adoption and Use Theory (UTAUT) is modified by introducing the role of government and perceived risk in using the systems. Using structural equation modelling to analyse data from an online survey (n = 500) in Singapore, we find that performance expectancy, effort expectancy, social influence, and trust in government to govern autonomous systems significantly and positively impact the behavioural intention to use autonomous systems. Perceived risk has a negative relationship with user acceptance of autonomous systems. This study contributes to the literature by identifying latent variables that affect behavioural intention to use autonomous systems, especially by introducing the factor of trust in government to manage risks from the use of these systems and filling the gap by studying the entire domain of autonomous systems instead of a narrow focus on one application. The findings will enable policymakers to understand the perceptions of the public in regard to adoption and regulation, and designers and manufacturers to improve user experience.
The soft underbelly of complexity science adoption in policymakingAraz Taeihagh
The deepening integration of social-technical systems creates immensely complex environments, creating increasingly uncertain and unpredictable circumstances. Given this context, policymakers have been encouraged to draw on complexity science-informed approaches in policymaking to help grapple with and manage the mounting complexity of the world. For nearly eighty years, complexity-informed approaches have been promising to change how our complex systems are understood and managed, ultimately assisting in better policymaking. Despite the potential of complexity science, in practice, its use often remains limited to a few specialised domains and has not become part and parcel of the mainstream policy debate. To understand why this might be the case, we question why complexity science remains nascent and not integrated into the core of policymaking. Specifically, we ask what the non-technical challenges and barriers are preventing the adoption of complexity science into policymaking. To address this question, we conducted an extensive literature review. We collected the scattered fragments of text that discussed the non-technical challenges related to the use of complexity science in policymaking and stitched these fragments into a structured framework by synthesising our findings. Our framework consists of three thematic groupings of the non-technical challenges: (a) management, cost, and adoption challenges; (b) limited trust, communication, and acceptance; and (c) ethical barriers. For each broad challenge identified, we propose a mitigation strategy to facilitate the adoption of complexity science into policymaking. We conclude with a call for action to integrate complexity science into policymaking further.
Development of New Generation of Artificial Intelligence in ChinaAraz Taeihagh
How did China become one of the leaders in AI development, and will China prevail in the ongoing AI race with the US? Existing studies have focused on the Chinese central government’s role in promoting AI. Notwithstanding the importance of the central government, a significant portion of the responsibility for AI development falls on local governments’ shoulders. Local governments have diverging interests, capacities and, therefore, approaches to promoting AI. This poses an important question: How do local governments respond to the central government’s policies on emerging technologies, such as AI? This article answers this question by examining the convergence or divergence of central and local priorities related to AI development by analysing the central and local AI policy documents and the provincial variations by focusing on the diffusion of the New Generation Artificial Intelligence Development Plan (NGAIDP) in China. Using a unique dataset of China’s provincial AI-related policies that cite the NGAIDP, the nature of diffusion of the NGAIDP is examined by conducting content analysis and fuzzy-set Qualitative Comparative Analysis (fsQCA). This study highlights the important role of local governments in China’s AI development and emphasises examining policy diffusion as a political process.
Governing disruptive technologies for inclusive development in citiesAraz Taeihagh
Abstract
Cities are increasingly adopting advanced technologies to address complex challenges. Applying technologies such as information and communication technology, artificial intelligence, big data analytics, and autonomous systems in cities' design, planning, and management can cause disruptive changes in their social, economic, and environmental composition. Through a systematic literature review, this research develops a conceptual model linking (1) the dominant city labels relating to tech-driven urban development, (2) the characteristics and applications of disruptive technologies, and (3) the current understanding of inclusive urban development. We extend the discussion by identifying and incorporating the motivations behind adopting disruptive technologies and the challenges they present to inclusive development. We find that inclusive development in tech-driven cities can be realised if governments develop suitable adaptive regulatory frameworks for involving technology companies, build policy capacity, and adopt more adaptive models of governance. We also stress the importance of acknowledging the influence of digital literacy and smart citizenship, and exploring other dimensions of inclusivity, for governing disruptive technologies in inclusive smart cities.
Why and how is the power of big teach increasing?Araz Taeihagh
Abstract: The growing digitalization of our society has led to a meteoric rise of large technology companies (Big Tech), which have amassed tremendous wealth and influence through their ownership of digital infrastructure and platforms. The recent launch of ChatGPT and the rapid popularization of generative artificial intelligence (GenAI) act as a focusing event to further accelerate the concentration of power in the hands of the Big Tech. By using Kingdon’s multiple streams framework, this article investigates how Big Tech utilize their technological monopoly and political influence to reshape the policy landscape and establish themselves as key actors in the policy process. It explores the implications of the rise of Big Tech for policy theory in two ways. First, it develops the Big Tech-centric technology stream, highlighting the differing motivations and activities from the traditional innovation-centric technology stream. Second, it underscores the universality of Big Tech exerting ubiquitous influence within and across streams, to primarily serve their self-interests rather than promote innovation. Our findings emphasize the need for a more critical exploration of policy role of Big Tech to ensure balanced and effective policy outcomes in the age of AI.
Keywords: generative AI, governance, artificial intelligence, big tech, multiple streams framework
Sustainable energy adoption in poor rural areasAraz Taeihagh
Abstract
A growing body of literature recognises the role of local participation by end users in the successful implementation of sustainable development projects. Such community-based initiatives are widely assumed to be beneficial in providing additional savings, increasing knowledge and skills, and improving social cohesion. However, there is a lack of empirical evidence regarding the success (or failure) of such projects, as well as a lack of formal impact assessment methodologies that can be used to assess their effectiveness in meeting the needs of communities. Using a case study approach, we investigate the effectiveness of community-based energy projects in regard to achieving long-term renewable energy technology (RET) adoption in energy-poor island communities in the Philippines. This paper provides an alternative analytical framework for assessing the impact of community-based energy projects by defining RET adoption as a continuous and relational process that co-evolves and co-produces over time, highlighting the role of social capital in the long-term RET adoption process. In addition, by using the Social Impact Assessment methodology, we study off-grid, disaster-vulnerable and energy-poor communities in the Philippines and we assess community renewable energy (RE) projects implemented in those communities. We analyse the nature of participation in the RET adoption process, the social relations and interactions formed between and among the different stakeholders, and the characteristics, patterns and challenges of the adoption process.
Highlights
• Community-based approaches aid state-led renewable energy in off-grid areas.
• Social capital in communities addresses immediate energy needs in affected areas.
• Change Mapping in Social Impact Assessment shows community-based RE project impacts.
• Long-term renewable energy adoption involves co-evolving hardware, software, orgware.
• Successful adoption relies on communal mechanisms to sustain renewable energy systems.
Smart cities as spatial manifestations of 21st century capitalismAraz Taeihagh
Globally, smart cities attract billions of dollars in investment annually, with related market opportunities forecast to grow year-on-year. The enormous resources poured into their development consist of financial capital, but also natural, human and social resources converted into infrastructure and real estate. The latter act as physical capital storage and sites for the creation of digital products and services expected to generate the highest value added. Smart cities serve as temporary spatial fixes until new and better investments opportunities emerge. Drawing from a comprehensive range of publications on capitalism, this article analyzes smart city developments as typifier of 21st century capital accumulation where the financialization of various capitals is the overarching driver and ecological overshoot and socio-economic undershoot are the main negative consequences. It closely examines six spatial manifestations of the smart city – science parks and smart campuses; innovation districts; smart neighborhoods; city-wide and city-regional smart initiatives; urban platforms; and alternative smart city spaces – as receptacles for the conversion of various capitals. It also considers the influence of different national regimes and institutional contexts on smart city developments. This is used, in the final part, to open a discussion about opportunities to temper the excesses of 21st century capitalism.
Highlights
• Recent academic literature on modern capitalism and smart city development are brought together
• Different interpretations and denominations of 21th century capitalism are mapped and synthesized into an overview box
• Six spatial manifestations of the smart city are identified and thoroughly described, with their major institutions, actors and resources
• Five different types of capital (natural, human, social, physical and financial) are mapped, along with an analysis of how further financialization affects conversion processes between them
• Options to mitigate exclusionary tendencies of capitalism in the digital age are explored, based on the varieties of capitalism literature
Digital Ethics for Biometric Applications in a Smart CityAraz Taeihagh
From border control using fingerprints to law enforcement with video surveillance to self-activating devices via voice identification, biometric data is used in many applications in the contemporary context of a Smart City. Biometric data consists of human characteristics that can identify one person from others. Given the advent of big data and the ability to collect large amounts of data about people, data sources ranging from fingerprints to typing patterns can build an identifying profile of a person. In this article, we examine different types of biometric data used in a smart city based on a framework that differentiates between profile initialization and identification processes. Then, we discuss digital ethics within the usage of biometric data along the lines of data permissibility and renewability. Finally, we provide suggestions for improving biometric data collection and processing in the modern smart city.
A realist synthesis to develop an explanatory model of how policy instruments...Araz Taeihagh
Abstract
Background
Child and maternal health, a key marker of overall health system performance, is a policy priority area by the World Health Organization and the United Nations, including the Sustainable Development Goals. Previous realist work has linked child and maternal health outcomes to globalization, political tradition, and the welfare state. It is important to explore the role of other key policy-related factors. This paper presents a realist synthesis, categorising policy instruments according to the established NATO model, to develop an explanatory model of how policy instruments impact child and maternal health outcomes.
Methods
A systematic literature search was conducted to identify studies assessing the relationships between policy instruments and child and maternal health outcomes. Data were analysed using a realist framework. The first stage of the realist analysis process was to generate micro-theoretical initial programme theories for use in the theory adjudication process. Proposed theories were then adjudicated iteratively to produce a set of final programme theories.
Findings
From a total of 43,415 unique records, 632 records proceeded to full-text screening and 138 papers were included in the review. Evidence from 132 studies was available to address this research question. Studies were published from 1995 to 2021; 76% assessed a single country, and 81% analysed data at the ecological level. Eighty-eight initial candidate programme theories were generated. Following theory adjudication, five final programme theories were supported. According to the NATO model, these were related to treasure, organisation, authority-treasure, and treasure-organisation instrument types.
Conclusions
This paper presents a realist synthesis to develop an explanatory model of how policy instruments impact child and maternal health outcomes from a large, systematically identified international body of evidence. Five final programme theories were supported, showing how policy instruments play an important yet context-dependent role in influencing child and maternal health outcomes.
Addressing Policy Challenges of Disruptive TechnologiesAraz Taeihagh
This special issue examines the policy challenges and government responses to disruptive technologies. It explores the risks, benefits, and trade-offs of deploying disruptive technologies, and examines the efficacy of traditional governance approaches and the need for new regulatory and governance frameworks. Key themes include the need for government stewardship, taking adaptive and proactive approaches, developing comprehensive policies accounting for technical, social, economic, and political dimensions, conducting interdisciplinary research, and addressing data management and privacy challenges. The findings enhance understanding of how governments can navigate the complexities of disruptive technologies and develop policies to maximize benefits and mitigate risks.
Navigating the governance challenges of disruptive technologies insights from...Araz Taeihagh
The proliferation of autonomous systems like unmanned aerial vehicles, autonomous vehicles and AI-powered industrial and social robots can benefit society significantly, but these systems also present significant governance challenges in operational, legal, economic, social, and ethical dimensions. Singapore’s role as a front-runner in the trial of autonomous systems presents an insightful case to study whether the current provisional regulations address the challenges. With multiple stakeholder involvement in setting provisional regulations, government stewardship is essential for coordinating robust regulation and helping to address complex issues such as ethical dilemmas and social connectedness in governing autonomous systems.
A scoping review of the impacts of COVID-19 physical distancing measures on v...Araz Taeihagh
Most governments have enacted physical or social distancing measures to control COVID-19 transmission. Yet little is known about the socio-economic trade-offs of these measures, especially for vulnerable populations, who are exposed to increased risks and are susceptible to adverse health outcomes. To examine the impacts of physical distancing measures on the most vulnerable in society, this scoping review screened 39,816 records and synthesised results from 265 studies worldwide documenting the negative impacts of physical distancing on older people, children/students, low-income populations, migrant workers, people in prison, people with disabilities, sex workers, victims of domestic violence, refugees, ethnic minorities, and people from sexual and gender minorities. We show that prolonged loneliness, mental distress, unemployment, income loss, food insecurity, widened inequality and disruption of access to social support and health services were unintended consequences of physical distancing that impacted these vulnerable groups and highlight that physical distancing measures exacerbated the vulnerabilities of different vulnerable populations.
Data Sharing in Disruptive Technologies Lessons from Adoption of Autonomous S...Araz Taeihagh
Autonomous systems have been a key segment of disruptive technologies for which data are constantly collected, processed, and shared to enable their operations. The internet of things facilitates the storage and transmission of data and data sharing is vital to power their development. However, privacy, cybersecurity, and trust issues have ramifications that form distinct and unforeseen barriers to sharing data. This paper identifies six types of barriers to data sharing (technical, motivational, economic, political, legal, and ethical), examines strategies to overcome these barriers in different autonomous systems, and proposes recommendations to address them. We traced the steps the Singapore government has taken through regulations and frameworks for autonomous systems to overcome barriers to data sharing. The results suggest specific strategies for autonomous systems as well as generic strategies that apply to a broader set of disruptive technologies. To address technical barriers, data sharing within regulatory sandboxes should be promoted. Promoting public-private collaborations will help in overcoming motivational barriers. Resources and analytical capacity must be ramped up to overcome economic barriers. Advancing comprehensive data sharing guidelines and discretionary privacy laws will help overcome political and legal barriers. Further, enforcement of ethical analysis is necessary for overcoming ethical barriers in data sharing. Insights gained from this study will have implications for other jurisdictions keen to maximize data sharing to increase the potential of disruptive technologies such as autonomous systems in solving urban problems.
Call for papers - ICPP6 T13P03 - GOVERNANCE AND POLICY DESIGN LESSONS FOR TRU...Araz Taeihagh
CALL FOR PAPERS
T13P03 - GOVERNANCE AND POLICY DESIGN LESSONS FOR TRUST BUILDING AND RESPONSIBLE USE OF AI, AUTONOMOUS SYSTEMS AND ROBOTICS
https://www.ippapublicpolicy.org/conference/icpp6-toronto-2023/panel-list/17/panel/governance-and-policy-design-lessons-for-trust-building-and-responsible-use-of-ai-autonomous-systems-and-robotics/1390
Abstract submission deadline: 31 January 2023
GENERAL OBJECTIVES, RESEARCH QUESTIONS AND SCIENTIFIC RELEVANCE
Artificial intelligence (AI), Autonomous Systems (AS) and Robotics are key features of the fourth industrial revolution, and their applications are supposed to add $15 trillion to the global economy by 2030 and improve the efficiency and quality of public service delivery (Miller & Sterling, 2019). A McKinsey global survey found that over half of the organisations surveyed use AI in at least one function (McKinsey, 2020). The societal benefits of AI, AS, and Robotics have been widely acknowledged (Buchanan 2005; Taeihagh & Lim 2019; Ramchurn et al. 2012), and the acceleration of their deployment is a disruptive change impacting jobs, the economic and military power of countries, and wealth concentration in the hands of corporations (Pettigrew et al., 2018; Perry & Uuk, 2019).
However, the rapid adoption of these technologies threatens to outpace the regulatory responses of governments around the world, which must grapple with the increasing magnitude and speed of these transformations (Taeihagh 2021). Furthermore, concerns about these systems' deployment risks and unintended consequences are significant for citizens and policymakers. Potential risks include malfunctioning, malicious attacks, and objective mismatch due to software or hardware failures (Page et al., 2018; Lim and Taeihagh, 2019; Tan et al., 2022). There are also safety, liability, privacy, cybersecurity, and industry risks that are difficult to address (Taeihagh & Lim, 2019) and The opacity in AI operations has also manifested in potential bias against certain groups of individuals that lead to unfair outcomes (Lim and Taeihagh 2019; Chesterman, 2021).
These risks require appropriate governance mechanisms to be mitigated, and traditional policy instruments may be ineffective due to insufficient information on industry developments, technological and regulatory uncertainties, coordination challenges between multiple regulatory bodies and the opacity of the underlying technology (Scherer 2016; Guihot et al. 2017; Taeihagh et al. 2021), which necessitate the use of more nuanced approaches to govern these systems. Subsequently, the demand for the governance of these systems has been increasing (Danks & London, 2017; Taeihagh, 2021).
Call for papers - ICPP6 T07P01 - EXPLORING TECHNOLOGIES FOR POLICY ADVICE.docxAraz Taeihagh
CALL FOR PAPERS
T07P01 - EXPLORING TECHNOLOGIES FOR POLICY ADVICE
https://www.ippapublicpolicy.org/conference/icpp6-toronto-2023/panel-list/17/panel/exploring-technologies-for-policy-advice/1295
Abstract submission deadline: 31 January 2023
GENERAL OBJECTIVES, RESEARCH QUESTIONS AND SCIENTIFIC RELEVANCE
Knowledge and expertise are key components of policy-making and policy design, and many institutions and processes exist – universities, professional policy analysts, think tanks, policy labs, etc. – to generate and mobilize knowledge for effective policies and policy-making. Despite many years of research, however. many critical ssues remain unexplored, including the nature of knowledge and non-knowledge, how policy advice is organized into advisory systems or regimes, and when and how specific types of knowledge or evidence are transmitted and influence policy development and implementation. These long-standing issues have been joined recently by use of Artificial Intelligence and Big data, and other kinds of technological developments – such as crowdsourcing through open collaboration platforms, virtual labour markets, and tournaments – which hold out the promise of automating, enhancing. or expanding policy advisory activities in government. This panel seeks to explore all aspects of the application of current and future technologies to policy advice, including case studies of its deployment as well as theoretical and conceptual studies dealing with moral, epistemological and other issues surrounding its use.
What factors drive policy transfer in smart city developmentAraz Taeihagh
Abstract
Smart city initiatives are viewed as an input to existing urban systems to solve various problems faced by modern cities. Making cities smarter implies not only technological innovation and deployment, but also having smart people and effective policies. Cities can acquire knowledge and incorporate governance lessons from other jurisdictions to develop smart city initiatives that are unique to the local contexts. We conducted two rounds of surveys involving 23 experts on an e-Delphi platform to consolidate their opinion on factors that facilitate policy transfer among smart cities. Findings show a consensus on the importance of six factors: having a policy entrepreneur; financial instruments; cities’ enthusiasm for policy learning; capacity building; explicit regulatory mechanisms; and policy adaptation to local contexts. Correspondingly, three policy recommendations were drawn. Formalizing collaborative mechanisms and joint partnerships between cities, setting up regional or international networks of smart cities, and establishing smart city repositories to collect useful case studies for urban planning and governance lessons will accelerate policy transfer for smart city development. This study sheds light on effective ways policymakers can foster policy learning and transfer, especially when a jurisdiction's capacity is insufficient to deal with the uncertainties and challenges ahead.
Perspective on research–policy interface as a partnership: The study of best ...Araz Taeihagh
This article serves as a blueprint and proof-of-concept of Singapore’s Campus for Research Excellence and Technological Enterprise (CREATE) programmes in establishing effective collaborations with governmental partners. CREATE is a research consortium between Singapore’s public universities and international research institutions. The effective partnership of CREATE partners with government stakeholders is part of its mission to help government agencies solve complex issues in areas that reflect Singapore’s national interest. Projects are developed in consultation with stakeholders, and challenges are addressed on a scale that enables significant impact and provides solutions for Singapore and internationally. The article discusses the lessons learnt, highlighting that while research–policy partnerships are widespread, they are seldom documented. Moreover, effective communication proved to be a foundation for an effective partnership where policy and research partners were more likely to provide formal and informal feedback. Engaging policy partners early in the research co-development process was beneficial in establishing effective partnerships.
Whither policy innovation? Mapping conceptual engagement with public policy i...Araz Taeihagh
Abstract
A transition to sustainable energy will require not only technological diffusion and behavioral change, but also policy innovation. While research on energy transitions has generated an extensive literature, the extent to which it has used the policy innovation perspective – entailing policy entrepreneurship or invention, policy diffusion, and policy success – remains unclear. This study analyzes over 8000 publications on energy transitions through a bibliometric review and computational text analysis to create an overview of the scholarship, map conceptual engagement with public policy, and identify the use of the policy innovation lens in the literature. We find that: (i) though the importance of public policy is frequently highlighted in the research, the public policy itself is analyzed only occasionally; (ii) studies focusing on public policy have primarily engaged with the concepts of policy mixes, policy change, and policy process; and (iii) the notions of policy entrepreneurship or invention, policy diffusion, and policy success are hardly employed to understand the sources, speed, spread, or successes of energy transitions. We conclude that the value of the policy innovation lens for energy transitions research remains untapped and propose avenues for scholars to harness this potential.
The rapid developments in Artificial Intelligence (AI) and the intensification in the adoption of AI in domains such as autonomous vehicles, lethal weapon systems, robotics and alike pose serious challenges to governments as they must manage the scale and speed of socio-technical transitions occurring. While there is considerable literature emerging on various aspects of AI, governance of AI is a significantly underdeveloped area. The new applications of AI offer opportunities for increasing economic efficiency and quality of life, but they also generate unexpected and unintended consequences and pose new forms of risks that need to be addressed. To enhance the benefits from AI while minimising the adverse risks, governments worldwide need to understand better the scope and depth of the risks posed and develop regulatory and governance processes and structures to address these challenges. This introductory article unpacks AI and describes why the Governance of AI should be gaining far more attention given the myriad of challenges it presents. It then summarises the special issue articles and highlights their key contributions. This special issue introduces the multifaceted challenges of governance of AI, including emerging governance approaches to AI, policy capacity building, exploring legal and regulatory challenges of AI and Robotics, and outstanding issues and gaps that need attention. The special issue showcases the state-of-the-art in the governance of AI, aiming to enable researchers and practitioners to appreciate the challenges and complexities of AI governance and highlight future avenues for exploration.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
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Orchestrator execution result
Defect reporting
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Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
Crowdsourcing, Sharing Economies and Development
1. Crowdsourcing, Sharing Economies and Development
by
Araz Taeihagh
Reference as:
Taeihagh, A. (2017). Crowdsourcing, Sharing Economies and Development, Journal of
Developing Societies, Vol 33(2): 191–222. DOI: 10.1177/0169796X17710072
Link to Publisher’s website:
http://dx.doi.org/10.1177%2F0169796X17710072
2. Taeihagh: Crowdsourcing, sharing Economy and Development
2
Crowdsourcing, Sharing Economies and Development
Araz Taeihagh, School of Social Sciences, Singapore Management University, 90 Stamford Road
Level 4, Singapore, 178903 Phone: +65 68280627,
Email: araz.taeihagh@new.oxon.org
ABSTRACT – What are the similarities and differences between crowdsourcing and sharing economy?
What factors influence their use in developing countries? In light of recent developments in the use of
IT-mediated technologies, such as crowdsourcing and the sharing economy, this manuscript examines
their similarities and differences, and the challenges regarding their effective use in developing
countries. We first examine each individually and highlight different forms of each IT-mediated
technology. Given that crowdsourcing and sharing economy share aspects such as the use of IT, a
reliance on crowds, monetary exchange, and the use of reputation systems, we systematically compare
the similarities and differences of different types of crowdsourcing with the sharing economy, thus
addressing a gap in the current literature. Using this knowledge, we examine the different challenges
faced by developing countries when using crowdsourcing and the sharing economy, and highlight the
differences in the applicability of these IT-mediated technologies when faced with specific development
issues.
KEYWORDS: Crowdsourcing, sharing economy, development, developing countries, virtual labour
markets, tournament crowdsourcing, open collaboration, IT-mediated crowds, asset hubs, peer-to-peer
sharing networks.
1 Introduction
Crowdsourcing is the IT-mediated engagement of crowds for the purposes of problem-solving, task
completion, idea generation and production (Howe 2006; 2008; Brabham 2008). Crowdsourcing
encompasses various types of platforms, such as virtual labour markets (VLMs), tournament
crowdsourcing (TC) and open collaboration (OC), which each have different roles and characteristics
(Estellés-Arolas and González-Ladrón-de-Guevara 2012; Prpić, Taeihagh and Melton 2015). Along
with the growth of crowdsourcing, another IT-mediated technology in the form of the sharing economy
is rapidly being developed. ‘Sharing economy’ is an umbrella term referring to the practices of sharing,
exchange or rental of goods and services to others through IT without the transfer of ownership. The
sharing economy promises to increase efficiency and effectiveness by reducing transaction costs and
increasing the rate of utilisation of goods and services. It has had a transformative effect on how goods
and services are provided (Welsum 2016; Schor 2014; Goudin 2016).
Both crowdsourcing and the sharing economy are becoming increasingly popular (Lehdonvirta and
Bright 2015; Cohen and Kietzmann 2014; Zervas, Proserpio and Byers 2016), but despite their rapid
adoption and development there are gaps in the literature. These IT-mediated technologies improve
efficiency and decrease transaction costs and information asymmetry, and share similarities in their use
of IT, reliance on crowds, monetary exchange, use of reputation systems, etc. However, the literature
in each domain tends to ignore the other or treats it as a singular form. Moreover, at times a platform is
categorised as both a sharing economy platform and a crowdsourcing platform by different scholars.
For instance, scholars distinguish between Amazon MTurk and TaskRabbit based on whether the task
can be performed as a virtual service that can be executed online or whether a physical service needs to
be performed locally (De Groen, Maselli and Fabo 2016; Aloisi 2015). However, there are many
3. Taeihagh: Crowdsourcing, sharing Economy and Development
3
instances where both of these platforms have been categorised as part of the sharing economy. This
issue is particularly prevalent when the topic under study relates to labour markets or commons.
In this work we aim to systematically compare the similarities and differences of various types of
crowdsourcing and sharing economies across a wide range of criteria to address the gap in the
literature and bring about a more nuanced understanding of these IT-mediated technologies.
Furthermore, it is being suggested that developing countries can take up crowdsourcing and sharing
economy platforms to address problems that are particular to development. In practice this can be
difficult to achieve because developing countries face unique and specialized problems, and our
knowledge about the various models of IT-mediated technologies is incipient. Thus, decisions are
made by the market place despite or beyond the influence of policy makers.
The benefit of a more nuanced understanding of these IT-mediated technologies for developing
countries is that industry and policy-makers can work together more effectively to leverage the new
potential of applying IT-mediated technologies such as crowdsourcing and sharing economy for
achieving development objectives while ensuring that they are implemented in ways that maximize
positive impacts and minimize negative side effects. This paper thus suggests which forms would be
more appropriate to which types of development issues, with particular focus on issues relating to
mobile and online activities, productivity and innovation as well as legal and governance challenges.
In the next section we provide a general overview before examining different types of sharing economy
and crowdsourcing in Section 3. Given that the sharing economy and crowdsourcing share
characteristics we then systematically compare the two in Section 4. Then, drawing on this
understanding, we examine different types of developing countries and focus on the challenges they
face in regard to crowdsourcing and the sharing economy in Section 5, followed by concluding remarks
in Section 6.
2 Background
The sharing economy
The sharing economy is described as a transformative and disruptive economic model in which the
consumption of physical goods, assets or services is carried out through rental, sharing or exchange of
resources using IT through crowd-based services or intermediates without any permanent transfer of
ownership (Lessig 2008; Botsman and Rogers 2010; Belk, 2014; Hamari et al. 2015; Dillahunt and
Malone, 2015; Goudin 2016). This is done to increase efficiency and effectiveness by reducing
transaction costs1
and information asymmetry, particularly for consumers, increase the rate of utilisation
of goods, recirculation of goods, exchange of services and sharing of productive assets, as well as
increase competition in the marketplace, reduce the complacency of suppliers and make services that
often exist in an informal fashion safer through formalisation (Goudin 2016; Schor, 2014; Welsum
2016; Hira and Reilly 2017).
Scholars have repeatedly criticised the term ‘sharing economy’ because it implies altruistic or positive
non-reciprocal social behaviour that can increase societal trust and increased cooperation between
individuals, when in fact the services involved are often fee-paying in nature and involve access to
goods or assets that individuals often use for economic benefit (Belk 2014; Eckhardt and Bardhi 2015;
Hamari et al. 2015).
1
Demary (2015) based on Dahlman (1979) elaborates that platforms enable transaction costs to be reduced by facilitating: a)
the finding of information and reduction of search costs; b) the checking of prices and decision making as well as bargaining
on price; and c) a reduction in the policing and enforcement costs by enabling payments via the platform.
4. Taeihagh: Crowdsourcing, sharing Economy and Development
4
Based on the aforementioned description, the key features of the sharing economy are:2
• A transformative and disruptive nature, as evidenced by the effects of services such as Uber
and Airbnb on the transportation and tourism sectors (Guttentag 2015; Ikkala and Lampinen
2015; Cannon and Summers 2014).
• The consumption and use of goods, services or assets through rental, sharing or exchange of
resources, which increases the utilisation rate (Goudin 2016).
• A heavy reliance on IT through online platforms and/or mobile devices. For instance, the
sharing economy relies on IT for identifying relevant individuals or businesses, exchanging and
aggregating relevant information (e.g. products, services, usage), booking of services and
payment of fees. Technological breakthroughs that have enabled such activities have reduced
transaction costs and increased the reach of the sharing economy (Gansky 2010; Belk 2014;
Goudin 2016).
• The direct engagement of crowds and/or intermediaries. The sharing economy focuses on
consumer markets through crowd-based online services or intermediaries (Hamari et al. 2015)
providing consumer-to-consumer (peer-to-peer) or business-to-consumer models. This
particular aspect of the sharing economy in which economic activity is carried out through
crowd-engagement directly connects to crowdsourcing (see Section 4). Moreover, a large
portion of the communications happens through word of mouth and social media (Gansky,
2010).
• The temporary nature of the engagement (e.g. temporary transfer of ownership) (Belk 2014),
rather than any permanent transfer of ownership of goods, distinguishes the sharing economy
from e-commerce which is buying and selling of goods and services online (Burt and Sparks
2003).
Crowdsourcing
Crowdsourcing is the IT-mediated engagement of crowds for the purposes of problem-solving, task
completion, idea generation and production in which the dispersed knowledge of individuals and groups
is leveraged through a mix of bottom-up innovative crowd-derived processes and inputs with efficient
top-down goals set and initiated by an organisation (Howe 2006; 2008; Brabham 2008). It is
continuously evolving and a variety of forms are emerging (for an in-depth review, see Prpić 2016 and
Prpić & Shukla, 2016). Crowdsourcing initiatives can be carried out by ‘propriety crowds’ that
organisations foster through their own in-house platforms or by using third-party crowdsourcing
platforms that provide the required IT infrastructure and ‘built-in crowds’ as a paid service (Bayus
2013).
In this work, we use the three generalised types of crowdsourcing from the literature that focus on
micro-tasking in Virtual Labour Markets, Tournament Crowdsourcing and Open Collaboration through
social media and the web (Estellés-Arolas and González-Ladrón-de-Guevara 2012; Prpić et al. 2015).
These general categorisations are not exclusive or exhaustive but they are useful for examining the
general characteristics of different types of crowdsourcing and sharing economy.
3 Types of Sharing Economy and Crowdsourcing
Both sharing economy and crowdsourcing are umbrella terms and encompass a wide range of IT-
mediated technologies which can be classified into different categories based on a diverse set of
features and applications. Below we examine various types of sharing economy and crowdsourcing.
Types of sharing economy
2
As Gansky (2010) points out, not all of the characteristics listed above need to be present in every sharing economy business.
5. Taeihagh: Crowdsourcing, sharing Economy and Development
5
The sharing economy has the potential to be applied in a diverse range of sectors, which include3
:
• Tourism and hospitality (Guttentag 2015; Ikkala and Lampinen 2015);
• Mobility and logistics (e.g. car-sharing, ride-sharing, bike-sharing and on-demand logistics
and delivery) (Cohen and Kietzmann 2014; Cannon and Summers 2014; Techcrunch 2015);
• Labour and service platforms (Thompson 2015; Fraiberger and Sundararajan 2015);
• Food and dining (Hendrickson 2013; Tanz, 2014);
• Goods and equipment (Morrissey 2015; Long 2013; Anderson 2016);
• Financial services (Ordanini et al. 2011; Zhang et al. 2014); and
• Other rapidly developing new areas of application.
This sector-based categorisation is perhaps the easiest method of classification but, as Kenny, Rouvinen
and Zysman (2015) point out, sectors are now blurring due to digitisation and use of IT platforms.
Belk (2010) suggests the concepts of ‘sharing in’ and ‘sharing out’ as a means of distinguishing between
sharing that is similar to family sharing (ownership as common) and sharing with strangers that does
not create any attachment or bonds. Demary (2015) reports on Smolka and Hienerth (2014) and their
categorisation of a sharing economy based on whether transactions are market-mediated or not, while
Kostakis and Bauwens (2014) and Oskam and Boswijk (2016) distinguish between types of sharing
economy by focusing on whether the sharing economy platform is centrally controlled or
open/decentralised and also whether the initiative is for profit or not. Cheng (2014) further expands the
consideration of whether the platform is for-profit or not-for-profit and its distributed or centralised
production aspects by also considering whether the application covers offline realms or not. Cheng
(2014) makes a distinction between peer-to-peer platforms and other types of sharing economy business
models but, as Westerbeek (2016) identifies, an overlap is still present between peer-to-peer platforms
and these other types of business model (such as collaborative consumption and the gift economy).4
Botsman and Rogers (2010) take a functional approach and distinguish between three types of sharing
economy based on whether the business model is: a) a redistribution market of used or pre-owned
goods; b) a product service system where consumers pay for access to the good as a service rather than
purchasing the good; and c) based on collaborative lifestyles (i.e. involving the sharing of non-physical
assets such as time, expertise and space). Andersson et al. (2013) take a similar functional approach
distinguishing business models based on peer-to-peer trading of digital and tangible materials, sharing
of goods and sharing of services. They also examine the characteristics of sharing platforms based on
the planning horizon for every transaction, assessing whether it is immediate (i.e. only a short time is
required for planning every transaction), recurring (a long time is required for setting up the first
transaction) or deferred (a long time is required for planning every transaction). Similarly, Demary
(2015) characterises peer-to-peer platforms based on the cost of transactions (i.e. whether supplier,
consumer, both suppler and consumer, or advertisers (in multisided platforms) pay the charges), while
Choudary (2015) focuses on the architectural framework (using a categorisation based on whether the
platform focuses on community building, the provision of infrastructure, or data) and patterns of
exchange in the platforms (based on whether platforms primarily exchange information, currency,
and/or goods and services).
In this study we adopt the sharing economy categorisation of Gansky (2010) and Rauch and
Schleicher (2015). These authors consider two business models in which a business either owns goods
or services and rents them out or creates a platform for the exchange of goods and services on a
temporary basis and makes a profit by charging fees to parties involved in a myriad of ways (as
3
For a recent survey of adoption of sharing economy in developing countries see Hira (2017).
4
Westerbeek (2016) defines peer-to-peer sharing as when the main objective of the business transaction can be reached
using a one-on-one one-off transaction between a provider and a user (e.g. in an Uber ride a certain location is reached after
a one-to-one transaction (the Uber ride)).
6. Taeihagh: Crowdsourcing, sharing Economy and Development
6
illustrated earlier by Demary 2015). Gansky (2010) names these two sharing economy models the
‘full mesh mode’ (company assets rented out to customers) and the ‘own-to-mesh mode’ (platforms
enabling peer-to-peer sharing of goods for a transaction or partnership fee rather than owning the
goods). Rauch and Schleicher (2015) name these two business models ‘asset hubs’ (the business owns
the goods or services and rents them out) or ‘peer-to-peer sharing networks’ (the business creates a
peer-to-peer platform for the exchange of goods and services on a temporary basis).
Types of Crowdsourcing
Virtual Labour Markets (VLMs)
A VLM is an IT-mediated market where individuals can provide online services that can be performed
anywhere (often by engaging in spot labour), offered by organisations generally through micro-tasks,
typifying the production model of crowdsourcing (Brabham 2008), in exchange for monetary
compensation (Prpić, Taeihagh and Melton 2014; Luz, Silva and Novais 2015).
Micro-tasks offered at sites such as Amazon’s Mechanical Turk (MTurk) and Crowdflower include
document translation, transcription, photo and video tagging, editing, sentiment analysis,
categorisation, data entry, and content moderation (Crowdflower 2016). These are activities that can be
divided into various steps (micro-tasks) that can be completed in parallel and at scale using human
computational power. Currently these tasks can be better performed through collective intelligence
rather than through artificial intelligence and automation. Furthermore, at the moment most of the
labourers working through VLM websites often work independently and anonymously and cannot form
teams or groups using the VLM platforms. This is a function of the current design of these platforms
and could (and probably will) change in the future to allow more sophisticated tasks to be performed.
At the moment, most of these micro-tasks require low to medium levels of skill and are at times
repetitive, meaning the compensation level per task is low.
Tournament Crowdsourcing (TC)
TC (Zhang et al. 2015; Glaeser et al. 2016) is another form of crowdsourcing in which organisations
post their problems to specialised IT-mediated platforms such as Eyeka or Kaggle or to in-house
platforms such as Challenge.gov (Brabham 2013). Here, with the help of the IT-mediated platform,
organisers form a competition and set the rules and prize(s) for the competition. Individuals or groups
can post their solutions through the specialised IT-mediated platform to be considered for a prize, which
range from a few hundred dollars to hundreds of thousands of dollars or even more.5
These TC platforms generally attract and maintain more specialised crowds who are interested in the
particular focus of the platform, which can differ widely from computer science (Lakhani et al. 2010)
and data science (Taieb and Hyndman 2014) to open government and innovation (The White House
2010). Relative to VLMs these TC platforms generally attract smaller numbers of more specialised
individuals, and members can choose to not be anonymous at these sites so as to benefit from the
reputational gains from their successful participations (Prpić, Taeihagh and Melton, 2015).
Open Collaboration (OC)
In the OC model of crowdsourcing, problems or opportunities are posted by an organisation to the
public through IT systems and crowds voluntarily engage in these endeavours generally without
expecting monetary compensation (Michel, Gil and Hauder, 2015; Prpić et al. 2015). Examples of this
type of crowdsourcing include starting an enterprise wiki or using social media and online communities
to gain contributions (Crowley et al 2014; Budhathoki and Haythornthwaite 2013; Mergel, 2015).
5
https://www.kaggle.com/competitions
7. Taeihagh: Crowdsourcing, sharing Economy and Development
7
The level of engagement from the crowd depends on a number of factors such the efficacy of the ‘open
call’ by the organisation, the crowd capital of the organisation as well as the reach and engagement of
the IT platform used (Prpić and Shukla 2013). As an example, as at May 2015, Twitter had more than
500 million users, out of which more than 310 million are active on a monthly basis.6
However, this
does not necessarily translate into significant engagement from the potential pool of users on the
platform. An open call might get the attention of celebrities or Nobel Laureates and get significant
traction and diffusion though their networks or, on the other hand, it might simply be ignored if the
organisation does not have ample influence within the network. Factors such as level of popularity and
level of prior engagement on the platform by the organisation, number of followers, number of retweets
or mentions the organisation garners and popularity of the followers and their level of reach in turn,
along with the content posted, are a small subset of the many factors that influence the level with which
crowds might engage with the open call (Cha et al. 2010).
4 Comparison of crowdsourcing with the sharing economy
As mentioned in the introduction of the article, although various forms of the sharing economy and
crowdsourcing can share a large number of common characteristics, the literature in each domain at
times ignores the other or treats it as a singular form. Additionly, in some instances a platform is
categorised as both a sharing economy platform and a crowdsourcing platform by different scholars.
For instance, scholars generally distinguish between Amazon MTurk and TaskRabbit given that the
former provides a virtual service that can be performed online and the latter provides a physical service
that needs to be performed locally (De Groen, Maselli and Fabo 2016; Aloisi 2015). This distinction is
fundamental to the works of Gansky (2010) and Rauch and Schleicher (2015) as they solely focus on
the exchange of physical goods or services that must be provided in person, which implicitly
differentiates between the sharing economy and crowdsourcing as crowdsourcing can be performed
virtually. However, there are numerous instances in the literature where both of these platforms have
been categorised as part of the sharing economy. This issue is particularly prevalent when the topic
under study relates to labour markets or commons (e.g. Amazon MTurk and Wikipedia). Nevertheless,
Westerbeek (2016) explicitly differentiates between crowdsourcing and sharing economy platforms by
pointing out the one-on-one peer-to-peer aspect to be the most important part of the sharing economy
that is not present in crowdsourcing.
As shown in sections 2 and 3 of this paper, crowdsourcing and the sharing economy both encompass a
wide range of activities and business models. Crowdsourcing refers to three generalised types of VLM,
TC and OC with varying levels of accessibility, crowd magnitude and scale as well as IT structure used
(Prpić, Taeihagh and Melton 2015). Below we expand and enhance this characterisation of
crowdsourcing types to cover the sharing economy in the form of asset hubs and peer-to-peer sharing
networks and further consider platform architecture and interactions (see Table 1). By carrying out this
systematic examination we address a key gap in the literature and bring to light a more nuanced picture
of the similarities and differences between the crowdsourcing and sharing economy types. A quick
examination of Table 1 shows that each of the five types of IT-mediated platforms have their own
unique set of characteristics while sharing commonalities with the other four types of crowdsourcing
and sharing economy platforms.
Accessibility
IT-mediated crowds can be examined based on the level of openness of their platform. Prpić et al.
(2015) distinguish between platforms based on whether the platform is open to the public free of charge
or requires payment for gaining access (and thus is private). OC platforms and peer-to-peer sharing
networks are considered public, while TC, VLMs, and asset hubs are considered private (see Table 1).
Accessing peer-to-peer sharing networks such as Uber can be as simple as downloading an app on a
mobile phone and a quick sign up, and in OC crowdsourcing similarly the payment of fees is not
6
https://about.twitter.com/company
8. Taeihagh: Crowdsourcing, sharing Economy and Development
8
required for accessing the service. Of course, the actual use of the service is an entirely different matter
and often requires payments in peer-to-peer sharing networks – unlike OC crowdsourcing.7
In the case of TC and VLMs, individuals or organisations need to pay a launch fee to start a
competition or access the spot labour (Prpić et al 2015). In a similar fashion, most for-profit asset
hubs require the payment of a fee to access the service offered.8
Table 1 - Comparison of different types of sharing economy and crowdsourcing
Anonymity and reputation systems
Anonymity in the context of crowdsourcing and the sharing economy refers to whether the participants
in the crowds in different types of platforms are anonymous with respect to their offline identity. OC
platforms have a variable level of anonymity because of the different contexts and natures of the
activities of a particular site, as well as user preferences (Prpić, Taeihagh and Melton 2015). VLMs
such as Amazon MTurk provide ‘methodological anonymity’ by providing unique numeric identifiers
to the requester as a means of connecting them with MTurk workers, which provides them with a high
level of anonymity. TC platforms do not necessarily require the matching of offline and online
identities, although strong incentives might exist for the crowds frequenting such sites to connect their
offline and online identities to advance their offline career. Moreover, both crowdsourcing and sharing
economy platforms use reputation systems to maintain and improve the participation of IT-mediated
crowds. Morschheuser, Hamari and Koivisto (2016) review the use of reputation systems in
crowdsourcing by examining the literature on the use of points/scores, leaderboards/rankings,
badges/achievements, levels, progression and reward systems, etc. Furthemore, new studies on the use
of reputation systems in the sharing economy are emerging (Slee 2013; Zervas et al. 2015; Ert et al.
2015).
7
A variety of payment systems are used for transactions in peer-to-peer networks that range from payments from
suppliers, consumers or both to payment by advertisers in multisided platforms (Demary 2015).
8
For instance, in the case of car-sharing companies Car2Go has a $35 registration fee (plus tax) and Zipcar has a
$25 one-time application fee (Car2Go 2016; Zipcar 2016). Both of these services offer plans catering to the needs
of their members ranging from pay-as-you-go plans to monthly plans that offer certain prepaid miles that a
member can use.
Accessibility Anonymity Crowd
magnitude
Nature of the
crowd
Platform architecture IT-structure Platform interactions
Virtual labour
markets
(e.g. Amazon
Mturk)
Private High Millions General Community building
and infrastructure
provision
Episodic Information, currency,
and virtual services
Tournament
crowdsourcing
(e.g. Kaggle)
Private Medium Hundreds of
thousands
Specialized Community building Episodic Information, currency,
and virtual services
Open collaboration
(e.g. Twitter)
Public Variable Hundreds of
millions
General Community building Collaborative Information
Asset hubs
(e.g. Zipcar, Car2go)
Private Low Hundreds of
thousands to a
few millions
Specialized Community building
and infrastructure
provision
Episodic Information and currency
Peer-to-peer
sharing networks
(e.g. Uber)
Mostly Public Low Hundreds of
thousands to
millions
General or
specialized
Community building,
infrastructure
provision and data
layer
Collaborative Information and currency,
in some instances
goods/services as well
9. Taeihagh: Crowdsourcing, sharing Economy and Development
9
Crowd magnitude
Crowd magnitude refers to the number of available individuals to implement crowdsourcing or sharing
economies by conducting activities such as performing a task or providing a service, which ultimately
dictates the rate and scale with which resources can be created or provided in each platform (Prpić,
Taeihagh and Melton 2015). Table 1 presents the magnitude of different crowds for each form of
sharing economy and crowdsourcing reviewed.
Codagnone, Abadie and Biagi (2016) provide a review of the numbers of registered contractors on
various sharing economy platforms, demonstrating that the size of largest crowds in peer-to-peer
sharing networks can reach into the millions. In the case of asset hubs, Car2go has over a million users
and Zipcar has close to a million users (Dryden 2015; Avis 2016). Prpić, Taeihagh, and Melton (2015)
report on the largest size of crowds in crowdsourcing platforms, which range from thousands of
participants to the hundreds of millions: OC platforms such as Twitter and Facebook have hundreds of
millions of members, while TC platforms and VLMs’ magnitude of crowds also range from hundreds
of thousands (Kaggle, eYeka) to millions (Crowdflower, Amazon MTurk).
Nature of the crowd
In crowdsourcing and the sharing economy, specialised crowds form around specific types of content
or service, while general crowds provide or perform a multitude of common tasks or services. The
nature of the crowds can influence the size of the potential crowd available for a specific endeavour,
as well as impacting the tasks assigned to the participants and the features of the IT used, for instance
the various forms of TC, are unlikely to reach the same size as general OC platforms or some of the
larger peer-to-peer sharing networks (Prpić et al. 2015).
Table 1 highlights that asset hubs and TC rely on specialised crowds, whereas OC crowdsourcing and
VLMs rely on general crowds that either form around multiple kinds of content (OCs) or services
(VLMs). Peer-to-peer sharing networks represent a more complex picture as their crowds can be
specialised or general. For example, an individual or organisation interested in an asset hub such as
Zipcar is largely interested in a specific type of good or service offered whereas in peer-to-peer sharing
networks individuals might be interested in specific services (such as in the case of Uber) or be more
generalised (such as in the case of TaskRabbit).
Platform architecture
Choudary (2015) examined a selection of IT platforms and categorised them based on their architectural
frameworks and configuration and their patterns of exchange. He identified that all platforms function
across three layers but the degree to which each layer is dominant varies:
1- Network-Marketplace-Community layer: comprises the individual members of the crowd and
their network of interactions with other members. The network interaction might be direct with
each other as in social networks or implicit in the case of markets in which buyers and sellers
interact regularly. In some instance, this implicit community is formed when there are no direct
interactions between the individual users but the platform leverages the data available from
individual users and benchmarks them with one another to create value.
2- Infrastructure layer: enables value creation in the platform via the provision of tools, services
and rules. The infrastructure system in itself does not create value but allows users to create
value using this infrastructure, such as in the case of platforms such as YouTube that facilitate
content creation, dissemination and monetisation.
3- Data layer: all platforms use data but the extent and intensity varies among them significantly.
At a minimum, data is used for connecting the users of a platform with relevant
goods/services/content. However, in some platforms data plays the leading role.
Table 1 highlights that OC, VLM and TC platforms all focus on value creation by creating a Network-
Marketplace-Community layer. VLMs (more so than TC platforms) also focus on providing tools and
services that facilitate the connection of individuals and organisations that demand work with crowds,
as well as providing templates, tools and APIs that facilitate the creating of tasks and the receiving of
results from the crowds. Asset hubs also strongly focus on the Network-Marketplace-Community layer
10. Taeihagh: Crowdsourcing, sharing Economy and Development
10
as well as infrastructure provision. Companies such as Zipcar and Car2go, for instance, operate based
on developing and maintaining sizeable fee-paying crowds and providing and maintaining an
infrastructure network of vehicles for their use. Peer-to-peer sharing networks arguably have the most
sophisticated architecture and rely on a mixture of Network-Marketplace-Community, infrastructure
and data layers that vary in terms of the functionality they provide. Asset hubs also utilise data layers
but given that asset hubs have more control over their own companies’ assets relative to peer-to-peer
sharing networks, which rely solely on users’ goods/services, it can be argued that data layers are far
more vital for the proper functioning of peer-to-peer networks. For instance, an asset hub such as Zipcar
can relocate their own vehicles to different locations for the provision of service, whereas a company
such as Uber has to utilise more sophisticate analytics to change the behaviour of their contracting
drivers and provide coverage in different areas.
Platforms’ IT structure
Prpić and Shukla (2013) distinguish between two types of IT structure, namely collaborative IT
structures and episodic IT structures, based on whether crowd members interact with each other through
the IT platform for the purpose of deriving resources from the crowd. We can extend this concept to
the sharing economy by examining whether IT-mediated crowds in the sharing economy need to
interact with one another directly through the platform for the purpose of accessing goods or services
(collaborative IT structures) or whether crowd members never need to directly interact with each other
through the IT platform (episodic IT structures).
Prpić, Taeihagh and Melton (2015) highlight that VLMs use episodic IT structures (e.g. Amazon MTurk
micro-tasks are carried out by individual crowd participants without interactions with each other, at
least at the moment) and OC crowdsourcing platforms are found to generally use collaborative IT
structures (e.g. social networks such as Twitter inherently exhibit collaborative IT structures due to
extensive crowd interactions and over time), while TC platforms can allow both forms (e.g. an
individual in a platform like kaggle can work separately from the others or can use the reputation system
and results from previous competitions in the platform, connect with others and form teams for
participating in the completion in the hope of increasing their chance of winning the tournament).
Similarly, in asset hubs, there is no need for the crowd members to connect with one another. For
instance, crowd members using car-sharing services such as Zipcar or Car2go do not interact with one
another and the central platform run by the asset holding company manages various coordination and
scheduling efforts. Needless to say, the situation is completely different for peer-to-peer sharing
networks as they directly rely on peer engagement and the collaborative aspect that the IT structure
provides to function properly.
Table 1 illustrates that peer-to-peer sharing networks and OC crowdsourcing share similar collaborative
IT structures while asset hubs along with VLMs and TC share similar episodic IT structures (not
necessitating direct interaction of participants through the platform). As such, platforms that rely on
collaborative IT structures require the existence, generation and maintenance of social capital to
function properly (Prpić and Shukla 2013).
Platform interactions
The dominant social and economic interactions in platforms revolve around the exchange of
information, good/services or currency (Choudary 2015). All of the platforms highlighted in Table 1
share one fundamental aspect in that they all facilitate the exchange of information. VLMs, TC and
asset hubs facilitate the exchange of information and currency in various forms. Furthermore, in VLMs
and TC virtual services are also exchanged through the platform. Initially, the transfer of information
from the individuals or organisations demanding work or expertise to workers and tournament
participants is carried out. This is followed by the exchange of information and flow of virtual services
in the form of the performance of tasks and provision of results and solutions.9
Finally, a currency
9
As described earlier, the distinction between virtual and physical services here is important. In VLMs and
crowdsourcing, as the service can be performed online the flow of the service is virtual whereas in the sharing
11. Taeihagh: Crowdsourcing, sharing Economy and Development
11
exchange is carried out for the compensation of the crowd for their services. OC platforms are voluntary
and often do not involve the exchange of currency or goods or services and thus the main form of
exchange through such platforms is free information and/or content.10
Asset hubs and peer-to-peer sharing networks both involve the sharing of information and currency,
generally through procedures such as: transfer of information on goods/services from provider (business
or individual) to consumer, followed by the transfer of money from consumer to provider and
subsequently the transfer of goods/services from the provider to the consumer. It is obvious that, unlike
as is the case with virtual goods/services, in the case of physical assets the exchange of goods/services
is not possible through the platform itself, although in some instances peer-to-peer sharing networks do
also track, facilitate and monitor the exchange of goods/services internally. Choudary (2015) highlights
that a peer-to-peer sharing network such as Uber can track the ‘transportation-as-a-service’ exchange
as it is aware of the path of the trip using GPS and mobile networks, which helps in terms of fee
calculation and the determination of the completion of the ride.
5 Crowdsourcing, the sharing economy and development
The aim of the previous section was to bring attention to the nuanced similarities and differences
between crowdsourcing and sharing economy platforms which can be used by developing countries
when attempting to leverage these IT-mediated technologies for development. The comparison revealed
that the five types of IT-mediated technologies examined (Asset Hubs and Peer-to-peer networks,
Virtual labour markets, Tournaments crowdsourcing and Open collaboration) do not replicate each
other and have unique attributes, while sharing commonalities with other forms. Understanding that
developing countries have different development priorities helps in better capturing the challenges they
face in the adoption of new technologies (Koch, 2015). One such approach that offers a more nuanced
understanding of developing countries and their characteristics by taking into account countries’ needs
as well as resources and institutional capacities is the multi-dimensional clustering system of different
types of developing countries. It categorises developing countries into five groups based on factors such
as levels of poverty and inequality, productivity and innovation, political constraints, and dependence
on external flows (Vázquez and Sumner, 2012; 2013; 2015). In this work each cluster of countries has
a specific developmental character and set of issues that cannot be reduced to a simple representation
using a single metric. In Table 2 we have developed a summary of the work by Vázquez and Sumner.
As with the introduction of any new technology, the proponents of crowdsourcing and the sharing
economy focus on the positive aspects, such as the ease with which individuals can connect, interact,
and exchange information, currency and goods and services, and promise positive societal
transformation. While often the initial focus of scholars with the introduction of new technologies is on
developed countries, developing countries can benefit from them as well. For instance, it is argued that
sharing economy platforms, particularly peer-to-peer sharing networks, can boost small-scale service
sectors in developing countries, as through the use of IT platforms they can reduce overhead costs and
require relatively smaller levels of capital investment, solving informational problems by quickly
matching consumers with suppliers (Ozimek 2014) and in fact recent survey data from Hira (2017)
suggests an exponential increase in founding of sharing economy and crowdsourcing start-ups in
developing countries.
While some scholars see the sharing economy and crowdsourcing as a potential pathway toward
sustainability that can give voice to consumers and increase social capital, income and reciprocity,
economy platforms that entail the provision of physical services (e.g. TaskRabbit) the flow of service is not
captured through the platform, meaning such platforms only allow the exchange of information and currency.
10
It must be pointed out that solely the exchange of information is predominant in OC crowdsourcing platforms.
In the sharing economy, it is possible that platforms solely facilitate the exchange of information, and exchange
of goods/services and currency is carried out outside the platform (such as in the case of platforms that rely on
advertisement and listing fees).
12. Taeihagh: Crowdsourcing, sharing Economy and Development
12
others warn of the potential for grave scenarios in which these platforms erode accountability and tax
bases, divide communities, discriminate against individuals, underpay individuals, destroy job security,
and result in the domination of markets by multinational corporations in the name of neoliberal
capitalism (Heinrichs 2013; Dillahunt and Malone 2015; Martin 2016; Reeves 2015; Stone 2012;
Edelman and Luca 2014; Hira and Reilly 2017). Similarly, Zvolska (2015) points out that while at the
moment emphasis is generally placed on the potential sustainability of the sharing economy, concrete
research substantiating these claims is scarce.
It must be pointed out that the success of development and the diffusion and use of innovative
technologies depends on social, political and institutional factors (Edquist 2005; Schor, 2014). As was
illustrated in the previous section, relative to their developed counterparts developing countries often
fall behind in terms of GDP, levels of productivity, innovation, governance and political freedoms and
have higher rates of poverty, income equality and dependence on external flows of cash. Given the
nuanced differences within each country group, a one-size-fits-all approach to the adoption of
crowdsourcing and the sharing economy in developing countries is not feasible. Below we focus on
some of the relevant challenges facing different types of developing countries, with a particular focus
on the governance and regulatory aspects.
Table 2 Characteristics of different types of developing Countries – Developed based on Vázquez
& Sumner research on groupings of developing countries (2012; 2013; 2015)11
11
Vázquez & Sumner (2013) point out that even with this more nuanced categorization of the developing countries, it is not
possible to perfectly match the group assignments of the countries. They point out that while Type C1 contains the most similar
group of countries, the case of India is atypical (Gini coefficient considerably lower than the group average. GDP 16% higher
in non-agricultural sectors relative to the group average. Lower exports of primary products, five times higher scientific article
Poverty Income
inequality
Productivity Innovation GDP Political
freedom
Governance CO2
emissions
External
flow
Type C1 - High poverty
rate countries with largely
traditional economies E.g.
(2005-2010): Sierra Leone;
Ethiopia; Rwanda; Haiti;
Bangladesh; Pakistan;India
Highest Moderate Lowest Lowest Lowest Very Low Poor Low High
Type C2 – Natural
resource dependent
countries with little
political freedom. E.g.
(2005-2010): Vietnam;
Tajikistan; Yemen;
Cameroon; Angola; Chad;
Congo
High Low Low Low Low Low - Poor Low Moderate
Type C3 - External flow
dependent countries with
high inequality E.g. (2005-
2010): Bolivia; Indonesia;
Thailand; Peru; Colombia;
Ukraine; Sri Lanka; Kenya
Moderate High Moderate Moderate Moderate High High Moderate High
Type C4 - Economically
egalitarian emerging
economies with serious
challenges of
environmental
sustainability and limited
political freedoms E.g.
(2005-2010): Iraq; Egypt;
China; Jordan; Azerbaijan;
Venezuela
Moderate
/Low
Lowest High High High Lowest Poor High Low
Type C5 - Unequal
emerging economies with
low dependence on
external finance, E.g.
(2005-2010): Turkey;
Brazil; Mexico; Argentina;
South Africa; Malaysia
Lowest High Highest Highest Highest Highest Highest Highest Lowest
13. Taeihagh: Crowdsourcing, sharing Economy and Development
13
Arguably the most important requirement for setting up and successfully operating crowdsourcing and
sharing economy platforms in the first instance is access to communication networks for activities such
as the exchange of information, currency and transactions among crowds (e.g. consumers with suppliers
or workers with tasks from employers). According to Vázquez and Sumner’s (2015) classification,
which was elaborated in the previous section and in Table 2, Type C1 and C2 countries with the worst
development indicators (i.e. higher levels of poverty and lower levels of labour productivity and
innovation capacity) are dealing with severe poverty problems and have more difficulty in
implementing such technologies to begin with. World Bank indicators on the diffusion of mobile phones
by country groupings, mobile cellular subscriptions per 100 people, and individuals and households
with access to internet suggest this is indeed the case (World Bank 2015; 2016).12
Furthermore, Type
C1 and C2 countries have higher levels of contribution from the agricultural sectors and larger portions
of the population that have difficulty in using online platforms for carrying out more sophisticated tasks
online such as participating in VLMs and TC that require higher capacity and access to computers rather
than mobile phones that facilitate local (mobile) sharing economy activities (relative to their
counterparts in C3 to C5 groupings, which have higher levels of urban population).
Research suggests that developed countries disproportionally hire more individuals from
crowdsourcing and sharing economy platforms than developing countries to conduct online and local
tasks (Codagnone, Abadie and Biagi 2016). Aside from issues relating to discrimination between
individuals (discussed in the next subsection), here again the transfer of higher skilled and higher paying
jobs within developing countries is not equal. Type C4 and C5 countries that generally have higher
levels of productivity and innovation are more likely to get the better paying jobs such as programming
and engage in specialised forms of IT-mediated technology such as TC. On the other hand, C1 to C3
countries will attract low to medium skilled work. Even in this case, Type C1 and to some extent C2
countries are at a disadvantage as it is more likely that individuals in these countries might not have the
ability to provide verifiable personal information or demonstrate the lack of a criminal record (Nguyen,
2014) as part of joining a platform that might bar them from participating in online platforms as well
as having more difficulty in transferring funds online. Therefore, it can be argued that, although a certain
level of outsourcing from developed countries to developing countries is happening, the economies that
have moved away from traditional agriculture and are more advanced will benefit more, which could
in fact further increase the gap between C4 and C5 countries and their C1 to C3 counterparts that have
more traditional economies.
Governance
Public governance is the process by which a society manages itself and organises its affairs and is a
bedrock for successful and stable economies (UN, 2007). Developing countries often suffer from
inefficiency in terms of the delivery of vital public services, inefficient revenue systems, poor
transparency and the inappropriate allocation of resources, which often manifest themselves in acute
problems in sectors such as healthcare (Shah, 2005; Berglof and Claessens 2006; Asante, Zwi and Ho
2006).
According to Ozimek (2014), poor governance and a lack of effective regulatory regimes in developing
countries combined with weak property rights make attracting the investment required for building
large companies with high reputational capital difficult. He argues that in the absence of good
governance practices, nimble decentralised crowd-based rating systems lower the bar for the existence
of an effective services industry and bypass the need for regulation, as users in these countries will trust
peer-based feedback systems that can inform them about quality of goods/services more that
production and four times lower dependence on external finance relative to the group average as well as better governance and
democracy indicators).
12
The most important difference between C1 and C2 countries according to Vázquez and Sumner (2013) is in terms of level
of primary exports (much higher in C2), quality of democracy (higher in C1) and dependency on external finance (higher in
C1).
14. Taeihagh: Crowdsourcing, sharing Economy and Development
14
government endorsed companies and will help them in avoiding fraud and wrongdoing. However,
Aloisi (2015) believes these ranking systems and approval ratings transfer the traditional role of
management to the users of the platforms, highlighting that with this transfer the recipients of such
reviews in the platforms are less protected from external manipulation and agendas. Furthermore, given
that most of crowdsourcing and sharing economy companies are commercial and seek profits (with the
exception of some OC crowdsourcing platforms and non-commercial peer-to-peer sharing networks)
Ozimek’s views about potential of IT platforms seem rather optimistic.
Codagnone, Abadie and Biagi (2016) have already documented instances of litigation in the US in
regard to crowdsourcing and the sharing economy concerning employee benefits, cost
reimbursements, violation of labour standards, incorrect classification as contractors, and minimum
wage and overtime payments. Uncontrolled price wars between firms can also affect workers,
employees and contractors. Stiff competition can result in price reductions by firms seeking to attract
more consumers and an increasing volume of business but this can also result in contracting drivers
being undermined, affecting the industry and ultimately consumers as a whole (Straits Times 2016). If
such issues are surfacing so quickly with the adoption of crowdsourcing and sharing economy
practices in developed countries such as the US and Singapore, which have strong governance and
regulatory regimes, as well as effective enforcement mechanisms relative to developing countries, the
counter argument that given the governance and regulatory deficits in developing countries a stronger
and stricter enforcement and oversight of these platforms is needed also seems plausible.
In developed countries, in response to some of these legal challenges, firms such as TaskRabbit, Uber
and Lyft have made adjustments to their business models. However, without adequate regulation being
in place, Type C1, C2 and C4 countries are susceptible to firms entering their markets and dominating
them while passing on the risks to workers, contractors and consumers (e.g. not having strict regulations
for mandating third-party insurance in ride-sharing platforms or protecting privacy and financial
information in both commercial crowdsourcing and sharing economy platforms that carry out currency
exchanges) and then dealing with any litigation afterwards, perhaps after a long period in which they
took advantage of the situation. This is further exacerbated because these countries (particularly C1 and
C2 types) are less capable of monitoring the activities of the platforms and ensuring the correct
collection of records and sufficient tax payments to the state.
Codagnone, Abadie and Biagi (2016) and Aloisi (2015) focus on the work-related challenges
surrounding IT-mediated platforms, examine the relevant literature and meticulously unpack issues
such as workplace health and safety, discrimination, and social arbitrage to address exploitation using
these platforms and facilitate employment online (e.g. Amazon MTurk) or locally (e.g. TaskRabbit).
Accordingly, they suggest:
• The development of a minimum wage and maximum working hours per day restrictions;
• Avoiding exclusivity clauses that tie workers to a particular platform;
• The inclusion of relevant forms of social protection and health insurance;
• The provision of liability insurance for damage to third parties;
• Privacy protection mechanisms for workers;
• Guarantees for avoiding algorithmic discrimination with respect to geographical preferences,
gender, ethnicity, race or age when matching individuals in platforms; and
• Mandating the portability of an individual’s performance across platforms.
Many of the suggested remedies are challenging to implement and are yet to be addressed in developed
countries, which further increases concerns in regard to developing countries. All of the developing
countries can benefit from improving their governance and regulatory capacity and capability relative
to developed countries. This in turn will facilitate addressing the aforementioned issues. As highlighted
in Table 2, Type C1, C2 and C4 countries have the highest levels of governance deficit, which
demonstrates the challenges in addressing the issues raised by Codagnone, Abadie and Biagi (2016)
and Aloisi (2015). Moreover, it is worth pointing out that the compounding effects of corruption and
15. Taeihagh: Crowdsourcing, sharing Economy and Development
15
restrictions on political freedom in these countries further exacerbate these problems, as such workers
in these countries will be more vulnerable relative to their Type C3 and C5 counterparts.
Highly publicised concerns about Uber, for instance, due to excessive charges from the surge-pricing
algorithm and drivers being accused of assault, resulted in blanket bans in some cities, as unlike the
traditional taxi industry Uber was initially not subject to strict regulations for pricing and licensing
(Gobble, 2015). However, the findings of the study by van den Broek (2015) suggest that, although
firms such as Airbnb and Uber try to hold on to their generic business models as much as possible, in
the face of regulatory constrains (mainly relating to drivers in the case of Uber and hosts in the case of
Airbnb) these firms have adapted their business models and have found ways to operate legally within
the set framework.
As such, the active participation of governments in developing countries and more effective regulation
of the affected sectors is paramount for gaining the benefits of IT-mediated platforms highlighted earlier
(e.g. even addressing shortcomings in provision of goods/services by the state as suggested by Ozimek
(2014)) and avoiding negative consequences such as labour law violations, discrimination,
infringements on privacy, etc. Type C3 and C5 countries, with higher governance capacities, are more
likely to be able to work with firms, or impose restrictions on them to encourage the adoption of positive
practices. Additionally, given the higher level of productivity in Type C4 and C5 countries, they can
utilise pull mechanisms to direct innovation in IT-mediated technology and provide funding and support
to companies that follow best practices. Research by Sadoi (2008) suggests that focusing on developing
local technological capabilities within a country is more successful than the provision of incentives to
firms for technology transfer to developing countries as successful transitions depend on countries
developing their own innovation hubs. As such, Type C1 to C3 countries should not just open markets
to external corporations but should exert some control and focus on improving levels of productivity
and innovation and perhaps given the complexity of the issues at hand and the severity of constrains
they face set stricter control mechanisms relative to their C4 and C5 counterparts, or even focus on
direct provision of services.
It is worth mentioning that some forms of crowdsourcing platform, particularly OC crowdsourcing,
rather than receiving support, might be strictly limited in some of the developing countries with lower
levels of political freedom (Type C4, C1 and C2) or actively used for reducing political freedom, as
new empirical research by Asmolov (2015) demonstrates that, using volunteers from crowdsourcing
platforms, it is possible to prevent collective action.
6 Conclusion
This paper examined the sharing economy and crowdsourcing and highlighted various types of each
IT-mediated technology. Afterwards, given the similarities of crowdsourcing and sharing economy in
terms of their use of IT, reliance on crowds, monetary exchange, use of reputation systems and the gap
in the literature in regard to their nuanced similarities and differences, the sharing economy and
crowdsourcing were systematically compared along several dimensions.
We advanced the literature on the sharing economy and crowdsourcing by providing a comparison of
their types across dimensions such as accessibility, crowd magnitude, nature of the crowd, anonymity,
platforms’ architectural frameworks, IT structure and interactions. This systematic comparison brought
a more nuanced understanding of these IT-mediated technologies and highlighted similarities and
differences between various types of crowdsourcing and sharing economy platforms, demonstrating
that each type of IT-mediated technology examined had unique attributes, while sharing commonalities
with other forms. It addressed a gap in the current literature where these IT-mediated technologies were
either ignored in the other domain or treated as a singular form or even at times categorised as both a
sharing economy platform and a crowdsourcing platform by different scholars. Of course, examinations
across these dimensions each include exceptions and the comparison should not be considered
definitive. Nevertheless, it allows future researchers to better differentiate between the crowdsourcing
16. Taeihagh: Crowdsourcing, sharing Economy and Development
16
and sharing economy. For instance, following Gansky (2010) and Rauch and Schleicher (2015) we
mainly focused on the commercial use of the sharing economy that focuses on the exchange of physical
goods or services that are carried out locally. This can be expanded in the future once research in the
field goes beyond the types of categorisations highlighted in the current paper and coalesces around a
more detailed categorisation of sharing economy types. This endeavour is currently under development,
particularly in regard to peer-to-peer networks.
In addition to the above contributions, we examined the use of crowdsourcing and the sharing economy
in developing countries. We went beyond the simple categorisation of developing economies based on
GDP and examined some of the challenges facing different groups of developing countries in
addressing crowdsourcing and the sharing economy. We suggested which forms would be more
appropriate when faced with different types of development issues, with particular focus on issues
relating to mobile and online activities, productivity and innovation as well as legal and governance
challenges, helping to highlight the differences in the applicability of these IT-mediated technologies
in specific contexts.
We hope that this research facilitates a more nuanced examination of the applicability of these
technologies in different types of developing countries, encourages researchers to study them more
rigorously in future, and helps industry and policy-makers to work together more effectively to
leverage the new potential of applying crowdsourcing and sharing economy for addressing
developmental challenges while maximizing positive impacts and minimizing negative side effects.
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