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This paper aims to document international evidence of the impact of a board-level governance bundle [size, independence, CEO duality, gender diversity and sustainability committee (SC)] on sustainability reporting (SR) and, separately, on its three dimensions (economic, environmental and social).

The sample includes 370 listed firms from 50 countries. A GRI standards-based disclosure index was constructed to quantify SR across various reporting media.

The baseline findings show that SC positively affects SR and its three dimensions. Board size also has a significant and positive impact on SR and two of its dimensions (economic and social). Similarly, board independence and CEO duality have a significant but negative association with SR and the same two dimensions. Finally, board gender diversity has no significant impact on SR and all its three dimensions.

The findings that only SC significantly influences SR, and its three dimensions, have important implications for corporate governance reforms internationally to improve SR in countries where such committees are not yet part of the board of directors’ sub-committees.

Overall, this study contributes to board characteristics–SR literature and holds significant theoretical and practical implications.

This study addresses the gap in research concerning student attitudes toward ethics and social responsibility (E&SR) within diverse organizational contexts, specifically as they transition into managerial and non-managerial roles.

A total of 425 business students from four universities participated. To determine statistically significant differences between potential managers and non-managers (M&NM), a paired comparison inferential t-test was employed.

The study revealed positive E&SR attitudes among business students. However, it differed from existing literature by finding a significant difference in perceived E&SR importance between aspirants in managerial and non-managerial positions. Notably, non-managerial students exhibited significantly stronger support for these principles.

This study, conducted in an emerging economy, offers a unique perspective by analyzing E&SR perceptions across both potential management and non-management employees. Given the direct and indirect influence employee perceptions have on business performance, this research sheds light on the crucial role of E&SR in management practices. The findings are further substantiated by robust data and include implications for human resource management, along with suggestions for future research directions.

This paper aims to investigate the role of field-configuring events (FCEs) and situational context in the institutionalisation of sustainability reporting (SR) in Pakistan.

This paper uses insights from the institutional logics perspective and qualitative research design to analyse the interplay of the institutional logics, FCEs, situational context and social actors’ agency for the institutionalisation of SR among leading corporations in Pakistan. A total of 28 semi-structured interviews were carried out and were supplemented by analysis of secondary data including reports, newspaper articles and books.

The emerging field of SR in Pakistan is shaped by societal institutions, where key social actors (regulators, enablers and reporters) were involved in the institutionalisation of SR through FCEs. FCEs provided space for agency and were intentionally designed by key social actors to promote SR in Pakistan. The situational context connected the case organisations with FCEs and field-level institutional logics that shaped their decision to initiate SR. Overall, intricate interplay of institutional logics, FCEs, situational context and social actors’ agency has contributed to the institutionalisation of SR in Pakistan. Corporate managers navigated institutional logics based on situational context and initiated SR that is aligned with corporate goals and stakeholder expectations.

For corporate managers, this paper highlights the role of active agency in navigating and integrating institutional logics and stakeholders’ expectations in their decision-making process. For practitioners and policymakers, this paper highlights the importance of FCEs and situational context in the emergence and institutionalisation of SR in developing countries. From a societal point of view, dominance of business actors in FCEs highlights the need for non-business actors to participate in FCEs to shape logics and practice of SR for wider societal benefits.

From a societal point of view, dominance of business actors in FCEs highlights the need for non-business actors to participate in FCEs to shape logics and practice of SR for wider societal benefits.

This paper focuses on the role of FCEs and situational context as key social mechanisms for explaining the institutionalisation of SR.

The purpose of this study is to investigate the moderating effect of board gender diversity on the relationship between sustainability reporting (SR) and earnings management (EM) in the East Africa Community (EAC).

The study analyzed a sample of 71 publicly traded companies from 2011 to 2021.

The study finds that both SR and board gender diversity have a negative and significant effect on EM and that board gender diversity moderates the relationship between SR and EM.

The findings suggest that boards should support the adoption of SR and increase female representation as a practical way to reduce EM. Policymakers should also implement appropriate measures, such as imposing mandatory SR and gender quotas on corporate boards, to address EM.

This research adds to the limited knowledge of SR and EM in the EAC and also fills a gap in the existing literature by investigating the influence of board gender diversity on the link between SR and EM.

This study aims to explore to what extent stakeholder engagement affects the sustainability reporting (SR) process and if it succeeds in facilitating the encounter between demand and supply of accountability, as well as the main challenges of this practice, by focusing on a crucial and under-investigated public sector area, the judicial system.

The study adopts an action research (AR) approach. Specifically, it focuses on a specific phase (i.e. stakeholder engagement) of the broader project that was carried on from 2019 in an Italian Public Prosecutor’s Office. Data were collected from multiple sources, i.e. written notes and reports gathered during meetings, the survey administered to stakeholders and the published sustainability reports.

Stakeholder engagement may be a valuable and effective tool for improving the level of accountability, as it increases the responsiveness of SR to the informative needs of stakeholders. However, the study also highlights some critical points that must be addressed to exploit this fully. Among these is the need to act upstream of the process by working on an accounting system that goes beyond the economic dynamics and can effectively answer the accountability demand.

The study contributes to theoretical and empirical knowledge by exploring a topic and a public sphere still limited investigated, i.e. the stakeholder engagement in sustainability in the judicial sector. The AR approach also presents some originality points, as it is low widespread in management and accounting literature.

Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

This study aims to examine the multifaceted connection between perceived socially responsible human resource management (PSR-HRM), job insecurity (JI), psychological availability (PA) and employee voice behavior (VB). Furthermore, it delves into the mediating roles of PA, and JI in the linkage between PSR-HRM and VB and the moderating role of PA in the connection between JI and VB.

The empirical data for this study were collected from a sample of 385 full-time employees in Vietnam. To analyze the data and explore the relationships among the constructs, partial least squares structural equation modeling (PLS-SEM) was used.

The study reveals positive relationships between PSR-HRM, PA and VB. Furthermore, it demonstrates that PA serves as a partial mediator in the nexus between PSR-HRM and VB, while JI similarly partially mediates this association. In addition, the research identifies a positive moderating effect of PA on the linkage between JI and VB.

Grounded in social exchange theory and social cognitive theory, this study uncovers significant relationships, providing nuanced insights into the intricate interplay among PSR-HRM, JI, PA and VB. It represents one of the initial investigations into the moderating influence of PA on the connection between JI and VB.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).

This experimental study aims to deal with the improvement of process performance of electric discharge drilling (EDD) for fabricating true blind holes in titanium alloy Ti6Al4V. Micro EDD was performed on Ti6Al4V and blind holes were drilled into the workpiece.

The effects of input parameters (i.e. voltage, capacitance and spindle speed) on responses (i.e. material removal rate, tool wear rate and surface roughness [SR]) were evaluated through response surface methodology. The data was analyzed using analysis of variance and multi-optimization was performed for the optimized set of parameters. The optimized process parameters were then used to drill deeper blind holes.

Blind holes have few characteristics such as SR, taper angle and corner radius. The value of corner radius reflects the quality of the hole produced as well as the amount of tool roundness. The optimized process parameters suggested by the current experimental study lower down the response values (i.e. SR, taper angle and corner radius). The process is found very effective in producing finished blind holes.

This experimental study establishes EDD as a feasible process for the fabrication of truly blind holes in Ti6Al4V.

For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.