Mobile media and ecologies of location

Abstract

This paper examines the ongoing significance of locative media and mobile-generated geocoded data, including their increasing integration into the core functionalities and business objectives of large social media and search services. In this article, I take a ‘communicative ecologies’ approach to explore how location-based services function as a dynamic system, with a fluid and shifting structure or set of relations. The evolution of the mobile social networking and search and recommendation service Foursquare provides a striking example of such a system. In the first half of the paper, I explore the company’s still-evolving business model, and their intricate corporate relationships with other key search, recommendation, and social media firms. In the second half, I trace how this dynamic engagement is also at play in the end uses of Foursquare and other location applications.

Keywords:

• locative media
• mobile media
• Foursquare
• communicative ecologies

Introduction

In one memorable interview moment from the project this article will go on to discuss, a participant reflected on her use of the location-sharing mobile phone app, Foursquare, by declaring, ‘I am Captain James Cook’. The implication was that this particular smartphone app was the vessel, if you like, supporting her own urban explorations. The historical comparison is an interesting one. Much, of course, has been said about Cook as an explorer in search of Terra Australis Incognita – the ‘unknown southern land’ (Edmond, 1997, p. 6). What is less often remarked on, but is revealing in the context of the discussion to follow, is the issue of who supported and profited from his voyages of discovery. The answer to both questions, of course, was the British Government and the British Empire. In the present context, then, if the Foursquare app (which is to be described shortly) is the ship Endeavour supporting this particular user’s urban explorations, the sponsor and major beneficiary is the parent company, Foursquare Labs Inc., with the information gathered through these ‘explorations’ facilitating the expansionist aims of Foursquare to become, in CEO Dennis Crowley’s words, ‘the location layer of the internet’ (Olanoff, 2013).

The approach adopted in this article is to examine a specific case concerning, on the one hand, the history and evolving business and revenue model of Foursquare, which, on the other hand, is then read against fieldwork examining individual end user engagement with Foursquare. That is to say, the article seeks to examine the ongoing significance of locative media and mobile-generated geocoded data, including their increasing integration into the core functionalities and business objectives of large social media and search services, while at the same time giving attention to the complexities and contradictions of end use, which can be seen to both validate corporate changes in strategy as well as complicate these business ambitions.

In examining Foursquare, I draw on the work of David Altheide (1995) and adopt a ‘communicative ecology’ approach. This is taken to be a productive framework for grasping, in Altheide’s (1995, p. 2) words, ‘how social activities are joined interactively in a communication environment with information technology’. Key to this approach is an acknowledgment that an ecosystem ‘does not exist as a thing, but is a fluid structure’ (Altheide, 1995, p. 11).

The communicative ecology framework is also productive in that it focuses attention not just on more immediate communication-related aspects of the contexts in which people operate, but also on the ways that they are, in Jo Tacchi’s (2006) words, ‘in turn imbricated in other structural, social, economic and cultural contexts’. For José van Dijck (2013), the communicative ecology approach is especially helpful in taking seriously the way specific new media platforms operate and their place within larger communicative ecosystems – that is, how they interconnect, how users engage with them, and so on. As van Dijck explains:

By taking apart single platforms into their constitutive components, we may combine the perspectives on platforms as techno-cultural constructs and as organized socioeconomic structures. But disassembling platforms is not enough: we also need to reassemble the ecosystem of interoperating platforms in order to recognize which norms and mechanisms undergird the construction of sociality and creativity. (Van Dijck, 2013, p. 25)

In this investigation of locative media platforms, emphasis is given to the dynamic nature of these businesses, the fluid and shifting structure or set of relations that characterise them, and the equally dynamic nature of individual and social engagement with them. The evolution of Foursquare provides a striking example of the precarious existence of locative media companies within such a complicated and dynamic communicative ecosystem. In the first half of the article, the company’s still-evolving business model is explored, as is their intricate corporate relationships with other key search, recommendation, and social media firms. In the second half, the intricacies and complications that attend end-use of Foursquare and other location applications are examined.

The business of foursquare

Foursquare, as noted, is a location-based mobile social networking and, more recently, search and recommendations service. It rose from the ashes of Dodgeball, the pioneering mobile service that New Yorkers Dennis Crowley and Alex Rainert created in 2000 and subsequently sold to Google in 2005. Foursquare was founded in 2009 by Crowley and Naveen Selvadurai (with Rainert joining soon after), and has grown to become a key player in the area of location-based mobile social networking and local search.

What set Foursquare apart from its competitors when it first launched, and was of particular appeal to its early adopter heavy users, was the emphasis it gave to its various gameplay elements, where each Foursquare user collected badges for venue check-ins, competed with friends over a check-in leader board, and with other users to become ‘mayor’ of venues. Each of these gameplay aspects is explained briefly in turn.

First, individual users could collect a variety of merit-style badges. Often whimsically titled, these were scaled to reward various progressive levels or stages of user engagement. So, for example, new users could achieve the ‘Newbie’ badge before progressing, following heavy enough check-in use over a given time, to unlocking the ‘Super User’ badge, or the ‘Swarm’ badge when a check-in was received in close temporal proximity to those of over 50 other fellow Foursquare users. In late 2011, Foursquare also introduced scaled achievement levels within each badge (as well as a small suite of additional badges) so as to, in their words, reward venue exploration and help show individual user ‘expertise’ (Level up, 2011). This meant, for instance, that an occasional café-goer might achieve level 2 of the ‘Fresh Brew’ badge, whereas a café-frequenting coffee aficionado might achieve level 10 of the same badge. As far as Foursquare is concerned, the second user is of far greater interest in terms of the check-in information and recommendations data they contribute to the service’s metrics.

Second, the app featured a dynamic table that mapped, in the form of a constantly updating leader board, who, in a given user’s social network, is achieving the most check-in points over a seven-day period. The aim is to encourage playful competition between members of a user’s social network and, presumably, drive up the number of total venue check-ins. Some venues also offer discounts and other deals for check-ins.

Third, Foursquare encouraged users to compete with each other to become ‘Mayor’. This is the honorary title given to an individual user who has checked in most frequently to the same venue over a 60-day period.

So successful was Foursquare’s gamification integration that it was rapidly replicated by other competing services. For instance, soon after Foursquare launched, Yelp introduced a ‘royalty’ system of its own. Rather than become ‘Mayor’ of a venue, Yelpers were competing to become Duke/Duchess (most check-ins to a venue), Baron/Baroness (most titles in a neighbourhood), and King/Queen (most in a city) (Siegler, 2010). Within the tech sector, numerous other companies have tried to follow Foursquare’s (and Yelp’s) lead, with Facebook, the Google-owned crowd-sourced traffic information service Waze, language-learning platform Duolingo, communication app Line, and numerous others, all incorporating game elements into their operations (Mishra, 2014).

Once the darling of US tech start-up scene, Foursquare has, however, faced a much tougher time in the few years that immediately followed its launch. In early 2011, the company made a much-publicised strategic shift in direction that has taken it away from its prior emphasis as a location-based mobile social networking app driven by game dynamics. This is a decision generally regarded as a response to persistent questions the company has faced by industry analysts questioning the long-term sustainability of its business (Isaac, 2013). It is also a result of reported slowing in user growth, including in emerging markets outside of the US (Evans, 2013).

Faced with these challenges, the company radically rethought its corporate strategy, specifically by redesigning the application itself and the services it offers end-users, and by further honing its still nascent business plan, including by building services to cater for business. The result is that Foursquare has turned away from leader boards, badges, and points, and is now focused instead on local search, discovery, and recommendations. Indeed, as of mid 2014, Foursquare Labs Inc. unbundled its operations: it spun-off the gameplay/check-in aspects of its service into a new app called Swarm (where, even here, the gameplay elements are being squeezed out in recent updates); and, the original Foursquare app has been redesigned as a dedicated search and recommendation service. As Foursquare’s former head of business development, Holger Luedorf, put it at the time, ‘we’re positioning ourselves as the location layer of the Internet’ (quoted in Panzarino, 2014).

The company’s plans, as Crowley describes them, are to ‘get most of [their] future sales from software that helps merchants track the behavior of potential customers’ (Crowley quoted in Chang & MacMillan, 2011). Foursquare already collects some revenue through strategic partnerships with competitors and a variety of companies – most significantly, a USD$15 million partnership with Microsoft that also sees the software giant making ‘substantial’ additional regular payments to Foursquare for access to its proprietary location data (Tate, 2014).

Foursquare’s merchant platforms, however, are quite different in that they encourage businesses to pay for help in analyzing the data generated by users through Foursquare’s service (Chang & MacMillan, 2011). In 2012, Foursquare also launched ‘promoted updates’, a service that allows its business clients to send advertising messages to users who are in the vicinity of a restaurant or other venue (Kelly, 2012). A further merchant feature was the Foursquare for Business app (Foursquare Blog, 2013a; Isaac, 2013). Launched in early 2013, it allows businesses to offer deals when users check-in, as well as send messages to regulars (Isaac, 2012). Additionally, by October 2013, the company had also opened up Foursquare Ads to small businesses around the world (Foursquare Blog, 2013b), and, a few months later, had partnered with ad tech company Turn to deliver ads to its users on desktop computers, tablets, and mobiles (Delo, 2014).

Key to the success of these corporate-focused initiatives was a major redesign of the actual Foursquare app around the ‘Explore’ feature. In essence, Explore is a recommendations and ratings system that utilizes a series of metrics drawn from each user and their social network history, including tips, likes, dislikes, popularity, local expertise, and so on (Kerr, 2012). This information is then targeted to that user (in Foursquare’s words) in the form of ‘recommendations for places that you would probably like to visit based on your profile and check-in history’ (Goldman, 2012). In a second development for its end-users, in late May 2013, Foursquare added what it called ‘super-specific search’ to Explore (Welch, 2013). This applies a range of filters to search results that combine common queries (such as price, opening hours, and so on), with additional information drawn from check-ins and user data. By September 2013, restaurant menu search capabilities had also been added (Sterling, 2013).

Foursquare’s ambitions for Explore extend beyond the compilation of location information of this sort, to also combine mobile, social, and location-based interactions with past and present user data to generate real-time and even predictive recommendations. To achieve this, Foursquare’s chief data scientist, Blake Shaw, explains how the company’s engineers work with and combine two interconnected data sets: social data (Foursquare’s ‘social graph’), and location-related data (their ‘places graph’). Both ‘graphs’ are composed of ‘nodes’ (things – people and places) and ‘edges’ (the connections between these things) (Shaw, 2012). Within its social graph, nodes are Foursquare users, and the edges are the connections linking these users to each other, including as a result of friendships, follows, ‘dones’ (tips posted by one user that other users then do), comments left by users, and the ‘co-location of people [who are on Foursquare] in the same physical space’ (Shaw, 2012). In the places graph, the nodes are places that are registered in its points of interest database, and the edges are comprised of a variety of different things: flow (‘how often people move from one place to another’); co-visitation (‘how many people have been to the same place before’); categories (the sorting of venues based on similarities between them); and menus, tips and shouts (which are described as data ‘which connects places because they share the same characteristics’) (Shaw, 2012).

Foursquare’s intention is to combine these datasets and develop from them responses to queries generated via the Explore feature. These responses are created in order to produce for users ‘realtime recommendations from signals [that combine] location, time of day, check-in history, friends’ preferences, and venue similarities’ (Shaw, 2012). The larger ambition of Foursquare’s engineers is to better understand the points of intersection between these two graphs. As Shaw (2012) asks, ‘What are the underlying properties and dynamics of these networks? How can we predict new connections? How do we measure influence? Can we infer real-world social networks?’

Foursquare’s ability to answer these questions will depend in large measure on the ongoing population of its places database. And, the company has taken a number of steps to try and facilitate continued and expanding supply of geolocational information flowing into its places database. Targeting their end users, Foursquare introduced at least three subtle adjustments to their mobile interface that aim to encourage ‘frictionless location sharing’. Firstly, there was the quiet removal of the ability for users of its iOS version to check in privately; in doing this, Foursquare ensured, in the words of one tech commentator, that ‘check-in data is accessible to users of the product, its API partners, and any possible suitors for acquisition’ (Panzarino, 2013). Secondly, for its iPhone app, a series of unobtrusive venue-related user feedback questions was introduced (Foursquare filling, 2013); these pop-up after one checks in to a location, and prompt users to respond to such questions as, ‘is it quiet here?’, ‘would you grab a quick bite to eat at this venue?’, ‘does it have Wi-Fi?’, and so on – feedback that enables the company to further populate its places database with crowd-sourced ‘rich’ venue data. Thirdly, Foursquare has also sought to position Explore as a ‘passive venue search system’ (Shaw, Shea, Sinha, & Hogue, 2012), and they encouraged iOS users to activate push notifications on their phones for nearby venue recommendations in order to move away from the prior reliance on users being the ones to initiate interaction with the service. These tweaks to Foursquare’s end-user interface of its mobile application to encourage greater venue and location sharing all form part of Foursquare’s larger ambitions to build a predictive mobile search and recommendation service.

Importantly, though, end-use of the company’s revamped flagship Foursquare application, and its spin-off Swarm application, no longer form the main source of geolocational data that Foursquare Labs Inc. extracts in order to build its places database. Rather, the rich user information in its database is populated increasingly by applications and platforms that access Foursquare’s location information via its application programming interfaces (APIs), including GPS-based navigation service Waze (owned by Google), short-form video sharing app Vine (owned by Twitter), visual discovery, collection, and storage site Pinterest, and mobile photo-sharing and social media service Instagram (owned by Facebook), among others. The Foursquare places APIs thus serve as key gateways to the platform’s ‘audience traffic’ – and these APIs are accessible to around 40,000 third-party developers (Barouch, 2013). As Jordan Frith (2015, p. 105) points out, ‘the huge database of location names and addresses’ that Foursquare has built up ‘has become an essential part of the location-based application ecosystem’. Indeed, Barouch (2013) argues that, such is the richness and importance now of Foursquare’s places database, ‘any destabilization of Foursquare or its developer tools would fundamentally affect the stability of the mobile web’. It is the richness of this geocoded ‘audience traffic’ that forms the company’s ‘core, saleable asset’ (Van Couvering, 2011, p. 198).

According to Carlos Barreneche, it is the further, interpretative work that is undertaken by Foursquare in making sense of its audience traffic and in developing sophisticated forms of ‘geodemographic profiling’ that is equally significant. Combining careful reading of patent documents and the technical language and tools of computer science (k-means algorithms, nearest neighbour rules, points-of-interest databases, and so on), Barreneche details how companies like Foursquare employ data mining and aggregation practices that utilise ‘records of location trails [and past check-ins] to produce the socio-spatial patterns that make up the segmentations that enable inferences about users’ identity and behaviour’ (Barreneche, 2012a, p. 339; see also; Barreneche, 2015). In this way, to adapt Mark Andrejevic’s (2007, p. 296) words, Foursquare is developing a portrait of ‘user activity made possible by ubiquitous interactivity [… that is] increasingly detailed and fine-grained, thanks to an unprecedented ability to capture and store patterns of interaction, movement, transaction, and communication’. Andrejevic refers to these processes as forms of ‘digital enclosure’. The aim, of course, is to exploit this data resource for commercial gain and to provide an environment in which this can occur and flourish.

And, yet, Foursquare’s cross-platform partnerships (with the likes of Waze, Vine, Pinterest, and others), and its maintenance of an ‘open’ API, potentially complicate this notion of a digital enclosure. This is for two reasons. Firstly, the fact that over 40,000 developers access Foursquare data via its places APIs suggests that an expanded sphere of corporate influence is perhaps a more accurate description of what is occurring via these tools than is captured by the term ‘enclosure’. As Taina Bucher (2013) points out, by offering third-party developers a way to access parts of a company’s data, ‘APIs have also become a useful way for these companies to extend their reach and growth across the Web’. Secondly, as API usage tends to generate more traffic than end use does (Bucher, 2013), in a very real sense the data that Foursquare draws on to populate its places database is, to use Mark Coté’s term, increasingly ‘motile’. That is to say, these data are in continual movement: they are generated through an array of location-based social media platforms (not just Foursquare), are stored and shifted across multiple proprietary servers, and, increasingly, move outside of end-user control (Coté, 2014, p. 123). Such cross-platform corporate deals and data access and sharing arrangements raise significant methodological, privacy, and other critical concerns that shall be returned to in the final section of this article. However, prior to examining these larger implications, and before turning to a consideration of end-use below, I wish to conclude this discussion of Foursquare’s monetisation strategies by reflecting on the business implications for the company of these cross-platform deals. As has been argued elsewhere (Frith, 2015; Wilken & Bayliss, 2015), Foursquare’s decision to rely heavily upon end-user engagement with other applications owned by more established competitors in order to enrich its own database is, in political economic terms, a risky strategy for the company for at least three reasons.

First, it is risky in terms of its possible impacts on its core user-base. The strategy focuses on Foursquare’s so-called ‘super users’, the ‘contingent of users and merchants who are seriously engaged by its platform’; once the company has a ‘dense, engaged, revenue-generating core of users’ (Gobry, 2012), its aim is to expand from this base. The trick for Foursquare will be in managing commercial and new user growth, without ostracizing its core constituency of high-end users – those initially drawn to its gameplay elements. And the slow but steady removal of gameplay elements, even from Swarm app, is likely not helping matters.

Second, it is risky in financial terms. Over its relatively short life, Foursquare has managed to attract investment funding totalling USD$162.4mil at a valuation of around USD$600mil. However, venture capital investment is by no means an endless stream, and the individuals and companies who have backed Foursquare and its founders will eventually want to see a return on their investment. While the USD$15mil deal with Microsoft mentioned earlier in this article clearly brings some financial stability to the firm’s operations, it remains the case that unless Foursquare can continue to grow its user base (which it has struggled to do) and/or its profit margins, one possible outcome is a loss-making (and potentially embarrassing) financial exit.

Third, the bold new path that Foursquare has embarked on is also risky in that it now places Foursquare in increasing and more direct competition with a number of larger, and financially well-heeled, competitors, including Google, Facebook, and Yelp, as well as Twitter. This points to one of the more perplexing features of the location services ecosystem in which Foursquare is operating: by allowing Instagram, Vine, and Waze to access location data from Foursquare’s API, Facebook and Google are actively contributing to the further enrichment of the ‘location “underlayer” database for the internet’ that Foursquare is building. It is this database, populated more extensively from these sources than from Foursquare’s own users, that could in fact prove to be Foursquare’s greatest asset. So long as this data-stream persists, Foursquare’s places database will become further enriched, fuelling Crowley’s desire for Foursquare to become the ‘location layer of the internet’ (Edwards, 2013), as well as pushing the firm closer to profitability, or, at very least, an acceptably higher exit valuation. Whether this cross-platform data-stream will persist, however, is by no means assured – as is evidenced by the initial steps Facebook has taken towards testing the viability of bringing the provision of Instagram’s geoservices in-house (Carr, 2014).

In this first section of this article, I have traced Foursquare’s still-evolving business plan (including its reliance on cross-platform partnerships), and documented the company’s dramatic strategic change in direction where it has systematically downplayed its gameplay functionality and played up the urban search and recommendation aspects of its service.

Foursquare and end-user engagement

The focus now shifts to consider end-user engagement with Foursquare within this same communications and mobile app ecosystem. A study was designed, consisting of eleven in-depth, qualitative, semi-structured interviews conducted in two batches (in May-July 2013 and in May 2014) in Melbourne, Australia, in order to explore how end-users have been and are engaging with Foursquare. Of the eleven interviewees, six were females and five were males, with an age range spanning 22–60. In terms of their occupations, two were university students, with the remainder professional workers employed across a variety of fields. These Melbourne interviews form the initial stage in a larger comparative project, developed in collaboration with Associate Professor Lee Humphreys of Cornell University, to examine Foursquare end-use in Melbourne, and then in New York City, where Foursquare originated. This article is restricted to reporting on the Melbourne interview material, and it does not address the comparative dimensions of the study. All interviews were professionally transcribed, and the analysis of the transcripts was completed using the initial categories that guided the interviews, and then complemented with a thematic analysis to compare responses across interviews.

Each interviewee was asked a series of questions relating to their use of Foursquare specifically. However, picking up on van Dijck’s point about situating this use within a wider ecosystem, participants were also asked how this use formed part of their wider engagement with social media and location-based services. This sample is not intended to be representative of Foursquare users, yet is still an important step in identifying the personal, social, and enduring economic significance of mobile locative media services.

On one level, the findings from these interviews serve as partial vindication of Foursquare’s decision to pivot away from being a check-in based SNS to a search and recommendation service. In general terms, the higher-end users interviewed tended to be those most invested in the gameplay aspects of the service – the mayorships, badges, and leader boards. This dedicated core of high end, and confirmed ‘super users’ (those given venue editing rights) were also among the more pedantic. They were the ones who expressed most concern about the accuracy of venue data, and, in some cases went to considerable lengths to rectify errors themselves, or to notify Foursquare of location pin and other inaccuracies.

At the other end of the spectrum, it was, as a rule, the less engaged, more casual users who tended to be more open to the increased commercialisation of the service. For them, special offers, deals, and discounts were of appeal. This was also the cohort of users who were more likely to maintain lists within the app, consisting of venues they encountered or saw recommendations for that they intended to visit at some point in the future. User growth, and thus revenue growth, it seems, is more likely to come from these less engaged users open to commercialisation initiatives, and it therefore makes sense that this is where Foursquare would focus its attention and resources.

On another level, however, talking to end users highlights clearly how complicated user engagement within this ecosystem is, and, as a consequence of this, that Foursquare faces a considerable struggle to maintain existing levels of engagement with their service and build further growth, especially in less concentrated markets like Melbourne.

The complexities of use are especially evident when we pay attention to how end-users understand and engage with the larger ‘app ecosystem’. For those interviewed, Foursquare was merely one of a larger suite of apps they used that might be considered as social networking services and which also provide various forms of location data. In addition to Foursquare, those that were explicitly mentioned included some obvious candidates, such as Facebook and Instagram, Twitter, Snapchat, Pinterest, Waze, Google+, the defunct Google Latitude, and Apple’s Find My Friends service, as well as less well-known services, including tvtag (formerly GetGlue, a social media service for TV fans), Twinkle (a now discontinued location-aware client for Twitter), social media service Path, and Strava (a location-based service specifically for runners and cyclists).

For the subjects of the study, this ‘app ecosystem’ is the site for complicated patterns of technological engagement. There are a number of dimensions to this engagement that I would like to detail here.

A key feature of user engagement with apps that emerged from the interview data, and specifically pertaining to location-based and social-networking services, concerns location disclosure and sharing. Location is rarely declared unthinkingly or universally (‘If people check me in [on Facebook] it makes me nervous.’). Rather, users are, as a rule, quite particular about whether to disclose geolocation information at all, and, if so, in what circumstances, when, and to whom. Given the choice, users prefer to control – to ‘curate’ – how their location information is disseminated.

Fundamental to this desire for control are various attempts at, or strategies for, compartmentalisation. According to the Melbourne interview data, compartmentalisation takes a number of forms, each guided by different motivations. For example, there is compartmentalisation that results from differential access to social networking services by those that comprise one’s immediate social network. For example, one participant told of how, when travelling, she would check into airports to tell family that she’d arrived safely – common practice for many travellers. This she would do, however, on Facebook as well as on Foursquare, as her parents have access to her Facebook, but not her Foursquare. The same participant also joined Path in order to be able to check in together at venues with her partner, ‘because he doesn’t use Facebook’.

There is also compartmentalisation that is sought by users as a way of maintaining distinct family, work, and other networks, such as in the following example.

‘Facebook is certainly personal, in terms of communicating with family, because my family is overseas, and Twitter is certainly more of a work thing…. Google+ is certainly like personal interest more, you know…’

One of the challenges for social networking service users that is said to flow from our postings across multiple social media platforms, and the seemingly endless flow of information that comes with this, is, in Michael Wesch’s (2009, p. 23) words, a sense of ‘an infinite number of contexts collapsing upon one another’ (see, also, Marwick & Boyd, 2011). One way we can read this passage is as a desire, on the part of this particular individual, to attempt to resist ‘context collapse’ by compartmentalising their social media use.

There is also compartmentalisation that occurs as a result of users’ understandings of the ‘technological affordances’ of particular location-based and social networking services. The term ‘technological affordance’ comes from human-computer interaction (HCI) and interaction design research where it refers to possible actions that are readily perceivable by an actor, and determined by the actor’s goals, plans, values, beliefs, past experiences, and so on (Gaver, 1991). One important sub-category of affordances is what William Gaver (1991) calls ‘hidden affordances’. These indicate that there are possibilities for action that are not perceived by an actor. In relation to ‘hidden affordances’, one interviewee admitted to pushing all his Foursquare check-ins to Twitter, but checked in via Facebook separately because ‘I’ve got the Facebook check-in that I use [to] tag people, the people I’m with properly, whereas I can’t seem to tag people on Foursquare’.

In terms of technological affordances as involving user perceptions and expectations, a number of interviewees expressed very clear views about the utility of the respective platforms that they use.

‘The value for me of Waze is not about people … [it is about] crowd source[d] traffic.’

‘Foursquare for me, it’s not about a network connection as such, but more about a record of where I’ve been, maybe where I’d like to go to…’

‘Facebook, that’s how I keep in touch [with] a lot of friends of mine. Twitter is … the main thing is the information that I get from it in terms of news, opinions and sort of current events…. Instagram, I guess it’s my interest in photography… and Foursquare is just, I see more as a diary I guess, of keeping a log of where I go and what I do.’

From these quotes, we get a glimpse of the complicated ecologies of app use that Foursquare is one part of. Many interviewees favour other platforms over it when it comes to food-related search and recommendations – the very thing that the company is aiming to refocus around.

‘If we are looking for somewhere to eat, or something like that, … Urbanspoon is better than Foursquare.’

‘[Yelp] kind of combines the best elements of Foursquare and Urbanspoon or whatever other review places there are.’

‘So I keep Foursquare for checking in and the game, and I keep Yelp for finding out what’s good and reviewing it.’

One issue that Foursquare users face in less population dense cities like Melbourne is the perception that the company’s attention is directed elsewhere. One participant, a super user and social media manager at a small inner city marketing firm, was fairly blunt in her assessment of Foursquare’s present status in Melbourne as a location-based service for business and for individuals:

‘From a business engagement perspective, they’ve really dropped the ball and they just haven’t done as much as they could…. [And, from an end user perspective], I don’t see a lot of activity Foursquare are doing to try and get new people to join.’

While this remark was made in an early interview, just as Foursquare had set about strengthening their services for business and releasing their Explore feature for users, the criticism holds. There is a wealth of commentary on Foursquare’s limitations, and the gaps in its places database, once you leave the big cities of New York, San Francisco, London, and so on. These same issues, and the lack of visible efforts to address them, are evident in Melbourne. Yelp, by contrast, launched in Australia (in Melbourne and Sydney) as early as 2011 (Kidman, 2011), and have retained the gameplay aspects they copied from Foursquare, and which, as noted, Foursquare is in the process of dispensing with altogether.

Finally, in addition to asking interviewees about their use of Foursquare and other apps, we also asked them about their understanding of the interactions that take place between apps within the larger communications ecosystem. In response, participants provided some rich insights concerning how they manage data across different platforms, and how they understand the business arrangements driving cross-platform partnerships.

With respect to how data is managed across different services, one interviewee viewed Foursquare fundamentally as a geolocational extension of Twitter:

‘So I guess that how I use Foursquare these days is a way of saying something on Twitter with location information.’

However, the tendency – as with the disclosure of location information in general – was caution and careful deliberation concerning when, where, and how geolocation information was pushed to other services:

‘I use the TomTom app, and I know that’s got integration with Facebook and all that sort of stuff, but I haven’t turned it on.’

‘I’ll push [Foursquare] check-ins to Facebook, maybe, once every three months and that’ll [only] be at a very public event.’

And, with respect to knowledge of corporate arrangements, a very small number of participants displayed some awareness of the cross-platform partnerships and corporate buyouts. For example, when asked whether they used location or GPS-enabled smartphone apps other than Foursquare, one participant responded:

‘Not really. Sometimes I’ll add it to Instagram, […] but that is powered by Foursquare anyway, so…’

These specific responses notwithstanding, one of the more striking features of participant answers to questions about cross-platform sharing and the handling of end-user data was how often these responses were characterised by contradiction and paradox, and misinformation. For example, when reflecting on her use of Facebook, one participant stated:

‘I wouldn’t consider [Facebook] as a location-based service that I use because I don’t often, I do a little bit, but I don’t geotag photos, I don’t try to check in that often…. I do it more on Instagram, but I don’t want to do it as much on [Facebook].’

What this participant does not appear to realise is that Facebook owns Instagram.

A second interviewee stated that they only tag Facebook with location some of the time, while noting earlier in the interview that all their Foursquare check-ins were pushed to Facebook. While a third offered the following reflection on their Google accounts:

‘I’m someone who has a ridiculous amount of my information on Google profiles. […] But they don’t share this information with other people.’

This particular participant appears oblivious to the various forms of geodemographic profiling and targeted marketing that Google employs using end user data (Barreneche, 2012a), and of the motility of social media and search data as it moves between a company like Google and various third parties (Coté, 2014).

Some reflections on geo-social media research issues

In this article, I have taken a communicative ecology approach to this examination of locative media service, Foursquare, including documenting its struggles to reinvent itself on the one hand, and detailing how Foursquare fits within wider patterns and associated complexities of locative media app use on the other hand. End use of Foursquare was revealed to both validate corporate changes in strategy as well as complicate these business ambitions through, to cite two examples, users exercising caution when disclosing location information, and by resisting use of the app as a food-related search and recommendations service.

While Foursquare has been selected here as a case study, it does, I believe, speak to an important set of larger issues. It reveals both the ongoing importance of place and location and how these and our experiences of them are increasingly mediated through mobile technologies. It also speaks to concerns and challenges that are common to the US and wider tech start-up scene, especially in making their business financially viable. In an oft-quoted adage, generally attributed to Silicon Valley entrepreneur Steve Blank (2010), ‘a startup is an organization formed to search for a repeatable and scalable business model’. For example, like Foursquare, Twitter, since going public, has also been experiencing flat-lining new user growth, and struggles with meaningful revenue generation (Levine, 2014).

What is more, this particular case study also speaks to a number of larger critical issues that, I argue, ought to underpin and inform future (locative) app ecologies research. It is these issues that I would like to reflect upon here in this final section.

The first of these issues concerns the need to take medium (or platform) specificity seriously when undertaking social and locative media research. As Barreneche and Wilken (in press) have argued elsewhere:

By not paying due and careful attention to the specifics of data extraction strategies, political and cultural economic analyses of new media services risk eliding key differences between new media platforms, and their respective software systems, patterns of consumer use, and individual revenue models.

In the case of Foursquare, how it extracts geocoded data, and how it interprets and uses this data, leads to quite particular ‘place ontologies’ – that is, ‘ways of categorizing the world’ which also ‘embody certain worldviews or modes of knowing the world’ (Barreneche, 2012b) – that are subtly but no less significantly different from those constructed by Google, Twitter, or Facebook. These differences matter, and need to be accounted for.

Nonetheless, the second research issue is that, at the same time, I contend that Van Dijck (2013) has a point when she suggests that we need to move beyond single platform studies to investigate the wider communicative ecology. As revealed in the end user interviews reported on in the second half of this article, Foursquare use does not occur in isolation; rather, it forms one (albeit quite specific) geomobile app within a larger app ecology. The study informing this article was designed so that Foursquare use formed a key point of departure for discussion of wider, intersecting (geolocative) app use. Such an approach is revealing of the points of comparison and contrast in how each app is built, used, and understood. In addition to this, larger multi-platform comparative studies are needed in order to understand the technological affordances of each (the issue of platform specificity noted above), the distinct as well overlapping patterns of end use (see Humphreys, 2012), and data sharing arrangements and cross-platform partnerships, and the implications of each (see, for example, Van Dijck, 2013).

The third issue relates to my belief that any critique of the technological affordances of specific platforms and how they shift over time must be coupled with political economic analyses of how the company (its corporate structure, business and revenue models, and so on) also shifts. What is striking about the above discussion of Foursquare as a business, for example, is its decision to shift its attention away from its former emphasis on gameplay and refocus its efforts around local search and recommendations. This move seems to have been driven by waning end user engagement with the game aspects of the service and plateauing new user growth. However, it also involves the introduction of a revamped app that requires certain socio-technical adjustments by end users if they are to continue to use the Foursquare app. Furthermore, it is worth noting that, in the midst of this overhaul, co-founder Naveen Selvadurai was asked by Dennis Crowley to leave Foursquare (Carr, 2013), and long-term business partner Alex Rainert apparently left of his own volition (Kafka, 2013). Subsequent to the developments described in this article, two further key senior Foursquare staff also departed, chief operating officer Evan Cohen and business development head Holger Luedorf (Isaac, 2014). Any future analysis of the business side of Foursquare would do well to account for these departures, their likely motivations, and their longer-term impacts, for the business, for investors, and for users.

Fourthly, the company data sharing arrangements and cross-platform deals noted in the first section of this article raise two important, interconnected research issues. The reach and impact of these corporate deals, and the power relations inherent in them, point to the ongoing importance of political economic analyses in examinations of new media (Mansell, 2004). However, the complexities of these arrangements at the same time suggest the need for methodological enrichment and extension in ways that account for the politics of the operation and use of application programming interfaces (APIs) (Bucher, 2012), the particular dynamics of search economics (Van Couvering, 2011), algorithmic processing (Gillespie, 2014), and of predictive analytics and data sorting (Bucher, 2012; Gerlitz & Helmond, 2013). In addition, cross-platform deals and data-sharing arrangements, and the ‘data motility’ of which Coté (2014) writes, pose many challenges for end user understanding of data retention and individual privacy. A key focus in existing scholarship on locative media and privacy has been on emphasising that users’ negotiations of locational privacy is, and ought to be, ‘intimately related to the ability to control the context in which one shares locational information’ (De Souza e Silva & Frith, 2012, p. 129). However, as Frith (in press) points out elsewhere, given the multitude of sources that feed Foursquare’s places database, the fact is that ‘people who use a variety of location-based services are interacting with Foursquare data without even knowing it’. Moreover, even if end users think they are clear about their own privacy settings, the entangled nature of the app ecology, and their own (partial) knowledge of corporate arrangements, algorithmic processes, terms of service documents, and device settings, complicate matters considerably.

Fifth, and finally, we must, I argue, balance political economic analyses with other analyses – including those that consider end use (as per this article), and (preferably in concert with) those of the policy and regulatory contexts in which these services operate, as well as production processes, and so forth (see, for example, Goggin, 2006). As Jo Tacchi (2006) reminds us, the business imperatives of a service like Foursquare cannot be understood in isolation as they are ‘in turn imbricated in other structural, social, economic and cultural contexts’.

All of this work is complicated by the fact that, as media theorist Matthew Fuller (2007, p. 1) notes, ‘complex objects such as media systems’ only ever settle ‘temporarily into what passes for a stable state’ before reforming and resettling, and so on, in a process that is ongoing. The issue, then, if I might put it in the language of locative media and mapping, is that, in analysing these services and their uses, the theoretical, methodological, and pedagogical pins we are trying to drop are situated in a fluid and ever-shifting technological landscape. This is a key challenge that we, as media and communication researchers, face in seeking to understand mobile media and their associated ecologies of location.

Disclosure statement

No potential conflict of interest was reported by the author.

Acknowledgements

This article is an output of the Australian Research Council (ARC)-funded project, ‘The Cultural Economy of Locative Media’ (DE120102114). I wish to thank the ARC for its financial support, Lee Humphreys, and Jenny Kennedy for her invaluable research assistance.

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Notes on contributors

Rowan Wilken

Rowan Wilken, PhD, is a Senior Lecturer in Media and Communication, Swinburne University of Technology, Melbourne, Australia, and holds an Australian Research Council funded research fellowship to investigate location-based services in the Swinburne Institute for Social Research. His present research interests include mobile and locative media, digital technologies and culture, theories and practices of everyday life, domestic technology consumption, and old and new media. He has published widely on mobile and location-based media. He is the co-editor (with Gerard Goggin) of Locative Media (Routledge, 2015) and Mobile Technology and Place (Routledge, 2012), and is the author of Teletechnologies, Place, and Community (Routledge, 2011). At present he is working on two books: Cultural Economies of Locative Media (Oxford University Press, forthcoming); and, an edited collection (with Justin Clemens), The Afterlives of Georges Perec (Edinburgh University Press, forthcoming).