Ordinal Optimization Based Algorithm for Hotel Booking Limits Problem

Purpose The importance of online reviews on online hotel booking has been widely acknowledged. However, not all online reviews affect consumers equally. Compared with common online reviews, key online reviews (KORs) have a greater influence on consumers' decisions and online hotel booking. This study takes the first step to investigate the factors affecting the identification of KORs and the role of KORs in online hotel booking.Design/methodology/approach To test the research hypotheses, this study develops a crawler to obtain 551,600 online reviews of 650 hotels in ten representative large cities in China. This study first uses a binary logistic regression to identify KORs by combining review content quality and reviewer characteristics and then uses a log-regression model to investigate the role of KORs in online hotel booking.Findings This study mined the factors affecting the identification of KORs by analyzing review contents and reviewer characteristics. Our results revealed that KORs play a mediating role in the effects of review content and reviewer characteristics on online hotel booking.Originality/value This study focuses on KORs, which have received limited attention in research but are important to practitioners. Specifically, this study investigates the antecedents and consequences of KORs. Our results enable hotel managers to manage online reviews effectively, particularly KORs.

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Users’ acceptance of innovative mobile hotel booking trends: UK vs. PRC

Information Technology & Tourism ◽

10.1007/s40558-018-0123-x ◽

2018 ◽

Vol 20 (1-4) ◽

pp. 9-36 ◽

Cited By ~ 3

Author(s):

Meng Tao ◽

Muhammad Zahid Nawaz ◽

Shahid Nawaz ◽

Asad Hassan Butt ◽

Hassan Ahmad

Keyword(s):

Hotel Booking

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Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems

Applied Sciences ◽

10.3390/app10062075 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2075 ◽

Cited By ~ 2

Author(s):

Shih-Cheng Horng ◽

Shieh-Shing Lin

Keyword(s):

Optimization Problems ◽

Solution Space ◽

Inequality Constraints ◽

Optimization Methods ◽

Ordinal Optimization ◽

Np Hard ◽

Constrained Optimization Problems ◽

Inequality Constrained Optimization ◽

Hard Problems ◽

Np Hard Problems

The stochastic inequality constrained optimization problems (SICOPs) consider the problems of optimizing an objective function involving stochastic inequality constraints. The SICOPs belong to a category of NP-hard problems in terms of computational complexity. The ordinal optimization (OO) method offers an efficient framework for solving NP-hard problems. Even though the OO method is helpful to solve NP-hard problems, the stochastic inequality constraints will drastically reduce the efficiency and competitiveness. In this paper, a heuristic method coupling elephant herding optimization (EHO) with ordinal optimization (OO), abbreviated as EHOO, is presented to solve the SICOPs with large solution space. The EHOO approach has three parts, which are metamodel construction, diversification and intensification. First, the regularized minimal-energy tensor-product splines is adopted as a metamodel to approximately evaluate fitness of a solution. Next, an improved elephant herding optimization is developed to find N significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the N significant solutions. The EHOO approach is tested on a one-period multi-skill call center for minimizing the staffing cost, which is formulated as a SICOP. Simulation results obtained by the EHOO are compared with three optimization methods. Experimental results demonstrate that the EHOO approach obtains a superb solution of higher quality as well as a higher computational efficiency than three optimization methods.

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