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Statistical methods play an important role in auditors’ analyses of auditee’s data. A key aspect in the statistical approach to auditing is assessing the strength of evidence for or against a hypothesis. Unfortunately, the often-used frequentist statistical methods cannot provide the statistical evidence that audit standards demand directly nor easily. In this article we discuss an alternative approach that can provide this evidence: Bayesian inference. Firstly, we explore the philosophical differences between frequentist and Bayesian inference. Secondly, we discuss misconceptions in the interpretation of frequentist statistical evidence, and finally we discuss how Bayesian inference allows the auditor to obtain and interpret statistical evidence in line with audit standards via its alternative to the p value, the Bayes factor.

The audit environment of today offers a wealth of information in the form of data. Consequently, data about the auditee is expected to guide and improve auditors’ approach to tests of details. However, to be able to make optimal use of this data, auditors must have tools that facilitate the effective and efficient use of quantitative information throughout an audit. In this article, we introduce Bayesian generalized linear modeling as a statistical framework to incorporate this information into tests of details, thereby enabling auditors to deliver a fine-grained and specifically tailored audit opinion to stakeholders. We begin with an introduction of Bayesian inference in audit sampling, then explain the main concepts underpinning Bayesian generalized linear modeling and show how this approach allows auditors to bridge the gap between analytics on integrally available data and analytics on data that is available on a sample basis, making optimal use of their information.

Bayesiaanse statistiek is een manier om efficiëntie en transparantie bij gestratificeerde steekproeven te vergroten. De reden hiervoor is dat Bayesiaanse statistiek de auditor dwingt om expliciet te maken welke informatie en kennis gebruikt wordt bij de evaluatie van de steekproef. In dit artikel worden twee belangrijke vormen van voorkennis beschreven die de auditor moet valideren. Vervolgens zijn deze vormen van voorkennis vertaald naar statistische modellen, die worden gebruikt bij het doen van een gestratificeerde steekproef. Afsluitend wordt gedemonstreerd hoe sommige van die modellen kunnen leiden tot nauwkeurigere foutschattingen en een transparantere audit.

Auditors who perform audit sampling are often interested in obtaining evidence for or against the hypothesis that the misstatement in a population of items is lower than a critical limit, the so-called performance materiality. Here, we propose to perform this hypothesis test using a Bayesian approach that involves the use of an impartial prior distribution, assigning equal prior probabilities to the competing interval hypotheses. Firstly, we argue that the impartial prior distribution is sensible for auditors because it is easy to justify, interpret, and explain. Secondly, we show that Bayes factors computed using this prior distribution have desirable statistical properties. Finally, we compare these Bayes factors with traditional p-values in an audit sampling context and elaborate on the merits of the impartial Bayesian hypothesis test.

Supplemental Material, sj-pdf-1-jsr-10.1177_10946705221079941 for Toward Multisensory Customer Experiences: A Cross-Disciplinary Bibliometric Review and Future Research Directions by Susan Stead, Ruud Wetzels, Martin Wetzels, Gaby Odekerken-Schröder and Dominik Mahr in Journal of Service Research

Supplemental Material, sj-pdf-2-jsr-10.1177_10946705221079941 for Toward Multisensory Customer Experiences: A Cross-Disciplinary Bibliometric Review and Future Research Directions by Susan Stead, Ruud Wetzels, Martin Wetzels, Gaby Odekerken-Schröder and Dominik Mahr in Journal of Service Research

Purpose: Service robots can offer benefits to consumers (e.g. convenience, flexibility, availability, efficiency) and service providers (e.g. cost savings), but a lack of trust hinders consumer adoption. To enhance trust, firms add human-like features to robots; yet, anthropomorphism theory is ambiguous about their appropriate implementation. This study therefore aims to investigate what is more effective for fostering trust: appearance features that are more human-like or social functioning features that are more human-like. Design/methodology/approach: In an experimental field study, a humanoid service robot displayed gaze cues in the form of changing eye colour in one condition and static eye colour in the other. Thus, the robot was more human-like in its social functioning in one condition (displaying gaze cues, but not in the way that humans do) and more human-like in its appearance in the other (static eye colour, but no gaze cues). Self-reported data from 114 participants revealing their perceptions oftrust, anthropomorphism, interaction comfort, enjoyment and intention to use were analysed using partial least squares path modelling. Findings: Interaction comfort moderates the effect of gaze cues on anthropomorphism, insofar as gaze cues increase anthropomorphism when comfort is low and decrease it when comfort is high. Anthropomorphism drives trust, intention to use and enjoyment. Research limitations/implications: To extend human–robot interaction literature, the findings provide novel theoretical understanding of anthropomorphism directed towards humanoid robots. Practical implications: By investigating which features influence trust, this study gives managers insights into reasons for selecting or optimizing humanoid robots for service interactions. Originality/value: This study examines the difference between appearance and social functioning features as drivers of anthropomorphism and trust, which can benefit research on self-service technology adoption.

Many psychologists do not realize that exploratory use of the popular multiway analysis of variance harbors a multiple-comparison problem. In the case of two factors, three separate null hypotheses are subject to test (i.e., two main effects and one interaction). Consequently, the probability of at least one Type I error (if all null hypotheses are true) is 14 % rather than 5 %, if the three tests are independent. We explain the multiple-comparison problem and demonstrate that researchers almost never correct for it. To mitigate the problem, we describe four remedies: the omnibus F test, control of the familywise error rate, control of the false discovery rate, and preregistration of the hypotheses.