Exploring Barcelona Smart Tourism through a Digital Transformation Lens

The evolution of urban tourism is increasingly shaped by digital technologies, with cities around the world embracing innovative models and digital transformation to enhance visitor experiences. Digital transformation refers to the integration of digital technologies into all aspects of an organization’s operations, fundamentally altering how it operates and delivers value to customers (Westerman et al., 2011). In the tourism context, digital transformation encompasses the adoption of innovative technologies to enhance the tourist experience, streamline operations, and drive sustainable growth. Building upon foundational studies by scholars such as Buhalis and Amaranggana (2014) and Gretzel et al. (2015) about smart tourism platforms and destinations, this blog explores a theoretical framework for understanding the key elements of smart tourism through a digital transformation lens. The elements of connectivity, data analytics, personalization, and sustainability are drawn from the Barcelona Smart Tourism Platform, reflecting a holistic approach to digital transformation in tourism.

Connectivity serves as the backbone of the digital transformation, enabling the integration of tourists, service providers, and city authorities, which is in line with the vision outlined by Law et al. (2016). Digital connectivity is a means of creating a cohesive ecosystem where information flows freely and efficiently. This connectivity manifests through various channels, including Wi-Fi hotspots, mobile apps, and digital signage, enabling tourists to access information, make bookings, etc. Moreover, by integrating disparate systems and stakeholders, connectivity enhances collaboration and coordination, fostering a more integrated approach to destination management.

Data analytics enables the city to gain actionable insights from the massive amount of data generated by tourist interactions. Drawing on methodologies outlined by Xiang et al. (2017) and Gretzel et al. (2015), Barcelona employs advanced analytics techniques to analyse visitor behaviour, preferences, and trends. This data-driven approach empowers the city to make informed decisions thereby optimizing the tourism experience, and enables stakeholders to tailor offerings, optimize resource allocation, and anticipate demand, thereby maximizing satisfaction and operational efficiency.

Personalization allows a fit to the individual needs and preferences of tourists (Wang et al., 2017). As Gretzel et al. (2021) noted the importance of techniques, Barcelona leverages AI-driven algorithms to customize offerings and recommendations for each visitor. From personalized itineraries to targeted promotions, this personalized approach enhances visitor satisfaction and fosters deeper engagement with the destination.

Sustainability of Barcelona is in line with principles outlined by Niñerola (2019), Barcelona integrates sustainability across all aspects of the visitor experience, from transportation to accommodation to attractions. This encompasses initiatives such as promoting eco-friendly modes of transportation, reducing waste through recycling programs, and supporting local communities through responsible tourism practices.

As echoed by scholars such as Buhalis (2020) and Gretzel (2021), Barcelona is creating smarter, more sustainable destinations through technology, exemplifying the potential of digital innovation in sustainable tourism by integrating connectivity, data analytics, personalization and sustainability. As cities around the world navigate the complexities of urban tourism in the digital age, the Barcelona Tourism Platform offers a good example, and a framework that others working in smart tourism can utilize.

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From word-of-mouth to star ratings: Platforms and the changing nature of trust in the informal sector

By Mindy Park, Arfive Gandhi, Yudho Giri Sucahyo 

Do digital platforms formalise informal cities? 

To some, the arrival and penetration of digital platforms in the vast informal economies of the global South cities may sound no longer new. To others, it still is a momentous opportunity to transform the informal sector. As the bustling streets fuse into the virtual marketplaces, the dynamics of transactions, trust, and community are being reshaped. To explore the renewed landscape of informal economies in the platform age, we illustrate the changing nature of trust by drawing on our recent research on platform use in the informal sector in Jakarta, Indonesia. 

Broadly, there are two important impacts of platformisation in the informal sector: incorporation and legitimisation. Platforms are seen to incorporate the informal sector into the broader economy. In doing so, certain platform features aim to guarantee the credibility and transparency of informal practices that are often deemed untrustworthy without active regulations in place. While some might frame this process as “formalisation of the informal sector”, in fact, platformisation itself hardly formalises existing sectors. What it does is simply insert the previously informal or marginalised groups into the wider urban economy, both as consumers and as traders.  

Then the legitimising effect is the impact of this insertion on trust building. What enables this trust are the new platform features (e.g., ratings/reviews and digital payment) that replace the traditional ways of building reputation and making transactions in the informal economy. Linking platforms with trust somewhat disguises us into thinking that there was simply no trust in the informal sector before platforms. On the contrary, trust was the most important factor that sustained the informal sector – word of mouth shaped trust and trust worked as an important “informal” institution even in the absence of formal regulations¹. In other words, with nothing much but mutual trust that people could resort to, trust has been the basis for their informal trade. 

With these incorporation and legitimisation effects, platformisation leads to evolving dynamics of trust amongst informal workers and consumers. In the traditional informal sector, the boundary of the interaction and social networks was largely within the physical informal city. The key change in the platformised city is that everyday interactions move beyond this physical informal space towards wider virtual networks that involve not only the informal class but also the middle and elite classes. That is, now the boundary that forms word-of-mouth has become much wider. The ratings and reviews formed by broader consumers (or simply the general public) shape reputations of platformised informal firms, not just the word-of-mouth formed within their existing social networks confined to the physical city.  

These dynamics are illustrated in our research in Jakarta. Here are several remarks and comments from the motorcycle drivers, street vendors and consumers in the informal sector, who now have become part of ride-hailing, delivery, and fintech platforms. These provide a glimpse of how differentiated their experiences are, and intriguingly, the way they express distrust against one another. For example: 

When asked about any negative or exploitative experiences working as platform drivers, they often mention how consumers or restaurant owners are treating them.  

•  “Sometimes passengers/customers use threatening or aggressive language” 
• “Their policies prioritise consumers. Drivers should only comply with the rules and ethical codes, which is not always easy” 
• “Platforms’ customer service only makes consumers more talkative” 
• “Platform fees include parking fees, but sometimes restaurants charge us these additional fees (due to misunderstandings of rules)” 

On the other hand, being entrepreneurs (albeit micro in scale), street vendors exercise more control over their business.  

• “I run this business, so I should take care of my customers myself. I can handle them myself and they’re (platforms) not part of it” 
• “In light of customer satisfaction, platform intervention is only a form of passive monitoring. Each business is already proactive in seeking solutions on its own” 

In the meantime, when asked about their overall experiences with platform use, some consumers are not entirely happy with drivers. 

• “Platform providers should carry out regular evaluations of workers on their digital platforms. So that workers with bad and dangerous ratings do not continue to work on the platform or are trained so that they do not endanger consumers” 
• “Platforms should try to increase motorcycle drivers’ digital literacy” 

Overall, the reality of platformisation is less aligned to claims of economic opportunities in incorporation and legitimacy in formalisation. Rather it presents new, more challenging domains around trust. With strong consumer beliefs in platforms’ contribution to transparency and a sense of distrust towards worker behaviour, these seem to amplify the distrust citizens already had against the informal sector (and vice versa). Adding fuel to this distrust may be heightened competition even amongst the drivers, street vendors and entrepreneurs. 

This might eventually hamper city-wide collective movements. With platform ecosystems still dependent on existing socio-cultural moral norms and class, negotiations between agents of differential power arise and contribute to shifting the consumer culture in platform use². Although there is growing conscientisation of the often exploitative and adverse impacts of platforms on workers across the globe, with a lack of trust, widespread collective action around platforms is less evident in the Indonesian context (but still, drawing only from a handful of comments above, we would not say it is conclusive yet). 

In the end, this is neither to romanticise traditional informality nor to critique consumers. Of course, trust only cannot fully fill the voids of formal institutions that are to serve broader purposes such as safety, protection as well as market efficiency. The solution also does not seem to be in denouncing “platform algorithms” that can make contributions to filling some (but with equal importance, not all) of the voids. Nevertheless, paying attention to the evolving dynamics of trust will shed new light on the way we understand the impacts of platforms and trust in the informal economy.  

As this evidence shows, we need to examine further the intersections of platforms and informality. What are the dynamics of legitimisation and extraction, control and autonomy, and order and freedom to make our platformised cities healthier?  

References

¹ Burbidge, D (2013) ‘Trust creation in the informal economy: the case of plastic bag sellers of Mwanza, Tanzania’, African Sociological Review, 17(1): 79-103. 

Odera, L.C (2013) ‘The role of trust as an informal institution in the informal sector in Africa’, Africa Development, 38(3-4): 121-146. 

² Rava, N & Lalvani, S (2022) ‘The moral economy of platform work’, Asiascape: Digital Asia, 9(0): 144-174, https://brill.com/view/journals/dias/9/1-2/article-p144_8.xml?language=en&ebody=pdf-89805.   

The power of PropTech: an under-researched topic in social sciences

PropTech (‘property technology’) refers to the use of digital technologies in the delivery of products and services in the real estate industry. Its primary goal is to innovate and optimize the ways in which properties are built, bought, sold, and managed. For example, artificial intelligence has been used to generate marketing materials and calculate credit scores; blockchain technologies have been employed to limit the risk of fraud; and virtual reality technologies have been applied to facilitate remote viewing and purchase.

PropTech is indeed a very broad sector, encompassing AI design tools, listing and management platforms, and smart home technologies, among other things, and it overlaps with other sectors such as FinTech (‘financial technology’) and ConTech (‘construction technology’). According to Zion Market Research, the global PropTech market size stood at US$19.5 billion in 2022 and is predicted to grow to US$32.2 billion by 2030.

In contrast to the hype in the business circle, academics studying the housing market have expressed concerns regarding the wide applications of PropTech. Desiree Fields (2022), a leading scholar on this topic, argues that PropTech has transformed housing financialization in the post-2008 era, particularly in the context of the single-family rental market where large-scale investment companies are able to exploit the US foreclosure crisis and forge a new asset class. With respect to the rising popularity of online listing platforms, Geoff Boeing, Julia Harten, and Rocio Sanchez-Moyano (2023) report that the already-advantaged communities tend to benefit more from these platforms. Moreover, Wainwright (2023) highlights that, while rental platforms claim to be objective, prejudices are usually built into their algorithmic designs. All these findings speak to the fact that PropTech has reconfigured the relationships among real estate professionals, investors, property owners, and tenants, as Joe Shaw (2020) argues.

To mitigate the negative impacts of PropTech on housing, Geoff Boeing et al. (2023) suggest that policymakers and practitioners: 1) make use of the data collected by online platforms to better understand market conditions; 2) collaborate with platform owners to improve algorithmic designs; 3) regulate platforms in terms of their data collection, processing, and usage. While these suggestions sound promising, their implementation seems challenging. Given the private nature of online platforms, they might be hesitant to share data, collaborate with the public sector, or fully comply with guidelines and regulations. Take Airbnb as an example, the platform has been accused of allowing its hosts to evade taxes and regulations and exaggerating housing crises in various localities.

Currently, there is a dearth of research on the social science aspects of PropTech, particularly its implications for equity, inclusion, and regulatory challenges. This gap could potentially be addressed by researchers specializing in development studies, science and technology studies, and urban studies. Compelling questions include, but are not limited to: How does the adoption of PropTech impact housing accessibility and affordability, particularly for disadvantaged groups? How does PropTech influence the dynamics of property ownership, rental markets, and housing finance? How does PropTech shape the global landscape of real estate in terms of the flow of investment, policies, and technologies? Please reach out if you are interested to discuss these questions further.

Digital tourism and marginal providers after the crisis

“Bouncing back” in tourism should not be about connecting local providers to platforms but ensuring that available online tools provide inclusive outcomes

As tourism has become global it has become an important part of the economy in a number of countries of the global south. It brings foreign currency into the economy and provides a surprising number of jobs to those who provide services. For all the ethical and environmental issues it poses, in countries such as Thailand, Indonesia, Tanzania and Rwanda, the loss of tourists during the pandemic led to crises. Vast swathes of workers and firms have had to move into other sectors with broader implications for economies.

A major imperative following the crisis has been to ensure that tourism can bounce back. Institutions such as the OECD and the UN, as well as development donors and governments have pushed recovery plans with significant “digital tourism” components. Embracing digital tourism is seen as a quick win. Tourists have grown more used to online platforms – whether that be booking hotels, arranging transportation, sharing tourism experiences or posting reviews. The vision of recovery plans is that if local providers can embrace platforms, not only will they become more efficient, but drive forward tourism demand.

Drawing on recent research examining platform practices of small/marginal tourism service providers in Indonesia and Rwanda [1], we argue these visions for digital tourism may have limits. This research highlights three major considerations: the contexts of the adoption of platforms by tourism firms, the inflexibility of tourism platforms, and how tourism development may better be guided by grassroots online practices.

Platform use by small service providers

With several decades of investment in internet connectivity, the costs and barriers to internet use have been reducing, leading to growing use. This is especially the case for businesses in tourism, where digital tourism is becoming the norm. In both Indonesia and Rwanda, major global platforms such as TripAdvisor, Uber, Booking.com, Airbnb, Google maps and Traveloka are now well-established.

With the growing ubiquity, we might say that platforms are moving from something that forward-thinking firms opt into, to being non-negotiable for all firms. It is now almost like an infrastructure that firms need to be part of. This is true even amongst more marginal service providers such as tour guides, tiny hotels and those providing cultural activities who would use mobile devices to be part of such platforms.

Whether they want to go online or not, they are aware they are being mapped, rated and discussed online.

The complexity of tourism platforms

At first glance platforms seem to offer significant potential. They are easy to sign up for tourism providers and provide a way to quickly reach and interact with tourists across the globe. They often offer services such as online payments and booking systems that can make operations more efficient.

However, for small tourism providers platforms remain a challenge. While it is easy to join, successfully harnessing these platforms requires a broad range of technical skills. Successful firms need to be adept at website design, digital media skills and social media use to be able to stand out.

Challenges are not just about the capabilities of service providers, platforms are often highly complex and inflexible. For example, in Rwanda, small hotels were spending time and resources trying to move up search rankings on platforms. In Indonesia, some providers of tourism services were trying to negotiate algorithmic pricing systems.

With local support from platforms often non-existent and limited flexibility, small providers in these countries often suffered in competition with larger and foreign providers who were better places to make gains from being online.

Agency of tourism service providers

Even with these significant challenges, small service providers were able to combine digital tools for benefits – using shared calendar software, mobile apps, cloud sharing, online translation and social media to collaborate with customers and better fit with their daily needs.

Moreover, in Indonesia some tourism enterprises have come together to collaborate in more social- or environmentally-orientated online spaces.  In some other countries, we have also seen the success of commercial platforms more attuned to small enterprise needs and activities (e.g. South African platform Nightbridge)

These types of activity are very different to the policy prescriptions of joining the platform “juggernauts” for pandemic recovery. They suggest alternative ways forwards for small tourism providers – by amplifying the bottom-up activities already occurring outside mainstream platforms, and by being aware that service providers are negotiating multiple platforms and online software.

Summary: Bouncing back and “digitalisation”

These experiences of tourism and the goals of pandemic recovery are mirrored in other sectors in the global south. Governments and donors are not sitting back but seeking to play an active part in recovery through support. And, like tourism, one of the areas that are repeatedly mentioned is “digitalisation” – supporting so-called “inefficient” small firms to connect and use digital platforms for economic gain. 

But as this research shows, the reality is that platforms pose challenges. Connecting online is often no longer the major barrier. Rather platforms fit poorly with the skills of small firms and their growing complexity favour better-financed firms. They rarely adapt to the challenges faced in global south contexts.

Blindly shepherding firms towards adopting large platforms may negatively affect small providers. Interventions should rather support more creative uses of technology and leverage the unique relationships and applications that could afford more inclusive outcomes.

[1] This article is based on the recently published paper:

Foster, C., & Bentley, C. (2022). Examining Ecosystems and Infrastructure Perspectives of Platforms: The Case of Small Tourism Service Providers in Indonesia and Rwanda. Communications of the Association for Information Systems, 50

An open-access version is available to download.

Distribution of Income from Motorcycle-Based Gig Work in Indonesia

When a consumer pays for motorcycle-based gig work, where does the money go?

Following the approach of an earlier, similar post on car ride-hailing,  and again using data gathered by the Fairwork Indonesia team in Jakarta, we can break this down using the generic model shown below:

a. Amount paid by customer: the service payment plus a platform fee (sometimes called an order or service or transaction processing fee) plus – sometimes – a tip.

b. Amount paid to platform: platforms typically take a commission (a set percentage of the customer service payment, usually between 10-25%) and often also charge a platform fee.

c. Amount paid to worker: all of the tip and the service payment minus the platform’s commission.  In some instances – at the end of a shift or at the end of a week – the worker might also get a bonus payment from the platform e.g. for completing a certain number of tasks or being available for work consistently and/or at particular times.  There may also be other criteria that impact access to bonus payments such as low order cancellation rates or high customer feedback ratings.  Bonuses are paid to the worker from the platform’s share which is taken from the platform’s commission; sometimes also from the platform fee; and in some instances more than this (in other words, in these cases, the worker earns more than the amount paid by the customer due to an additional subsidy taken by the platform from investment or other sources of capital).

The two charts below show the distribution of customer payments for two motorcycle-based gig work platforms (which were charging a 20% gross commission on the customer service payment plus a fee).  Figure 1 presents data for riders who own their own motorcycle (the majority of riders in our sample).  Figure 2 presents data for riders who finance their vehicle through loan repayments or (less frequently) rental.

We can draw a number of conclusions:

i. Shares of the Pie: the worker’s true net income (i.e. after work-related costs have been taken into account) is a significant share – around two-thirds – of the total payment made by the customer.  Aside from the net income earned by the worker, the great majority of the customer payment is captured by large private businesses; typically multinationals – the platform, fuel companies, vehicle finance houses, telecom providers.  A significant chunk of vehicle servicing and maintenance costs even goes this way via parts, oil, tyres, etc.

ii. Fuel Costs: fuel makes up a very significant proportion of costs: around 80% of costs for bike owners; about half of costs for those who finance their motorcycle.  It is therefore not surprising that the price of fuel is always at the forefront of workers’ minds: a relatively small rise can cause quite a significant reduction in their net income.

iii. Financing vs. Owning: as expected, the net income of those who finance their vehicle is a lower proportion of customer payment than that of vehicle owners.  In absolute terms, these two groups take home about the same net income (non-owners’ net income was about 5% lower).  It’s not completely clear how this happens but one contributing factor is that workers who finance their bikes work longer hours in order to help towards earning the extra to cover their repayments: an average 78-hour week compared to a 66-hour week for those who owned their bikes.

iv. Bonuses and Platform Subsidies: as noted below, the figures here are calculated on the basis of 23.5% of rider income deriving from platform bonus payments.  The platform gross commission plus fee represent just over 32% of the customer payment; yet the platform’s net earning is 5% or 6% only.  In other words, and absent unknown factors, the platform is on average paying substantially more than its entire commission to workers.

On this basis, one can calculate the tipping point at which platforms earn nothing and are having to subsidise worker income from investment or other sources of capital.  As illustrated in Figure 3, for this instance, this will happen when worker bonuses make up more than 30% of their income.  Yet one can find examples in Indonesia where the effect of bonuses is to more than double workers’ basic pay (i.e. bonuses make up more than 50% of worker income).  In such circumstances platforms must be significantly subsidising gig work from capital. If this is widespread, it may help to explain why so many gig work platforms report operating losses.

Network effects – the greater value of a platform to users as more users participate – would predict the emergence of monopoly (single seller of services to customers) and monopsony (single buyer of services from workers).  Yet this has not happened in most gig economy markets – including those of Indonesia – which, instead, are oligopolies/oligopsonies, meaning there is competition between platforms for both customers and workers.  It is that competition which in part motivates the payment of bonuses to workers.

Notes:

– Although insurance is shown as 0%, there are small payments against this item by some workers; just that they are so negligible a component that they rounded down to zero percent.

– The average figures we have included are that 25% of rider income is made up from tips and bonuses, of which tips make up 1.5%.  This must be seen as a very rough-and-ready average because platforms’ bonus payment schemes are continuously changing; their availability typically varies between workers (e.g. with tiered systems such that the highest bonus payments are only accessible by workers who meet particular criteria on workload, availability, cancellation rates, customer ratings, etc.); and workers’ ability to meet the targets necessary for bonus payment varies from day to day.  Bonuses are typically also only achievable for those working very long shifts: some of our sample were working 15- and in a couple of instances 18-hour days.

– The figures here do not take into account any customer-side promotions that platforms occasionally run; the assumption being that these may not alter the share of rider income.

– Fairwork data from South Africa showed riders’ net income to be 55% of the total customer payment, but this did not separately account for bonuses, which will increase the percentage.  Overall, distribution of income will vary between platforms and locations so the figures above should be seen as illustrative rather than universal.

– Research work reported in this blogpost was supported by the German Federal Ministry for Economic Cooperation and Development (BMZ), under a commission by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ).

Post by Richard Heeks, Treviliana Putri, Paska Darmawan, Amri Asmara, Nabiyla Risfa, Amelinda Kusumaningtyas & Ruth Simanjuntak.

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Distribution of Income from Ride-Hailing in Indonesia

When a customer takes a taxi journey from a ride-hailing platform, where does the money go?

Using data gathered by the Fairwork Indonesia team in Jakarta, we can now break this down using the generic model shown below:

a. Amount paid by customer: the fare for the ride plus a platform fee (sometimes called an order or service or transaction processing fee) plus – sometimes – a tip.

b. Amount paid to platform: platforms typically take a commission (a set percentage of the customer fare, usually between 10-25%) and often also charge a platform fee.

c. Amount paid to worker: all of the tip and the fare minus the platform’s commission.  In some instances – at the end of a shift or at the end of a week – the worker might also get a bonus payment from the platform e.g. for completing a certain number of rides or being available for work consistently and/or at particular times of peak demand.  There may also be other criteria that impact access to bonus payments such as low order cancellation rates or high customer feedback ratings.  Bonuses are paid to the worker from the platform’s share which is taken from the platform’s commission; sometimes also from the platform fee; and in some instances more than this (in other words, in these cases, the worker earns more than the amount paid by the customer due to an additional subsidy taken by the platform from investment or other sources of capital).

The two charts below show the distribution of customer payments for two car ride-hailing platforms (which were charging a 20% gross commission on the customer fare plus a fee).  Figure 1 presents data for drivers who own their own vehicles (the minority of car taxi drivers in our sample).  Figure 2 presents data for drivers who finance their vehicle through loan repayments or (less frequently) rental.

We can draw a number of conclusions:

i. Worker Share of the Pie: the worker’s true net income (i.e. after work-related costs have been taken into account) is a minority share – around one-third – of the total payment made by the customer.

ii. Large Business Share of the Pie: aside from the net income earned by the worker, the great majority of the customer payment is captured by large private businesses; typically multinationals – the platform, fuel companies, vehicle finance houses, telecom providers.  A significant chunk of vehicle servicing and maintenance costs even goes this way via parts, oil, tyres, etc.

iii. Fuel Costs: fuel makes up a very significant proportion of costs: around 90% of costs for vehicle owners, who spend more on fuel than they earn in net terms; about half of costs for those who finance their vehicle.  It is therefore not surprising that the price of fuel is always at the forefront of workers’ minds: a relatively small rise can cause quite a significant reduction in their net income.

iv. Financing vs. Owning: not surprisingly, the net income of those who finance their vehicle is a lower proportion of customer payment than that of vehicle owners.  In absolute terms, these two groups take home about the same net income.  It’s not completely clear how this happens but one contributing factor is that workers who finance their vehicles work longer hours in order to help towards earning the extra to cover their repayments: an average 70-hour week compared to a 65-hour week for those who owned their cars.

Notes:

– Although insurance is shown as 0%, there are small payments against this item by some workers; just that they are so negligible a component that they rounded down to zero percent.

– The average figures we have included are that 15% of driver income is made up from tips and bonuses, of which tips make up 1.5% (i.e. one tenth of the extra).  This must be seen as a very rough-and-ready average because platforms’ bonus payment schemes are continuously changing; their availability typically varies between workers (e.g. with tiered systems such that the highest bonus payments are only accessible by workers who meet particular criteria on workload, availability, cancellation rates, customer ratings, etc.); and workers’ ability to meet the targets necessary for bonus payment varies from day to day.  Bonuses are typically also only achievable for those working very long shifts: some of our sample were working 15- and in a couple of instances 18-hour days.

– The figures here do not take into account any customer-side promotions that platforms occasionally run; the assumption being that these may not alter the share of driver income.

– Fairwork data from South Africa showed a similar financial distribution, with ride-hailing taxi drivers’ net income being 32% of the total customer payment.  However, distribution of income will vary between platforms and locations so the figures above should be seen as illustrative rather than universal.

– Research work reported in this blogpost was supported by the German Federal Ministry for Economic Cooperation and Development (BMZ), under a commission by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ).

Post by Richard Heeks, Treviliana Putri, Paska Darmawan, Amri Asmara, Nabiyla Risfa, Amelinda Kusumaningtyas & Ruth Simanjuntak.

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A Better Way to Research Digital Platforms

In a new European Journal of Development Research paper – “Conceptualising Digital Platforms in Developing Countries as Socio-Technical Transitions” – I and my co-authors argue that there is a better way to research digital platforms.

Digital platforms play an ever-growing role within international development, and a body of research has emerged as a result.  This research offers valuable insights but we find three lacunae:

– Current work collectively identifies a whole set of factors at micro-, meso- and macro-levels that shape the trajectory of digital platforms.  But no research to date can encompass all of the factors and levels.

– Current work has been narrow and a-historical: it analyses the platform but not the existing ways of organising or delivering the particular social, economic or political activity that the platform competes with.

– Current work looks at either implementation and growth of platforms, or at their impact, but not both.  Yet implementation, scaling and impact of platforms are inextricably intertwined.

Our paper therefore uses a different and more holistic approach.  Understanding digital platforms as socio-technical transitions, it uses the multi-level perspective (MLP: see summary diagram below) as its analytical framework.

Using this framework, it analyses a successful ride-hailing platform – EasyTaxi in Colombia.  Although there were some challenges in applying the MLP framework, it addressed the three shortcomings of earlier work:

– It covers the broad range of factors that shape platforms at micro-, meso- and macro-level.

– By focusing on transition, it encompasses both the before and after of platform introduction.

– It analyses the platform lifecycle from initial innovation, though implementation and growth, to impact.

Thus, for example, the MLP explains how prior context and profile of traditional taxi driving created the landscape of infrastructure and incentives behind rapid scaling of the platform.  It also explains development impact: how resource endowments shifted between stakeholders; the formation and formalisation of institutional forces; and the changing distribution of power in the market.

On this basis, we recommend use of the multi-level perspective to researchers wanting to fully understand implementation and impact of digital platforms.

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Digital Platforms as Institutions

How should we understand digital platforms from an institutional perspective?

The paper, “Digital Platforms and Institutional Voids in Developing Countries”, suggests a four-layer model of institutional forms, and illustrates this using ride-hailing platforms as an example.

Layer 1: Digital Institutions.  Platforms themselves are institutions into which digitised routines and rules have been designed based on the digital affordances of the platform. Ride-hailing examples include algorithmic decision-making such as driver—customer matching, or price setting.

Layer 2: Digitally-Enabled Institutions.  Some institutional functions rely on digitised routines and rules within the platform but involve human intermediation.  Ride-hailing examples include checks on driver credentials for market entry, or adjudication of deactivation decisions.

Layer 3: Business Model Institutions.  These are broader rules and routines determined by the platform company as part of its business model, which govern participation in the platform but which exist outwith the digital platform.  Ride-hailing examples include control over vehicle entry into the market, determination of driver employment status, or setting the balance of supply and demand.

Layer 4: Stakeholder-Relation Institutions.  These are the connections or disconnections to other market or domain institutions.  Ride-hailing examples include relations to external stakeholders such as government agencies and trade unions.

Analysis of field evidence from Colombia and South Africa suggests that the first two types of institution are associated with the filling of prior institutional voids, and with market improvements.  The latter two institutional forms are more related to the maintenance, expansion or creation of institutional voids, and to market inequalities.

We look forward to further work applying and revising this institutional model of platforms.

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How Platforms Change Markets: The Lens of “Institutional Voids”

Do digital platforms change markets for better or worse?

To help understand this, we used the lens of institutional voids in the World Development paper, “Digital Platforms and Institutional Voids in Developing Countries”.  This argues that markets don’t work properly because they have institutional shortcomings or voids: inadequate provision of information, limited matching of buyers and sellers, poor management of transactions, ineffective market regulation, etc.

A promise of digital platforms is that they will fill these voids and change markets for the good.  We investigated this using evidence from Colombia and from the South Africa Fairwork project on taxi markets before and after the advent of three e-hailing platforms: Bolt, EasyTaxi and Uber.

The “before” picture was far from perfect.  Institutional voids led to markets with problems including high costs, crime, insecurity, opportunism, informality and discrimination.  As predicted, the gig economy platforms filled some of the institutional voids that led to this profile.  This reduced costs and risks for both drivers and passengers, improved vehicle and service quality, and enabled employment for those excluded from the traditional market.

Yet, in contrast to past research on business and institutional voids in the global South, we found that void-filling is not all that platforms companies do.  They also maintain some voids, such as lack of information and lack of formal employment status for drivers.  They expand some voids, such as lack of information available to government.  And they create some voids by circumventing the regulatory roles performed by government agencies and driver collective bodies.

The core impact of these additional strategies is to increase the relative power of the platform company vis-à-vis other market stakeholders and to make the market much more unequal.  Going far beyond the typical role of business, platform companies have internalised the institutions for the entire gamut of market functions; collapsing an entire organisational field into themselves.  The previously-distributed and -dissipated institutional power that the platform companies have concentrated into themselves is thus unprecedented, particularly given the duopolistic nature of the markets that are often created.

Filling institutional voids is not wholly beneficial – our research also identified problems caused by the digitalisations and formalisations that platforms bring.  But our key recommendation is a need to identify and address the voids that these companies retain or make.  Actions needed include information provision to address customer–driver asymmetries; revitalised state control over market supply–demand imbalance; new legislation to address lack of employment rights for workers; and more effective worker collectivisation.

Our research represents a novel insight into the relation between platforms, institutions and markets, and we look forward to further work applying these ideas to other sectors and contexts.

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Digital Platforms as Development Infrastructure

I’m going to argue here that digital platforms should be understood as development infrastructure[1].

In recent years, there’s been a renewed emphasis on the value and role of infrastructure in international development[2].  Official development assistance for infrastructure has therefore risen but there remains a significant infrastructure financing gap[3].

It may be something of an exaggeration to say, as Paul Collier does, that “the west’s aid agencies ‘pulled out of infrastructure long ago, and started financing social stuff instead. That’s important, but there’s a need to get back to financing the basic [physical and organisational] infrastructure’ because ‘without it countries can’t develop’”[4].  However, while western agencies are still funding infrastructure, it is certainly true that China particularly has stepped in to try to fill the gap left by lack of western funding for development infrastructure; especially via its Belt-and-Road initiative[5].  This gap-filling includes digital infrastructure.

When we think of digital and infrastructure, the focus has been on telecommunications: fibre-optic cabling, mobile networks and the like.  But digital platforms should also be seen as infrastructure.  As development processes digitise and dematerialise, platforms become the “infra-structure” for society: lying beneath and increasingly forming the foundation and site for economic, social and political activity.

Platforms store development assets, just like a grain silo.  Platforms transport development assets, just like a road or railway.  Platforms import and export development assets, just like a port.  Platforms enable transactions of development assets, just like a marketplace.

Digital platforms thus perform the developmental functions not just of physical but also of institutional infrastructure.  For example, as marketplaces, they combine within themselves the institutional infrastructure functions of participant aggregation and certification, transaction facilitation, payment and regulation[6].

The Chinese state has recognised this.  Its Digital Silk Road initiative funds traditional digital infrastructure but it also encompasses support for the spread of Chinese digital platforms to low- and middle-income economies of the global South[7].  These platforms are then becoming a key part of national economic infrastructure in these countries[8]. Will western governments recognise platforms’ infrastructural importance to development?  And, if so, how should and will they respond?

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[1] Graphic: https://e.huawei.com/en/publications/global/ict_insights/201810161444/analysts/201906101000

[2] Bhattacharya, A., Romani, M. & Stern, N. (2012) Infrastructure for Development: Meeting the Challenge, London School of Economics; Donaubauer, J., Meyer, B., & Nunnenkamp, P. (2016) Aid, infrastructure, and FDI. World Development, 78, 230-245; DFID (2020) International Development Infrastructure Commission Recommendations Report, Department for International Development, UK

[3] UNCTAD (2020) Official international assistance plays a key role in financing for sustainable development, SDG Pulse

[4] Hellowell, M. & Wakdok, S. (2021) Disaster relief, Prospect, March, 48-51

[5] Huang, Y. (2016) Understanding China’s Belt & Road initiative. China Economic Review, 40, 314-321.

[6] Heeks, R., Eskelund, K., Gomez-Morantes, J. E., Malik, F., & Nicholson, B. (2020) Digital Labour Platforms in the Global South: Filling or Creating Institutional Voids?, Working Paper no.86, Centre for Digital Development, University of Manchester, UK

[7] Bora, L. Y. (2020) Challenge and perspective for Digital Silk Road. Cogent Business & Management, 7(1), 1804180; Choudary, S.P. (2020) China’s country-as-platform strategy for global influence, TechStream, 19 Nov

[8] Keane, M., & Yu, H. (2019) A digital empire in the making: China’s outbound digital platforms. International Journal of Communication, 13, 4624-4641.