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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.

 

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What can we learn about e-commerce in Africa from Jumia’s IPO filing?

30 April 2019 1 comment

 

There has been growing discussion about the potential of e-commerce in developing countries. This discussion intensified recently when pan-African e-commerce firm Jumia went public in the US, becoming the “first African unicorn”.

The IPO prospectus, a 270-page outline of the firm released as part of this filing, has sparked much debate. Elsewhere, TechCrunch has dug into the financial intricacies of Jumia, and online debates have raged linked to the question “Is Jumia really an African firm?” (see here and here).

I won’t detail those two issues here. Instead, below I will discuss the insights that the prospectus provides us about e-commerce platforms operating in Africa. This is especially useful as we have been struggling with a lack of detail on e-commerce, with firms reluctant to share commercially sensitive information.

 

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1) The market for e-commerce in Africa is already large, and growing rapidly
Jumia announces that it had 4 million active customers in the year 2018, growing rapidly from 2.7 million in 2017. This translates into “Gross Merchandise Volumes”  (GMV) of €828.3m in 2018, from €507m in 2017. As these figures suggest, e-commerce is bringing in a significant number of customers on the continent and is growing at a rate of over 50% in the last year.

Regionally, the largest countries for Jumia’s business are Nigeria (29% of GMV) and Egypt (20.5%). Interestingly, in the largest 5 markets, only 50% of goods are delivered in primary cities with the rest evenly split between secondary cities and rural areas. Most customers are mobile users, which comprise 81% of all traffic in 2018.

 

2) …but making profits is a challenge. Jumia is a loss-making firm
The growing size of e-commerce has not yet translated into profits. Jumia made a €170m loss in 2018.

This is attributed to a number of factors. As a platform, sales are mainly made by third-party sellers (90%). So the €828m GMV translates to just €130.6m revenue for Jumia.  Jumia then faces high costs for operating including warehousing, delivery, sales and advertising and the platform. Once these are considered, the €130.6m turns into a €170m loss.

How does Jumia plan to become profitable in the future? By becoming a market leader in Africa, Jumia will rely on expanding markets to increase sales volumes over the coming years. Alongside this, they describe the potential of reduced costs in fulfilment (storing and delivering goods) and growing use of mobile payments that should make each transaction more efficient over time.

 

3) The role and prospects for Jumia platform sellers
Jumia has 81,000 sellers and the prospectus hints that quite a number of these are African, although there is no data provided. Interestingly, commissions from sellers for sales only contributed moderately to revenues. Jumia makes similar amounts of revenue from services such as fulfilment (delivery costs to buyers) and “value-added services” (premium services for sellers).

As Jumia grows, its aim is to attract ever more sellers to the platform. “Competition between sellers is…essential to our monetization, as it increases the appetite for sellers to use our services”. This statement suggests that the platform will move low margins and high competition, with sellers relying on value-added services as a source of expansion. For local sellers, these future directions suggest concerns about this platform supporting widespread upgrading of local SMEs in Africa.

 

4) Data is an important component of the relationship with sellers
We know that data is vital to many platforms, and Jumia is no different. A seller score “..a data-driven scoring of a seller’s performance” is a key aspect of Jumia’s relationship with sellers. The score is important to the success of sellers, where platform algorithms use this score to determine the order of products in searches. Jumia also gamifies the “seller score”, associating it with a range of other advantages for sellers.

In order to support sellers, Jumia supports third-party financial services for its sellers, using this score as a way to demonstrate creditworthiness. This mirrors alternative credit-scoring schemes we have seen elsewhere in Africa.

 

5) African e-commerce platforms face significant and often unexpected risks
Many of the challenges for Jumia are similar to those challenges of e-commerce elsewhere [1]. One key challenge repeatedly mentioned is the “…failed deliveries, excessive returns, late collections, unrecoverable receivables and voucher abuse”, with 14% of all sales (by revenue) being failed deliveries or returns. These risks particularly emerge from the use of cash payments on delivery (COD), a common approach to payments for online goods in developing countries.

Beyond this challenge, an eye-opening aspect of the prospectus is the unexpected challenges encountered by Jumia:

  • In Kenya, a Jumia warehouse was robbed and €500K of merchandise was stolen
  • In Egypt, a €5000 fine was imposed by authorities due to the platform offering “unlisted drugs”
  • In Kenya, a scam involving electronic payments suppliers led to €550K losses
  • Jumia has undertaken investigations around allegations of internal fraud and bribes in countries such as Nigeria and Morocco connected to relationships with platform sellers

 

6) Policy challenges for e-commerce in Africa are significant
In line with other platforms, Jumia operates a relatively “asset light” way across multiple countries in Africa. Even with close to a billion euro in GMV, and with offices in 18 countries, it is a tiny direct employer with around 4,800 staff in Africa.

This “asset light” approach, however, comes into collision with African governments’ desire to regulate e-commerce. Jumia discusses the challenges faced in a number of countries including taxes, particularly VAT on imports; uneven data protection rules, and restriction of cross-border personal data transfer; and regulation on financial and mobile payments.

There has been pressure for harmonising such rules globally in order to support cross-border business models of digital firms such as Jumia. However, African governments are keen to ensure that they are able to operate their economies appropriately, including collecting taxes and nurturing local firms, in the face of e-commerce imports [2]. It is therefore understandable that policy might vary according to the political goals of different nations in Africa. These challenges are likely to intensify coming years given current disagreements on harmonising e-commerce at the WTO, and African CFTA discussions on e-commerce rules still at an early stage [3].

Platform firms, therefore, require careful mapping of national rules and regulations, and in Africa the “asset light” model may only be viable to well-funded platforms. For smaller platforms, it may be better to focus on a smaller subset of countries.

 

In summary, the Jumia prospectus indicates that digital economies are expanding and we can expect to see a growing set of firms operating across the continent. However, given the challenges encountered by Jumia, easy profit and growth are not a given. For platform sellers, e-commerce provides new potential market opportunities, but careful consideration of how platforms best support them, will be vital to success.

 

References
[1] UNCTAD (2015) Information Economy Report 2015 – Unlocking the Potential of E-Commerce for Developing Countries, UNCTAD, Geneva, Switzerland.
[2] Azmeh, S. & Foster, C. (2018) Bridging the Digital Divide and Supporting Increased Digital Trade: Scoping Study, Discussion Paper, GEG Africa, Pretoria, South Africa. http://www.gegafrica.org/item/782-bridging-the-digital-divide-and-supporting-increased-digital-trade-scoping-study
[3] Foster, C. & Azmeh, S. (2018) E-Commerce and the African Continental Free Trade Agreement (AfCFTA), Discussion Paper, GEG Africa, Pretoria, South Africa. http://infomediation.net/publication-e-commerce-and-the-african-continental-free-trade-agreement-afcfta/

 

Digitally Removing the Middleman for Development: Trouble Brewing in East African Tea?

11 February 2019 1 comment

How do new digital technologies enable firms to develop? One process often highlighted is disintermediation, where digital technologies allow firms to “cut out the middleman”. Exploring the Kenyan tea auction we suggest that these ideas need to be rethought. Digital technologies bring change, but may lead to more challenging conditions for smaller firms.

 

Kenya_mombasa_tea_auction_480_feb2012_2

The Mombasa auction. Source: Wikimedia Commons

 

One of the benefits often associated with digital technologies is the potential for disintermediation – or put more simply “cutting out the middleman”. This concept forms the basis for many hopes for development around digital technologies [1].

In the early days of digital technologies, it was found that they often failed to cut out the middleman due to the “digital divide” where digital skills, infrastructure quality and cost limited the use of technologies in smaller firms. But as firms have adopted technologies and with appropriate applications these foundational claims for digital development are important to revisit.

 

Digitalising the tea sector

Tea is an important export in East Africa and twice a week sellers come together in the Mombasa tea auction to trade tea with international buyers. The tea auction emerged during the colonial era, and with its antiquated traditions, slow speed, and accusations of corruption, there have been demands to move online.

An online auction would speed up the processes of trading by cutting out the middlemen in tea value chains (see below) and allowing tea producers to sell more directly to international buyers.

 

combined

Roles of middlemen in the tea value chain: The tea trade centre in Mombasa, home to the tea auction (left); tea tasting (middle); auction warehousing of tea lots (right).
Source: Photos courtesy of Laura Mann.

 

The auction seems a good fit for digital disintermediation in terms of economic models of transactions [2]. Trade is predictable with a limited number of traders and a strong sectoral governing body. With falling costs of online access in the region, a digital auction seemed viable, particularly as competitor regions such as Sri Lanka and India are already in the process of digitalising their auctions.

 

Challenges faced in the tea sector

While on paper the case seems promising, change has not taken place as expected. An “e-auction” trial was abandoned and over the past decade, digitalisation has been slow and frequently resisted.

In discussion with key stakeholders involved in the auction, we identified three challenges:

  • The nature of transactions: Tea transactions are often seen as generic and simple to trade, and so well suited to online exchange. But tea trading is becoming more complex.  Tasting the quality of tea, for example, is important to buyers who are mixing different teas together to produce retail products, and there is also a growth in value-added teas where buyers need extra information about ethical standards they want met. These factors make moving trading online more complex, where more complex factors need to be included in a digital system.
  • The types of institution: Well-established rules and governance in the tea sector limit the ability to reform the tea auction. The balance of power in sectoral bodies is often skewed towards middlemen, exactly those who might be cut out by digital technologies. This meant that any kind of reform was strongly resisted by sectoral bodies.
  • Middlemen adaptation: Eventually after much resistance, aspects of the tea auction were partially digitalised such as e-payments and digital auction catalogues. This did have an effect of reducing certain roles connected to the auction. But the intermediaries did not disappear. They adapted and took up new roles. For example, tea brokers who were previously important in facilitating payments repositioned themselves as providers of auction intelligence and price data for small tea producers.

A key finding related to these challenges was that international firms, dissatisfied with the slow pace of change, began to sidestep the auction by becoming involved in “direct sales” with selected producers, supported by digital technologies.

 

Making sense of digital disintermediation

The future for tea trade in East Africa is fragmentation which may be detrimental to smaller tea producers. Smaller tea producers were not connected enough to become part of “direct sales” with international firms. With the auction only slowly digitalising, it is falling behind as the centre of trading.

For the analysis of digital disintermediation, the case highlights the need for careful consideration of transactions: the nature of transactions, the role of institutions and potential externalities (such of adaptation of middlemen) [3]. These are factors that implementers might consider to better support small producers’ development outcomes from digitalisation – what are the institutional bodies that need to buy in? Which stakeholders should be considered? etc.

More than this though, a greater awareness of the way actors use their power as change occurs is crucial. Such an approach is very different from the abstract, economic approach normally used to explore digital disintermediation [4]. From this perspective a very different view of development emerges. In the Mombasa auction case, it has not been transformed. Through the challenges and strategic activities of more powerful actors, digital transactions are solidifying the relationships of those who are already well linked, and able to capture resources.

 

This post summarises a recent book chapter: ‘Making Sense of Digital Disintermediation and Development: The Case of the Mombasa Tea Auction’ by Chris Foster, Mark Graham and Timothy Mwolo Waema.

The chapter is part of the new MIT Press book ‘Digital Economies at Global Margins’. The book is available as an open-access PDF from the IDRC website

 

 

Notes:

[1] A good example is the World Development Report (2016) on ‘Digital Dividends’, but many other projects often uncritically assume similar concepts.

[2] In economics, disintermediation is often associated with transaction costs, and an analysis of how digital technologies change aspects of transactions costs: information costs (gathering information about transactions) and coordination cost (co-ordinating the exchange of goods) (e.g. Wigand 1997).

[3] The study of transaction costs can be split into two differing perspectives. The “neoclassical approach” focussing on the mechanics of transactions such as coordination and information costs, and “property rights approaches” which explore wider aspects of transactions such as rules, regulations and externalities (Allen 1999). We suggest that digital disintermediation has been too focussed on narrow “neoclassical” perspectives to date.

[4] Contemporary institutional analysis often explores political power and settlements in shaping institutions. We also stress this aspect here, highlighting the importance of power in shaping institutions, and in turn the outcomes of digital disintermediation.

 

 

References:

Allen, D.W. (1999) Transaction Costs, in Encyclopedia of Law and Economics, B. Bouckaert & G. De Geest (eds), Edward Elgar, Cheltenham, UK, pp. 893–926.

Wigand, R.T. (1997) Electronic Commerce: Definition, Theory, and Context. The Information Society, 13(1), pp. 1–16.

World Bank (2016) World Development Report 2016: Digital Dividends, World Bank, Washington, D.C.

How Many Platform Workers Are There in the Global South?

29 January 2019 Leave a 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/

Industry 4.0 to Digital Industrialisation: When Digital Technologies meet Industrial Transformation

23 April 2018 1 comment

As digital technologies increasingly permeate all aspects of our physical world, many believe that we are moving into a hyper-connected, intelligent society and economy. One of the emerging concepts underpinning this potential transformation is the Fourth Industrial Revolution, or Industry 4.0.

What is Industry 4.0?

According to the proponents of Industry 4.0, each industrial revolution has shifted manufacturing opportunities and patterns of specialisation, enabled by key technological developments as illustrated in Figure 1.

industry 4.0 timeline2

Figure 1. Industry 4.0 trajectory (source: Author based on [1] and [2])

The vision of Industry 4.0 includes digitalising all elements of industrial activities to achieve a highly flexible, distributed production and service network. Through advancements of technologies such as Artificial Intelligence (AI), advanced automation and robotics, 3D printing, big data and Internet of Things, a tighter integration of digital and physical elements will allow machine-to-machine interactions and a mode of operation that provides more efficient production. In an absolute Industry 4.0 world, every object and all machinery in the factory will be interconnected to share data and operate without much human presence [2].

This of course, is only viable when an advanced level of technological, social and economic integration occurs. Given that technologies progress at an unpredictable rate, and that their real-world applications often lead to unexpected outcomes, it is difficult to know how (or whether) industry 4.0 will manifest. Nevertheless, recent studies warn us that this industrial change can drive uneven global development even further.

Shifting focus from manufacturing to “digital industrialisation”

AI and robotics may take 800 million jobs by 2030 in the world, and emerging economies such as China and India could be hit the hardest, losing 236 and 120 million jobs by 2030 respectively [3]. The costs of operating robots and 3D printers in furniture manufacturing in the US is predicted to be cheaper than Kenyan wages in 2033 [4], indicating that the lower labour cost may no longer be the main attribute ensuring competitiveness in a global market.

Given that industrialisation has long been considered to play a vital role in economic growth of developing countries, the development implications of this transformation have been mainly discussed in manufacturing, albeit with a negative perspective: changing patterns and geography of production [2] (such as re-shoring manufacturing back to high-income countries) and technological unemployment in labour-intensive manufacturing industry [4].

However, I would like to bring more attention to the development of the “digital” side of this industrial transformation – which I will refer to as digital industrialisation. This is a work-in-progress concept that encompasses not only the technological integration of digital technologies into manufacturing, but also the extensive re-organisation of an economy to digitalise production processes.

Some work on this has already been carried out within the DIODE (Development Implications of Digital Economies) network [5], but we need more research to build a better picture of the current and future landscape: for example, how digital industrialisation can take place in small-scale, localised production networks in the global South [6] and how the economic models emerging within the digital economy (such as platform economy and gig economy) may impact innovation and manufacturing processes globally [7].

I will further argue that this impending industrial transformation is best understood as a continuous process rather than a goal to reach – something that terms such as industry 4.0 tend to project. Rather than focusing on the potential winners and losers in this race, we need to elucidate how this transformation can take place in an inclusive and sustainable manner.


[1] Lasi, H., Fettke, P., Kemper, H. G., Feld, T. and Hoffmann, M. (2014) ‘Industry 4.0.’ Business and Information Systems Engineering, 6(4), pp. 239–242.

[2] Hallward-Driemeier, M. and Nayyar, G. (2018) Trouble in the making? The future of Manufacturing-led development. Washington, DC: The World Bank.

[3] Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., Sanghvi, S., (2017) Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute.

[4] Banga, K. and te Velde, D. (2018) Digitalisation and the future of manufacturing in Africa. London: Overseas Development Institute.

[5] Bukht, R. and Heeks, R. (2017) Defining, conceptualising and measuring the digital economy. GDI Development Informatics Working Paper 68. Centre for Development Informatics, University of Manchester, UK.

[6] Seo-Zindy, R., & Heeks, R. (2017) ‘Researching the emergence of 3D printing, makerspaces, hackerspaces and fablabs in the global south: A scoping review and research agenda on digital innovation and fabrication networks‘, Electronic Journal of Information Systems in Developing Countries, 80(1), pp. 1–24.

[7] UNCTAD (2017) The ‘new’ digital economy and development, UNCTAD Technical Notes on ICT for Development no.8. Geneva: United Nations Conference on Trade And Development.

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