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Posts Tagged ‘Digital Labour’

Trust Issues and Ride-Hailing Platforms in Lagos, Nigeria.

The idea of building trust is often central to the adoption and use of technology platforms in general such that the processes and governance of these platforms ought to align with the realities of user-groups which are essential for a seamless service. Since 2013, the entry of ride-hailing platforms in Nigeria has increased because of an overall technology awareness in Lagos and continuous successes of existing ride-hailing companies such as Uber and Taxify (see Table 1). Ease of access, trip predictability and ease of fare calculations and payment, amongst other things have improved.

Despite its growing impact on urban transport in Nigeria, the industry has suffered several challenges such as insecurity and lack of safety for user-groups. Prior to ride-hailing platforms, the notion of trust has been integral for taxi businesses or technologies to thrive. For instance, a passenger who builds a bond with a local taxi driver such that the driver runs personal errands such as dropping off school kids.

Trust in simple terms is the belief in the ability of someone or something. There has been increasing interest in the concept of trust in online transactions since the development of the internet and e-commerce in the early 1990s (1). The concept of ‘trust’ encapsulates both offline environments and online environments such that the difference lies in the varying characteristics of these environments as well as the context in which trust is formed and maintained. In technology, “it is a belief that a specific technology has the attributes necessary to perform as expected in a given situation in which negative consequences are possible” (2).Risks and uncertainties are exacerbated because users lack total control of the processes governing ride-hailing apps.

Table 1: Ride-hailing companies in Lagos
Source: Author’s fieldwork

In the ride-hailing industry in Lagos, both drivers and passengers are aware of the risk in engaging with a complete stranger via an app which is monitored by platform companies through data analytics and algorithms. Unlike the conventional taxi industry, user-groups often build trust in platform companies based on the efficiency and reliability of their apps over time. For example, Mr Ayo, the Taxify driver has just accepted his first trip for the day, but later declines because the rider would only pay via an ‘online bank transfer’ and from experience, the driver does not trust this process because it is often a fraudulent tactic used by riders without money. Using a third-party banking app to make a transfer to the driver’s account gives the rider more power in this situation because the payment could be reversed in 24 hours if reported by the rider. If it were a card-paid trip, the driver would feel safer because the ride-hailing app acts as an intermediary between both parties such that if a conflict occurs, it can be resolved amicably.

One of the many instances where the rider loses trust is through trip manipulations by drivers.  Since Uber slashed the base fare of trips by 40% in Lagos, drivers have reacted with strategies for increasing the fare of trips through manipulative techniques (3). In 2017, Lockito, designed for testing geofencing-based apps, was being used in inflating fares by manipulating the distance of a trip.  For example, a trip that should be about 5.9km would be double the distance when the Lockito app is being used (see Figure 1).

Although drivers are responsible for altering the GPS function in the Uber app, riders become aware that the app is also vulnerable to fraudulent activities. Riders frequently monitor the Uber app, drivers’ behaviour and prefer cash payments to card payments to avoid being defrauded during trips. Although there are other factors involved such as low smartphone and card penetration overall (4), the psychological construct of trust remains central to the reliability and predictability of drivers, riders, and the algorithms behind ride-hailing apps.

Figure 1: Incorrect GPS reading vs correct GPS reading
Source: BrandSpurNG (2017)

Regardless of ride-hailing platforms’ success in Nigeria, trust issues surrounding usability and culture remain a stumbling block especially for indigenous start-ups like Oga-Taxi. More research would be needed to understand the implications on user behaviour and what coping strategies are needed to thrive in an increasingly ‘networked’ environment as well as how these strategies may create new realities in the global South.

References.

  1. Li, F., Pieńkowski, D., van Moorsel, A. & Smith, C. (2012). A Holistic Framework for Trust in Online Transactions. International Journal of Management Reviews, 14(1), pp. 85-103
  2. McKnight, D. G., Carter, M., Thatcher, J.B., & Clay, P.F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems, 2(2), pp. 12 – 32.
  3. Adegoke, Y. (2017). Uber drivers in Lagos are using a fake GPS app to inflate rider fares, Quartz Africa, 13 Nov
  4. appsafrica (2015). Can Uber really work in Lagos, Nigeria? appsafrica, 2 Jun
  5. BrandSpurNG (2017). Uber Drivers In Lagos Using Fake GPS App To Inflate Fares – Report, Nairaland Forum, 14 Nov

Analysing the Perceptions of Digital Gig Workers: Mining Emotions from Job Reviews

In a previous post, we provided a discussion of how the analysis of user-generated content (e.g. comments/posts on social media and/or job review sites) can help in understanding perceptions of digital gig workers. The prevailing assumption is that generally, digital gig workers contend with non-standard working conditions, e.g. the lack of social security coverage, long working hours, lower salaries, and the lack of benefits. Nevertheless, it is believed that digital gig workers in the Global South in particular perceive their jobs as being better than local benchmarks (i.e. office-based work).

To test the above assumptions, we developed and employed automatic text analysis methods to answer the following research questions:

  • How do digital gig workers feel about their jobs?
  • Which topics pertaining to decent work standards do they frequently talk about?
  • Are there any differences—in terms of sentiments and topics—across different geographic locations, or across genders?

We hereby present the results of analytics in the way of answering the questions above.

Firstly, we collected online posts published by digital gig workers from Glassdoor, a web-based platform for sharing reviews of companies and their management. Focussing on reviews of the digital gig companies Upwork, Fiverr and Freelancer, we retrieved a total of 567 reviews, 297 of which include geographic metadata (i.e. the geographic location associated with the account/profile of the user posting a review). For our text analysis, we made use of the Pro and Con fields that each review came with.

Based on the NRC Emotion Lexicon, a dictionary-based emotion detection method (implemented in the R statistical package) was applied on the reviews, classifying them according to Robert Plutchik’s eight basic emotions: Joy, Trust, Fear, Surprise, Sadness, Anticipation, Anger, and Disgust. We then grouped the reviews as either coming from the Global North or the Global South based on the geographic metadata attached to them. Shown in the figure below are the 15 most frequent emotion-bearing words found within reviews, represented according to the emotions they express. Bars in amber correspond to words prevalent in reviews from the Global North (GN) while those in blue pertain to those in reviews from the Global South (GS). 

Riza GNGSemotion

It can be observed that there are more words within GS reviews containing emotions that are clearly positive. All of the 15 words associated with Trust were found more often in GS reviews. Furthermore, 10 and 8 words associated with Joy and Anticipation, respectively, were more frequent in GS reviews. These results support the belief that digital gig workers in the Global South (GS) do express positive feelings towards their jobs.

Meanwhile, our results show that digital gig workers from both GN and GS express negative emotions. On the one hand, GS reviews were the source of 11 and 10 words associated with Anger and Fear, respectively. On the other hand, 15 and 11 words associated with Sadness and Disgust, respectively, were contained in GN reviews. This suggests that generally speaking, digital gig workers do have to contend with less than ideal working conditions, which in turn trigger such negative emotions.

Finally, 10 words associated with Surprise came from GN, 5 from GS. It is worth noting though that this particular emotion can either be negative or positive depending on context.

These results are but “teasers” to the full results of our automated analysis. Further details including the topics/themes towards which such emotions are targeted, as well as answers to the second and third research questions stated above, will be presented by Dr Victoria Ikoro in the upcoming 3rd Annual ICT4D North Workshop to be held in the Management School of the University of Liverpool on the 6th June 2019.

 

How Many Platform Workers Are There in the Global South?

29 January 2019 1 comment

In developing countries, there has been a rapid increase in the gig economy and in the presence of digital labour platforms: defined as “a set of digital resources – including services and content – that enable value-creating interactions between consumers and individual service-providing workers”[1].

But how many workers actually work for such platforms?

I am not going to provide a reliable answer to that question but I will give some kind of ballpark figure.

We start by dividing out two types of platform work: digital gig work that involves digitisable tasks like data entry, writing copy, web design, accounting, etc; and physical gig work that involves a physical task like taxi driving, food delivery, domestic work, etc.  A previous estimate[2], updated to account for growth, would be that there were something like 10 million active digital gig workers in the global South at the start of 2019 (and around ten times that number registered on digital labour platforms but with 90% of them inactive).

So how many physical gig workers are there?  I’m going to break this down by continent since the extent of physical gig work seems to vary significantly between the three main continents of the global South.

Africa

Calculations here are based on extrapolations from just two economies, and seek to take account of wealth and population[3].  Current research for the Fairwork project estimates around 30,000 physical gig workers in South Africa; about half in taxi-driving and the rest mainly in delivery and domestic work.  Estimates for Nigeria[4] plus re-use of some of the same ratios found in South Africa, suggest 20,000 such workers.  Accounting for GDP per capita and population suggests around 60 workers per US$1,000 GDP/capita and per 1 million population; i.e. per US$1bn GDP.  Multiplying up to the overall GDP of Africa produces an estimate of c.130,000 physical gig workers in Africa.  However, given there are at least 100,000 in Egypt alone, we can at least double that to 250,000.

Asia

Similar calculations can be undertaken in Asia, based on numbers associated with platforms in India and Indonesia.  Extrapolating from estimates for taxi-driving and food delivery platforms in India[5], I estimate around 2 million physical gig workers in India.  For Indonesia[6], the figure is closer to 1 million.  Accounting for GDP suggests around 800 workers per US$1bn of GDP.  Multiplying up to the overall GDP of Asia (excluding Japan) produces an estimate of roughly 18 million physical gig workers in developing Asia.

However, there is an alternative approach, which is to exclude China in this calculation, which produces a figure of 9 million, and then take at face value claims that Didi Chuxing employs 21 million physical gig workers in China[7].  This would lead to an estimate of 30 million physical gig workers in developing Asia.

Latin America

Here, I’ve taken a simpler approach based on some national and continent-wide estimates of taxi driving[8] and then re-using ratios from the South Africa work.  This produces an estimate of something like 2 million physical gig workers in Latin America.

Summary

The basis for these estimates is flimsy, and the extrapolations are worse, so please attach a strong health warning to this material.  Better still, come up with some improved statistics.  But my ballpark figure is that there are at least 30 million platform-based gig workers in the global South; 10 million digital and just over 20 million physical.  And that the figure could be more than 40 million, which would be around 1.5% of the global South workforce.

A proportion of these workers are not relying on this as their primary source of income.  For digital gig workers, this number is anything from two-thirds to a half[9].  It may be somewhat less for the physical gig economy, so another ballpark would be that around 15-20 million workers in developing countries are relying on digital platforms for their primary source of income.

(Annual turnover is an issue for another day but, globally and summing figures for the digital gig economy[10] and main physical gig platforms Uber[11] and Didi Chuxing[12], it must be at least US$50bn.)

 

[1] Adapted from Constantinides, P., Henfridsson, O., & Parker, G. G. (2018). Introduction—Platforms and Infrastructures in the Digital Age, Information Systems Research, 29(2), 381-400

[2] Heeks, R. (2017) Decent Work and the Digital Gig Economy, GDI Development Informatics Working Paper no.71, University of Manchester, UK

[3] An alternative approach would seek to extrapolate in terms of numbers of Internet users but that is correlated with GDP, and the figures still point to a strong under-representation of Africa in platform labour and strong over-representation of China.  Put another way, factors other than wealth and Internet access are needed to explain national differences in the proportions working in the platform economy.

[4] E.g. https://www.vanguardngr.com/2018/08/uber-monthly-passenger-base-in-nigeria-hits-267000/ and https://technext.ng/2018/08/17/max-ng-3-5-things-should-know-about-ride-hailing-platform/

[5] E.g. https://qz.com/india/1385653/uber-ola-drivers-pay-the-price-for-indias-fuel-price-rise/ and https://www.livemint.com/Companies/cYbdfsYk93HFhMuC0XgaNN/Swiggy-Zomato-hike-delivery-boy-salaries-as-competition-gro.html and https://economictimes.indiatimes.com/small-biz/startups/newsbuzz/zomato-swiggy-and-ubereats-paying-higher-cash-on-delivery/articleshow/65142563.cms

[6] e.g. http://buscompress.com/uploads/3/4/9/8/34980536/riber_7-s1_sp_h17-051_59-67.pdf and https://www.thejakartapost.com/academia/2018/11/21/the-gig-economy-and-skills-traps-in-indonesia.html

[7] E.g. https://technode.com/2018/03/19/didi-1-5-billion-abs/ and https://www.sustainabletransport.org/archives/6317

[8] E.g. https://www.reuters.com/article/us-uber-brazil/uber-rival-apps-join-forces-in-brazil-to-stem-tide-of-regulation-idUSKBN1D71KE and https://www.ft.com/content/7bf04e08-1d63-11e8-aaca-4574d7dabfb6 and https://www.globalfleet.com/en/smart-mobility/latin-america/news/chile-imposes-regulations-ride-hailing-companies and https://www.forbes.com/sites/jonathanmoed/2018/12/20/is-uber-operating-illegally-in-its-fastest-growing-region/#74c69e161925

[9] Heeks, R. (2017) Decent Work and the Digital Gig Economy, GDI Development Informatics Working Paper no.71, University of Manchester, UK

[10] Heeks, R. (2017) Decent Work and the Digital Gig Economy, GDI Development Informatics Working Paper no.71, University of Manchester, UK

[11] E.g. https://www.cnbc.com/2018/08/15/uber-q2-2018-revenue-bookings-slow-slightly.html

[12] E.g. https://kr-asia.com/losing-300m-in-2017-didi-chuxing-wants-to-turn-a-profit-in-2018-amid-fierce-competition

Social Media Analytics for Better Understanding of the Digital Gig Economy

27 April 2018 3 comments

Owing to the proliferation of digital platforms facilitating online freelance work such as Upwork, Fiverr and Amazon Mechanical Turk, the number of digital gig workers has been continuously increasing worldwide. In 2015, there were as many as 48 million digital gig workers [1]; between 2016 and 2017, a 25% increase in the number of such workers was reported [2].

Digital gig work is indeed attractive to many, with a number of benefits that such independent workers are perceived to enjoy, e.g., flexible working hours, reduced transportation costs, wide range of projects to choose from. However, there exist potentially distressing issues, e.g., lack of job security, tough competition, substandard wages, which are especially pronounced in developing country settings [3]. Whereas traditional media such as news were unable to pinpoint or bring attention to these concerns, social media analysis–done manually by Cision in 2017–provided a window to the thoughts of independent workers which led to the fine-grained identification of issues that they are faced with [4].

As part of the currently ongoing Social Media Analytics Research and Teaching @ Manchester (SMART@Manchester) project funded by the University of Manchester Research Institute (UMRI), we aim to automatically gain insight into people’s perceptions of digital gig work, based on their posts on social media platforms such as Twitter and Facebook, as well as on review sites such as Glassdoor.

Specifically, we wish to test the currently prevailing assumption that digital gig work is experienced differently in the Global South compared to the Global North. Workers tend to make comparisons with their local benchmarks (i.e., office-based work), and it is believed possible that in the Global North, digital gig work is worse than prevailing benchmarks, whereas in the Global South it is better.

The following are some of the research questions that will be addressed as part of this case study.

  1. How do digital gig workers feel about their jobs?
  2. Which topics pertaining to decent work standards do they frequently talk about?
  3. Are there any differences—in terms of sentiments and topics—across different geographic locations, or across genders?

The first question can be answered by opinion mining while the second is addressable by topic identification. To determine whether there are differences with respect to opinions and topics, between the Global North and South or between genders, results from opinion mining and topic identification need to be combined with social media content metadata (e.g., geographic locations). 

In the way of opinion mining, we are currently investigating the use of an automatic emotion identification tool called Illuemotion which was developed by University of Manchester final-year Computer Science student, Elitsa Dimova. The web-based tool, a screenshot of which is provided below, is underpinned by a neural network model that analyses tweets to determine the most dominant emotions expressed, which can be any of anger, fear, joy, love, sadness, surprise and thankfulness.

The image below shows one of the tweets directly fetched by the tool from Twitter (via their API) when supplied with “#upwork” as input query. The tweet, which speaks of hidden dangers of being a digital gig worker, was detected by Illuemotion as expressing sadness and fear. One of our next steps is to apply the tool on a collection of thousands of tweets to allow us to analyse them across different geographic regions as well as genders.

As we are analysing data that pertains to human emotion, ethical considerations are being taken into account, especially bearing in mind that we also do not wish to compromise any of the digital gig workers who are social media users. For example, many Twitter users are unaware that what they post publicly can be used to identify or (reverse) look them up. They also have a right to be forgotten (i.e., they can delete their posts as well as their accounts). Overall what this means for us researchers who make use of their data is that in scholarly publications, we should provide only aggregated results and ensure that we do not include any identifiable information. These and other ethical considerations were discussed in detail in the recently concluded symposium in the Academy of Management Specialised Conference on Big Data entitled, “Ethical and Methodological Considerations for Management Research in the Digital Economy” held at the University of Surrey from the 18-20th April.

As well as two other SMART@Manchester case studies, the above described research questions on perceptions of digital gig work and our proposed approaches will be presented in the upcoming 4th International Workshop on Social Media World Sensors (Sideways 2018) co-located with the 15th European Semantic Web Conference to be held in Heraklion, Crete, Greece from the 3rd-7th June.

References:

[1] Kuek, S.C. et al. (2015) The Global Opportunity in Online Outsourcing. World Bank, Washington, DC. Available at: http://documents.worldbank.org/curated/en/138371468000900555/The-global-opportunity-in-online-outsourcing

[2] Lehdonvirta, V. (2017) The online gig economy grew 26% over the past year, The iLabour Project, Oxford Internet Institute. Available at: http://ilabour.oii.ox.ac.uk/the-online-gig-economy-grew-26-over-the-past-year/

[3] Heeks, R. (2017) Decent Work and the Digital Gig Economy: A Developing Country Perspective on Employment Impacts and Standards in Online Outsourcing, Crowdwork, etc, Centre for Development Informatics, Global Development Institute, University of Manchester. Available at: http://hummedia.manchester.ac.uk/institutes/gdi/publications/workingpapers/di/di_wp71.pdf

[4] Rubec, J. (2017) Study: The Dark Side of the Gig Economy, Cision. Available at: https://www.cision.com/us/2016/12/the-dark-side-of-the-gig-economy/

Do Outsourcing Clients Want Decent Digital Work?

22 December 2017 Leave a comment

There are growing concerns that digital gig work – supplied by platforms like Mechanical Turk, Upwork, Freelancer, etc – falls short of decent work standards.  (For further details see the working paper, “Decent Work and the Digital Gig Economy”.)  To address this, and as discussed previously in this blog, there are plans to encourage new ethical standards.

But almost all evidence on this to date comes from workers.  The voices of only a few platforms have been heard, and there seems to be no evidence from clients.  Yet clients are central to decent digital work standards: if they create incentives for platforms to improve, that will be a powerful motivation.  Conversely, if clients don’t care, it removes a key driving force from the gig economy ecosystem.

So, what evidence can be found?

Here, I summarise Babin, R., & Myers, P. (2015) Social responsibility trends and perceptions in global IT outsourcing, Proceedings of the Conference on Information Systems Applied Research, v8, n3663.  This in turn summarises results from surveys conducted during 2009-2014 by the International Association of Outsourcing Professionals.

The survey was specifically about corporate social responsibility (CSR) in IT outsourcing.  So: a) it is not exactly about digital gig work but a broader category of outsourcing; b) the survey may encourage some level of “virtue signalling”: respondents wanting to appear more socially-responsible than they are in reality.  Nonetheless, it offers some relevant guidance about client attitudes to decent digital work.

In general terms, half the respondents were US-based; half were non-US; a fair reflection of gig work clients.  They ranged from SMEs to multinationals and just over half had a written CSR policy.  They are thus larger and more formally-CSR-inclined than the modal micro-enterprise client for digital gig work, but important given the increasing involvement of firms in gig outsourcing.

Key findings include the following:

– Nearly half “often” or “always” gave preference to outsourcing providers who had demonstrable CSR capability.

– Nearly two-thirds expected CSR consideration to become “more” or “much more” important in their future IT outsourcing.

– The largest factor in evaluating CSR capabilities of an outsourcing provider was its labour practices (see figure below).

Figure: Key factors in evaluating the CSR capabilities of an outsourcing provider, survey median (IAOP, 2009-14)

At least for this group of clients, then, the type of labour practices covered by proposed decent digital work standards were the top CSR issue; and CSR was quite widespread as a determinant in digital-related outsourcing (only 5% said they never used CSR as a determinant).

This gives some basis for believing – at least among larger clients for digital gig work – that an appetite exists for better employment and working conditions; an appetite that can encourage platforms to change.

Decent Digital Work and the FairWork Foundation

31 October 2017 1 comment

How can we improve standards for digital gig workers: those undertaking micro-work and online freelancing via platforms like Upwork and Mechanical Turk?

The recent research paper – “Decent Work and the Digital Gig Economy” – explains why such standards are needed.  With up to 70m workers worldwide registered for online work and growth rates of 20-30% per year, this is already a sizeable activity.  It is especially popular with the c.80% of workers based in middle- and low-income countries, who often see online work as better than local alternatives.  However, this ignores the chronic precarity and structural inequality associated with such work: damaging outcomes that will only spread if nothing is done.

But what should be done?

The paper develops an inventory of “Decent Digital Work” standards.  This is a comprehensive set of guidelines that integrates two things: first, the global decent work standards set by the ILO; second, the actions needed to address specific digital gig economy problems.

A key value for this inventory is as a comparator with other decent work initiatives.  For example, the paper analyses the way in which two major initiatives – SA8000, and the Ethical Trading Initiative – do and do not cover the requirements for decent digital work.

Below, a further comparison is undertaken, between the Decent Digital Work standards, and the criteria adopted by the FairWork Foundation; an initiative aiming to rate and certify gig economy platforms.  The table indicates those elements which are the same in both standards; those where a completely-different element is included; and those where there is some variation in the element.

From this, three things can be concluded:

a) A number of Decent Digital Work standards are absent in the FairWork Foundation certification criteria. Several of these relate to the broader context for work, would be outwith the scope of an individual platform, and therefore are not relevant to platform certification. However, those identified under “Employment” and “Work Conditions” can form part of a further discussion to consider their relevance to certification.

b) Some elements (e.g. around access to digital work opportunities, and accounting for worker costs other than unpaid time) speak to the particular conditions of gig workers from the global South. This is the location for the great majority of gig workers: already for digital gig workers; increasingly for physical gig workers. As such, the FairWork Foundation must ensure its global North origins do not skew its focus.

c) The FairWork Foundation should review how prevalent the non-competition and non-disclosure agreement problems are, and whether they are worth including. (Human review of task instructions – something unlikely to be practicable for most platforms – appears to have been dropped from later versions of the certification criteria; hence, its inclusion in brackets.)

As noted in the Decent Work and the Digital Gig Economy paper’s action research agenda, next steps here would be:

– Survey of worker, client and platform views about identified standards.

– A multi-stakeholder dialogue to agree a minimum set of certification standards and evaluation methods.

– Parallel research on the impact of standards and certifications in the gig economy, and analysing the costs and benefits of interventions such as standards and certifications at micro- and macro-level.

This is just one example of the application of the Decent Digital Work standards.  We hope you can identify other uses . . .

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