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Modular construction is an innovative method that enhances the performance of building construction projects. However, the performance of steel modular construction has not been systematically understood, and the existing measurement methods exhibit limitations in effectively addressing the features of steel modular building construction. Therefore, this study aims to develop a new performance measurement framework for systematically examining the performance of steel modular construction in building projects.

This study was conducted through a mixed-method research design that combines a comprehensive review of the state-of-the-art practices of construction performance measurement and a case study with a 17-story steel modular apartment building project in Hong Kong. The case project was measured with data collected from the project teams and other reliable channels, and the measurement practices and findings were referenced to establish a systematic performance measurement framework for steel modular construction.

Considering steel modular construction as a complex socio-technical system, a systematic performance measurement framework was developed, which considers the features of steel modular construction, focuses on the construction stage, incorporates the views of various stakeholders, integrates generic and specific key performance indicators and provides a benchmarking process. Multifaceted benefits of adopting steel modular construction were demonstrated with case study, including improved economic efficiency (e.g. nearly 10% cost savings), improved environmental friendliness (e.g. approximately 90% waste reduction) and enhanced social welfare (e.g. over 60% delivery trips reduction).

This paper extends the existing performance measurement methods with a new framework proposed and offers experience for future steel modular construction. The measured performance of the case project also contributes in-depth understanding on steel modular construction with benefits demonstrated. The study is expected to accelerate an effective uptake of steel modular construction in building projects.

This study proposes a systematic and comprehensive circular economy (CE) performance measure as an instrument to operationalize and quantify circularity. It seeks to evaluate the relative contribution of blockchain technology to evaluate various measures in this study. A general research agenda for investigating blockchain capabilities to performance assessment in CE is presented.

Empirical survey data from 32 CE and blockchain experts are collected to inform this study. Inductive reasoning, heatmaps, and a middle-range theory building approach are utilized to generalize theoretical patterns for blockchain support of CE performance assessment and measurement. A series of propositions are then developed as a middle-range theory for the true, false, perceptible, and hidden affordances of blockchain technology capabilities for CE performance measurement.

Overall, sixteen performance measures and metrics are identified and examined. The authors find most of these measures and metrics – based on literature and expert opinion – can be supported using blockchain technology capabilities. Four major blockchain capabilities, transparency and traceability, reliability and security, smart contracts, and incentivization and tokenization are shown to have varying potential support for CE performance assessment. There needs to be an evaluation of true, false, perceptible, and hidden affordances of blockchain technology capabilities for CE performance measurement in future studies.

Blockchain application for CE, and specifically performance measurements, is a new area. Research and practice evaluation on this issue is important but needs substantially additional investigation to help CE progress. This study provides a framework for evaluation and a foundation for future research at the nexus of CE, blockchain technology, and performance measurement.

Current literature recognised big data as a digital revolution affecting all organisational processes. To obtain a competitive advantage from the use of big data, an efficient integration in a performance measurement system (PMS) is needed, but it is still a “great challenge” in performance measurement research. This paper aims to review the big data and performance measurement studies to identify the publications’ trends and future research opportunities.

The authors reviewed 873 documents on big data and performance carrying out an extensive bibliometric analysis using two main techniques, i.e. performance analysis and science mapping.

Results point to a significant increase in the number of publications on big data and performance, highlighting a shortage of studies on business, management and accounting areas, and on how big data can improve performance measurement. Future research opportunities are identified. They regard the development of further research to explain how performance measurement field can effectively integrate big data into a PMS and describe the main themes related to big data in performance measurement literature.

This paper gives a holistic view of big data and performance measurement research through the inclusion of numerous contributions on different research streams. It also encourages further study for developing concrete tools.

Amidst the scarcity of resources, it is undisputable that an effective public procurement performance measurement system (PMS) is required particularly in county governments, especially for Kenya to realize its ambitions in devolved governance system. County governments cannot be effectively evaluated on their performance if the long-term, strategic impact of public procurement processes and projects is not captured. Arising from this backdrop, this study aims to determine the predictors of strategic procurement performance metrics (SPPM) adoption in public procurement PMS of county governments.

Anchored on institutional theory and public sector scorecard model, a survey research design was adopted where data were collected through census from 115 respondents working in procurement, finance and stores department of Kakamega county government. Data were collected using questionnaire (75.56% response rate) and key informant interviews, and analyzed by using multiple regression model and ordinal logistic regression models.

Multiple regression model and ordinal logistics regression revealed that national government support negatively and significantly, and regulatory framework positively and significantly affects the adoption of SPPM.

There is need for formal mechanism that will enable the national government in partnership with the council of governors to be proactively involved in developing procurement performance measurement capacity of county governments. This study’s findings also provide suggestions for a working regulatory framework required for the adoption of SPPM by county governments.

This work adds value to the prevailing body of knowledge on public procurement PMS in the public sector.

This paper aims to improve our understanding of performance measurement systems in the health and care sector, by focussing on employee reactions to core performance measurement practices. Targets and monitoring are hypothesised to be associated with employee perceptions of job control, supportive management and job demands, which in turn, are expected to be linked to employee-wellbeing and organisational commitment.

Matched employee workplace data are extracted from a nationally representative and publicly available survey. Structural equation models are estimated.

Performance measurement systems are neither perceived as resources nor additional demands. Setting many targets and a focus on productivity can lead to negative employee outcomes, since these positively correlate with perceptions of job demands, which negatively correlate with employee wellbeing. However, monitoring financial performance and monitoring employee performance may be helpful to managers, as these are positively associated with employee perceptions of job control and supportive management, which positively correlate with job satisfaction and organisational commitment and, negatively, with anxiety. Overall, common criticisms of performance measurement systems in healthcare are questioned.

Given the lack of consensus on how performance measurement systems can influence employee experiences and outcomes, this study combines theories that argue for performance measurement systems in managing operations with models developed by psychologists to describe how perceptions of the work conditions can affect employee attitude and wellbeing. A conceptual model is therefore developed and tested, and potential direct and indirect effects of performance measurement systems in the health sector are inferred.

This study aims to explore the potential dynamics between performance measurement at the organisational level and emerging urban development projects at the macro-institutional field level of sustainability governance and accountability.

Using a theoretical triangulation between three theories, namely contingency theory, institutional theory and social cognitive theory, this study investigates not only the macro-micro dynamics, but also the (recursive) micro-macro dynamics between performance measurement and urban development. Using an Egyptian public sector urban development organisation and its sustainable energy project as an empirical example, interviews, documents and observations were collected.

The dynamics emerged between field urban development projects and the (unintended) organisational implementation of the performance measurement system, the sustainability key performance indicators (KPIs) reporting system. Contributing to previous literature, these dynamics have been institutionalised through (three) interrelated levels: the (macro-field) urban development contingencies and pressures for sustainability KPIs reporting, the (organisational) institutionalisation of the urban development performance measurement system and then the (micro-organisational) cognitive role of sustainability KPIs reports in (re)making political urban development decisions.

This study faced some limitations that paved the way for future research axes. For political and security reasons, difficulties were encountered in conducting interviews with government actors in the sustainable energy project under study. Also, due to the practical separation of the environmental sustainability system from the sustainability KPIs reporting system in this case study, environmental sustainability is outside the scope.

Sustainability reports may influence public sector decision-making processes in a specific urban development context. These KPIs reports may also increase public sector management opportunities for urban auditing, transparency, accountability and sustainability governance. These KPIs may also guide public sector management to lower prices in poor villages to increase smart energy consumption and improve community health.

Sustainability reports may increase decision-makers' understanding of consumer behaviours and societal changes. This may help in making appropriate political decisions to improve their welfare and regular smart energy consumption. Not only urban citizens, but this social advantage may also extend to urban development employees through employees' promotion, training and access to government-funded academic and professional scholarships.

This study is an attempt to develop current public sector performance measurement analyses in the emerging urban development field using a triadic analytical approach. This study also fed the literature with an extended case study that clarified the (multi-level) and (two-way) dynamics between performance measurement and urban development.

The conceptual foundations of performance measurement and management (PMM) are predominantly rooted in control systems research. However, the appropriateness of this paradigm for volatile and uncertain environments has been questioned. This paper explores whether grounding PMM in social systems theory and viewing uncertainty from an organisational behaviour perspective provides new insights into the PMM theory–practice gap.

A framework, rooted in social systems theory and practice theory, is created that describes how organisational behaviour shapes the social processes associated with organisational change. Semi-structured interviews of 35 people from 16 organisations coupled with thematic analysis are employed to identify the organisational behavioural characteristics that influence how PMM is executed in practice. PMM is then reconceptualised from the perspective of this social systems-based framework.

This investigation proposes (1) performance management is concerned with elements of PMM-related practices open to flexible interpretation by human agents that change the effectiveness of organisational practices, whereas performance measurement is concerned with elements of PMM-related practices not open to interpretation but deliberately reproduced to provide a consistent comparison with the past; (2) the purpose of PMM should be to achieve organisational effectiveness (OE) and (3) the mechanisms underlying performance management and performance measurement are social intervention and embeddedness, respectively.

This first social systems perspective of PMM advances the development of PMM's theoretical foundations by providing a behaviour-based interpretation of, and framework for, PMM-mediated organisational change. This competing approach has strong links to practice.

This paper aims to develop a performance measurement (PM) system for fourth-party reverse logistics (RL). In this regard, it proposes simultaneous utilization of a procedural approach and hybrid use of existing performance frameworks.

The paper first describes fundamentals of RL and PM approaches in literature to build a basis for further discussion. It then carries out a systematic review of the literature on PM in RL to highlight current trends and approaches in this regard. At last, it develops a PM system for fourth-party RL by exploring relevant performance frameworks and by defining a procedure in detail.

The paper identifies the ongoing interest in development PM in RL, and the gap and the need for the development of comprehensive PM system for fourth-party RL. It also identifies the best approach is to use existing performance frameworks in literature and a procedural approach together as they complement each other rather than contradicting.

The paper fills a gap in literature by developing a comprehensive PM framework for the fourth-party RL. The presented procedure is well suited to be used as part of strategic management process in any fourth-party RL organization. It forces users a top-down approach, from strategies to measures, so that a strong linkage is established in the process. When used together with the proposed PM framework, it guarantees a comprehensive and balanced PM system.

The paper identifies the need for the development of a PM framework as well as a measurement system for the fourth-party RL in the literature. In this regard, it develops a PM framework first, and then a procedure on how to implement it. As a result, users can effectively use them as a part of strategic management process of their organizations.

The fourth-party RL is growing and very promising mode of RL for well-being of society, by offering a strong path to sustainable development in the midst of diminishing resources in the world. The existence of a well measurement system is crucial for the healthy operations and development of fourth-party RL organizations. The paper is offering a strong PM model in this regard.

The paper fulfills an identified need to study how to develop a PM system for fourth-party RL.

This paper aims to present a simple and innovative fuzzy methodology-based lean performance measurement system (L-PMS) for an Indian automotive supply chain. The paper also enlightens the influence of coronavirus disease 2019 (COVID-19) on supply chains and the practical implications of the unprecedented disruptions on the performance measurement systems.

The L-PMS is divided into three phases. In the first phase, the key performance indicator (KPI) list, as deemed fit by the organization, is prepared using literature and suggestions from the case organization. The list contains 61 KPIs measuring 24 performance dimensions in seven functional areas of the supply chain. In the second phase, the KPI performance data (actual, best and worst) are collected using the enterprise resource planning (ERP) system. In the last phase, the leanness score of the case organization is calculated at four levels – KPI, dimension, functional area and overall organization.

The overall leanness score of the case organization is 60%. The case organization uses KPIs from all seven functional areas, but it needs to improve the number of KPIs in administration and supplier management functional areas. The case organization uses only quantitative KPIs. However, the performance dimensions at the middle level are adequate. The leanness level of the case organization in different areas is highly variable (ranges from 45% to 91%).

The major limitation of the study is that the case study is done at a single organization.

The managers at the different levels of the hierarchy can use the lean performance measurement score to leverage the better performing areas/dimensions/KPIs and improve poor performing areas/dimensions/KPIs. The lean performance measurement at functional area level can help leadership to give responsibility to different people for the improvement of leanness with respect to different dimensions/functional areas. The disruptive impact of COVID-19 should clearly be understood by the managers to make appropriate decisions based on the severity as measured at different levels.

According to the authors' best knowledge, this is the first lean performance measurement application at the four hierarchical levels (KPI, performance dimension, functional area and overall organization).

This study examines the relationship between the innovation ecosystem and performance measurement models. Although the innovation ecosystem and measurement models are widely recognized, the existing literature lacks a comprehensive understanding of the relationship between the proposed themes. Furthermore, it does not reveal how studies can be grouped to propose a thematic typology of the relationship.

The authors present a systematic literature review conducted in the Web of Science and Scopus databases, from a textual corpus that aided the proposition of the typology that aims to provide answers regarding the addressed themes.

The results of this review are based on a total of sixty peer-reviewed articles from the innovation ecosystem literature and performance measurement models between 1995 and 2020. The results make several contributions to the literature. First, by integrating evidence from empirical studies, the authors identified a typology formed by three classes: (1) ecosystem agents (2) analytical focus and (3) structured measurement tools. Second, the authors verified the relationship between the themes and discovered the existence of gaps to be filled, with the proposition of three drivers. Third, the authors presented a comprehensive mapping of field studies with a descriptive analysis of the textual corpus.

The results of the research provide important implications for researchers, managers and policy makers. Furthermore, the authors suggest directions for future research, including the need to examine the performance of the entire innovation ecosystem, integrating the different agents that exist for performance measurement.