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This study aims to investigate whether auditors compromise their independence for economically important clients in family business settings.

The authors empirically examine the research question based on China for the years 2011 to 2020. The dependent variable is the auditors’ propensity to issue modified audit opinions, which is a proxy for auditor independence. The authors use relative client audit fees as a proxy for client importance. To address endogeneity issues in the selection of family firms, the authors use the two-stage least squares regression model and, subsequently, the propensity score matching and Hausman firm fixed effect modeling.

This study reveals that the propensity to issue modified audit opinions is positively correlated with client importance. Big-N auditors are more likely to issue modified audit opinions for their economically important family firm clients, whereas such evidence is not found for non-Big-N auditors. Results are consistent and robust to endogeneity test and sensitivity analysis.

This study enriches the literature on auditor independence and the effect of family firms’ ownership structure factors on audit reporting behavior for their economically important clients. Findings may prove useful for managers and practitioners interested in family business.

The paper intends to review academic research on peer to peer (P2P) accommodation sharing, notably Airbnb, for 2010–2022 and to identify the knowledge gaps for future research directions.

Numerous databases were searched using keywords. Based on the central theme of the research papers, the papers were divided into eight segments—consumer behavior, host behavior, host–guest relationship (HGR), trust in Airbnb, dominant theories in Airbnb, Airbnb regulation, Airbnb and hotels and macro impacts of Airbnb. In-depth content analysis resulted in the final 101 papers for inclusion.

The review advances comprehension of the Airbnb phenomenon by enriching the literature with new and most recent studies. Most existing Airbnb research has been conducted in Europe, USA/Canada, followed by Asian countries like China, Singapore, S. Korea and India. Future studies should include South America, Africa and other developing nations. More cross-cultural studies are required to understand consumer and host behavior in different cultural settings. Numerous proposals to fulfill the research gaps identified by the paper are discussed.

The study will give better insights into the spiraling P2P accommodation economy. The study will be useful to researchers, scholars, Airbnb, the hotel industry, vacation rental players and destination marketing organizations by relating the study findings to practical competition analysis. The study provides deeper insights into the decision-making process of both guests and hosts by examining the relevant motivators and constraints. It will also assist the Airbnb platform in identifying its strength over the traditional hotel industry and other vacation rentals. The findings will also assist policymakers in better controlling the Airbnb phenomena by providing a comprehensive view of the micro and macro environment.

The paper includes the most recent studies from Asian countries like India, Singapore, China, Korea and Taiwan, not covered by earlier reviews. Prior studies mainly focused on European and American countries. Also, the paper tried to cover the macro impacts of Airbnb in-depth and the effects of COVID-19.

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.

We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.

The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

Due to the influence of complex and intersecting factors, self-governed public open spaces (POSs) (managed by local communities) are subject to collective action dilemmas such as tragedy of the commons (overexploitation), free-riding, underinvestment and mismanagement. This review paper adopts a multi-dimensional and multi-tier social-ecological system (SES) framework proposed by McGinnis and Ostrom, drawing on collective action theory to explore the key institutional-social-ecological factors that impact POS self-governance.

In this paper, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was utilized to systematically screen and review the relevant literature for the period from 2000 to 2023 in three databases: Web of Science, Scopus and Google Scholar. A total of 57 papers were chosen for in-depth analysis.

The literature review identified and categorized several variables associated with the self-organizing system of POS; consequently, an SES-based POS management framework was developed for the first time, consisting of 114 institutional-social-ecological sub-variables from different dimensions and three levels. Compared to ecological factors, among others, governance organizations, property-rights systems, socioeconomic attributes and actors' knowledge of SES have been commonly and primarily studied.

There is still room for the refinement of the conceptual SES-based POS collective action framework over the time (by adding in new factors), and indefinitely empirical research validating those identified factors is also worth to be undertaken, particularly testing how SES factors and interaction variables affect the POS quality (collective action).

The findings of this study can provide local policy insights and POS management strategies based on the identification of specific SES factors for relevant managers. Moreover, this research makes significant theoretical contributions to the integration of the SES framework and collective action theory with POS governance studies.

Unlike other types of corporate disclosure, corporate political disclosure (CPD), which is the disclosure of corporate political contributions and the related governing policies and oversight mechanisms, does not provide completely new information to stakeholders. Some of the information disclosed in CPD is available from other public records (e.g. the Federal Election Committee website or OpenSecrets website). Given this unique feature of CPD, it is interesting to investigate the cost and benefit tradeoff for firms of altering their CPD practice in response to policy and political uncertainty.

This study employs recently developed indexes of aggregate economic policy uncertainty (EPU) and a novel dataset of CPD transparency to examine the impact of EPU on CPD transparency and how the proprietary cost of corporate political activities moderates this association. The sample consists of S&P 500 companies from the 2012 to 2019 period.

The authors document that firms mitigate the heightened information asymmetry associated with higher aggregate EPU by increasing CPD transparency. The positive association between EPU and CPD is less pronounced for firms that are more sensitive to EPU, for firms that more actively manage EPU through corporate political contributions or lobbying activities and for firms that are followed by more analysts. The authors also find that more transparent CPD helps to mitigate the information asymmetry caused by heightened EPU. This study’s results hold when the authors control for other types of voluntary corporate disclosure.

This study contributes to the emerging literature on the determinants of CPD transparency by identifying EPU's positive impact on CPD transparency. This study also provides empirical evidence that the proprietary costs arising from the controversial nature of corporate political activities dampen firms' incentives to provide transparent CPD in response to heightened EPU, and that information on corporate political activities gathered and processed by financial analysts seems to lower the marginal benefit to companies of publicizing CPD on their own website.

Within a gated community, management of common property presents great challenges. Therefore, the diagnostic social ecological system (SES) framework proposed by Elinor Ostrom providing a holistic understanding of complex collective action problems in terms of management of commons is used to investigate key institutional-social-ecological factors influencing collective action in the context of gated communities.

In this paper, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used to systematically screen and review the relevant literature from 2000 to 2022, where 28 papers were selected for further analysis.

The study systematically identifies and categorises a series of variables related to self-organizing management in the gated community, and consequently a SES-based gated community management framework is developed. Based on the conceptual framework, the paper discusses logical interrelationships of institutional-social-ecological factors and their impacts on collective action performance of gated communities.

Apart from requiring empirical validation, the conceptual SES-based gated community management framework is certainly subject to continuous improvement in terms of refinement and addition of other potential determinants of gated community collective action.

Not only the review paper provides updates on the latest gated-community collective action research, it also contributes theoretically by conceptualizing the SES framework and its institutional–social–ecological design principles in gated community management. Studying these factors should also be of practical significance because the findings ultimately offer policy insights and management strategies that help policy-makers, property developers and local communities to govern such neighbourhood common resources efficiently and sustainably.

The authors investigate how managers adapt their financial reporting and disclosure practices in response to the COVID-19 pandemic through changes in accounting estimates (CAEs).

The authors define the pandemic period as starting on March 1, 2020. The sample consists of 9,575 CAEs disclosed in quarterly (10-Qs) and annual (10-Ks) financial reports by US firms between January 1, 2004 and May 31, 2022. The authors perform multivariate analyses of the impact of the COVID-19 pandemic on the incidence of CAEs and on whether the impact of CAEs on firms' financial performance and reporting quality changes during the pandemic.

In the examination of the CAE footnote disclosures in the quarterly (10-Qs) and annual (10-Ks) reports of US companies, the authors find no evidence that the incidence of CAEs in 10-Ks or the number of firms reporting CAEs are significantly different in the pre-pandemic and pandemic periods, but the incidence of CAEs in 10-Qs is significantly higher in the pandemic period than in the pre-pandemic period. The authors also find that the number of CAEs related to revenue recognition increase significantly in the pandemic period, but CAEs in other categories decrease, with the sharpest drop seen in the liabilities category. Further investigation suggests that although the dollar impact of 10-K CAEs on current financial statements is higher during the pandemic period, firms with CAEs, especially positive CAEs, in either 10-Ks or 10-Qs are less likely to use CAEs to boost earnings in the pandemic period. However, the authors find evidence that firms tend to use CAEs to “big bath” current earnings and create reserve for future period. The authors have not observed any significant differences in how the various phases of the pandemic affect the reporting of CAEs. Additionally, there is no evidence to suggest that financially distressed firms report more or fewer CAEs during the pandemic.

The results are consistent with the notion that, during the pandemic, firms exercise greater caution in their CAE disclosures, refraining from using CAEs as a means of boosting earnings but as a strategy to create reserve for future period. The paper highlights the challenges that various stakeholders face when assessing a company's current and future financial performance based on management's accounting estimates.

This study captures the impact of the COVID-19 pandemic on the incidence of CAEs and CAEs' impact on the financial performance and financial reporting quality of firms during the pandemic.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.

This paper aims to present a multi-fidelity surrogate-based optimization (MFSBO) method for computationally accurate and efficient design of microfluidic concentration gradient generators (µCGGs).

Cokriging-based multi-fidelity surrogate model (MFSM) is constructed to combine data with varying fidelities and computational costs to accelerate the optimization process and improve design accuracy. An adaptive sampling approach based on parallel infill of multiple low-fidelity (LF) samples without notably adding computation burden is developed. The proposed optimization framework is compared with a surrogate-based optimization (SBO) method that relies on data from a single source, and a conventional multi-fidelity adaptive sampling and optimization method in terms of the convergence rate and design accuracy.

The results demonstrate that proposed MFSBO method allows faster convergence and better designs than SBO for all case studies with 49% more reduction in the objective function value on average. It is also found that parallel infill (MFSBO-4) with four LF samples, enables more robust, efficient and accurate designs than conventional multi-fidelity infill (MFSBO-1) that only adopts one LF sample during each iteration for more complex optimization problems.

A MFSM based on cokriging method is constructed to utilize data with varying fidelities, accuracies and computational costs for µCGG design. A parallel infill strategy based on multiple infill criteria is developed to accelerate the convergence and improve the design accuracy of optimization. The proposed methodology is proved to be a feasible method for µCGG design and its computational efficiency is verified.