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Drawing on the theoretical lens of the job demands-resources model, this study builds upon and tests a conceptual model that links customer prioritization, product complexity, business ties, job stress and customer service performance. Conceptualizing customer prioritization and product complexity as job demands and business ties as personal job resources, this research explicates the mediating process by which customer prioritization and product complexity affect customer service performance through job stress and its boundary conditions. The purpose of this paper is to offer a theoretical framework in which business ties moderates the mediated relations of customer prioritization and product complexity to customer service performance.

Structural equation modeling and a moderated mediation analysis were used on a unique multi-level, multi-respondent data set of 248 participants from 124 small and medium-sized enterprises in Turkey.

This study finds that both customer prioritization and product complexity increase job stress. In addition, this paper finds that business ties have a bitter-sweet nature as a personal resource and reverse the relation of customer prioritization to job stress while strengthening the negative direct relation of product complexity to job stress. Finally, this study finds that the indirect relation of customer prioritization to customer service performance through job stress is contingent on business ties. Specifically, this paper finds that high levels of business ties negate the indirect relation of customer prioritization to customer service performance while low levels of business ties exacerbate the negative effects of customer prioritization to customer service performance, channeled through job stress.

The findings demonstrate the critical role that personal networks play in reducing job stress and enhancing customer service performance for small and medium-sized enterprises that adopt customer-centric strategies such as customer prioritization. Nevertheless, the results suggest that the managers need to cognizant of the undesirable consequences of business ties may have on job stress when boundary-spanners handle a wide range of products/services that are technically complex. Accordingly, this study recommends small and medium-size enterprise managers and owners should be cautious in resource allocation to establish informal, personal ties with suppliers, competitors, customers and other market collaborators.

This paper offers a deeper perspective of the relations of customer prioritization and product complexity to job stress and customer service performance. This study also specifies business ties as a personal coping resource, which decreases the undesirable consequences when used in small and medium enterprises that adopt customer-centric strategies.

The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.

An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.

The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.

In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.

The aim of this study is to describe the view of age‐related prioritisation in health care among physicians and healthcare politicians and to compare their views regarding gender and age.

Swedish physicians (n=390) and politicians (n=310), mean age 52 years, answered an electronic questionnaire concerning age‐related priority setting in healthcare. The questionnaire had fixed response alternatives with possibility of adding comments.

A majority of the participants thought that age should not influence prioritisation, although more physicians than politicians thought that younger patients should be prioritised. There were also significant differences concerning their views on lifestyle‐related diseases and on who should make decisions concerning both vertical and horizontal prioritisation. The comments indicated that the politicians referred to ethical principles as a basis for their standpoints while the physicians often referred to the importance of biological rather than chronological age.

Web‐based surveys as a method has its limitations as biased samples and biased returns could cause major problems, such as limited control over the drop‐outs. The sample in this study was, however, judged to be representative.

The results indicate that supplementary guiding principles concerning prioritisation in healthcare are needed in order to facilitate decision‐making concerning resource allocation on a local level.

This paper adds important knowledge about decision makers' views on age‐related priorities in healthcare, thus contributing to scientific base for prioritisation in healthcare and the ongoing debate in society.

This study aims to develop a software risk prioritization model using analytic hierarchy process (AHP) and further validate the usability attributes of the model in prioritizing operational, technical, technological, strategic and environmental software risks.

Questionnaire was used to collect data from software practitioners to evaluate the usability attributes of the AHP-software risk prioritization model. Accordingly, partial least square-structural equation modeling was used to analyze the collected data.

Results reveal that the developed AHP-software risk prioritization model is efficient and effective in facilitating software risk factor prioritization. In addition, results suggest that the experts are satisfied with the learnability, accessibility and navigation capability of the model. Besides, results indicate that the model provides a useable interface and system design for content availability of information needed by software practitioners in evaluating and prioritizing operational, technical, technological, strategic and environmental risk. Furthermore, results show that the experts intend to adopt the model to prioritize identified software risk in their firm.

Methodologically, the developed AHP-software risk prioritization model is faced with issues such as inconsistency in judgments, weakness of confronting ambiguities and uncertainties of high complexity. Empirically, data were collected from software practitioners in Malaysia to validate the AHP-software risk prioritization model. Hence, results from this study cannot be generalized to other software practitioners in different countries.

This study developed a software risk prioritization model to evaluate and prioritize software risks that occur in software organizations by deploying AHP to carryout risk factor priority selection. Moreover, the model provides risk knowledge as guidelines for evaluating software risks in software organizations.

The developed AHP-software risk prioritization model computes risk prioritization factor priority selection and further supports software practitioners and evaluates risks and associated risk factors. Besides, this study develops an instrument that can be used in project risk management to validate the usability attributes of software risk approaches.

This research designs use case and class diagram to show how the AHP-software risk prioritization model evaluates and prioritizes software risks factors by using risk evaluation questions. Additionally, the AHP-software risk prioritization model computes, evaluates and prioritizes software risk factors using risk factor priority selection for software project management.

An ongoing debate in the field of organizational decision-making concerns the use of intuition versus analytical rationality in decision-making. For the purpose of contributing to this debate we use a rich empirical dataset built from a longitudinal study of information technology project prioritization in a large financial institution to investigate how managers make space for the use of intuition in decision-making. Our findings show that during project prioritization meetings, senior decision makers apply three different techniques: bringing-in project intangibles, co-promoting intuitive judgments, and associating intuitive judgments with shared group context, when they make space for intuition in decision processes.

In this chapter, we update stakeholder salience research using the new lens of stakeholder work: the purposive processes of organization aimed at being aware of, identifying, understanding, prioritizing, and engaging stakeholders. Specifically, we focus on stakeholder prioritization work — primarily as represented by the stakeholder salience model — and discuss contributions, shortcomings, and possibilities for this literature. We suggest that future research focus on stakeholder inclusivity, the complexity of prioritization work within intra-corporate markets, the integration of stakeholder prioritization with other forms of stakeholder work, and the development of managerial tools for multiobjective decision making within the strategic management context.

Manufacturing companies struggle to manage production disturbances. One step of such management deals with prioritising those disturbances which should undergo root cause analysis. The focus of this work is on two areas. First, investigating current challenges faced by manufacturing companies when prioritising root cause analysis of production disturbances. Second, identifying the stakeholders and factors impacted by production disturbances. Understanding the current challenges and identifying impacted stakeholders and factors allows the development of more efficient prioritisation strategies and, thus, contributes to the reduction of frequency and impact of disturbances.

To achieve the intended purpose of this research, a qualitative approach was chosen. A series of interviews was conducted with practitioners, to identify current challenges. A series of focus groups was also held, to identify the impacted stakeholders and factors by disturbances.

Various challenges were identified. These are faced by manufacturing companies in their prioritisation of production disturbances and relate to the time needed, criteria used, centralisation of the process, perspective considered and data support. It was also found that a wide range of stakeholders is impacted by production disturbances, surpassing the limits of production and maintenance departments. Furthermore, the most critical factors impacted are quality, work environment, safety, time, company results, customer satisfaction, productivity, deliverability, resource utilisation, profit, process flow, plannability, machine health and reputation.

The current situation regarding root cause analysis prioritisation has not been identified in previous works. Moreover, there has been no prior systematic identification of the various stakeholders and factors impacted by production disturbances.

Several authors have proposed different approaches to help practitioners deal with the complexity of prioritising improvement projects and initiatives. However, these approaches have been developed as “generic” methods which do not consider the specific needs, objectives and capabilities of different industries and organisations. The purpose of this paper is to present an integrated methodology that prioritises improvement projects or initiatives based on two key performance objectives, cost and quality, specifically important for low volume‐high integrity product manufacturers.

The paper reviews some of the most commonly used prioritisation methods and the theory and logic behind the proposed prioritisation methodology. Then, the prioritisation methodology is empirically tested, through a case study, in a world class manufacturing organisation.

The results obtained from the case study indicate that the integrated methodology proposed in this paper is an effective alternative for low volume‐high integrity products manufacturers to identify, select and justify improvement priorities.

Selection and prioritisation of projects and initiatives are key elements for the successful implementation of improvements. The integrated methodology presented in this paper intends to aid organisations in dealing with the complexity that is normally handled over the selection and prioritisation of feasible improvement projects.

This paper presents a novel methodology that integrates two commonly used approaches in industry, Process Activity Mapping (PAM) and Failure Mode and Effect Analysis (FMEA), to prioritise improvements. This methodology can help, in particular, organisations embarked in the manufacture of low volume‐high integrity products to take better decisions and align the focus of improvement efforts with their overall performance and strategic objectives.

Given the critical role of project prioritization and selection process in Six Sigma efforts, this study aims to analyse the relevant literature to answer this question: What types of project prioritization and selection methods have been used in Six Sigma research?

The study implemented the systematic literature review (SLR) method to identify and review all relevant previous studies.

The study revealed that 59 articles focused on the topic used 111 methods, analytic hierarchy process appeared as the most frequently used method with 12 articles (20%) and one-third of the methods used in the current Six Sigma project selection literature contained multi-criteria decision-making methods. In total, 61% of 59 articles were not published in the journals ranked by the ABDC’s list. Only 17% of the articles reviewed in this study were published in journals ranked as B category and 12% of the articles were published in A category journals.

The findings of this literature review may help Six Sigma practitioners and researchers accurately identify project prioritization and selection methods, considering that qualitative and quantitative scientific methods guarantee to make better decisions than “gut feelings” of the decision makers in this process.

Although a variety of studies focused on the topic, an SLR is lacking in the area of Six Sigma project prioritization and selection. Therefore, this study was constructed using the SLR method to analyse the topic.

Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.

A Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.

Results of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.

This paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.