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The purpose of this paper is the development of an empirically based typology of condition based maintenance (CBM) approaches, including the relevant characteristics and requirements.

An exploratory case study was conducted in a major gas production facility. The CBM typology that resulted from this case study was subsequently tested against a large set of CBM literature.

In the literature, CBM is usually presented as a single theory or practice. The paper finds that CBM in fact includes several different approaches and that each of the approaches is only suitable in situations where the specific characteristics of the approach match the situational characteristics. Aided by these findings, a new typology for CBM was developed. The typology is based on the method for obtaining the expected value, or trend (through statistical vs analytical modeling) and the type of data used (process vs failure data). A subsequent literature survey reveals that the proposed typology is applicable for the categorization of a large number of CBM cases found in the literature.

One of the most important requirements in selecting and using a CBM approach is the availability and integration of various types of knowledge, in particular process engineering and maintenance engineering knowledge. Practitioners can use these insights to assess current CBM cases, and identify the key characteristics of current and future use of various CBM types.

This paper presents a novel and empirically based framework for the classification of the different CBM types. Such frameworks were lacking in the current literature. The paper adds to maintenance engineering literature by identifying the key dimensions of the various types along with their key requirements.

The purpose of this paper is to outline some key aspects such as material systems used, phenomenological and statistical process modeling, techniques applied to monitor the process and optimization approaches reported. All these need to be taken into account for the ongoing development of the SLM technique, particularly in health care applications. The outcomes from this review allow not only to summarize the main features of the process but also to collect a considerable amount of investigation effort so far achieved by the researcher community.

This paper reviews four significant areas of the selective laser melting (SLM) process of metallic systems within the scope of medical devices as follows: established and novel materials used, process modeling, process tracking and quality evaluation, and finally, the attempts for optimizing some process features such as surface roughness, porosity and mechanical properties. All the consulted literature has been highly detailed and discussed to understand the current and existing research gaps.

With this review, there is a prevailing need for further investigation on copper alloys, particularly when conformal cooling, antibacterial and antiviral properties are sought after. Moreover, artificial intelligence techniques for modeling and optimizing the SLM process parameters are still at a poor application level in this field. Furthermore, plenty of research work needs to be done to improve the existent online monitoring techniques.

This review is limited only to the materials, models, monitoring methods, and optimization approaches reported on the SLM process for metallic systems, particularly those found in the health care arena.

SLM is a widely used metal additive manufacturing process due to the possibility of elaborating complex and customized tridimensional parts or components. It is corroborated that SLM produces minimal amounts of waste and enables optimal designs that allow considerable environmental advantages and promotes sustainability.

The key perspectives about the applications of novel materials in the field of medicine are proposed.

The investigations about SLM contain an increasing amount of knowledge, motivated by the growing interest of the scientific community in this relatively young manufacturing process. This study can be seen as a compilation of relevant researches and findings in the field of the metal printing process.

The purpose of this paper is to measure the relationships between advanced manufacturing technologies (AMTs) categories (stand-alone, intermediated and integrated systems) implementation and design, process and commercial benefits obtained.

A survey is designed with benefits gained from AMT implementation as well as its categories, which is applied to the maquiladora industry. A structural equation model with data from 383 responses is used to measure the relationship between AMT categories and benefits gained using nine hypotheses that are tested statistically significant using partial least squares. Also, using conditional probabilities, a sensitivity analysis reports how low and high levels from AMT implementation influence on the obtained benefits.

Integrated systems are the most important AMT for maquiladoras and have the strongest impact on design, processes and commercial benefits.

Data obtained support the model, but results may be different in another industrial sector and countries with different labor culture and technological level.

Managers in maquiladora industry must focus their attention on integrated manufacturing systems, because high implementation levels guarantee the biggest probability to gain benefits in design, production process and commercial.

The relationship between AMT and their benefits has not been measured in depth, and this paper contributes to understand that problem. In addition, this paper is the first to report a sensitivity analysis that enables managers to acknowledge the probability of obtaining certain benefits.

Distinguishing phishing emails from legitimate emails continues to be a difficult task for most individuals. This study aims to investigate the psycholinguistic factors associated with deception in phishing email text and their effect on end-user ability to discriminate phishing emails from legitimate emails.

Email messages and end-user decisions collected from a laboratory phishing study were validated and analyzed using natural language processing methods (Linguistic Inquiry Word Count) and penalized regression models (LASSO and Elastic Net) to determine the linguistic dimensions that attackers may use in phishing emails to deceive end-users and measure the impact of such choices on end-user susceptibility to phishing.

We found that most participants, who played the role of a phisher in the study, chose to deceive their end-user targets by pretending to be a familiar individual and presenting time pressure or deadlines. Results show that use of words conveying certainty (e.g. always, never) and work-related features in the phishing messages predicted higher end-user vulnerability. On the contrary, use of words that convey achievement (e.g. earn, win) or reward (cash, money) in the phishing messages predicted lower end-user vulnerability because such features are usually observed in scam-like messages.

Insights from this research show that analyzing emails for psycholinguistic features associated with computer-mediated deception could be used to fine-tune and improve spam and phishing detection technologies. This research also informs the kinds of phishing attacks that must be prioritized in antiphishing training programs.

Applying natural language processing and statistical modeling methods to analyze results from a laboratory phishing experiment to understand deception from both attacker and end-user is novel. Furthermore, results from this work advance our understanding of the linguistic factors associated with deception in phishing email text and its impact on end-user susceptibility.

Dynamic capabilities (DCs) need renewing to respond to changes emerging in the environment, and organizations must build up their capacities to sustain good performance levels. This study aims to identifying and characterizing the DCs existing in public health sector organizations by surveying the DC-related areas in health organizations, contributing to broader and more systematized knowledge in this field.

The authors sent a questionnaire to 245 professionals with leadership and management positions in healthcare organizations in this study. The authors used multivariate methods to validate the variables used to measure the DCs.

In addition to highlighting the impact of DCs on public health organizations' performance, the study’s results allowed the authors to identify hidden capacities in the organizations of this sector, which only emerge when resource management difficulties occur. These changes and difficulties may interact with users and/or professionals' needs and make organizational management a particular challenge aggravated by quick responses to ensure the organization's survival.

This study contributes to the literature's call for a deeper understanding of the role of DCs and contribute to a greater practical understanding of how these capabilities influence the performance of such organizations.

The purpose of this paper is to perform a comparative analysis between the productivity metrics recommended in the literature and those that companies in the knowledge-intensive services sector use in practice.

To collect information, a systematic review of the literature was used, to apply virtual surveys and interviews among managers of different companies representing the sector. For data analysis, categorical optimal scales, homogeneity tests, tetrachoric correlation matrices, word clouds and association coefficients for dichotomous variables were used.

There are association patterns between the metrics used and the nature of the work performed. Despite the heterogeneity observed in the productivity metrics, categorization guidelines related to the traditional, human resources and customer-oriented approaches emerge.

Possible neglects using metrics aimed at valuing the intellectual capital immersed in human resources are evident, particularly in the follow-up to autonomy, knowledge management, human capital, teamwork, training and capacity building metrics, among others. Conversely, face-to-face monitoring metrics, such as absenteeism, are overvaluation.

The approaches and metrics discussed and the results obtained, provide information so that knowledge-intensive companies have a reference framework to identify and select useful metrics to assess the work carried out by their workforce.

This paper presents the UE‐Eurostat methodology to design and implement a system of surveys of inbound tourism. It has been thought for the homogeneisation of the information systems of the EU countries and for the development of the cooperation in the field of tourism statistics between the European Union and other relevant extra‐Europe regions. Furthermore, the methodology wants to give a common framework of analysis for the collection and processing of comparable statistics at each territorial level. The general research process has been split up into nine fundamental steps which represent the stages a researcher has to follow in order to obtain reliable data on visitor flows, visitors' and trip characteristics, consumption behaviour and opinions and impressions on the trip and the visit. It has been implemented for closed areas (areas with political borders, e.g. a country, geographical borders, e.g. a island, artificial borders, e.g. a museum, a theme park, an archaeological area) and open areas (a macro‐region, a region, a single tourist destination, e.g. a city, or tourist site, e.g. a square).

The purpose of this paper is to develop a model that incorporates the effect of rejection cost on optimal maintenance planning decisions. Such a model will help in further modelling the interrelationships between preventive maintenance and quality control policy.

In this paper a model is developed for obtaining optimal preventive maintenance interval based on block replacement policy to incorporate the effect of rejection cost. An illustrative example is presented to compare economic performance of the proposed model (M2) and the conventional model (M1). Model M1 stands for optimal preventive maintenance interval without considering the rejection cost and model M2 stands for optimal preventive maintenance interval considering the rejection cost. The comparison is done for different production rates, costs of rejection and cost of lost production. The impact of control chart parameters on preventive maintenance decision is also studied.

In this paper it is found that model M2 gives better results as compared to model M1. The improvements are more significant at higher production rate, lower cost of lost production and higher rejection cost. The impact of control chart parameters on preventive maintenance planning decision becomes significant as the cost of rejection increases.

Conventionally only the down time cost and the cost of repair/replacement are considered for optimal maintenance interval determination. However in the case of machine tools, failure may not always bring the system immediately under complete breakdown but may lead to the functioning of system with degraded performance like process shift from in‐control state to out‐of‐control state. It results into poor quality and thus may lead to higher rejection cost. The cost of rejections may be significantly high in some production systems and, if not incorporated properly during maintenance planning decision may adversely affect the effectiveness of the maintenance planning. Hence the approach presented in this paper gives a better way of maintenance planning. Though the work presented here is illustrated through a simple example considering a single component operating as a part of machine, the approach can be extended to multi‐component system.

The outcome is of significant value as it opens up a new perspective into the development of integrated model for maintenance planning and quality control decisions for reducing the operating costs associated with the manufacturing processes.

To support ongoing industry efforts to reduce the cost of energy (CoE) of offshore wind compared to other types of energy sources, researchers are applying scientific models and thought processes to identify potential areas of improvement and optimization. This paper aims to introduce a conceptual framework from a supply chain management (SCM) perspective, aimed at promoting the reduction of CoE in the offshore wind energy industry.

Using conceptual arguments from current academic literature in SCM, a comprehensive framework is presented that clarifies how SCM practices can be used by offshore wind energy industry to reduce CoE.

The offshore wind energy sector is a young industry that must reduce CoE to compete with other forms of energy. Applying a supply chain perspective in the offshore wind industry has hitherto been limited to the academic community. This paper offers a SCM framework that includes three interdependent aspects of reducing CoE – innovation, industrialization and supplier partnering – to guide the industry towards sources to reduce CoE.

SCM is a broad research area; thus, the presented framework to reduce the CoE is open for further development.

The paper provides insights into how the CoE can be reduced through innovation, industrialization and partnering in the offshore wind energy supply chain.

The paper offers a seminal contribution by introducing a SCM framework to understand sources and approaches to reduce CoE in the offshore wind energy industry.