Browsing by Author "Rahman, Imran"

Now showing 1 - 4 of 4
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    Breaking barriers for Bangladeshi female solo travelers
    Bernard, Shaniel; Rahman, Imran; McGehee, Nancy G. (Elsevier, 2022-01-01)
    Asian Muslim women's travel habits are sorely under-researched. In response to various calls for research in this area, this study utilizes Hofstede's five cultural dimensions to determine how Bangladeshi cultural values inhibit and/or enhance travel constraints for solo Muslim female travelers and the subsequent effects on solo travel behavior. We propose solo travel as a strategic tourism development tool to achieve mobility rights and gender equality particularly for destinations that are highly populated with more women than men. Introducing an interpretivist qualitative approach, the study extracted both survey and open-ended responses from 307 frequent Bangladeshi solo travelers that were recruited from a women-only English-speaking Facebook Bangladeshi travel group. The findings reveal that this group is constrained by a unique combination of intrapersonal, interpersonal, and structural factors. Power-distance, masculinity, and uncertainty-avoidance also play key roles. Sustainable and practical applications are outlined for destination management organizations, travel planners, policy makers, non-governmental organizations, and for-profit tour companies that benefit both Bangladeshi solo female travelers and those with whom they interact.
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    Electrical Load Disaggregation and Demand Response in Commercial Buildings
    Rahman, Imran (Virginia Tech, 2020-01-28)
    Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. In recent decades due to increasing electricity demand, there is an increased likelihood of electrical power systems experiencing stress conditions. These conditions lead to a limited supply and cascading failures throughout the grid that could lead to wide area outages. Demand Response (DR) is a method involving the curtailment of loads during critical peak load hours, that restores that balance between demand and supply of electricity. In order to implement DR and ensure efficient energy operation of buildings, detailed energy monitoring is essential. This information can then be used for energy management, by monitoring the power consumption of devices and giving users detailed feedback at an individual device level. Based on the data from the Energy Information Administration (EIA), approximately half of all commercial buildings in the U.S. are 5,000 square feet or smaller in size, whereas the majority of the rest is made up of medium-sized commercial buildings ranging in size between 5,001 and 50,000 square feet. Given that these medium-size buildings account for a large portion of the total energy demand, these buildings are an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique to disaggregate the power of individual HVACs using machine learning classification techniques, where a single power meter is used to collect aggregated HVAC power data of a building. This method is then tested over a number of case studies, from which it is found that the aggregated power data can be disaggregated to accurately predict the power consumption and state of activity of individual HVAC loads. The second work focuses on a DR algorithm involving the determination of an optimal bid price for double auctioning between the user and the electric utility, in addition to a load scheduling algorithm that controls single floor HVAC and lighting loads in a commercial building, considering user preferences and load priorities. A number of case studies are carried out, from which it is observed that the algorithm can effectively control loads within a given demand limit, while efficiently maintaining user preferences for a number of different load configurations and scenarios. Therefore, the major contributions of this work include- A novel HVAC power disaggregation technique using machine learning methods, and also a DR algorithm for HVAC and lighting load control, incorporating user preferences and load priorities based on a double-auction approach.
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    The incidence of environmental status signaling on three hospitality and tourism green products: A scenario-based quasi-experimental analysis
    Rahman, Imran; Chen, Han; Bernard, Shaniel (Elsevier, 2023-03-01)
    This study examined whether environmental status signaling (ESS) applied to purchase situations involving three environmental products in hospitality and tourism: an environment-friendly car, an organically-produced wine, and a green hotel. Findings from three scenario-based quasi-experimental studies suggested that ESS differed across the type of green product and the consumption setting. When status motive was high, consumers would purchase the environment-friendly car over its more-luxurious conventional counterpart across all consumption settings. Higher purchase intention was also found for the more luxurious hotel over the environment-friendly hotel and for the organically-produced wine over the better-rated conventional wine in private settings. These effects disappeared in the public setting. Moreover, ESS was independent of whether green products were priced equal or more. Recommendations on how to promote the personal benefits of green products and improve performance, design, and packaging of the green products were provided to practitioners in the hospitality and tourism industry.
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    Sustainability Communication in Hotels: The Role of Cognitive Linguistics
    Bernard, Shaniel; Rahman, Imran; Douglas, Alecia (Sage, 2023-03-06)
    Efficiently communicating sustainability initiatives is critical to generating positive attitudes and pro-environmental behavior in hotel consumers. However, research on the combined effect of various message factors to improve environmental message persuasiveness is scant. To fill this gap, two studies were conducted with a sample of onsite and online hotel guests to offer new insights into the combined effect of language design elements that identify connectives and prepositional phrases with message content as essential grounding components of persuasion. Our results demonstrate the effectiveness of restriction-based language design on booking intention through nuanced mechanisms involving perceived environmental performance, perceived greenwashing, and environmental concern. This study contributes to the growing literature on sustainability marketing by examining the design and integration of linguistic tools that hospitality managers can use in their sustainability communication campaigns. Additional practical and theoretical implications are provided.