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Many hotels allocate guests to specific rooms immediately after reservation. This happens because individual rooms are sold (and there is no concept of room type) or because the assignment is done by hand at reservation or because of a connection with a channel manager, which is immediately fixing the room number after a reservation request. This early allocation is suboptimal, and it causes the unnecessary rejection of some reservations when the hotel has a high occupancy level. The purpose of this paper is to investigate different room allocation algorithms, including an optimal one (called RoomTetris), aiming at higher occupancy levels and profitability.

The methodology is based on theoretical results and experimentation. The optimality or the proposed RoomTetris algorithm is demonstrated. Experiments are executed in different contexts, including realistic ones, through the adoption of a hotel simulator, to measure the improvements in the occupancy rate of the optimal and heuristic strategies with respect to random or sub-optimal assignments of rooms.

The main results are that smart allocation algorithms can greatly reduce the rejection rate (reservation requests which cannot be fit into the hotel room plan) and improve the occupancy level, the percentage of available rooms or beds sold for the various periods.

This analysis can be extended by considering cancellations and overbookings. A second possibility to add flexibility in room allocation for hotels having more than one type of rooms is that the hotel can upgrade and offer a high-price room to the customer, which given an even large flexibility to fix rooms by shifting customers to other compatible types. In addition, more complex integrations with revenue management can also be considered, for cases in which the cost of a room depends on the number of guests.

Given that the difference in occupancy rate of the optimal algorithm is particularly large in high season and high-request periods, periods which are usually associated to higher rates and higher volumes, the proposed algorithm will improve the main financial performance indicators such as revenue per available room by an even bigger multiplier, depending on the hotel pricing policy. Because the room allocation process can be completely automated, the adoption of appropriate smart allocation algorithms represents a low-hanging fruit to be picked by efficient hotel managers.

To the best of the knowledge this is the first proposal of an optimal algorithm (with proof of optimality) for the considered problem.

很多酒店, 特别是私人、家庭经营型、或者精品酒店, 在客人预定后立刻分派指定的房间给客人。这往往是因为独立房间售卖（没有特殊房型概念）或者因为客人在预定时, 工作人员手动指派房间, 亦或者是因为预订系统与渠道管理系统链接, 直接在预定后指派房间号。这种早期的分派程序是不优化的, 往往在酒店住房率高的时候, 会造成一些不必要的房间预定失败, 继而带来的利润损失。本论文旨在研究不同房间指派参数配置, 包括最优系统（RoomTetris）, 使得酒店达到更高住房率的同时产生高利润。

本论文采用理论讨论和实验等研究方法, 并展示了提出的RoomTetris参数的最优性。本论文还将其参数放在不同的情景中做实验, 以显示其提高酒店针对随机或者次优化分派的最佳启发式策略中的住房率。

研究结果表明智能型分派参数能够大大降低预定失败率（预定需求不能符合酒店房型供给）, 并且提高住房率和利润。住房时间并不是必须的参数, 极具个性化服务, 比如让客人选房间号, 可能导致利润损失（因为最优房间分派无法实现）, 房型的设计也应该参与到最优房间分派的效果中来。

预定取消和超额预定的情况也应该加入到分析中来。第二种对于拥有不止一种房型的酒店来说, 可能增加房间分派的情况在于为客人升级房型, 这样可以将客人转到其他适合房型以解决房间分派问题。此外, 更复杂系统兼容财务管理系统应该被考量, 有的时候, 房间的成本取决于客人的数量。

由于最优算法的住房率区别在于旺季和高预定时段, 也就是高房间价格和高预定量, 本论文提出的最佳算法将提高主要财务指标, 比如RevPAR（平均客房收益）。由于房间分配系统可以完全实现自动化, 那么采用智能分派系统无疑是有效酒店管理中的优质选择。

据作者所知, 此文章是首篇关于此类话题的研究优质算法（且被证实其最佳）。

This study aims to analyze how different room-committing practices affect the occupancy and profitability of hotels and it critically reviews the role of minimum-length-of-stay (MLOS) requirements given these findings.

The approach uses statistical analysis of simplified contexts to develop understanding, and simulations of more complex situations to confirm the relevance in realistic contexts.

The study demonstrates that proper solutions of the room-committing problem improve occupancy and profitability, in particular, for hotels working in high-season and high-occupancy situations. Smart committing algorithms diminish the role of MLOS requirements. More demand can be accepted without sacrificing late-arriving long reservations.

To the best of the authors’ knowledge, this work, building upon a previous one cited in this paper, is the first to rigorously study the room-committing problem and to demonstrate its relevance in practical situations and its implications on MLOS rules.

This study aims to analyze the tradeoff between two potential marketing strategies for service providers, namely, market segmentation on the basis of reservation lead time and cooperation with third parties.

This paper proposes an optimization model to describe market segmentation strategy after cooperating with third parties by taking hotels for example.

The results show that the profitability of adopting two strategies simultaneously is lower than that with market segmentation alone under some cases, which is relevant with attributes of travel agencies, such as switch rate and market share.

This study indicates that cooperation with third parties has a negative impact on profitability of hotels using market segmentation in some cases. However, randomness of demand, customer loyalty and existence of cancellation should be considered in further research.

In an e-commerce era, hotels with market segmentation based on reservation lead time, are not required to cooperate with third parties under a number of situations (e.g. high switch rates and small market sizes of travel agencies). In addition, hotels should revise the segmentation strategy based on the change rate of potential demand of individual customers. Furthermore, hotels should enhance customer loyalty, strengthen cooperation with travel agencies that possess large market shares or small switch rates.

The study preliminarily formulates the optimal market segmentation strategy on the basis of reservation lead time after cooperating with third parties, which contributes to the revenue management.

The importance of service firms cooperating with digital platforms is widely acknowledged. The authors study three contractual relationships (fixed-cost, cost-sharing, and profit-sharing) between service firms (specifically hotels) and digital platforms in a highly fragmented service supply chain to examine which of these contract types optimizes profits.

The authors extend prior models analyzing the optimal expected total profit from the travel service firm (hotel)–digital platform relationship, providing new insights into each contract type’s ability to coordinate decentralized systems and optimize profits for both parties.

This study finds that fixed cost contracts cannot coordinate the decentralized system. Cost-sharing contracts can coordinate the decentralized system but only allow one channel profit split. In contrast, profit-sharing contracts may not always perfectly coordinate the decentralized system but support alternative profit allocations. Practically, both profit-sharing and cost-sharing contracts are preferable to fixed-cost contracts.

The paper includes implications for travel service firm managers to consider when structuring contracts with digital platforms to focus on profit optimization. Profit-sharing contracts are most preferable when cost and revenue data are fully shared between parties, while cost-sharing contracts are preferable over fixed-cost contracts.

This study extends prior investigations into the utility of different contract types on the optimal profit of a travel service firm (hotel)-digital platform provider relationship. The research fills a gap in the literature concerning the contracts used in these relationship types.

The purpose of this paper is to describe criteria for selecting and processes for implementing a self‐service online resource reservation using the University of Virginia (U.Va.) Library as a case study.

The paper chronicles the identified needs, selection criteria, and deployment strategy undertaken by a U.Va. Library task force in early to mid‐2010 along with the unexpected pitfalls and policy implications uncovered by the process.

The U.Va. Library selected and rolled out a product meeting a variety of public service needs, EMS Enterprise, in the summer/fall of 2010, with lessons learned about the intricacies of IT infrastructure, public service policies and procedures, and the varied resource reservation needs of the library's libraries and labs.

The paper demonstrates how the addition of a self‐scheduling reservation system has helped fill a long‐awaited need and adds value to the U.Va. Library's Services.

The purpose of this research is to investigate online buyers' views for and against disintermediation of hotel reservation.

A structured questionnaire was developed and a telephone survey was conducted in Hong Kong in March 2008.

Empirical findings of 283 Hong Kong residents who had previously booked hotel rooms online showed that experienced online buyers were more positive towards technology‐assisted hotel room reservations and less positive towards travel agents than their less experienced peers.

Hoteliers should ensure that their rooms are marketed appropriately in different types of online and offline channels.

The study demonstrates the existence of significantly different views among different groups of online buyers based on their online purchase experiences. With the increasing popularity of internet applications, hotel managers and travel agents should set practical plans to meet the needs of online buyers.

This study aims to examine the effect of task and technology characteristics on the compatibility of technology and tasks, as well as examine the reciprocal effect between the task-technology fit and the use of information systems.

The study took place in 36 star hotels from one-star to four-star hotels in some cities and districts in South Kalimantan Province. There were 24 hotels in Banjarmasin, 7 hotels in Banjarbaru and 1 hotel in each area of Banjar, Tanah Bumbu, Tabalong, Hulu Sungai Utara and Barito Kuala. The hotels chosen were those implemented the information and communication technology as supporting administrative activities to serve hotel customers. The population was the front office staff in the existing hotels as the users of the information technology. The sampling technique used in this research was the questionnaire distribution in accordance with the number of population. Data were collected from the filled questionnaires. From the 239distributed questionnaires, 164 (68.62 per cent) were returned and used as the research data.

Task characteristics and technology characteristics have a significant and positive effect on task-technology fit, in which the higher the task characteristics and technology characteristics, the higher the task-technology fit. The task-technology fit and the use of information systems are positive and reciprocal. This means that the higher the task-technology fit, the higher the use of information systems.

The originality of this study is reciprocal relationship between the variables of use with the task-technology fit. Some researchers have found the compatibility of technological tasks affecting the use of information systems, namely, Lin and Huang (2008), Norzaidi and Salwani (2009), Larsen et al. (2009), McGill and Klobas (2009), D’Ambra and Wilson (2013), Im (2014) and Chang et al. (2015). On the other hand, in task-technology fit theory, Goodhue and Thompson (1995) state that use affects the task-technology fit.

This paper aims to assess the impact of information and communication technology (ICT) on customer satisfaction in Jamaican hotels.

An online survey approach was used with 213 hotel guests in Jamaica.

The study found that there was a significant relationship between ICT adoption and hotel guest satisfaction.

It is hoped that other researchers will refine the proposed research model.

It is believed that the findings of the study can be used to guide hotel managers regarding the ICT components with the greatest influence on customer satisfaction, which by extension can lead to improved hotel performance.

These findings can assist policy makers at the national level in formulating optimal strategies to improve the Jamaican hotel industry.

The paper proposes a research model, which can provide useful insights to hotel administrators regarding increasing the ability to satisfy customers.

Voice command and voice search are becoming increasingly popular in the hospitality and tourism industry, with many hotels and travel companies investing in voice-enabled technology to improve guest experiences and streamline operations. This technology allows travellers to manage their travel plans, request services and get information through natural voice commands on any voice-enabled device. Voice assistants are also multilingual, allowing hotels to customise responses to guests who do not speak the local language. Angie, a multilingual, in-room voice assistant, is an example of this technology. It can fulfil guest requests, answer common questions about the property and create streamlined access to a wide range of hotel amenities, such as ordering room service or requesting extra towels. Hotels can control questions and responses to assist stretched staff and provide upsell and advertising revenue through digital promotions or recommended onsite amenities or discounts. In addition, voice command technology can be used to book travel and find things to do at a destination. Google Assistant can help with travel plans like booking a hotel, checking flight status and finding things to do at a destination. In conclusion, voice command and voice search technology are transforming the hospitality and tourism industry by improving guest experiences, reducing operational costs and increasing revenue.

The purpose of this paper is to focus on the pricing strategy for viral marketing in the context of the hotel industry and propose three typical price‐network size schedules to be tested. The paper further proposes controlled experiments in hotel industry context to examine the performance of those schedules.

A simulated hotel booking web site with basic reservation functions will be constructed. Participants should be randomly drawn and asked to encourage their friends from social networks to book with the participant to achieve a better price. Each participant will be given a special promotion code embedded with different pricing information. The number of customers attracted, total revenue and time to achieve a sell‐out was collected after the experiments.

These were the expected performance differences associated with different network pricing strategies in the viral marketing campaigns.

Since participants do not need to pay for the booking, it is difficult to control their intentions. Also, in the quantity discount scenario, individual preference may not be met.

First, this paper proposed the possible pricing strategy to promote the effectiveness of viral marketing in hotel industry. Second, by initiating business with customers directly through viral marketing, hotels can have more control over the inventory and product prices.

Through a better understanding in viral marketing from the perspective of network discount strategies, the marketing opportunities enabled by social networks can be extended. This paper suggests that employing a price‐network size schedule that offers deduction in price as network size increases may be an effective tool in attracting potential customers to make purchases in a timely manner. This paper further suggests experiments to test typical pricing schedules that could be utilized in practices. The proposed experiments can provide a deeper insight into the implementation of viral marketing for hotel managers.