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The previous sections have reviewed the concept of trust, trust models, and trust indicators from various research domains. The following section moves on to discuss how this trust- related research applies to the GIS domain and to geospatial data selection and use.

2.3.1 Risk as a Precondition of Trust

As was discussed in Section 2.1.1, risk is a vital precondition of trust. Since risk can arguably be considered high in GIS dataset use – e.g., risks associated with selecting inappropriate data, risks of data misuse and risks of data misinterpretation – it follows that there is an inherent need for trust at some level in order to facilitate effective dataset selection and use. Misuse of geospatial data and use of datasets that are not fit for an intended purpose can potentially lead to high financial costs, lead to legal actions being taken, have ecological or social impact, and even result in a loss of human life. In recent years, when production and public availability of geospatial data has significantly increased, risks associated with selecting inappropriate data have also increased, meaning that dataset users’ trust in dataset producers, dataset providers and the datasets themselves plays a crucial role in establishing effective producer-consumer relationships, which in turn facilitate effective use of geospatial data. As with e-Commerce, this means that it is important that effective means of communicating the trustworthiness of datasets and their producers and providers are identified within the GIS domain.

2.3.2 Initial and Experiential Trust

In the last decade the production, availability, and sharing of geospatial data has significantly increased (Wang and Huang, 2007; Brown et al., 2013), with a corresponding increase in the availability of Spatial Data Infrastructures (SDIs), web-based catalogues, portals, standards and services. For instance, since 1993 when the concept of a Spatial Data Infrastructure (SDI) was formally initiated, more than 100 local, regional, national, and global SDIs have been established across the world (Maguire and Longley, 2005). Geospatial data can now be

accessed via catalogues and portals which document data from different providers. In parallel, there are increasing numbers of professional and non-professional geospatial data consumers searching for data to fit their specific needs. Geospatial data producers and providers, especially the ones that are new and have no established reputation, face a challenge in terms of engendering sufficient trust to convince data consumers to acquire and use their datasets. Geospatial data providers have to ensure that first-time data consumers can and will establish sufficient trust (in this case, fragile initial trust) to establish a new consumer-provider relationship. As with e-Commerce, it is therefore essential to identify informational aspects (i.e., geospatial-specific trust triggers) that can help to promote user trust in geospatial data providers and the datasets that they offer. Conversely, when searching for datasets to meet their needs data consumers face a challenge in terms of assessing the trustworthiness of data providers and the quality of the datasets that they offer. If data providers do not engender enough trust at the initial trust-building stage, first-time consumers will not engage in a provider-customer relationship. While initial trust is critical in attracting new data consumers, maintaining trusting relationships can be even more demanding. Where there is a choice, geospatial data users will be highly unlikely to continue any relationship after having a negative experience with a data provider, either directly or via the quality of their datasets. Experiential trust is, therefore, an essential part of more established and long term GIS consumer-provider relationships.

2.3.3 Vertical and Horizontal Trust

International organisations and initiatives such as the OpenGIS Consortium Inc. (OGC) (OGC, 2014b), International Organisation for Standardisation Technical Committee (ISO/TC 211) (ISO/TC211, 2014a), INSPIRE (INSPIRE, 2014d), A Quality Assurance Framework for Earth Observation (QA4EO) (QA4EO, 2014), Dublin Core (DCMI, 2014a), and many more, are actively working on establishing and supporting geospatial data and metadata standards. In the GIS community, international standards on geospatial data quality largely concentrate on providing guidelines for, and quality control of, metadata records. These standardisation activities directly relate to vertical trust: when making a dataset selection decision, consumers (or users) of geospatial data consider the international standards supported by datasets, drawing on and establishing vertical trust (recall, this is trust between an individual and an organisation) in so doing. Adherence to international standards may indicate to data users that datasets are either of good quality or are at least supported with adequate documentation to enable effective fitness-for-use evaluation. Although standardised supporting documentation is important in the dataset assessment phase, data users usually seek advice and recommendations from their colleagues and peers in the data discovery phase – the formulation of and reliance on horizontal trust.

2.3.4 Technological and Relational Trust

Technological trust in the GIS context relates to data consumers’ trust in technologies that allow access to, acquisition and use of geospatial data. These technologies can include:

 national and regional SDI systems that provide access to the catalogues of metadata;  data portals that provide an interface for data discovery and acquisition;

 producers’ websites that allow purchase or free download of datasets;

 technologies and algorithms used to collect and compute the data in the datasets; and

 Web GIS applications.

Relational trust in the GIS context refers to a geospatial data user’s belief that a dataset provider will demonstrate favourable behaviour in the future – in essence, it is a measure of a users’ willingness to accept vulnerability (perhaps in terms of the perceived suitability of a dataset for a given purpose) based upon positive expectations concerning the dataset provider’s future behaviour. This may be assessed by means of the availability of valid contact information and the ability to contact the data provider in the future, or the availability of a customer support service to respond to user queries regarding datasets and services that a provider offers.

2.3.5 Credibility and Benevolence

In the GIS domain credibility relates to a data consumer’s belief that a given data provider is capable of reliably supplying data of good quality and providing services of a high standard. GIS dataset providers can include commercial companies that sell data, governmental organisations, SDIs providing catalogues of available geospatial data, spatial data clearinghouses, data portals, or providers of VGI; the reputation of such providers directly relates to the credibility dimension of trust. The corresponding benevolence dimension of trust in the GIS domain relates to a data provider’s good intentions towards data consumers, even where there is no direct financial gain. It is hypothesised that dataset users might be more likely to acquire data from a provider if they have established a belief that the provider will assist in any further queries regarding the data that the provider has supplied.

2.3.6 Web Trust Model

As discussed in section 2.1.2, the Web Trust Model (McKnight et al., 2002a) is composed of four high-level constructs – disposition to trust, institution-based trust, trusting beliefs, and trusting intentions – which, when combined together, lead to trust-related behaviours. In the GIS context disposition to trust will especially affect trusting beliefs of novice users that are not very familiar with the domain. It is anticipated that, when selecting a suitable dataset,

users with generally low propensity to trust others will be particularly cautious and considerate when assessing datasets and their quality. Presence of well-defined and established geospatial data trust indicators (trust triggers) could be particularly important to this type of users because they would probably require as much information as possible available to them to make a data selection decision. Disposition to trust will also be highly influenced by a data consumer’s previous experiences. Institution-based trust in the GIS context concerns producers’ compliance with international standards or any applicable certification programmes; dataset repositories, data portals and clearinghouses may be recognised by geospatial data users as third-party institutions that can be trusted to facilitate successful data acquisition. Trusting beliefs in the GIS context can be defined as geospatial data consumers’ confidence that data providers will supply data of good quality (provided with well-documented metadata records) and will act in a favourable manner – for instance, will reply to further queries, send notifications about any dataset updates or discovered issues, warn about potential errors, suggest dataset application areas, etc. Trusting intentions refer to a geospatial data consumer’s belief that a dataset is fit for its intended purpose and willingness to acquire geospatial data from a data provider. Finally, trust-related behaviours in the GIS context will be witnessed when data consumers acquire and use datasets from data providers.

2.3.7 Multi-Dimensional Trust Model

The Multi-Dimensional Trust Model (Tan and Sutherland, 2004) (see section 2.1.2) is very similar to the Web Trust Model already discussed. This model also includes institutional and dispositional trust, trusting intentions and trust related behaviours (online purchase behaviour), but additionally presents interpersonal trust. Interpersonal trust in this model’s context, and when applied to the GIS domain, would likely manifest in geospatial data consumers’ trust in given data providers, in peer advice and recommendations, and in journal papers or technical reports that provide dataset quality checks.

Trust clearly has the potential to have a major impact on users’ geospatial dataset selection and quality evaluation processes. When searching for a suitable geospatial dataset, users may come across new data repositories or unknown data providers, in which case they have to decide whether to engage into a trusting relationship (initial trust) with that provider. Users may reflect on their previous experiences with geospatial data producers and providers to decide whether or not to return to a dataset provider to acquire future data sets (experiential trust). Furthermore, in any decision to trust a dataset provider, a user is essentially making an assessment of the provider’s credibility, the technological trustworthiness of the provider or producer, and the observance of standards set by higher orders (vertical trust). Users may also contact their peers, work colleagues or friends to get advice and recommendations on

seek information on projects or companies who have previously used a given dataset (horizontal trust), or they may look to journal papers, expert reviews and technical reports where dataset quality checks have been reported when making a selection decision (horizontal trust). When selecting from several dataset options, some users may be more keen on datasets that adhere to international standards and are supported with standardised metadata documentation (institutional and vertical trust). In contrast, in situations where consequences of data misuse are very severe, users may choose not to select datasets themselves but to use a third-party organisation to select datasets for them (institutional trust, risk precondition, third-party credibility).

The mapping between trust concepts and GIS dataset selection and use suggests that trust plays a vital role in geospatial data selection and use. Every time geospatial data users select a dataset to use, they are likely to have to make a trusting decision, often without even realising they are doing so. Consequently, the research reported in this thesis aims at identifying geospatial data trust and quality indicators upon which users rely when selecting a dataset that fits their needs.