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This paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's four-component model of antecedents for ethical decision-making. This study derives implications for accounting and auditing scholars and practitioners.

This research is rooted in the hermeneutics tradition of interpretative accounting research, in which the reader and the texts engage in a form of dialogue. To substantiate this dialogue, the authors conduct a theoretically informed, narrative (semi-systematic) literature review spanning the years 2015–2020. This review's narrative is driven by the depicted contexts and the accounting/auditing practices found in selected articles are used as sample instead of the research or methods.

In the thematic coding of the selected papers the authors identify five major ethical challenges of AI-based decision-making in accounting: objectivity, privacy, transparency, accountability and trustworthiness. Using Rest's component model of antecedents for ethical decision-making as a stable framework for our structure, the authors critically discuss the challenges and their relevance for a future human–machine collaboration within varying agency between humans and AI.

This paper contributes to the literature on accounting as a subjectivising as well as mediating practice in a socio-material context. It does so by providing a solid base of arguments that AI alone, despite its enabling and mediating role in accounting, cannot make ethical accounting decisions because it lacks the necessary preconditions in terms of Rest's model of antecedents. What is more, as AI is bound to pre-set goals and subjected to human made conditions despite its autonomous learning and adaptive practices, it lacks true agency. As a consequence, accountability needs to be shared between humans and AI. The authors suggest that related governance as well as internal and external auditing processes need to be adapted in terms of skills and awareness to ensure an ethical AI-based decision-making.

The purpose of this paper is to use stakeholder theory as the theoretical reference framework to study the influence of internationalization (geographic international diversification) and social performance on multinational companies’ (MNCs) reputation.

The authors confirm the research hypotheses using a sample of 113 US MNCs in the chemical, energy and industrial machinery sectors during the period 2005-2010.

This study contributes to the literature in three ways. First, it incorporates literature on internationalization to study the possible connection between geographic international diversification and social performance in MNCs. Second, it sheds light on the debate between corporate social responsibility (CSR) and the reputation of MNCs in a very diverse transnational context in which MNCs must meet the needs of stakeholders at both local and global levels. Third, it incorporates the mediating role of social performance in the relationship between geographic international diversification and the firm’s reputation.

Prior studies have hardly analyzed this relationship, which becomes especially relevant for MNCs, since their implementation of advanced CSR practices in the different markets in which they operate will gain them a good reputation, not only in specific local contexts but also globally, benefitting the organization as a whole and enabling it to gain internal consistency (improvement in internal efficiency), transparency and legitimacy.

This article aims to elaborate the context-sensitive nature of credibility assessment by examining how such judgments are made in online discussion in times of uncertainty caused by Finland's intent to join the North Atlantic Treaty Organization (NATO) in spring 2022.

The empirical findings draw on the qualitative content analysis of 3,324 posts submitted to a Finnish online discussion in February–March 2022. It was examined how the participants of online discussion assess the credibility of information sources referred to in debates on the NATO membership. It is assumed that the believability of the author of information is indicative of his or her expert power, for example based on the credentials of a scholar, while the credibility of information content, for example the provision of factual evidence is indicative of the source's informational power.

Political decision-makers, particularly the President of Finland were assessed as most credible information sources, due to their access to confidential knowledge and long-time experience in politics. The credibility assessments differed more strongly while judging the believability of researchers. On the one hand, their expertise was praised; on the other hand, doubts were presented about their partiality. Fellow participants of online discussion were assessed most negatively because information sources of these types are associated with low expert and informational power.

As the study concentrated on credibility assessments made in a Finnish online discussion group, the findings cannot be extended to concern the credibility judgments occurring information in other contexts.

The study is among the first to characterize the role of expert and informational power in credibility assessment in times of uncertainty.

The purpose of this study is to shed light on the tools, processes and negotiations involved in the formation of acceptable current values in the context of goodwill impairment testing. The study raises the questions of how a current value for goodwill becomes a faithful representation and how one expectation about the future becomes more convincing than other expectations.

Drawing on the study of associations, the analysis presents a case study of a large, internationally active organisation. By combining field notes, interview transcripts and a variety of documents, the qualitative analysis focusses on strategies and mechanisms of persuasion.

The findings reveal how epistemological objectivity of current values forms in three moments of relational becoming that codify, depersonalise and proceduralise the valuation task. Further, the study suggests that a convincing argument forms with the help of four enablers: a bricolage of inscriptions, methodological mystification, transformed professional identities and a practical need for closure.

The study contributes with an analysis and illustration of financial accounting as practice, elaborating on the meaning and construction of faithful representation in cases of measurement uncertainty.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The purpose of this research is to explore the effects of supplier selection method on key procurement outcomes such as procurement lead time (PLT), supplier performance and buyer team size.

Data were collected from a sample of 124 archival contract records from the US Department of Defense. A multiple regression model and multivariate analysis of covariance/analysis of covariance models were used to test the effects of source selection method on pertinent procurement outcomes.

The trade-off (TO) source selection method increases PLT, as does the number of evaluation factors and the number of proposals received. Substantially larger sourcing teams are also associated with the TO source selection method. Nonetheless, the TO method results in better supplier performance.

TO source selections yield superior supplier performance than low-bidder methods. However, they are costly in terms of time and personnel. Any assessment of supplier value should consider not only the price premium for higher performance but also the transaction costs associated with the TO method.

Very little research addresses a buying team’s evaluation of supplier-offered value ex ante and whether that value assessment materializes into actual value-added supplier performance. Low bidder tactics are pervasive, but price (i.e. sacrifice) is only one component of value. Benefits from superior supplier performance may yield greater overall value. If value is critical to the buyer, a TO source selection method – versus a low-bidder approach – is the appropriate tool because of higher supplier performance ex post.

The aim of this study is to empirically explore and analyze the concrete tasks of output measurement and the inherent challenges related to these tasks in a traditional and autonomous professional public work setting – the judicial system.

The analysis of the tasks is based on a categorization of general performance measurement motives (control-motivate-learn) and main stakeholder levels (society-organization-professionals). The analysis is exploratory and conducted as an empirical content analysis on materials and reports produced in two performance improvement projects conducted in European justice organizations.

The identified main tasks in the different categories are related to managing resources, controlling performance deviations, and encouraging improvement and development of performance. Based on the results, key improvement areas connected to output measurement in professional public organizations are connected to the improvement of objectivity and fairness in budgeting and work allocation practices, improvement of output measures' versatility and informativeness to highlight motivational and learning purposes, improvement of professional self-management in setting output targets and producing outputs, as well as improvement of organizational learning from the output measurement.

The paper presents empirically founded practical examples of challenges and improvement opportunities related to the tasks of output measurement in professional public organization.

This paper fulfils an identified need to study how general performance management motives realize as concrete tasks of output measurement in justice organizations.

The work of internal auditors is relevant to their host entities' reporting processes; however, few researchers have examined how internal auditors’ competency and objectivity affect their resistance to pressure from host entities regarding their reports. Thus, the main objective of this study is to examine the influence of internal audit functions' (IAF) quality factors on chief audit executives' (CAEs) ability not to modify internal audit report.

This study uses data from the Global Internal Audit Common Body of Knowledge to investigate the relationship between IAF quality and auditor resistance to pressure related to changes in internal audit reports. IAF quality is calculated using a composite measure comprising four IAF quality components. Auditors' resistance is measured using the extent to which internal auditors experienced a situation wherein they were directed to modify a valid audit finding in a report.

The analyses provide evidence that CAEs experience, certification, training and objectivity were all significantly associated with resistance to pressure. In other words, a greater quality of IAF leads to a greater ability to resist pressure to change their reports.

Despite the statistically significant results that confirm the impact of IAF competence and objectivity on the resistance of CAEs to pressure, some other factors should be considered simultaneously in future research. In addition, the study sample contains 2,193 CAEs from different regions, environments, sectors and business areas. Focussing on a particular environment, sector or organisation size may generate different results.

The following practical implications are proposed: First, internal audit regulators will find this study helpful in formulating strategies for creating balanced relationships between CAEs and other authorities and users. Second, CAEs can be encouraged to undergo constant training and complete professional development (as required by the Institute of Internal Auditors [IIA] standard). Finally, it would be interesting to apply this study to a particular environment, sector and size.

This study builds on the limited research that investigates the relationship between IAFs’ quality and the resistance of CAEs to pressure. It extends Calven’s (2021) study that investigates the impact of adherence to the IIA's Core Principles on the likelihood of IAFs modifying valid audit findings. This study examines the influence of IAF quality factors on CAEs' ability not to modify internal audit report.

This study, first, reviews the existing literature on electronic word-of-mouth (eWOM) and, using communication theory, examines its impact on its readers’ decision-making processes. Second, this paper aims to propose some elements of eWOM communications that might be further researched.

A literature review of relevant academic articles produced 97 works related to social communication theory, eWOM and new artificial intelligence trends in hospitality. Thereafter, potential avenues for future research were explored.

The study results showed: valence, relevance, understandability and visual cues are the most important antecedents of message usefulness and the reader’s behavioral intentions; source credibility is the sender characteristic that most affects the reader’s behavioral intentions and consumer susceptibility to interpersonal influence is the receiver characteristic that most influences their attitudes and behavioral intentions. In addition, the study highlights four relevant aspects for future research. First, more research into online fake reviews is needed to better understand sender motivations. Second, companies should actively manage negative reviews. Then, the careful choice of platforms on which companies promote their products/services. Finally, the role of artificial intelligence in increasing the effectiveness of eWOM in the hospitality industry.

This paper integrates – grounded on communication theory – results from previous studies about the central elements of communication (message, sender and receiver) and discusses the main trends in hospitality-related eWOM. In addition, the paper examines the potential of specific eWOM elements as future lines of research, in particular: fake reviews, strategies for dealing with negative reviews, the eWOM platform used and artificial intelligence applications.

Este estudio, en primer lugar, revisa la literatura existente sobre el boca-oído electrónico (eWOM) y, utilizando la teoría de la comunicación, examina su impacto en los procesos de toma de decisiones de sus lectores. En segundo lugar, proponemos algunos elementos de las comunicaciones eWOM que podrían investigarse más adelante.

Una revisión de la literatura de artículos académicos relevantes produjo 97 trabajos relacionados con la teoría de la comunicación social, eWOM y nuevas tendencias de inteligencia artificial en hotelería. A partir de entonces, se exploraron posibles vías de investigación futura.

Los resultados del estudio señalaron: 1) la valencia, la relevancia, la comprensibilidad y las señales visuales son los antecedentes más importantes de la utilidad del mensaje y las intenciones de comportamiento del lector; 2) La credibilidad de la fuente es la característica del emisor que más afecta las intenciones de comportamiento del lector; 3) La susceptibilidad del consumidor a la influencia interpersonal es la característica del receptor que más influye en sus actitudes e intenciones de comportamiento. Además, el estudio destaca cuatro aspectos relevantes para futuras investigaciones. Primero, se necesita más investigación sobre las reseñas falsas en línea para comprender mejor las motivaciones del emisor. En segundo lugar, las empresas deben gestionar activamente las críticas negativas. Luego, la cuidadosa elección de las plataformas en las que las empresas promocionan sus productos/servicios. Por último, el papel de la inteligencia artificial en el aumento de la eficacia de eWOM en la industria hotelera.

Este artículo integra –con base en la teoría de la comunicación– resultados de estudios previos sobre los elementos centrales de la comunicación (mensaje, emisor y receptor) y analiza las principales tendencias en eWOM relacionadas con la hostelería. Además, el artículo examina el potencial de elementos específicos de eWOM como líneas futuras de investigación, en particular: revisiones falsas, estrategias para lidiar con críticas negativas, la plataforma eWOM utilizada y aplicaciones de inteligencia artificial.

Palabras clave: eWOM, Teoría de comunicación, Revisiones falsas, Revisiones negativas, Inteligencia artificialTipo de artículo: Revisión de la literatura

本研究, 首先, 回顾了现有的关于电子口碑（eWOM）的文献, 并利用传播理论, 研究了它对读者决策过程的影响。其次, 我们提出了一些可能需要进一步研究的电子口碑传播的要素。

对相关学术文章的文献回顾产生了97篇与社会传播理论、eWOM和酒店业新的人工智能趋势有关的作品。此后, 对未来研究的潜在途径进行了探讨。

研究结果显示。1）价值、相关性、可理解性和视觉线索是信息有用性和读者行为意图的最重要的前因; 2）来源的可信度是最能影响读者行为意图的发送者特征; 3）消费者对人际影响的易感性是最能影响他们态度和行为意图的接受者特征。此外, 该研究还强调了未来研究的四个相关方面。首先, 需要对网上虚假评论进行更多的研究, 以更好地了解发送者的动机。其次, 公司应该积极管理负面评论。然后, 谨慎选择公司推广其产品/服务的平台。最后, 人工智能在提高酒店业eWOM的有效性方面的作用。

本文以传播理论为基础, 整合了以往关于传播中心要素（信息、发送者和接受者）的研究结果, 并讨论了与酒店业相关的电子WOM的主要趋势。此外, 本文还研究了特定的eWOM要素作为未来研究方向的潜力, 特别是：虚假评论、处理负面评论的策略、使用的eWOM平台和人工智能应用。

关键词：eWOM, 传播理论, 虚假评论, 负面评论, 人工智能。