Skill was not found to impact trust. In addition, trust has a significant positive impact on long-term orientation of the relationship among SMEs.
The origins of the structural equation modelling (SEM) have its roots in three disciplines: sociology, psychology and economics. In marketing SEM starts its application in November 1982 in the issue published by the Journal of Marketing Research (Bollen 1989).
SEM grows out of and serves purposes similar to multiple regression, but in a more powerful way which takes into account the modeling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, multiple latent independents each measured by multiple indicators, and one or more latent dependents also each with multiple indicators. SEM may be used as a more powerful alternative to multiple regression, path analysis, factor analysis, time series analysis, and analysis of covariance. That is, these procedures may be seen as special cases of SEM, or, to put it another way, SEM is an extension of the general linear model (GLM) of which multiple regression is a part.
In this analysis we apply SEM to examine the influence of reputation, flexibility, information exchange, power and skill on trust and consequently on long-term orientation. ...
ng-term orientation is a central theme in marketing currently, it is crucial to know what are the variables that help explain successful long-term relationship building.
The analysis is structured as follows. First, we give a brief description of SEM and its importance for marketing research, then we provide the bases for the interrelationship between the variables used in the model; this is followed by description of the design of the analysis and finally, discussion of the result is provided.
Description of structural equation modelling and its applicability to the field of marketing
SEM is usually viewed as a confirmatory rather than exploratory procedure, using one of three approaches:
1. Strictly confirmatory approach: A model is tested using SEM goodness-of-fit tests to determine if the pattern of variances and covariances in the data is consistent with a structural (path) model specified by the researcher. However as other unexamined models may fit the data as well or better, an accepted model is only a not-disconfirmed model.
2. Alternative models approach: One may test two or more causal models to determine which has the best fit. There are many goodness-of-fit measures, reflecting different considerations, and usually three or four are reported by the researcher. Although desirable in principle, this AM approach runs into the real-world problem that in most specific research topic areas, the researcher does not find in the literature two well-developed alternative models to test.
3. Model development approach: In practice, much SEM research combines confirmatory and exploratory purposes: a model is tested using SEM procedures, found to be deficient, and an alternative model is then tested based on changes suggested by SEM modification indexes. This