ORIGEN NUEVOS OPERADORES TURÍSTICOS

Several researches signify that between 50% to 60% of changes during construction projects take place due to poor quality of information design (Kirby et al., 1988). Review of the literature data and information science has revealed several dimensions of information quality. Information quality dimensions are very broad as a general subject in organisations. Pipino et al. (2002) measured stakeholder perceptions of information quality in healthcare,

33 finance and bank firms and listed its dimensions including; accessibility, appropriate amount of data, believability, completeness, concise representation, consistent representation, ease of manipulation, free of error, interoperability, objectivity, relevancy, reputation, security, timeliness, understand-ability and value-added. The main four dimensions of information has been investigated in model-sound, dependent, useful and usable (Kahn et al., 2002). Further, Kahn et al (2002) provided benchmarking for evaluating method of providing information and delivering dependent and usable information to consumers. Yang et al. (2005) highlighted five dimensions of quality for evaluating web information including; accuracy, accessibility, usability, usefulness and interaction.

An understanding of the quality of information is essential to comprehend its role in information management in the field of construction engineering .It has been pointed out that information quality plays a crucial role in construction engineering, and especially in the design phase, poor information quality leads to poor drawings (Westin and Sein, 2013). There are several dimensions for benchmarking the quality of information. Most of the scholars in AEC industry focused on three main information quality including accessibility, accuracy and interoperability. The dimensions of poor quality information have been highlighted (Marshal, 2004) which is inaccurate, incomplete and inaccessible. In addition Marshal (2004) believed that organisations require strategic information management system based on those core factors to improve information quality. Moreover, Gorla et al. (2010) highlighted the dimensions of information quality as information accuracy, and information accessibility and information interoperability. Curry et al. (2013) highlighted accessibility and interoperability are key criteria in order to manage information in AEC industry. It is vital to access to different source of information. Interoperability is a main challenge in information interaction between sources. The benchmarking of information quality depends on the use of the information and what is recognised as poor information in one case may not be applicable in another case.

34 Information Collecting Information Organising Information Exchanging Information Reusing Information Quality Information Accessibility Information Accuracy Information Interoperability

Figure 2-7 Information quality impact on information lifecycle in Structural Engineering

Figure 2-7 shows that information quality has a significant influence in the entire information lifecycle (Information collection, information organising, information exchanging and information reusing) in structural engineering information management. The three main dimensions of information quality consist of: Information accessibility, information accuracy and information interoperability. this research attempts to obtain the expert overview for validation of these dimensions. In the following sub-sections each information quality dimension in the context of structural engineering disciplines is described in details.

2.2.1.1 Information Accessibility

Information retrieval is a well-establish research in engineering information management area. Information access in engineering sector has been surveyed by Liu et al. (2008) essentially to improve information management performance within AEC industry. A number of researches argued that engineers spend two-third of their time due to obtain output results from their work and they spend one-third of their time on searching and accessing to design information (McMahon et al., 2004, Hertzum and Pejtersen, 2000). It is very vital in engineering product design area that information be organised and structured for efficient retrieval (Chandrasegaran et al., 2013).

Accessibility in the collection of information is an essential requirement in structural engineering information management. Structural engineers usually have distinct information requirements. This information consists of unstructured data, semi-structured data and

35 structured data set in relational data warehouse. The availability of information are not efficient in AEC industry due to vast volumes and complex information (Lyman and Varian, 2010). The fundamental function is to select the most useful information in the requested time frame. This underscores the importance of classification of information in information accessibility, especially when the organisation has to handle large volumes of information (Dash and Lin, 2003).

Information Access Oth er d isci plin es in form atio n _Pr od uc t F un cti_on In_fo rm ati_on Process Information

Figure 2-8 Information Access Dimensions

Reviewing the state of the art literature shows that there are three information categories which are vital to be accessible in structural engineering design sector (See Figure 2-8). The first category is the raw information which may be collected from various sources for example; architect, client, local authorities and building services. Structural engineers can be consumers of some sort of information or producer of other sort of information (Sacks et al., 2000). The second category is related to information of predicting behaviour of product. Chandrasegaran et al. (2013) argued that to design physical engineering structure mapping between function and structure is often a critical challenge. Sometimes behavioural functions of certain structures are not predicted accurately due to lack of sufficient access to product functional information. The information has to be recorded and updated regularly to enable decision making in the analysis and design phases.

Information representation is very vital for structural engineering information management system and behaviour function prediction of structure products should be accessible and presented well by system to designers to make accurate decisions. And finally the third category is the structural process information. Engineering processes are very information and knowledge intensive process (Liu and Young, 2004). At the end of structural engineering process, there is extensive information accumulated that would be potentially be utilised in upcoming projects (Liu et al., 2008).

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Figure 2-9 Knowledge availability in structural engineering processes (Caviers et al., 2011)

In future projects structural engineers will access to information related to previous projects in early of stage design while this information can be documented in efficient way. Caviers et al. (2011) called this approach of information management as early integrated approach. The graph in Figure 2-9 presents the relationship between available volumes of knowledge in each phase of structural engineering design in a traditional integration method in contrast with an early integration method. As it can be seen in this graph in traditional information management system the available knowledge in early conceptual design stage is very low and it’s gradually increase towards detailed design. On the other hand, in early integration information management system most of the knowledge can be retrieved at the early conceptual design stage.