Academic literature on the topic 'Client acceptance decision'

Artificial intelligence is one of the most innovative and trending technologies in the hotel sector. It is transforming the hotel sector into a novel environment in business-client relationships. For this reason, this study seeks to find, through a literary review and a causal study carried out by experts in technologies and hotels, using the methodology of fuzzy cognitive maps, what the determining factors in the decision of hotels to accept and implement artificial intelligence in their hotels and in their services are. The list obtained is made up of the following factors (in order of relevance in this decision): top management support, perceived reliability of the technology, security, relative advantage, support from information systems providers, technological organizational readiness, government pressure or incentives, compatibility, business partner pressure, customer pressure, complexity, pressure from competitors, perceived cost, characteristics of the leader or manager, and size of the company.

It may be easy to think that because insurance companies are so profitable it is acceptable to engage in ethically or legally questionable practices to ensure that clients receive needed treatment services and that one is paid at the level one thinks appropriate. Mental health clinicians should remember the ethics of their profession and always act in keeping with this. Further, it is important to fulfill all contractual obligations agreed to with insurance companies. This chapter explains these obligations, the challenges and dilemmas that commonly arise, and ways to respond to them in an ethically appropriate manner. The ethical pitfalls that commonly arise when working with insurance companies are explained, and specific recommendations for avoiding these pitfalls are provided. Further, guidance is offered on how to assess key elements of contracts with insurance companies so that thoughtful and informed decisions can be made about participation with these companies before difficulties are experienced.

The significance of spiritual health in the Māori model of health. Author notes resistance in secular society to the term ‘spiritual’ and broadens the term to encompass our search for meaning: why we are here, why we exist. Maslow and our esteem needs, notably an acknowledgement of our capacities, achievements, and adequacy, by others and ourselves, to gain a sense of ‘being useful in the world’. The definition of spirituality from the Oxford Textbook of Spirituality in Healthcare as ‘the way people engage with the purpose and meaning of human existence’. Using the framework from acceptance and commitment therapy, the author’s personal experience of defining his personal values. His reflection on his own personal beliefs and his personal journey to self-discovery. The concept of inner child work and its role in existential health and healing. A client’s own experience of finding his inner child. The concept of ‘best me’. Maslow’s theory of motivation. The concept of flourishing and the author’s personal self-assessment. The US psychiatrist Dr M. Scott Peck’s book The Road Less Travelled, and his insight that it takes courage to make tough life decisions. The power of the stories we tell ourselves.

Gaining new and keeping existing clients or customers can be well-supported by creating and monitoring feedbacks: “Are the customers satisfied? Can we improve our services?” One of possible feedbacks is allowing the customers to freely write their reviews using a simple textual form. The more reviews that are available, the better knowledge can be acquired and applied to improving the service. However, very large data generated by collecting the reviews has to be processed automatically as humans usually cannot manage it within an acceptable time. The main question is “Can a computer reveal an opinion core hidden in text reviews?” It is a challenging task because the text is written in a natural language. This chapter presents a method based on the automatic extraction of expressions that are significant for specifying a review attitude to a given topic. The significant expressions are composed using significant words revealed in the documents. The significant words are selected by a decision-tree generator based on the entropy minimization. Words included in branches represent kernels of the significant expressions. The full expressions are composed of the significant words and words surrounding them in the original documents. The results are here demonstrated using large real-world multilingual data representing customers’ opinions concerning hotel accommodation booked on-line, and Internet shopping. Knowledge discovered in the reviews may subsequently serve for various marketing tasks.

Many organisations are currently dealing with long standing legacy issues in clean up, decommissioning and demolition projects. Industry is required to ensure that all bulk articles, substances and waste arisings are adequately characterised and assigned to the correct disposal routes in compliance with UK legislation and best practice. It is essential that data used to support waste sentencing is of the correct type, quality and quantity, and that it is appropriately assessed in order to support defensible, confident decisions that account for inherent uncertainties. AMEC has adopted the Data