Dietary analysis and nutritional behaviour in people with and without age-related macular disease

Background and Aims: Consumption of antioxidant nutrients can reduce the risk of progression of age-related macular degeneration (AMD) - the leading cause of visual impairment in adults over the age of 50 years in the UK. Lutein and zeaxanthin (L&Z) are of particular interest because they are selectively absorbed by the central retina. The objectives of this study were to analyse the dietary intake of a group of AMD patients, assess their ability to prepare and cook healthy food, and to make comparisons with people not affected by AMD. Methods: 158 participants with AMD were recruited via the UK charity The Macular Society, and fifty participants without AMD were recruited from optometric practice. A telephone interview was conducted by trained workers where participants completed a 24 hour food diary, and answered questions about cooking and shopping capabilities. Results: In the AMD group, the average L&Z intake was low in for both males and females. Those able to cook a hot meal consumed significantly more L&Z than those who were not able. Most participants were not consuming the recommended dietary allowance of fibre, calcium, vitamin D and E, and calorific intake was also lower than recommendations for their age-group. The non-AMD group consumed more kilocalories and more nutrients than the AMD group, but the L&Z intake was similar to those with AMD. The main factor that influenced participant’s food choices was personal preference. Conclusion: For an ‘informed’ population, many AMD participants were under-consuming nutrients considered to be useful for their condition. Participants without AMD were more likely to reach recommended daily allowance values for energy and a range of nutrients. It is therefore essential to design more effective dietary education and dissemination methods for people with, and at risk of, AMD.

Benefit segmentation of international travelers to Macau, China

The purpose of this study is threefold: (1) to identify the underlying benefits sought by international visitors to Macau, China, which has emerged as a popular gambling destination in Asia; (2) to segment tourists visiting Macau by employing a cluster analysis based on the benefits sought; and (3) to examine any salient differences between the segment groups with regard to their behavioral characteristics, socio-economic characteristics, and demographic profiles. A convenience sample was used to collect data in the Macau International Airport, in the Macau Ferry Terminal, and at the border gate with Mainland China. A total 1,513 useful surveys were retained for data analysis. Cluster analysis discloses four distinct clusters: "convention and business seekers," "family and vacation seekers," "gambling and shopping seekers," and "multi-purpose seekers." Based on the results of our findings, several managerial implications are discussed. © Taylor & Francis Group, LLC.

An investigation into the factors affecting e-commerce adoption decisions by SMEs:a study in Saudi Arabia

Electronic commerce (e-commerce) has become an increasingly important initiative among Small and Medium Enterprises (SMEs) as both a great opportunity and as a source of competition. The factors affecting adoption decisions of e-commerce by SMEs have been well documented, but there is a paucity of empirical studies that examine the adoption of e-commerce in the Arab world. The aim of this chapter is to provide insights into the salient e-commerce adoption issues by focusing on Saudi Arabian businesses. This chapter investigates the state of e-commerce adoption and analyses the factors that determine the extent to which SMEs in Saudi Arabia are inclined towards deploying e-commerce technologies. This research was designed using a qualitative approach through exploratory case studies selected from firms in Saudi Arabia. The findings contribute towards a better conceptual and practical understanding of the main factors driving SMEs to adopt e-commerce. The study has found that the level of e-commerce implementation has yet to mature and customer readiness for Internet shopping has to improve before e-commerce reaches the levels of maturity seen in other regions of the world. This study highlights several directions for future inquiry and implications for policymakers and managers who are involved in efforts to introduce complex innovations such as e-commerce into their organisations or are interested in expanding their e-commerce applications and generating more revenue.

An investigation into the adoption of electronic commerce among Saudi Arabian SMEs

Despite the proliferation of e-commerce adoption by SMEs and the world-wide growth of e-commerce, in general there is a paucity of empirical studies that examine the adoption of e-commerce by SMEs in the Middle East. In this paper, the authors provide insights into the salient e-commerce adoption issues by focusing on Saudi Arabian SMEs. This research was designed using a qualitative approach through in-depth case studies selected from firms in Saudi Arabia. The findings contribute toward a better conceptual and practical understanding of the main factors driving SMEs to adopt e-commerce. The authors find that the level of e-commerce implementation has yet to mature and customer readiness for Internet shopping must improve before e-commerce reaches the levels of maturity seen in other regions of the world. This study highlights directions for future inquiry and implications for information and technology managers and policymakers in developing Arab nations.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.