Suchergebnisse

PurposeThe purpose of this study is to develop a performance measurement model for service operations using the analytic hierarchy process approach.Design/methodology/approachThe study reviews current relevant literature on performance measurement and develops a model for performance measurement. The model is then applied to the intensive care units (ICUs) of three different hospitals in developing nations. Six focus group discussions were undertaken, involving experts from the specific area under investigation, in order to develop an understandable performance measurement model that was both quantitative and hierarchical.FindingsA combination of outcome, structure and process‐based factors were used as a foundation for the model. The analyses of the links between them were used to reveal the relative importance of each and their associated sub factors. It was considered to be an effective quantitative tool by the stakeholders.Research limitations/implicationsThis research only applies the model to ICUs in healthcare services.Practical implicationsPerformance measurement is an important area within the operations management field. Although numerous models are routinely being deployed both in practice and research, there is always room for improvement. The present study proposes a hierarchical quantitative approach, which considers both subjective and objective performance criteria.Originality/valueThis paper develops a hierarchical quantitative model for service performance measurement. It considers success factors with respect to outcomes, structure and processes with the involvement of the concerned stakeholders based upon the analytic hierarchy process approach. The unique model is applied to the ICUs of hospitals in order to demonstrate its effectiveness. The unique application provides a comparative international study of service performance measurement in ICUs of hospitals in three different countries.

PurposeThe purpose of this paper is to help managers to successfully plan, implement, and operate enterprise resource planning (ERP) projects using a risk management framework.Design/methodology/approachThis paper adopted a combined literature review and case study method. Using literature review, the paper first identified major issues of managing ERP projects and develops a risk management framework for managing those issues. The proposed risk management framework was then applied to a ERP implementation project of a UK‐based energy services group and its effectiveness for managing ERP projects implementation had been demonstrated. Additionally, the risk factors as identified from the case application are compared with the risk factors from the previous researches so as to suggest mitigating measures.FindingsAll the risk factors are categorized into planning, implementation and operations phases along with project processes, organizational transformation and information technology (IT) perspectives. Project implementation phase is the most vulnerable to failure. The case study results reveal that the effect of other projects on on‐going ERP project, management of overall IT architecture and non‐availability of resources for organizational transformation are most critical from likelihood and impact perspectives. Managing risk across various phases of project and equal emphasize to effective project management, organizational transformation and IT adoption are the key to success in ERP implementation.Practical implicationsThe risk factors, which were identified using literature review and the case study, have great significance as mitigating measures of those risks may result successful implementation of ERP projects in the industry. Additionally, proposed risk management framework could be customized to implement ERP projects elsewhere.Originality/valueERP projects are risky as they are capital intensive, technically complex, and call for organizational transformation. There are both success and failure stories. However, both researchers and practitioners agree, that if it can be implemented and operated successfully and benefits should be achievable. Although there are many studies on ERP implementation, little has been discussed on managing risks of ERP projects. Therefore, this paper bridges the gap.

Objective Experimentally investigate maneuver decision preferences in navigating ships to avoid a collision. How is safety (collision avoidance) balanced against efficiency (deviation from path and delay) and rules of the road under conditions of both trajectory certainty and uncertainty. Background Human decision error is a prominent factor in nautical collisions, but the multiple factors of geometry of collisions and role of uncertainty have been little studied in empirical human factors literature. Approach Eighty-seven Mechanical Turk participants performed in a lower fidelity ship control simulation, depicting ownship and a cargo ship hazard on collision or near-collision trajectories of various conflict geometries, while controlling heading and speed with the sluggish relative dynamics. Experiment 1 involved the hazard on a straight trajectory. In Experiment 2, the hazard could turn on unpredictable trials. Participants were rewarded for efficiency and penalized for collisions or close passes. Results Participants made few collisions, but did so more often when on a collision path. They sometimes violated the instructed rules of the road by maneuvering in front of the hazard ship's path. They preferred speed control to heading control. Performance degraded in conditions of uncertainty. Conclusion Data reveal an understanding of maneuver decisions and conditions that affect the balance between safety and efficiency. Application The simulation and data highlight the degrading role of uncertainty and provide a foundation upon which more complex questions can be asked, asked of more trained navigators, and decision support tools examined.

Objective: The aim of this study was to explore the impact of prior information on spatial prediction and understanding of variability. Background: In uncertain spatial prediction tasks, such as hurricane forecasting or planning search-and-rescue operations, decision makers must consider the most likely case and the distribution of possible outcomes. Base performance on these tasks is varied (and in the case of understanding the distribution, often poor). Humans must update mental models and predictions with new information, sometimes under cognitive workload. Method: In a spatial-trajectory prediction task, participants were anchored on accurate or inaccurate information, or not anchored, regarding the future behavior of an object (both average behavior and the variability). Subsequently, they predicted an object's future location and estimated its likelihood at multiple locations. In a second experiment, participants repeated the process under varying levels of external cognitive workload. Results: Anchoring influenced understanding of most likely predicted location, with fairly rapid adjustment following inaccurate anchors. Increasing workload resulted in decreased overall performance and an impact on the adjustment component of the task. Overconfidence was present in all conditions. Conclusion: Prior information exerted short-term influence on spatial predictions. Cognitive load impaired users' ability to effectively adjust to new information. Accurate graphical anchors did not improve user understanding of variability. Application: Prior briefings or forecasts about spatiotemporal trajectories affect decisions even in the face of initial contradictory information. To best support spatial prediction tasks, efforts also need to be made to separate extraneous load-causing tasks from the process of integrating new information. Implications are discussed.

Objective The aim of this study was to understand factors that influence the prediction of uncertain spatial trajectories (e.g., the future path of a hurricane or ship) and the role of human overconfidence in such prediction. Background Research has indicated that human prediction of uncertain trajectories is difficult and may well be subject to overconfidence in the accuracy of forecasts as is found in event prediction, a finding that indicates that humans insufficiently appreciate the contributions of variance in nature to their predictions. Method In two experiments, our paradigm required participants to observe a starting point, a position at time T, and then make a prediction of the location of the trajectory at time NT. They experienced several trajectories from the same underlying model but perturbed by random variance in heading and speed. Results In Experiment 1A, people predicted linear paths well and were better in heading predictions than in speed predictions. However, participants greatly underestimated the variance in predicted location, indicating overconfidence. In Experiment 1B, the effect was replicated with frequencies rather than probabilities used in variance estimates. In Experiment 2, people predicted nonlinear trajectories poorly, and overconfidence was again observed. Overconfidence was reduced on the more difficult predictions. In both main experiments, those better at predicting the mean were not better at predicting the variance. Conclusions Predicting the level of uncertainty in spatial trajectories is not well done and may involve qualitatively different abilities than prediction of the mean. Application Improving real-world performance at prediction demands developing better understanding of variability, not just the average case. Biases in prediction of uncertainty may be addressed through debiasing training and/or visualization tools that could assist in more calibrated action planning.

Abstract
Hurricane forecasts are often communicated through visualizations depicting the possible future track of the storm. The cone of uncertainty (COU) is a commonly used visualization, but the graphic is prone to misinterpretation such as thinking only locations contained within the cone's boundary are at risk. In this study, we investigated the utility of conveying hurricane forecast tracks using a set of animated icons, each representing an instance of a possible storm path. We refer to this new visualization as animated risk trajectories (ARTs). We measured nonexperts' perception of risk when viewing simplified, hypothetical hurricane forecasts presented as ARTs or COUs. To measure perception of risk for each visualization type, we designed experiments to have participants make decisions to evacuate individual towns at varying distances from the most likely forecast path of a storm. The ARTs led to greater risk perception in areas that fell beyond the cone's boundaries. Nonexperts' interpretation of risk was impacted by the visual properties of the ARTs, such as the distribution of the icons, including their density, and whether the distribution was unimodal or bimodal. This supports the suggestion that ARTs can have value in communicating spatial–temporal uncertainty.

Significance Statement
Because of the inherent uncertainties in weather forecasts and emergency management planning, communicating hurricane risk to the public is a unique challenge for decision-makers. Our study investigated the effect of conveying uncertainty in hurricane forecast tracks using a distribution of animated icons [animated risk trajectories (ARTs)], which correspond to potential hurricane tracks that evolve over time. This visualization was compared with a simplified cone-of-uncertainty graphic, such as those used by the National Hurricane Center. We found that ARTs offered flexibility in conveying information about hurricane risk, such as the magnitude of the risk via the number of icons and the location of the risk via the distribution of icons.

Objective The ability of people to infer intentions from movement of other vessels was investigated. Across three levels of variability in movements in the path of computer-controlled ships, participants attempted to determine which entity was hostile. Background Detection of hostile intentions through spatial movements of vessels is important in an array of real-world scenarios. This experiment sought to determine baseline abilities of humans to do so. Methods Participants selected a discrete movement direction of their ship. Six other ships' locations then updated. A single entity displayed one of two hostile behaviors: shadowing, which involved mirroring the participant's vessel's movements; and hunting, which involved closing in on the participant's vessel. Trials allowed up to 35 moves before identifying the hostile ship and its behavior. Uncertainty was introduced through adding variability to ships' movements such that their path was 0%, 25%, or 50% random. Results Even with no variability in the ships' movements, accurate detection was low, identifying the hostile entity about 60% of the time. Variability in the paths decreased detection. Detection of hunting was strongly degraded by distance between ownship and the hostile ship, but shadowing was not. Strategies employing different directions of movement across the trial, but also featuring some runs of consecutive movements, facilitated detection. Conclusions Early identification of threats based on movement characteristics alone is likely to be difficult, but particularly so when adversaries employ some level of uncertainty to mask their intentions. These findings highlight the need to develop decision aids to support human performance.

Objective We examine the effects of two different kinds of decision-aiding automation errors on human–automation interaction (HAI), occurring at the first failure following repeated exposure to correctly functioning automation. The two errors are incorrect advice, triggering the automation bias, and missing advice, reflecting complacency. Background Contrasts between analogous automation errors in alerting systems, rather than decision aiding, have revealed that alerting false alarms are more problematic to HAI than alerting misses are. Prior research in decision aiding, although contrasting the two aiding errors (incorrect vs. missing), has confounded error expectancy. Method Participants performed an environmental process control simulation with and without decision aiding. For those with the aid, automation dependence was created through several trials of perfect aiding performance, and an unexpected automation error was then imposed in which automation was either gone (one group) or wrong (a second group). A control group received no automation support. Results The correct aid supported faster and more accurate diagnosis and lower workload. The aid failure degraded all three variables, but "automation wrong" had a much greater effect on accuracy, reflecting the automation bias, than did "automation gone," reflecting the impact of complacency. Some complacency was manifested for automation gone, by a longer latency and more modest reduction in accuracy. Conclusions Automation wrong, creating the automation bias, appears to be a more problematic form of automation error than automation gone, reflecting complacency. Implications Decision-aiding automation should indicate its lower degree of confidence in uncertain environments to avoid the automation bias.

Previous research has suggested that providing informative cues about interrupting stimuli aids management of multiple tasks. However, auditory and visual cues can be ineffective in certain situations. The objective of the present study was to explore whether attention-directing tactile cues aid or interfere with performance. A two-group posttest-only randomized experiment was conducted. Sixty-one participants completed a 30-min performance session consisting of aircraft-monitoring and gauge-reading computer tasks. Tactile signals were administered to a treatment group to indicate the arrival and location of interrupting tasks. Control participants had to remember to visually check for the interrupting tasks. Participants in the treatment group responded to more interrupting tasks and responded faster than did control participants. Groups did not differ on error rates for the interrupting tasks, performance of the primary task, or subjective workload perceptions. In the context of the tasks used in the present research, tactile cues allowed participants to effectively direct attention where needed without disrupting ongoing information processing. Tactile cues should be explored in a variety of other visual, interruptladen environments. Potential applications exist for aviation, user-interface design, vigilance tasks, and team environments.

Objective This experiment examined performance costs when processing two sources of information positioned at increasing distances using a flat panel display and an augmented reality head-mounted display (AR-HMD). Background The AR-HMD enables positioning virtual information at various distances in space. However, the proximity compatibility principle suggests that closer separation when two sources of information require mental integration assists performance, whereas increased separation between two sources hurts integration performance more than when a single source requires focused attention. Previous studies have provided inconsistent findings regarding costs associated with increased separation. Few of these experiments have examined separation for both focused and integration tasks, compared vertical and lateral separation, or measured head movements. Method Three experiments collectively examined these issues using a flat panel display and a virtual display presented with an HMD, where the separation of information varied laterally or vertically during a focused attention (digit reading) task and an information integration (mental subtraction) task. Results There was no performance cost for either display when information was increasingly separated. However, head movements mitigated performance costs by preserving accuracy at larger separations without increasing response time. Conclusion Head movements appear to mitigate performance costs associated with presenting information increasingly far apart on flat panel displays and HMDs. Both eye scanning and head movements appear to be less effortful than expected. Application These findings have important implications for design guidelines regarding the placement of information presented on flat panel displays and, more specifically, HMDs, which can present information 360° around the user.

Objective Three experiments sought to understand performance limitations in controlling a ship attempting to meet another moving ship that approached from various trajectories. The influence of uncertainty, resulting from occasional unpredictable delays in one's own movement, was examined. Background Cognitive elements of rendezvous have been little studied. Related work such as the planning fallacy and bias toward underestimating time-to-contact imply a tendency toward late arrival at a rendezvous. Methods In a simplified simulation, participants controlled the speed and/or heading of their own ship once per scenario to try to rendezvous with another ship. Forty-five scenarios of approximately 30 s were conducted with different starting geometries and, in two of three experiments, with different frequencies and lengths of the unexpected delays. Results Perfect rendezvous were hard to obtain, with a general tendency to arrive late and pass behind the target vessel, although this was dependent on the angle of approach and relative speed. When occasional delays were introduced, less frequent but longer delays disrupted performance more than shorter but more frequent delays. Where delays were possible, but no delay occurred, there was no longer evidence of a general tendency to more frequently pass behind the target ship. Additionally, people did not wait to see if the unpredictable delays would occur before executing a course of action. Different control strategies were deployed and dual axis control was preferred. Conclusions The tendency to arrive late and the influence of the possibility of uncertain delays are discussed in relationship to control strategies.

Objective: The aim of this study was to validate the strategic task overload management (STOM) model that predicts task switching when concurrence is impossible. Background: The STOM model predicts that in overload, tasks will be switched to, to the extent that they are attractive on task attributes of high priority, interest, and salience and low difficulty. But more-difficult tasks are less likely to be switched away from once they are being performed. Method: In Experiment 1, participants performed four tasks of the Multi-Attribute Task Battery and provided task-switching data to inform the role of difficulty and priority. In Experiment 2, participants concurrently performed an environmental control task and a robotic arm simulation. Workload was varied by automation of arm movement and both the phases of environmental control and existence of decision support for fault management. Attention to the two tasks was measured using a head tracker. Results: Experiment 1 revealed the lack of influence of task priority and confirmed the differing roles of task difficulty. In Experiment 2, the percentage attention allocation across the eight conditions was predicted by the STOM model when participants rated the four attributes. Model predictions were compared against empirical data and accounted for over 95% of variance in task allocation. More-difficult tasks were performed longer than easier tasks. Task priority does not influence allocation. Conclusions: The multiattribute decision model provided a good fit to the data. Applications: The STOM model is useful for predicting cognitive tunneling given that human-in-the-loop simulation is time-consuming and expensive.