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Measuring the Big Data Knowledge Divide Using Wikipedia

Big data is of increasing importance; yet – like all digital technologies – it is affected by a digital divide of multiple dimensions. We set out to understand one dimension: the big data ‘knowledge divide’; meaning the way in which different groups have different levels of knowledge about big data [1,2].

To do this, we analysed Wikipedia – as a global repository of knowledge – and asked: how does people’s knowledge of big data differ by language?

An exploratory analysis of Wikipedia to understand the knowledge divide looked at differences across ten languages in production and consumption of the specific Wikipedia article entitled ‘Big Data’ in each of the languages. The figure below shows initial results:

  • The Knowledge-Awareness Indicator (KAI) measures the total number of views of the ‘Big Data’ article divided by total number of views of all articles for each language (multiplied by 100,000 to produce an easier-to-grasp number). This relates specifically to the time period 1 February – 30 April 2018.
  • ‘Total Articles’ measures the overall number of articles on all topics that were available for each language at the end of April 2018, to give a sense of the volume of language-specific material available on Wikipedia.

‘Big Data’ article knowledge-awareness, top-ten languages*

ko=Korean; zh=Chinese; fr=French; pt=Portuguese; es=Spanish; de=German; it=Italian; ru=Russian; en=English; ja=Japanese.
Note: Data analysed for 46 languages, 1 February to 30 April 2018.
* Figure shows the top-ten languages with the most views of the ‘Big Data’ article in this period.
Source: Author using data from the Wikimedia Toolforge team [3]

 

Production. Considering that Wikipedia is built as a collaborative project, the production of content and its evolution can be used as a proxy for knowledge. A divide relating to the creation of content for the ‘Big Data’ article can be measured using two indicators. First, article size in bytes: longer articles would tend to represent the curation of more knowledge. Second, number of edits: seen as representing the pace at which knowledge is changing. Larger article size and higher number of edits may allow readers to have greater and more current knowledge about big data. On this basis, we see English far ahead of other languages: articles are significantly longer and significantly more edited.

Consumption. The KAI provides a measure of the level of relative interest in accessing the ‘Big Data’ article which will also relate to level of awareness of big data. Where English was the production outlier, Korean and to a lesser extent Chinese are the consumption outliers: there appears to be significantly more relative accessing of the article on ‘Big Data’ in those languages than in others. This suggests a greater interest in and awareness of big data among readers using those languages. Assuming that accessed articles are read and understood, the KAI might also be a proxy for the readers’ level of knowledge about big data.

We can draw two types of conclusion from this work.

First, and addressing the specific research question, we see important differences between language groups; reflecting an important knowledge divide around big data. On the production side, much more is being written and updated in English about big data than in other languages; potentially hampering non-English speakers from engaging with big data; at least in relative terms. This suggests value in encouraging not just more non-English Wikipedia writing on big data, but also non-English research (and/or translation of English research) given research feeds Wikipedia writing. This value may be especially notable in relation to East Asian languages given that, on the consumption side, we found much greater relative interest and awareness of big data among Wikipedia readers.

Second, and methodologically, we can see the value of using Wikipedia to analyse knowledge divide questions. It provides a reliable source of openly-accessible, large-scale data that can be used to generate indicators that are replicable and stable over time.

This research project will continue exploring the use of Wikipedia at the country level to measure and understand the digital divide in the production and consumption of knowledge, focusing specifically on materials in Spanish.

References

[1] Andrejevic, M. (2014). ‘Big Data, Big Questions |The Big Data Divide.’ International Journal of Communication, 8.

[2] Michael, M., & Lupton, D. (2015). ‘Toward a Manifesto for the “Public Understanding of Big Data”.’ Public Understanding of Science, 25(1), 104–116. doi: 10.1177/0963662515609005

[3] Wikimedia Toolforge (2018). Available at: https://tools.wmflabs.org/

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Social Media Analytics for Better Understanding of the Digital Gig Economy

27 April 2018 1 comment

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

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

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

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

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

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

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

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

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

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

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

References:

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

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

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

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

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

23 April 2018 1 comment

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

What is Industry 4.0?

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

industry 4.0 timeline2

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

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

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

Shifting focus from manufacturing to “digital industrialisation”

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

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

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

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

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


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

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

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

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

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

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

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

Why is it Important to Know about the Origins of ICT4D Champions?

At the most recent gathering of information and communication technology for development (ICT4D) scholars in the North of England I asked how important and significant it is to research the origins of champions and leaders in international development. Understanding the origins of key development actors appeared to be a pressing matter to me when researching ICT4D champions[i] in South Africa: it seemed unlikely that one could proactively identify, deploy, develop and support such individuals without understanding their genesis. However, I felt uncertain about the extent research colleagues share this sense of significance. ICT4D North (of England) provided a great opportunity to ask colleagues for their opinions about this issue.

Those attending my 10 minute talk were requested to consider how significant they think understanding the factors contributing to someone becoming an ICT4D champion are and to indicate their assessment on a scale of 1 to 10 before I shared my arguments.

I continued to reason that understanding the origins of ICT4D champion is very significant based on the following three perspectives:

A conceptual perspective on the nature of development

Champions and leaders are included within development conceptualisations around agency – the capacity of individual actors to act on behalf of themselves or others towards increased well-being and development.

Consider the recent Information Technology for Development journal special issue, ‘Conceptualizing Development in ICT4D’: all seven contributions – one way or the other – included an emphasis on agency in their framing of development. Authors of three papers in the special issue were present at ICT4D North, so this observation was illuminated from those sources:

1) Jimenez and Zheng examined the relationship between innovation and development and argued for the importance of the individual’s agency therein, hence a human-development perspective;

2) Poveda and Roberts argued for the importance of agency to challenge structural root causes of unjust social norms, hence a social justice perspective on development through critical-agency;

3) Ismail et al. framed development from an institutional perspective and, amongst other things, showed the importance of spokespersons – lead agents – for the marginalised in impact sourcing initiatives.

As such, understanding the various roles of actors – including leaders such as champions and others – and the factors that shape their agency, cannot be omitted from a development research agenda, because it is so central to our understanding of the concept ‘development’.

A perspective on international development project performance

International aid and development initiatives are most often delivered by means of projects. Unfortunately these projects have an uneven success track record. Tangible, broad-based evidence is elusive but to illustrate, the World Bank Independent Evaluation Group project performance ratings[ii] indicated a failure rate of 50% until 2000 and 39% to 2010.

ICT4D projects perform no better, with less than 20% of initiatives considered successful and as many as a third being outright failures[iii]. Interestingly, from a cross-cutting analysis of critical success factors for information systems projects[iv] it was found that the top three most prevalent drivers of success were people factors – issues such as facilitation of participation, sponsorship and competence building; these are all leadership-related aspects.

How significant are leaders and leadership in mainstream development discourses? To get a clearer sense of this I examined World Development – the largest and most impactful development studies journal[v]. On average, only one article that empirically examines leaders and leadership in development was published annually over the last 20 years. This seems disproportionate to the acknowledged importance of key individuals in development practice and is inadequate to progressively build knowledge in this area. Understanding the role and nature of leading actors – such as champions and other leaders – is critically important in order to succeed with development projects, yet inadequately addressed through development research, including a better grasp of their roots.

A champion-specific perspective

The literature on the origins of champions is somewhat of an enigma. We analysed a core set of systematically selected research papers about champions of information systems innovations[vi] to gain insight into current perspectives. This analysis revealed four prevailing concepts of champion origins from which two axes can be derived:

  • Born vs. made: some authors argue that becoming a champion is the result of an innate predisposition. While context and external interventions may impact the likelihood that this predisposition is expressed in champion behaviour, it does not alter that predisposition. The key task for organisations, therefore, is identification of those who have a champion’s profile. Others argue that (almost) anyone can become a champion through appropriate development and training: these, rather than profiling exercises, thus become the focus of organisational intervention.
  • Emergent vs. appointed: some authors see champions as naturally emerging within any project or situation of innovation. These individuals take an interest in a particular cause and then begin to champion it. Organisations may affect this via general contextual interventions, but they would not get directly involved at the level of the individual. Others argue that one needs to plan the presence of champions: individuals must be identified, sometimes explicitly assigned the role of champion, before championing can begin.

My empirical research of ICT4D champions establishes that the factors leading to someone becoming a champion extend beyond these. Origins are affected by a mix of contingency factors – environmental factors, social networks, personal characteristics, organisational factors, skills and education, and personal experiences – that influence them over a longitudinal time period, during which a trigger – an opportunity, experience, or a new technology – catalyses a person into actively championing a specific cause, innovation or ICT4D initiative. So, the inadequacy of our current understanding provides the impetus to further explore the role and nature of key agents – leaders such as champions and others – in ICT4D projects, including a better grasp of their genesis.

In sum, I argue, from these three perspectives, that our current understanding of leading actors – such as ICT4D champions – is inadequate. Considering these issues around agency in development, it is contended that current champions in ICT4D are incidental, because we have insufficient understanding of the origins of champions. Better understanding of the origins of champions is significant because it is the necessary first step to proactively identify, develop, deploy and support such individuals in our initiatives. Ultimately this could lead to more successful development in practice.

ICT4D North participants were then asked to revisit their initial assessment after I shared my arguments, thereby examining the persuasiveness of my narratives. Here are some of the responses:

Post Its

Here are lessons I’ve learned from their feedback:

  • The conceptual links between agency, leaders and champions should be further developed and clarified.
  • The notion of ‘origins of a champion’ invokes a connotation of ‘place of origin’ as opposed to a more holistic interest in all the factors that play a role in a champion’s formation[i].
  • It was encouraging to see an upwards trajectory in participants’ perceptions about the significance of the topic after considering my arguments. This should be strengthened in future work by attending to the lessons learned here.

Huge thanks to all participants who attended the ICT4D North (of England) second annual workshop hosted by the University of Sheffield!

[i] For an introduction to what we have learned so far about ICT4D champion origins see: Renken, J. C. & Heeks, R. B. (2017). A Conceptual Framework of ICT4D Champion Origins. 14th International Conference on Social Implications of Computers in Developing Countries (IFIP WG 9.4). Yogyakarta, Indonesia.

[i] For a definition of ICT4D champions see: Renken, J. C. & Heeks, R. B. (2013). Conceptualising ICT4D Project Champions. The 6th International Conference on Information and Communications Technologies and Development. Cape Town, South Africa.

[ii] World Bank Independent Evaluation Group. (2015). IEG World Bank project performance ratings. Washington, DC: The World Bank

[iii] Heeks, R. (2003). Most E-government-for-Development Projects Fail : How can Risks be Reduced? Manchester: Institute for Development Policy and Management.

[iv] Broader in scope than ICT4D projects by including developed country data, see: Irvine, R. & Hall, H. (2015). Factors, Frameworks and Theory: A Review of the Information Systems Literature on Success Factors in Project Management. Information Research, 20(3), 1-46.

[v] Based on: McKenzie, D. (2017). ‘The State of Development Journals 2017: Quality, Acceptance Rates, and Review Times’, Development Impact, 21 February 2017, Available at: https://blogs.worldbank.org/impactevaluations/state-development-journals-2017-quality-acceptance-rates-and-review-times

[vi] Renken, J. C. & Heeks, R. B. (2014). Champions of Information System Innovations: Thematic Analysis and Future Research Agenda. UK Academy for Information Systems (UKAIS) International Conference. Oxford, UK.

 

Big Data and Urban Transportation in India

12 February 2018 Leave a comment

What effect are big data systems having on urban transportation?

To investigate this, the Centre for Internet and Society was commissioned by the Universities of Manchester and Sheffield, to conduct a study of the big data system recently implemented by the Bengaluru Metropolitan Transport Corporation (BMTC).  The “Intelligent Transport System” (ITS) took three years to reach initial operational status in 2016, and now covers the more than five million daily passenger journeys undertaken on BMTC’s 6,400 buses.

ITS (see figure below) processes many gigabytes of data per day via three main components: vehicle tracking units that continuously transmit bus locations using the mobile cell network; online electronic ticketing machines that capture details of all ticketing transactions; and a passenger information system with linked mobile app to provide details such as bus locations, routes and arrival times.

ITS Architecture (Mishra 2016)[1]

At the operational level the system is functioning moderately well: the data capture and transmission components mainly work though with some malfunctions; and the passenger-facing components are present but have data and functionality challenges that still need to be fully worked-through.  Higher-level use of big data for tactical and strategic decision-making – optimising routes, reducing staff numbers, increasing operational efficiency – is intended, but not yet evidenced.

Just over a year since full roll-out, this is not unexpected but it is a reminder that big data systems take many years to implement: in this case, at least four years to get the operational functions working, and years more to integrate big data into managerial decision-making.

Nonetheless some broader impacts can already be seen.  Big data has changed the mental model – the “imaginary” – that managers and politicians have of bus transport in Bengaluru.  Where daily operations of the bus fleet and bus crews were largely opaque to management prior to ITS, now they are increasingly visible.  Big data is thus changing the landscape of what is seen to be possible within the organisation, and has already resulted in plans for driver-only buses, and a restructuring that is removing middle management from the organisation: a layer no longer required when big data puts central management in direct contact with the operational front line.

Big data is also leading to shifts in power.  Some of these are tentative: a greater transparency of operations to the general public and civil society that may receive a step change once ITS data is openly shared.  Others are more concrete: big data is shifting power upwards in the organisation – away from front-line labour, and away from middle managers towards those in central management who have the capabilities to control and use the new data streams.

For further details of this study, see Development Informatics working paper no.72: “Big Data and Urban Transportation in India: A Bengaluru Bus Corporation Case Study”.

[1] Mishra, B. (2016) Intelligent Transport System (ITS), presentation at workshop on Smart Mobility for Bengaluru, Bengaluru, 10 Jun https://www.slideshare.net/EMBARQNetwork/bmtc-intelligent-transport-system

Can Proximity WiFi Networks Offer An Alternative Distribution Channel To Publishers?

Facebook’s changing its algorithm, again. For news publishers it exposes their vulnerability of relying on a platform that will always put its own interests first. So the scramble goes on to reach audiences in meaningful ways.

It draws the focus to how alternative, decentralised technology infrastructure can challenge these powerhouse gatekeepers of the Internet, offering communities alternative ways to create and share stories about their local life. In the growing trend of multimedia and mobile content consumption what digital tools make local, relevant and valuable information accessible and ‘findable’ to audiences that would otherwise be drowned out by the noise of the Internet?

Field trials of lightweight community connectivity systems go some way to evidencing a different approach that fit with the UN’s sustainable cities and communities goal. Rather than relying on Google and Facebook as distribution platforms, decentralised proximity technology serves news and information in hyperlocality. The technical determiner of news relevance is no longer an Internet giant’s algorithm, but rather hyperlocal place.

WiFi-based distribution systems offer accessible networks without the need for applications. In a prototype deployed in three remote villages of Armenia, online content from seven news providers was adapted as static pages served and distributed as offline content in hyperlocal locations such as bus stops, health centres or village buildings. It allows content to be tailored to place. The decentralised typology allows publishers to pinpoint to the nearest few meters what content is consumed where by who: invaluable information in the battle to find a viable business model. This precision of news data analytics is not available to publishers via the Internet giants, particularly not in rural, or restricted environments such as Armenia. Moreover when fully operational, one device can connect to the Internet to receive real-time content and then distribute amongst other devices offline when the network is in mesh formation thus acting as a solution to information access where Internet connectivity is problematic, limited or costly.

Distributing content in this way overcomes a central problem: many people want to find information relevant to where they are and who is close to them via their mobile phone – what Facebook would describe as ‘meaningful interactions‘.  It also needs exploring as an alternative for the millions of people worldwide without reliable Internet connectivity. This type of infrastructure has the added benefit of easily adding tools for community storytelling. Users are encouraged to write and share stories about their own life, via for example a WordPress blog, thus emerging their own habits and preferences about what content they want to consume where, even to some extent shaping a place-based news agenda.

However, even in rural communities, where most users have mobile phones but cannot afford a data connection, the tendency is to rely on social networks such as Facebook or in the Russian territories Odnoklassniki. They understand and expect distribution that’s always new, always instant, always two-way. An offline decentralised mobile-ready alternative needs much explaining, and technical support to keep it operational in outdoor environments.

The impact of location-based services within the journalistic context has only been addressed in a handful of studies to date. Nyre et al. (2012) developed a location-based news project called LocaNews that found it difficult to write for location. Schmitz Weiss (2013) found that newsrooms use place-based mobile applications mainly for traffic or weather reports while young adults were consuming news on their smartphones and there was a high use of location-based services including consumption of local news content through such services. Thus pointing to a gap between what news consumers are doing and using on their smartphones and what news organizations are able to provide when it comes to geo-located news content.

A step change is needed to emerge meaningful user-friendly and efficient alternatives that map to user demand. More projects future scoping information systems that can disrupt news disruption are needed. These need to explore:

  • What comparisons can be drawn between the effectiveness of web applications, beacons, WiFi typologies, geofencing, narrowcasting, Near Field Communication, Global Positioning Systems, Radio Frequency Identification and more to explore new mix-and-match models of hyperlocal distribution.
  • How can users be inspired to engage with offline mesh formations perhaps via cost sharing, community engagement or other publishing benefits
  • To understand content popularity based on place. While there is some understanding to what is consumed where there is little work to unpick why. An ability to understand why content is more popular based on location would further enable news providers to provide better experiences.
  • The availability of news data consumption analytics in hyperlocal place for further opportunities to understand and match instantly what is needed at location: a new model to be explored at scale.
  • Relevant revenue opportunities such as digital placemaking, active citizen nudges paid on commission, conversion of passing footfall, and  cost-saving benefits for communities. Further insights into revenue possibilities for publishers in politically pressured environments would be of particular value.

Guest post by Clare Cook @cecook co-founder of the Media Innovation Studio

For further details please refer to the project report for CAST – Discovery Amplification Sustainability and Interactions, a WiFi proximity broadcasting prototype deployed in three remote villages of Armenia.

Do Outsourcing Clients Want Decent Digital Work?

22 December 2017 Leave a comment

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

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

So, what evidence can be found?

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

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

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

Key findings include the following:

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

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

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

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

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

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

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