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Due to advances in both software and hardware, obsolescence risk refers to the fear that a product will soon become obsolete, which can be very high for technological products such as smartwatches or smartphones. Drawing on the perceived risk theory and innovation resistance, this study examines the effects of different obsolescence risks on consumer resistance to smartwatches.

A sequential explanatory approach using a mixed method was adopted in this study. In Study 1, we conducted semi-structured and in-depth face-to-face interviews with 16 individuals to identify the most important obsolescence risks affecting consumers’ resistance to smartwatches. This qualitative study develops a novel theoretical model based on interpretive results, including technological, economic, functional, and aesthetic obsolescence risks. In Study 2, we tested our theoretical model by applying partial least-squares structural equation modeling to a sample of 298 smartwatch users.

The results show that consumer resistance to smartwatches is affected by technological, economic, functional, and aesthetic obsolescence risks.

Although most extant studies have focused on the factors influencing the adoption and use of consumer electronics, little is known about the role of obsolescence risk in consumers’ resistance to these products.

Until now there has been surprisingly little research on the causes of and the remedies for skills obsolescence. This study tries to fill that gap to some extent by analysing the relation between risk factors and skills obsolescence. Moreover, the role remedies play to counter skills obsolescence is analysed. Four empirical analyses that relate skills obsolescence to risk factors and remedies are presented. We find that most risk factors identified in the literature can be validated empirically. The remedies for skills obsolescence are not effective in all situations: the results show that there is considerable variation in the effectiveness of the remedies across different types of skills obsolescence. Although current available data do not allow a comprehensive analysis, which also takes account of relations between the various types of skills obsolescence, the results obtained are plausible and offer a starting point for further research.

Energy consumption has increased significantly since the 1970s, which has increased demand for sufficient infrastructure in the oil and gas industry. Many top-notch oil and gas companies invested in and equipped their facilities with high-capacity electrical equipment to meet high demand and benefit from high revenues. This is becoming a challenge nowadays for old facilities in the oil and gas industry, as most of the electrical equipment installed has reached or even exceeded its lifetime. Moreover, many of the original equipment manufacturers (OEMs) for electrical equipment from the 1980s are no longer in market today. Therefore, the aim of this study is to develop a proactive, cost-effective obsolescence management framework for electrical equipment in the oil and gas industry, considering the aging factor of the equipment.

Firstly, the study begins with gathering available information and identifying criteria. Secondly, the data collection is evaluated by subject-matter-experts (SMEs) in asset management field to ensure compliance with updated international standards and relevant regulatory requirements. Thirdly, a multi-criteria decision-making process is used to rank criteria. Finally, a scoring system is developed to measure the electrical equipment obsoleteness.

The developed framework will assist decision-makers in making informed decisions about maintenance, replacement or upgrades, using knowledge from previous studies and experts’ input. The result finding indicates that considering aging correction factors when measuring equipment obsoleteness leads to accurately and correctly predicting the electrical equipment obsoleteness score.

Previous studies have addressed obsolescence management without taking equipment age into account, regardless of how the equipment is performing. Thus, the lack of a comprehensive obsolescence management framework that accounts for both cost-effectiveness and the aging factor in the oil and gas industry poses a critical challenge.

This paper aims to highlight the expanding link between facility management (FM) and building automation and control systems (BACS) through a review of literature. It examines the opportunities and challenges of BACS for facility managers and proposes solutions for mitigating the risks associated with BACS implementation.

This paper reviews various research papers to explore the positive influences of BACS on FM, such as support with strategic decision-making, predictive maintenance, energy efficiency and comfort improvement. It also discusses the challenges of BACS, including obsolescence, interoperability, vendor lock-in, reliability and security risks and suggests potential solutions based on existing literature.

BACS offers numerous opportunities for facility managers, such as improved decision-making, energy efficiency and comfort levels in office buildings. However, there are also risks associated with BACS implementation, including obsolescence, interoperability, vendor lock-in, reliability and security risks. These risks can be mitigated through measures such as hardware and software obsolescence management plans, functional requirement lists, wireless communication protocols, advanced feedback systems and increased awareness about BACS security.

To the best of the authors’ knowledge, no prior academic research has been conducted on the expanding link between FM and BACS. Although some papers have touched upon the opportunities and challenges of BACS for FM, this paper aims to provide a comprehensive overview of these findings by consolidating existing literature.

Since technological lifecycles do not always match hardware/software (HW/SW) lifecycles, obsolescence becomes a major issue in system lifecycle management as it can cause premature and unscheduled replacement of HW/SW subsystems. The purpose of this paper is to report a dynamic model to predict the obsolescence dates for HW/SW subsystems.

The dynamic model estimates obsolescence dates for HW/SW subsystems based on graph theory concept. The model depicts the stages of subsystem obsolescence through transmittances composed of probability and time-distribution elements. The model predicts probability and mean time to obsolescence for line replaceable units (LRUs) over the lifetime of the system. An illustrative example in signaling systems used in a train control system was used to demonstrate the application of this model.

Generally, the short timespan for HW/SW subsystems, which are periodically replaced with newer technologies, results in the development of new product lines by suppliers while they try to support legacy systems for a reasonable period of time. Obsolescence of HW/SW subsystems increases operation and maintenance costs as legacy systems are typically more expensive to maintain. The costs can be reduced by an optimum time to obsolescence derived from the model.

This research adds to the body of knowledge on asset management and maintenance strategy. This paper may be of particular interest to reliability, maintainability and availability practitioners and project managers.

The originality of this paper lies in developing a graph-based model that predicts probability and mean time to obsolescence for LRUs over the lifetime of the system.

The purpose of this paper is to address the need for further development of tools that could be used to mitigate obsolescence within the built environment. Literature reviewed within this paper indicates a distinct gap in research, allowing for rising obsolescence-driven investments within asset systems. In addition to further conceptual development, case study testing is required to validate the use of certain existing methods.

This paper has developed a Boolean obsolescence assessment tool, which was then tested within a case study environment. This year-long case study provided real world data across three asset systems within an operational building.

The findings from this preliminary case study indicate that a Boolean tool of this type has the potential to provide significant insight into obsolescence mitigation. Such a tool, implemented in accordance with onsite asset management processes, has the ability to mitigate and avoid obsolescence-driven investments.

This case study is limited because of its length and size. To mitigate the effects that may have been captured, this research project has been developed and continued.

The model featured within this paper originated from an untested obsolescence indexing technique. This model was adapted and extended to improve its accuracy and functionality, which also involved adding weighting mechanisms, resulting in not only an original model but a novel set of results because of the current lack of explicit testing of similar models.

An industry which manufactures and sells products that are liable to failure (e.g. TV sets, tape recorders, washing machines or refrigerators), usually has a department for “service after sales”. Such a department must distribute information on how to repair products, and has to supply spare parts to replace defective components during a given number of years. We call these parts service parts. Three phases can be distinguished in the life‐cycle of service parts:

An industry which manufactures and sells products that are liable to failure (e.g. TV sets, tape recorders, washing machines or refrigerators), usually has a department for “service after‐sales”. Such a department must distribute information on how to repair products, and also supply spare parts to replace defective components during a given number of years. We call these spare parts service parts. Three phases can be distinguished in the life‐cycle of service parts: the initial, the repeat, and the final phase.