The measurement of attribution of blame in the self-service technology context

It is argued that attribution of blame (AoB) will differ in the Self-Service Technology (SST) context versus the interpersonal services context, due to the inherent elements of the SST environment, thereby making it a construct worthy of further research in the SST setting. This paper presents a first step in this pursuit by validating a multiple-item instrument of AoB in the SST context, which, to the researchers’ knowledge, has not been done previously. The paper comments on the surprising lack of valid, unidimensional instruments to measure each of the dimensions of AoB (locus, controllability and stability), even in the interpersonal services context. Preliminary results of a pre-test and pilot study support a three-dimensional measurement model of attribution of blame, in the SST setting.

The influence of locus of attribution, level of employee authority and employee concern on consumers postrecovery evaluations

This study employed a 3 x 2 x 2 full-factorial, between-subjects design experiment involving locus of attribution, concern, and the level of authority of the employee performing the recovery on consumers’ postcomplaint evaluations. The research, conducted in a restaurant context, involved a sample of 411 undergraduate students. Findings suggest that customers are less dissatisfied and more likely to revisit the restaurant when the location of the cause of the failure is internal (i.e., they are to blame). In addition, consumers are less likely to repurchase when the manager, as opposed to the waiter, is responsible for the service failure.

Effectiveness of Liechtenstein entities to prevent the application of Australian income tax attribution regimes

Analyses the most common structures of Liechtenstein. The Anstalt, Stiftung, Trust, Business Trust and Company are described and the taxation consequences for an Australian investor considered. The analysis covers the CFC, FIF, transferor trust, deemed entitlement and anti-avoidance rules in Australian income tax law.

Challenges in collaborative writing : addressing authorship attribution

Purpose – Increasing pressure to enhance research coupled with a desire for a broadening of
academic input, are prompting greater levels of collaboration. Research collaboration can generate
notable benefits but can also pose a variety of challenges. The purpose of this paper is to explore the
reasons, facilitators, benefits and challenges of academic collaboration. It also provides suggestions to
manage identifiable risks and enhance team dynamics.
Design/methodology/approach – This is a conceptual paper exploring prior literature in relation
to the contentious points of research collaboration, particularly in regard to authorship attribution.
Findings – The authors present two checklists that researchers can utilise to ensure the successful
completion of collaborative projects. The checklists incorporate the main factors required for effective
collaborative work and research, and form a foundation for discussion among team members.
Originality/value – The paper draws upon experiences, observations, academic literature and
protocols, and provides strategies and recommendations to enhance collaboration and authorship
attribution. The two checklists presented in the paper are value-adding for team members.

Automatic attribution of ancient Roman imperial coins

Classification of coins is an important but laborious aspect of numismatics - the field that studies coins and currency. It is particularly challenging in the case of ancient coins. Due to the way they were manufactured, as well as wear from use and exposure to chemicals in the soil, the same ancient coin type can exhibit great variability in appearance. We demonstrate that geometry-free models of appearance do not perform better than chance on this task and that only a small improvement is gained by previously proposed models of combined appearance and geometry. Thus, our first major contribution is a new type of feature which is efficient in terms of computational time and storage requirements, and which effectively captures geometric configurations between descriptors corresponding to local features. Our second contribution is a description of a fully automatic system based on the proposed features, which robustly localizes, segments out and classifies coins from cluttered images. We also describe a large database of ancient coins that we collected and which will be made publicly available. Finally, we report the results of empirical comparison of different coin matching techniques. The features proposed in this paper are found to greatly outperform existing methods.