Resumo

VIEIRA, Armando Luís. Exploring data collection innovations by examining the effects of relationship marketing on performance in times of crisis. Innovar [online]. 2014, vol.24, n.53, pp.75-82. ISSN 0121-5051.  https://doi.org/10.15446/innovar.v24n53.43911.

Abstract: The scientific quality, rigour, and impact of a considerable number, if not the vast majority, of marketing studies depend on an effective data collection process. Most of the researchers in these and related areas rely on traditional data collection tools, such as face-to-face, pen-andpaper, or online questionnaires, which are not very effective, both in terms of the time and money required to gather a reasonable number of observations. Bearing in mind that crises should also be an opportunity for researchers and institutions to develop more productive and effective research tools and procedures, the aim of this research is twofold: 1) to test a model that relates relationship marketing (RM) efforts with performance; and 2) to explore innovative, more effective, data collection tools to be employed in a marketing research context. To this end, this study proposes and tests a model of the effects of RM antecedents and mediators on objective performance. The empirical work draws on the perceptions of 4,389 firm representatives in terms of their relationships with their counterparts in hotels, collected by using app Generation npolls . Structural equation modelling results suggest that commitment is the strongest determinant of share of business, and fully mediates the impact of relationship quality (with satisfaction and trust as first-order dimensions) on objective performance. From an innovative, methodological perspective, this study demonstrates that it is possible to collect a significantly high number of observations in a very short period of time, with considerable advantages when compared to traditional data collection procedures.

Palavras-chave : innovation; data collection; Relationship marketing; seller objective performance; structural equation modelling.

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