Convergence produces a flood of data because technology allows the collection of records of interactions and transactions, and also the storage, inter-linking and sharing of this information. The potential for detail and cross-linking between this data is endless. Hill (2008) provides a narrative to illustrate the cross-connections in an everyday situation:
We can’t see how the street is immersed in a twitching, pulsing cloud of data.…This is a new kind of data, collective and individual, aggregated and discrete, open and closed, constantly logging impossibly detailed patterns of behaviour.
Such data emerges from the feet of three friends, grimly jogging past, whose Nike+ shoes track the frequency and duration of every step, comparing against pre-set targets for each individual runner.
This is cross-referenced with play list data emerging from their three iPods. Similar performance data is being captured in the engine control systems of a stationary BMW waiting at a traffic light, beaming information back to the BMW service centre associated with the car’s owner.
The traffic light system itself is capturing and collating data about traffic and pedestrian flow, based on real-time patterns surrounding the light, and conveying the state of congestion in the neighbourhood to the traffic planning authority for that region, which alters the lights’ behaviour accordingly. (That same traffic data is subsequently scraped by an information visualisation system that maps average travel times on to house price data, overlaid onto a collaboratively produced and open map of the city.)
A predominant behaviour within convergence is ‘the search’. Users are able to search within this information and then ‘find, filter, and forward’ (Pesce 2008). The ‘targeted search’ has become a new form of advertising. The profile of a user is known, via a record of past spending and internet behaviour, and advertising is presented that is targeted to their needs. It is yet to be seen whether users accept or trust this development (Metz 2009).
From a technical perspective, there are several ways in which information can be collected. For instance, web browsing usually involves the use of ‘cookies’, which work in the background, usually unknown to the user. These ‘cookies’ store the browser’s clicks, which can create a history of the user’s purchasing trends, political leanings and even sexual inclinations. These cookies are accessed by different websites that the user visits (Chacksfield 2009). Knowing this information about possible customers allows advertisers to gather knowledge about customer habits, interests and behaviour, which in turn can influence pricing decisions (Odlyzko 2003). The information-gathering process is often a one-way process. Although customers and individuals can have a lot of information gathered about them, there is little chance for individuals to collect consolidated detailed first-hand information about organisations.
Sometimes, this data is harnessed to provide a reputation profile of a user that is then made available to other users. Drawing on past behaviour by a user, a
profile is generated automatically by the software that can help other users predict how the user might behave in the future. An example of such a feature is the interface of eBay. Buyers can access a number value that gives a sense of the honesty and reliability of different sellers. This feature has been touted as one of the reasons behind the success of eBay. Although reputation systems are of great interest to trust researchers, the aspect of conventional reputation systems is not addressed in my research. This is because I argue that these types of systems are not dealing with trust, but a related concept of reputation. I am interested in how an individual develops a trust interaction rather than how someone receives advice from a community.
1.4.e Trust and risk in converged and provisional digital environments Convergent computing and new digital environments allow a wide variety of people to enter into a diverse range of transactions with people from all over the globe, sometimes in a one-off transaction with a fast result. However, the information that helps an individual build a context such as rules and customs is limited. There may be little or no build-up to the situation or opportunity to gather more information (Cheshire & Cook 2004). There is a limited number of exchanges for parties to get to know each other and understand each other’s perspectives (Nooteboom 2005).
Some of the risks or losses at stake for an individual engaging in a trust interaction can be identified, while others cannot be foreseen (Lacohée et al. 2006). In the area of health information digital exchange, a user might be given incorrect information that may have adverse effects. Within the context of on-line dating, somebody may pretend to have certain qualities (for instance, being single rather than married) that may result in disappointment for another. On-line dating might result in a meeting that could result in physical danger. Other risks are more difficult to identify. These risks include violations of privacy (control over personal information) and security (safety to self and devices). For instance, a user with a mobile device enters a shopping centre, which is wirelessly connected to the internet. Unknown to the user, her contact details are taken from the device and listed in a database that can be accessed by a range of people. Past purchases
made by the user are automatically calculated and the user is given advertisements tailored specifically to her desires. She makes a new purchase of an item flagged as an object that can be utilised by terrorists, thus an alert is activated that her behaviour should be documented. Her photo and purchase time are uploaded to a database.
Other risks include ‘information injustice’, which is when information provided by an individual is taken out of context and used to draw interpretations that may be unfair (van den Hoven 1999). For instance, a comment reflecting religious beliefs on a social networking site could be used in a job selection process. There may be an imbalance in the amount and access different parties in an interaction hold. Also, information provided by individuals may be used against their interests. For instance, data about an individual’s preferences is used by advertisers to create targeted campaigns that motivate the individual to part with his or her money. So, trust needs to be constitutive of every practice and experience and is not simply a matter for unusual or particular settings.
In summary, current digital environments have the following characteristics relevant to the design of digital environments that attempt to enable trust: a provisional status (resulting in an expectation of temporariness for users); increasing power for users (more aspects of technology can be created and configured by non-professionals); provocation for the user to disclose a range of information, some private; and the creation of a flood of data that can be searched and archived in ways that cannot be predicted. The type of trust and risk interactions that can evolve in these types of environments is infinite, as users can enter into a limitless range of interactions with an increasing range of potential partners. Some more prosaic examples include the on-line purchases of items by a user. Without the user’s knowledge, his or her transactions are observed by a third party and the pattern of the consumption is sent to advertisers who can target the user based on the background knowledge about the user’s predilections.