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This paper aims to develop a crisis readiness framework for road traffic crisis response for law enforcement agencies in the United Arab Emirates (UAE).

A Delphi method was used that combined questionnaire-based survey and the analytical hierarchy process to collect quantitative and qualitative data from an expert panel of crisis readiness professionals on how they prioritise and weigh the different strategic criteria, sub-criteria and performance indicators in the context of law enforcement agencies’ traffic response.

The findings of this paper resulted in the identification, ranking and validation of ten key dimensions of crisis readiness clustered into three distinct sets of priority rankings: response planning, resources, training and coordination; information management and communication and risk and hazard assessment; and early warning, legal and institutional frameworks, recovery initiation and property protection. The results additionally established the relative priority of sub-criteria for each criterion and validated a broad set of key performance indicators (KPIs) for the top six ranked criteria.

The findings are based on a single case study focused on a specific area of operation within crisis response and one group of organisations of the UAE police sector. This potentially places a constraint on the wider generalisation of the findings to different operational areas and agencies, as they may have different priorities or organisational conditions that have implications for the framework application and the relative importance of certain criteria and sub-criteria.

This paper provides strategic guidance in the form of a prioritised list of criteria, sub-criteria and KPIs that can direct efforts to optimise different dimensions of crisis readiness at a strategic and operational level.

This paper makes an original contribution in identifying the key criteria and performance indicators of crisis readiness for road traffic situations. The findings contribute a comprehensive strategic readiness framework that supports planning and decision-making for the development of organisational capacities that can enhance response times of police to road traffic crises. This framework ranks dimensions of crisis readiness and key sub-criteria in order of priority and validates the key components of crisis readiness that can support practitioners to structure, standardise and benchmark key processes and elements of crisis response.

This study aims to validate the KAIIAE model in the public sector and analyse the interrelationships between Enablers and Results in the King Abdullah II Award for Excellence (KAIIAE) model to better understand the dynamic logic behind improving excellence results.

The methodology used is structural equation modelling (partial least squares technique), and the data were obtained from the assessment processes for the KAIIAE for 98 Jordanian public organisations.

The findings showed that the award model has proven to be a reliable and valid framework, as the criteria and sub-criteria were highly correlated. The set of Enablers was strongly related to the set of Results, and the synergies between the critical criteria confirmed the importance of leadership, strategy, and processes for the organisation's excellence results. It was found that the new significant and direct relationships between “People” and “People Results” and between “Partnerships and Resources” and both “Society Results” and “Key Results” improved the understanding and implementation of the model. There was a significant interrelationship between model criteria, excluding the relationship between “Society Results” and “Key Results”, since the government's main objective is to serve society. A new structural model for the KAIIAE with the new relationships was suggested.

This paper concentrated only on the public sector, although the excellence model has been implemented in the private sector. Features of the different types of organisations were not tested. Furthermore, the variations in size, covered fields, number of employees and provided services need to be investigated further.

Government award bodies can use these research findings to develop a new model version for public sector awards by combining a cohesive array of elements for any government organisational need or application. The study suggested adding new criteria or relationships or eliminating those that are not significant and have no impact on public sector organisations. The findings will assure the organisations' managers that the KAII excellence model criteria are highly correlated and synergised for public sector organisations. These criteria perform efficiently once they are considered and implemented in an interconnected manner rather than managing them independently, which makes up their management framework. This knowledge about the validity of the structure of the model allows public sector organisations to benefit fully from the self-assessment processes and improve the organisation's management, enhancing their faith in excellence award models.

This study has contributed to the existing literature on the reliability and validity of business excellence models as a framework for implementing, evaluating, and improving excellence, particularly in the government sector. Several important insights have emerged from this study. The relevant analysis supports a new model structure for excellence in the public sector. Among the model structure relationships, the study identified the existence of new direct relationships between enablers and results. New significant and direct relationships are added to the model that advances the implementation and understanding of the model. Moreover, it informs theory about Excellence Award implementation in developing countries within the context of Jordan – a topic that has previously received limited attention in the international business excellence literature. Since there is limited research on the suitability of implementing the KAIIEA excellence model (that is based on nine criteria of the EFQM model) in public organisations, this work suggested introducing a new modification to the model to suit the characteristics of the public sector.

The considerable prominence of the government sector has drawn attention to the excellence parameters within its organisations. Hence, a lack of studies and inadequate knowledge in the governmental management system have limited testing excellence in the public sector. This paper provides support that the excellence model (KAIIAE model that is EFQM based) is an appropriate framework by identifying direct and significant model interrelationships for the public sector based on the actual and sufficient performance of its entities so as to drive the changes of the next model generation. This is the first study that attempts to comprehend and describe the validation of the KAIIAE model in the public sector.

The purpose of this paper is to propose a quality function deployment analytic hierarchy process (QFD‐AHP) framework to improve quality and competitive positioning within the financial services context. The applicability of the model is demonstrated through a case study.

Results of two empirical surveys were utilized to detect the “Voice of the Customer”. The first identified main bank selection criteria and questionnaires and exploratory factor analysis were employed. The second utilized structured interviews for the development of the first house of quality (HOQ), relating customer requirements to key market segments. The AHP was used to determine the intensity of the relationships within the HOQ.

In total, six key selection criteria were designated. Analysis incorporated customer needs and evaluations for the bank's current position, future goals related to specific market segments, and competition allocations. “Pricing” was the most important criterion, followed by “effective services” and “location”.

Data analysis was carried out with data from one relatively large organization. Future research might comprise additional financial institutions and possible comparative analysis regarding criteria and modeling alternatives. In addition, integration of tools such as fuzzy AHP to account for the volatility of bank selection criteria could be of great interest.

Prioritizing selection criteria is a valuable tool to help managers focus future improvement efforts and goals in a sector which faces important challenges due to the global financial crisis. As selection criteria and relative importance change due to turbulent financial situation, models such as the proposed allow adaptation, echoing the evolving voice of the customer in the key attributes and performance improvement priorities.

The paper develops a model that integrates the AHP and the QFD through which banks will identify key customer segments’ needs, as a first step in their effort to improve quality and performance. The authors emphasized the research design to foster face validity.

The purpose of this paper is to help Taiwan TV‐shopping companies to effectively select the key capabilities by introducing a multiple criteria decision‐making method, the analytic network process (ANP), that has been employed in many areas of managerial decision making.

Reviewing the literatures about the balanced scorecard and interviewing the executives obtains the perspectives and criteria to construct the hierarchy for selecting the key capabilities of TV‐shopping companies. Selecting perspectives and criteria of the balanced scorecard are interrelated. Owing to the interdependent relations in decision making, ANP is applied, which captures the outcome of dependency between the perspectives and criteria for handling such problems.

After reviewing the literatures about the balanced scorecard and interviewing the executives, ten critical criteria are retained: proposal, employee satisfaction, data integrity, risk, treatment, customer satisfaction, brand, revenue growth, cost, budget.

According to the ten important criteria, Taiwan TV‐shopping companies could select the optimal key capabilities more effectively. Moreover, a practical application of ANP presented in section 4 is generic and also suitable to be exploited for Taiwan TV‐shopping companies.

As TV‐shopping channels have been increasing and the industry prospering, a more specified and scientific selection method is essential for the more complex decision making of determining key capability, a critical source of core competitiveness. The paper contributes to selecting the optimal key capabilities more effectively.

Governments in developing countries manage their considerable state assets for public service delivery directly. In Indonesia, the Directorate of State Asset Management responsible for developing the national strategy for state asset optimization requires the determination of key elements and prioritization tools. The purpose of this paper is to show that a simple calculation using the combination of the balanced scorecard (BCS) and analytical hierarchy process (AHP) will help in the prioritization of strategy development.

A questionnaire survey of 131 multistakeholder respondents to identify the most important key elements and the best alternative for asset optimization was done in this study.

The respondents agree on the most important key elements, and that the best alternative for asset optimization is the efficient maintenance of assets. Competitive human resources comprise the recommended second key element, and that improvements in asset performance and value will improve public service as the second-highest alternative. This study also shows the importance of the integration of asset optimization in existing government strategic instruments supported by a comprehensive data set related to public assets and their performance.

This paper provides a new contribution to integrating asset optimization strategies as the core of the organization’s performance and prioritization strategies. Additional BSC perspectives are suggested, with the inclusion of AHP for prioritization. In addition, this study includes the opinions of all the stakeholders, from external users to the central management. The flexibility of the tools to adapt to the existing strategic framework will allow their application by different agencies and in different countries.

The purpose of this paper is to present a framework for evaluation and ranking of potential surrogates to select the optimum surrogates and test it for five selected social resilience indicators in a disaster context. Innovative resilience assessment approaches are required to capture key facets of resilience indicators to deepen the understanding of social resilience. Surrogates can adequately represent the target indicator that is difficult to measure, as surrogates are defined as key facets of a target indicator.

To optimize the selection of surrogates, five key evaluation criteria were used. Disaster management experts completed an online survey questionnaire and evaluated three potential surrogate options. Surrogates were then ranked using PROMETHEE, a multi-experts multi-criteria group decision analysis technique.

A framework was devised to evaluate and rank potential surrogates to assess social resilience in a disaster context. The findings revealed that the first ranked surrogate can be the most critical facet of a resilience indicator of measure. In most instances, highly experienced cohort of practitioners and policy makers have aligned their preferences of surrogates with the overall ranking of surrogates obtained in this study.

The surrogate approach can also be tested in different disaster and geographic contexts. The resilience indicators used in this study to explore surrogates are largely applicable in all contexts. However, the preference of surrogates may also vary in different contexts.

Once the surrogate is selected through an evaluation process proposed in this paper, the resilience status can be updated regularly with the help of the selected surrogate. The first ranked surrogate for each of the social resilience indicator can be applied, since the findings revealed that the first ranked surrogate can be the most critical facet in the context of the social resilience indicator being measured.

The framework and the selection of optimal surrogates will assist to overcome the conceptual and methodical challenges of social resilience assessment. The applicability of selected surrogates by practitioners and policymakers in disaster management will play a vital role in resilience investment decision-making at the community level.

The surrogate approach has been used in the fields of ecology and clinical medicine to overcome the challenges in measuring difficult to measure indicators. The use of surrogates in this study to measure social resilience indicators in a disaster context is innovative, which was not yet explored in resilience measurement in disaster management.

This paper discusses the concept, definition and usage of Key Activity Indicators (KAIs) and their integration within a Performance Measurement and Management system (PMM).

The actual definition and application areas of the KAIs are determined through a systematic literature review. Successively, a thorough definition of Key Activity Indicators is provided, along with a set of criteria for their deployment. Lastly, a case involving a Large Scale Retail Trade (LSRT) company is reported to report an example for guiding KAIs adoption.

This research shows that the scientific background concerning KAIs is still not mature. Moreover, the paper defines the role of KAIs for measuring operational activities and their possible connection with Key Performance Indicators (KPIs).

Although KAIs have been introduced and discussed in the scientific literature; there is no evidence of criteria to deploy these indicators, leaving organizations without any guidance for their operational implementation.

From an academic standpoint, the study provides an overview of the usage of KAIs within the present scientific contributions, showing the advancements of this research field. From an industrial standpoint, the research proposes a set of criteria for the organizational deployment of KAIs.

The study investigates the concept of KAIs that, besides being originally conceived within World Class Manufacturing (WCM), has not received much attention in the scientific literature.

The purpose of this study was to prioritize the appropriate generic contractor selection criteria for Engineering–Procurement–Construction (EPC) projects in the construction industry.

Proceeding from a review of previous studies and validation by a small group of experts, a preliminary set of 16 criteria was first identified. This was followed by three rounds of Delphi surveys: firstly, with 64 experienced participants confirming the relevance of the 16 criteria; secondly, with a reduced subgroup of 47 more experienced participants scoring the importance of each; and finally, providing the opportunity for these 47 to revise their scores in the light of knowing the aggregated results of the previous round.

The results show the consensus view, of which the most important criteria are ranked as past performance, project understanding, technical attributes, key personnel, health and safety, past experience, time, management, financial, contractual and legal, quality, cost, relationships, environmental and sustainability, organizational and industrial relations, and geographic location.

The findings are useful for both practitioners and academics in making a significant contribution to the body of knowledge of the EPC process. This will assist in providing a better understanding of criteria importance and pave the way to developing an EPC contractor selection model involving the criteria most needed to objectively identify potential contractors and evaluate tenders.

The purpose of this paper is to determine the significant variables leading to successful implementation of enterprise resource management (ERM) and its predecessor concepts of enterprise resource planning, supply chain planning, electronic commerce, and radio frequency identification systems. An implementation roadmap is presented using four stages for implementing ERM systems: planning, development, implementation, and testing. The roadmap indicates when and where the significant success variables would appear and how a firm might manage the implementation process.

In the research, the key success criteria and key implementation drivers uncovered by literature, case studies, and interviews were used. A survey instrument was constructed and the survey hosted on a web site where practitioners from industry were invited to supply opinions. The data were analyzed by using correlation models and one‐way analysis of variance (ANOVA) to develop cause‐effect diagrams (CE) for overall ERM systems and each component of ERM. Based on the controlled error of the ANOVA process, the CE diagram was used to depict the degree to which variables influence implementation success.

The research results have helped uncover the key significant variables that contribute to successful ERM implementation.

The proposed implementation roadmap indicates when and where the significant success variables would appear and how a firm might manage the implementation process.

Despite better accessibility and flexibility, peer-to-peer (P2P) lending has suffered from excessive credit risks, which may cause significant losses to the lenders and even lead to the collapse of P2P platforms. The purpose of this research is to construct a hybrid predictive framework that integrates classification, feature selection, and data balance algorithms to cope with the high-dimensional and imbalanced nature of P2P credit data.

An improved synthetic minority over-sampling technique (IMSMOTE) is developed to incorporate the randomness and probability into the traditional synthetic minority over-sampling technique (SMOTE) to enhance the quality of synthetic samples and the controllability of synthetic processes. IMSMOTE is then implemented along with the grey relational clustering (GRC) and the support vector machine (SVM) to facilitate a comprehensive assessment of the P2P credit risks. To enhance the associativity and functionality of the algorithm, a dynamic selection approach is integrated with GRC and then fed in the SVM's process of parameter adaptive adjustment to select the optimal critical value. A quantitative model is constructed to recognize key criteria via multidimensional representativeness.

A series of experiments based on real-world P2P data from Prosper Funding LLC demonstrates that our proposed model outperforms other existing approaches. It is also confirmed that the grey-based GRC approach with dynamic selection succeeds in reducing data dimensions, selecting a critical value, identifying key criteria, and IMSMOTE can efficiently handle the imbalanced data.

The grey-based machine-learning framework proposed in this work can be practically implemented by P2P platforms in predicting the borrowers' credit risks. The dynamic selection approach makes the first attempt in the literature to select a critical value and indicate key criteria in a dynamic, visual and quantitative manner.