Grocery product pricing and Australian supermarket consumers : gender differences in perceived importance levels

Grocery shopping is an essential and routine activity. Although long regarded the responsibility of the female spouse, modern social and demographic shifts are causing men to become more engaged in this task. This is the first study to analyse gender differences with respect to the criterion of grocery product price within an Australian supermarket retail environment. A stratified sample of 140 male and 140 female grocery shoppers was surveyed. Results showed that men considered price attributes of products as being significantly lower in importance than did women. Additionally, men displayed lower levels of price nvolvement, reported referencing shelf price to a lesser extent, and gave lesser consideration to promotional tactics focusing on low price. Although men on average buy fewer items than do women, they spend more money for each item they purchase. This higher expenditure per item appears to be driven, at least in part, by a lack of price referencing. This research has implications for gender studies and consumer behaviour disciplines in relation to grocery shopping.

Modelling word meaning using efficient tensor representations

Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive tasks. Many of these models use geometric representations of words to store semantic associations between words. Often word order information is not captured in these models. The lack of structural information used by these models has been raised as a weakness when performing cognitive tasks. This paper presents an efficient tensor based approach to modelling word meaning that builds on recent attempts to encode word order information, while providing flexible methods for extracting task specific semantic information.

Feasibility of using health data sources to inform product safety surveillance in Queensland : a report for the Queensland Injury Prevention Council

This report provides an evaluation of the current available evidence-base for identification and surveillance of product-related injuries in children in Queensland. While the focal population was children in Queensland, the identification of information needs and data sources for product safety surveillance has applicability nationally for all age groups. The report firstly summarises the data needs of product safety regulators regarding product-related injury in children, describing the current sources of information informing product safety policy and practice, and documenting the priority product surveillance areas affecting children which have been a focus over recent years in Queensland. Health data sources in Queensland which have the potential to inform product safety surveillance initiatives were evaluated in terms of their ability to address the information needs of product safety regulators. Patterns in product-related injuries in children were analysed using routinely available health data to identify areas for future intervention, and the patterns in product-related injuries in children identified in health data were compared to those identified by product safety regulators. Recommendations were made for information system improvements and improved access to and utilisation of health data for more proactive approaches to product safety surveillance in the future.

Product labelling can confer sustained increases in expected and actual satiety

Expected satiety has been shown to play a key role in decisions around meal size. Recently it has become clear that these expectations can also influence the satiety that is experienced after a food has been consumed. As such, increasing the expected and actual satiety a food product confers without increasing its caloric content is of importance. In this study we sought to determine whether this could be achieved via product labelling. Female participants (N=75) were given a 223-kcal yoghurt smoothie for lunch. In separate conditions the smoothie was labelled as a diet brand, a highly-satiating brand, or an ‘own brand’ control. Expected satiety was assessed using rating scales and a computer-based ‘method of adjustment’, both prior to consuming the smoothie and 24 hours later. Hunger and fullness were assessed at baseline, immediately after consuming the smoothie, and for a further three hours. Despite the fact that all participants consumed the same food, the smoothie branded as highly-satiating was consistently expected to deliver more satiety than the other ‘brands’; this difference was sustained 24 hours after consumption. Furthermore, post-consumption and over three hours, participants consuming this smoothie reported significantly less hunger and significantly greater fullness. These findings demonstrate that the satiety that a product confers depends in part on information that is present around the time of consumption. We suspect that this process is mediated by changes to expected satiety. These effects may potentially be utilised in the development of successful weight-management products.

Tensor query expansion : a cognitively motivated relevance model

In information retrieval, a user's query is often not a complete representation of their real information need. The user's information need is a cognitive construction, however the use of cognitive models to perform query expansion have had little study. In this paper, we present a cognitively motivated query expansion technique that uses semantic features for use in ad hoc retrieval. This model is evaluated against a state-of-the-art query expansion technique. The results show our approach provides significant improvements in retrieval effectiveness for the TREC data sets tested.

Ageing and use of complex product interfaces

This paper presents an experiment designed to investigate if redundancy in an interface has any impact on the use of complex interfaces by older people and people with low prior-experience with technology. The important findings of this study were that older people (65+ years) completed the tasks on the Words only based interface faster than on Redundant (text and symbols) interface. The rest of the participants completed tasks significantly faster on the Redundant interface. From a cognitive processing perspective, sustained attention (one of the functions of Central Executive) has emerged as one of the important factors in completing tasks on complex interfaces faster and with fewer of errors.

Using visuals to explore the contextual aspects of user-product interactions

Experience underlies all kinds of human knowledge and it is dependent on context. People’s experience within a particular context-of-use determines how they interact with products. Methods employed in this research to elicit human experience have included the use of visuals. This paper describes two empirical studies that employed visual representation of concepts as a means to explore the experiential and contextual component of user- product interactions. One study employed visuals that the participants produced during the study. The other employed visuals that the researcher used as prompts during a focus group session. This paper demonstrates that using visuals in design research is valuable for exploring and understanding the contextual aspects of human experience and its influence on people’s concepts of product use.

Comparative study of DFSS in product and service innovation

Six sigma has proven itself as a major quality initiative in the last two decades. It is a philosophy which provides a systematic approach to applying numerous tools in the framework of several quality improvement methodologies. The most widely used six sigma methodology is DMAIC, which is best suited for improving existing processes. In order to build quality into the product or service, a proactive approach like Design for Six Sigma (DFSS) is required. This paper provides an overview of DFSS, product innovation, and service innovation. The emphasis is on comparing how DFSS is applied differently in product and service innovation. This paper contributes by analysing the existing literature on DFSS in product and service innovation. The major findings are that the DFSS approach in services and products can be differentiated along the following three dimensions: methodology, characteristics, and technology.

Managing product quality and reliability under new challenges

This paper presents the findings of an investigation of the challenges Australian manufacturers are currently facing. A comprehensive questionnaire survey was conducted among leading Australian manufacturers. This paper reports the main findings of this study. Evidence indicates that product quality and reliability (Q & R) are the main challenges for Australian manufacturers. Design capability and time to market came second. Results show that there is no effective information exchange between the parties involved in production and quality control. Learning from the past mistakes is not proving to have significant effects on improving product quality. The technological innovation speed is high and companies are introducing as many as 5 new products in a year. This technological speed has pressure on the Q & R of new products. To overcome the new challenges, companies need a Q & R improvement model.

A design approach to teaching new product development

The educational unit new product development, taught within the industrial design program at the Queensland University of Technology (QUT) introduces the relationship between product design and commercialisation to third year industrial design undergraduate students. In which, they are exposed for the first time to product strategy development aimed at meeting consumer expectations, whilst at the same time achieving corporate objectives. Delivered content such as intellectual property, market opportunities, competitor analysis and investor requirements are taught within the thirteen week semester timeframe. New product development theory is not a new field. However, the design approach to teaching this theory and more importantly how designers can use it in the design process is novel. This paper provides an overview of the curriculum design of this unit as well as its incremental development over the past four year duration period. Student project outcomes and more importantly the process and tools from this unit are also discussed and presented.

Individual user behaviour modelling for effective web recommendation

With the growth of the Web, E-commerce activities are also becoming popular. Product recommendation is an effective way of marketing a product to potential customers. Based on a user’s previous searches, most recommendation methods employ two dimensional models to find relevant items. Such items are then recommended to a user. Further too many irrelevant recommendations worsen the information overload problem for a user. This happens because such models based on vectors and matrices are unable to find the latent relationships that exist between users and searches. Identifying user behaviour is a complex process, and usually involves comparing searches made by him. In most of the cases traditional vector and matrix based methods are used to find prominent features as searched by a user. In this research we employ tensors to find relevant features as searched by users. Such relevant features are then used for making recommendations. Evaluation on real datasets show the effectiveness of such recommendations over vector and matrix based methods.

Clustering of web users using the tensor decomposed models

We propose to use the Tensor Space Modeling (TSM) to represent and analyze the user’s web log data that consists of multiple interests and spans across multiple dimensions. Further we propose to use the decomposition factors of the Tensors for clustering the users based on similarity of search behaviour. Preliminary results show that the proposed method outperforms the traditional Vector Space Model (VSM) based clustering.

A people-to-people recommendation system using Tensor Space Models

Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.

Integrating collaborative filtering and search-based techniques for personalized online product recommendation

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.