A
Alireza Golmohammadi
Researcher at University of Tulsa
Publications - 12
Citations - 250
Alireza Golmohammadi is an academic researcher from University of Tulsa. The author has contributed to research in topics: Quality (philosophy) & Service (business). The author has an hindex of 6, co-authored 8 publications receiving 112 citations. Previous affiliations of Alireza Golmohammadi include University of Arkansas & Wilfrid Laurier University.
Papers
More filters
Journal ArticleDOI
How Technology is Changing Retail
Venkatesh Shankar,Kirthi Kalyanam,Pankaj Setia,Pankaj Setia,Alireza Golmohammadi,Seshadri Tirunillai,Tom Douglass,John Hennessey,J.S. Bull,Rand Waddoups +9 more
TL;DR: In this article, the authors provide a deep discussion of and look ahead on how technology is changing retail, starting with a classification of technologies that impact retailing, in particular in the COVID-19 and beyond world.
Journal ArticleDOI
Cross-cultural comparison of Chinese and Arab consumer complaint behavior in the hotel context
TL;DR: In this article, the authors combine the cultural dimensions of Hofstede, 1980, Hoffmann, 2001 and Schwartz (2006) to form a new theoretical model for examining cross-cultural consumer complaint behavior.
Journal ArticleDOI
Booking on-line or not: A decision rule approach
TL;DR: In this paper, the authors explore the behavior of Iranian tourists in the online environment to specify how different factors affect tourists' decision to use online hotel booking websites and use a Dominance-based rough set (DRS) in order to model tourists' choice.
Journal ArticleDOI
Negative online reviews and consumers’ service consumption
TL;DR: In this paper, the authors examine how psychological distance and prior investments in information search influence service consumers' reactions to negative eWOM, and find that when consumers perceive the service consumption to be psychologically proximal, temporal investments in online information search diminish the impact of negative e-WOM on behavioral intentions.
Journal ArticleDOI
Importance analysis of travel attributes using a rough set‐based neural network: The case of Iranian tourism industry
TL;DR: Developing a hybrid neural network that will be able to predict tourists' overall satisfaction of their travel experience and prioritizing the travel attributes based on their proportional impact on tourists' Overall satisfaction in Iran.