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This paper aims to propose an integrated use of balanced scorecard (BSC), data envelopment analysis (DEA) and game theory approach as an enhanced performance measurement technique to determine and rank the importance of manufacturing indicators of a steel company as a real case study.

An efficiency change ratio is defined to examine the characteristic function of each coalition which is super-additive. Then, the Shapley value index is used as the solution of the cooperative game to determine the importance of the BSC indicators of the company and rank order them.

The results reveal that “profitability rate” is the most important BSC indicator, whereas “customer satisfaction” is the least significant one. The ranking order of the importance of all BSC indicators makes it possible for the senior managers of the organization to realize the importance of each index separately and to improve the profitability and the number of customers by presenting programs according to the budget and time constraints.

The main contribution of this paper lies in the adoption of a game theory approach to performance measurement in the industrial sector that determines and ranks the importance of manufacturing indicators.

Considering the direct impact of retailers' performance on the economy, this paper aimed to propose a comprehensive framework to evaluate the performance of different branches of a retailer.

Through a case study, the weights of indicators were calculated by the best-worst method (BWM) and the branches' performance was appraised using data envelopment analysis (DEA).

The branches were ranked in terms of performance, and sensitivity analysis and statistical tests were conducted to realize the weaknesses and strengths of the branches. Then, some strategies were proposed using strengths, weaknesses, opportunities and threats (SWOT) analysis to improve the performance of the weak branches.

This paper contributes to previous studies on the evaluation of retailers' performance by proposing a triple framework based on resilience, sustainability and sales-marketing indicators. This paper focused on branches' operations and branches' optimization by improving performance in terms of these three indicators. This paper also offers a qualitative and quantitative analysis of retailers' performance, which has received less attention in previous studies.

Researchers and practitioners have recently been interested in corporate sustainability performance (CSP). However, knowledge on measuring CSP is limited. Many CSP-measurements are eclectic, without guidance for contextual applications. This paper aims to develop a conceptual framework that categorizes, explains and evaluates measurements based on their accuracy and precision and provides a guideline for their context-specific application.

The authors conducted a systematic literature review of an initial sample of 1,415 papers.

The final sample of 74 papers suggested four measurement categories: isolated indicators, indicator frameworks, Sustainability Balanced Scorecards (SBSC) and Sustainability Performance Measurement Systems (SPMS). The analysis reveals that isolated indicators are inaccurate and imprecise, limiting their application to organizations with delimited, specific measurements of parts of CSP due to the risk of a GIGO-effect (i.e. low-quality input will always produce low-quality output). CSP-indicator frameworks are imprecise but accurate, making them applicable to organizations that handle a more significant amount of CSP data. They have a risk of greensplashing, i.e. many indicators not connected to the industry, organization or strategy. In contrast, SBSCs are precise but inaccurate and valuable for organizations desiring a comprehensive strategic management tool with limited capacity to handle sustainability issues. They pose a risk of the streetlight effect, where organisations do not measure relevant indicators but what is easy to measure.

The ideal CSP-measurement was identified as SPMSs, which are both precise and accurate. SPMSs are useful for organizations with complex, comprehensive, connected and tailored indicators but are methodologically challenging.

In the past few decades, performance measuring systems have become important managerial tools for healthcare organizations. Healthcare performance metrics are a useful tool in understanding how healthcare organizations achieve their goals while satisfying the needs of their patients and conforming to national and international standards. Various efforts have been made to assess healthcare performance. Most of these measures are focused on a single perspective or developed by a single source to meet management and strategic objectives on time.

We develop a review of the literature to shed light on the measures used to assess performance in the healthcare sector at various points in time, as well as to establish a thorough understanding of healthcare performance measurement.

Developing real-time digital traceability of metrics and an integrative perspective that increases the actionability of information acquired is an attractive potential made possible by the introduction of new technologies and the digitization of data.

We conclude that a proper measurement system should be one to combine patient, physician, non-medical staff and system perspective, which will further facilitate the assessment of healthcare performance and the comparative function.

The quest for improved facilities management (FM) delivery is receiving immense focus through the incorporation of innovative technologies such as cyber-physical systems (CPS). The system’s high computational capabilities can aid in the abatement of some of the challenges plaguing FM functions. However, the requisite ingredients for the uptake of the system for FM have still not gained scholarly attention. Because performance measurement is a vital index in determining the outcome of FM methods, this study aims to investigate the influence of performance measurement indicators that are influential to the uptake of CPS for delivering FM functions.

A qualitative technique was adopted using the Delphi technique. The panel of experts for the study was selected through a well-defined process based on stipulated criteria. The experts gave their opinions in two rounds before consensus was attained on the identified performance measurement indicators, whereas methods of data analysis were measures of central tendency, inter-quartile deviation and Mann–Whitney U test.

Results from this study showed that 11 of the performance indicators were of very high significance in the determination of the uptake of CPS for FM functions, whereas 5 of the indicators were proven to be of high significance. Furthermore, there was no statistical difference in the opinions of the experts based on their affiliation with academic institutions and professional practice.

The findings of this study contribute practically by aiding policymakers, facility managers and relevant stakeholders with the vital knowledge of delivery mandates for efficient FM services that can spur the uptake of digital technologies such as CPS.

This study contributes to the body of knowledge as it unveils a roadmap of the expected performance output and its accompanying evaluation that would drive the adoption of a promising technology such as CPS in the delivery of FM tasks.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

The author aims to study and predict the sustainability governance performances of firms using an advanced grey prediction model. The case implication of the prediction model is also studied considering select firms in the Indian context.

The author has proposed an advanced grey prediction model, the first-entry grey prediction model (FGM (1, 1)) for forecasting the sustainability governance performances of firms. The proposed model is tested using the periodic data of sustainability governance performances of 10 Indian firms.

The author observes that the majority of firms (6 out of 10) show dipping performances for sustainability governance for the future predicted period. This throws insights into the direction of improving good governance practices for Indian firms.

The idea and motivation for sustainability-focussed governance need a bi-directional focus from the side of managers that act as the agents and from the side of shareholders that act as the principals, as seen from an agency theory perspective for sustainability governance.

Sustainability governance culture can be inculcated to a firm at the strategic level by having a bi-directional focus from managers and shareholders, so as to enhance the social and environmental sustainability performances.

The governance performance evaluations for firms particularly in developing countries were not dated back more than a decade or two. Hence, the author implements a prediction model that can be best suited, when there are small periodic data sets available for prediction.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

This paper examines the effect of ESG ratings and its dimensions (environmental, social and governance) on the financial performance of JSE-listed firms included in FTSE/JSE Responsible Investment Index.

The paper employs panel data covering 40 JSE-listed firms included in FTSE/JSE Responsible Investment Index between 2015 and 2019. The paper employs the two-stage least squares (2SLS) instrumental variable regression technique to estimate the effect of ESG ratings and its dimensions (environmental, social and governance) on both accounting- and market-based performance indicators.

The results of the two-stage least squares instrumental estimation analysis reveal that investment in ESG initiatives improves both accounting- and market-based indicators of financial performance. Of the ESG pillars, the paper finds environmental initiatives improves firms' financial bottom line and market performance, while a firm's social and governance practices are observed to have no effect on a firm's accounting and market performance measures.

The insights from this study proffers policy implications for firms' management, investors and regulatory authorities.

As far as the authors are concerned, this paper presents the first empirical analysis on the contribution of ESG ratings on financial performance in South Africa.