Market integration of domestic and imported seafood: Insights from the Sydney Fish Market*

Australia has experienced a significant increase in seafood imports over the past two decades. Concurrently, Australian seafood producers have raised concerns that the low market prices of imported fish may negatively affect the prices of domestically produced seafood and, subsequently, the profitability of the Australian fishing industries. To validate this concern, this study examines the relationship between prices of domestically produced seafood and imported fish. Price data from the Sydney Fish Market (SFM), Australia’s largest auction wholesale fish market and fish import data are used for a cointegration analysis which is conducted using the bivariate Johansen test. Results indicate that prices of most domestic species traded within the SFM are not cointegrated, implying that they largely develop independently of each other. However, imported fish, particularly fresh imports, were found to be cointegrated with Australian produced fresh fish supplies traded on the SFM. Although the law of one price (LOP) was only confirmed to hold for some price pairs, the results suggest a partial substitution relationship between imports and domestically caught fish. This implies that prices of domestically produced fish within the Australian market are likely impacted by price dynamics within the international seafood market.     

Assessing Consumer Fraud Risk in Insurance Claims

Abstract

We present an unsupervised learning method for classifying consumer insurance claims according to their suspiciousness of fraud versus nonfraud. The predictor variables contained within a claim file that are used in this analysis can be binary, ordinal categorical, or continuous variates. They are constructed such that the ordinal position of the response to the predictor variable bears a monotonic relationship with the fraud suspicion of the claim. Thus, although no individual variable is of itself assumed to be determinative of fraud, each of the individual variables gives a “hint” or indication as to the suspiciousness of fraud for the overall claim file. The presented method statistically concatenates the totality of these “hints” to make an overall assessment of the ranking of fraud risk for the claim files without using any a priori fraud-classified or -labeled subset of data. We first present a scoring method for the predictor variables that puts all the variables (whether binary “red flag indicators,” ordinal categorical variables with different categories of possible response values, or continuous variables) onto a common –1 to 1 scale for comparison and further use. This allows us to aggregate variables with disparate numbers of potential values. We next show how to concatenate the individual variables and obtain a measure of variable worth for fraud detection, and then how to obtain an overall holistic claim file suspicion value capable of being used to rank the claim files for determining which claims to pay and the order in which to investigate claims further for fraud. The proposed method provides three useful outputs not usually available with other unsupervised methods: (1) an ordinal measure of overall claim file fraud suspicion level, (2) a measure of the importance of each individual predictor variable in determining the overall suspicion levels of claims, and (3) a classification function capable of being applied to existing claims as well as new incoming claims. The overall claim file score is also available to be correlated with exogenous variables such as claimant demographics or highvolume physician or lawyer involvement. We illustrate that the incorporation of continuous variables in their continuous form helps classification and that the method has internal and external validity via empirical analysis of real data sets. A detailed application to automobile bodily injury fraud detection is presented.     

Using Bids,Arguments and Preferences in Sensitive Multi-unit Assignments: A p-Equitable Process and a Course Allocation Case Study

Bonus distribution in enterprises or course allocation at universities are examples of sensitive multi-unit assignment problems, where a set of resources is to be allocated among a set of agents having multi-unit demands. Automatic processes exist, based on quantitative information, for example bids or preference ranking, or even on lotteries. In sensitive cases, however, decisions are taken by persons also using qualitative information. At present, no multi-unit assignment system supports both quantitative and qualitative information. In this paper, we propose MUAP-LIS, an interactive process for multi-assignment problems where, in addition to bids and preferences, agents can give arguments to motivate their choices. Bids are used to automatically make pre-assignments, qualitative arguments and preferences help decision makers break ties in a founded way. A group decision support system, based on Logical Information Systems, allows decision makers to handle bids, arguments and preferences in a unified interface. We say that a process is p-equitable for a property p if all agents satisfying p are treated equally. We formally demonstrate that MUAP-LIS is p-equitable for a number of properties on bids, arguments and preferences. It is also Pareto-efficient and Gale–Shapley-stable with respect to bids. A successful course allocation case study is reported. It spans over two university years. The decision makers were confident about the process and the resulting assignment. Furthermore, the students, even the ones who did not get all their wishes, found the process to be equitable.     

Consumer Attachments to Human Brands: The “Oprah Effect”

Human brands offer an interesting complement to product brands in that consumer attachments to human brands take on personal relationship qualities that can engender greater affiliation and loyalty. At present, one of the most powerful human brands in the developed world is that of Oprah Winfrey. This research uses the Oprah brand as a context in which to explore and expand the existing model of human brand attachment because of the enduring strength and consistency of how she has cultivated her brand. The existing model of human brand attachment identifies autonomy, relatedness, and competence as the antecedents of attachment strength. However, the expanded model in the current research introduces and validates brand personality appeal (and its sub‐dimensions of favorability, originality, and clarity) as an important moderating factor in the relationship between these antecedents of attachment and attachment outcomes. Two studies utilizing both qualitative and quantitative data investigate the impact of these factors on attachment strength as manifested in emotional attachment behaviors, markers of relationship quality, and marketplace responses in the form of brand loyalty and purchase intentions.