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Purpose – Accounting research has long shown the effect of subjectivity in performance evaluation. This study investigates one form of subjectivity in performance evaluation: flexibility in weighting performance measures examining decisions made by supervisors about weighting. Empirical studies show that the performance-measure weights are only partially consistent with the predictions of the agency theory and they are a still outstanding issue.

Methodology/approach – We develop an experiment to analyse supervisor decision-making, manipulating two factors: internal organisational interdependence and the level of managerial performance. We derive hypotheses along with both economic and behavioural approaches. The economic approach is based on agency theory predictions and the controllability principle while the behavioural approach is drawn on the organisational justice theory. We argue that in low interdependence contexts the supervisor's decision confirms the agency theory predictions, while in high interdependence conditions weighting decisions could be driven by behavioural considerations of fairness perceptions of the evaluation process and the level of managerial performance.

Findings – We find that in low interdependence contexts the supervisor's decision confirms the agency theory predictions, while in high interdependence contexts it does not. The results indicate that the supervisor's decision stems from the integration of economic and behavioural perspectives.

Research and social implications – The theoretical framework can be useful for interpreting the supervisor decision-making and the weighting process.

Originality – The economic and behavioural approaches allow us to understand flexibility in weighting performance measures suggesting that, in addition to economic considerations, a behavioural perspective may also be relevant in explaining subjective weighting.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

The author presents new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape with fixed point well in the interior of the (0,1) interval: Instead the author usually finds populations dominated by “optimists” who uniformly overweight best outcomes in risky options. The choice pairs used here mostly do not provoke similarity-based simplifications. In a third experiment, the author shows that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.

The purpose of this paper is to establish a new approach for solving the expansion term problem.

This study develops an expansion term weighting function derived from the valuable concepts used by previous approaches. These concepts include probability measurement, adjustment according to situations, and summation of weights. Formal tests have been conducted to compare the proposed weighting function with the baseline ranking model and other weighting functions.

The results reveal stable performance by the proposed expansion term weighting function. It proves more effective than the baseline ranking model and outperforms other weighting functions.

The paper finds that testing additional data sets and potential applications to real working situations is required before the generalisability and superiority of the proposed expansion term weighting function can be asserted.

Stable performance and an acceptable level of effectiveness for the proposed expansion term weighting function indicate the potential for further study and development of this approach. This would add to the current methods studied by the information retrieval community for culling information from documents.

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.

Evaluation of energy and climate policy interactions is a complex issue, whereas stakeholders' preferences incorporation has not been addressed systematically. The purpose of this paper is to present an integrated weighting methodology that has been developed in order to incorporate weighting preferences into an ex ante evaluation of climate and energy policy interactions.

A multi‐criteria analysis (MCA) weighting methodology which combines pair‐wise comparisons and ratio importance weighting methods has been elaborated. It initially introduces the users to the evaluation process through a warming up holistic approach for an initial rank of the criteria and then facilitates them to express their ratio relative importance in pair‐wise comparisons of criteria by providing them an interactive mean with verbal, numerical and visual representation of their preferences. Moreover, it provides a ranking consistency test where users can see the degree of (in)consistency of their preferences.

Stakeholders and experts in the energy policy field who tested the methodology stated their approval and satisfaction for the combination of both ranking and pair‐wise comparison techniques, since it allows the gradual approach to the evaluation problem. In addition, main difficulties in MCA weights elicitation processes were overcome.

The methodology is tested by a small sample of stakeholders, whereas a larger sample, a broader range of stakeholders and applications on different climate policy evaluation cases merit further research.

The novel aspect of the developed methodology consists of the combination of ranking and pair‐wise comparison techniques for the elicitation of stakeholders' preferences.

The purpose of this study is to examine whether financial analysts mislead investors in recognizing the differential persistence of the three cash flow components of earnings, defined by Dechow et al., in forecasting annual earnings.

The paper uses Mishkin's econometric approach to compare the persistence of the cash flow components within and across the historical, analysts' and investors' weightings.

It is found that financial analysts' weightings of the cash flow components are more closely aligned with the historical relations than are investors' weightings, both in direction and in magnitude. The degree of analysts' mis‐weighting is economically small and much lower than the degree of investors' mis‐weighting. Moreover, the extent of both investors' and analysts' mis‐weightings of the cash components is generally smaller for firms with greater levels of analyst following, a proxy for the quality of the information environment.

The findings suggest that financial analysts' bias in weighting the cash components of earnings is at best a partial explanation for investors' bias.

This study is important to academics and the investment community that relies upon financial analysts as information intermediaries, because the ability of analysts to incorporate value‐relevant information in their published expectations may impact securities prices.

The study is the first to document the weightings of the cash components of earnings by financial analysts. In addition, this paper provides evidence that financial analysts, as information intermediaries, are less biased than investors in processing not only the accrual but also the cash components of earnings.

In the context of sharing economy, the superhost program of Airbnb emerges as a phenomenal success story that has transformed the tourism industry and garnered humongous popularity. Proper performance evaluation and classification of the superhosts are crucial to incentivize superhosts to maintain higher service quality. The main objective of this paper is to design an integrated multicriteria decision-making (MCDM) method-based performance evaluation and classification framework for the superhosts of Airbnb and to study the variation in various contextual factors such as price, number of listings and cancelation policy across the superhosts.

This work considers three weighting techniques, mean, entropy and CRITIC-based methods to determine the weights of factors. For each of the weighting techniques, an integrated TOPSIS-MOORA-based performance evaluation method and classification framework have been developed. The proposed methodology has been applied for the performance evaluation of the superhosts (7,308) of New York City using real data from Airbnb.

From the perspective of performance evaluation, the importance of devising an integrated methodology instead of adopting a single approach has been highlighted using a nonparametric Wilcoxon signed-rank test. As per the context-specific findings, it has been observed that the price and the number of listings are the highest for the superhosts in the topmost category.

The proposed methodology facilitates the design of a leaderboard to motivate service providers to perform better. Also, it can be applicable in other accommodation-sharing economy platforms and ride-sharing platforms.

This is the first work that proposes a performance evaluation and classification framework for the service providers of the sharing economy in the context of tourism industry.

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.