Antecedents of Significant Digital Development Research

This post is a cheat because it’s actually summarising a paper on organisational – not digital development – research.

It’s by the leading organisational theorist – and confutation of nominative determinism – Richard Daft, and I read it just before I started my PhD.

Based on a survey of organisational researchers, its findings feel relevant to digital development.  Significant research . . .

– Is an outcome of the researcher’s involvement in the real world

– Is an outcome of the researcher’s own interests, resolve and effort

– Is chosen on the basis of intuition

– Is an outcome of intellectual rigour

– Reaches into an uncertain world to produce something that is clear, tangible and well-understood

– Focuses on real problems

– Is concerned with theory, with a desire for understanding and explanation

Not-so-significant research is the opposite: expedient, quick and easy, lacking personal commitment from the researcher, lacking theoretical thought and effort, and so on.

While planning and clarity mark out the latter stages of significant research, it is the outcome of an organic process of intuition, integration of ideas from different fields or chance meetings, that starts with uncertainty.  Precisely planned, tidy, clean and clearly-defined research most likely leads to small results (research funders please take note!).

That all seems to fit equally-well with digital development research but, of course, these criteria come from a researcher perspective, not that of other stakeholders.  See what you think.

If you’d like to read the paper, it’s not so easy to find:

 – Daft, R. L. (1984). Antecedents of significant and not-so-significant organizational research. In: T.S. Bateman & G.R. Ferris (eds), Method and Analysis in Organizational Research. Reston Publishing, Reston, VA, 3-14.

Or, there’s a firewalled update:

– Daft, R. L., Griffin, R. W., & Yates, V. (1987). Retrospective accounts of research factors associated with significant and not-so-significant research outcomes. Academy of Management Journal30(4), 763-785.

Income of Gig Work vs. Previous Job in Pakistan

Richard Heeks, Iftikhar Ahmad, Shanza Sohail, Sidra Nizamuddin, Athar Jameel, Seemab Haider Aziz, Zoya Waheed, Sehrish Irfan, Ayesha Kiran & Shabana Malik

Does the transition to gig work improve incomes in Pakistan?

Many workers join gig work platforms in the belief that their incomes will improve, but is this borne out in practice?  To investigate, the Centre for Labour Research interviewed 94 workers based on six platforms across three sectors: ride-hailing, food delivery, and personal care.

Of these, 51 were able to tell us what their previous monthly income had been in their most-recent employment prior to joining the platform[1].  Stated income varied from the equivalent of US$60 per month up to U$1,200 per month, and averaged US$220 per month[2].

After moving into gig work, average gross income was slightly higher, at US$240 per month but, as the graph below shows, there was a much more differentiated picture behind the average, with around 40% of respondents earning less gross income (red-bordered blue columns) than they had done previously.

However, as the graph also shows, things looked worse when comparing net income (orange columns).  For the great majority of prior jobs, work-related costs were small (only work-to-home transport, which we calculated based on typical commuting journeys in Pakistan to be just under US$18 per month; i.e. less than 10% of average gross income).  But for gig work – much of which relies on journeys by vehicle and continuous internet connectivity – the costs of petrol, maintenance and data eat heavily into gross income.  In addition, for some (only a few in our Pakistan sample) there are costs of renting their vehicle.[3]

These costs represented, on average, 65% of gross income and knocked average net income for gig workers down to just US$85 per month.  When we compare before-and-after for net income, then, we found more than 70% of our sample were earning less than in their previous job, and 45% earned over US$100 per month less.

This was especially an issue for ride-hailing drivers and it does reflect the particular circumstances during our interview period of late 2021 to early 2022: a drop-off in demand for travel due to Covid, and a steep rise in petrol prices.  Indeed, so bad was the problem that just over a fifth – 21 of the 94 – were reporting negative income.  That is, they were effectively paying to go to work as their costs exceeded their gross income; something to which the platforms responded in May 2022 by dropping the commission taken from drivers to 0%.

While recognising the challenging period for gig workers covered by our fieldwork, nonetheless, this does suggest that – by and large – gig work is not delivering the income boost that workers often hope for.  They may, for example, be lured by gross income figures, not realising how much lower net income will be.  Gig work does provide a livelihood – 40% of our sample were unemployed in the immediate period prior to joining – but it is not really fulfilling its promise.  It also falls far from decent work standards: five-sixths of those we interviewed took home less than a living wage.

If you’d like to know more, please refer to the 2022 Fairwork Report on Pakistan’s gig economy.


[1] Those who stated what their prior employment had been gave the following job descriptions: BPO operator, Teacher (2), Housekeeper, Shopkeeper, Gas company worker (2), Safety officer, Business person, Tanker driver, Ride-hailing driver with another platform (3), Traditional taxi driver (3), Farmer, Builder, Computer operator, Cook, Technician, Shop assistant, Domestic worker, Government worker

[2] This average is some way above the overall average earnings of US$140 per month but well below formal sector average monthly salary of US$480.

[3] For further detail, see this discussion of the breakdown of ride-hailing passenger payments.

Digital public goods platforms for development

Nicholson, B. Nielsen, P. Sahay, S. Saebo, J. Digital public goods platforms for development: The challenge of scaling The Information Society available open access at: https://www.tandfonline.com/doi/full/10.1080/01972243.2022.2105999

Recently there has been an explosion of research into digital platforms.  To provide an indication of the size of the output, a quick search on Google Scholar provided 3270000 “hits”, 39900 in 2022 alone to date with publications across diverse disciplines including management, information systems, economics and more.   In the realm of ICT4D, discourse has focused on how platforms may enable socio-economic development (Nicholson et al 2021) however there is a paucity of examples of empirical research on how this may be realised.  

Digital platforms are defined according to their principal purpose and identifies two broad categories: transaction platforms and innovation platforms. Transaction platforms refer to a two or multi sided marketplace mediated by the platform.  Innovation platforms act as “foundations upon which other firms can build complementary products, services or technologies” (Gawer, 2009, p. 54).

Most prior empirical research on digital platforms involves commercial, for-profit platforms situated in the regulative institutions of the Global North.  Inherent in this prior work is an assumption of “monetisation” and the capitalist market forces, and little is known about platforms that are donor supported and aimed at socio economic development.    

A forthcoming paper attempts to address the knowledge gap by conceptualising innovation platforms as public goods and asking:

How can innovation platforms be public goods?

A goal of the article is to identify the challenges of simultaneously scaling up digital platforms and developing them into public goods.  Empirically, the focus is on health, specifically the empirical example is the District Health Information System (DHIS2). 

The relevance of public goods in development is well-established in the domain of health.  Initiatives driven by global health organisations such as the World Bank and World Health Organization aim to promote digital public goods. Digital Square, a marketplace initiative in digital health, has developed a Global Goods Guidebook and a Global Goods Maturity Model.  Before and during the pandemic, open-source systems have been launched to support outbreak management, such as the Surveillance Outbreak Response Management and Analysis System (SORMAS). SORMAS intuitively displays features of a public good: it is free of charge, open source, independent from tech companies, and interoperable with other platforms such as DHIS2.

Turning to theory of public goods leads us to the economics discipline and centres on two main principles: non-rivalry and non-exclusion. “Goods” such as crime control, flood defences etc. are provided because of failure of the market mechanism.  Government thus intervenes either financially, through such mechanisms as taxation or licensing, or with direct provision.   Public goods are non-rivalrous, implying that one individual’s consumption of the good does not influence what is available for others. They are also non-excludable, in the sense that no one can be excluded from consumption of a public good. 

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Consider a lighthouse where one navigator’s use of the light does not prevent other navigators from doing the same. Many potential public goods exhibit only one of these properties resulting in the tragedy of the commons which can be illustrated with the example of a village pasture. Unrestricted access (non-exclusion) to the commons – pasture belonging to the village as a whole – leads to its degradation (rivalry). However, some scholars question the inevitability of depletion of common pool resources when they are managed in a bottom-up, cooperative way by those most dependant on them.  Under certain conditions, individuals govern themselves collectively, and without market pressures or government regulation, to obtain benefits, even if the temptation to freeride is present.

Global public goods are goods whose benefits cross borders and are global in scope for example eradication of infectious diseases where it is impossible to exclude any country from benefiting and each country will benefit without preventing another.

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The district health information system or DHIS2 supports decentralized routine health management. The architecture is designed with a generic core that enables local innovation and anyone with internet access can at any time download the most recent version of DHIS2, the source code, as well as required libraries and third-party products (such as Chrome or Firefox browsers). DHIS2 also comes with a set of bundled apps, developed by University of Oslo or through its partners in the Global South (such as HISP Tanzania, an independent entity with close collaboration with Oslo) available in an “app store.” It is similar in concept to Apple App Store or Google Play and some DHIS2 apps are also available on these platforms too. The platform architecture allows local innovation as apps, increasing its potential relevance globally.

Due to its openness and flexibility, it is impossible to know the exact number of DHIS2 implementations. It is known that ministries of health and other organizations in more than 100 developing countries use DHIS2, together covering an estimated population of 2.4 billion people.  In November 2020, the ministries of health in 73 countries (primarily developing countries) used DHIS2, out of which 60 were nationwide implementations, and 13 were in the pilot stage. In addition, 22 Indian states used DHIS2. There is also a range of other organizations using DHIS2 independently for reporting in the countries they are operating, including PEPFAR, Médecins Sans Frontières (MSF), International Medical Corps, Population Services International (PSI), and Save the Children.

We can explore the “qualification” of DHIS2 as a public good by considering some of the challenges experienced by developers in Oslo and other implementation sites examined as tensions and paradoxes.  In a seminal paper on paradoxes and theory building, Poole and van de Ven (1989) identify a paradox as “concerned with tensions and oppositions between well-founded, well-reasoned, and well-supported alternative explanations of the same phenomenon” (565). 

Consider the story of the product lead of the DHIS2 analytics team response to the challenge of prioritizing requests by developing a roadmap prioritization matrix. Most use-cases need analytics functionality, and a wide variety of requests are directed to this team. The product lead estimates that the analytics team can only accommodate about half the requests at any stage of the product development cycle. The question facing this individual is: “which requests should be prioritized, coming from whom, and in which release cycle?” The primary implementations of DHIS2 are users from governments in low- and middle-income countries, according to the product lead, who tend to not actively voice their requests for changes in functionality. These groups are constrained by physical separation often across great distance, limiting ability to meet in person and develop social relationships. By contrast, users from donor organizations and other users in the West, tend to have closer proximity and resources to visit Oslo and “make their voices heard,” resulting in greater influence over the DHIS2 functionality development. This mismatch led the product manager to develop this “objective” prioritization methodology.  From the perspective of public goods, the dynamics of donors’ activity affects the rivalry / excludability conditions as their greater influence means that other users are relatively excluded, and access is rivalrous depending on this influence.

There are also paradoxical consequences of scaling at the macro and micro levels.  While the Oslo development team add in their releases of new features for strengthening outputs and analysis towards a generic global platform, the typical user in a district of a developing country requires basic functionalities, and the new features often detract instead of increasing the software’s value for the users.  At the macro-level, the development team are seeking to cater to the universe of users, including district users, researchers, and data analytic experts in multiple country contexts. This requires them to continuously add new features, often for increasingly sophisticated use. This process went counter to the needs at the micro-level of the users in district offices, who want specific and easy to use functionalities for their everyday use.  Thinking again from a theoretical standpoint, the malleability of a digital good compared to the oft cited example of a static lighthouse is clearly evident. The drive towards generic global features at the macro level causes rivalry and excludes some users at the local more micro level.  

Overall, the more macro interests of the donors and drive towards a global generic platform appear incompatible with the smaller players who become increasingly marginalized. Furthermore, their capacity for collective action is limited by structural factors.   This challenges DHIS2’s status as a public good as we can see rivalry and exclusion creeping in.

The problem is not insurmountable, collective action and subsidiarity offer helpful mechanisms of governance. Two main subsidiarity conditions are known to be helpful related to effectiveness and necessity: that action should be taken at the level where it is most effective and that action at the higher level should be taken when lower levels cannot achieve the set goals by themselves. This is in line with ongoing efforts by Oslo to build South-South community-based networks and thereby decentralization into the Health Information System Programme (HISP) network. 

References

Gawer, A. (2009). Platform dynamics and strategies: from products to services. Platforms, markets and innovation45, 57.

Nicholson, B., Nielsen, P., & Sæbø, J. (2021). Digital platforms for development. Inf. Syst. J.31(6), 863-868.





Digital Inequality Beyond the Digital Divide

How can we understand digital inequality in an era of digital inclusion?

As the open-access journal paper, Digital Inequality Beyond the Digital Divide: Conceptualising Adverse Digital Incorporation in the Global South” explains, the digital divide has been an essential and powerful concept that links digital systems with inequality.

But it is no longer sufficient.  A majority of the global South’s population now has internet access and is included in, not excluded from, digital systems.  Yet, as the figure below illustrates, that inclusion also brings inequalities – the small farmers in digital value chains losing out to large intermediaries; the gig workers whose value and data are captured by their platforms; the communities disempowered when they are digitally mapped.

Figure 1: From an Exclusion-Based to an Inclusion-Based Perspective on Digital Inequality

We need a new conceptualisation to explain this emerging pattern.  I refer to this as “adverse digital incorporation”, defined as inclusion in a digital system that enables a more-advantaged group to extract disproportionate value from the work or resources of another, less-advantaged group.

As shown below, I have inductively built a model of adverse digital incorporation, based around three aspects:

Figure 2. Conceptual Model of Adverse Digital Incorporation

Future digital development research can apply this model deductively to cases of digital inequality, and can further investigate the digitality of adverse digital incorporation. 

For digital development practitioners, the challenge will be to achieve “advantageous digital incorporation”: designing digital interventions that specifically and effectively reduce existing inequalities.  This means going beyond digital equity to digital justice: addressing the underlying and contextual causes of inequality not just its surface manifestations.

For further details, please refer to the paper; “Digital Inequality Beyond the Digital Divide: Conceptualising Adverse Digital Incorporation in the Global South”.

How Does Technology Affect Smart City Governance?

What is a Smart City?

A Smart City (SC) capitalises on technology, proper governance and collaborations between the various stakeholders to comprehensively promote city prosperity and eventually improve the quality of citizens’ lives.

Figure 1. Envisaging the smart city[1]

Cities are agglomerations of economic, social, and cultural benefits[2]. On the other hand, cities are increasingly confronted with issues such as diminishing public management efficiency, backward infrastructure, traffic congestion, environmental pollution, and general security concerns, among others.

The Smart City is a concept that has evolved around the world to solve urban problems and enhance urban development. Several municipalities, such as Cape Town, Ottawa, San Diego, Southampton, Barcelona, Seoul, and Shanghai, have developed SCs to serve citizens better and improve the quality of citizens’ lives.

What is Smart City Governance?

New governance patterns are required to manage SCs. The governance models for SCs could be divided into two categories:

  • Some of the governance models are technology-driven, focusing on the role of big data and technology.
  • Other governance models emphasise the human and institutional factors,  such as the role of governance structures, citizen-centricity, social capital, human resources and stakeholders.

At the intersection of these two, Smart City Governance (SCG) emerges mainly due to the growing roles of technology and human capabilities in the functioning of cities, which gives the government the opportunity to optimise the governance process and outcomes. A typical description of SCG is “crafting new forms of human collaboration through the use of ICTs to obtain better outcomes and more open governance processes” [3].

How does technology affect SCG?

The technology revolution has altered the city governance model. The impact of technology on governance models is roughly in two directions. One is to use technology to strengthen the government-centric bureaucratic model, and the other is to use technology to distribute decision-making power to more stakeholders.

  • Technology contributing to the concentration of power

The case in Shenzhen, China shows how technology can strengthen a top-down governance model. The Shenzhen government propagated a programmatic document for SCG, the Shenzhen Municipal New-Type Smart City Construction Master Plan, in 2018[4]. In this plan, the SC structure of Shenzhen includes three layers and two supports, as outlined in the figure below.

The primary layer is the SC Sensory Network System, which mainly includes sensor networks, communication networks, and computing storage centres; the middle layer provides support for government decision-making, which is composed of the Urban Big Data Centre and SC Operation and Management Centre; the top application layer includes four parts public services, public safety, urban governance and smart industries.

In this scenario, technology is the core element of governance and is used to strengthen the government’s decision-making and implementation capabilities. In this kind of governance model, technology is used to collect public management-related data and information, help make governmental decisions and finally reinforce the rationality and efficiency of government.

Figure 2. Shenzhen’s smart city structure [5]

  • Technology contributing to the decentralisation of power

On the other hand, technology may give impetus to the bottom-up governance model. For example, in the case of Amsterdam Smart City (ASC)[6], the Amsterdam Economic Board governs and funds it using an open web-based platform. This platform allows stakeholders to communicate and disseminate information in a fair and transparent manner. Furthermore, open-house programmes and open gatherings help citizens communicate and empower themselves. This case demonstrates how technological innovation has aided in the distribution of information and power to more stakeholders in ASC.

Figure 3. Amsterdam Smart City

In conclusion, data and information bestow stakeholders’ power and legitimacy in urban governance to a certain extent. From the standpoint of technology, the power distribution of data and information may affect the governance model towards decentralisation or concentration.

References

[1] https://www.arcweb.com/industries/smart-cities

[2] https://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/citiesoftomorrow/citiesoftomorrow_final.pdf

[3] Bolívar, M. P. R., & Meijer, A. J. (2016). Smart governance: Using a literature review and empirical analysis to build a research model. Social Science Computer Review, 34(6), 673–692. https://doi.org/10.1177/0894439315611088

[4] http://www.sz.gov.cn/zfgb/2018/gb1062/content/post_4977617.html

[5] Hu, R., (2019). The state of smart cities in China: The case of Shenzhen. Energies, 12(22), p.4375

[6] https://amsterdamsmartcity.com/

Workshop on China’s Digital Expansion in the Global South

Credit: ASPI https://chinatechmap.aspi.org.au/

China is fast-emerging as a global digital superpower and has a rapidly-growing digital presence in other low- and middle-income developing countries of the global South.  Yet research to date has been relatively limited on this rising phenomenon which is having important economic, social, political and geopolitical impacts.

This online workshop – held 1000-1730 (UK time/BST) on Thursday 21st July 2022 – presented new findings based on primary research in the global South, and also provided a space to reflect on the agenda and collaborations for future research.

Recordings of the presentations in the three main workshop session can be found at: https://www.youtube.com/playlist?list=PLjghFTNvDEIyEUpx7nlYqWDKeA5JkWczL

The workshop timetable is shown below:

1000-1200:

The Future Research Agenda on China’s Digital Expansion – Richard Heeks, Angelica Ospina, Chris Foster, Ping Gao, Xia Han, Nicholas Jepson, Seth Schindler & Qingna Zhou (University of Manchester)

Learning Along the Digital Silk Road? Technology Transfer, Power, and Chinese ICT Corporations in North Africa – Tin Hinane El Kadi (London School of Economics)

China’s Digital Expansion in Africa: South to South Cooperation or South Dominance? – Grace Wang (Stellenbosch University)

1300-1445:

Chinese Digital Platform Companies’ Expansion in the Belt and Road Countries – Yujia He (University of Kentucky)

Global Developments of Chinese E-commerce Livestreaming: Case of AliExpress and Lazada in Southeast Asia – Xiaofei Han (Carleton University)

Transnational Governance behind Chinese Platforms’ Overseas Content Moderation: A Case Study of TikTok’s Global Reach to Southern and South-eastern Asia – Diyi Liu (University of Oxford)

1500-1645:

The Chinese Surveillance State in Latin America? Evidence from Argentina and Ecuador – Maximiliano Vila Seoane (National Scientific and Technical Research Council, Argentina) & Carla Álvarez Velasco (Institute of Higher National Studies, Ecuador)

China’s Expansion in Brazilian Digital Surveillance Markets: Between Public Actors and Foreign Enterprises – Esther Majerowicz (Federal University of Rio Grande do Norte) & Miguel Henriques de Carvalho (Federal University of Rio de Janeiro)

Alibaba in Mexico: Adapting the Digital Villages Model to Latin America – Guillermo J. Larios-Hernandez (Universidad Anahuac Mexico)

1645-1730:

Future Research Agenda Activity

The workshop was co-hosted by the University of Manchester’s Centre for Digital Development and Manchester China Institute

Graphic credit: ASPI at https://chinatechmap.aspi.org.au/

COVID-19 and the Unsettled Questions of Digital Governance

A meeting on e-commerce at the World Trade Organisation, source: WTO photos

How will ongoing debates on digital governance shape the future of digital development?

One of the important implications of the COVID-19 pandemic has been the further acceleration of growth in the digital economy and the expansion of cross-border digital flows. Driven by the pandemic, large and small businesses across the world adapted their business models by shifting completely or partially to internet-based models. As a result, digital transactions, within and across countries, increased dramatically over the last couple of years. While measurement of such flows is challenging, some reports estimate that global Internet Protocol (IP) traffic was expected to more than triple between 2017 and 2022 and that domestic and international IP traffic in 2022 will exceed all Internet traffic up to 2016.

This growth has intensified the debates around digital governance. These debates have begun prior to the pandemic as the growth in the digital economy on the one hand and the move by some states to adopt “interventionist” digital policies drove intense discussions on how to govern the digital world and where to draw the line between sovereignty of states on the one hand and the need to adopt international rules and norms to maintain the global nature of the digital world.

The success of some countries, particularly China, in building digital capacities and firms through selective, and often limited, integration in the global digital market have intensified those debates as other countries began to look to the Chinese model as a guidance for their digital strategies. As a result, questions around the appropriate forum to govern digital issues, the limits of state power vis-à-vis international rules and norms, and the applicability of such rules to different economies have dominated digital policy debates for a number of years. Some of those debates have taken place within regional blocs such as the European Union (EU) and the Association of Southeast Asian Nations (ASEAN) while others have taken place within international bodies that are focused on digital governance such as the Internet Governance Forum (IGF).

While these debates continued in different forums, a difficult link between digital governance and the international trading system was established largely as a result of pressure from the advanced economies. Issues such as data flows, source code and algorithms, and cybersecurity, amongst others, became increasingly linked to trade regimes with recent trade agreements adopting digital chapters that include rules on a range of digital issues.

While the link between trade agreements and the digital world is not always clear (while some cross-border flows are trade flows, a huge percentage of these flows are not trade-related, and the two are very difficult to separate), the trade regime offered an established forum with the ability to produce binding and enforceable rules to govern the digital space. Today, negotiations on digital issues continue in a number of multilateral, regional, and bilateral trade forums as states pursue different visions of the digital economy and how to govern digital flows. The advanced economies, in particular the United States, the EU, Japan, and Australia in addition to emerging economies such as India and China are the key drivers of these processes.

The implications of such processes for development issues are profound, and often overlooked. The economic and social value of data, for instance, is not yet fully understood and, as such, it is unclear what adopting binding international rules around data flows will exactly entail. Some argue that developing countries will benefit from global open data policies as it gives them an opportunity to integrate in the digital economy and to achieve technological progress. Others, however, question this position and argue that developing countries should resist any rules that could undermine their policy space to adopt digital policies. As discussions on these issues continue in different forums, more engagement from digital development scholars is needed.

In the context of the dramatic expansion of the digital economy driven by the pandemic, better understanding the implications of digital governance for digital development particularly in lower-income and smaller developing countries is crucial to help shape the processes driving digital governance and to ensure that digital rules do not undermine the objectives of economic and social development that are increasingly tied to digital issues in today’s world.

The Organisational Context for Successful ICT4D Practitioners

How can their organisational context best support those who implement ICT4D projects?

People – designers, builders, operators, champions – are critical to the successful implementation of ICT4D projects.  The digital development organisations that employ these practitioners already know that.  But what they know far less about is how to create a supportive organisational context that will improve ICT4D practitioner performance and, hence, ICT4D project success rates.

I have therefore been undertaking field research in East Africa designed to tease out components of supportive context, based on interviews in five organisations which were a mix of NGOs and social enterprises.  To date, I have identified six “habits of highly-effective digital development organisations”:

1. Reinforcing Mission Congruence

The most-effective contexts were those in which ICT4D practitioners were given a clear sense of how their work fitted with the organisation’s wider mission, which typically related to social impact.  As well as giving practitioners the bigger picture of their contribution, this also helped create a unity of purpose with shared goals of making a difference.

2. Strong Non-Monetary Rewards

Money is tight in most digital development organisations but they can successfully motivate their practitioners with non-monetary rewards.  Flexibility on working hours and opportunities for work-life balance came up repeatedly in this category, alongside recognition from peers of one’s contribution.

3. Involvement in Monetary Reward-Setting

A role for non-monetary rewards does not mean money is unimportant – it is!  But just as important as the amount was the process by which pay was calculated.  Supportive contexts were those where pay was transparently calculated and openly discussed, and hence where ICT4D practitioners felt involved in the process of decision-making.

4. Support for Career Progression

To make their best contribution to ICT4D projects, practitioners needed to feel that they were making progress in their careers.  Though often backed by direct mentoring, organisational support here varied by career stage.  Early-career practitioners had a strong perceived need for skills development: not narrow task-specific skills but a broad and hybrid mix of technical and non-technical capabilities.  This worked best where their organisation offered them a mix of different roles but also ensured access to high-quality digital tools and infrastructure.  Mid-career professionals also wanted growth opportunities but they focused less on technical skills and more on being given the autonomy and responsibility to develop leadership capabilities.

5. Meeting Personal Goals

ICT4D practitioners give their best to their projects and their organisation when they have a perception of reciprocation; particularly in terms of being helped to achieve their personal goals.  Goals of social impact and skills-building for career progression were mentioned already, but supportive contexts could provide other things – networks of stakeholder relations to build social capital for the future, and facilitation of personal development projects.

6. Socio-Emotional Support

ICT4D often has a technical bias but practitioners worked best in cultures attuned to the human side of work, and in which they felt their whole selves were recognised.  These were organisations that were more like “families” than “well-oiled machines”; in which peers and managers cared about wellbeing and would take time to listen and engage with personal problems; and in which socialisation and hence a sense of belonging were actively encouraged.

These findings may themselves have some specificity to East African digital development organisations.  Each organisation may thus need to identify the dimensions of organisational support that will work with its particular ICT4D practitioners.  Nonetheless, these six habits should be a useful starting point for all organisations.

If you would like further details about the six habits, or my ongoing work using these to develop interventions for digital development organisations, then feel free to contact me: epiphania.kimaro[@]manchester.ac.uk

Photo credit: Gladness Mayenga

Global South researchers succeeding against the odds: how are they different?

Understanding the Context

How are some global South researchers able to overcome contextual constraints and become highly cited?

There is a clear research divide between the global South and the global North[1] in terms of research investment and capabilities. The average national expenditure on research and development in Southern countries is 0.38% compared to 1.44% in Northern countries[2]. The number of researchers per million population in 2017 was 713 in the global South and 4,351 in the global North[3]. This had implications on the volume and impact of scientific outputs produced by the global South in comparison to the global North. Excluding China and India, in 2018 global North countries produced an average of more than 35,000 scientific and technical journal articles per country while global South countries produced 4,000 journal articles per country, out of which less than 2% made it to the top 1% most cited articles globally. This can be partially explained by the lower levels of investment and English proficiency, smaller relative populations of researchers, institutional exclusion factors and/or biases against Southern researchers when it comes to accepting their papers in top tier journals or awarding grants.

Despite all of the aforementioned challenges, there are a few Southern researchers who are able to achieve better outcomes than their peers. Such researchers could provide valuable insights and lessons that might help to better understand and even mitigate the current North–South divide in research outputs and citation. This blog post will highlight some of the valuable insights emerging from our recently published study that attempted to uncover publication-level and individual-level factors underlying the outperformance of information systems researchers in Egypt.

The Method

 This study employed the “data-powered positive deviance” (DPPD) methodology that uses digital datasets to identify positive deviants (those performing unexpectedly well in a specific outcome measure that is digitally recorded, mediated or observed) and potentially also to understand the characteristics and practices of those positive deviants (PDs) if digitally recorded.

Three main steps were conducted to identify and characterise PDs, as shown in Figure 1:

  • In the Define step, we defined our study population and the performance indicators that will be used to assign a score for each researcher. The study population comprised 203 information system researchers in Egyptian public universities. Six well-known citation metrics (h-index, g-index, hc-index, hi-index, aw-index and m-quotient) were calculated for each researcher using Publish or Perish and Google Scholar bibliometrics. Several citation metrics were used to avoid putting certain groups at a disadvantage due to factors such as the length of their research career, the size of their research departments, the age of their papers or their publication strategies.
  • The Determine step aims at identifying the PDs based on the scores calculated in the previous step. In this study, PDs or outliers were defined as researchers who significantly outperformed their peers in at least one of the six citation metrics. The interquartile (IQR) method was used to identify those outliers based on their deviation from the median, i.e. lying beyond the 1.5*IQR added to the third quartile in at least one of the six citation metrics.
  • The third step, Discover, consists of three main stages. In Stage 1, primary data was collected through in-depth interviews from a sample of PDs to explore practices, attitudes and attributes that might distinguish them from non-PDs. During Stage 2, the key findings from Stage 1 plus other predictors of research performance drawn from the literature were used to design a survey tool. That survey then targeted the whole population and tested if the proposed differentiators were significantly different between the two groups. Finally, in Stage 3, the Scopus database was used as the basis for analysis of researcher publications; extending and validating some of the findings identified in the previous stages.

Figure 1: Summary of the applied DPPD method

 What we found

 A combination of data sources (interviews, surveys, publications) and analytical techniques (PLS regression, topic modelling) were used to identify significant predictors of positively-deviant information system researchers. One of the key findings was that PDs contributed to the creation of roughly half (48%) of the publications and achieved nearly double (1.7x) the total number of citations of non-PDs despite representing roughly one-eighth (13%) of the study population. While there were significant predictors of outperformance that are structural (e.g. gender, academic rank and role, workplace perceptions), our focus in this post is on highlighting factors that are transferable i.e. practices and strategies that are to some extent within the control of the individual researchers. Table 1 provides a summary of such factors.

Individual-Level Predictors

 

Positive Deviants

Non-Positive Deviants

Travelling abroad to obtain their PhD degree

More PDs got their PhDs from global North countries 

Fewer non-PDs got their PhDs from global North countries

International research collaborations

Frequently part of multi-country research teams 

Seldom part of multi-country research teams

Co-authorship

Published more papers with foreign reputable authors

Published fewer papers with foreign reputable authors

Securing research grants and travel funds 

Secured more grants and travel funds

Secured fewer grants and travel funds

Research approach

Less inclined to do radical research

More inclined to do radical research

Student supervisions

Supervised a larger number of postgraduate students

Supervised a smaller number of postgraduate students

Capacity development  

More PDs took scientific writing and English writing courses

Fewer non-PDs took scientific writing and English writing courses

Publication-Level Predictors

Length of paper

Longer papers

Shorter papers

Length of abstract

Longer abstracts

Shorter abstracts

Length of title

Longer titles

Shorter titles

Number of authors and affiliations

More authors and affiliations

Fewer authors and affiliations

Number of references

More references

Fewer references 

Publication type

More journal articles and fewer conference papers

More conference papers and fewer journal articles

Quality of journals

Higher SJR journals

Lower SJR journals

Publishers

Published more in Elsevier Journals

Published less in Elsevier Journals

Topics

PDs publish fewer papers covering business process management and neural networks and published more papers in wireless sensor networks and hardware systems

Non-PDs publish more papers covering business process management and neural networks and published fewer papers in wireless sensor networks and hardware systems

 Table 1: Significant transferable predictors of outperformance

The analysis also included a visualization of topic prevalence over time for the PD corpus and non-PD corpus as presented in Figure 2. It shows topics, such as Classification Models, where PDs were early movers and then they were followed by NPDs. There is a greater prevalence of Expert Systems and GIS-related topics in the PD corpus in comparison to the NPD corpus. Conversely, there is lower prevalence of Neural Networks and Business Process Management & Process Mining. There are also topics that had very similar proportions over time for both groups, such as Social Network Mining.

Figure 2: Topic proportions of PD corpus (left) and non-PD corpus (right) over time

 Implications for practice and policy

This analysis cannot, of course, guarantee that applying these factors more broadly would lead to the same outcomes achieved by PDs. Nonetheless, there would be value in individual Southern researchers reflecting on the research- and paper-related behaviours that have been shown associated with positively-deviant research profiles. For instance, Southern researchers work in contexts of resource limitation, hence, research grants and travel funds are of outmost importance. Including partners from Northern universities (as PDs do) increases the chances of securing the funds as those partners are more familiar with grant procurement processes and more experienced in writing proposals. Studying abroad also seems to put Southern researchers at an advantage as it does not just equip them with the technical know-how and the degree needed to pursue their academic careers, but also helps them establish channels of collaboration with their supervisors and their PhD granting universities, long after they returned to their home countries. Those long standing relationships provide further access to research grants either directly or via joint grant applications.

In terms of paper-related strategies, Southern researchers could avoid low-visibility local conferences and can select journals instead as they are more likely to deliver citations. Publishing with more authors (domestic and international) could also help pay for journal publication fees, with fees split across more authors or paid from overseas sources. Publishing with foreign authors could also help Southern researchers overcome the institutional biases[4] among editors, reviewers in single-blind or open review systems, and readers. PDs’ preference for working on established research areas rather than on radical research topics may also help in relation to institutional barriers, with research that builds incrementally on existing ideas and literature being more likely to be accepted for publication by referees, and cited by others working in the established area. Hence, Southern researchers seeking more citations could consider contributing to mainstream topics that build on existing work. Along the same lines, having multiple authors and affiliations increases the likelihood of citations, as each author has their own network and bringing those networks together can increase readership. Similarly, publishing papers with a larger number of references increases paper visibility through citation-based search in databases that allow it, such as Google Scholar, and through the “tit-for-tat” hypothesis i.e. authors tend to cite those who cite them.[5]

Higher education institutions and higher education policy makers may also reflect on the findings, and consider strategic implications for training, resource provision, collaborations, etc. For example, English and scientific/formal writing courses were associated with PD performance; such courses could be prerequisites for starting a PhD research. There could be more academic training designed around research grant writing and providing guidance on funding bodies that researchers can apply to. International research collaborations appeared as an important predictor of PDs; so, university senior managers and policy makers can explore ways to reduce barriers and increase opportunities for overseas PhD study, post-PhD return, and ongoing joint research projects with global North universities.

Citation rates are, of course, not the “be all and end all” of research: there are and should be other motivations and indicators of research. However, we hope the findings presented here can provide valuable “food for thought” for global South researchers.

 ________ 

[1] The terms “South” and “Southern” will be used to refer to countries classified as upper-middle income, lower-middle income, and low income. Accordingly, the terms “North” and “Northern” will be used to refer to countries that are members of the OECD (Organisation for Economic Co-operation and Development) or are classified as high-income economies by the World Bank based on estimates of gross national income per capita.

[2] Blicharska, M., Smithers, R. J., Kuchler, M., Agrawal, G. K., Gutiérrez, J. M., Hassanali, A., Huq, S., Koller, S. H., Marjit, S., Mshinda, H. M., & Masjuki, H. (2017). Steps to overcome the North-South divide in research relevant to climate change policy and practice. Nature Climate Change, 7(1), 21–27.

[3] World Bank. (2020). Science & Technology Indicators. World Bank.

[4] Karlsson, S., Srebotnjak, T., & Gonzales, P. (2007). Understanding the North-South knowledge divide and its implications for policy: A quantitative analysis of the generation of scientific knowledge in the environmental sciences. Environmental Science and Policy, 10(7–8), 668–684.; Gibbs, W. W. (1995). Lost science in the third world. Scientific American, 273(2), 92–99.; Leimu, R., & Koricheva, J. (2005). What determines the citation frequency of ecological papers? Trends in Ecology & Evolution, 20(1), 28–32.

[5] Webster, G. D., Jonason, P. K., & Schember, T. O. (2009). Hot topics and popular papers in evolutionary psychology: Analyses of title words and citation counts in evolution and human behavior, 1979–2008. Evolutionary Psychology, 7(3), 147470490900700300.

 

Understanding smart tourism and smart tourism ecosystems

As a fashionable and novel tourism agenda, the theory of smart tourism is still evolving but the literature brings out three perspectives. First, tourists . The primary starting point of smart tourism is to fully satisfy the tourists’ need for scenic spots and create more value for them. In this sense, smart tourism is seen as a new pattern of tourism operation, which regards the tourist as the basic service object [1][2][3]. Second, managers (e.g., in government and tourism enterprises). Smart tourism is about achieving a comprehensive and thorough system which aims to offer accurate, convenient and ubiquitous tourism information applications, as well as a range of travel services [4]. In this case, the managers refer to the local scenic area managers and staff, government officials, and the company offering the technology  [5]. Third, technology. Although the main point of the smart tourism system is the service, the capabilities and foundation of smart tourism is technology[6]. This refers to the highly systematic, detailed interaction between physical tourism and information resources [7], including digital data exchange[8].

Accompanied by the development of information and communication technologies (ICTs), the evolution of tourism goes through three stages: traditional tourism, e-tourism and smart tourism. Traditional tourism refers to people moving to countries outside their usual environment[9]. With evolution of technology, e-tourism emerged to address users’ interactivity and web-based technology was used to enhance the tourism experience and information governance, which is considered an early step of smart tourism[10].Subsequently, e-tourism evolved into smart tourism, building on technology infrastructure and ICTs (e,g. cloud service, big data). Importantly, smart tourism emphasises explicitly the support of variously smart activities and value-addition through the dynamic interaction between different actors[11]. The difference between e-tourism and smart tourism is detailed in the table below.

  e-Tourism Smart Tourism
Sphere digital bridging digital & physical
Core technology websites sensors & smartphones
Travel phase pre-& post-travel during trip
Lifeblood information big data
Paradigm interactivity technology-mediated co-creation
Structure value chain / intermediaries ecosystem
Exchange B2B, B2C, C2C public-private-consumer collaboration
Table 1 Comparison of e-tourism and smart tourism [12]

Smart tourism systems

Based on the development of ICTs, the smart tourism system (STS) is regarded as a complex system based on a digital service platform to support smart tourism, which addresses the innovation service provided to different stakeholders[7] [9]. Specifically, it emphasizes the actors’ intelligent demands of value-added through information service creation, delivery and exchange[13]. Thereby, the STS is characterized by value co-creation through the integration of sources into both micro and macro levels[3][8]. The table below presents the established system in China based on literature and reality [14][15].

STS Sub-system STS Functionalities STS Instances
Forecasting system Passenger flowWeather forecastQueuing-time forecast Wind speed sensorHumiture sensorImage recognition
Panoramic virtual reality system Virtual tourism experienceVirtual community Panoramic photographyVR 
Intelligent management system Smart vehicle and transportReal-time traffic Crowd handling RFID·        Video surveillanceTourist-flow monitoring 
Smart guide system Scenic spot interpreterPersonalized tours route E/Robot tour map Mobile app Electronic map Voice navigation
Smart recommend system Scenic spot recommendation Recommended route QR codeMobile app
Table 2 Smart tourism systems in China

Figure 1 demonstrates the conceptual model of a non-profit smart tourism system, which is summarized from the current literature[15].The objective is that the STS provides service to government, enterprise, tourists, and residents [16]. Importantly, these applications are not isolated and individual but also service the interaction requirements. For example, from the perspective of tourism, the STS faces the tourist (T), the connection between tourists (T2T), and the interaction needs between the tourist and government (T2G).

Figure 1 A conceptual model of structuring STS adapted from [15]

Smart tourism ecosystem (STE)

When linking the ecosystem with the smart tourism system, an STE can be established, which contains the characteristic of both the smart tourism system and wider ecosystem components. An STE consists of two layers.  The information ecology layer emphasizes the interaction between humans, firms, technology and their environment[9].This indicates the importance of the different actors related to information behaviour and information systems. The service system layer emphasizes the interactions through institutions and technologies to provide services to the beneficiaries, to exchange resources, and to co-create value [12].

Gretzel and Werthner defined the STE as “a tourism system that uses smart technology to create, manage, and provide smart tourism services/ experiences”[17].It is characterized by intensive information sharing and value co-creation. Buhalis and Amaranggana indicated that an STE aims to provide sustainable, enhanced/rich, valuable travel service and experience [6].To reach this, digital ecosystems that provide technical resources and facilitate interactions within and between stakeholders form the core of STE[17]. In other words, the generation of tourism experiences always requires extensive coordination and cooperation between different industry stakeholders and government players [18].

As shown in Figure 2, Gretzel & Werthner proposed a schematic representation of an STE [17].Their study describes an STE as an interactive space supported by a digital ecosystem and containing various types of actors, which are distinguished as tourism consumers (TC), residential consumers (RC), tourism suppliers (TS), other industry suppliers (OS), government agencies, destination marketing organizations (DMO) and intermediaries. These actors are not necessarily discrete, as a single player can play multiple roles. Moreover, this model is also adopted by other researchers like Brandt et al, who proposed an social media analytics (SMA)-enabled STE model, which also indicated the RC, TC, TS and government as vital actors [19]. The tourism consumers (TCs) have resources and, because they have access to the digital ecosystem, can be organized among themselves or mixed with closely related residential consumers (RCs) and act as producers. Through smart technology, tourism providers (TS) or other business-focused groups can connect and create new service offerings. In an ecosystem, the main source of ‘food’ for a ‘species’ is data/information, and the effective conversion of this food into rich tourism experiences can lead to a longer life for the ‘species’. Telecom companies and banking/payment support service providers representing other industry providers (OS) are vital ‘predators’ in the ecosystem and provide essential information to the system. Destination marketing organizations (DMOs) perform traditional information brokering, marketing, and quality control functions, while various intermediaries facilitate transactions through innovative data and devices [9]. Government utilization of social media in the STE could enhance the development and the value co-creation of local tourism [12].

Figure 2 Smart tourism ecosystem [17]

To conclude, STE studies regard the STE as a complex information and service ecosystem, which involves multiple actors like government, tourists, platform providers, residents etc. The main characteristics of STE are 1) the dynamic interactions between multiple actors; 2) the value co-creation during the actors’ interactions; 2) sustainable development; 4) the creation and exchange of tourism resources; 5) the innovation service. Thus, STEs could facilitate the interaction between actors, the exchange and creation of tourism resources, and value creation. This could further improve the tourism experiences and enhance the sustainable development of smart tourism. 

References

[1] Yao, G. (2012). Analysis of smart tourism construction framework. Nanjing University of Posts and Telecommunications (The Social Sciences Edition), 14(2), 5e9.

[2] Fu, Y., & Zheng, X. (2013). China smart tourism development status and counter- measures. Development Research, 4, 62e65.

[8] Hunter, W. C., Chung, N., Gretzel, U., & Koo, C. (2015). Constructivist research in smart tourism. Asia Pacific Journal of Information Systems, 25(1), 105–120.

[3] Shafiee, S., Ghatari, A. R., Hasanzadeh, A., & Jahanyan, S. (2019). Developing a model for sustainable smart tourism destinations: A systematic review. Tourism Management Perspectives, 31, 287-300.

[4] Jin, W. (2012). Smart tourism and the construction of tourism public service system. Tourism Tribune, 27(2), 5e6.

[5] Huang, C., Goo, J., Nam, K., & Yoo, C. (2016). Smart tourism technologies in travel planning: The role of exploration and exploitation. Information & Management, 54(6), 757-770. doi: 10.1016/j.im.2016.11.010

[6] Buhalis, D., & Amaranggana, A. (2013). Smart tourism destinations. In Z. Xiang, & L. Tussyadiah (Eds.), Information and communication technologies in tourism 2014 (pp. 553e564). Cham, New York: Springer.

[7] Law, R., Buhalis, D., & Cobanoglu, C. (2014). Progress on information and communication technologies in hospitality and tourism. International Journal of Contemporary Hospitality Management, 26(5), 727–750.

[9] Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179-188.

[10] Werthner, H., & Ricci, F. (2004). E-commerce and tourism. Communications of the ACM, 47(12), 101-105.

[11] Lamsfus, C., Martín, D., Alzua-Sorzabal, A., & Torres-Manzanera, E. (2015). Smart tourism destinations: An extended conception of smart cities focusing on human mobility. In information and communication technologies in tourism 2015 (pp. 363-375). Springer, Cham.

[12]Park, J. H., Lee, C., Yoo, C., & Nam, Y. (2016). An analysis of the utilization of Facebook by local Korean governments for tourism development and the network of smart tourism ecosystem. International Journal of Information Management, 36(6), 1320-1327.

[13] Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: lessons from tourism and hospitality. Journal of Service Management. 

[14] Zhu, W., Zhang, L., & Li, N. (2014). Challenges, function changing of government and enterprises in Chinese smart tourism. Information and Communication Technologies in Tourism, 10.

[15] Wang, X., Li, X. R., Zhen, F., & Zhang, J. (2016). How smart is your tourist attraction?: Measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach. Tourism Management, 54, 309-320. 

[16] Zhang, L., Li, N., & Liu, M. (2012). On the basic concept of smarter tourism and its theoretical system. Tourism Tribune, 27(5), 66–73.

[17]Gretzel, U., Werthner, H., Koo, C., & Lamsfus, C. (2015). Conceptual foundations for understanding smart tourism smart tourism ecosystems. Computers in Human Behavior, 50, 558-563.

[18]Mill, R. C., & Morrison, A. M. (2002). The tourism system. Kendall Hunt

[19]Brandt, T., Bendler, J., & Neumann, D. (2017). Social media analytics and value creation in urban smart tourism  ecosystems. Information & Management, 54(6), 703-713