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The purpose of this paper is to achieve the multi-objective optimization design of novel tubular switched reluctance motor (TSRM).

First, the structure and initial dimensions of TSRM are obtained based on design criteria and requirements. Second, the sensitivity analysis rules, process and results of TSRM are performed. Third, three optimization objectives are determined by the average electromagnetic force, smoothing coefficient and copper loss ratio. The analytic hierarchy process-entropy method-a technique for order preference by similarity to an ideal solution-grey relation analysis comprehensive evaluation algorithm is used to optimize TSRM. Finally, a prototype is manufactured, a hardware platform is built and static and dynamic experimental validations are carried out.

The sensitivity analysis reveals that parameters significantly impact the performance of TSRM. The results of multi-objective optimization show that the average electromagnetic force and smoothing coefficient after optimization are better than before, and the copper loss ratio reduces slightly. The experimental and simulated results of TSRM are consistent, which verifies the accuracy of TSRM.

In this paper, only three optimization objectives are selected in the multi-objective optimization process. To improve the performance of TSRM, the heating characteristics, such as iron loss, can be considered as the optimization objective for a more comprehensive analysis of TSRM performance.

A novel motor structure is designed, combining the advantages of the TSRM and the linear motor. The established sensitivity analysis rules are scientific and suitable for the effects of various parameters on motor performance. The proposed multi-objective optimization algorithm is a comprehensive evaluation algorithm. It considers subjective weight and objective weight and fully uses the original data and the relational degree between the optimization objectives.

This study extends prior accounting research on decision aids (DAs) relating to face validity. Specifically, this study aims to examine the effects of face validity through the presence of two levels of bias in DA output. The presence of bias in a DA will not affect how statistically informative an aid is but will decrease the face validity. The findings suggest that non-expert DA users recognize the bias in the DA’s suggestions as evidenced by users’ low agreement with the aid; however, they do not adjust for the bias in their performance, suggesting that non-expert users do not learn from the DA.

This repeated-measures experimental design allows us to examine performance effects over time in response to different levels of bias in the DA output. The participants in the study are provided with outcome feedback to examine learning effects.

The findings suggest that non-expert DA users recognize the bias in the DA’s suggestions as evidenced by users’ low agreement with the aid; however, they do not adjust for the bias in their performance, suggesting that non-expert users do not learn from the DA. Although users of an unbiased DA strongly agree with the DA’s output, individual performance deteriorates over time. Initially, the users of an unbiased DA perform better than those who use a biased DA; however, over time, the performance of users of an unbiased aid deteriorates and the performance of users of the biased aid does not improve.

Companies developing DAs may need to consider the effects of using a DA under circumstances different from those under which the aid was developed and that may lead to the biased DA output. This study has implications for firms that design, develop and use DAs.

This study considers a yet unexamined face validity issue – observable bias in DA output. This study examines deterministic DAs designed to assist the decision-maker through their ability to combine multiple cues in a systematic and consistent manner. This study has implications for firms that design, develop and use DAs. Firms need to consider the effects of using a DA under circumstances different from those under which the aid is developed, thereby, potentially leading to biased DA output. Each additional variable added to the DA will be associated with an incremental cost in a DA’s development, use and modification. The results of this study provide insights contributing to the information available for cost–benefit analyses conducted when developing a DA or when considering the modification of existing aid. Failure to change a DA because of face validity issues alone may result in a decline in user performance. Thus, the cost of modifying a DA must be weighed against the benefits resulting from improved performance. This study contributes insights into how users’ responses to DA bias could affect the assessments of the benefits of including an omitted variable in a DA.

The Keirsey Temperament Sorter (KTS) is a psychometric instrument that can be useful to researchers interested in investigating the impact of personality traits in accounting practice and education. The KTS may be used to investigate: (a) the nature of personality of accounting practitioners, faculty and students; and (b) how personality traits affect the performance of accounting practitioners, faculty and students. This paper provides KTS users with the empirical and conceptual information necessary to conduct research in these areas. The psychometric properties and underlying theory of the KTS are examined, as are its limitations. The paper also compares the KTS to the Myers-Briggs Type Indicator (MBTI), another widely used psychometric instrument. The two instruments are compared in order to determine the relative advantages of each for accounting research.

Auditors and audit researchers consider decision aids important for overcoming biases and for improving the effectiveness and efficiency of audits (see Messier, 1995 for a review). Decision aids are used to increase judgment consistency, and, therefore, have potential to improve both the effectiveness and efficiency of audits. This study investigates three levels of justification requirements (no justification, unconditional justification, and justification of disagreement) to determine their effects on decision-makers' agreement with an aid and performance in decision-making. We find that an unconditional justification requirement leads to better performance when there is no decision aid, but leads to no better performance or increased agreement with the aid when a decision aid is present. Justification of disagreement with a decision aid, however, leads to increased agreement with the aid's advice in all conditions. We also find partial support that justification of disagreement leads to better performance than unconditional justification. For those who would like to see a decision aid have more influence, justification of disagreement with a decision aid could be an easily and economically implemented management policy or software design component.

The Editor and Associate Editors at AABR would like to thank the many excellent reviewers who have volunteered their time and expertise to make this an outstanding publication. Publishing quality papers in a timely manner would not be possible without their efforts.