Inferring Market Structure from Customer Response to Competing and Complementary Products

We consider customer influences on market structure, arguing that market structure should explain the extent to which any given set of market offerings are substitutes or complements. We describe recent additions to the market structure analysis literature and identify promising directions for new research in market structure analysis. Impressive advances in data collection, statistical methodology and information technology provide unique opportunities for researchers to build market structure tools that can assist “real-time” marketing decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46981/1/11002_2004_Article_5088105.pd

Flying to Quality: Cultural Influences on Online Reviews

Customers increasingly consult opinions expressed online before making their final decisions. However, inherent factors such as culture may moderate the criteria and the weights individuals use to form their expectations and evaluations. Therefore, not all opinions expressed online match customers’ personal preferences, neither can firms use this information to deduce general conclusions. Our study explores this issue in the context of airline services using Hofstede’s framework as a theoretical anchor. We gauge the effect of each dimension as well as that of cultural distance between the passenger and the airline on the overall satisfaction with the flight as well as specific service factors. Using topic modeling, we also capture the effect of culture on review text and identify factors that are not captured by conventional rating scales. Our results provide significant insights for airline managers about service factors that affect more passengers from specific cultures leading to higher satisfaction/dissatisfaction

1000 praktických příkladů početních ze života

monografi

Usage patterns of advanced analytical methods in tourism research 1988-2008: a six journal survey

Advanced analytical methods alone do not warrant progress in scientific discovery. But their frequency of use, whether individually or in combination, and their variation over time reflect the researchers' perceived benefit. This survey covers more than 4,600 articles with more than 2,000 applications of advanced (multivariate) methods. Regression-Based Methods and Exploratory Factor Analysis account for 45% of all applications. In third place and by far the fastest growing analytical instrument is Structural Equation Modeling (SEM), followed by clustering techniques. Numerous other methods are in occasional use. Best practice examples, smart combinations of analytical methods, and underutilized methods with a promising application potential are identified. Typical pitfalls and shortcomings get diagnosed. Three of the most popular method classes and application areas, viz. scale development, SEM, and classification methods, are portrayed in greater detail and highlighted regarding their tourism-specific mode of employment

Analyzing Destination Images: A Perceptual Charting Approach

Heterogeneity of perceptions is a neglected issue in market segmentation studies. Only recently parametric approaches toward modeling segmented perception-preference structures such as combined MDS and Latent Class procedures have been introduced. A completely different nonparametric method is based on topology-sensitive vector quantization (VQ) for consumers-by-brands-by-attributes data. It maps the segment-specific perceptual structures into bar charts with multiple brand positions exhibiting perceptual distinctiveness or similarity. A brief introduction into the VQ methodology is followed by a sample study on three urban destinations competing on the world travel markets. City images serve as the underlying behavioral constructs. Preferential data are based on respondents\u27 comes-closest-to-ideal-city judgments and incorporated into the perceptual positions of city profiles. Perceptual charting works on two levels of aggregation named prototypes and perceptual sub-structures. The results demonstrate how this method prevents the analyst from drawing erroneous conclusions due to uncontrolled aggregation