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We review what is known and what is still unknown about the process of revealing the impact of unreliability on travel choices. We do this from the perspective of a demand-modelling practitioner who wishes to allow for the benefits from improved reliability in the assessment of a transport scheme. We discuss the travel responses affected by unreliability, the requirements from the data used to model these responses, the explanatory variables used in these models and the additional information required as input when applying them. One of our findings is that there is a conflict between existing studies in their conclusions about the aversion to early arrival. Another notion is that it is unclear whether the common simplified treatment of the distribution of preferred arrival times is acceptable. We also suggest that the dominance of departure time shifting as a primary response to unreliability might refute the common assumptions about travellers' choice hierarchy, which was established without considering the impact of unreliability; this raises questions about the robustness of assignment models that do not allow time shifting.

The purpose of this paper is to enhance trust in e-commerce multi-agent systems by presenting a model, called RUU, to select the most trustworthy provider agent based on learning from previous interactions and computing reliability, unreliability and uncertainty.

The methodology comprises analyzing the most representative existing trust models, while a new concept was proposed and measured as unreliability. To make decision about the agents, RUU integrated reliability, unreliability and uncertainty components and used the TOPSIS multi-criteria decision method to select the most trustworthy provider agent. To evaluate the RUU model, the experimentation was carried out in two stages. First, the average accuracy of the model was investigated by simulating RUU in a multi-agent environment. Second, the performance of the model was compared with other related trust models.

The experimental results revealed that RUU model outperforms current models in providing accurate credibility measurements and computing an accurate trust mechanism for agents, also presenting a decision-making process to choose the most trustworthy provider agent.

The model presented based on different mathematical computations that take time to be calculated, which is a big limitation of computational models.

RUU enables an agent to make effective and sound decisions in light of the uncertainty that exists in e-commerce multi-agent environments.

This paper is beneficial to enhance the fulfilment of purchasing between provider and requester agents. In fact, the proposed model can ensure critical transactions performed securely in e-commerce multi-agent environments.

This study focuses on unintended negative consequences of IT, called technostress. Given that employees are recognized as a major information security threat, it makes sense to investigate how technostress resulting from employees' constant interaction with IT influences the likelihood of security incidents. Although past research studied the concept of security-related technostress, the effect of IT use itself on employees’ extra-role activities such as security-related behaviors is unanswered. Thus, this paper aims to provide an understanding of the negative impact of technostress on employee information security policy (ISP) compliance.

Drawing on technostress literature, this research develops a research model that investigates the effect of technostress on employee intention to violate ISPs. It also extends the dimensionality of technostress construct by adding a new dimension called “techno-unreliability” that shows promising results. The authors use online survey data from a sample of 356 employees who have technology-based professions. We apply the structural equation modeling technique to evaluate the proposed research model.

Findings showed that IT use imposes high-level perceptions of a set of technostress creators, which makes users rationalize their ISP violations and engage in non-compliant behaviors. Further analysis of each dimension of technostress showed that techno-complexity, techno-invasion and techno-insecurity account for higher ISP non-compliant behaviors.

This study provides a new understanding of technostress to the context of information security and emphasizes on its negative impact on employee ISP compliance behaviors.

Availability of military systems is of major concern for military planners at both tactical (battle) level and at strategic level (long‐term national planning). Availability factors critically affect the operational effectiveness during military operations. Military systems are complex and lend themselves to simulation approach for availability estimation as analytical solutions are extremely difficult. The purpose of this paper is to discuss the method of systems modeling to approach the simulation for availability estimation of military systems.

Availability measures are needed for two main domains of application: peacetime operations and battlefield situations. Availability measures include not only inherent availability of interest to designers/manufacturers, but also operational availability and field/service availability. The simulation approach adopted here involves discrete event simulation (DES) techniques using Monte Carlo methods since a network of events can be included in the model. A system engineering approach is emphasized, starting with system representation and characterisation, and using system aggregation techniques.

Modeling involves hierarchical models and network diagrams for events. First the system is described by a hierarchical model; the events and transitions are represented with state transition diagrams (STD). The simulation scheme would be based on initial resources or inventory as military operations proceed, with random variates for event times or rates. The availability as a function of time A(t) is arrived at. The reliability and maintainability models are simulated with probability distributions or using empirical distributions. The methods of data collection and analysis, and sensitivity analysis are mentioned. The methodology is explained with two case studies from the authors' work. The approaches of other workers in recent years are summarised.

The paper shows that the simulation models can suitably be modified to include their applications for army and navy military operations. Also, with proper data on all major subsystems of interest for the weapon platform and accurate past war data, it is possible to fine‐tune the models for online use during military campaigns. The availability figures thus obtained may also be used for procurement decisions for long‐term and strategic planning.

The running cost of over 300 PCs has been studied at Rolls‐Royce and Associates. The company helpdesk system was employed to provide detail of the frequency and nature of the PC component failure and the time taken for the repairs. The cost of unreliability was then calculated as the cost of replacing each faulty component plus the cost of unavailability while waiting for the repair. It was found that for most PC components there is a higher probability of failure, leading to a higher cost of unreliability, in the early part of life. The conclusion is drawn that bulk purchase of PCs will lead to higher running costs in the initial period and for many organisations a rolling replacement policy would spread costs more evenly. The helpdesk was found to be an excellent source of information for improving the quality of maintenance procedures.

The aim of this paper is to permit system reliability analysts/managers/practitioners/engineers to analyse system failure behaviour more consistently and plan suitable maintenance actions accordingly.

The paper adopted three important tools, namely, root cause analysis (RCA), failure mode effect analysis (FMEA), and non‐homogeneous Poisson point process (NHPPP), to build an integrated and helpful framework, able to facilitate the maintenance managers in decision making. The factors contributing to system unreliability were analysed using RCA and FMEA. The failure data related to the components are modelled using NHPPP models and are used to optimise maintenance decisions (repair or replacements) based on cost dimensions.

The paper finds that the in‐depth analysis of a system using RCA and FMEA helps to create a knowledge base to deal with problems related to process/product unreliability. From the results it is observed that NHPPP models adequately analyse time‐dependent rate of occurrence of failures. Thus, assisting the maintenance analyst in development of suitable maintenance strategy by properly understanding the mechanism of failure (through modeling of failure data); adopting adequate aging management actions (such as predictive or periodic testing) to predict or detect the degradation of components; and performing cost analysis.

The contemporaneous adoption of the three proposed techniques for failure analysis will help system reliability engineers/managers/practitioners not only to understand the failure behaviour of component(s) in the system, but also to plan/adapt suitable maintenance practices to improve system reliability and availability.

Maintaining a high level of availability of weapon systems during battles becomes important from the point of view of winning the battle. Due to attrition factors (failure due to battle damage and unreliability) and logistic delays in the repair process, maintaining the required level of availability is difficult. In this paper, we develop a simulation model for availability of fighter aircraft considering multiple failures causing system failure and logistic delays in the repair process. The methodology is based on discrete event simulation using Monte Carlo techniques. The failure time distribution (Weibull) and the repair time distribution (exponential) for the considered subsystems of the aircraft and the logistic delay time distribution (log‐normal) for the logistic factors spares, crew and equipment were chosen with suitable parameters. The results indicate the pronounced decrease in availability (as low as less than 10 per cent in some cases) due to multiple failures and logistic delays. The results are, however, highly sensitive to a combination of reliability, maintainability and logistic delay parameters.

The purpose of this study is to examine the effect of individual components' reliability on a system's reliability. The system refers to the Financial Management Information System (FMIS), the US Federal Highway Administration's (FHWA) web‐based project approval and tracking software. Its components are 61 project information fields.

The analysis would view each highway project‐funding request as an activity with cycle‐dependent performance for which success probability can be calculated as Reliability, R. The reliability analysis of the 61 FMIS fields results in a series system with R_sys the “estimated reliability” of finding “true” values in all 61 information fields during one highway‐related project funding authorization review.

Of an estimated 200 projects approved, there was previously estimated a 50 percent to 80 percent unreliability rate, while the study found an unreliability rate of approximately 80 percent.

Owing to the nature of federal government software, data can be very difficult to acquire in this working environment, but a simple calculation was relatively successful in confirming the “estimated reliability” of finding “true” values and showing how the reliability could dramatically decrease.

The paper contributes to the applicability of reliability analysis to project approval software, showing the progression from estimated data to bounding the estimate using reliability theory.

Easily employable quantitative supply chain complexity (SCC) measures considering the significant dimensions of complexity as well as the drivers that represent those dimensions are limited in the literature. The purpose of this paper is to propose an integrated interpretive structural modeling (ISM) and a graph-theoretic approach to quantify SCC by a single numerical index considering the interdependence and the inheritance of the SCC drivers.

In total, 18 SCC drivers identified from the literature are clustered according to the significant dimensions of complexity. The interdependencies established through ISM and inheritance values of SCC drivers are mapped into a Variable Permanent Matrix (VPM). The permanent function of this VPM is then computed and the resulting single numerical index is the measure of SCC.

A scale is proposed by computing the minimum and maximum threshold values of SCC with the help of expert opinions of the Indian automotive industry. The complexity of commercial and passenger vehicle sectors within the automotive industry is measured and compared using the proposed scale. From the results, it is identified that the number of suppliers, increase in spare-parts due to shortened product life-cycle and demand uncertainties increase the SCC of the passenger vehicle sector, while number of parts, products and processes, variety of products and process and unreliability of suppliers increase the complexity of the commercial vehicle sector. The result indicates that various SCC drivers have a different impact on determining the SCC level of these two sectors.

The authors propose an integrated method that can be readily applied to measure and quantify SCC considering the significant dimensions of complexity as well as the interdependence and the inheritance of the SCC drivers that contribute to those dimensions. This index further helps to compare the complexity of the supply chain which varies between industries.

The purpose of this paper is to study the following research question: how can information systems (ISs) be the real sources of uncertainty for supply chain management (SCM), despite their positive roles in the effectiveness of modern supply chain (SC) organization?

A typology of the uncertainties linked to ISs and their usage is developed. Then, five explanatory case studies conducted in diverse industries allow to characterize this typology.

The case studies allow to identify the most frequently recurring uncertainties, namely, “limited IS capacity” and “data unreliability.” In addition, “company size” does not seem to be a relevant dimension for assessing the vulnerability of a SC to uncertainties linked to ISs. On the other hand, the “business sector,” with its implied power influences, was found to be a relevant dimension.

This research does not allow to rank uncertainties according to their level of criticality.

This paper enables SC and information systems managers, as well as ISs/SC project managers to be aware of the need to consider ISs not only as the vectors for performance, but also as the factors for uncertainty in SCs.

ISs are generally considered to be a vector of performance. Nevertheless, their use remains problematic in many companies. This paper falls within both the ISs and SCM research areas. In the latter field, typologies of the uncertainties are common, but often ignore the uncertainties linked to ISs and their usage. The authors suggest envisaging ISs as the potential factors for uncertainty in the context of SCM.