THE EFFECT OF EMPLOYEE UNIFORM ON JOB SATISFACTION: A CASE OF THE HOUSEKEEPING DEPARTMENT IN A LUXURY FIVE-STAR HOTEL IN HONG KONG, CHINA

Purpose – The aim of this study is to advance the understanding of Hong Kong’s housekeeping culture by examining how employee uniforms and the image they project influence job satisfaction amongst the housekeeping department employees of a luxury five-star Hotel in Hong Kong, China. Design – Using a purposive sampling method, ten semi-structured interviews were conducted with the housekeeping staff of a luxury five-star Hotel in Hong Kong. Content analysis was conducted to identify data patterns. Findings – Findings categorized four determinants of uniform influencing job satisfaction: fit to wearer, appropriate materials, color and design, and hotel brand image. The findings confirm that staff uniforms play an important role in demonstrating the hotel’s brand identity, improving job satisfaction, operational efficiency, and staff-management relationships. Apart from the aesthetic design, management should put operational practicality and functionality into account by getting employees to participate when launching and implementing any change initiatives on staff uniforms. Communication and mutual understanding between management and employees are imperative in understanding each other’s concerns. Originality of the research – Using a qualitative approach, the results offered an empirical basis to guide hotel management and administrators in making decisions about uniforms

Implications of serial measurements of natriuretic peptides in heart failure: insights from BIOSTAT‐CHF

No abstract available

Design of an Intelligent Customer Identification Model in e- Commerce Logistics Industry

The emergence of e-commerce in recent years has lead to revolutionary changes in the logistics industry, as e-commerce relies heavily on efficient logistics to deliver the online goods to customers in a short period of time. Compared with traditional logistics, e-commerce orders, with a high variety of goods but small in quantity, are generally received from large number of customers worldwide. With a huge customer base, it is challenging for logistics service providers (LSPs) to provide satisfactory time-critical logistics services to meet the diversified customer requirements. In order to differentiate its services from others e-commerce LSPs, it is important to identify potential target groups of customers, and their behaviour so as to attract their attention. In this paper, an intelligent customer identification model (ICIM) is designed to support data analysis for managing customer relationships in a systematic way. The ICIM integrates the k-means clustering algorithm and the C4.5 classification algorithm in order to be able to deal with both continuous and discrete attributes for extracting valuable hidden knowledge. This effectively supports the identification of actual customer needs, and the classification of new customers in the future with minimum time for developing customer relationship management (CRM) recommendations to customers, thus improving business performance. Through a pilot study in a freight forwarding company in Hong Kong, it provides a real world demonstration and validation of data mining for CRM in the emerging e-commerce logistics industry

Evaluation of various fluid-film models for use in the analysis of squeeze film dampers with a central groove

Experimental vibration responses of squeeze film dampers (SFDs) are obtained with four different central groove depths, two types of lubricant and various unbalance levels. Highly non-linear fluid stiffness and damping are observed, the damping being sensitively related to oil viscosity and unbalance. Existing oil film models are applied to predict the SFD behaviour. A special groove-two land model is able to predict the vibration behaviour of a very shallow grooved SFD and the conventional two-land theory is applicable to a SFD with a very deep groove. These observations provide useful guidelines for designing a shallow or deep grooved SFD-rotor assembly

Modulation of MHC expression on human endothelial cells by sera from patients with systemic lupus erythematosus

10.1006/clin.1993.1133Clinical Immunology and Immunopathology683321-326CLII

FeWO₄Cl as cathode material for lithium rechargeable battery

Lithium has been electrochemically and chemically intercalated into layered FeWP4Cl. The discharge curve between FeW04Cl and Li2FeWO4Cl is constituted by three potential plateaux and two domains where the cell voltage decreases rapidly upon the intercalation of lithium ions. A wide bi-phase domain is obtained for x in LixFeW04Cl ranging between 0.0 and 0.85. The limit of a solid solution extends in the vicinity of the LiFeWO4Cl composition. The theoretical and practical discharge capacities for Li1-xFeWO4Cl are 79.03 mAh/g and 78.45 mAh/g respectively. The structures of FeWO4Cl and LiFeWO4Cl were determined: from single crystal analysis and by Rietveld refinement of the powder X-ray diffraction pattern respectively. The modifications of the Fe-O bond lengths emphasize the iron reduction during the discharge process. Moreover, the strong change of Fe-Cl distance suggests a reversible framework modification

Using fuzzy-based association rule mining to improve production systems for chemical product development

In chemical product development, challenges lie in the determination of appropriate ingredients and parameter settings that lead to the desired product attributes. This relies heavily on the past knowledge and experience of the domain experts to generate feasible product candidates for verification. In this paper, a fuzzy-based association rule mining model (FbARM) is developed to provide knowledge support during chemical product development. Fuzzy-based association rule mining is applied to discover hidden relationships between parameters and the resultant product quality, followed by the use of fuzzy logic to generate recommendations on parameter settings. The feasibility of the FbARM is verified by means of a case study in a personal-care products manufacturing company. The results demonstrate the practical viability of the FbARM, while the learning ability of the FbARM allows a continuous improvement of the fuzzy rules, which is of paramount importance in responding to the changing requirements of the chemical industry