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Despite the well-recognized importance of recycled water, the study of industry-peer pressure on recycled water is relatively new. This study investigates how organizations experience and react to industry-peer pressure to set recycled water targets. Additionally, this study investigates the role of board chairs involved in sustainability committees in contributing to responses to industry-peer pressure.

Using Eviews 12, this study employed a pooled logistic regression model to analyze data from 1,346 firms on Taiwan and Taipei exchanges (2017–2020).

The findings revealed that frequency-based imitation drives recycled water target-setting diffusion. However, there is no direct relationship between outcome-based imitation and recycled water target-setting. Notably, outcome-based imitation drives the adoption of recycled water target-setting of firms with board-chair membership in sustainability committees.

This study faces certain data limitations. First, this study primarily focuses on water recycling. Future research could explore other ways to reduce water usage, such as using water-efficient equipment. Second, this study gathered information solely on the presence or absence of a board chairperson on the sustainability committee. Future researchers could explore the impact of the composition of sustainability committee on recycled water target-setting. Lastly, the sample used in this study is restricted to Taiwan's corporations that existed during 2017–2020. Future researchers may consider adopting a longitudinal design in other economies to address this limitation.

The findings of this study offer several guidelines and implications for recycled water target-setting and the composition of sustainability committees. It responds to an urgent call for solutions to water shortages when pressure from governments and nongovernmental organizations is relatively absent. The number of industry peers that have already set recycled water targets is indispensable for motivating firms to set their own recycled water targets. In terms of insufficient water-related regulatory pressure and normative pressure, this study found evidence suggesting that the direct motivation for setting recycled water targets stems from mimetic pressures via frequency-based imitation. The evidence in this study suggests that policymakers should require companies to disclose their peers’ recycled water target information, as doing so serves as an alternative means to achieving SDG 6.3.

Recycled water target-setting might be challenging. Water recycling practices may face strong resistance and require substantial additional resources (Zhang and Tang, 2019; Gao et al., 2019; Gu et al., 2023). Therefore, this study suggests that firms should ensure the mindfulness of board members in promoting the welfare of the natural environment when making recycled water target-setting decisions. To reap the second-mover advantage, firms must consider the conditions in which board members can more effectively play their role. Corporations may help their chairpersons in setting recycled water targets by recruiting them as members of sustainability committees. Meanwhile, chairpersons tend to activate accurate mental models when the water conservation performance of pioneering industry peers is strong enough to indicate the potential benefits of adopting recycled water target-setting. Investors’ and stakeholders’ understanding of how the composition of sustainability committees is related to recycled water target-setting may help to identify the potential drivers of firms’ water responsibility. Investors and stakeholders should distinguish firms in terms of the board chair’s membership of their sustainability committee and focus on water-use reduction outcomes in the industry. This study provides insights into circumstances whereby chairpersons help to restore the water ecosystem.

This study explains how frequency-based and outcome-based imitation are two prominent mechanisms underlying the industry-peer pressure concerning recycled water target-setting. Moreover, this study fills literature gaps related to the moderating roles of board-chair membership in sustainability committees concerning industry-peer pressure on recycled water target-setting.

This study aims to examine how different uses of performance measurement systems (PMS) enable or hinder organizational ambidexterity (OA), intended as the simultaneous pursuit of exploitation and exploration.

Following a qualitative research design, we gathered data through semi-structured interviews, observations and reviews of documents at four departments of an automotive firm.

We contribute to operations management research and practice by demonstrating how PMS, which are typically associated with exploitation, can also foster exploration and enable organizations to become ambidextrous. Specifically, we show how PMS can be structured and used in more agile ways and, in relation to innovation, we identify which PM practices should be introduced and with what effects and those that should be avoided. We also contribute to organization theory by highlighting how a single management tool can promote the achievement of both exploration and exploitation.

In investigating PMS uses and their effects, we identify several positive practices. For example, we show how managers can use PMS more effectively and how targets could be deployed to stimulate creativity and innovation. We also emphasize the need for managers to opt more often for team incentives rather than individual ones to encourage the collaboration needed for OA.

We provide in-depth insight into how PM tools affect an organization’s ability to pursue exploitation and exploration, thus contributing to research in operations, innovation and organization theory.

The purpose of this study is to understand how executives in technology companies relate to targets for gender equality, especially pertaining to top management.

The study draws on 19 interviews of CEOs, senior line managers and HR directors in ten technology companies operating in Finland. The method is (reflexive) thematic analysis.

Previous studies on the role of executives in promoting gender equality provide somewhat mixed results: while their role is vital, senior leaders may not be inclined to support gender equality targets and measures. Drawing on critical feminist theorizing, this study identifies three ways in which the executives in technology companies related to gender equality targets: endorsing, negotiating and resisting. However, all these responses were constrained by the executives’ assumption that their companies are meritocratic. The study illustrates how executives’ narrow understanding of gender equality and reliance on the presumably well-working systems, combined with underlying doubts about the competence of women, hinder the advancement of women to top management.

While previous studies have evaluated targets to increase the number/percentage of women, both in certain “ideal case” companies and in terms of their effectiveness more broadly, this study discusses how technology company executives navigate these targets in relation to women's assumed “competence”.

The aim of this study is to empirically explore and analyze the concrete tasks of output measurement and the inherent challenges related to these tasks in a traditional and autonomous professional public work setting – the judicial system.

The analysis of the tasks is based on a categorization of general performance measurement motives (control-motivate-learn) and main stakeholder levels (society-organization-professionals). The analysis is exploratory and conducted as an empirical content analysis on materials and reports produced in two performance improvement projects conducted in European justice organizations.

The identified main tasks in the different categories are related to managing resources, controlling performance deviations, and encouraging improvement and development of performance. Based on the results, key improvement areas connected to output measurement in professional public organizations are connected to the improvement of objectivity and fairness in budgeting and work allocation practices, improvement of output measures' versatility and informativeness to highlight motivational and learning purposes, improvement of professional self-management in setting output targets and producing outputs, as well as improvement of organizational learning from the output measurement.

The paper presents empirically founded practical examples of challenges and improvement opportunities related to the tasks of output measurement in professional public organization.

This paper fulfils an identified need to study how general performance management motives realize as concrete tasks of output measurement in justice organizations.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This study aims to explain how negative workplace interactions are formed by the application of a performance management system (PMS).

The study draws from unique in-depth interviews with service workers who resigned from an accounting shared service centre (SSC), discussing the reasons behind the resignations. Following an abductive approach, organisational justice theory is used to analyse the service workers' perceptions of negative interactions and to link the negative interactions to the use of the PMS.

The findings suggest that negative workplace interactions are characterised by cost consciousness, inequality and competitiveness. These interactions are attributed to the use of a PMS in the centre and are related to perceptions of distributive, procedural and interactional injustice.

Managers and leaders of shared service–type organisations should not rely on PMSs as an all-encompassing solution; instead, they should acknowledge the fairness of the use of PMSs. Moreover, HR professionals should choose and train employees to apply PMSs fairly. Fairness is important in work allocation, resourcing, monitoring, giving feedback, recognising good performance, promotion and interaction between peers.

This study contributes to the literature by taking an overall perspective on PMSs to analyse and explain the unintended negative consequences of a PMS in a highly scripted and monitored work environment that is usually considered appropriate for such a system's use. Through the analysis, the study highlights pitfalls in the use of a PMS and the importance of interactional injustice not only between but also within organisational levels.

This study aims to examine the role of performance management systems (PMSs) in enabling logic blending to manage institutional complexity and tensions arising from coexisting institutional logics.

This research uses a case study of an Australian non-government organisation (NGO) operating in an institutional field dominated by the state government, in which policy reform jolted the balance between institutional logics. Data was collected through semi-structured interviews, archival documents and observations.

We find the policy reform required the NGO to transform from a wholly care focus to accommodate a more balanced approach with a focus on care coupled with efficiency, outcome delivery and performance measurement. The NGO responded by revising its purpose, strategy and operational model and by seeking to address the imperatives of two dominant and often competing care and managerial logics. We find this was achieved through logic blending, in which PMSs played a pivotal role, with the formalisation and collaboration processes mobilising different elements of PMSs, mobilising some elements differently or not mobilising some elements at all.

This study highlights the central role of PMSs in managing tensions between and the complexity arising from coexisting institutional logics through logic blending, a form of enduring compromise. This study extends the accounting logics and performance management literature by developing the understanding of what constitutes logic blending and how it is distinct from other forms of compromise.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

While extant research does mention performance management systems as antecedent to a management accountant’s role, and that there is tension between both, there is little detailed research. Thus, this paper aims to investigate the extent to which a performance management system interacts with the role of a management accountant.

The study is a cross-sectional field study, using interviews with paired management accountants and operative managers in 16 multinational organisations in Germany. The perspectives of both management accountants and operative managers are analysed separately. The role episode model theoretically informs the study.

The findings reveal management accountants distinguish between three roles of scorekeeping, controlling and business support, similar to prior literature. By contrast, operating managers are concerned with the value-adding and non-value-adding character of activities and thus support a dichotomy of management accountants’ roles. Drawing upon the role episode model, this study elucidates the interplay between performance management systems and the roles of management accountants, which encompass both role-taking and role-making dynamics. Additionally, this study contributes to management control literature by operationalising the components of a performance management system framework and linking them to the role of management accountants, as proposed by role antecedents in previous literature. The study also uncovers factors influencing role-taking and role-making, alongside examining the repercussions of role consensus or conflict based on the interaction with the operating manager.

This paper is subject to the normal limitations of case study research and generalisation. The findings may also be influenced by the cultural context of the study.

An updated role episode model is presented, highlighting further performance management systems’ components. The study also reveals factors enabling and/or inhibiting the management accountants’ business support role and the impact of role consensus/conflict.

South Africa had formally introduced a policy of inflation targeting (IT) in February 2000. By December 2001, the governor of the South African Reserve Bank, after reading the latest statistics, was concerned with the disappointing economic data. Economic activity had slowed drastically, to the point that the country appeared to be heading for a recession. The gloomy statistics forced the governor to consider whether the country had pursued the right policy. Persistently high unemployment, one legacy of the apartheid era, meant that South Africa did not have the luxury of waiting for new policies to bear fruit. With the inflation forecast to exceed the mandated target, the governor would have to tighten monetary policy, which would further restrict investment. Was it is time for South Africa to change course?