How to stop binge drinking and speeding motorists : effects of relational-interdependent self-construal and self-referencing on attitudes toward social marketing

How can marketers stop speeding motorists and binge drinking? Two experiments show that the beliefs consumers have about the degree to which they define themselves in terms of their close relationships (i.e., relational-interdependent self-construal (RISC)) offer useful insights into the effectiveness of communications for two key social marketing issues—road safety (Study 1, New Zealand sample) and alcohol consumption (Study 2, English sample). Further, self-referencing is a mechanism for these effects. Specifically, people who define themselves in terms of their close relationships (high-RISCs) respond most favorably to advertisements featuring a dyadic relationship (two people), and this favorable response is mediated by self-referencing. In contrast, people who do not include close relationships in their sense of self (low-RISCs) respond most favorably to self-reference advertisements featuring solitary models.

Empirical Support For an Item and Relational Conceptualization of Sponsorship

This paper outlines how commercial sponsorship can be conceptualized using an item and relational information framework, and supports this with empirical data. The model presented allows for predictions about consumer memory for sponsorship information, and hence has both theoretical and practical value. Data are reported which show that sponsors considered congruent with an event benefit by providing consumers with sponsor-specific item information, while sponsors considered incongruent benefit by providing sponsor-event relational information. Overall the provision of sponsor-event relational information is shown to result in superior memory to the provision of sponsor-specific item information, which is superior to basic sponsor mentions.

Empirical support for an item and relational conceptualization of sponsorship

This paper outlines how commercial sponsorship can be conceptualized using an item and relational information framework, and supports this with empirical data. The model presented allows for predictions about consumer memory for sponsorship information, and hence has both theoretical and practical value. Data are reported which show that sponsors considered congruent with an event benefit by providing consumers with sponsor-specific item information, while sponsors considered incongruent benefit by providing sponsor-event relational information. Overall the provision of sponsor-event relational information is shown to result in superior memory to the provision of sponsor-specific item information, which is superior to basic sponsor mentions.

A multilevel model of work-life : examining the impact of group structure and relational job design

One in five Australian workers believes that work doesn’t fit well with their family and social commitments. Concurrently, organisations are recognising that to stay competitive they need policies and practices that support the multiple aspects of employees’ lives. Many employees work in group environments yet there is currently little group level work-life balance research. This paper proposes a new theoretical framework developed to understand the design of work groups to better facilitate work-life balance. This new framework focuses on task and relational job designs, group structures and processes and workplace culture.

Information Literacy Research : The Evolution of the Relational Approach

This chapter introduces the reader to the relational approach to information literacy, its evolution and use in contemporary research and emerging directions. It presents the relational approach, first introduced by Australian information literacy researchers, as an integration of experiential, contextual and transformational perspectives. The chapter opens with a reflection on the wider information literacy domain. It then addresses the development of the approach, its fundamental elements and characteristics, and explores the adoption of the approach in key contexts including education, workplace and community settings. The chapter explores significant studies that have contributed to its evolution and reflects on the impact of the development of the relational framework and related research. The chapter concludes with a focus on directions emerging from the relational understanding ofinformation literacy and potential implications.

Relational regulation theory and the role of social support and organisational fairness for nurses in a general acute context

Aims and objectives. To present a novel approach to nurse stress by exploring the demand–control–support model with organisational justice through the lens of relational regulation theory. Background. Nursing is often stressful due to high demands and dissatisfaction with pay, which impacts the mental well-being and productivity of nurses. Design. A cross-sectional design. Methods. A validated questionnaire was sent to the work addresses of all nursing and midwifery staff in a medium-sized general acute hospital in Australia. A total of 190 nurses and midwives returned completed questionnaires for the analyses. Results. The multiple regression analyses demonstrated that the model applies to the prototypical context of a general acute hospital and that job control, supervisor support and outside work support improve the job satisfaction and mental health of nurses. Conclusions. Most importantly, supervisor support was found to buffer the impact of excessive work demands. Fairness of procedures, distribution of resources and the quality and consistency of information are also beneficial. Relational regulation theory is applied to these findings as a novel way to conceptualise the mechanisms of support and fairness in nursing. Relevance to clinical practice. The importance of nurses’ well-being and job satisfaction is a priority for improving clinical outcomes. Practically, this means nurse managers should be encouraging nurses in the pursuit of diverse relational activities both at work and outside work.

Worldly feelings made material : cosmopolitan aesthetics, affect and recent relational art

The openness and compassion implicit in the social transaction of recent philosophies of cosmopolitanism is reflected in the aims of the body of interpersonal, process-driven artworks commonly referred to as relational art. In attempting to bring art into life, specifically as a point of intervention in the lives of its spectators, the affective power required to realize the communal and participatory aims of many of these artworks is central. Relational art practices invite the individualising distinctiveness of the spectator yet ultimately seek the collective affect of the artwork’s formulated community. Like cosmopolitanism, this is a felt community where the obligatory affective investment is imagined as open and empathic built on mutual exchange and generosity. They suggest that it doesn’t matter so much what we feel about art but what and how we feel through art. The artworld’s public spheres have become increasingly affective worlds, where the artwork’s coerced and managed human relations are conceived as interstices for a more open exchange with art and each other. With reference to Sydney Biennale’s recent All My Relations exhibition, this paper will interrogate how worldly feelings are made material by the requisite emotional and aesthetic labour of feeling for and with others in relational art.

Improving work and life balance : building a multilevel model of work-life balance focusing on group structure and relational job design

One in five Australian workers believes that work doesn’t fit well with their family and social commitments. Concurrently, organisations are recognising that to stay competitive they need policies and practices that support the multiple aspects of employees’ lives. Many employees work in group environments yet there is currently little group level work-life balance research. This paper proposes a new theoretical framework developed to understand the design of work groups to better facilitate work-life balance. This new framework focuses on task and relational job designs, group structures and processes and workplace culture.

Integration of sentiment analysis into customer relational model : the importance of feature ontology and synonym

Online business or Electronic Commerce (EC) is getting popular among customers today, as a result large number of product reviews have been posted online by the customers. This information is very valuable not only for prospective customers to make decision on buying product but also for companies to gather information of customers’ satisfaction about their products. Opinion mining is used to capture customer reviews and separated this review into subjective expressions (sentiment word) and objective expressions (no sentiment word). This paper proposes a novel, multi-dimensional model for opinion mining, which integrates customers’ characteristics and their opinion about any products. The model captures subjective expression from product reviews and transfers to fact table before representing in multi-dimensions named as customers, products, time and location. Data warehouse techniques such as OLAP and Data Cubes were used to analyze opinionated sentences. A comprehensive way to calculate customers’ orientation on products’ features and attributes are presented in this paper.

Multi-shot person re-identification via relational stein divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.

Reversible and semi-blind relational database watermarking

Database watermarking has received significant research attention in the current decade. Although, almost all watermarking models have been either irreversible (the original relation cannot be restored from the watermarked relation) and/or non-blind (requiring original relation to detect the watermark in watermarked relation). This model has several disadvantages over reversible and blind watermarking (requiring only watermarked relation and secret key from which the watermark is detected and original relation is restored) including inability to identify rightful owner in case of successful secondary watermarking, inability to revert the relation to original data set (required in high precision industries) and requirement to store unmarked relation at a secure secondary storage. To overcome these problems, we propose a watermarking scheme that is reversible as well as blind. We utilize difference expansion on integers to achieve reversibility. The major advantages provided by our scheme are reversibility to high quality original data set, rightful owner identification, resistance against secondary watermarking attacks, and no need to store original database at a secure secondary storage.

Managing knowledge for asset management : shifting from process to relational frames

The purpose of this paper is to review existing knowledge management (KM) practices within the field of asset management, identify gaps, and propose a new approach to managing knowledge for asset management. Existing approaches to KM in the field of asset management are incomplete with the focus primarily on the application of data and information systems, for example the use of an asset register. It is contended these approaches provide access to explicit knowledge and overlook the importance of tacit knowledge acquisition, sharing and application. In doing so, current KM approaches within asset management tend to neglect the significance of relational factors; whereas studies in the knowledge management field have showed that relational modes such as social capital is imperative for ef-fective KM outcomes. In this paper, we argue that incorporating a relational ap-proach to KM is more likely to contribute to the exchange of ideas and the devel-opment of creative responses necessary to improve decision-making in asset management. This conceptual paper uses extant literature to explain knowledge management antecedents and explore its outcomes in the context of asset man-agement. KM is a component in the new Integrated Strategic Asset Management (ISAM) framework developed in conjunction with asset management industry as-sociations (AAMCoG, 2012) that improves asset management performance. In this paper we use Nahapiet and Ghoshal’s (1998) model to explain antecedents of relational approach to knowledge management. Further, we develop an argument that relational knowledge management is likely to contribute to the improvement of the ISAM framework components, such as Organisational Strategic Manage-ment, Service Planning and Delivery. The main contribution of the paper is a novel and robust approach to managing knowledge that leads to the improvement of asset management outcomes.

Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.