Call for Expressions of Interest for postdoctoral Marie Curie Incoming Fellowship at Copenhagen Business School

Marie Sklodowska-Curie Individual Fellowships – Call for Expressions of Interest

Copenhagen Business School – Department of Digitalisation

Location: Copenhagen, Denmark

Salary:  The salary range for the fellowship will fall within the range of € 66 000 -€70 000 per annum (salary levels 6-8), subject to negotiation in accordance with the Danish Ministry of Finance guidelines. The grant includes a family allowance of € 500/month for eligible researchers.

Hours: Full time

Contract type: Fixed term

Closing date for Expressions of Interest: 30 June 2019

Closing date for submission of applications: 11 September 2019


The Fellowship

The Marie Sklodowka Curie Individual Fellowships (MCIFs) are prestigious fellowships funded by the European Commission. They offer a generous allowance for 12-24 months including mobility, family and research allowance. Applicants can be from any country, must hold a PhD (or equivalent), and must not have resided or carried out their main activity in Denmark for more than 12 months in the 3 years immediately prior to the MCIF deadline on 11 September 2019. MCIFs follow a bottom-up approach, that is research fields and topics are chosen freely by the applicants, in collaboration with their prospective hosts.



The Department of Digitalization (DIGI) at Copenhagen Business School (CBS) welcomes expressions of interest from postdoctoral researchers with an excellent track record of research and publication to apply jointly with a supervisor from DIGI/CBS to Marie Sklodowska-Curie Individual Fellowship Scheme. Selected candidates will receive dedicated support from a DIGI Senior Professor, as well as CBS’s Research Support Office to develop their proposal and application for submission to the European Commission by 11 September 2019.  The successful candidates will benefit from travel funding provided by CBS to visit the host professor for 1-2 days in July/August 2019 to discuss and plan the full proposal for submission to the European Commission. The host at CBS is Professor Anoush Margaryan.


Research themes

Expressions of Interest that are aligned with one of more of the following research themes are welcome:

  1. Workplace learning and skill development practices in emergent forms of digital work such as crowdwork in online labour platforms
  2. Policy and practice implications of crowdwork and other emergent forms of digital work for pedagogy in higher education settings
  3. Practices and processes of worker self-organisation and networking for learning, knowledge sharing and skill development in crowdwork

Candidates with a (interdisciplinary) background in, including but not limited to, Learning Sciences, Workplace Learning, Education, Sociology, Psychology, Communication Studies, Information Science, Business and Organisational Studies/Human Resource Development and a strong interest in workplace learning research are welcome to apply.


How to express your interest

Expressions of interest should include:

  1. Your CV including a full list of your publications, research grants and projects
  2. A max 2-3 page Research Proposal Summary aligned with one or more of the above themes
  3. A max 1-2 page motivation letter identifying synergies with key research areas listed above

Please email your EoI pack as one single PDF file attachment, putting ‘MCIF Expression of Interest’ as your email subject to Bodil Sponholtz, by Sunday 30 June 2019. 

Expressions of interest will be selected on the basis of the quality of the research idea and the candidate’s track record. Successful candidates will be informed by Monday 8 July 2019 when they will be invited to make a formal application to the European Commission with their CBS host Professor Anoush Margaryan.

To allow sufficient time for institutional approvals, the final draft of the full proposal is expected to be ready for review by the host by Tuesday 3 September 2019.

For further information and to arrange an informal discussion please contact Professor Anoush Margaryan


New paper on learning practices in crowdwork

My paper “Workplace learning in crowdwork: Comparing microworkers’ and online freelancers’ practices” has been accepted for publication in the Journal of Workplace Learning.  Here is the abstract:

Purpose: This paper explores workplace learning practices within two types of crowdwork– microwork and online freelancing. Specifically, the paper scopes and compares the use of workplace learning activities (WLAs) and self-regulatory learning strategies (SRL strategies) undertaken by microworkers and online freelancers. We hypothesised that there may be quantitative differences in the use of WLAs and SRL strategies within these two types of crowdwork, because of the underpinning differences in the complexity of tasks and skill requirements.

Methodology: To test this hypothesis, a questionnaire survey was carried out among crowdworkers from two crowdwork platforms – Figure Eight (microwork) and Upwork (online freelancing). Chi-square test was used to compare WLAs and SRL strategies among online freelancers and microworkers.

Findings: Both groups use many WLAs and SRL strategies. Several significant differences were identified between the groups. In particular, moderate and moderately strong associations were uncovered, whereby OFs were more likely to report (i) undertaking free online courses/tutorials; and (ii) learning by receiving feedback. In addition, significant but weak or very weak associations were identified, namely OFs were more likely to learn by (i) collaborating with others; (ii) self-study of literature; and (iii) making notes when learning. In contrast, MWs were more likely to write reflective notes on learning after the completion of work tasks, although this association was very weak.

Contribution: The paper contributes empirical evidence in an under-researched area – workplace learning practices in crowdwork. Crowdwork is increasingly taken up across developed and developing countries. Therefore, it is important to understand the learning potential of this form of work and where the gaps and issues might be. Better understanding of crowdworkers’ learning practices could help platform providers and policymakers to shape the design of crowdwork in ways that could be beneficial to all stakeholders. The paper outlines several implications for the design of crowdwork.


Crowdworkers’ motives to take up crowdwork

One of the issues I explored as part of the survey of crowdworkers was their motives to engage in this form of work.

Specifically, the participants were presented with a list of 16 motives synthesised from crowdwork literature and asked to select all motives that applied in their case.  Participants were also given an opportunity to indicate any other motives not included in the list.

Findings (Figure 1) suggest that the key motives are to earn supplementary or main income, with crowdwork tasks considered fun and a fruitful way of spending one’s time while earning money. Also, being their own boss and having control over their own schedule were indicated relatively frequently as reasons to take up crowdwork. Few crowdworkers appear to engage in this form of work due to inability to find or perform traditional work (10% and 4% respectively).


Figure 1. Crowdworkers’ motives to engage in crowdwork (n=182)

Comparing the responses of microworkers and online freelancers (Fig. 2), more online freelancers report crowdwork as their primary source of income corraborating findings from previous studies of crowdwork.

Also, we observe that considerably fewer online freelancers than microworkers report crowdwork tasks being fun as a key motive, but considerably more online freelancers cite being their own boss, having control over their schedule, following their passion, having more choice of the projects they can do, and earning more through crowdwork than they could through traditional work as key reasons to take up crowdwork.


Figure 2. Online freelancers’, OF (n=15) and microworkers’, MW (n=167) motives to engage in crowdwork 

Other motives indicated by the crowdworkers included:

  • “As more people use [crowdwork platform] to find people for project work, the network effect almost requires that I use it; it is an important source of work referrals.”
  • Learning skills and gaining work experience”.
  • “I just finished my studies and am currently searching for a traditional (part-time) employment in addition to crowdwork, mostly for insurance reasons.”
  • “In [my country] the incomes from traditional work are terrible“.
  • “It’s best online earning platform I’ve found so far.”


Do crowdworkers self-identify as entrepreneurs?

As part of the survey of crowdworkers’ learning practices, I asked the participants about their self-identity as entrepreneurs.

In particular, I asked the crowdworkers if they considered themselves to be entrepreneurs in line with the following definition: “‘Entrepreneur’ means a person who organises and manages their own business exercising considerable personal initiative and taking on financial risk. Entrepreneurs include people who are self-employed, those who are a sole owner, partner or a majority shareholder of a small, medium, or large company”.

The majority of crowdworkers (52%) reported not identifying as entrepreneurs (Figure 1):


Figure 1. Crowdworkers’ reported self-identity as entrepreneurs (n=182)

More online freelancers than microworkers reported self-identifying as entrepreneurs (Figure 2).  Those who selected ‘other’ stated that they weren’t entrepreneurs yet, but were planning to start a business in the future.

EntreprenuersByGroupFigure 2. Reported entrepreneurial self-identity among microworkers, MW (n=167) and online freelancers, OF (n=15) 


Crowdworkers’ use of self-regulated learning strategies

My previous blogposts in the series on crowdworkers’ learning practices are available here, here, here, and here.

One aspect of the surveys of crowdworkers learning practices I’ve been recently conducting has been focused on scoping the range and frequency of use of of self-regulated learning (SRL) strategies undertaken by crowdworkers.

The questionnaire included a sub-scale of 34 items grounded in Zimmerman’s 3-phase model of self-regulated learning scoping crowdworkers’ strategies of planning, implementing and reflecting on their workplace learning.

The initial results from the sample of 167 microworkers and 15 online freelancers are as follows:

  • The possible range of SRL scores based on the questionnaire: 0 – 102
  • The actual SRL score ranges in this sample are:
    • microworkers: 4-99
    • online freelancers: 10-76
  • The sub-groups by SRL score are:
    • low SRL: 0-34
    • medium SRL: 35-70
    • high SRL: 71-102

1. What is the distribution of high, low and medium SRL scores among this sample?

SRLScoresFigure 1. Distribution of SRL scores among the sample of crowdworkers, percentages (n=182, including microworkers (MW) n=167 and online freelancers (OF) n=15).  

The bell curve distribution of SRL scores is in line with our previous surveys among ‘conventional’ knowledge workers.


2. What are the most prevalent SRL strategies among crowdworkers across the three phases of Zimmerman’s model?

By ‘most prevalent’ I mean SRL strategies that crowdworkers reported using ‘most of the time’ and ‘always’ (those who reported using a strategy only ‘sometimes’ are excluded from this analysis).

The initial results are shown in Figures 2-4 below.

Overall, we observe that crowdworkers use a wide range of self-regulated learning strategies across all phases, setting and modifying their own learning goals, strategies and performance standards and reflecting on their learning from crowdwork tasks.

Self-efficacy and intrinsic motivation are prevalent among crowdworkers (determined by responses to statements such as ‘important to learn new things in crowdwork tasks’, ‘confident can handle most demands in crowdwork’, prefers tasks that require to learn something new’, ‘meets learning goals’).

Despite the nature of crowdwork tasks that are designed to be completed individually and autonomously, some crowdworkers apply social learning strategies. Examples of social learning strategies are reaching out to others (36% of microworkers and 33% of online freelancers reported doing this most of the time or always); considering how what they have learned from crowdwork may be of interest to their peers (27% of microworkers and 39% of online freelancers do this most of the time or always); or sharing their learning from crowdwork with others (30% of online freelancers and 13% of microworkers).

The patterns of use of SRL strategies are broadly similar across both groups, but there are some differences, most notably:

  • More microworkers report regularly allocating time to work on their learning goals (22% of microworkers vs 7% of online freelancers)
  • More microworkers report regularly reflecting on their performance on crowdwork tasks (64% of microworkers vs 13% of online freelancers)
  • More microworkers report regularly sharing their reflections on their learning with others (20% of microworkers vs 0% of online freelancers)

Further analysis of the data will help determine if these differences are statistically significant and develop possible explanations for the differences.


Figure 2. SRL Planning strategies among crowdworkers 



Figure 3. SRL implementation strategies among crowdworkers



Figure 4. SRL reflection strategies among crowdworkers






Nature of work tasks in crowdwork: Microwork vs online freelancing

As part of the survey of crowdworkers’ learning practices, I explored the nature of tasks they undertake within crowdwork platforms using two sets of previously validated and published scales: the Classification Structure of Knowledge-intensive Processes and some items from the ‘Knowledge Characteristics’ sub-scale of the Work Design Questionnaire.

In particular, the participants were asked to indicate which of the following 15 task types most closely described their typical crowdwork tasks (they could choose all options that applied):

  1. My crowdwork tasks are mostly routine
  2. My crowdwork tasks are highly reliant on formal processes
  3. My crowdwork tasks don’t give me freedom to decide what should be done in any particular situation
  4. My crowdwork tasks are mostly systematically repeatable
  5. My crowdwork tasks are highly reliant on formal standards
  6. My crowdwork tasks are dependent on integration across functional or disciplinary boundaries
  7. My crowdwork tasks are improvisational/creative
  8. My crowdwork tasks are highly reliant on my deep expertise/personal judgement
  9. My crowdwork tasks are dependent on collaborating with others
  10. My crowdwork tasks are highly reliant on my own individual expertise/experience
  11. My crowdwork tasks involve solving problems that have no obvious correct answer
  12. My crowdwork tasks involve dealing with problems I have not met before
  13. My crowdwork tasks require unique ideas/solutions to problems
  14. My crowdwork tasks require me to use a variety of skills to complete the work
  15. My crowdwork tasks require me to use a number of complex or high-level skills

Here are the results from the two groups: microworkers (Figure 1) and online freelancers (Figure 2). Among microworkers the three most prevalent characterisations of tasks were routine, systematically repeatable and requiring a variety of skills. And among online freelancers the most prevalent tasks were those that required a variety of skills and uniquie ideas and solutions, dealing with novel problem, and were reliant on their own individual expertise and experience. 


Figure 1. Microworkers’ perceptions of the nature of their crowdwork tasks 


Figure 2. Online freelancers’ perceptions of the nature of their crowdwork tasks

I’m working on a paper comparing these findings with earlier findings from a study of how conventional workers describe the nature of their tasks to see if there are any statistically significant differences and whether a similar typology emerges from the crowdwork settings. Stay tuned!



Workplace learning activities in crowdwork: 2016 vs 2017 survey

Yesterday, I wrote about the initial findings of a survey scoping the range and frequency of use of workplace learning activities among crowdworkers.   

This is the second iteration of the survey, the first survey was carried out in March 2016 within the same two platforms, but involving different participants. In the 2016 sample a larger proportion of crowdworkers reported using social learning activities, as well as online courses and tutorials. 

I plan to compare the demographic details and other key characteristics of the participants of both survey iterations to identify potential explanations for these differences.