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Driving is an integral component of many operational systems, and any small improvement in driving quality can have a significant effect on accidents, traffic, pollution, and the economy in general. However, making improvements is challenging given the complexity and multidimensionality of driving as a task. In this paper, we investigate the effectiveness of nudging to improve driving performance. In particular, we leverage a smartphone application launched by our industry partners to send three types of nudges through notifications to drivers, indicating how they performed on the current trip with respect to their personal best, personal average, and latest driving performance. We measure the resulting driving performance using telematics technology (i.e., real-time sensor data from an accelerometer, Global Positioning System (GPS), and gyroscope in a mobile device). Compared with the “no-nudge” control group, we find that personal best and personal average nudges improve driving performance by approximately 18% standard deviations of the performance scores calculated by the application. In addition, these nudges improve interaccident times (by nearly 1.8 years) and driving performance consistency, as measured by the standard deviation of the performance score. Noting that driving abilities and feedback seeking may vary across individuals, we adopt a generalized random forest approach, which shows that high-performing drivers who are not frequent feedback seekers benefit the most from personal best nudges, whereas low-performing drivers who are also frequent feedback seekers benefit the most from the personal average nudges. Finally, we investigate the potential mechanism behind the results by conducting an online experiment in a nondriving context. The experiment shows that the performance improvements are directly driven by the changes in participants’ effort in response to different nudges and that our key findings are robust in alternative (nondriving) settings. Our analysis further shows that nudges are effective when the variability in reference points is low, which explains why the personal best and personal average nudges are effective, whereas the last score nudge is not. This paper was accepted by Vishal Gaur, operations management.

Peer-to-peer (P2P) federated cloud system has emerged as a promising service delivery model wherein multiple cloud service providers (CSPs) collaborate and share resources and services among themselves to fulfill the spikes in demand from their cloud service consumers (CSCs). It facilitates CSPs to accomplish the committed service level agreements (SLAs) to their CSCs. However, the lack of preexisting trust relationships and unawareness about the cloud infrastructure and service delivery performance among CSPs in a distributed environment poses a risk to the quality of service (QoS) being delivered. We address this challenge by proposing a trust model, PRTrust, for a peer-to-peer federated cloud system that capitalizes the triangular relationship of performance, risk, and trust for the participating CSPs. The main contributions of this work are: (1) providing a logical design and architecture for trust and performance management in a peer-to-peer federated cloud system; (2) providing a two-tier weighted performance evaluation mechanism for CSPs; (3) providing a risk evaluation mechanism for CSPs based on their current performance level; (4) providing an improvised mechanism to evaluate and manage personalized reputation-based trust for the participating CSPs; (5) providing a CSP selection mechanism from a trusted list of CSPs, using the evaluated performance-based risk, for sharing the resources and services in a peer-to-peer federated cloud system. The proposed PRTrust model has shown better threat resilient behavior in dealing with malicious peer CSPs when compared with the reference EigenTrust model.

Purpose The purpose of this study is to contribute an actor-network-theory (ANT) perspective of how algorithms are used and whether this reflects positively on organisational practices and related perceptions of organisation performance, by both customers and employees. Furthermore, the organisation’s view of adopting algorithms to manage their performance is explored. Design/methodology/approach The current study is a study set in this virtual space, as it seeks to use data available online from different stakeholders of the case organisation. Online “virtual space” studies have become more common over the past few years. Primary and secondary data are shared online, for instance on relevant rating websites for customers (such as TrustPilot) and employees (such as Indeed), which serve as a reflection of the organisational performance and the communication of their values. This type of study has become more prevalent, either as a study undertaken online fully (Hine, 2007) or partially (for instance Baxter et al., 2019), which provides a different perspective to traditionally carried out face-to-face interactions. Findings The current study has contributed insights from a global case organisation in the logistics sector originating in the Middle East (ME). The case organisation clearly indicates that its algorithm, in line with particular presentations and discussions on online platforms, is focussed on analysing limited factors, which are prediction of accurate delivery timeslot, preferred and precise location for delivery and speed of delivery overall. This reflects an inherent bias of the delivery industry, which the case organisation operates in, and may focus attention on delivering on those objectives, with a detrimental effect on the organisation’s broader organisational mindset. This refers to long-term values and sustainability-related objectives. Originality/value Findings will reflect on whether algorithms can be used to align a multi-national organisation to achieve its desired performance and related desirable results. This will be one of the main contributions of the current study, which engages with a multi-national organisation in the logistics sector that is based in the ME region.

This paper addresses the problems of gaming behaviour as expressed in untruthful reporting, manipulation of data and performance measures, and distortion of information. We apply game-theoretical p...

PurposeThe objective of this paper is to propose an approach to comparatively analyze the performance of drugs and consumable products warehouses belonging to different healthcare institutions.Design/methodology/approachA Cluster Analysis is completed in order to classify warehouses and identify common patterns based on similar organizational characteristics. The variables taken into account are associated with inventory levels, the number of SKUs, and incoming and outgoing flows.FindingsThe outcomes of the empirical analysis are confirmed by additional indicators reflecting the demand level and the associated logistics flows faced by the warehouses at issue. Also, the warehouses belonging to the same cluster show similar behaviors for all the indicators considered, meaning that the performed Cluster Analysis can be considered as coherent.Research limitations/implicationsThe study proposes an approach aimed at grouping healthcare warehouses based on relevant logistics aspects. Thus, it can foster the application of statistical analysis in the healthcare Supply Chain Management. The present work is associated with only one regional healthcare system.Practical implicationsThe approach might support healthcare agencies in comparing the performance of their warehouses more accurately. Consequently, it could facilitate comprehensive investigations of the managerial similarities and differences that could be a first step toward warehouse aggregation in homogeneous logistics units.Originality/valueThis analysis puts forward an approach based on a consolidated statistical tool, to assess the logistics performances in a set of warehouses and, in turn to deepen the related understanding as well as the factors determining them.

Buildings consume a large proportion of global primary energy and building performance management requires massive data inputs. Key Performance Indicator (KPI) is a tool used for comparing different buildings while avoiding problems caused by heterogeneous data sources. However, silos of building and energy consumption data are separate, and the linkages between a KPI formula and different data sets are often non-existent. This paper develops an ontology-based approach for automatically calculating the KPI to support building energy evaluation. The proposed approach integrates building information from BIM and energy and environmental information collected by sensor networks. A KPI ontology is developed to establish a KPI formula, thereby linking static and dynamic data generated in the building operation phase. Each KPI can be defined by inputs, a formula and outputs, and the formula consists of parameters and operators. The parameters can be linked to building data or transformed into a SPARQL query. A case study is investigated based on the proposed approach, and the KPIs for energy and environment are calculated for a real building project. The result shows that this approach relates the KPI formula to the data generated in the building operation phase and can automatically give the result after defining the space and time of interest, thus supporting building performance benchmarking with massive data sets at different levels of details. This research proposes a novel approach to integrating the KPI formula and linked building data from a semantic perspective, and other researchers can use this approach as a foundation for linking data from different sources and computational methods such as formula created for building performance evaluation.

Purpose University department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in measuring the efficiency of academic departments in data envelopment analysis (DEA) deals with crisp data, which may be, often, imprecise, vague, missing or predicted. Current literature offers various approaches to addressing these uncertainties by introducing fuzzy set theory within the basic DEA framework. However, current fuzzy DEA approaches fail to handle missing data, particularly in output values, which are prevalent in real-life evaluation. Thus, this study aims to augment these limitations by offering a fuzzy DEA variation. Design/methodology/approach This paper proposes a more flexible approach by introducing the fuzzy preference programming – DEA (FPP-DEA), where the outputs are expressed as fuzzy numbers and the inputs are conveyed in their actual crisp values. A case study in one of the top higher education institutions in the Philippines was conducted to elucidate the proposed FPP-DEA with fuzzy outputs. Findings Due to its high discriminating power, the proposed model is more constricted in reporting the efficiency scores such that there are lesser reported efficient departments. Although the proposed model can still calculate efficiency no matter how much missing and unavailable, and uncertain data, more comprehensive data accessibility would return an accurate and precise efficiency score. Originality/value This study offers a fuzzy DEA formulation via FPP, which can handle missing, unavailable and imprecise data for output values.

Service-continuity in distributed computing can be enhanced by designing self-organized systems, with a non-fixed structure, able to modify their structure and organization, as well as adaptively react to internal and external environment changes. In this paper, an architecture exploiting a bio-inspired management approach, i.e., the functioning of cell metabolism, for specialized computing environments in Service-Oriented Networks (SONs) is proposed. Similar to the processes acting in metabolic networks, the nodes communicate to each other by means of stimulation or suppression chains giving rise to emergent behaviors to defend against foreign invaders, attacks, and malfunctioning. The main contribution of this work is a novel bio-inspired methodology for SON analysis to improve the network reliability and robustness for maintaining service-continuity. To show the effectiveness of the proposed computational framework, an embedded Field-Programmable Gate Array (FPGA) prototyped SON for a relevant healthcare imaging application is also outlined. In particular, our case study extracts and analyzes the Cerebral Vascular Tree from Magnetic Resonance Angiography series via a Maximum Intensity Projection algorithm; the proposed solution addresses and implements some basic issues of an interesting diagnosis tool for cerebral aneurysm detection. The prototyped system was tested and evaluated in terms of execution time and used resource analysis, by achieving a 4× speed-up factor compared to the software counterpart.

This research was conducted to identify and analyze the Comparative Study of Quality Management Practices and Management Performance at Central MUI before and after ISO 9001: 2015 implementation and the relationship between Quality Management Practices and Management Performance before ISO implementation, the relationship between Quality Management Practices and Management Performance after ISO implementation. , The sampling method used was purposive sampling with a sample size of 100 respondents. Measurements were made using questionnaires and then the data were processed using SPSS software. This research was conducted at the central MUI. The results of this study indicate that Quality Management Practices before ISO and Quality Management Practices after ISO 9001: 2015 have a strong relationship in influencing the success of ISO 9001: 2015 which is implemented in the central MUI and the Performance of the Central MUI Management before ISO and the Performance of the Central MUI Administrators after ISO 9001 : 2015 has a very strong influence in influencing the success of ISO 9001: 2015 which is implemented at the central MUI. So it can be said that the Quality Management Practices and Performance Management at the Central MUI before and after the implementation of ISO 9001: 2015 have improved so that it needs to be maintained and maintained in order to stay good in implementing ISO 9001: 2015 which is implemented by the central MUI.

PurposeThis paper proposes an integrated knowledge visualization and digital twin system for supporting strategic management decisions. The concepts and applications of strategic architecture have been illustrated with a concrete real-world case study and decision rules of using the strategic digital twin management decision system (SDMDS) as a more visualized, adaptive and effective model for decision-making.Design/methodology/approachThis paper integrates the concepts of mental and computer models and examines a real case's business operations by applying system dynamics modelling and digital technologies. The enterprise digital twin system with displaying real-world data and simulations for future scenarios demonstrates an improved process of strategic decision-making in the digital age.FindingsThe findings reveal that data analytics and the visualized enterprise digital twin system offer better practices for strategic management decisions in the dynamic and constantly changing business world by providing a constant and frequent adjustment on every decision that affects how the business performs over both operational and strategic timescales.Originality/valueIn the digital age and dynamic business environment, the proposed strategic architecture and managerial digital twin system converts the existing conceptual models into an advanced operational model. It can facilitate the development of knowledge visualization and become a more adaptive and effective model for supporting real-time management decision-making by dealing with the complicated dependence of constant flow of data input, output and the feedback loop across business units and boundaries.

While research has documented the framing effects of performance data on decision-making, little is known about the impact of data visualizations on performance gaming. We propose that performance ...

The number of startups in Indonesia has surpassed 2,000 companies, but only a handful have survived and thrived in the VUCA world. One of the failure causes is the lack of good business management in translating the company’s vision and strategy into clear and measurable objectives. For a project-based renewable energy startup, as the current study’s focus, it is necessary to focus not only on generating revenue but also on achieving the company’s vision and mission to sustain. To address this, we formulated an integrated framework related to the Performance Management System of the renewable energy startup. We combined the Balanced Scorecard as a tool for communicating the company’s strategies, Key Performance Indicators as a corporate performance tracking tool, and Objectives and Key Results for a daily basis or short-term goal-setting tool. It is also complemented by the idea of Ethical Climate to embed ethics into the system. Finally, quantifiable company performance measures will be obtained to assist the startup in the decision-making process related to appropriate strategies for corporate sustainability.

The ecological compensation mechanism is regarded as the direction for the future management of the ecological environment of the river basin, which has become a global concern. Ex-post assessments on the performance of ecological compensation programs contribute to further improvement and optimization in the process of exploration. This study proposes a novel performance assessment approach to address the issue of environmental efficiency evaluation with uncertainty by systematically integrating data envelopment analysis (DEA), bootstrap, regression, and exponential smoothing. The last two methods are used to fill in missing data, DEA super-SBM is applied to measure the performance, and bootstrap is adopted to stimulate more data. This approach is applied to performance measurement of Xin'an river basin in China. Validated by benchmark comparisons and statistical tests, the outcomes indicate that this integrated conceptual method can serve as an effective way to gauge environmental efficiency with ecological compensation when missing data are presented. The results obtained from the case highlight the limited positive effect of ecological compensation. Marginal utility brought about by the ecological compensation investment fund is declining, and the fund utilization in some projects also appears to be low. Consequently, suggestions for future optimization on fund and performance management, evaluation method and compensation mode are provided.

The purpose of this research is to improve talent performance through an integrated development program to nurture and increase company competitiveness. Research is carried out by identifying problems and analyzing the factors that affect talent performance and then designing a system to fix the existing problems. From the analysis of existing data, we find that improving talent performance requires a comprehensive approach. From this research we can also conclude that an integrated learning system is very effective in increasing competence and talent performance. The combination of formal and informal learning, theory and practice provides an effective learning system.

Purpose This paper aims to evaluate the usefulness of two conceptual frameworks: levers of control (LOC) (Simons, 1995) and performance management systems (PMSs) (Ferreira and Otley, 2009) for studying PM practices using a case study. Design/methodology/approach A case study method is used whereby data are collected through semi-structured interviews, examination of the group’s annual reports and internal documents. Findings A key finding of this study is the use of a PMS at the case company which is formally structured and with objectives, mechanisms and processes designed beyond a mere “performance measurement system.” While the case analysis indicates that most of the key components of the two frameworks are featured in the company’s PMS design, the uses of Simons’ (1995) LOC, however, are not consistent with the notion of “balance” as advocated by the model. Research limitations/implications The evidence presented in this study is based on one large manufacturing company, and hence the findings cannot be generalized. Practical implications The findings of this study can be used in enriching the design of current proposed theoretical frameworks and also in encouraging management accounting researchers to continue the efforts of studying performance management (PM) practices. Originality/value A deeper understanding of PM practices using holistic frameworks has yet to receive more contested efforts from management accounting researchers. This paper attempts to contribute to this endeavor and fill in the gap in this area of research.

The increase availability of operational data from the fleets of cars in the field offers opportunities to deploy machine learning to identify patterns of driver behaviour. This provides contextual intelligence insight that can be used to design strategies for online optimisation of the vehicle performance, including compliance with stringent legislation. This paper illustrates this approach with a case study for a Diesel Particulate Filter, where machine learning deployed to real world automotive data is used in conjunction with a reliability inspired performance modelling paradigm to design a strategy to enhance operational performance based on predictive driver behaviour. The model-in-the-loop simulation of the proposed strategy on a fleet of vehicles showed significant improvement compared to the base strategy, demonstrating the value of the approach.

Purpose There is an increasingly growing interest in treading beyond the traditional university goals of teaching and research to focus on their contribution to society through knowledge transfer (KT) performance activities. This paper aims to determine how performance management systems (PMSs) encourage lecturers to engage in the transfer of knowledge from higher education institutions (HEIs) to society. Design/methodology/approach This study is based on a sample of 3,812 Spanish university lecturers surveyed about different PMS tools – strategic plans, budgeting, meetings and reward systems – and different KT activities – research and development contracts, patents, extended patents, licenses, services and spin-offs – for the 2011–2016 period. A logit model was applied for the statistical analyzes. Findings As expected, enabling the use of these tools generally increases the probability of producing KT while only in some cases does coercive use reduce it. Moreover, combining enabling and coercive uses does not increase the likelihood of KT performance. Practical implications University policymakers and managers should reduce the gap between KT and research and teaching by, for example, examining the effects of their performance management practices on scholars’ perceptions and their subsequent behavior. Originality/value Despite previous literature states that coercive use decreases performance, the authors reveal that this is not the case for KT performance in HEIs. According to the findings, a specific record of KT, i.e. a coercive strategic plan tool, has a consistently positive effect on all four KT activities as follows: R&D contracts, patents, extended patents and licenses.

The purpose of this study is to develop a performance management design at Yayasan Pembinaan Anak Cacat Jakarta. The performance management design is utilised as a strategic step in managing the performance of the employees at YPAC Jakarta. The study is categorized as qualitative and quantitative descriptive method. Using the theoretical approach to business strategy and Human Resources Management (HRM) strategy from Miles and Snow (1984). Moreover, the performance management theory proposed by Armstrong (1994) and Aguinis (2013) is observed as well. To measure the performance, Mixed model Theory by Spencer and Spencer’s (1993) is put into account. The data collection method used is using interviews with the sources; Management Board, Managing Director and General Manager. Another method of data collection is to use a survey using a questionnaire which distributed to 72 respondents to obtain employee’s perception in regard to the implementation of performance management at YPAC Jakarta. The list of questions in the questionnaire is adopted and necessarily modified from the previous study which was developed by Weiss and Hartle (1997). The result of this study shows that the performance management that must be improved is at the stage of the performance appraisal as well as of the performance review, which each has below- average values of 3,12 out of 3,2. However, the results of the planning and execution stages of performance actually shows that there are several aspects that are still lacking. Based on the identified problems in the performance management implementation, therefore the improvement plan is drafted.

Purpose World Cup tournament is one of the most popular international organizations in football. The purpose of this paper is to investigate the overall performance of World Cup 2018 teams via multi-criteria decision-making (MCDM) approaches. Design/methodology/approach The presented approach adopts entropy integrated Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Aggregated Sum Product Assessment (WASPAS) approaches to weight the criteria and evaluate the performance of World Cup 2018 teams. Initially, the authors compute weight criteria via Shannon Entropy method. Then, the authors compute and compare the results of TOPSIS and WASPAS methods so as to evaluate the performance of World Cup 2018 teams. Findings According to TOPSIS and WASPAS results, Belgium has demonstrated better performance in comparison to other teams. As per to the empirical results, both methods have shown a significant harmony in terms of performance figures. There is also strong positive correlation between TOPSIS and WASPAS method results. This result confirms the stability of the analysis. Originality/value This paper contributes to sport performance management literature by using MCDM methods in FIFA World Cup 2018 teams. To the best of the authors’ knowledge, this is the first paper to measure performance of an international football organization via MCDM methods.

In order to deliver real-time feedback to support employee development and rapid innovation, many companies are replacing formal review-based performance management with systems that enable frequent and continuous employee evaluation. Real-time feedback applications enable supervisors and employees to give, seek, and receive competency-based feedback using their computers, smartphones, or other devices. In this study, we examine the role of one such real-time feedback application to understand its effects on employee performance appraisals. First, we seek better understanding of how workplace relationships affect employee feedback across managers, colleagues, and direct reports and find that feedback tends to be more critical when given by managers. What is even more important from an industry perspective is the role of preferential treatment and retaliation. Managers can be more transactional, but colleagues are not. We also highlight a series of gender observations: men rate women higher than men, and women rate men and women similar to how men rate men. We conclude by finding that positive real-time feedback has a stronger effect on an employee’s future ratings than negative feedback. Our findings have direct implications for the design and implementation of performance management systems and highlight how companies can use information systems to create an innovative human resource operation that delivers flexibility and agility.

PurposeThis work investigates the influence of the organizational life cycle (OLC) on the performance management system (PMS) design and the performance of projects in Brazilian industries of software development.Design/methodology/approachThe study employed descriptive statistics and structural equations modeling on the data and information obtained from 115 Brazilian industries of software development with maturity in project management. Data collection was carried out through a survey applied to managers of the organizations mapped and certified by management maturity models Brazilian software process improvement (MPS-BR) or capability maturity model integration (CMMI).FindingsResults revealed that OLC influences the PMS design dimensions (scope, aggregation and opportunity) and project performance. Regarding effects observed on the performance of projects, only the scope dimension's influence was validated by the analyses, and the other dimensions were not significant considering the investigated sample.Originality/valueThe paper shows evidence of how OLC can influence PMS design and project performance. The analyses can help managers to improve their actions in the PMS design, increasing confidence and generating more robust and sustainable results in the business model.

PurposeThis work aims to measure the performance of an electric motors supply chain (SC) by using the Triple E performance measurement model to assess three performance dimensions simultaneously: efficiency, efficacy and environmental impact.Design/methodology/approachThis research presents a real case of a Brazilian company as an example for applying the Triple E performance measurement model and the discrete-event simulation to assess the performance of an SC.FindingsPerformance measurements for the three dimensions were the following: (1) for efficiency, a high inventory cost in the SC, driven by the distribution center (DC) (49.7% total cost); an order cycle time operating with an average of 21.7 days and a logistic channel transport capacity of 88%; (2) for efficacy, a service level of 98% for all channels; (3) for environmental impact, the SC emits a total of 395,733 kg of CO2 annually, with the DC and the regional distribution center (RDC2) being the largest emitters.Originality/valueThis work allowed to measure the performance of an SC interface (manufacturing process, a DC and three regional distribution centers). It was possible to measure the inventory holding cost and out-of-stock inventory costs, order cycle time, SC service level, transport capacity utilization and CO2 emissions in the transportation process of each SC stage and the whole SC. This study can be used as a decision support guide for academics and practitioners to measure and improve the SC performance.

As Organizational Behavior Management was borne out of Applied Behavior Analysis, so too is its approach to assessing environmental variables before implementing solutions for behavior change. This...

Given that nonprofits embrace diverse perspectives when adopting performance measurement, one key area of scholarly attention is understanding what leads nonprofits to embrace different performance...

Interacting with software using keyboard and mouse will soon be history. Voice, speeches, gestures are the next big thing in communication. IoT will certainly produce a large amount of people-related data and it is this data that is going to be really useful for decision taking purposes. HR would need to incorporate technology to successfully control the organizational human resources. IoT will allow us to access our data and other applications like Continuous Performance Management, where digital culture is required to create a genuinely valuable connection between an employee and his or her staff. It also helps to exchange thoughts and experiences through social media collaboration which is a driving force for all employees. The paper tries to analyze the role of IoT in People analytics, challenges associated with it and how Delloitte Canada has gone about implementing it across their organization.

The research on relationships among vehicle operating speed, roadway design elements, weather, and traffic volume on crash outcomes will greatly benefit the road safety profession in general. If these relationships are well understood and characterized, existing techniques and countermeasures for reducing crash frequencies and crash severities could potentially improve, and the opportunity for new methodologies addressing and anticipating crash occurrence would naturally ensue. This study examines the prevailing operating speeds on a large scale and determines how traffic speeds and different speed measures interact with roadway characteristics and weather condition to influence the likelihood of crashes. This study used three datasets from Washington and Ohio: 1) Highway Safety Information System (HSIS), 2) the National Performance Management Research Dataset (NPMRDS), and 3) National Oceanic and Atmospheric Administration (NOAA) weather data. State-based conflated databases were developed using the linear conflation of HSIS and NPMRDS. The results show that certain speed measures were found to be beneficial in quantifying safety risk. Annual-level crash prediction models show that increased variability in hourly operating speed within a day and an increase in monthly operating speeds within a year are both associated with a higher number of crashes. Safety practitioners can benefit from the current study in addressing the issue of speed and weather in crash outcomes.

Feedback is a common intervention used to address performance issues in clinical and organizational settings. Most research on feedback focuses on manipulating either the parameters of feedback del...

This research highlights the application of Balanced Scorecard methodology on relevant key performance indicators, with high impact on the development of Romanian educational services. Without pretending to be a comprehensive study with generalizable results, this research reveals the analysis of fifteen performance indicators, integrated in three dimensions: institutional capacity, educational efficiency and quality management. The Balanced Scorecard model enables an efficient management in order to deliver the best value to all educational stakeholders. We consider that the findings will have significant implications both on the educational decision makers and the managers of educational institutions from Romania, as the integration of relevant performance indicators in this survey purposefully challenge their minds.

Purpose This purpose of this study is to structure complex problems to be solved with greater efficiency, optimising the relationship between root causes (RC) relevance of the problem and utilisation of human resources to treat them, minimising the use of manpower in problem-solving activity and thus contributing to greater productivity within organisations. Design/methodology/approach The authors built an approach under the concepts of theory of constraints and multiattribute and multiobjective decision-making methods that were applied in a real complex problem of the low development of Brazilian space industry, by theoretical perspective. Also, the authors submitted it in a simulation environment to assess in which situations it is successful considering number of problem’s RC, system complexity and number of people in the system. Findings The approach was successful on the real case, finding the optimal relationship between the RC relevance and the number of people involved to treat them. For certain complex problem inputs configurations, simulation results reveal that the approach is reliable obtaining more than 95% chance of success in finding the optimal relationship, when comparing with traditional prioritising methods. Originality/value This approach introduces an unprecedented way to locate and evaluate non-physical constraints within a system, which is used to determine RC relevance, as well as an unprecedented way of defining a single optimal solution for structuring a problem, considering the relevance of RC and the use of human resources. The approach is useful for organisations in general which often need managing complex problems with few resources.

The objective of this study is to examine the effect of posted speed limit change on travel speeds. To do this, we use the National Performance Management Research Data Set (NPMRDS), which is a unique and rich dataset provided via the Federal Highway Administration (FHWA). Particularly, we focus on the impact of increased posted speed limit, implemented during June 2015, on interstate highways in Wisconsin including urban and rural areas between 2014 and 2016 by employing a quasi-experimental model with a panel dataset. Our findings clearly demonstrate that the posted speed limit change from 65 to 70 increases travel speed by 1.7 mph after controlling for location and time specific fixed-effects. Specifically, the posted speed limit change on Wisconsin interstate highways increases average travel speed of passenger and freight vehicles by 1.87 and 1.16 mph, respectively. Findings also show that speed variations have increased after the speed limit change, and higher variations do not occur in freight travel speeds, but in passenger vehicle travel speeds. These variations differently exist between urban and rural areas. We suggest that transportation agencies should consider these variations by vehicle types and places when they increase posted speed limit.

PurposeFragmentation can inhibit joint goals and performance measures. Therefore, the purpose of this study is to investigate the level of fragmentation between public, private and third sectors in a hybrid organization's performance management systems and the effects of this fragmentation to propose hypotheses and new research methods for future studies.Design/methodology/approachThe inductive research design was based on a mixed method approach. As empirical data, a survey, interviews and documents were used in this case study examining a hybrid organization called Welfare Alliance.FindingsThe results showed low-level fragmentation in the performance management system of the hybrid. Although the level of fragmentation was low-level, it affected the hybrid's ability to implement joint performance goals and measures. Performance management practices suffered as a consequence.Originality/valueAs a theoretical contribution to research addressing performance management in hybrids, the study proposes new concepts and theoretical hypotheses concerning fragmented performance management systems in hybrids. These theoretical hypotheses propose how performance goals and measures can become fragmented because they isolate service production units and activities from each other. The proposed hypotheses for future studies also attempt to provide explanations for how fragmentation can spread from one management function to another (i.e. from goal setting to performance measurement).

Purpose This paper aims to provide owner/managers with access to an evidence-based study for the purpose of improving practice in performance measurement (PM) in their small and medium-sized enterprises (SMEs). Design/methodology/approach As a qualitative case study, it adopted semi-structured interviews to explore the best practices in PM from 32 SMEs in Malaysia. The interviewees were the owner/manager, the supervisor and/or the person who is responsible for human resources in the firm. Thematic analysis was conducted. Findings First, it should be no surprise, as the study echoes previous research, that the traditional, complex PM systems are not functionally suitable for most SMEs. Second, the study identifies five common capabilities (5Cs) in human capital that these SMEs require in their PM practice, all of which are claimed to contribute critically to SME success and sustainability. Research limitations/implications While qualitative studies can obtain in-depth results, it is encouraged to continue similar research with additional samples for comparison, including quantitative studies and SMEs in other countries. Practical implications The findings help SME owners/managers benchmark their current practices in PM and improve toward what peer firms have been doing well to benefit organizational development in a feasible tone. Originality/value This research informs SME practitioners of the 5Cs mechanism found to be effective for long-term organizational survival – including during unprecedented times such as an economic downturn or a pandemic.

Building construction projects suffer from many dangers such as cost and time overruns. A major reason for the failure of such projects is the lack of measurements of the construction performance. There is usually a disparity of judgment among the stakeholders when it comes to the perception of failure and success of a building construction project because it is based on personal indices. The aim of this study is to identify and rank the key performance indicators in their relative importance as a way to assist in building construction performance. A thorough literature review was conducted and 10 key performance indicators were identified. A questionnaire survey and interviews were used to collect data and the results were analyzed using the analytical hierarchical process, pair-wise comparison. As a result, the highest prioritized key performance indicator was safety, followed by time effectiveness and client satisfaction. The least prioritized key performance indicator was the environmental performance. The results of this paper may serve as a guideline in improving building construction projects.

PurposeThis paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).Design/methodology/approachA semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.FindingsThe review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.Originality/valueGiven the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

This paper investigates the intention to use an electronic Performance Management System (e-PMS) through a conceptual framework derived from the Technology Acceptance Model where several constructs were put under investigation. The positivist approach was adopted in view of collecting primary data from a sample of 52 employees working at XYZ Holdings Limited through a structured survey questionnaire comprising of 25 items segregated under 6 sections. Research findings revealed a positive individual relationship between each independent variable; perceived usefulness, perceived ease of use, attitude and accessibility with intention to use the e-PMS respectively. Gender, age, grade and working experience were found to be statistically insignificant. However, when analyzing the constructs simultaneously as a whole model, only perceived ease of use and attitude were found to be statistically significant towards the intention to use the software. The derived model will be useful not only to the management of XYZ Holdings Limited but also to other companies which intend to adopt a new technology while at the same time serves as a base for future research in this field.

Previous studies have applied various benchmarking approaches to measure the performance of organisations that are responsible for maintaining the pavements under their jurisdiction. However, there...

PurposePolicymakers implementing pay-for-performance schemes within general practice should seek to design schemes which work with rather than against the professional values and goals of general practitioners. In this way, schemes are more likely to enhance the practitioners' engagement. The purpose of this paper is to show how this was done in two case studies of pay-for-performance design and present the lessons from this study for policymakers.Design/methodology/approachA Most Similar Systems collective case study of the design of two pay-for-performance schemes for general practitioners, the United Kingdom's Quality and Outcomes Framework (QOF) and the New Zealand’s Performance Management Programme (PMP) was undertaken, involving 26 semi-structured interviews with policymakers, documentary and literature analysis.FindingsInnovation in processes was found in both case studies which facilitated engagement by general practitioners in the formulation and implementation of these schemes. These were careful selection of highly skilled design teams, use of principle-based negotiation techniques and academic mediation of indicator selection. In addition, in England the majority of members in the combined QOF design team were general practitioners. The evidence from these two case studies reinforces approaches to scheme design which seek to harness rather than challenge medical professional values and which maximise the participation of general practitioners in the design process. Achieving funder/practitioner collaboration should be a key goal in the policymaking process.Practical implicationsPay-for-performance scheme designers can improve their ability to engage general practitioners in scheme design and scheme uptake by adopting approaches which actively engage general practitioners as designers and users of such schemes.Originality/valueThis study compares two contemporaneous processes of pay-for-performance scheme design and implementation in similar systems of general practice funding and delivery at the national level, offering a rare quasi-experimental opportunity for learning lessons from comparative analysis.

We use meta-analytic structural equation modeling (MASEM) to examine how organizational planning leads to managerial performance. Specifically, we test a theoretically-driven model of how participa...

8th CIRP Conference of Assembly Technology and Systems, CATS 2020, online, 29 Sep 2020 - 1 Oct 2020; Procedia CIRP 97, 418-422 (2021). doi:10.1016/j.procir.2020.05.261 special issue: "8th CIRP Conference of Assembly Technology and Systems / Edited by Sotiris Makris", Published by Elsevier, Amsterdam [u.a.]

The detection of cells with sub-optimal performance and the identification of the root-cause of such performance is a crucial and challenging task in Network Performance Management (NPM). The contemporary NPM approaches, being reactive, silo-based, and highly expert-reliant, are not viable options for such tasks anymore, particularly in the emerging complex heterogeneous mobile networks. The state-of-the-art research in the field of data-driven Artificial Intelligence (AI) is a ray of hope for developing innovative solutions for such NPM tasks. However, the scarcity of holistic and detailed real network data limits the potential of this approach. In this study, we present a comprehensive AI-driven framework for the auto-diagnosis of cells with sub-optimal performance in a real network. We have explored and shared insight about an untapped comprehensive Call Detail Record (CDR) dataset from a real network operator. The outcome is anonymous and annotated data made public to encourage further research in this domain. We employ a $K-means$ clustering method that exploits CDR data and domain experts’ input for the identification of particular types of cell performances. Next, a support vector machine-based classifier is developed for real-time applications which classifies the network nodes based on their performance with an accuracy of 97.69%. Subsequently, we introduce an algorithm that uses the classification results for the root-cause analysis of sub-optimal performance by leveraging network topography and area knowledge. The method succeeds in reaching the outcomes of an expert-led root-cause analysis and beyond. At the same time, the algorithmic approach limits the manual root-cause analysis to 30 possible scenarios per hour as opposed to analysis of 759 cells, thus it reduces the workload of an expert significantly. In the broad picture, the proposed AI framework lays the foundation towards zero-touch mobile network and service management starting with automated NPM and root-cause analysis.

PurposeThis paper aims to organise, in a general typology, the different purposes of performance management systems and processes (PMSPs) that are discussed across the performance management literature in different functional areas.Design/methodology/approachThe typology is developed based on a traditional review of the performance management literature from three types of functional areas, represented by operations and production management, management accounting and human resource management.FindingsThe cross-functional typology illustrates how the different types of purposes discussed in the literature can be organised in a hierarchical structure. In this way, the basic purpose of organisational value creation for PMSPs can be decomposed into two layers of sub-purposes, the first specifying the domain and the second outlining the specific managerial use of PMSPs.Practical implicationsThe presented typology may help managers across different functional areas map the purposes of their PMSPs; this mapping will not only provide the basics for understanding a PMSP's potential value for an organisation but also serve as an important input for PMSP design.Originality/valueThe presented typology has a broader scope than existing typologies of purposes in research and, consequently, better interrelates and tracks the various types of purposes discussed across different functional areas. This contributes not only to our understanding of performance management as a cross-functional field but also to research on the use and design of PMSPs in organisations.

In recent days, the single and multiple economies depend upon the human capital to build a valuable service. The individual employee level is important to process and maintain the whole organization. Consequently, performance management is needed at each employee level and the business level to implement a system in order to measure the employee performance and provide growth based on the performance. In data mining applications, the knowledge discovery of interest in Human Resources Management (HRM) is applicable. To extract the knowledge significant data mining classification techniques were used. The scope of this work compares the predictive analyzing of theC4.5 algorithm, Naive Bayes and Fuzzy logics are made by comparing its accuracy. This paper proposed a framework to help human resource to monitor the employee performance. The exact accuracy of the proposed framework found to be more efficient in terms of the accurately predicting the outcome of the employee.

PurposeThis paper investigates the role of boundary objects and boundary work in the integration of risk management (RM) and performance management (PM) systems. In particular, the paper combines theoretical insights with an empirical focus to examine how shared contexts are created through the boundary work performed by key actors across knowledge boundaries.Design/methodology/approachThe paper develops an exploratory qualitative case study from a local government context. The methodology is based on document analysis and semi-structured interviews.FindingsBoundary objects can act as knowledge integration mechanisms, allowing key actors to understand the meanings and uses of RM and PM practices. The paper shows how collaborative versus competitive boundary work exerted by key actors can explain the creation of shared contexts leading to integration between RM and PM.Originality/valueThe results contribute to the debate about the integration of RM with other managerial systems. Differently from previous research, the integration theme is addressed in the present work by looking specifically to the integration between RM and PM. In doing so, the role of both boundary objects and the boundary work performed by relevant actors to demarcate their legitimacy and autonomy over preferred practices is portrayed.

Adept IP address management (IPAM) practice incorporates the effective management of the three foundational aspects of IP networking: IP address subnetting and tracking, dynamic host configuration protocol, and domain name system. Network managers responsible for maintaining accessibility to an IP network need to keep IPAM information organized, accurate, and secure. The most commonly applied network management approach is that of the FCAPS model from a functional perspective and ITIL® from a service management perspective. This chapter discusses common IPAM tasks within the context of the FCAPS model, then relates functional mapping of these tasks to ITIL® process areas. The FCAPS model covers the following key functions within the practice of network management: fault management, configuration management, accounting management, performance management, and security management.

The article is devoted to the use of digital technologies in performance management at the enterprise. It focuses on the possibility of including projects for the increase in labor productivity within the framework of рпогйу program “Improving 1аЬог efficiency and етр1оуте эирроГ;” in Ше strategy for the development of the information society of the Russian Federation for 2017-2030. The article also provides a list of technical and economic factors of performance planning, which can be identified and analyzed using the technology of "digital twins" in the system of automated planning and production preparation (CPS Cyber Planning firmware module).

Purpose Most green building (GB) materials, which are used widely in the construction sector in Malaysia, perform poorly in terms of energy efficiency and sustainability. Nevertheless, during maintenance planning of these materials, the focus is often directed towards comfort and design instead. However, as GB material construction projects grow in scale and complexity, interconnections between the activities and processes can be noticed during problematic planning performance management to monitor the GB material components for corrective and preventive maintenance actions. Design/methodology/approach The concept of GB material maintenance planning for sustainable development and the main features of information and communication technology tools and techniques are based on analysis of literature reviews of GB material scenarios. Findings The results show how decision-making support in maintenance planning can be unsuccessful and how planning decisions can frame the content of an integrated system to analyse information and reduce risks of GB material failure. Originality/value The paper concludes that implementing a research framework for developing such a system can help improve the sustainable performance of maintenance planning of GB material economic, social and environmental issues.