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The purpose of this paper is to argue that if mental health services are to genuinely support the recovery of those who they serve then recovery principles must permeate all facets of the organisation, in particular human resources and workforce development.

This paper draws on the principles of recovery-focused approaches to people who use services and explores how these might guide a recovery-focused approaches to human resources and workforce issues.

The recovery principles like recognising and utilising the expertise of lived experience, co-production and shared decision making, peer support, focusing on strengths and becoming an expert in your own self-care all have as much relevance for creating a recovery-focused workforce as they do in the recovery journeys of those who use services. Everyone who uses services is “more than a mental patient” and everyone who provides services is “more than a mental health practitioner” – we need to use all the assets that everyone brings.

Although there has been a great deal of discussion about the features of recovery-focused services, there has been little, if any, consideration of extending the principles of recovery to human resources. The aim of this paper is not to offer a blue print but to begin an exploration of what a recovery-focused approach to workforce issues might look like.

This study aims to offer an overview of hospitality and tourism research on artificial intelligence (AI) and its impact on the industry. More specifically, this study examines hospitality and tourism AI research trends in hospitality and tourism customer service experience creation and delivery, service failure and recovery, human resources and organizational behavior. Based on the review, this study identifies the challenges and opportunities and provides directions for future studies.

A narrative synthesis approach was used to review the hospitality and tourism research on AI and its impact on various aspects of the industry.

AI and AI applications in customer service experience creation and delivery and its possible effects on employees and organizations are viewed as a double-edged sword. Although the use of AI and AI applications offers various benefits, there are also serious concerns over the ethical use of AI, the replacement of human employees by AI-powered devices, discomfort among customers and employees and trust toward AI.

The paper offers an updated holistic overview of AI and its implications in different facets of the hospitality and tourism industry. Challenges and opportunities are discussed to foster future discussions on the use of AI among scholars and industry professionals.

Organizational researchers studying well-being – as well as organizations themselves – often place much of the burden on employees to manage and preserve their own well-being. Missing from this discussion is how – from a human resources management (HRM) perspective – organizations and managers can directly and positively shape the well-being of their employees. The authors use this review to paint a picture of what organizations could be like if they valued people holistically and embraced the full experience of employees’ lives to promote well-being at work. In so doing, the authors tackle five challenges that managers may have to help their employees navigate, but to date have received more limited empirical and theoretical attention from an HRM perspective: (1) recovery at work; (2) women’s health; (3) concealable stigmas; (4) caregiving; and (5) coping with socio-environmental jolts. In each section, the authors highlight how past research has treated managerial or organizational support on these topics, and pave the way for where research needs to advance from an HRM perspective. The authors conclude with ideas for tackling these issues methodologically and analytically, highlighting ways to recruit and support more vulnerable samples that are encapsulated within these topics, as well as analytic approaches to study employee experiences more holistically. In sum, this review represents a call for organizations to now – more than ever – build thriving organizations.

This paper tests a research model that examines work engagement (WENG) as a mediator between green human resource management (GHRM) and absenteeism and green recovery performance.

To gauge the aforesaid linkages, the authors used data collected from employees and their supervisors in the international five-star chain hotels in Istanbul, Turkey. One-hundred and eighty-two respondents completed the surveys.

GHRM fosters WENG. As predicted, WENG alleviates absenteeism and triggers green recovery performance. Employees' favorable perceptions of GHRM (cognitive evaluation) give rise to higher WENG (emotional response), which in turn directs their behaviors such as reduced absenteeism and higher green recovery performance (behavioral response).

No empirical study has gauged the linkage between GHRM and WENG and nonattendance behavior or absenteeism so far. Moreover, there are few empirical pieces in the current literature that have tested the mechanism through which GHRM is associated with green and/or nongreen outcomes.

This study aims to explore the recovery performances of chatbots (vs human employees) and help firms use chatbots to carry out effective service recovery.

Two experiments were conducted to test the proposed hypotheses.

The results show that compared with human employees’ recovery, chatbots’ recovery leads to lower customer satisfaction and revisit intention. This effect is more significant for symbolic recovery instead of economic recovery. Perceived distributive and interactional justice mediate the interaction effect of recovery provider and recovery strategy on recovery performance. Using immediate recovery rather than delayed recovery can attenuate chatbots’ poor performances in symbolic recovery.

This study enriches the chatbot research and the service recovery literature by deploying chatbots into the service recovery setting. Using an integrated theoretical model including recovery strategy and recovery timing, this study provides substantive insight into how firms can enhance chatbots’ recovery performances.

本研究旨在探索聊天机器人（与人类员工相比）的服务补救表现, 并帮助公司使用聊天机器人进行有效的服务补救。

本研究进行了两个实验来检验提出的理论假设

结果表明, 与人类员工的服务补救相比, 聊天机器人的服务补救导致顾客满意度和再惠顾意愿降低。 这种效应对于象征补救而非功利补救更为显著。 分配公平和互动公平在服务补救提供者和补救策略的交互作用对补救表现的影响中起到了中介作用。 使用立即补救而不是延迟补救可以减轻聊天机器人象征补救方面的不良表现。

本研究通过将聊天机器人部署到服务补救环境中丰富了聊天机器人研究和服务补救文献。 本研究通过构建包括服务补救策略和补救时机在内的综合理论模型, 为企业如何提高聊天机器人的服务补救表现提供了实质性的见解。

A systems perspective of waste management allows an integrated approach not only to the five basic functional elements of waste management itself (generation, reduction, collection, recycling, disposal), but to the problems arising at the interfaces with the management of energy, nature conservation, environmental protection, economic factors like unemployment and productivity, etc. This monograph separately describes present practices and the problems to be solved in each of the functional areas of waste management and at the important interfaces. Strategies for more efficient control are then proposed from a systems perspective. Systematic and objective means of solving problems become possible leading to optimal management and a positive contribution to economic development, not least through resource conservation. India is the particular context within which waste generation and management are discussed. In considering waste disposal techniques, special attention is given to sewage and radioactive wastes.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.