Questionnaire Design (ETAM, TTF)

The design of questionnaires is of utmost importance particularly when it comes to the application of the Extended Technology Acceptance Model (ETAM - Davis; extended by Morris[1]) and the Task-Technology Fit concept (Goodhue / Thompson [2]) in innovation marketing. Both approaches have been proven of special value for the determination of technology acceptance and user requirements analyses.
ETAM serves the assessment of system usage by users. It translates the vast concept of prototype evaluation into well-defined research questions with recognised statistical benchmarks that can be answered in a valid and reliable way. Technology acceptance is defined as the degree to which individual users will use a given system when usage is voluntary or discretionary. Studies on ETAM have shown the importance of perceived usefulness and perceived ease of use as determinants of attitudes and intentions to use a technology. The quality of system experience (quality of use) has been found to significantly influence user perceptions of usefulness and ease of use, while the mere amount of system experience turned out not to.
However, ETAM requires actual system usage by users. If this pre-requisite can not be provided, it is possible to draw on projective assessment approaches such as the Task-Technology Fit concept. Core of the model is the relevance of the correspondence between technology and the tasks of the user for the achievement of individual performance impacts. If TTF is decomposed into its single components, it can be also applied as diagnostic instrument to evaluate the fulfilment of user requirements by systems or services.

Multivariate Analysemethoden

At present, multivariate methods of analysis are one of the fundaments of empirical research in real sciences. They allow for statistically based analyses of large amounts of data as well as of samples of smaller size. Quality of data is strongly dependent on the measurement. Generally speaking it can be stipulated that the information content of the data increases with raising level of scales and more arithmetic operations and statistical ratios can be applied. Where the object of investigation and the data allows for, multivariate methods of analysis represent an effective method to obtain mathematically traceable and checkable results, which can be easily operationalised. However, thoughtless usage of multivariate methods might quickly turn out as a source of misinterpretation, since a statistically significant correlation does not necessarily constitute a sufficient condition for the existence of a causally determined interrelation.[3] Thus, the underlying hypotheses have to be scrutinised with respect to their validity.

Social Network Analysis

Social Network Analysis is both a research approach and a method to analyse social structures. It serves the analysis of relational data employing formal methods based on graph theory. Social Network Analysis is applied in different areas of social and economic sciences. As to innovation management one of its main tasks in this context is the representation and analysis of innovation systems and their structures. Objects of investigation are amongst others the formation of clusters and their impact on the innovation potential, the analysis of communication and information flows, the distribution of knowledge and the interaction between the actors of the innovation system.

[2] Goodhue, D. L., and R. L. Thompson (1995): Task-Technology Fit and Individual Performance. MIS Quarterly 19 (2), pp. 213-236.

[3] Backhaus, Klaus et al. (1996): Multivariate Analysemethoden: Eine anwendungsorientierte Einführung. 8. verb. Aufl., Springer.