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The Veterans Healthcare Administration (VHA) has promoted Specialty Care Neighborhoods (SCN) to enhance the coordination of services between primary and specialty care. Care coordination agreements (CCAs) were included as a critical element in the SCN program. The purpose of this paper is to examine the role of these documents in the successful implementation of SCNs.

Content, quality, and perceived usefulness of CCAs from 19 SCN sites were evaluated. CCA content was defined as the presence or absence of eight key components: contact information, process for urgent consults, process for e-consults, content of consults, primary and specialty care responsibilities, expected response time, discharge criteria, and review criteria. CCA quality was based on a qualitative assessment of CCA content; and perceived usefulness was based on a qualitative assessment of interview responses from CCA users. CCA characteristics were compared to SCN implementation levels using descriptive statistics. SCN implementation level was defined and measured by VHA Specialty Care Services.

Participating sites with medium-high or high SCN implementation levels had CCAs with more key components and of higher quality than sites with medium-low to medium SCN implementation levels. Perceived usefulness of CCAs was not associated with implementation level.

Since this study built on a quality improvement effort to facilitate care coordination, a rigorous research approach was not used. Specific CCA components could not be examined nor could specific hypotheses be tested due to the small and diverse sample. Findings presented are only preliminary.

The examination of CCAs suggests that these documents may be helpful to improve communication among primary and specialty care providers by explicitly stating agreed upon processes, mechanisms and criteria for referrals, roles and responsibilities for the co-management of patients, and timelines for review of CCAs.

This small study suggests that high-quality CCAs, which include a number of key components, can facilitate the implementation of coordinated care. Key characteristics of CCAs are identified in this study, including measures of CCA content, quality, and usefulness, which can be used in future efforts to develop and evaluate efforts to improve care coordination.

Purpose: The purpose of this chapter is to elaborate on the major conceptual and practical considerations of the use of robots, artificial intelligence and service automation (RAISA) in travel, tourism, and hospitality companies (TTH).

Design/methodology/approach: The chapter develops a conceptual framework of the major issues related to the use of RAISA in the travel, tourism and hospitality context.

Findings: The findings indicate that while there is a creeping incursion of RAISA into TTH, there are major concerns that the TTH industry has to consider in regard to automating TTH services.

Practical implications: In a practical sense, the chapter identifies the decisions that TTH industry professionals need to take when dealing with RAISA technologies. Furthermore, the chapter elaborates on the impacts RAISA have on business operations, marketing management, human resources and financial management of TTH companies. The TTH industry has to adjust its practices and communicate with its workforce in ways as not to increase Luddite tendencies and resistance among employees.

Social implications: The analysis shows that there is an upcoming era in which automation of services will be so advanced that wealthy countries may not need to import labour to make up with its own aging workforce, suggesting that RAISA and its further development has the potential for disrupting society and international relations.

Originality/value: This chapter provides a comprehensive review of the issues related to the use of RAISA in the TTH industry, including the drivers of RAISA adoption in tourism, advantages and disadvantages of RAISA technologies compared to human employees, decisions that managers need to take, and the impacts of RAISA on business processes. It shows how macroenvironmental pressures shape the microeconomic decisions to use RAISA in a TTH context.

The purpose of this paper is to establish the progress of information and communication technology (ICT) based on a review of papers published in tourism and hospitality journals between 2009 and 2013.

Based on three major databases, 107 journal papers were retrieved and reviewed. The papers were grouped into two major categories, consumer and supplier, which generally comprise the key players in the industries.

A content analysis showed that hospitality and tourism industries use ICT in different functional units and for different applications. This, in turn, indicates their wide adoption in the industry. Industrial implications are discussed.

On the basis of the content analysis, industry practitioners can learn about up-to-date practices and decide how to take advantage of recent technological developments.

A major contribution of this paper is the comprehensive review of recently published papers in tourism and hospitality journals from the perspectives of consumer and supplier.

Organizations often view current market situations from the customers’ viewpoints. To do so, they face an incredibly vast volume and variety of data. Data visualization makes huge amounts more accessible and understandable and helps to communicate complex information more accurately and effectively. This chapter discusses how to utilize user-generated data in the tourism industry to enhance the customer experience through data visualization tools. Five analysis tools are used to identify the factors that contribute to hotel guests’ dissatisfaction in five top city destinations. Identifying and eliminating dissatisfaction is the first step in enhancing the quality of the experience and in fostering loyalty in the long run.

This study aims to identify the key determinants of Airbnb rating scores.

This study is based on a sample of 127,257 listings across 43 cities. A total of 24 explanatory variables were identified, and they were further grouped into host verification information, communication, policy of renting, space, information about environment, price and experience of hosting. Both Tobit and ordered logit models were used to perform the analysis.

The results indicate that good communication, large space and provision of information about the listings’ environment have a positive effect on users’ satisfaction, whereas experience of hosting negatively influences users’ satisfaction.

This study contributes to the peer-to-peer accommodation literature by affording a more complete understanding about guest satisfaction and its determinants.

This study aims to explore the dimensions and sub-factors of Airbnb hosts’ affinity-seeking strategies. It also aims to build a conceptual framework of hosts’ affinity-seeking strategies and their impact on Airbnb guests’ feelings of affection toward their host and/or the homestay and their behavioral intentions.

Based on 150,161 Inside Airbnb online reviews in three major US cities, this study uses semantic analysis to explore processes through which hosts’ affinity-seeking strategies are constructed.

A conceptual framework is proposed to identify two dimensions (warmth and competency) and their sub-factors (“presenting friendly attitudes,” “showing personality traits,” “providing service and help” and “promoting social interaction and sharing”) of Airbnb homestay hosts’ affinity-seeking strategies. The framework shows a positive relationship between these strategies and guests’ affection and behavior in response.

The research findings provide valuable insights to hosts for improving their affinity and strengthening their competitive advantages. They also offer guidance to destination management organizations on how to build a positive destination image.

To the best of the authors’ knowledge, this study is the first to conceptualize Airbnb homestay hosts’ affinity-seeking strategies. It contributes to the literature by incorporating social cognition theory and service theory in the analysis of these strategies.

The purpose of this study is to examine the effects of host attributes and travelers’ frequency of past stays and their interaction on the likelihood of repeat purchase of home-sharing services at both the host and listing levels.

A combination of econometrics analyses using a large-scale, granular online observational data set collected from a home-sharing platform was performed.

Travelers exhibit salient loyalty to home-sharing services. At the host level, host attributes including acceptance rate and listing capacity positively affect travelers’ likelihood of repeat purchase; such effects diminish as travelers’ frequency of past stays with a host/listing increases. At the listing level, confirmation efficiency and acceptance rate are critical, and travelers’ frequency of past stays matters.

Responding to the call for more research on customer loyalty of sharing economy, this study instantiated on a home-sharing website in China and adds a unique perspective to the research domain, but its findings may not be generalized in other settings.

This study identifies the factors affecting customers’ repeat purchase behaviors at both the host and listing levels, allowing the hosts, webmasters of home-sharing websites and even hoteliers to advance specific tactics to promote repeat purchase among travelers.

Loyalty was measured with real-time internet-enabled observational data about travelers’ actual repeat purchase behavior on a home-sharing website, rather than assessing consumers’ behavioral intentions through the conventional survey method. Two specific levels of customer loyalty were analyzed, including the ones towards a service provider (host) and a service product (listing).

This study aims to provide an in-depth understanding of the complex nature of Airbnb user experience by analyzing the pattern and sentiment of online reviews and assessing the relationships among review scores.

Big data analysis is conducted using Airbnb users’ online reviews of 16 US cities; correlation is run on review scores.

The key themes of Airbnb users’ online reviews are “clean,” “location,” “stay,” “home,” “place,” “host,” “neighborhood” and “recommend” and users have positive Airbnb experiences in general. The score of “cleanliness” significantly affects the “overall review” score.

This study is exploratory in nature; mixed methods should be used in the future to measure the relationship between user experience and extracted themes. As the context is in the USA in the current study, comparisons of review patterns across different countries and regions are necessary for later studies. Furthermore, future studies should consider Airbnb users’ demographics, personality and lodging preferences.

It is important for Airbnb hosts to maintain a clean and accessible property. Both Airbnb hosts and hoteliers should enhance the attributes that generate positive customer reviews. Each city should develop different strategies based on the performance of “cleanliness” and “overall review.”

This study supplements the existing literature in Airbnb user experience by analyzing online reviews in 16 US cities via Leximancer 4.0.

分析Airbnb网络评论：论美国16城市的用户体验

本论文旨在深入了解Airbnb用户体验的复杂属性, 通过分析网络评论以及网络评分之间的关系来找寻规律和判断用户情感偏差。

本论文采用大数据方式, 样本为美国16个城市的Airbnb用户网络评论; 本论文还对网络评分做出相关性分析。

研究结果表明, Airbnb用户网络评论的关键主题为“清洁性”、“地理位置”、“居住体验”、“家”、“地点”、“招待主人”、“邻里”、以及“推荐”和用户总体来说对Airbnb有着积极体验。“清洁度”分数对“整体评论”有显著影响。

本论文属于开拓性研究作品; 未来项目可以采用混合方法来衡量用户体验与主题的关系。本论文的研究背景是美国, 未来研究可以做不同国家和区域的评论模式对比分析。此外, 未来研究还应该考虑Airbnb用户的统计数据指标、个性、以及住宿设施偏好。

对于Airbnb住宿提供者来说, 保持设施的清洁和可使用性是非常重要的。对于Airbnb住宿主人和酒店经营者来说, 加强服务属性来提高网络好评是非常重要的。每个城市都应该基于“清洁度”和“整体评论”来开发不同的战略。

本论文补充了现有Airbnb用户体验的文献, 通过Leximancer 4.0 软件来分析美国16个城市的网络评论

This study aimed to identify distinctive characteristics of hotel guests and understand their perception of guestroom technologies (GRTs) in hotels by utilizing the theory of consumer innovativeness and the social influence theory.

The data were collected from 268 hotel guests. The K-means clustering algorithm was employed to identify participants based on their views on the significance of technologies provided in guest rooms. A multivariate analysis of variance was applied to investigate if there were significant differences among clusters regarding social influence on hotel bookings, technology innovativeness and technology expertise and knowledge.

Two clusters were identified: technology compassionates and casual users of technology. Findings revealed that technology compassionates are more influenced by their friends when booking a hotel compared to casual users of technology. The ability to link up multiple wireless mobile devices, accessible outlets and mobile websites were the most critical GRTs for technology compassionates.

Technology should be considered a crucial part of the hotel guest experience. Hoteliers ought to continue investing in smart technologies to improve their guests' experiences. GRTs can reduce overhead staff costs while giving guests more control over their stay by utilizing everyday items like smartphones and offering them more power over their lodging experience.

This study advances the existing literature on GRTs by identifying which GRTs produce the most customer satisfaction. Moreover, this study explores the impact of social influence, innovativeness as a personality trait and having expert knowledge of technologies on preferences for GRTs.