Sentence examples for generate satisfaction from inspiring English sources

to determine what elements of the attention in Emergency, generate satisfaction and whichones generate dissatisfaction.

Nyaga et al. (2010) showed that collaborative activities, such as information sharing, joint report of efforts, and dedicated investments, create trust and involvement that generate satisfaction and improve performance.

In contrast, the items in the Detractors domain generate disappointment, but do not necessarily generate satisfaction when they are met.

Thus, new forms of organization of these systems have been designed to better treat chronic conditions, aiming to be more equitable, cost-effective and to generate satisfaction and quality of life for their users.

Some organizations have decided to use raw (unadjusted) comparisons based on the argument that health care organizations and practitioners need to adapt to their own patient populations and provide whatever is needed to generate satisfaction in the groups they serve.

An SEM test suggested that airport design features and pleasant scent have a positive influence on traveler enjoyment, generating satisfaction.

This study enhances our understanding on the determinants of online purchase intention and satisfaction as well as on how a proper web atmospheric design alters consumer shopping experience which entices favourable purchase intention and generates satisfaction.

We compared consultations with regular as well as unfamiliar GPs, and from this, we derived the components that generated satisfaction at the first meeting and over time.

Communicating personal information to another person and having them respond to these disclosures is essential to developing intimacy with another person (Reis & Shaver, 1988) and generating satisfaction in close relationships (Collins & Miller, 1994).

The comparisons indicate that the proposed approach can effectively generate customer satisfaction models and that their modeling results outperform those based on the other three methods in terms of mean absolute errors and variance of errors.

Rough set (RS)- and particle swarm optimization (PSO)- based adaptive neuro-fuzzy inference system (ANFIS) approaches are proposed to generate customer satisfaction models in affective design that address fuzzy and nonlinear relationships between affective responses and design attributes.