Projective techniques have considerable potential to study consumer behaviour and are widely used in commercial market research and psychology, but not in tourism and hospitality research. This chapter demonstrates that tourism and hospitality researchers can collect richer data from smaller samples by using projective techniques, which provide more flexibility and allow the combination of multiple projective methods to triangulate findings. Projective techniques are qualitative methods that reach the subconscious of respondents by asking them to interpret information or complete tasks, which circumvent normative responses that create social desirability bias. Five techniques are outlined: collage, choice ordering, word association, photo elicitation and a scenario expressive technique. The study found that the most successful instrument for reducing social desirability bias was word association, while the least successful was photo-expression. The limitations are the highly resource intensive nature of rigorous analysis, ambiguous stimuli impacting on the complexity of data elicitation and codification, and variations in interpretation of the meaning of the results.
Tourism and hospitality involve participation, involvement and interaction. More inductive research using ethnographic and participatory approaches, where researchers critically reflect on interactions, attitudes and behaviours, is needed in the fields of tourism and hospitality. Ethnographies and participatory approaches bring the researcher into a study. These approaches portray how people interact with their environment and surroundings, encounter socio-political situations, or add meaning to the very places they reside in, with each establishing meaningful insight on a destination, its community and a place’s identity. Tourism as a leisure activity involves participation, involvement and interaction. For researchers, better understanding how people interact in different destinations is assessed though interrelationships among the visitors and those delivering the service. This chapter outlines ethnography and participatory approaches as a research method linked to destinations, communities and place identity.
An empirical and theoretical research gap on the real-time on-site behavior of attendees at community festivals was investigated using a relativistic ontology and a social constructivist epistemology combined with a mixed research strategy of hermeneutic phenomenology, ethnographic participant observation technique and grounded theory method.
Reflections on Methods and their Applications
Edited by Willem H. van Boom, Pieter Desmet and Peter Mascini
Paulo Albuquerque and Bart J. Bronnenberg
We present an illustration of how marketing and structural models can be applied in a public policy context. We describe the demand model in Albuquerque and Bronnenberg (2012) to evaluate the impact of the 2009 federal policy measure known as the “Car Allowance Rebate System” program (or “Cash for Clunkers”) on prices and demand in the auto sector.
Rebecca Kirk Fair and Laura O’Laughlin
Despite the wide scope for survey evidence used in litigation, the relevance and usefulness of expert-submitted surveys in any legal context is dependent on how they are designed and implemented. The avoidance of bias in survey evidence is central to a survey’s admissibility and the probative weight accorded to the survey expert’s testimony. This chapter discusses possible sources of bias and describes methods and techniques that a survey expert can use to minimize this bias.
Greg M. Allenby and Peter E. Rossi
Bayesian econometric methods are particularly well suited for analysis of marketing data. Bayes theorem provides exact, small-sample inference within a flexible framework for assessing particular parameters and functions of parameters. We first review the basics of Bayesian analysis and examine three areas where Bayesian methods have contributed to marketing analytics – models of choice, heterogeneity, and decision theory. We conclude with a discussion of limitations and common errors in the application of Bayes theorem to marketing analytics.
Asim Ansari and Yang Li
The field of “Big Data” is vast and rapidly evolving. In this chapter, strict attention is paid to challenges that are associated with making statistical inferences from big data. We characterize big data by the four Vs (volume, velocity, variety and veracity) and discuss the computational challenges in marketing applications using big data. We review stochastic approximation, variational Bayes, and the methods for wide data models.
Peter E. Rossi
This chapter summarizes the major methods of causal inference and comments on the applicability of these methods to marketing problems.
This chapter offers an overview of Conjoint Analysis, with an eye toward implementation and practical issues. After reviewing the basic assumptions of Conjoint Analysis, I discuss issues related to implementation; data analysis and interpretation; and issues related to ecological validity. In particular, I discuss recent evidence regarding consumers’ attention in Conjoint Analysis surveys, how it may be increased and modeled, and whether responses in Conjoint Analysis surveys are predictive of real-life behavior. Each section concludes with practical recommendations.