GBM-5003 v1

Arora, S. D., & Mathur, S. (2020). Hotel pricing at tourist destinations – A comparison across emerging and developed markets. Tourism Management Perspectives, 35.
This study compares hotel rents across tourist and non-tourist destinations for an emerging (India) and a developed (USA) market. Furthermore, the results show that hotels at tourist destinations enjoy a rent premium across markets. However, this rent premium is positively associated with a star rating only in emerging markets and is stable in developed markets. The study aims to help hotel managers by providing a contextual framework for making these strategic investment decisions.

Carter, L. L. (2019). Equivalence and research design optimization for international market segmentation. Journal of Marketing Development & Competitiveness, 13(3), 10–24.
The massive growth of global competition has led firms to attempt various international market segmentation schemas; however, creating effective cross-border strategies is often fraught with unique challenges. This study aims to address the issues concerning equivalence and propose a two-stage approach towards effective international market segmentation. The study consolidates equivalence issues specific to international research that may increase bias into the segmentation process.

Kalia, P., & Paul, J. (2021). E-service quality and e-retailers: Attribute- based multi-dimensional scaling. Computers in Human Behavior, 115.
Digital retail is a technology-driven business. Customers shop with the help of cutting-edge self-service technologies deployed by marketers to enhance customer experience and e-service quality (e-SQ). This paper attempts to compare the similarity and dissimilarity between top e-retailers based on customer perception grounded in seven dimensions of e-SQ using data from an important emerging market. The multi-Dimensional Scaling (MDS) technique was applied to analyze the respondents' similarity judgments to draw an aggregate perceptual map of the selected e-retailers.

Yadegaridehkordi, E., Nilashi, M., Nizam Bin Md Nasir, M. H., Momtazi, S., Samad, S., Supriyanto, E., & Ghabban, F. (2021). Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques. Technology in Society, 65.
This study investigates the travelers' choice behavior toward green hotels through existing online travel reviews on TripAdvisor. Accordingly, a method combining segmentation and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques were developed to segment travelers based on their provided reviews and prioritize green hotel attributes based on their importance level in each segment.