Search results

The purpose of this paper is to present an overview of the concept of early warning signs in projects and explain how a performance measurement system can be utilized as a source of data for an early warning approach signaling that a project is about to experience problems at some stage in the future.

Combination of action research and semi‐structured interviews and document analysis supplemented by a post‐mortem analysis after project close‐out.

Detection of early warning signals in projects can be better enabled through the application of a performance measurement system with properly defined key performance indicators. Utilization of this tool can positively affect the overall success of the project.

The case study involved only one project from the oil and gas industry.

The empirical case study was developed to illustrate the usefulness of exploiting a performance measurement system in a project. A procedure was demonstrated for developing and implementing an early warning system based on performance measurement, and specific performance indicators have been described for other projects to copy.

This paper highlights the gap in the literature concerning the link between early warning and project management and the link between early warning and performance measurement. It offers a new idea on how performance measurement can be used as an effective early warning system and is intended to be primarily of use to project management practitioners and practically‐oriented academics who are interested in developing fresh insights into new approaches for better management of projects.

This paper aims to explore the concept of early warning signs (EWSs) in offshore-outsourced software development (OOSD) projects at the team level. It also aims to identify the EWSs of failure in the onshore-offshore project context and understand how they are perceived by responsible managers.

A grounded theory approach is followed by gathering data from 19 failed OOSD projects using project managers from client and vendor sides as the key informants.

This study identified 13 EWSs of failure in five categories of trust and team cohesion, common project execution structures, awareness of shared work context, collaboration between teams and onshore-offshore team coordination capabilities. EWSs were found to comprise two components: early warning issues and early signals of failures.

India-based vendors’ data in the study formed the primary weakness of the work regarding generalizability, even though it brought homogeneity to data. Lack of triangulation of failure data through client or vendor peers proved impossible in this research as failure remains a very sensitive topic. Dual composition of EWSs could be applied to institutionalize an early warning tool in projects.

The paper develops an exploratory model of EWSs of failure and project failure in the OOSD project context. The two-component framework of EWSs allows project managers to eliminate false positives while identifying EWSs. It contributes to the information system failure, risk management and information technology offshoring research streams.

This paper presents a model for predicting crises in small businesses using early‐warning signals. It summarises the results of two separate studies carried out in Finland (with 72 per cent response) and the UK (26 per cent) on the decision process of corporate analysts (Finland) and bank managers (UK) in predicting the failure of small and medium‐sized enterprises (SMEs). Both studies consist of seven main headings and over 40 sub‐headings of possible factors leading to failure. Weighted averages were used for both studies to show the importance of these factors. There are significant similarities in the results of the two studies. Management incompetence was regarded as the most important factor, followed by deficiencies in the accounting system and attitude towards customers. However, low accounting staff morale was considered a very important factor in Finland but not in the UK. Unlike Finland, the UK results emphasised the importance of accounting systems and internal control. These two studies differ from previous studies as managerial auditing elements like the importance of internal control departments (UK evidence) and budgetary control systems were included. Similarities in the results of these surveys conducted under two separate EU environments imply that it would be interesting and beneficial to extend these studies to other member states.

The study aims to expand on the concept of an early warning system (EWS) by introducing weak-signal detection, human-in-the-loop (HIL) verification and response tuning as integral parts of an EWS's design.

The authors bibliographically highlight the evolution of EWS over the last 30+ years, discuss instances of EWSs in various types of organizations and industries and highlight limitations of current systems.

Proposed system to be used in the transforming of weak signals to early warnings and associated weak/strong responses.

The authors contribute to existing literature by presenting (1) novel approaches to dealing with some of the well-known issues associated with contemporary EWS and (2) an event-agnostic heuristic for dealing with weak signals.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0513.

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models, presents the results and compares them with probit model results. Finds the ANN models generally at least as good as the probit, although both types improved their accuracy rates (for Type I and Type II errors) when early warning signals were included. Believes ANN “is a promising technique” although more research is required, and suggests some avenues for this.

The purpose of this paper is to research the impact of foreign direct investment (FDI) on China's economic growth, so as to measure reasonable scales of FDI and the safe coefficient of China's FDI utilization, make timely predictions, and suggest specific foreign capital management and controlling strategies for the policy makers to adopt under various conditions.

This paper builds early warning systems (EWSs)for China's FDI utilization, applies grey correlation model GM (1,1) to predict early warning indexes, and uses both of the grey correlation and analytic hierarchy process (AHP) to evaluate the weights of the indexes.

The paper finds that FDI can promote China's economic growth, make great contribution to the technology spillover and improve China's employment environment as well as the quality of employment. But its contribution is less than the domestic capital in the aspects of China's industrial structure, area structure and trade structure adjustment, and more seriously, FDI exacerbates the imbalance of the area distribution in China. Moreover, foreign capital focuses on the occupation and monopoly of the domestic market, which will reduce import and export trade and harm the development of China's economy.

Owing to data constraints, this paper is not detailed and comprehensive enough, and needs further exploration in the empirical work.

Given the strong evidence of the EWS for FDI utilization, this paper finds a precise way to evaluate the influence of FDI on China's economic growth, by which the government can implement different capital management and controlling strategies to smooth the openness process of FDI in China.

This paper applies EWS into the FDI utilization to evaluate the safe coefficient and achieve the warning indexes, which are evaluated by the combination of the grey correlation and AHP.

Available information for evaluating the possibility of hospitality firm failure in emerging countries is often deficient. Oversampling can compensate for this but can also yield mixed samples, which limit prediction models’ effectiveness. This research aims to provide a feasible approach to handle possible mixed information caused by oversampling.

This paper uses mixed sample modelling (MSM) when evaluating the possibility of firm failure on enlarged hospitality firms. The mixed sample is filtered out with a mixed sample index through control of the noisy parameter and outliner parameter and meta-models are used to build MSM models for hospitality firm failure prediction, with performances compared to traditional models.

The proposed models are helpful in predicting hospitality firm failure in the mixed information situation caused by oversampling, whereas MSM significantly improves the performance of traditional models. Meanwhile, only partial mixed hospitality samples matter in predicting firm failure in both rich- and poor-information situations.

This research is helpful for managers, investors, employees and customers to reduce their hospitality-related risk in the emerging Chinese market. The two-dimensional sample collection strategies, three-step prediction process and five MSM modelling principles are helpful for practice of hospitality firm failure prediction.

This research provides a means of processing mixed hospitality firm samples through the early definition and proposal of MSM, which addresses the ranking information within samples in deficient information environments and improves forecasting accuracy of traditional models. Moreover, it provides empirical evidence for the validation of sample selection and sample pairing strategy in evaluating the possibility of hospitality firm failure.

The global financial and economic crisis resulting from the US housing crisis has shown that house prices can have far-reaching consequences for the real economy. For macroprudential supervision, it is, therefore, necessary to identify house price bubbles at an early stage to counteract speculative price developments and to ensure financial market stability. This paper aims to develop an early warning system to signal speculative price bubbles.

The results of explosivity tests are used to identify periods of excessive price increases in 18 industrialized countries. The early warning system is then based on a logit and an ordered logit regression, in which monetary, macroeconomic, regulatory, demographic and private factors are used as explanatory variables.

The empirical results show that monetary developments have the highest explanatory power for the existence of house price bubbles. Further, the study reveals currently emerging house price bubbles in Norway, Sweden and Switzerland.

The results implicate a new global housing boom, particularly in those countries that did not experience a major price correction during the global financial crisis.

The ordered logit model is an advanced approach that offers the advantage of being able to differentiate between different phases of a house price bubble, thereby allowing a multi-level assessment of the risk of speculative excesses in the housing market.

The late 2000s Great Recession led to a surge of bank failures in the USA with nearly 300 banks failing from 2009 to 2010. Recalling the farm crises of the 1980s where the farm sector was pinpointed as one of the major precursors of such crises, this study is an attempt to validate if the agricultural sector can once again be considered as a major instigator of the current financial crises.

An early warning model is developed based on factors that may cause bank failures, with special attention given to the role of the agricultural lending portfolios of commercial banks. The model will have several time period versions that will determine the length of time prior to the actual bank bankruptcy declarations that early warning signals could be detected.

The empirical results indicate that credit exposure to the farm sector does not necessarily enhance a bank's tendency to fail or its probability of success or survival. This lends support to the reality that agricultural loan delinquency rates are consistently below the banks' overall loan delinquency rates, thus confirming that agricultural lenders are in relatively stronger financial health. This study instead finds that costly funding arrangements, increasing interest rate risk, and declining asset quality can be possible early warning signals that can be detected as far back as two or three years before eventual bank failure.

This study differentiates itself from previous studies by its special focus on the role of the agricultural finance industry in the ensuing economic crises. This study's early warning model also presents an extended version of previous empirical models as it accounts for measures of capital adequacy, asset quality, management risk, profitability, liquidity risk, loan portfolio composition and risk, funding arrangement, structural and macroeconomic variables.

The purpose of this paper is to propose a framework to detect adverse drug reactions (ADRs) using internet user search data, so that ADR events can be identified early. Empirical investigation of Avandia, a type II diabetes treatment, is conducted to illustrate how to implement the proposed framework.

Typical ADR identification measures and time series processing techniques are used in the proposed framework. Google Trends Data are employed to represent user searches. The baseline model is a disproportionality analysis using official drug reaction reporting data from the US Food and Drug Administration’s Adverse Event Reporting System.

Results show that Google Trends series of Avandia side effects search reveal a significant early warning signal for the side effect emergence of Avandia. The proposed approach of using user search data to detect ADRs is proved to have a longer leading time than traditional drug reaction discovery methods. Three more drugs with known adverse reactions are investigated using the selected approach, and two are successfully identified.

Validation of Google Trends data’s representativeness of user search is yet to be explored. In future research, user search in other search engines and in healthcare web forums can be incorporated to obtain a more comprehensive ADR early warning mechanism.

Using internet data in drug safety management with a proper early warning mechanism may serve as an earlier signal than traditional drug adverse reaction. This has great potential in public health emergency management.

The research work proposes a novel framework of using user search data in ADR identification. User search is a voluntary drug adverse reaction exploration behavior. Furthermore, user search data series are more concise and accurate than text mining in forums. The proposed methods as well as the empirical results will shed some light on incorporating user search data as a new source in pharmacovigilance.