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The purpose of this paper is to propose a novel weighting algorithm for fuzzy information axiom (IA) and to apply it to the evaluation process of 3D printers.

As a decision-making tool, IA method is presented to evaluate the performance of any design. Then, weighted IA methods are investigated and a new weighting procedure is introduced to the literature. Then, the existing axiomatic design methods and the proposed new method are classified into two groups: weighting based on information content and weighting based on design ranges. The weighting based on information content approach consists of four methods including pessimistic and optimistic approaches. The philosophy of the weighting based on design ranges is to narrow design ranges in order to decrease fuzziness in the model. To prove the robustness and the performance of the proposed weighting method, the results are compared with the existing methods in the literature. Then, the new approach is applied to evaluate 3D printers.

The results of the proposed study show that the proposed weighting algorithm has better performance than the old ones for IA. Therefore, the proposed weighting algorithm should be used for the weighting tool of IA thereafter.

An effective weighting method compatible with the philosophy of IA method has been proposed. Furthermore, the performances of 3D printers are compared by using the proposed method.

Previous experiments demonstrated the value of relevance weighting for search terms, but relied on substantial relevance information for the terms. The present experiments were designed to study the effects of weights based on very limited relevance information, for example supplied by one or two relevant documents. The tests simulated iterative searching, as in an on‐line system, and show that even very little relevance information can be of considerable value.

This research investigates when and how brands influence attribute importance weights. Most past studies modelling consumer decision processes treated the brand of a product as an attribute parallel to the price, color or size of a product, and as a result, those studies assigned an equal (i.e. non-contingent) importance weight across brands for each attribute. In contrast, this study introduces a brand-contingent attribute-weighting process, in which brand is a higher-order construct that influences attribute importance.

This study presents a multi-level choice model in which the importance weight of an attribute can vary across brands. This study then estimates the model using real purchase data and survey data from an airline industry.

This study finds that attribute importance weights are contingent upon two aspects of a brand – the perceived relative position of the brand and consumers’ brand usage experiences. Specifically, when consumers perceive a brand to be inferior to its competitors in a given attribute, they generally place greater weight on that attribute for that brand. In contrast, when consumers perceive a brand to be superior to its competitors in a given attribute, only consumers with extensive brand usage experiences place greater weight on that attribute for that brand.

The findings provide managerial insights on brand positioning and segmentation strategies using consumers’ brand usage experiences.

This study advances the literature on consumer decision processes by modeling an attribute-weighting process that is contingent upon brands. The present study models this process based on consumer behavior theories and estimates the model using real market data.

The purpose of this paper is to attempt to establish the pattern of multi‐objective and multi‐dimensional grey fuzzy forecasting with feedback based on the theories of grey system and fuzzy recognition.

First, according to the given weights, the weighting integrated value of samples were computed. Second, the method of fuzzy recognition with single index was employed to calculate the fuzzy classification of the integrated value. According to the cause analysis, the fuzzy classification of the integrated value is used to compute the weights of indexes. In the same way, repeating the above processes, the weighting integrated value and fuzzy classification with given accuracy are retrieved at the same time. Finally, the authors calculate the correlation coefficient between the weighting integrated values and forecasting objects, according to the principle of maximal relativity, optimizing the weighting integrated value of samples, establishing the fuzzy forecasting pattern, and checking the model's precision. A numeric example is also computed in the last part of the paper.

The results are convincing: not only that the pattern of multi‐objective and multi‐dimensional grey fuzzy forecasting with feedback based is valid, but also the model's applied prediction accuracy is higher, where the test samples' mean forecast accuracy of groundwater dynamic levels is 96.50 percent.

The method exposed in the paper can be used to predict groundwater dynamic levels and even for other similar forecast problems.

The paper succeeds in realising both a prediction pattern and application of predicting groundwater dynamic levels by using the newest developed theories of grey system and fuzzy recognition.

INSTRUCT is an interactive computer program illustrating some of the techniques which have been suggested for use in future text retrieval systems. The program is designed as a computerised teaching aid for students in schools of librarianship and information science. A previous paper has provided a general overview of the novel retrieval facilities provided by INSTRUCT. The present paper discusses the means by which these facilities have been implemented: in particular, detailed descriptions are given of the nearest‐neighbour search algorithm that underlies the string similarity and ranked output modules of the program, and of the term‐weighting schemes that are used. The paper concludes with a discussion of potential enhancements to the present program.

This paper proposes a benefit of the doubt (BoD) approach to construct and analyse teacher effectiveness scores (i.e. SET scores).

The BoD approach is related to data envelopment analysis (DEA), a linear programming tool for evaluating the relative efficiency performance of a set of similar units (e.g. firms, departments, individuals, etc.) who use (possibly multiple) inputs to produce (possibly multiple) outputs in operation environments typically characterised by no reliable information on the prices of inputs and/or no (exact) knowledge about the “functional form” of the production or cost function.

A major appeal of BoD is the flexibility in the construction of the SET scores. In particular, BoD puts teacher performances into a relative perspective to be evaluated optimally, thereby accounting for different values and interpretations that teachers attach to “good teaching”. Furthermore, if available, stakeholder opinion can be easily incorporated into the evaluation. The swift identification of teachers' relative strengths and weaknesses is another advantage. The advantages show in the application.

Several issues specific to the construction and analysis of SET scores have remained largely untouched, and hence unresolved, in the literature. One particular blank area is the weighting and aggregation of SETs into teacher performance scores. This paper contributes to the literature in that it presents a methodology that addresses this issue. To illustrate the usefulness of BoD for teacher evaluations with summative intentions and/or formative purposes, the methodology is applied to SET data collected at a university college in Brussels (Belgium).

The concept of the ‘specificity’ of the terms used in an information retrieval system has come up several times in the last few years. In fact, there are two different concepts which have been described by this term. It is perhaps helpful to distinguish between them.

The purpose of this paper is to propose a non‐parametric methodology to construct composite scores of citizen satisfaction with local police services. More precisely, the paper advocates a custom made version of the popular Data Envelopment Analysis approach, also referred to as the “Benefit‐of‐the‐Doubt” model. The key advantage of this approach is that it weights the citizen satisfaction rates with the multiple local police functions and tasks into the composite score in an endogenous manner, thereby allowing for different values and interpretations of “good local policing” among police services. The methodology is illustrated with citizen satisfaction data on a sample of Belgian local police services.

The paper develops a multidimensional measure of local police effectiveness based on citizen satisfaction measures. It uses a non‐parametric evaluation methodology related to the popular DEA‐model. The paper looks for strengths and weaknesses in the performances of local police services both at the micro level (per local police service) and at the macro level (region).

With an average overall satisfaction score of 91.94 per cent, it seems safe to say that the majority of the citizens are generally satisfied with local police services. The BoD‐model identifies per local police service the basic functionalities that citizens rated relatively highly and poorly. Results show that urbanization per se does not drive the satisfaction scores of the participating local police services. Of much more importance are the regional disparities. Participating local police zones in Flanders receive higher satisfaction scores than those in the Walloon Region (the two big regions in Belgium). One of the findings is that the BoD‐model offers both conceptual and practical advantages in the evaluations of local police services. BoD grants each police service the benefit‐of‐the‐doubt in the effectiveness evaluations. The BoD‐model also identifies the factors of strength and weakness that explain the citizen satisfaction scores.

To the authors’ knowledge, this is the first time that multidimensional scores of citizen satisfaction are used in the effectiveness evaluations of local police services. The paper uses a methodology that accounts for the own particular circumstances of the local police services (by assigning in an endogenous manner weights in the evaluations).

This paper synthesizes the extant research on the outcome effect in the accounting domain, focusing primarily on the context of performance evaluation. It reviews the current state of our knowledge about this phenomenon, including its underlying cognitive and motivational causes, the contexts in which the outcome effect is observed, the factors that influence its various manifestations, and ways in which undesirable outcome effects can be mitigated. It also considers various perspectives about the extent to which outcome effects represent undesirable judgmental bias, and whether this distinction is necessary to motivate research on this topic. The paper is intended to motivate and facilitate future research into the effects of outcome knowledge on judgment in the accounting context. Therefore, we also identify important unanswered questions and discuss opportunities for future research throughout the paper. These include additional consideration of instances in which the outcome effect is reflective of bias, how this bias can be effectively mitigated, ways in which outcome information influences judgment (regardless of whether this influence is considered normative), and how the underlying causes of the outcome effect operate singly and jointly to bring about the outcome effect. We also consider ways that future research can contribute to practice by determining how to encourage evaluators to retain and incorporate the relevant information conveyed by outcomes, while avoiding the inappropriate use of outcome information, and by enhancing external validity to increase the generalizability of experimental results to scenarios frequently encountered in practice.

This paper aims to examine how balanced scorecard (BSC) users change their judgement processes according to qualitative changes in the BSC. Prior experimental studies have found that decision-makers do not fully adapt their judgements according to changes in financial reports, known as functional fixation. Although previous research has examined functional fixation in several management accounting-related disciplines, the research has not been completely successful in developing a deeper understanding of the cognitive processes that are responsible for the occurrence of this judgemental bias.

To fill this gap, a combination of structural modelling and a process tracing method that monitors participants’ information acquisition to better understand the underlying cognitive processes that affect BSC users’ judgements is used.

Overall, the results indicate that functional fixation is present both from an input–output (structural modelling) and a process tracing perspective. Stable general individual differences, particularly in terms of intuitive versus deliberative preferences in decision-making, influence the probability of functionally fixated behaviour. Additionally, previous findings concerning the over-reliance on financial information in the BSC setting is replicated. Using process data, it was found that BSC users rely more on financial measures than on non-financial measures in the pre-decisional phase of exercising their judgement.

This paper contribute to management accounting research on the BSC by investigating two cognitive biases (functional fixation and overreliance on financial measures) from an input–output and a process tracing perspective.