Does risk matter? Disengagement from risk management practices in information systems projects

Risk management and ‘routine-based reliability’ is considered fundamental to project performance. Existing theories of project risk management do not fully explain why project managers stop practicing risk management information systems (IS); however, constructs drawn from organisation theory offer insights into how and why such disengagement occurs. The study examines risk management practices in 21 IS projects within 10 organisations. By focusing on risks that resulted in significant events and mapping backwards over time the practices associated with those risks, we identify that in all but five projects the manager had disengaged from prescribed risk management before executing risk responses. In most projects, the majority of formally identified and assessed risks remained unallocated and untreated. A laddering technique was used to help explain why this transpired. We found five key underlying beliefs that governed project managers’ risk management attitudes and actions.     

Exploring the factors affecting myopic drivers’ driving skills and risk perception in nighttime driving

The aim of this study was to investigate how various factors affect myopic drivers’ nighttime driving skills and nighttime risk perception. A total of 400 myopic drivers and 100 non-myopic drivers participated in the study. The participants were asked to complete a questionnaire that included demographic information, a quality of life scale, a nighttime driving skills scale, and a nighttime risk perception scale. To explore the correlations and differences among demographic information, quality of life, myopic drivers’ nighttime driving skills and nighttime risk perceptions, bivariate correlation analysis and analyses of covariance were conducted on data from 364 valid myopic drivers’ questionnaires and 81 valid non-myopic drivers’ questionnaires. Moreover, a structural equation model was built to explore the predictors of myopic drivers’ driving skills and risk perception at night and to measure the relationships among various factors. The results indicate that non-myopic drivers reported a lower level of nighttime risk perception than myopic drivers, whereas their nighttime driving skill was significantly higher than that of myopic drivers. Female myopic drivers reported significantly higher risk perception and lower driving ability than males. Additionally, drivers with at least a bachelor degree had higher nighttime risk perception than drivers without a college degree. As the degree of myopia increased, nighttime driving ability decreased, and nighttime risk perception improved. Myopic drivers’ quality of life was the strongest predictor of nighttime driving skills and nighttime risk perception. These findings suggest that further studies of myopic drivers’ driving behaviors should not only consider their visual characteristics, but also explore the relationship between their quality of life and driving behaviors, especially for highly myopic drivers.

     

Knowledge discovery using neural approach for SME’s credit risk analysis problem in Turkey

This study proposes a knowledge discovery method that uses multilayer perceptron (MLP) based neural rule extraction (NRE) approach for credit risk analysis (CRA) of real-life small and medium enterprises (SMEs) in Turkey. A feature selection and extraction stage is followed by neural classification that produces accurate rule sets. In the first stage, the feature selection is achieved by decision tree (DT), recursive feature extraction with support vector machines (RFE-SVM) methods and the feature extraction is performed by factor analysis (FA), principal component analysis (PCA) methods. It is observed that the RFE-SVM approach gave the best result in terms of classification accuracy and minimal input dimension. Among various classifiers k-NN, MLP and SVM are compared in classification experiments. Then, the Continuous/Discrete Rule Extractor via Decision Tree Induction (CRED) algorithm is used to extract rules from the hidden units of a MLP for knowledge discovery. Here, the MLP makes a decision for customers as being “good” or “bad” and reveals the rules obtained at the final decision. In the experiments, Turkish SME database has 512 samples. The proposed approach validates the claim that is a viable alternative to other methods for knowledge discovery.     

A question of risk?

Deploying new technology can be fraught with problems. The key to a successful rollout is to ensure that cost implications are fully understood and that all stakeholders' interests are promoted through the card scheme.     

Beyond human error taxonomies in assessment of risk in sociotechnical systems: a new paradigm with the EAST ‘broken-links’ approach

Abstract

Risk assessments in Sociotechnical Systems (STS) tend to be based on error taxonomies, yet the term ‘human error’ does not sit easily with STS theories and concepts. A new break-link approach was proposed as an alternative risk assessment paradigm to reveal the effect of information communication failures between agents and tasks on the entire STS. A case study of the training of a Royal Navy crew detecting a low flying Hawk (simulating a sea-skimming missile) is presented using EAST to model the Hawk-Frigate STS in terms of social, information and task networks. By breaking 19 social links and 12 task links, 137 potential risks were identified. Discoveries included revealing the effect of risk moving around the system; reducing the risks to the Hawk increased the risks to the Frigate. Future research should examine the effects of compounded information communication failures on STS performance.

Practitioner Summary: The paper presents a step-by-step walk-through of EAST to show how it can be used for risk assessment in sociotechnical systems. The ‘broken-links’ method takes a systemic, rather than taxonomic, approach to identify information communication failures in social and task networks.     

The mediating role of the dimensions of the perceived risk in the effect of customers’ awareness on the adoption of Internet banking in Iran

One of the major issues banks are faced with in providing Internet Banking (IB) services is the adoption of these services by the customers. This study seeks answer to the question that whether bank customers’ awareness of the services and advantages of IB is effective in reducing the negative effect of customers’ perceived risk on their intention of IB adoption. To this end, the two constructs of the dimensions of the perceived risk and IB awareness are simultaneously considered. Besides, in the research model, the effect of IB awareness on each dimension of the perceived risk and the effect of these dimensions on intention of IB adoption by the customers are investigated. The results indicate that IB awareness acts as a factor reducing all dimensions of the perceived risk (including time, financial, performance, social, security, and privacy). In addition, it was found out that except for social risk, other dimensions of the perceived risk have significantly negative effect on the intention of IB adoption. Finally, proving the direct and positive effect of IB awareness on adoption intention, it was concluded that the dimensions of customers’ perceived risk plays a mediating role in the positive effect of IB awareness on IB adoption intention. In this respect, management approaches centered on the concept of IB awareness are offered for reducing the dimensions of customers’ perceived risk.     

Skewing users’ rational risk considerations in social commerce: An empirical examination of the role of social identification

Social commerce has emerged as a new platform that enables users to conduct shopping assisted by inputs from other members and to publicly comment on transactions or products. It therefore adds a social aspect to traditional online commerce environments. Nevertheless, the role of the social facet embedded in such transactions in influencing user behaviors is not fully understood. In this study, we rely on theories of risk deterrence in decision-making and the “risky/choice shift” logic to suggest that the social identification of users regarding their community members skews the way they consider risks in decision-making on these sites. Using data from 175 users of etsy.com, we show that perceived commerce risk reduces intentions to buy from the website and that perceived participation risk curtails intentions to post comments on social commerce forums. The findings further show that the influence of these risk assessments is reduced when the degree of social identification with the website community increases; these risk considerations become negligible in decision-making processes when ’social identification is one standard deviation above the mean. Hence, users’ social identification with the social commerce website community skews their rational decision-making.     

An immunity based network security risk estimation

There are two kinds of risk-estimation methods for the network security: static and real-time. The static methods estimate the network risk through statically evaluating the network value, security holes, and the occurring frequency of security events[1], e.g., COBRA1), OCTAVE[2], etc. Focusing on the static factors of the target system, the static methods can only make a rough estimation of the security risk that the network faced in the past, and cannot in real-time evaluate the risk of …     

Consumers’ perceived risk: sources versus consequences

In this study, we examine an important factor that affects consumers’ acceptance of business-to-commerce (B2C) electronic commerce—perceived risk. The objective of this paper is to examine the definition of perceived risk in the context of B2C electronic commerce. The paper highlights the importance of perceived risk and the interwoven relation between perceived risk and trust. It discusses the problem of defining perceived risk in prior B2C research. This study proposes a new classification of consumers’ perceived risk based on sources. It highlights the importance of identifying the sources of consumer’s risk perceptions in addition to the consequences dimensions. Two focus group discussion sessions were conducted to verify the proposed classification. Results indicate that Internet consumers perceive three sources of risk in B2C electronic commerce: technology, vendor, and product.     

Does body mass index increase the risk of low back pain in a population exposed to whole body vibration?

The aim of this study was to determine whether body mass index (BMI) influences the risk of low back pain (LBP) in a population exposed to whole body vibration (WBV). For this a self-administered questionnaire was sent to 467 participants, driving occupational vehicles. Vibration measurements were performed according to ISO 2631-1 on a representative sample (n=30) of this population. For each participant, we calculated the current root mean square (r.m.s.) over an 8 h (A(8)) working day. The questionnaire response rate was 47% (n=221). We did not find a significant correlation between BMI and the onset of LBP in the last 7 days (r=0.07, p=0.34) nor for LBP in past 12 months (r=-0.30, p=0.63). No significant increased risk was found for the onset of LBP with the increase of BMI, neither for the last 7 days (OR 1.02; 95% CI: 0.93-1.23) nor for the past 12 months LBP (OR 0.98; 95% CI: 0.89-1.09). Introducing the interaction with WBV exposure in the logistic regression model, did not result a significant increased risk in the onset of LBP-7 days (OR 0.97; 95% CI: 0.92-1.01) nor in the onset of LBP 12 months (OR 0.97; 95% CI: 0.93-1.01) either. Occupational participants exposed to WBV, with a high BMI do not have an increased risk for the development of LBP.     

Comparison of individual,ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance

Supply chain finance (SCF) becomes more important for small- and medium-sized enterprises (SMEs) due to global credit crunch, supply chain financing woes and tightening credit criteria for corporate lending. Currently, predicting SME credit risk is significant for guaranteeing SCF in smooth operation. In this paper, we apply six methods, i.e., one individual machine learning (IML, i.e., decision tree) method, three ensemble machine learning methods [EML, i.e., bagging, boosting, and random subspace (RS)], and two integrated ensemble machine learning methods (IEML, i.e., RS–boosting and multi-boosting), to predict SMEs credit risk in SCF and compare the effectiveness and feasibility of six methods. In the experiment, we choose the quarterly financial and non-financial data of 48 listed SMEs from Small and Medium Enterprise Board of Shenzhen Stock Exchange, six listed core enterprises (CEs) from Shanghai Stock Exchange and three listed CEs from Shenzhen Stock Exchange during the period of 2012–2013 as the empirical samples. Experimental results reveal that the IEML methods acquire better performance than IML and EML method. In particular, RS–boosting is the best method to predict SMEs credit risk among six methods.

     

An integrated AHP–DEA methodology for bridge risk assessment

The traditional analytic hierarchy process (AHP) method can only compare a very limited number of decision alternatives, which is usually not more than 15. When there are hundreds or thousands of alternatives to be compared, the pairwise comparison manner provided by the traditional AHP is obviously infeasible. In this paper we propose an integrated AHP–DEA methodology to evaluate bridge risks of hundreds or thousands of bridge structures, based on which the maintenance priorities of the bridge structures can be decided. The proposed AHP–DEA methodology uses the AHP to determine the weights of criteria, linguistic terms such as High, Medium, Low and None to assess bridge risks under each criterion, the data envelopment analysis (DEA) method to determine the values of the linguistic terms, and the simple additive weighting (SAW) method to aggregate bridge risks under different criteria into an overall risk score for each bridge structure. The integrated AHP–DEA methodology is applicable to any number of decision alternatives and is illustrated with a numerical example.     

Applying forensic techniques to information system risk management – first steps

Over the past 15 months we have been discussing End-to-End Digital Investigation (EEDI) and how we can use structured approaches to system modeling to help solve security incidents and perform incident post mortems. Our next task is to apply some of these techniques to information security and risk management.     

Why do drivers maintain short headways in fog? A driving-simulator study evaluating feeling of risk and lateral control during automated and manual car following

Drivers in fog tend to maintain short headways, but the reasons behind this phenomenon are not well understood. This study evaluated the effect of headway on lateral control and feeling of risk in both foggy and clear conditions. Twenty-seven participants completed four sessions in a driving simulator: clear automated (CA), clear manual (CM), fog automated (FA) and fog manual (FM). In CM and FM, the drivers used the steering wheel, throttle and brake pedals. In CA and FA, a controller regulated the distance to the lead car, and the driver only had to steer. Drivers indicated how much risk they felt on a touchscreen. Consistent with our hypothesis, feeling of risk and steering activity were elevated when the lead car was not visible. These results might explain why drivers adopt short headways in fog. Practitioner Summary: Fog poses a serious road safety hazard. Our driving-simulator study provides the first experimental evidence to explain the role of risk-feeling and lateral control in headway reduction. These results are valuable for devising effective driver assistance and support systems.     

Statmap—a 3-dimensional model for oil spill risk assessment

Stochastic oil spill models have traditionally focussed on the production of statistics for oil on the sea surface and along shorelines. Environmental risk analyses of potential spills therefore have tended to neglect potential risks to organisms in the water column. StatMap fills this void, supplying in addition statistics on the spatial distribution of hydrocarbon concentrations in the water column. This paper describes the model concepts and provides examples of model input and output.     

A stochastic model for forward–reverse logistics network design under risk

Attention with reverse logistics networks has increased during the last decade since their economic impact has been increasingly important and as environmental legislation has been becoming stricter. In this paper, A multi-period multi-echelon forward–reverse logistics network design under risk model is developed. The proposed network structure consists of three echelons in the forward direction, (suppliers, facilities and distribution centers) and two echelons, in the reverse direction (disassembly, and redistribution centers), first customer zones in which the demands are stochastic and second customer zones in which the demand is assumed to be deterministic, but it may also assumed to be stochastic. The problem is formulated in a stochastic mixed integer linear programming (SMILP) decision making form as a multi-stage stochastic program. The objective is to maximize the total expected profit.     

Connectivity and the measurement of operational risk: an input–output approach

In this article I propose a framework for the analysis of the interdependencies within a financial institution that is based on the input–output framework originally developed by Leontiev (1941). After discussing the state of the art of operational risk measurement, I briefly review the foundations of input–output analysis and explain how to build an input–output model at the business unit level for a financial institution. I also discuss the suitability of an input–output model in capturing the impact on operational risk losses of the interdependencies within a financial institution and then present, through some numerical examples, how to implement the model within a quantitative framework for the measurement of operational risk. In Ersilia, to establish the relationships that sustain the city's life, the inhabitants stretch strings from the corners of the houses, white or black or gray or black-and-white according to whether they mark a relationship of blood, of trade, authority, agency. When the strings become so numerous that you can no longer pass among them, the inhabitants leave: the houses are dismantled; only the strings and their supports remain. From a mountainside, camping with their household goods, Ersilia's refugees look at the labyrinth of taut strings and poles that rise in the plain. That is the city of Ersilia still, and they are nothing. Italo Calvino, Invisible Cities (1972)     

Overall availability and risk analysis of a general robot–safety device system

We analyse the availability of a general robot–safety device system characterised by the feature of cold standby and by an admissible risky state. In contrast to the previous literature, we allow a general failure-free time distribution for the robot and, as an example, we present computational results for Coxian failure and repair time distributions. In order to decide whether the risky state is admissible, we introduce a risk criterion based on the notion of rare events. The criterion is always satisfied in the case of fast repair.     

Do you know your customer? Bank risk assessment based on machine learning

Know Your Customer (KYC) data can serve as a valuable risk assessment tool for banks by providing information that can identify customers who are more likely to default on a loan. This study aims to provide an accurate risk assessment tool using unique KYC data and machine-learning techniques to overcome problems in existing risk detection methods. This study proposes that the bank branch is the best level at which to determine the degree of default risk, and can also provide insight into patterns of suspicious transactions. Bank managers and regulators can focus on suspicious behavior at specific branches to increase overall compliance and reduce the risk of illegal activity.