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This study reexamines the sustainability of fiscal policy in Sweden.

To test the sustainability of fiscal policy, two approaches are used; the methodology of Kejriwal and Perron (2010), testing for multiple structural changes in a cointegrated regression model and time-varying cointegration test of Bierens and Martins (2010), and Martins (2015).

Using the first approach of testing for multiple structural changes in a cointegrated regression model, the results indicate that government spending and revenue are cointegrated with two breaks. An estimation of a two-break long-run model shows that the slope coefficient increases from 0.678 to 0.892 from the first to the second regime, implying that fiscal deficits were weakly sustainable in the first two regimes, from 1800 to 1943, and from 1944 to 1974. Further, results from time-varying cointegration test indicate that cointegration between spending and revenue in Sweden is time-varying. Fiscal deficits were found to be unsustainable for the periods 1801–1811, 1831–1838, 1853–1860 , 1872–1882, 1897–1902, 1929–1940 and 1976–1982 and weakly sustainable over the rest of the study period.

A number of implications arise from this study: (1) Accounting for breaks in cointegration analysis and in the estimation of the level relationship between spending and revenue is very important because ignoring breaks may lead to an overestimated slope coefficient and hence a bias on the magnitude of fiscal deficit sustainability. (2) In testing for cointegration between spending and revenue, assuming a constant cointegrating slope when it is actually time-varying can also be misleading because deficits can be sustainable for a period of time and unsustainable over another period.

The contribution of this study is three-fold; first, the study uses a long series of annual data spanning over a period of two centuries, from 1800 to 2011. Second, because of the importance of structural change in economics, to examine the existence of a level relationship between spending and revenue, the study uses the methodology of Kejriwal and Perron (2010) to test for multiple structural changes in a cointegrated regression model, as well as time-varying cointegration of Bierens and Martins (2010) and Martins (2015).

This paper aims to examine the relationship between different types of shareholders that command share ownership, family, institutions or external blockholders and earnings management. In addition, it examines the effect of company size on earnings management.

The sample includes 67 companies listed in the Mexican Stock Exchange for the period 2005-2015. The sample composition is quite industry-balanced. A cross-sectional version of the Jones model (1991) is to measure the earnings management. The GMM (generalized method of moments) model is also estimated.

The results show that family and institutional ownership reduce the earnings management, but the impact is different depending on the company size.

The results show that there is a clear relationship between increasing participation of family and institutional investors and a reduction in earnings management. This is consistent with the literature that establishes that ownership is an effective regulatory mechanism that limits earnings management through closer supervision and involvement in management.

For companies’ corporate governance and regulatory authorities, the results of this study may serve to improve the decision-making.

This study shows that ownership structure can provide corporate governance in Mexican listed companies with different monitoring and control capacities to influence companies’ strategies, particularly in relation to the discretion of earnings management.

The International Network of Research Management Societies (INORMS) celebrated its 20^th anniversary in 2021. It was established to increase communication among research management societies. The need for a formal international research management community developed because there was (1) increased international funding of research, (2) the number of international research collaborations was growing, and (3) there was a need to understand research regulations in other countries. INORMS sought to address these issues through international congresses and by providing a forum for member societies to work more closely together on common issues. Membership in INORMS steadily increased over the years. The 20^th anniversary meeting was highlighted with the signing of the Hiroshima Statement that described a research manager’s principles and responsibilities, which include collegiality, inclusiveness, professionalisation, innovation, and accountability. This chapter summarises the factors that led to the formation of INORMS and its history.

Strategy scholars have widely recognized the central role that narratives play in the construction of organizational identities. Moreover, storytelling is an important strategic asset that firms can leverage to inspire employees, excite investors and engage customers' attention. This chapter illustrates how advancements in computational linguistic may offer opportunities to analyze the stylistic elements that make a story more convincing. Specifically, we use a topic model to examine how narrative conventionality influences the performance of 78,758 craftsmen selling their handmade items in the digital marketplace of Etsy. Our findings provide empirical evidence that effective narratives display enough conventional features to align with audience expectations, yet preserve some uniqueness to pique audience interest. By elucidating our approach, we hope to stimulate further research at the interface of style, language and strategy.

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.

Recent studies have shown that people who attend electronic dance music events and use drug checking services (DCS) are a predominantly white male, highly educated middle-class population. However, there is still a lack of data beyond sociodemographic characteristics that must be addressed. This paper aims to describe the drug use patterns and protective behavior strategies (PBS) used by testers and nontesters at Boom Festival 2018 and analyze the relationship between these behaviors and the decision to use the DCS.

This is an exploratory research based on a cross-sectional design using baseline data collected at the Boom Festival from testers (N = 343) and nontesters (N = 115).

Nontesters presented, in general, slightly higher frequencies of use for most drugs, whereas testers tended to adopt PBS more frequently. Moreover, testers planned their drug use more often than nontesters and set more limits on the amount of drugs they used in one session. Both of these behaviors work as predictors for using the DCS.

Our data suggest that DCS might not be easily accessible to all people who use drugs, reaching almost exclusively highly educated people that already apply several harm reduction strategies. Actions should be taken to promote service accessibility.

To the best of the authors’ knowledge, this paper is the first to compare the demographics, drug use and PBS adoption of DCS users with nonusers who attended the same festival.