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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.

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.

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.

This paper aims to analyze and examine how an organization from the financial sector prioritizes its business processes and what criteria are adopted to select the most appropriate process for improvement projects.

This descriptive research is based on an exploratory approach. Qualitative methodology was applied to a case study through on-site observation, documentation analysis and semi-structured interviews.

The results confirm criteria mentioned in the literature, such as financial aspects and strategic impacts, but also raised a new critical issue: automation potential of manual processes, reflecting the current movement of process automation.

As a limitation of this study, it is worth mentioning the application in only one organization in the financial market and the small number of respondents, even though they occupy leadership positions in the organization.

As a practical implication, the present work offers a direction for managers of the financial sector in structuring and applying models for prioritizing processes aimed at organizational efficiency.

Automation solutions for process improvement need careful study to minimize impacts in human resources reduction. In this sense, the eligibility of a process for automation must be carefully considered.

This paper presents the evolution of the process prioritization model adopted by a large institution in the financial market, which has a significant presence in the Brazilian and international markets as a commercial and wholesale bank.

In order to create sustainable health systems, many countries are introducing ways to prioritise health services underpinned by a process of health technology assessment. While this approach requires technical judgements of clinical effectiveness and cost effectiveness, these are embedded in a wider set of social (societal) value judgements, including fairness, responsiveness to need, non-discrimination and obligations of accountability and transparency. Implementing controversial decisions faces legal, political and public challenge. To help generate acceptance for the need for health prioritisation and the resulting decisions, the purpose of this paper is to develop a novel way of encouraging key stakeholders, especially patients and the public, to become involved in the prioritisation process.

Through a multidisciplinary collaboration involving a series of international workshops, ethical and political theory (including accountability for reasonableness) have been applied to develop a practical way forward through the creation of a values framework. The authors have tested this framework in England and in New Zealand using a mixed-methods approach.

A social values framework that consists of content and process values has been developed and converted into an online decision-making audit tool.

The authors have developed an easy to use method to help stakeholders (including the public) to understand the need for prioritisation of health services and to encourage their involvement. It provides a pragmatic way of harmonising different perspectives aimed at maximising health experience.

All health care systems are facing increasing demands within finite resources. Although many countries are introducing ways to prioritise health services, the decisions often face legal, political, commercial and ethical challenge. The research will help health systems to respond to these challenges.

This study helps in increasing public involvement in complex health challenges.

No other groups have used this combination of approaches to address this issue.

The main research attempts guiding questions about management research agendas had been relevance questions versus rigor questions. Researchers have also attempted to set management research agendas in particular sectors. To the best of the authors’ knowledge, no research, however, has addressed the infrastructural and foundational questions of what moral priorities and ethical principles should guide the future development of management research. Because the Islamic theological approach is a “transcendental values integration” approach, it presents a potentially viable source of reference particularly for scholars interested in ethical philosophical paradigmatic approaches. Islamic literature has presented guiding principles as to how to balance priorities through the Jurisprudence of Priorities (Fiqh Al-Awlawiyyat). The purpose of this exploratory conceptual paper is to synchronize the Islamic background literature on the jurisprudence of priorities with management research development and agendas. The research is exploratory in nature.

The paper is conceptual, merging Islamic literature with management research for the development of a framework to potentially guide management researchers in prioritizing their research agendas.

The research resulted in the conceptualization of a framework aiding researchers in the prioritization of their research agendas.

The research has implications for management scholars who are interested to prioritize their research projects and agendas. The research presents a schematic diagram and guiding framework through which scholars can reflect on their choice of research topics.

The research is also relevant to funding agencies as they devise the funding priorities in the management field.

This paper addresses the unique and foundational question of what moral priorities and ethical principles should guide the future development of management research. The authors build on a religious-philosophical approach, drawing on the Islamic jurisprudence of priorities as a literature base. The authors, therefore, address the key principles of responsible research regarding how it can be relevant on the infrastructural level to society and how the benefit to key stakeholders should be tackled. To the authors’ knowledge, this was not done in previous literature.

Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area.

The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data.

Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior.

The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain.

La Inteligencia Artificial (IA) ofrece muchos beneficios para mejorar la práctica del marketing predictivo. Sin embargo, plantea preocupaciones éticas relacionadas con la priorización de clientes, la concentración de cuota de mercado y la manipulación del consumidor. Este artículo explora estas preocupaciones éticas desde una perspectiva contemporánea, basándose en las experiencias y perspectivas de profesionales en IA y marketing predictivo. El estudio tiene como objetivo contribuir a la literatura de este ámbito al proporcionar una perspectiva moderna sobre las preocupaciones éticas del uso de la IA en el marketing predictivo, basándose en las experiencias y perspectivas de profesionales en el área.

Para realizar el estudio se realizaron entrevistas semiestructuradas durante seis semanas con 14 participantes con experiencia en sistemas habilitados para IA en marketing, utilizando técnicas de muestreo intencional y de bola de nieve. Se utilizó un análisis temático para explorar los temas que surgieron de los datos.

Los resultados revelan que el uso de la IA en marketing podría tener consecuencias no deseadas, como perpetuar sesgos existentes, violar la privacidad del cliente, limitar la competencia y manipular el comportamiento del consumidor.

El estudio identifica siete temas y los comparan con el modelo de Ashok para proporcionar una perspectiva estructurada para interpretar los resultados. El marco presentado por esta investigación es único y puede utilizarse para respaldar investigaciones éticas que abarquen aspectos sociales, tecnológicos y económicos dentro del ámbito del marketing predictivo.

人工智能（AI）为改进预测营销实践带来了诸多益处。然而, 这也引发了与客户优先级、市场份额集中和消费者操纵等伦理问题相关的观点。本文从当代角度深入探讨了这些伦理观点, 充分借鉴了人工智能和预测营销领域专业人士的经验和观点。旨在通过现代视角提供关于在预测营销中应用人工智能时所涉及的伦理观点, 为该领域做出有益贡献。

本研究采用了目的性和雪球抽样技术, 与14位在人工智能营销系统领域具有丰富经验的参与者进行为期六周的半结构化访谈。研究采用主题分析方法, 旨在深入挖掘数据中显现的主要主题。

研究结果表明, 在营销领域使用人工智能可能引发一系列意外后果, 包括但不限于加强现有偏见、侵犯客户隐私、限制竞争以及操纵消费者行为。

本研究通过明确定义七个独特的主题, 并采用阿肖克模型进行基准比较, 为读者提供了一个结构化的视角, 以解释研究结果。所提出的框架具有独特之处, 可有效支持在跨足社会、技术和经济领域的预测营销中展开的伦理研究。

This paper aims to examine the integration of lateral transshipment and road vulnerability into the humanitarian relief chain in light of affected area priority to address equitable distribution and assess the impact of various parameters on the total average inflated distance traveled per relief item.

After identifying comprehensive critical criteria and subcriteria, a hybrid multi-criteria decision-making framework was applied to obtain the demand points’ weight and ranking in a real-life earthquake scenario. Direct shipment and lateral transshipment models were then presented and compared. The developed mathematical models are formulated as mixed-integer programming models, considering facility location, inventory prepositioning, road vulnerability and quantity of lateral transshipment.

The study found that the use of prioritization criteria and subcriteria, in conjunction with lateral transshipment and road vulnerability, resulted in a more equitable distribution of relief items by reducing the total average inflated distance traveled per relief item.

To the best of the authors’ knowledge, this study is one of the first research on equity in humanitarian response through prioritization of demand points. It also bridges the gap between two areas that are typically treated separately: multi-criteria decision-making and humanitarian logistics.

This is the first scholarly work in Shiraz focused on the equitable distribution system by prioritization of demand points and assigning relief items to them after the occurrence of a medium-scale earthquake scenario considering lateral transshipment in the upper echelon.

The paper clarifies how to prioritize demand points to promote equity in humanitarian logistics when the authors have faced multiple factors (i.e. location of relief distribution centers, inventory level, distance, lateral transshipment and road vulnerability) simultaneously.

The current study is designed to investigate the factors that foster the framing of destination competitiveness and establish the factors that drive the contribution of tourism innovations to economic growth in smart tourism destinations.

A four-year panel data were extracted from the World Economic Forum's travel and tourism competitiveness index and data were analysed using Poisson Pseudo Maximum Likelihood regression model.

The findings demonstrate that both the enabling environment and airport infrastructure significantly affect tourism's impact on the economy of the selected smart European tourism destinations. Conversely, human resources and general infrastructure display a negative correlation with tourism's contribution to the economy. However, no data in the sample support the idea that tourism policies, government prioritization or readiness of tourism information and communication technologies impact tourism's contribution to the economy. Additionally, the marginal effects indicate that improving the enabling environment and airport infrastructure can generate additional benefits for the economy through tourism.

The uniqueness of this study is the integration of smart tourism destinations with the measure of destination competitiveness to provide an empirical bridge that links tourism competitiveness to economic growth.

This paper adds to the ongoing debate on judgements, opportunity evaluation and founder identity theory and shows that founders vary in their prioritisation and combination of judgement criteria, linked to their respective social founder identity. It further reveals how this variation among founder identity types shapes their perception of distinct entrepreneurial opportunities and the forming of first-person opportunity beliefs.

This study uses a qualitative approach by presenting three business scenarios to a sample of 34 first-time founders. It adopts a first-person perspective on their cognitive processes during the evaluation of entrepreneurial opportunities using verbal protocol and content analysis techniques.

The theorised model highlights the use of similar categories of judgement criteria by individual founders during opportunity evaluation that followed two distinct stages, namely search and validation. Yet, founders individualised their judgement process through the prioritisation of different judgement criteria.

The authors provide new insights into how individuals individuate entrepreneurial opportunities through the choice of different judgement criteria that enable them to develop opportunity confidence during opportunity evaluation. The study also shows that first-time founders depict variations in their cognitive frames that are based on their social identity types as they assess opportunity-related information and elicit variations in reciprocal relationships emerging between emotion and cognition. Exposing these subjective cognitive evaluative processes provides theoretical and practical implications that are discussed as well.