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This study aims to examine the impact of corporate governance (CG) on anti-corruption disclosure (A-CD), paying particular attention to the FTSE 100. Notably, it examines how board and audit committees’ characteristics affect the quantity and quality of anti-corruption disclosure.

Data from FTSE 100 firms, spanning the period from 2014 to 2020, were analysed using the regression of the Poisson fixed effect and GEE analyses.

The findings show that gender diversity, audit committee expertise and the independence of the audit committee are positively associated with both quantity and quality of anti-corruption disclosure. Notably, no statistically significant relationships were identified between anti-corruption disclosure and factors such as board size, role duality or board meetings.

The findings provide valuable insights for decision-makers and regulatory bodies, shedding light on the elements that compel UK companies to enhance their anti-corruption disclosure and governance protocols to alleviate corruption and propel efforts towards ethical behaviour.

This study makes a notable contribution to the sparse body of evidence by examining the influence of board and audit committee attributes on anti-corruption disclosure subsequent to the implementation of the UK Bribery Act in 2010. Specifically, to the best of the authors’ knowledge, this study assesses for the first time the impact of board and audit committee mechanisms on both the quantity and quality of anti-corruption disclosure.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

The purpose of this paper is to examine the antecedents of organizational ambidexterity of foreign ventures in an emerging market. Organizational ambidexterity, the simultaneous pursuit of exploration and exploitation, represents a key innovation strategy. Yet, the driving factors of such innovation strategies for foreign ventures competing in emerging markets have been underresearched. In this study, unpacking the construct of organizational ambidexterity into two dimensions (i.e. the combined dimension [CD] and the balance dimension [BD]), the authors aim to investigate how firm-level and industry-level factors drive foreign ventures in pursuing exploration and exploitation and maximizing the benefits of both.

This study adopts the hierarchical multiple regression approach using a sample of foreign ventures operating in high-tech manufacturing industries in China.

The authors find that the firm-level factor of strategic flexibility leads positively to the CD of organizational ambidexterity, whereas the industry-level factor of technological turbulence has a significantly positive impact on the BD.

This study provides important insights into the driving factors of organizational ambidexterity for foreign ventures competing in emerging markets.

A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

This study examines the roles of cross-sectional dependence, asymmetric structure and country-to-country policy variations in the inflation-poverty reduction causal nexus in selected sub-Saharan African (SSA) countries from 1981 to 2019.

To account for cross-sectional dependence, heterogeneity and policy variations across countries in the inflation-poverty reduction causal nexus, this study uses robust Hatemi-J data decomposition procedures and a battery of second-generation techniques. These techniques include cross-sectional dependency tests, panel unit root tests, slope homogeneity tests and the Dumitrescu-Hurlin panel Granger non-causality approach.

Unlike existing studies, the panel and country-specific findings exhibit several dimensions of asymmetric causality in the inflation-poverty nexus. Positive inflationary shocks Granger-causes poverty reduction through investment and employment opportunities that benefit the impoverished in SSA. These findings align with country-specific analyses of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo. Also, a decline in poverty causes inflation to increase in the Congo Republic, Madagascar, Nigeria, Senegal and Togo. All panel and country-specific analyses reveal at least one dimension of asymmetric causality or another.

All stakeholders and policymakers must pay adequate attention to issues of asymmetric structures, nonlinearities and country-to-country policy variations to address country-specific issues and the socioeconomic problems in the probable causal nexus between the high incidence of extreme poverty and double-digit inflation rates in most SSA countries.

Studies on the inflation-poverty nexus are not uncommon in economic literature. Most existing studies focus on inflation’s effect on poverty. Existing studies that examine the inflation-poverty causal relationship covertly assume no asymmetric structure and nonlinearity. Also, the issues of cross-sectional dependence and heterogeneity are unexplored in the causal link in existing studies. All panel studies covertly impose homogeneous policies on countries in the causality. This study relaxes this supposition by allowing policies to vary across countries in the panel framework. Thus, this study makes three-dimensional contributions to increasing understanding of the inflation-poverty nexus.

The novelty addressed here is undertaken by using tailor-made and fully characterized starch nanoparticles (SNPs) having a particle size ranging from 80 to 100 nm with a larger surface area, biodegradability and high reactivity as a starting substrate for cadmium ions and basic dye removal from wastewater effluent. This was done via carboxylation of SNPs with citric acid via esterification reaction using the dry preparation technique, in which a simple, energy-safe and sustainable process concerning a small amount of water, energy and toxic chemicals was used. The obtained adsorbent is designated as cross-linked esterified starch nanoparticles (CESNPs).

The batch technique was used to determine the CESNPs adsorption capacity, whereas atomic adsorption spectrometry was used to determine the residual cadmium ions concentration in the filtrate before and after adsorption. Different factors affecting adsorption were examined concerning pH, contact time, adsorbent dose and degree of carboxylation. Besides, to validate the esterification reaction and existence of carboxylic groups in the adsorbent, CESNPs were characterized metrologically via analytical tools for carboxyl content estimation and instrumental tools using Fourier-transform infrared spectroscopy (FTIR) spectra and scanning electron microscopy (SEM) morphological analysis.

The overall adsorption potential of CESNPs was found to be 136 mg/g when a 0.1 g adsorbent dose having 190.8 meq/100 g sample carboxyl content at pH 5 for 60 min contact time was used. Besides, increasing the degree of carboxylation of the CESNPs expressed as carboxyl content would lead to the higher adsorption capacity of cadmium ions. FTIR spectroscopy analysis elucidates the esterification reaction with the appearance of a new intense peak C=O ester at 1,700 cm⁻¹, whereas SEM observations reveal some atomic/molecules disorder after esterification.

The innovation addressed here is undertaken by studying the consequence of altering the extent of carboxylation reaction expressed as carboxyl contents on the prepared CESNPs via a simple dry technique with a small amount of water, energy and toxic chemicals that were used as a sustainable bio nano polymer for cadmium ions and basic dye removal from wastewater effluent in comparison with other counterparts published in the literature.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

This study aims to examine the enablers of productivity of enterprise-level Agile development process using modified total interpretative structural modeling (TISM). The two main objectives of the current study are to determine the variables influencing enterprise-level agile development productivity and to develop modified TISM for the corresponding components.

To identify enablers of the productivity of enterprise-level agile software development process a literature review and opinions of domain experts were collected. A hierarchical relationship among variables that show direct and indirect influence is created using the modified TISM (M-TISM) technique with Cross Impact Matrix-Multiplication Applied to Classification analysis. This study examined and analyzed the relationships between the determinants within the enterprise using a M-TISM technique.

With the literature review, the study could identify ten enabling factors of the productivity of Agile development process at the enterprise level. Results depict that program increment (PI) planning and scalable backlog management, continuous integration and continuous delivery (CI/CD), agile release trains (ART), agile work culture, delivery excellence, lean and DevOps practices, value stream mapping (VMS), team skills and expertise, collaborative culture, agile coaching, customer engagement have an impact on the productivity of enterprise-level Agile development process. The results show that team collaboration, agile ways of working and customer engagement have a greater impact on productivity improvement for enterprise-level Agile development process.

The developed model is useful for organizations employing scaled Agile development processes in software development. This study provides a recommended listing of key enablers, that may enable productivity improvements in the Agile development process at the enterprise level. Strategists should focus on team collaboration and Agile project management. This study offers a modified TISM model to academicians to help them understand the effects of numerous variables on maintaining the productivity of an enterprise-level Agile. The identified characteristics and their hierarchical structure can help project managers during the execution of Agile projects at the enterprise level, more effectively, increasing their success and productivity.

The study addresses the gap in the literature by interpretative relationships between the identified enabling factors. The model validation is carried out by a panel of nine experts from several information technology organizations deploying Agile software development at the enterprise level. This unique method broadens the knowledge base in Agile software development at scale and provides project managers and practitioners with a practical foundation.

This paper makes a conceptual contribution by intersecting two strands of literature (return to work following health issues and industrial relations) to facilitate our understanding of the potential role of social dialogue in supporting return to work (RTW) following the diagnosis of a chronic illness. It conceptualises the levels and channels through which various actors and their interactions may play a role in RTW facilitation within the actor-centred institutional framework.

The paper uses an exploratory design based mainly on desk research but is also informed by roundtable discussions done in six countries as part of a larger project.

The conceptual and analytical framework (CAF) is developed to explain how various actors interact together in ways shaped by the RTW policy framework and the industrial relations systems, resulting in a continuum of RTW facilitation situations.

There is limited research on return-to-work policies following diagnosis of chronic illness from a comprehensive actor-oriented perspective. The existing literature usually focusses on just one stakeholder, overlooking the role of social dialogue actors. By bridging the two streams of literature and incorporating all potential actors and their interactions in a unitary model, the proposed framework provides a valuable tool to further discuss how successful RTW after a diagnosis of chronic illness can be facilitated.

This study aims to systematically review the health risk assessment of the concentration of heavy metals in Pistacia species globally.

The authors systematically searched PubMed, Science Direct, Scopus and Google Scholar to identify all articles published between 1 January 2002 and 20 August 2022. A total of 33 studies met the authors’ inclusion criteria, and their data were extracted. Additionally, the potential risk to human health was assessed by calculating the target hazard quotient and hazard index for both child and adult consumers.

The estimated daily intake for heavy metals in the included studies ranged from 9.72 × 10^–9 to 7.35 (mg/day) in the following order: zinc (Zn) > mercury (Hg) > iron (Fe) > lead (Pb) > copper (Cu) > aluminum (Al) > nickel (Ni) > chromium (Cr) > manganese (Mn) > cadmium (Cd) > arsenic (As) > selenium (Se) > cobalt (Co). Among the studies that investigated heavy metals in Pistacia species around the world, the non-carcinogenic risk for all species of Pistacia was determined to be less than 1, except for Pb and Hg in Pistacia lentiscus.

The soil near the industrial area contained excessive amounts of heavy metals, which led to the transfer of heavy metals to plants. Owing to the insufficiency of the number of studies that examined heavy metals in Pistacia species, further monitoring and investigations were recommended.