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This paper aims to focus on the use of qualitative research methods to gain a better understanding of the performance management system (PMS) of one of the largest retailers in North America. The motivation for the research was to assess whether the PMS at one of the world’s largest retail companies was congruent with the most recent thinking and research in the management accounting literature.

Using open-ended interviews, the paper seeks to develop relevant hypotheses emerging from the dimensions of the Strauss and Corbin’s qualitative research methodology (1998). A qualitative methodology was used because it provides a structured approach and analytical techniques that can build upon existing theory and literature.

The qualitative evidence collected during the course of the research indicates that financial measures were predominantly used by the company in its PMS, and that this reliance on financial measures may be an artifact of the industry in which the company operates. The retail industry is highly competitive, and it is very sensitive to changes in customer tastes and behavior, as well as shareholder and financial market pressures. In addition to financial measures, it was found that operational management developed certain non-financial performance measures and that this development may have been a response by operational managers to wider stakeholder pressures and external influences. However, these performance measures appear to be not fully integrated in the PMS and are therefore de-coupled and relatively unimportant in, or entirely absent from, top-level decision-making.

The conclusions of the paper provide support for the concepts of isomorphism and de-coupling as found in the literature of new institutional theory.

The case study approach has enabled to explore and gain further understanding of management accounting practices, particularly performance measurement and management, in their natural setting. Strauss and Corbin’s (1998) grounded theory methodology was adopted because it provides a structured set of analytical steps and systematic analytical techniques for handling and interpreting data and theory building.

This paper aims to show that the extent to which convergence/divergence of a company's quality policies and practices towards/away from those of Six Sigma benchmark policies and practices mirror and anticipate the divergence of its sigma metric (SMs) from quantitative Six Sigma benchmarks. Further, the paper proposes to evaluate the robustness of the quality processes of these three companies and to compare them to that of the Six Sigma benchmark by subjecting these processes to the twin performance shocks of the benchmark Six Sigma 1.5σ allowance for process drift and a 25 percent tightening of customer requirements.

Using a novel methodology more appropriate to the critical quality characteristics of typical service industry companies, the paper computes a set of SMs for each company that is richer and broader than the metrics found in standard Six Sigma tables. This new methodology is based on the empirically observed defect rates that are currently being generated by a service process. Further, based on the available empirical data, the paper compared these metrics to the Six Sigma benchmarks.

First, the paper shows that it is possible to compute a broad array of Six Sigma metrics for service businesses based on defect rate data. Second, the results confirm the central proposition of the research to the effect that the divergence/convergence of the qualitative characteristics of a company's quality system from benchmark Six Sigma policies and practices mirror and anticipate the convergence/divergence of the company's quality metrics from the Six Sigma benchmark. Third, the research produced the unanticipated result that the quantitative quality performance of high‐performing service businesses on the Six Sigma metrics are much lower than anticipated and below what is normally achieved by their manufacturing counterparts. The results were also used to do an evaluation of the Taguchi robustness of service processes.

First, the paper demonstrates that traditional Six Sigma computational methodology for generating Six Sigma metrics that is prevalent in manufacturing applies equally to service businesses. Second, the parallel convergence of the qualitative characteristics of a company's quality system towards Six Sigma practices and its quantitative metrics towards the Six Sigma benchmark means that primacy must be given to quality practices as the drivers of quality improvement. Third, the fact that high‐performing service businesses achieve Six Sigma measures that are so low compared to their manufacturing counterparts seems to point either to some key measurement challenges in deploying Six Sigma in service industries or to the need to further change Six Sigma methodology to make it more applicable to these businesses.

The purpose of this paper is to identify the metrics used in the literature to measure social issues in sustainable supply chains.

A systematic literature review was conducted to identify peer-reviewed articles containing metrics pertaining to social issues in the supply chain. A structured content analysis of each identified article was conducted to extract the metrics. This analysis provided a basis for a frequency analysis to determine how often the various metrics appeared in the literature. The metrics were also analyzed to determine whether they: simultaneously addressed the other areas of the triple bottom line, namely, environmental and/or economic issues; were quantitative or qualitative metrics; and could be classified as absolute, relative or context-based metrics.

A total of 53 unique metrics were identified. The analysis of the results showed that a limited number of environmental (3 metrics) and economic (11 metrics) issues were addressed by the metrics as well. A combination of quantitative (39.6 per cent) and qualitative (60.4 per cent) measurements were used. The vast majority of the metrics (90.6 per cent) were further classified as absolute metrics.

This paper presents one of the first in-depth analyses of metrics used to measure social issues in supply chains. This is important because social issues are often overlooked in research focused on performance measurement in sustainable supply chains.

The purpose of this paper is to present supply chain metrics and to propose a fuzzy-based performance evaluation method for lean supply chain.

To understand the overall performance of cost competitive supply chain the paper investigates the alignment of market strategy and position of the supply chain. Since lean is applicable in many supply chains, the authors propose a set of metrics to evaluate supply chain performance. Moreover, the paper uses a fuzzy model to evaluate the performance of cost competitive supply chains. Fuzzy is an appropriate model method when uncertainty is present. It also allows modelling of a significant number of performance metrics across multiple supply chain elements and processes. Competitive strategy can be achieved by using a different weight calculation for different supply chain situations.

Research provides optimal metrics for lean supply chains. The proposed method can measure the performance of lean supply chains using a fuzzy approach and competitive strategies.

The metrics which have been selected to measure the performance of lean supply chains is particularly applicable for high volume, low-price products.

By identifying optimal performance metrics and applying performance evaluation methods, managers can predict the overall supply chain performance under lean strategy. By identifying performance for each metric they can also categorize the existing performance and optimise them accordingly.

This study provides a performance evaluation method for supply chain managers to assess the effects of lean tools and competitive strategies.

This paper aims to define and describe test methods and metrics to assess industrial robot system agility in both simulation and in reality.

The paper describes test methods and associated quantitative and qualitative metrics for assessing robot system efficiency and effectiveness, which can then be used for the assessment of system agility.

The paper describes how the test methods were implemented in a simulation environment and real-world environment. It also shows how the metrics are measured and assessed as they would be in a future competition.

The test methods described in this paper will push forward the state of the art in software agility for manufacturing robots, allowing small and medium manufacturers to better utilize robotic systems.

The paper fulfills the identified need for standard test methods to measure and allow for improvement in software agility for manufacturing robots.

Most strategies stumble in the implementation phase. This article outlines a market‐validated process, and practical guidelines, for deploying well‐calibrated metrics to optimize implementation. The primary audience is mid‐level and senior executives charged with the responsibility for implementing strategy.

This approach focuses on how to design and deploy a balanced set of performance metrics to guide the implementation of strategy. It reviews conventional approaches and pitfalls, citing examples from a diverse array of businesses, then presents “best practices” for measuring what’s important. A key thesis is that good metrics reinforce implementation, while poor metrics actually interfere with implementation.

Misaligned metrics often impede implementation, eliciting counterproductive behavior from key managers. A better approach involves creating and deploying a smaller set of multidimensional metrics, closely aligned with the firm’s strategies. Successful firms move beyond simple budgetary indicators: they formulate a small set of metrics that directs management focus outside the firm (into the marketplace); translate qualitative aspirations into quantitative targets, using a common language; align the firm’s metrics with other managerial systems (like rewards) to motivate and galvanize the management team.

The approach and logic described are universal, but the actual metrics may need to be adapted to fit the strategies, stakeholders, and competitive position of each firm, and refined over time to dovetail with the firm’s budgetary process.

Strategy programs need to be expanded to focus on the implementation process – where performance measurement is instrumental. More attention should be given to simplifying and distilling performance indicators, and broadening and its stakeholders will help facilitate implementation, and ultimately, enhance stakeholder value. managerial perspective so that implementation challenges can be flushed out and resolved.

The rational, practical approach described offers managers specific guidelines for bringing strategies to life – for bridging the gap between aspirations and real performance. It illustrates common pitfalls, and outlines how to measure and optimize performance, improve implementation, and galvanize the management team.

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

To understand human resource (HR) practices outcomes on HR decision making, strategic human resource management (HRM) and organizational performance by exploring the HR data quality along with descriptive and predictive financial and non-financial metrics.

This work utilizes the grounded theory method. After the literature was reviewed, 113 HR managers of multinational and national companies in India were interviewed with a semi-structured questionnaire. The collected interview data was analyzed with NVivo 8.0 software.

It is interesting to uncover the descriptive and predictive non-financial and financial metrics of HR practices and their influence on organizational performance. It was found that HR data quality moderates the relationship between the HR practices outcome and HR metrics. This study found that HR metrics help in HR decision-making for strategic HRM and subsequently affect organizational performance.

This study has uniquely provided the descriptive and predictive non-financial and financial metrics of HR practices and their impact on HR decision making, strategic HRM and organizational performance. This study highlights the importance of data quality. This research offers insights to the HR managers, HR analysts, chief HR officers and HR practitioners to achieve organizational performance considering the various metrics of HRM. It provides key insights to the top management to understand the HR metrics' effect on strategic HRM and organizational performance.

Since the 1980s, higher education institutions in many developed Western countries have been facing competition for resources, have undergone economic rationalisation, adopted a New Public Management style of performance management and aspired to meet global standards of quality. This chapter explores the self-tracking practices of academic institutions and workers as they negotiate a field that has moved away from a quality evaluation system based primarily on social reputation towards one based increasingly on quantified outcome indicators. Universities typically measure research performance not only in terms of quantity of outputs but also the ‘attention capital’ they receive, for example, the number of citations or awards and prizes. These metrics and the emphasis on attention capital generally encourage a culture of competition rather than collaboration, while promoting the ‘celebrification’ of academic life. We argue that this trend has been intensified by technologies that gamify research achievements, continuously update citation and ‘read’ counts, and promote networked reputation. Under these conditions, academic institutions and workers have attempted to pursue a variety of positioning strategies that represent different degrees of conformity, resistance and compromise to the power of metrics.

As the complexity and scope of technical and social challenges increase, solutions to those challenges must be addressed by collaborative research and intellectual capital sharing efforts involving multiple organizations. One prominent type of research collaborative is the government-university-industry R&D partnership, an organizational form found in many countries. These collaboratives pose special management challenges, as they must combine the efforts of researchers coming from very different institutional and organizational cultures in order to capitalize their own intellectual capital. Many such partnerships have failed due to the inability to bridge these cultural gaps. The purpose of this paper is to propose a framework for establishing and managing these partnerships, using principles and constructs drawn from institutional theory, organizational learning, alliance theory, and innovation management.

The examples of the NASA Laboratories, which are incubating several companies, are analyzed to show how this framework can highlight key attributes of successful research collaboratives.

The recurring pattern from these diverse case studies shows that the presence of internal and external champions, appropriate technology, and patient risk capital make a difference in winning in a competitive environment. However, part of the same pattern perhaps is the lack of any identifiable recipes for success - critical factors appear to be situation specific.

In light of the findings from the seven case studies the authors presented, they recommend using a hybrid portfolio approach in assessing the success of technology transfer and commercialization efforts.