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Construction projects have become increasingly long, complex and costly with waste and inefficiencies and often fail to achieve the desired results. Integrated project delivery (IPD) is believed to change these problems. A reasonable and fair profit distribution mechanism is a critical factor for ensuring the success of the IPD projects. This study aims to investigate the strategies of all participants in the profit distribution of an IPD project with respect to the factor of the effort level.

This study describes the influence of owners and participants on profit distribution due to their respective efforts in the IPD project alliance. The influence of effort level on profit distribution is discussed based on the Holmstrom-Milgrom model of asymmetric information game theory and principal-agent theory, combined with incentive compatibility (IC) constraints and individual rationality (IR) constraints.

The results show that the optimal level of effort by each participant optimizes the profit distribution of an IPD project. At the same time, in the revenue incentive contract, the effort level of the participants is positively correlated with the profit distribution, proportional to their contribution coefficient and inversely proportional to the square of the cost of their creative activities in terms of effort. Each party of an IPD project can adopt a series of measures to improve their own effort level and choose the optimal level of effort based on the profit distribution, while satisfying their own utility maximization.

This study introduces the Holmstrom-Milgrom model in the principal-agent theory to explore the influence of the effort level on profit distribution in IPD projects. The quantitative model can contribute to establish a fair and efficient profit distribution scheme for the IPD projects.

The chapter proposes to investigate online reputation of hospitality brands and its measurements. Brand reputation is generally defined as an overall appraisal of a company by its stakeholders, which is the result of the company's past actions and predictions about the company's future (Ferguson, Deephouse, & Ferguson, 2000). Being viewed as the opinion shared among a group of stakeholders (Dowling, 2008), it plays an important role in the tourism industry. With the progress of Information Communication Technologies (ICTs), reviews and user-generated contents of destinations and of hospitality companies together with the related emerging brand reputation can influence consumers' behaviors and choices. Brand reputation analysis could be more useful in the hospitality brand management when integrated with brand image and brand identity analysis, mainly because in tourism businesses and destinations, brands are typically affected by an inherent fragility determined by the service nature of products (Casarin, 1996). According to Biel (1991), the meanings that consumers assign to a brand are synthesized into brand associations formed by the components perceived to underlie the brand's image. As well as brand reputation, strong, positive and unique associations reinforce a brand and increase its equity that requires significant internal brand identity efforts, which should create a corresponding brand image through integration in overall marketing programmes (Keller, 2003). It makes sense to develop an analytical research approach that compares online brand reputation (OBR) with brand association matching as a measure of the alignment between brand identity and brand image in hospitality companies. This comparative analysis emerging from brand reputation, brand image and brand identity analysis can reveal divergent situations (i.e., high brand reputation and low brand association matching) and orient brand managers in reviewing online brand communication. Brand reputation and brand image analysis will be contextualized in an online community as a social setting that is considered to be a new type of market (Muniz & O'Guinn, 2001). We focus on hospitality online communities populated by consumers and other actors such as influencers and bloggers: their brand perception could be separately compared with brand identity that we will extract from company communications including presentational information and brand-related press releases found on websites, nonfinancial narrative from annual reports, and interviews with managers published in mainstream media sources. In our analysis we will focalize on a cluster of luxury hospitality companies integrating a netnographic and text-mining techniques. We will use both the techniques in order to (1) extract and study brand associations in terms of brand reputation, brand image, and brand identity; (2) develop indicators of brand reputation and brand association matching; and (3) discuss their utility in the management of the hospitality company brands.

Millions of ratings and reviews about products are available on the Internet for free, and they are used by academic researchers in the tourism sector. Data from websites like TripAdvisor are replacing or complementing traditional questionnaires and interviews. The authors are proposing a methodology to estimate the percentage accounted for by the sample of self-interviewed individuals over the total study population, in order to calculate the reliability of the results obtained. Average percentages obtained for hotels cannot be easily generalized due to the high dispersion in participation rates among hotels, even in the same city. Participation levels for tourist attractions are substantially lower than those for hotels and are likely biased, due to the fact that some tourists evaluate places without actually visiting them, merely after viewing them from the outside.

Hotels are immersed in a very competitive environment and hoteliers have to plan and redesign their strategies to stay in the hospitality industry while faced with a steady rise in competition. Hoteliers can employ horizontal differentiation strategies and pricing decisions to gain a competitive advantage over their competitors. The goal of our work is to analyse the effect of pricing and horizontal differentiation strategy of a hotel on its online reputation and to analyse if the hotel location and agglomeration of competitors moderates their relationship with online reputation.

With a sample of 264 hotels from Paris, an empirical study is developed that aims to analyse, using regression techniques, the impact of price, differentiation, location and competitive environment on online ratings given by consumers in the hospitality context.

The paper provides empirical evidence of how a good location improves the online reputation of a company and how pricing strategies should take into account the location and number of competitors since a good location allows premium prices to be valued positively by consumers while an inappropriate location can produce the opposite effect. Depending on location, the number of competitors can intensify or reduce the effect of price on online reputation. Finally, online reputation only benefits from horizontal differentiation strategies when the degree of agglomeration is low.

This work provides insights about how hotels can strengthen their online reputation through pricing and differentiation strategies, incorporating elements of their environment such as location and competition in this decision-making process.

酒店處於一個競爭非常激烈的經營環境中，營運者要面對競爭日益加劇的挑戰，在這情況下，他們必須計劃並重新設計其策略，以在酒店服務業中仍能繼續其業務。營運者可採用水平分化策略和定價決策，以取得比同業更大的競爭優勢。本研究旨在分析，若酒店採用價格及水平差異之策略，這對它們的網上聲譽會起什麽效應；研究亦探討酒店所處的位置及競爭者的凝聚會否減弱其與網上聲譽的關係。

研究樣本為來自巴黎264間酒店。研究人員採用實驗研究法，透過迴歸分析法，去分析在酒店服務的背景下，價格、差異化、地點和競爭環境如何影響消費者所給予的網上評分。

本文提供了經驗性證據，證明良好的地點會改善公司的網絡聲譽； 同時，亦證明釐定價格策略時，經營者應考慮競爭者的位置和數目，這是因為良好的地點會讓消費者正面評價溢價，而不合適的地點則會導致相反的效應。根據所在的地點，消費者的數目能增強或減弱價格對網絡聲譽的影響。最後，本文也證明了網絡聲譽只會在凝聚程度低的情況下才會受惠於水平分化策略。

本研究提供啟示，使我們更明瞭酒店如何能透過價格及差異化策略，同時亦透過把它們的環境元素如地點和競爭等納入決策程序中，來強化網絡聲譽。

Online consumer reviews have become increasingly important for consumer decision-making. One of the most prominent examples is the hotel industry where consumer reviews on websites, such as Bookings.com, TripAdvisor and Venere.com, play a critical role in consumers’ choice of a hotel. There have been a number of recent studies analyzing various aspects of online reviews. The purpose of this paper is to investigate their effects in terms of hotel occupancy rates.

This paper measures through regression analysis the impact of three dimensions of consumer reviews (i.e. review score, review variance and review volume) on the occupancy rates of 346 hotels located in Rome, isolating a number of other factors that might also affect demand.

Review score is the dimension with the highest impact. The results suggest that after controlling for other variables, a one-point increase in the review score is associated to an increase in the occupancy rate by 7.5 percentage points. Regardless the review score, the number of reviews has a positive effect, but with decreasing returns, implying that the higher the number of reviews, the lower the beneficial effect in terms of occupancy rates is.

The findings quantify the strong association of online reviews to occupancy rates suggesting the use of appropriate reputational management systems to increase hotel occupancy and therefore performance.

A major contribution of this paper is its comprehensiveness in analyzing the relation between online consumer reviews and occupancy across a heterogeneous sample of hotels.

How to get a lighter and stronger anti-rolling torsion bar has become a barrier for the development of high-speed railway vehicles. The purpose of this paper is to realize the multi-objective optimization of an anti-rolling torsion bar with a Modified Non-dominated Sorting Genetic Algorithm III (MNSGA-III), which aims to obtain a better design scheme of an anti-rolling torsion bar device.

First, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) uses a simulated binary crossover (SBX) operator and a polynomial mutation operator, while the MNSGA-III algorithm proposed in this paper introduces an arithmetic crossover and an adaptive mutation operator to change the crossover and mutate operator in NSGA-III. Second, two algorithms are tested by ZDT3, ZDT4 functions. Both algorithms set the same population size and evolutionary generation, and then compare the results of NSGA-III and MNSGA-III. Finally, MNSGA-III is applied to the multi-objective model of an anti-rolling torsion bar which is established by taking the mass and stiffness of the torsion bar as the optimization object. After that, it obtains the Pareto solution set by solving the multi-objective model with MNSGA-III. The only optimal solution selected from the Pareto solution set is compared with the traditional design scheme of an anti-rolling torsion bar.

The MNSGA-III converges faster than NSGA-III. Besides, MNSGA-III has better diversity of Pareto solutions than NSGA-III and is closer to the ideal Pareto frontier. Comparing with the results before the optimization, it shows that the volume of the anti-rolling torsion bar reduces by 1.6 percent and the stiffness increases by 3.3 percent. The optimized data verifies the effectiveness of this method proposed in this paper.

The simulated binary crossover operator and polynomial mutation operator of NSGA-III are changed into an arithmetic crossover operator and an adaptive mutation operator, respectively, which improves the optimization performance of the algorithm.

This paper aims to define and demonstrate the importance of crowdsourcing for the improvement of diverse functions of hotels, the advantages and disadvantages of this technique and, specifically, the relevance of crowdvoting for enhancing hotel sales, and diverse hotel performance dimensions.

A total of 45,103 opinions from Booking.com, a sample of 184 questionnaires addressed to hotel managers and partial least squares (PLS)-path modeling were combined to contrast the hypotheses.

The results empirically show the direct and positive impact of the opinions of the crowd on the amount of hotel sales that do not depend on physical intermediaries, and the impact that this has on the performance dimensions of hotels.

The paper shows the relevance of following strategies addressed at increasing the customers’ recommendations in the main social media or virtual travel agencies, as a mechanism to reduce tourism and hospitality organizations’ traditional weaknesses and increase their long-term performance.

The novelty of crowdsourcing, means that few works (although with an explosive increase and with high impact) have focused on this aspect in the literature, especially in the tourism and hospitality literature. The results of our research open new areas of research and contrast the relevance of crowdsourcing and specifically crowdvoting for the success of hotels. In addition, the methodology employed, by mixing the data from a social media, with a questionnaire addressed to managers, can open new ideas for future works.

This study aims to investigate the relationships between service quality, perceived value and hotel guest satisfaction, drawing upon data from TripAdvisor – an online travel agent (OTA) platform. The study also investigates the mediating role of perceived value on the relationship between service quality and satisfaction, as well as the moderating role of hotel star ratings on all direct and indirect relationships.

Data for this study were collected via Web scraping from August–October 2018. Data were collected from 192 three- to five star-rated hotels in Kuala Lumpur, Malaysia. Partial least squares – structural equation modeling was used for data analysis. Furthermore, importance-performance map analysis (IPMA) was performed to identify the most important items of service quality and perceived value in improving customer satisfaction.

The findings of this study provide support for all direct and indirect relationships for three-star and four- and five-star hotels. Moreover, the results indicate that perceived value mediates the relationship between service quality and customer satisfaction. These results support the moderating role of hotel star ratings for the relationship between service quality and perceived value. The results also show that after perceived value, three-star hotels looking to improve customer satisfaction should prioritize improving the quality of their services, sleep quality, cleanliness and rooms. Four- and five-star hotels, on the other hand, should prioritize service, cleanliness, room and sleep quality.

OTA platforms collect a wealth of data pertaining to large number of hotels; nevertheless, few studies to date have drawn on this data to examine a pre-determined conceptual framework developed based on the literature. As such, this study makes a valuable methodological contribution to the tourism and hospitality literature. In terms of theoretical contributions, this study examines the mediating role of perceived value between service quality and satisfaction using OTA data. In addition, this study assesses the moderating role of hotel star ratings for the direct and indirect effects of service quality on satisfaction. Using IPMA, this study compares the importance and performance of service quality indicators to generate satisfaction between three-star and four- and five-star hotels.

本论文检测了服务质量、价值感知、和酒店顾客满意度之间的关系, 使用TripAdvisor的数据—OTA。本论文还检测了价值感知对服务质量和满意度之间的中介作用, 以及酒店星级评价对其中直接和间接关系的调节作用。.

本论文采样通过网络爬虫技术, 截取了2018年八月至十月之间的数据。研究样本为192家马来西亚Kuala Lumpur地区的三星-五星酒店。样本分析方法为PLS-SEM。此外, 本论文采样IPMA分析法来找出提高顾客满意度中的服务质量和价值感知中最重要的因子。.

研究结果指出了三星、四星、五星酒店的直接和间接关系。此外, 研究还显示了服务质量和顾客满意度关系的价值感知中介作用。研究结果还指出了酒店星级评价对服务质量和价值感知关系的调节作用。此外, 研究还指出, 除了价值感知, 如果三星酒店想提高顾客满意度, 那么他们应该优先提高其服务质量、睡眠质量、清洁度、和房间。另一方面, 四星和五星酒店应该优先提高其服务质量、清洁度、房间、和睡眠质量。.

OTA平台搜集大量酒店数据, 但是很少作品研究这些数据, 以检测根据文献提出的理论模型。因此, 本论文在方法论上对旅游酒店文献做出宝贵贡献。理论贡献而言, 本论文使用OTA数据检测了价值感知对服务质量和满意度关系之间的中介作用。此外, 本论文检测了酒店星级评价对服务质量和满意度之间直接和间接关系的调节作用。本论文使用IPMA方法, 比较各种服务质量指标的重要性对在三星、四星、五星酒店的提高满意度的不同作用。.

This study examines whether a higher interest alignment between online travel agencies (OTAs; hosting platform) and hotels (business owners) will intensify review manipulation activities.

With a panel data set collected from a Chinese online travel agency and a travel search engine, the authors develop a matching-based difference-indifference approach to examine the presence of partnership-intensified review manipulation.

The authors find that the ratings of agency's partner hotels (with a higher interest alignment) are abnormally higher than those of matched non-partner hotels (with a lower interest alignment), after they are benchmarked with their ratings on the search engine (without a partnership business model). Further, the analysis results indicate that this partnership-intensified manipulation deteriorates the hotel's sales performance because of damaged customer trust and satisfaction.

Previous studies implicitly assume that review manipulator is independent from the hosting platform. This is the first study examining the role of the hosting platform in review manipulations.

With the growth of social media and online communications, consumers are becoming more informed about hotels' services than ever before. They are writing online review to share their experiences, as well as reading online review before making a hotel reservation. Hotel customers considered it as reliable source and it influences customers' hotel selection. Most of these reviews reside in unstructured format, scattered across in the Internet and inherently unorganized. The purpose of this study was to use predictive text analytics to identify sentiment drivers from unstructured online reviews.

The research used sentiment classifications to analyze customers' reviews on hotels from TripAdvisor. In total, 9,286 written reviews by hotel customers were scrapped from 442 hotels in Malaysia. A detailed text analytic was conducted and was followed by a development of a theoretical framework based on the hybrid approach. AMOS was used to analyze the relationship between customer sentiments and overall review rating.

With the use of Structural Equation Modeling (SEM) and clustering technique, a list of sentiment drivers was detected, i.e. location, room, service, sleep, value for money and cleanliness. Among these variables, service quality and room facilities emerged as the most influential factors. Sentiment drivers obtained in this study provided the insights to hotel operators to improve the hotel conditions.

Although this study extended the existing literature on sentiment analysis by providing valuable insights to hoteliers, it is not without its limitations. For instance, online hotel reviews collected for this study were limited to one specific online review platform. Despite the large sample size to support and justify the findings, the generalizability power was restricted. Thus, future research should also consider and expand to other type of online review channels. Therefore, a need to examine these data reside various social media applications, i.e. Facebook, Instagram and YouTube.

This study highlights the significance of hybrid predictive model in analyzing the unstructured hotel reviews. Based on the hybrid predictive model we developed, six sentiment drivers emerged from the data analysis, i.e. location, service quality, value for money, sleep quality, room design and cleanliness. This consideration is critical due to the ever-increasing unstructured data resides in the online space. This explores the possibility of applying data analytic technique in a more efficient manner to obtain customer insights for hotel managerial consideration.

This study analyzed customer sentiments toward the hotel in Malaysia with the use of predictive text analytics technique. The main contribution was the list of sentiment drivers and the insights needed to improve the hotel conditions in Malaysia. In addition, the findings demonstrated motivating findings from different methodological perspective and provided hoteliers with the recommendation for improved review ratings.