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This study examines whether different strategy archetypes deploy specific performance metrics to support their strategic goals and priorities. If so, does alignment of strategy and metrics positively impact organisational performance?

The conceptual framework and hypotheses are couched in Contingency Theory. The role of business strategy as a moderating variable is tested using MANOVA, followed by post hoc pairwise comparisons. The results are based on cross-sectional survey data from 372 manufacturing and service organisations in Italy.

The overall contingency effect of business strategy in selecting and deploying performance metrics and their effect on organisational performance is supported. However, the group-wise post hoc analyses show support only for Prospectors but not for Defenders and Analysers.

This research lends further support in favour of the Contingency Theory from a new geographic context (Italy) that there are no universally best performance metrics that drive organisational performance. However, more research is needed to understand why the theory only holds for certain strategic archetypes and not across all archetypes.

Managers can direct resources and effort towards designing and deploying the “right” type of performance metrics suitable for their strategic orientation and thus optimise organisational performance.

This is a rare study that tests the moderating role of business strategy using all four strategic archetypes of the Miles and Snow typology. It deploys both financial and non-financial measures and uses a very large sample of both manufacturing and service organisations from a relatively unexplored region of the world. The study provides additional evidence in favour of the Contingency Theory whilst advocating for more research to refine our understanding of why the contingency perspective is not so important for firms that are not the first-in.

In academics and industry, significant efforts have been made to lead planners and control teams in evaluating project performance and control. In this context, numerous control metrics have been devised and put into practice, often with little emphasis on analyzing their underlying concepts. To cover this gap, this research aims to identify and analyze a holistic list of control metrics and their functionalities in the construction industry.

A multi-step analytical approach was conducted to achieve the study’s objectives. First, a holistic list of control metrics and their functionalities in the construction industry was identified. Second, a quantitative analysis based on social network analysis (SNA) was implemented to discover the most important functionalities.

The results revealed that the most important control metrics' functionalities (CMF) could differ depending on the type of metrics (lagging and leading) and levels of control. However, in general, the most significant functionalities include managing project progress and performance, evaluating the look-ahead level’s performance, measuring the reliability and stability of workflow, measuring the make-ready process, constraint management and measuring the quality of construction flow.

This research will assist the project team in getting a comprehensive sensemaking of planning and control systems and their functionalities to plan and control different dynamic aspects of the project.

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.

This paper aims to identify and empirically analyze useful and applicable metrics for measuring and managing the sustainability performance of small and medium-sized enterprises (SMEs).

To achieve the objective of the paper, potential metrics were adopted from previous research related to industrial sustainability and an empirical analysis was carried to assess the applicability of the metrics by collecting empirical data from Italian footwear SMEs using a structured questionnaire. The SMEs were selected using a convenience sampling method.

The results of the within-case analysis and the cross-case analysis indicate that the majority of the metrics were found to be useful and applicable to each of the SMEs and across the SMEs, respectively. These metrics emphasized measuring industrial sustainability performance related to financial benefits, costs and market competitiveness for the economic sustainability dimension; resources for the environmental sustainability dimension; and customers, employees and the community for the social sustainability dimension.

Apart from the within-case analysis and cross-case analysis, it was not possible to conduct statistical analysis since a small number of SMEs were accessible to collect empirical data.

The findings of the paper have considerable academic, managerial and policy implications and will provide a theoretical basis for future research on measuring and managing industrial sustainability performance. By providing a set of empirically supported metrics based on the triple bottom line approach (i.e. economic, environmental and social metrics), this paper contributes to the existing knowledge in the field of industrial sustainability performance measurement.

ESG (Environment, Social, Governance) has taken on increased importance in recent years for all stakeholders, with the S dimension now taking on a stronger focus in the real estate space. This paper proposes a new metric to be used in the S space to assess improvements in aspects such as gender equality and cultural diversity in real estate. It adds to the S metrics currently available to see the more effective delivery of the S dimension into real estate investment decision-making.

A new S metric in ESG is proposed and validated. Using this metric, examples regarding gender equality and cultural diversity are assessed among leading real estate players in Australia. This S metric is assessed over a number of time periods to demonstrate the improvements in gender equality and cultural diversity in these major real estate players.

This new S metric is seen to be highly effective and robust in capturing the changes in various aspects of the S dimension in ESG in the real estate space today; particularly concerning gender equality and cultural diversity. It is clearly able to demonstrate the significant changes in increased participation of women at the more senior leadership levels by leading players in the real estate space.

With ESG becoming a critical issue in the real estate sector, issues involved in the S space will take on increased significance going forward. This is critical, as the elements of the S dimension such as gender equality and cultural diversity are important aspects for an effectively functioning real estate industry. The S metric developed in this paper can be used for benchmarking purposes over time, as well as between real estate players, between sub-sections within a real estate organisation, and comparing against other industry sectors. It is also relevant in all organisations, and is not just limited to the real estate sector. Additional metrics in the S space are an important development to further empirically assess the effective delivery of the S dimension of ESG in the real estate sector and more broadly.

This paper specifically proposes this new S metric in ESG in the real estate industry. This is a key issue for the real estate industry going forward at all levels, as it will facilitate a more diverse real estate industry and more effective real estate investment decision-making. This S metric is applicable in all organisational sectors where the S dimension of ESG is important.

The purpose of this study is to revisit brand performance metrics (BPMs) (brand affinity, brand content and knowledge, brand image, brand ethics and brand value) and evaluate the moderated mediation effect of relationship quality (mediator) and relationship duration (moderator) in brand performance and customer loyalty relationship in an Indian banking context.

The research model was tested in the Indian banking sector. The primary data was collected from the 1,000 account holders of five Indian public and private banks. The data was analysed and validated using exploratory factor analysis and confirmatory factor analysis. Structural equation modelling and the Hayes process were used for testing the hypotheses.

The study results established BPMs as a four-dimensional structure comprising brand affinity, brand content and knowledge, brand image, brand ethics and brand value. The BPMs significantly positively impact relational quality (RQ) and customer loyalty. Further results also prove the existence of moderated mediation effect on BPMs and customer loyalty link and portray that the impact of BPMs on customer loyalty is mediated by the RQ and influenced by relationship duration.

The study is confined to the Indian banking sector. It did not examine the dimension-wise impact of brand performance indicators on RQ and customer loyalty. Future research is required to explore their influence in banking and other sectors.

The study findings suggest that to enhance brand performance, banks need to follow excellence in every conduct, take immediate actions against inappropriate behaviour, consistently update their relevant and valuable contents (news, videos, white papers, e-books, case studies, FAQ’s, photos, etc.) on their websites and also introduce loyalty schemes to reimburse customers’ interests with some substantial benefits such as rebates, discounts, annual gifts and extraordinary or additional services. These strategies can pave the way for enhancing long-term quality relationships between customers and their service providers and increasing customer loyalty.

To the best of the authors’ knowledge, the study is a maiden attempt to assess the effect of BPMs on customer loyalty in the presence of RQ and at the value of relationship duration/length. Besides, the study results also prove the existence of moderated mediation effect and portray that the impact of customer equity and relational benefits on customer loyalty is influenced by relationship duration and mediated by RQ.

Because of the COVID-19 pandemic and changing market demands, competition for manufacturing industries is increasing and they face numerous challenges. In such a case, it is necessary to use multiple strategies, technologies and practices to improve organizational performance and, as a result, to integrate them for ease of adoption. The purpose of this research is to identify advanced Industry 4.0 technologies, operational excellence (OPEX) strategies and reconfigurable manufacturing system (RMS) practices. The study also computes their weights, as well as identifies and prioritizes the performance metrics for the same.

A thorough review of relevant articles was conducted to identify 28 OPEX strategies, RMS practices and advanced technologies, as well as the 17-performance metrics. The stepwise weight assessment ratio analysis approach was used to compute the weights of the selected practices, while the WASPAS approach was used to prioritize the performance metrics. While developing the framework, the industry expert’s expertise was incorporated in the form of their opinions for pairwise comparison.

According to the study findings, advanced Industry 4.0 technologies were the most prominent for improving organizational performance. As a result, integrating Industry 4.0 technologies with OPEX strategies can assist in improving the performance of manufacturing organizations. The prioritized performance metrics resulted in the production lead time ranking first and the use of advanced technologies ranking second. This emphasizes the significance of meeting dynamic customer needs on time while also improving quality with the help of advanced technologies.

The developed framework can help practitioners integrate OPEX strategies and advanced technologies into their organizations by adopting them in order of importance. Furthermore, the ranked performance metrics can assist managers and practitioners in evaluating the manufacturing system and, as a result, strategic planning for improvement.

According to the authors, this is a novel approach for integrating OPEX strategies with advanced Industry 4.0 technologies, and no comparable study has been found in the current literature.

This paper aims mainly at introducing applied statisticians and econometricians to the current research methodology with non-Euclidean data sets. Specifically, it provides the basis and rationale for statistics in Wasserstein space, where the metric on probability measures is taken as a Wasserstein metric arising from optimal transport theory.

The authors spell out the basis and rationale for using Wasserstein metrics on the data space of (random) probability measures.

In elaborating the new statistical analysis of non-Euclidean data sets, the paper illustrates the generalization of traditional aspects of statistical inference following Frechet's program.

Besides the elaboration of research methodology for a new data analysis, the paper discusses the applications of Wasserstein metrics to the robustness of financial risk measures.

This paper aims to investigate how congruent keywords are used in information security policies (ISPs) to pinpoint and guide clear actionable advice and suggest a metric for measuring the quality of keyword use in ISPs.

A qualitative content analysis of 15 ISPs from public agencies in Sweden was conducted with the aid of Orange Data Mining Software. The authors extracted 890 sentences from these ISPs that included one or more of the analyzed keywords. These sentences were analyzed using the new metric – keyword loss of specificity – to assess to what extent the selected keywords were used for pinpointing and guiding actionable advice. Thus, the authors classified the extracted sentences as either actionable advice or other information, depending on the type of information conveyed.

The results show a significant keyword loss of specificity in relation to pieces of actionable advice in ISPs provided by Swedish public agencies. About two-thirds of the sentences in which the analyzed keywords were used focused on information other than actionable advice. Such dual use of keywords reduces the possibility of pinpointing and communicating clear, actionable advice.

The suggested metric provides a means to assess the quality of how keywords are used in ISPs for different purposes. The results show that more research is needed on how keywords are used in ISPs.

The authors recommended that ISP designers exercise caution when using keywords in ISPs and maintain coherency in their use of keywords. ISP designers can use the suggested metrics to assess the quality of actionable advice in their ISPs.

The keyword loss of specificity metric adds to the few quantitative metrics available to assess ISP quality. To the best of the authors’ knowledge, applying this metric is a first attempt to measure the quality of actionable advice in ISPs.

This paper aims to investigate the effect of business incubation metrics based on an adaptation of the lean start-up (LS) framework on start-up survival after incubation. This study also analyzes the obstacles in implementing the LS framework as incubation metrics.

This study uses mixed methods. Quantitative research using multiple linear regression was applied to the data of 30 start-ups incubated at Bandung Techno Park for the 2014–2017 period and survival tracking data after the incubation. A qualitative approach to complete the explanatory work was conducted through in-depth interviews with 12 respondents, including start-up graduates from the incubation program, program managers and mentors.

This study confirms that several LS incubation metrics significantly affect start-up sustainability after incubation. In addition, this study also explains several problems in applying the LS discipline that needs attention to increase incubation success.

Research was conducted only at one technology business incubator (TBI) model that focuses on digital start-ups in the emerging ecosystem. Research results can be biased in different situations and ecosystems.

The explanation of the relationship of LS-based incubation metrics to the survival of start-ups, as well as the challenges of their implementation, can be a reference for TBI management to consider and prioritize intervention strategies, thereby improving TBI’s business processes and increasing the success rate of incubated start-ups.

The creation of university start-ups and spin-offs has become a key performance indicator mandatory for technology universities in Indonesia. The existence of TBI institutions in universities as channels of technology commercialization is essential. The incubator’s success in creating a new technology-based company will have a significant social impact on the surrounding environment.

Although the LS method is popular in start-up communities and among practitioners, it is rarely used in the incubation process at universities. These results can be considered for university TBIs to explore LS as an incubation management tool to increase the success rate of incubated start-ups.