Structural equation models and causal analyses in Usability Evaluation δ 1 x 1 δ 1 x 1 δ 2 x 2 δ 2 x 2 δ 3 δ 3 δ 4 x 4 δ 4 x 4 δ 5 x 5 δ 5 x 5 δ 6 x 6 δ 6 x 6 δ 7 X 7 X 1 δ 7 X 7 δ 8 x 8 δ 8 x 8 δ 9 δ 10 x 9 x 10 X 2 δ 9 δ 10 x 9 x 10 Y 1 ζ 1 Konfirmatorische Faktorenanalyse nalyse mit abhängigen latenten Variablen ab Konfirmatorische Faktorenanalyse a e mit unabhängigen latenteten Konstrukten Y 2 n ζ 2 This paper illustrates the importance of th interplay between business indicators and border areas of better usability (Joy of Use) and aesthetics of a system. Simple payment systems or, for example, purchase recommendations tailored to the customer, also influencethe users experience (Joy of Use). The present research approach takes these influencing variables into account. Sonja Treppner / Hochschule für Musik und Theater Hannover / Hochschule Merseburg (FH)
Structural equation models and causal analyses in Usability Evaluation S. Treppner Hochschule für Musik und Theater Hannover, IJK Hannover Hochschule Merseburg (FH), University of Applied Sciences and Arts, Fakultät II - Informatik und Kommunikationssysteme Universität Hildesheim, sonja.treppner@uni-hildesheim.de Abstract The examination of causal relationships is of great interest to science. The traditional method for examining causality is the implementation of an experiment. The manipulation of influencing variables and the measurement of a resulting effect, which can occur with a time delay, is carried out in a controlled situation. Examining these relationships is possible for sciences such as physics, because controlled experiments can be conducted. In the social sciences and in economics, in which these controlled experiments are not feasible and in which latent variables are frequently employed, one resorts to structural equation models in order to examine causal relationships between non-experimental data if the variables exhibit a linear relationship. Although Usability Research takes place within an integrative approach of engineering sciences and psychology, it largely ignores business approaches and related research areas. The theoretical elaboration of the work and the practical part of the study combine both evaluation methods and offer new approaches in an integrative interplay. State of research Cause and effect relationships are often at the centre of business management theory and practice. 1 For several decades, suitable statistical methods for empirical examination of such relationships have been developed especially in social research, which have entered the business literature under terms such as structural equation analysis or causal analysis. 2 Causal analysis consists of a combination of regression analytical approaches and factor analytical 1 cf. Homburg/Pflesser, 2000, p. 635. 2 cf. Bollen, 1989, p. 4 et seqq. Fachhochschule Schmalkalden 1
approaches. This results in quantitative, interpretable relationships between latent and measurable model variables if the calculated values are not random, but can be significantly explained by the model construction. 3 Initial business applications of this multivariate method of analysis have been derived from the area of marketing. 4 Customer relationships have been in existence since there have been commercial exchanges between suppliers and demanders. However, the customer relationship only became an explicit object of market research and science at the end of the 70s. At this time, theories and concepts for understanding the relationships between industrial suppliers and their demanders, as well as for understanding the relationships within distribution channels were developed in Business-to- Business-Marketing. At about the same time, the suitability of the traditional marketing approach for the service sector was questioned. This led to the development of service marketing which, among others, concerns itself with the particularities of exchanges between suppliers and demanders of services. In the 80s, the rapid development of information technology promoted the emergence of database marketing. Database marketing primarily aims at directing customer relations by collecting, analyzing and supplying customer-related data. Together, the three research approaches form the conceptual basis from which relationship marketing originated. Since the middle of the 80s, the concept of relationship marketing has been researched and formulated in detail. It was quickly accepted by important areas of science and is widespread today - explicitly or implicitly. Within the scope of Electronic Customer Relationship Management (ecrm), relationship marketing connects with the areas of information and communication technologies. While the term customer relationship management demands the integration of the conceptual idea of relationship marketing, the term electronic underlines the central role of information and communication technologies. From the perspective of information technology, ecrm implies the automation of customer management processes in the areas of distribution, marketing and services. New information and communication technologies augment the existing communication and distribution channels and enable a direct and individualized sales approach. From 1970 until today, traditional relationship management has evolved and has become closely linked with the requirements of database marketing and electronic Customer Relationship Management. In parallel, the scientific areas of Usability Engineering and Usability Evaluation developed from traditional Software Ergonomics. Although both the area of 3 cf. Bickhoff et al., 2003, p. 53. 4 cf. Bagozzi/Yi, 1994; Bagozzi, 1982; Bagozzi, 1980. 11. Nachwuchswissenschaftlerkonferenz 14. April 2010 2
Electronic Customer Relationship Management and the research area of Usability Evaluation exhibit integrative research approaches, they are very rarely or never associated with each other. This is very remarkable, particularly since a large part of web portals can already be found on the Internet as ECRM systems. Usability research examines the target achievement of interactive systems regarding the satisfaction of the visitors needs with respect to the underlying task. In the process, the internal attributes of an application system determine its quality during application in the context of use. Following DIN standard 66272 one can define Usability as follows: Usability comprises characteristics (understandability, learnability, operability), which refer to the resources that are necessary for using a system as well as to the individual assessment of such a use by a specified or hypothetical group of users. Following DIN EN ISO 9241-11 one can therefore define Usability as denoting the extent to which a product can be used by a user in a specified context of use to achieve specified goals with effectiveness, efficiency and satisfaction. Usability thus consists of several components and is closely connected with measurable characteristics of an interactive application system, which, following Nielsen, are called usability factors. This includes qualities such as learnability, efficiency, memorability, fault tolerance and satisfaction. With the help of these criteria, Usability measurement is operationalized. Research approach: Correlation calculation (with the Pearson correlation coefficient) Bivariate analyses within the interval scale measurement: Analysis of variance Multivariate methods / regression analysis Causal analysis, i.e., the addition of measurement methods including formative and reflective indicators (the so-called latent variables) Areas of customer satisfaction management are assigned to the latent variables. Summary: This paper illustrates the importance of the interplay between business indicators and border areas of better usability (Joy of Use) and aesthetics of a system. A memorable layout grid, which meets the guidelines of cognitive theory, is not the only factor that affects the Usability of a system. Simple payment systems or, for example, purchase recommendations tailored to the customer, also influence the users experience (Joy of Use). The present research approach takes these influencing variables into account. Fachhochschule Schmalkalden 3
References: Bagozzi, R. P. (1982): Introduction to special issue on causal modelling, Journal of Marketing Research, Jg. 19, H. 4, S. 403. Bagozzi, R. P. (1980): Causal models in Marketing; New York. Bagozzi, R. P./Yi, Y. (1994): Advanced topics in structural equation models, in: Bagozzi, R.P. (Ed.), Advanced methods of marketing research, Cambridge, p. 1 et seqq. Bollen, K. A. (1989): Structural equations with latent variables; New York et al. Bickhoff et al., 2003, p. 53. Bollen, Kenneth A.: Lennox, Richard (1991): Conventional Wisdom in Measurement: A Structural Equation Perspective. In: Psychological Bulletin, Vol. 110, No. 2. pp. 305-314. Bollen, Kenneth A.: Ting, Kwok-fai (2000): A Tetrad Test for Causal Indicators. In: Psychological Methods, Vol. 5, No.1, pp. 3-22. Chin, Wynne W.: Newsted, Peter R. (1998): Structural Equation Modelling Analysis with Small samples Using Partial Least Squares. In: Hoyle, Rick H. (Ed.): Statistical Strategies for Small Sample Research. Thousand Oaks et al. 1998, pp. 307-341 Diamantopoulos, Adamantios, Winkelhofer, Heidi M. (2001): Index Construction with Formative Indicators: An Alternative to Scale Development. In: Journal of Marketing Research; Vol. 38, No. 2, pp. 269-277. Edwards, Jeffrey R.; Bagozzi, Richard P. (2000): On the Nature and Direction of Relationships between Constructs and Measures. In: Psychological Methods, Vol. 2. pp. 155-174. Eggert, Andreas; Fassott, Georg (2003): Zur Verwendung formativer und reflektiver Indikatoren in Strukturgleichungsmodellen-Ergebnisse einer Metaanalyse und Anwendungsempfehlungen. Working Paper VHB-Pfingsttagung, Zürich 2003. Homburg, Christian; Dobratz, Andreas (1991): Iterative Modellselektion in der Kausalanalyse. In: Zeitschrift für betriebswirtschaftliche Forschung, Vol. 43, No. 3, pp. 213-237. Homburg, C. (1989): Exploratorische Ansätze der Kausalanalyse als Instrument der Marketingplanung; Frankfurt/Main. Homburg, C./Pflesser, C. (2000): Strukturgleichungsmodelle mit latenten Variablen Kausalanalyse, in: Herrmann, A./Homburg, C. (Eds.), Marktforschung : Methoden, Anwendungen, Praxisbeispiele, Wiesbaden, p. 633 et seqq. Jöreskog, K. G. /Sörbom, D. (1996): LISREL 8: a users`s reference guide; Chicago. Reinartz, Werner; Krafft, Manfred; Hoyer, Wayne D. (2003): Measuring the Customer Relationship Management Construct and Linking it to Performance Outcomes. Working Paper. INSEAD, Fontainebleau 2003. Schnell, Rainer; Hill, Paul B.; Esser, Elke (1999): Methoden der empirischen Sozialforschung. 6. Ed., München 1999. 11. Nachwuchswissenschaftlerkonferenz 14. April 2010 4