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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 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.

This paper examines the socio-political affordances of metrics in research evaluation and the consequences of epistemic injustice in research practices and recorded knowledge.

First, the use of metrics is examined as a mechanism that promotes competition and social acceleration. Second, it is argued that the use of metrics in a competitive research culture reproduces systemic inequalities and leads to epistemic injustice. The conceptual analysis draws on works of Hartmut Rosa and Miranda Fricker, amongst others.

The use of metrics is largely driven by competition such as university rankings and league tables. Not only that metrics are not designed to enrich academic and research culture, they also suppress the visibility and credibility of works by minorities. As such, metrics perpetuate epistemic injustice in knowledge practices; at the same time, the reliability of metrics for bibliometric and scientometric studies is put into question.

As metrics leverage who can speak and who will be heard, epistemic injustice is reflected in recorded knowledge and what we consider to be information.

This paper contributes to the discussion of metrics beyond bibliometric studies and research evaluation. It argues that metrics-induced competition is antithetical to equality and diversity in research practices.

The aim of this paper is to review metrics and develop a framework for measuring leagile supply chain. Metrics that are applicable in the lean, agile and leagile strategies are identified in the literature and are then combined into a framework that can reflect both agile and lean strategies – the leagile supply strategy.

This work is based on the systematic literature review. Literature was collected, then lean and agile metrics were extracted, analysed, counted and grouped into the framework. Findings are compared against literature on leagile supply chain.

Findings indicate that there are sets of metrics specific to lean strategy, such as are process-focused, cost, productivity, inventory and delivery-based metrics, and specific to agile such as flexibility, responsiveness, information sharing and cooperation. There are also metrics common for both strategies; they are related to time, quality and customer satisfaction. Lean measures are tangible and focused on internal processes and products, while agile measures are targeted at external environment.

The framework could be used by practitioners as a starting point for performance system design.

There is a need to stop looking at lean and agile as separate and distinct supply strategies. Results of this research indicate that lean and agile are interlinked, both are focusing on customer satisfaction and quality. Applying a proposed set of metrics enables to design supply chain measurement system that reflects both strategies to measure leagile supply chain. The framework could be used by practitioners as a starting point for performance system design.

This paper aims to describe an interdisciplinary and innovative research conducted in Switzerland, at the Geneva School of Business Administration HES-SO and supported by the State Archives of Neuchâtel (Office des archives de l'État de Neuchâtel, OAEN). The problem to be addressed is one of the most classical ones: how to extract and discriminate relevant data in a huge amount of diversified and complex data record formats and contents. The goal of this study is to provide a framework and a proof of concept for a software that helps taking defensible decisions on the retention and disposal of records and data proposed to the OAEN. For this purpose, the authors designed two axes: the archival axis, to propose archival metrics for the appraisal of structured and unstructured data, and the data mining axis to propose algorithmic methods as complementary or/and additional metrics for the appraisal process.

Based on two axes, this exploratory study designs and tests the feasibility of archival metrics that are paired to data mining metrics, to advance, as much as possible, the digital appraisal process in a systematic or even automatic way. Under Axis 1, the authors have initiated three steps: first, the design of a conceptual framework to records data appraisal with a detailed three-dimensional approach (trustworthiness, exploitability, representativeness). In addition, the authors defined the main principles and postulates to guide the operationalization of the conceptual dimensions. Second, the operationalization proposed metrics expressed in terms of variables supported by a quantitative method for their measurement and scoring. Third, the authors shared this conceptual framework proposing the dimensions and operationalized variables (metrics) with experienced professionals to validate them. The expert’s feedback finally gave the authors an idea on: the relevance and the feasibility of these metrics. Those two aspects may demonstrate the acceptability of such method in a real-life archival practice. In parallel, Axis 2 proposes functionalities to cover not only macro analysis for data but also the algorithmic methods to enable the computation of digital archival and data mining metrics. Based on that, three use cases were proposed to imagine plausible and illustrative scenarios for the application of such a solution.

The main results demonstrate the feasibility of measuring the value of data and records with a reproducible method. More specifically, for Axis 1, the authors applied the metrics in a flexible and modular way. The authors defined also the main principles needed to enable computational scoring method. The results obtained through the expert’s consultation on the relevance of 42 metrics indicate an acceptance rate above 80%. In addition, the results show that 60% of all metrics can be automated. Regarding Axis 2, 33 functionalities were developed and proposed under six main types: macro analysis, microanalysis, statistics, retrieval, administration and, finally, the decision modeling and machine learning. The relevance of metrics and functionalities is based on the theoretical validity and computational character of their method. These results are largely satisfactory and promising.

This study offers a valuable aid to improve the validity and performance of archival appraisal processes and decision-making. Transferability and applicability of these archival and data mining metrics could be considered for other types of data. An adaptation of this method and its metrics could be tested on research data, medical data or banking data.

The purpose of this paper is to investigate the perception of marketing managers in a transition country Montenegro with regards to marketing metrics. The paper examines the degree in which managers are familiar with the way marketing metrics are applied and how important they are in the process of making business decisions in a company operating in a Montenegro.

Data was collected during 2020 through a survey of 171 randomly selected companies and was analyzed using structural equation model and the statistical method of analysis of variance tests.

The obtained results show that managers are quite familiar with financial and non-financial metrics. Both groups are applied to a significant degree, as managers believe that these indicators provide valuable information needed during the decision-making process. Still, more emphasis is placed on the knowledge, implementation and importance of non-financial metrics compared to financial metrics. This is probably due to the specificities of the economic activities of the companies operating in Montenegro, as most of them are service companies, which is why non-financial metrics (such as consumer metrics) are the most important indicators when it comes to ascertaining the market position of the company. Additionally, in recent years the primary focus in Montenegro, as country that is still in the process of transformation from planned economy to a free-market form, has been placed on strengthening of competitiveness and advancing the market orientation of companies. This led to an increase in the importance that managers in transition countries attach to non-financial metrics.

The fact that the survey only covers companies from one country is its limitation.

The obtained results will have a significant empirical contribution, which is reflected in providing guidelines for managers on how to improve the system of measuring and controlling marketing performance, all that to strengthen the competitiveness of the company, and can serve managers of hierarchy levels in a company as guidelines for making decisions on the implementation of marketing strategy and marketing metrics, to improve business performance, multi-context customer interaction, cost-saving and strengthen competitiveness.

Obtaining necessary knowledge management and implementing marketing metrics are important conditions for consideration when it comes to the continuous monitoring and improvement of business results, increasing competitiveness and advancing the market position of the company.

The originality stems from the analysis of the interconnection that exists between marketing metrics and strategic decision-making, which is expected to be positively reflected in the development of society, i.e. strengthening the competitiveness of companies based on knowledge management achieved through the assessment of the degree of knowledge, the implementation and the significance of each of the metrics covered within this research in business decision-making processes. The paper provides insights into the extent to which managers understand the meaning of these indicators and are able to combine different marketing metrics to obtain more complex indicators, serving as necessary inputs when making strategic business decisions.

Despite the general recommendation of using a combination of multiple criteria for research assessment and faculty promotion decisions, the raise of quantitative indicators is generating an emerging trend in Business Schools to use single journal impact factors (IFs) as key (unique) drivers for those relevant school decisions. This paper aims to investigate the effects of using single Web of Science (WoS)-based journal impact metrics when assessing research from two related disciplines: Business and Economics, and its potential impact for the strategic sustainability of a Business School.

This study collected impact indicators data for Business and Economics journals from the Clarivate Web of Science database. We concentrated on the IF indicators, the Eigenfactor and the article influence score (AIS). This study examined the correlations between these indicators and then ranked disciplines and journals using these different impact metrics.

Consistent with previous findings, this study finds positive correlations among these metrics. Then this study ranks the disciplines and journals using each impact metric, finding relevant and substantial differences, depending on the metric used. It is found that using AIS instead of the IF raises the relative ranking of Economics, while Business remains basically with the same rank.

This study contributes to the research assessment literature by adding substantial evidence that given the sensitivity of journal rankings to particular indicators, the selection of a single impact metric for assessing research and hiring/promotion and tenure decisions is risky and too simplistic. This research shows that biases may be larger when assessment involves researchers from related disciplines – like Business and Economics – but with different research foundations and traditions.

Consistent with the literature, given the sensibility of journal rankings to particular indicators, the selection of a single impact metric for assessing research, assigning research funds and hiring/promotion and tenure decisions is risky and simplistic. However, this research shows that risks and biases may be larger when assessment involves researchers from related disciplines – like Business and Economics – but with different research foundations and trajectories. The use of multiple criteria is advised for such purposes.

This is an applied work using real data from WoS that addresses a practical case of comparing the use of different journal IFs to rank-related disciplines like Business and Economics, with important implications for faculty tenure and promotion committees and for research funds granting institutions and decision-makers.

The purpose of this paper is to highlight challenges for service firms communicating Environmental, Social and Governance (ESG) efforts to customers. Specifically, it focuses on the relationship between ESG metrics and reporting and customer perceptions of social innovativeness.

The empirical material comprises three years of data (2018–2020) covering more than 100 firms from three sources: (1) Social Innovation Index (Sii), which is collected as part of the American Innovation Index (Aii), (2) Bloomberg Sustainability Accounting Standards Board (SASB) ESG and (3) Datamaran.

ESG metrics and reporting do not suffice to explain customer perceptions of social innovativeness. Rather, a firm's industry plays the prominent role in affecting these perceptions where service firms are at a disadvantage as customers perceive services as less socially innovative compared to goods.

While ESG metrics and reporting provide important information for investors and regulators, they are not reflected in customers' perceptions of firms' social innovativeness, and services are at a disadvantage relative to goods. Therefore, services researchers and managers must advance their knowledge regarding how to better link ESG metrics and report to customers' perceptions.

The paper offers a first large-scale, cross-industry investigation of how ESG metrics and reporting impact customer perceptions of social innovativeness, leading to a research agenda on communication of ESG.

In this paper, we consider the Heisenberg groups which play a crucial role in both geometry and theoretical physics.

In the first part, we retrieve the geometry of left-invariant Randers metrics on the Heisenberg group H_2n+1, of dimension 2n + 1. Considering a left-invariant Randers metric, we give the Levi-Civita connection, curvature tensor, Ricci tensor and scalar curvature and show the Heisenberg groups H_2n+1 have constant negative scalar curvature.

In the second part, we present our main results. We show that the Heisenberg group H_2n+1 cannot admit Randers metric of Berwald and Ricci-quadratic Douglas types. Finally, the flag curvature of Z-Randers metrics in some special directions is obtained which shows that there exist flags of strictly negative and strictly positive curvatures.

In this work, we present complete Reimannian geometry of left invarint-metrics on Heisenberg groups. Also, some geometric properties of left-invarainat Randers metrics will be studied.

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.