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Global warming exacerbates sea level rise and extreme weather events that cause severe flooding, resulting in lost productivity and property damage. To reduce the impact of flooding, residents are avoiding purchasing homes in high-risk areas. There are numerous studies on the relationship between flood hazards and housing prices in developed countries, but few in developing countries. Therefore, this study aims to investigate the relationship between flood hazards and housing prices in Hat Yai, Songkhla, Thailand.

This study uses spatial-lag, spatial error and spatial autoregressive lag and error (SARAR) models to analyze the effect of flood risk on property prices. The main analysis examines the degree of flood risk and housing rental prices from our survey of 380 residences. To test the robustness of the results, the authors examine a different data set of the same samples by using the official property valuation from the Ministry of Finance and the flood risk estimated by the Southern Natural Disaster Research Center.

The SARAR model was chosen for this study because of the occurrence of spatial dependence in both dependent variable and the error term. The authors find that flood risk has a negative impact on property prices in Hat Yai, which is consistent with both models.

This study is one of the first to use spatial econometrics to analyze the impact of flood risk on property prices in Thailand. The results of this study are valuable to policymakers for benefit assessment in cost–benefit analysis of flood risk avoidance or reduction strategies and to the insurance market for pricing flood risk insurance.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

To promote the success of peer-to-peer accommodation, this study examines the effects of online host–guest interaction as well as the interaction's boundary conditions of listing price and reputation on listing popularity.

Using 330,686 data collected from Airbnb in the United States of America, the authors provide empirical evidence to answer whether social-oriented self-presentation and response rate influence listing popularity from the perspective of social exchange theory (SET). In addition, the authors investigate how these two kinds of online host–guest interactions work with listing price and reputation to influence listing popularity.

The results reveal the positive association between online host–guest interaction and listing popularity. Notably, the authors find that listing price strengthens but listing reputation weakens the positive effects of online host–guest interactions on listing popularity in peer-to-peer accommodation.

This study is the first attempt to adopt SET to explain the importance of online host–guest interactions in influencing listing popularity as well as examine the moderating role of listing price and reputation on the above relationship.

A supplier may sell not only to one buyer (sole relationship configuration) but also to the buyers competitors (shared relationship configuration) for a specific product category. This study examines the performance implications when suppliers establish shared relationships with the buyer’s competitors.

Secondary data are used to test hypotheses relating a supplier’s relationship configurations to its operational performance. A seemingly unrelated regression approach (SUR) is applied to analyze the data, followed by endogeneity checks of the empirical findings.

The study shows that suppliers with less-shared ties with buying firms’ competitors exhibit superior inventory efficiency and asset turnover. Thus, suppliers can improve operational efficiency by creating relatively exclusive, deep and trust-based relations instead of more extensively shared and shallower relationships.

Based on agency theory as a theoretical lens and aerospace industry data, this research contributes by addressing the supplier’s perspective and linking its operational efficiency performance with its chosen supply relationship configuration.

Suppliers need to understand the performance implications of choosing relatively exclusive relationships versus shared relationships with buying firms. The research provides new insights for managers and can guide their supply chain decision-making.

Little is known about how a supplier’s relationship configurations can elevate, or impair, its operational efficiency. While conventional wisdom holds that suppliers should focus on multiple avenues of revenue growth by selling to buyers’ competitors, this study demonstrates that more sales to a buying firm’s rivals might, in fact, reduce a supplier’s efficiency.

This study aims to verify the significance of Andersen (2008) corporate risk management (CRM) framework in Asian emerging markets (AEMs) to control firm risk and improve firm performance.

The cross-sectional analyses are performed on a sample of 4,609 firms across nine Asian emerging countries using 2SLS estimation technique.

The empirical findings show that the adoption of CRM not only enhances firm performance by increasing the firm ability to capitalize on the market opportunity but also plays a significant role in reducing firm risk. The findings of this study assert that by institutionalizing risk management practices into an integrated CRM framework, the firm can reap multiple benefits by maintaining better contractual agreements and strategic partnerships with key stakeholders.

The study shifts the focus of CRM away from Western countries toward AEMs, which has been afflicted by high risks and uncertainties. The effectiveness of CRM against firm risk is established by dividing firm risk into firm-specific risk and systematic risk. Furthermore, this study also establishes that CRM not only leads to high returns but also reduces firm operational and production costs. Overall, the study provides a compelling argument to implement CRM for improving organizational performance and managing risks in a strategic and integrated manner. The findings are also relevant to risk management practitioners, as well as to academicians interested in the broader fields of corporate finance and strategy.

Using technology to meet national security expectations and requirements is not new. Nations attempt to strike a balance between security and the (expressed or otherwise) privacy needs of citizens. Attacks (physical or cyber) on citizens shift the equilibrium point towards security. In contrast, civil liberties organisations act to preserve or increase privacy. The purpose of this paper is to explore Australian attitudes towards privacy and surveillance during the COVID-19 pandemic. In addition, this paper aims to discover what (if any) factors contribute to societal acceptance of privacy encroachment implicated by surveillance programs.

Data collection occurred during 2021 using a cross-sectional survey comprising a variety of self-assessment questions. In addition, anchoring vignettes were introduced as a means of contextualising complex concepts, i.e. privacy and security. Finally, latent class analysis (LCA) was used to identify homogenous patterns within the data, referred to as “classes” for the analysis of trust.

First, the survey revealed that citizens appear to be unconcerned about surveillance in public and private spaces (although this may be a temporary effect resulting from the pandemic). The potential for identification, however, does raise concerns. Second, LCA surfaced a specific group that were more likely to trust entities and showed less concern about surveillance in society. Finally, even this latter group displayed a “trust deficit” in specific organisations (private businesses and social media firms).

The tension between security and privacy remains, even in a post-pandemic world; therefore, the authors consider that the results, whilst interesting, are preliminary. Notwithstanding this, the findings provide insight into Australian attitudes towards privacy and surveillance and, consequently, provide input into public policy.

This is the most recent survey of the Australian public concerning this issue. The analysis of the effect of the pandemic on attitudes provides further value.

This study aims to investigate the effect of public corruption on the performance and risk of financial institutions domiciled in the USA..

This study uses the US Department of Justice’s (DOJ) Public Integrity Section Reports to proxy corruption. The analysis is performed by bank size and includes robustness checks for omitted variables and endogeneity concerns.

The results show that a corrupt environment is associated with lower bank performance without a reduction in risk. Larger banks tend to underestimate the increase in credit risk. Small- and medium-size banks seek to “re-capture” returns in corrupt districts by reducing their liquidity.

The implication of this research is that financial institutions do not thrive in corrupt environments and are unlikely to participate in corrupt practices. Overall, this study documents the tangible harm inflicted by corrupt practices.

A practical implication is that banks may attempt to re-capture lower returns resulting from corrupt environments by extending more risky loans, specifically, commercial real estate loans.

This study demonstrates the costly impact of corruption on large and small banks. While larger banks report higher share of non-performing loans, smaller banks show an increase in the provision for loan and lease losses, suggesting that smaller banks may be more risk averse.

Prior studies investigate corruption in US firms while excluding financial institutions. This study fills this gap by investigating the effect of public corruption on the performance and risk of financial institutions domiciled in the USA.

Despite the widespread studies on the attitudes about OA, there exists little comparative evidence about the opinions of author and non-author parties at a global level in a social context. To bridge the gap, this study first investigated the opinions of the users who posted at least one tweet about OA in 2019. Then, it zoomed in to explore the views of the OA-interested tweeters, i.e. the users who have posted five or more tweets about OA.

Using a content analysis method, with an opinion-mining approach, this study examined a sample of 9,268 OA-related tweets posted by 5,227 tweeters in 2019. The sentiments were analyzed using SentiStrength. A threshold of at least five tweets was set to identify the OA-interested tweeters.

Academics and scholars, library and information professionals, and journals and publishers were the main OA-interested tweeters, implying that OA debates have not been widely propagated from its traditional audience to the general public. Despite an overall positive attitude, the tweeters showed negative perspectives about the gold and hybrid models, validity and quality, and costs and funds. The negativity depended on the OA features tweeted, the tweeters' occupations and gender, as well as the trends.

The low societal impact of the OA debates calls for solutions to attract the public's attention and to exploit their potential to achieve the OA ideals. The OA stakeholders' divergence necessitates finding solutions to remedy the pitfalls. It also underlines the need for scrutiny into social layers when studying society's opinions and behaviors in a social network.

This is the first study in estimating the extent of the societal impact of OA debates, comparing the social OA stakeholders' opinions and their dependence on the OA features tweeted, the tweeter roles and gender and the tweet trending status.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2022-0502

The purpose of this paper is to investigate the influence of macroeconomic cyclical indicators and country governance on bank non-performing loans in African countries.

Data was collected from the 53 African countries covering 2005–2021. The paper develops an empirical model to examine the impact of country governance in reducing macroeconomic cycle-induced adverse effects on bank credit risk. This research estimates Random Effects models and the General Method of Moment to examine the link between microeconomic and governance factors on bank non-performing loans. Stata version 15.1 was used to conduct panel regression analysis.

The findings of the study revealed that the generalized method of moments findings contributes valuable insights into the persistence of NPLs over time and the specific effects of variables on NPL levels. The study findings highlight that the debt-to-GDP ratio, unemployment, regulatory quality, government effectiveness and inflation have significant relationships with NPLs, shedding light on their specific contributions to credit risk dynamics.

The focus on a specific set of determinants for NPLs, which may not capture all the factors that influence NPL levels. Thus, the study did not consider the impact of macroeconomic shocks, such as natural disasters or global economic crises, which can have a significant impact on NPLs.

Policymakers should prioritize maintaining sustainable debt levels, promoting employment growth and controlling inflation rates to mitigate credit risk and reduce nonperforming loans. Also, enhancing regulatory quality and government effectiveness is crucial in ensuring financial stability and minimizing non-performing loans in Africa.

This paper provides a new possible solution to minimise bank non-performing loans risk by examining interactions of country governance regarding the macroeconomic cycle behaviour.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0729

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.