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The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).

Lerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.

The results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.

Literature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).

This exploratory study discusses the policy learning process of the development of disaster risk reduction (DRR) policy.

The paper discusses how DRR has and has not developed in Thailand through the two major disasters: the 2004 Indian Ocean Tsunami and the 2011 Great Flood. The information was collected by documentary analysis to gain a historical and critical understanding of the development of the system and policy of DRR in Thailand. Additionally, key stakeholders' interviews were undertaken to supplement the analysis.

The paper demonstrates that Thailand's DRR development has been “reactive” rather than “proactive”, being largely directed by global DRR actors.

Being a small-scale study, the sample size was small. The analysis and argument would be consolidated with an increase in the number of interviews.

The model can help deconstruct which dimension of the learning process a government has/has not achieved well.

The application of the “restrictive-expansive policy learning” model, which identifies different dimensions of policy learning, reveals that the Thai government's policy learning was of a mixed nature.

This study aims to investigate whether various types of risks faced by the publicly listed commercial banks of India and Bangladesh are driven by market power and provides comparative insights from both economies.

By using the adjusted Lerner index to gauge bank market power and applying the generalised methods of moments (GMM) regression approach, the research delved into the relationship between bank market power and three distinct facets of risk across a sample of 26 publicly listed commercial banks in India and 22 listed banks in Bangladesh spanning from 2011 to 2022.

The results indicate that for Bangladesh, both “competition fragility” and “competition stability” viewpoints coexist simultaneously across all risk types, supporting a nonlinear relationship between market power and risk. However, in the Indian context, a nonlinear association exists only in the case of credit risk, while the relationship with insolvency risk is linear, substantiating the “competition fragility view”. Apart from market power and bank-specific variables, GDP growth rate has emerged as a prominent driver across all risk categories in both countries.

The filtration of banks is a limitation that might have influenced the outcomes. This study recommends that the Reserve Bank of India encourages further bank consolidation. Along the same line, Bangladesh Bank should closely oversee the growing competitive landscape. Furthermore, the regulators must monitor the elevated levels of non-performing loans to reduce credit risk so as to bolster the stability of their respective banking sectors.

This comparative study is the first attempt to analyse the market power and risk relationship and includes a novel bank-specific variable, i.e. technology, apart from other established variables.

This paper aims to quantitively assess the resilience of residential properties to urban flooding in Port Harcourt, Nigeria, and assess whether they vary at spatially aggregated scales relative to the level of flood exposure.

The study synthesizes theoretical constructs/indicators for quantifying property level resilience, as a basis for measuring resilience. Using a two-stage purposive/stratified randomized sampling approach, 407 questionnaires were sent out to residents of 25 flood-prone areas, to solicit information on the resilience constructs as indicated by the adaptation behaviors of individual households and their property attributes. A principal component analysis approach is used as a mechanism for weighting the indicators, based on which aggregated spatial-scale resilience indices were computed for the 25 sampled areas relative to their levels of flood exposure.

Area 11 located in the moderate flood zone has the lowest resilience index, while Area 20 located in the high flood zone has the highest resilience index. The resilience indices for the low, moderate and high flood zone show only minimal and statistically insignificant differences indicating maladaptation even with incremental levels of flood exposure.

The approach to resilience measurement exemplifies a reproducible lens through which the concept of “living with floods” can be holistically assessed at the property level while highlighting the nexus of the social and technical dimensions.

The study moves beyond theoretical conceptualization, to empirically quantify the complex concept of property-level flood resilience.

The purpose of this paper is to explore flood-prone area residents' preferences of flood-resilient housing technologies (HTs), to understand the factors influencing their choices. Flood-resilient HTs can reduce damage and disruption at a household level, particularly in areas where large-scale community schemes are not available or feasible. People’s perception of floods and their preferences of flood-resilient HTs are among many very important factors influencing the adoption of these technologies. Therefore, these perceptions and preferences must be well understood before implementation of these technologies can occur. However, studies on these two important factors are lacking in literature, particularly in the sub-Saharan African context.

Nigerian residents’ preferences of flood-resilient HTs were explored by focusing on five frequently flooded areas around the Niger and Benue river basins in Kogi State, Nigeria. Thirty-eight chat, video and voice call interviews were conducted with participants across five case study areas: Lokoja, Idah, Bassa, Ajaokuta and Koton Karifi. The interviews, informed through an illustrated brochure, covered residents’ experiences and perceptions of floods. This was done to gain an understanding of the factors influencing the choice of flood-resilient HTs adopted and those preferred.

This study confirms that residents in these five focus areas show similar characteristics to other floodplain residents as encapsulated in protection motivation theory. The flood-resilient HTs discussed in this study include flood-avoidance, flood-recoverability and flood-resistance strategies, as well as neighbourhood-scale approaches. Flood-resistance and flood-recoverability strategies rated highly in terms of suitability and envisaged efficiency in mitigating flooding in Kogi State. Although the measures were mostly agreed to be potentially effective and successful on a household scale, there were concerns as to flood mitigation on a neighbourhood scale.

Pre-existing flood-resilient HTs were not extensively discussed in the literature review but were included to have a sense of the participants’ mitigation behaviour, as well as their potential to adopt (or not) new measures after adopting previous ones.

The results provide supporting evidence of the factors influencing the choice of and/or intention to adopt flood-resilient HTs, highlighted in literature. Results also contribute to literature by providing further insight into flood-resilient measures already adopted by residents, as well as their preferred HTs from the options presented. The implications of these findings and methodological considerations in this research are fully discussed in this paper.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

Disasters and other emergencies are increasing, with millions of people affected by events like earthquakes, fires and flooding. The use of mobile emergency alert systems (MEAS) can improve people’s responses by providing targeted alerts based on location and other personal details. This study aims to understand the factors that influence people’s willingness to share the personal information that is needed to provide context-specific messaging about a threat and protective actions.

Drawing on protection motivation theory (PMT), this study proposes and tests a model of willingness to use personalised MEAS that incorporates key factors related to an individual’s appraisal of a potential threat (i.e. perceived vulnerability and severity) and coping capacity (i.e. response efficacy and self-efficacy), with deterrents like response cost and privacy concern. This study uses survey data from 226 respondents in New Zealand and SmartPLS to assess the model.

The results show how willingness to use MEAS is influenced by people’s appraisal of an emergency threat and their perception of how using MEAS would help them to cope effectively. Fear and perceived severity are significant motivators of MEAS use, along with coping appraisal. However, when the negative influences of privacy concern and response cost are strong enough, they can dissuade use, despite knowing the risks.

The study addresses a gap in research on the use of alert systems like MEAS, which require sharing of personal information and continuous engagement such as the real-time disclosure of one’s location. It confirms the significance of factors not studied in prior research, such as privacy concerns, that can dissuade use. This study also extends the application of the PMT in the context of emergency management.

Flood preparedness and response from the perspective of community engagement mechanisms have been studied in scholarly articles. However, the differences in flood mitigation may expose social and behavioural challenges to learn from. This study aimed to demonstrate how text mining can be applied in prioritising existing contexts in community-based and government flood mitigation and management strategies.

This investigation mined the semantics researchers ascribed to flood disasters and community responses from 2001 to 2022 peer-reviewed publications. Text mining was used to derive frequently used terms from over 15 publications in the Scopus database and Google Scholar search engine after an initial output of 268 peer-reviewed publications. The text-mining process applied the topic modelling analyses on the 15 publications using the R studio application.

Topic modelling applied through text mining clustered four (4) themes. The themes that emerged from the topic modelling process were building adaptation to flooding, climate change and resilient communities, urban infrastructure and community preparedness and research output for flood risk and community response. The themes were supported with geographical flood risk and community mitigation contexts from the USA, India and Nigeria to provide a broader perspective.

This study exposed the deficiency of “communication, teamwork, responsibility and lessons” as focal themes of flood disaster management and response research. The divergence in flood mitigation in developing nations as compared with developed nations can be bridged through improved government policies, technologies and community engagement.

Informed by the resource-based view of the firm, dynamic capabilities theory and contingency theory, this study examines the impact of Industry 4.0 (IR4.0) technologies adoption on firm performance (FP) while accounting for the mediating role of innovation ambidexterity (IA) and moderating roles of contextual and methodological factors that drive the performance gains of the phenomenon.

A random-effect model in comprehensive meta-analysis (CMA) is used to synthesize 113 studies in 115 independent samples with 192,188 observations.

This analysis demonstrates that IR4.0 digital technologies are directly related to financial and non-financial performance, disclosing that the performance effect on non-financial is the largest. Moreover, there is a complementary partial mediation role of the impacts of IR4.0 on FP by IA. Furthermore, this focal relationship is moderated by boundary-spanning conditions: contextual factors – firm size, business type, economic development, industry sector and methodological factors – proxy of FP, sample size and study type.

The results imply that IR4.0 produces financial and non-financial benefits by enabling firms to develop dynamic capabilities like innovation ambidexterity, which informs managers and practitioners that unless IR4.0 technologies and IA strategies are combined together to generate superior FP, IR4.0, in and of itself, would produce a less positive impact on FP than the combined impact of IR4.0 and IA. Therefore, managers should focus on converting IR4.0 resources to dynamic capabilities like IA by leveraging open innovation strategies or building IR4.0-based coordination mechanisms by creating cross-unit business synergies.

To the best of the authors' knowledge, per the literature review, this is the first meta-analysis structural equation modeling study on the interplay between IR4.0, innovation ambidexterity and firm performance.

This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.

This review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.

The review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.

The review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.