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By using small reference samples, the calculation method of confidence value and prediction method of confidence interval for multi-input system are investigated. The purpose of this paper is to offer effective assessing methods of confidence value and confidence interval for the simulation models used in establishing guidance and control systems.

In this paper, first, an improved cluster estimation method is proposed to guide the selection of the small reference samples. Then, based on analytic hierarchy process method, the new calculation method of the weight of each reference sample is derived. By using the grey relation analysis method, new calculation methods of the correlation coefficient and confidence value are presented. Moreover, the confidence interval of the sample awaiting assessment is defined. A new prediction method is derived to obtain the confidence interval of the sample awaiting assessment which has no reference sample. Subsequently, by using the prediction method and original small reference samples, Bootstrap resampling method is used to obtain more correlation coefficients for the sample to reduce the probability of abandoning the true.

The grey relational analysis is used in assessing the confidence value and interval prediction. The numerical simulations are presented to demonstrate the effectiveness of the theoretical results.

Based on the selected small reference samples, new calculation methods of the correlation coefficient and confidence value are presented to assess the confidence value of model awaiting assessment. The calculation methods of maximum confidence interval, expected confidence interval and other required confidence intervals are presented, which can be used in assessing the validities of controller and guidance system obtained from the model awaiting assessment.

The purpose of this paper is to provide classification confidence value to every individual sample classified by decision trees and use this value to combine the classifiers.

The proposed system is first theoretically explained, and then the use and effectiveness of the proposed system is demonstrated on sample datasets.

In this paper, a novel method is proposed to combine decision tree classifiers using calculated classification confidence values. This confidence in the classification is based on distance calculation to the relevant decision boundary (distance conditional), probability density estimation and (distance conditional) classification confidence estimation. It is shown that these values – provided by individual classification trees – can be integrated to derive a consensus decision.

The proposed method is not limited to axis‐parallel trees, it is applicable not only to oblique trees, but also to any kind of classifier system that uses hyperplanes to cluster the input space.

A novel method is presented to extend decision tree like classifiers with confidence calculation and a voting system is proposed that uses this confidence information. The proposed system possesses several novelties (e.g. it not only gives class probabilities, but also classification confidences) and advantages over previous (traditional) approaches. The voting system does not require an auxiliary combiner or gating network, as in the mixture of experts structure and the method is not limited to decision trees with axis‐parallel splits; it is applicable to any kind of classifiers that use hyperplanes to cluster the input space.

Measurement of information security assurance (ISA) is an important but difficult task. This paper aims to propose a framework, which helps in refining information security requirements into controls whose effectiveness can be measured. This work also provides aggregation techniques to combine these measurements so as to obtain an indicator for ISA at the organizational level.

A top-down approach of refining security objectives to measurable independent tasks is carried out using assign graph as the model. This captures the various objectives and their interrelationships whose initial values and relative impacts are obtained from experts. Using fuzzy cognitive model (FCM), these initial values are combined together to obtain an indicator for ISA at the firm's level.

The two applications of the framework revealed that interrelationships do exist between the different controls employed in actual security implementations and that these dependencies are seldom accounted for. When those few controls that are to be measured are clearly identified, the security experts can focus their attention on them and ensure their correct implementation and appropriate measurement. The extent of impact of a single control on the overall security picture of the firm can also be found using this approach.

While the framework is generic, the assurance values obtained are context-sensitive. This is primarily because of the subjectivity involved in assigning impact measures and initial values.

This work helps in answering two difficult questions in information security management: “what to measure?” and “how to quantify the overall security assurance of the organization?” This assists the information security team in identifying and refining those controls that needs to be appropriately emphasized. The proposed framework helps the top management in doing “what-if” analysis, thereby aiding their decision-making for information security investments.

The novel framework proposes a top-down approach for security control refinement and a bottom-up approach for combining the confidence values to obtain an indicator for ISA. This work identifies and accommodates the possibilities of having interdependencies between security controls. The proposed aggregation method using FCM is being applied for the first time in information security context and provides convergence even in the presence of cyclic dependencies amongst the controls.

The purpose of this paper is to determine which design and operational attributes of e-commerce websites consumers use to assess website quality. Cue utilization theory is used to examine the explanatory power, robustness and relevance of the WebQual model. Results indicate which WebQual dimensions are the most relevant and salient to website users. These dimensions are categorized by their perceived and confidence values. A second study is conducted about how website users evaluate and utilize the WebQual dimensions.

Survey methodology was utilized to provide insight into the nomological validity of the WebQual model by examining it through a cue utilization lens.

The first study categorizes the WebQual dimensions on their ability to provide a diagnostic measure of website quality, and consumer confidence in their ability to use these cues when judging the website's overall quality. The second study presents results of each dimension in relation to the quality evaluation of an actual e-commerce website. Additional analysis also revealed gender differences in cue utilization.

This study provided insight into WebQual-based research and identified original differences in cue utilization across genders. Results suggest that it may be beneficial for brand managers to focus on a subset of quality dimensions, rather than assume that consumers are comfortable using all website attributes to formulate quality judgments. These, results contribute to multiple literatures by providing a model that developers can utilize to focus on the deterministic characteristics of overall website quality. Further, the cue utilization perspective provides additional avenues for fruitful further research into consumer decision-making in the e-commerce context.

The purpose of this study is to examine the consumer-side effects of “under new management” (UNM) signs. The authors integrate cue-utilization theory and relevance theory to guide hypotheses about the conditions under which these signs are and are not beneficial.

Two consumer-based experiments were used to examine the quality and reputation effects of restaurants signaling a management change on potential and existing customers.

The results suggest that positive and negative effects are possible. The direction of these effects is contingent upon consumers’ prior experience, type of service (i.e. search/experience) and the relevance of the signal.

The study is limited to one industry (i.e. restaurants) and examines the effects of market signals on perceived quality and reputation. In addition, this research brought forth the notion of “signal relevance” and suggested that it may be explicitly tied to attributions. However, this assertion must examine multiple signals (relevant/irrelevant) and their contingent effects on consumer perceptions.

The findings advise businesses to use caution when using signals such as an “UNM” sign, as they appear to have different effects depending on the experience of the consumer with the service and the relevance of the signal.

This research contributes to the literature on cue utilization theory to understand the effects of marketplace cues on consumer perceptions. It contributes to marketing theory and practice by proposing a model of cue effects based on prior customer experience, type of service and cue relevance.

Changes in Australian segment reporting standards over the last decade changed the required disaggregation of segment information. The purpose of this paper is to investigate whether increased disaggregation has implications for users' confidence in decisions based on segment reports and perceptions of segment reporting usefulness.

Using an experiment based on the differences between the original AASB 1005 and the more detailed requirements of AASB 114, the authors test whether segment report users' confidence in forecasting and their perceptions of segment report usefulness differ between the different information sets provided under these standards.

It was found that the more disaggregated or finer reports based on AASB 114 provide significantly more confidence to users, compared to the coarser segment reports based on the original AASB 1005, but this is not associated with differences in segment report usefulness scores.

The authors' experiment is based on AASB 1005 and AASB 114 and the results cannot be generalized to differences with other reporting standards. Examination of differences in recently released AASB 8 may reveal different implications for users' confidence and perceptions of usefulness. More generally, other tests of usefulness are needed to confirm whether opinions of usefulness that are not confirmed by decision‐making practices provide a reliable basis for determining usefulness.

By confirming that decision makers' confidence can be increased by the provision of finer information sets, the authors' results have practical implications for accounting standard setting.

By testing the impact of report differences on user decision confidence, the paper addresses a previously overlooked issue.

The purpose of this paper is to present an extended version of the Elaboration Likelihood Model (ELM‐model) to explain and predict which of the four cognitive processes that are distinguished in the literature, with respect to Country of Origin (CoO), can be expected to occur: the halo‐effect, the summary construct‐effect, the product attribute‐effect or the default heuristic‐effect.

Contrary to most of the previous theoretically‐oriented work on cognitive CoO‐effects, the epistemological background of the CoO‐ELM model proposed in this paper is of an inductive nature with theoretical propositions being derived from empirical data already gathered in the existing studies.

The outcome of this paper is a flow chart model leading to a set of theoretical propositions on which cognitive CoO‐effects can be expected to occur under different situational contexts.

This paper only focuses on the explanation of cognitive CoO‐effects, not on affective or conative/normative effects. Also, the CoO‐ELM model applies only to the processing of consumers' prior knowledge about a country's products and not about the country itself. Finally, the CoO‐ELM model still needs to be subjected to empirical verification. An important implication of this paper is that the CoO‐ELM framework makes the bulk of empirical data become more transparent given the four effects of cognitive CoO‐processes.

The CoO‐ELM model provides marketing practitioners with an easy and practical tool for the management of CoO‐cues.

This paper is the first attempt trying to catch all the cognitive CoO‐effects previously identified within a theoretically solid framework.

The purpose of this paper is to investigate the impact of investor confidence on mutual fund performance in two relatively vulnerable but leading emerging markets, India and Pakistan.

A pooled ordinary least squared (OLS) model is used to look at two alternative measures of investor confidence and test for the relationship between investor confidence and mutual fund returns. To check the robustness of the findings, the authors also implement two-stage least squares and generalized method of moments techniques to control for unobserved heterogeneity, simultaneity and dynamic endogeneity problems in the regressors.

The paper finds that the returns of mutual funds are positively associated with investor confidence and an interaction effect exists between investor confidence and persistence in performance. The paper also confirms that returns from mutual funds are associated with different fund characteristics such as fund size, turnover, expense, liquidity, performance persistence and the fund’s age. These findings remain robust to alternative model specifications and measures of investor confidence.

While the previous literature mainly focuses on mutual fund characteristics and the macroeconomic determinants of mutual fund returns, this paper demonstrates that investor confidence plays an important role in determining mutual fund performance. The authors attribute this finding to two relatively unique features of the emerging markets in the study. A lack of awareness of mutual funds as being a low-cost investment vehicle and the interplay of cultural and behavioral changes have prevented investor’s savings from being channeled into investment products, away from gold or property.

With the condition monitoring system on airplanes, failures can be predicted before they occur. Performance deterioration of aircraft engines is monitored by parameters such as fuel flow, exhaust gas temperature, engine fan speeds, vibration, oil pressure and oil temperature. The vibration parameter allows us to easily detect any existing or possible faults. The purpose of this paper is to develop a new model to estimate the low pressure turbine (LPT) vibration parameter of an aircraft engine by using the data of an aircraft’s actual flight from flight data recorder (FDR).

First, statistical regression analysis is used to determine the parameters related to LPT. Then, the selected parameters were applied as an input to the developed Levenberg–Marquardt feedforward neural network and the output LPT vibration parameter was estimated with a small error. Analyses were performed on MATLAB and SPSS Statistics 22 package program. Finally, the confidence interval method is used to check the accuracy of the estimated results of artificial neural networks (ANNs).

This study shows that the health conditions of an aircraft engine can be evaluated in terms of this paper by using confidence interval prediction of ANN-estimated LPT vibration parameters without dismantling and expert knowledge.

With this study, it has been shown that faults that may occur during flight can be easily detected using the data of a flight without expert evaluation.

The health condition of the turbofan engine was evaluated using the confidence interval prediction of ANN-estimated LPT vibration parameters.

The purpose of this paper is to supply an in-depth description of the educators’ values, beliefs and confidence changing from a traditional learning environment to a learning environment integrating technology.

The descriptive case study design was employed using descriptive statistical analysis and inductive analysis on the data collected.

Themes on a high level of confidence, the importance of professional development and training, self-motivation, and excitement about the way technology can enhance the learning, along with concerns over the lack of infrastructure and support for integrating technology, and about the ability of students to use the technology tools for higher ordered thinking surfaced.

Additional research may include a more diverse population, including educators at the kindergarten to high school level. Another recommendation would be to repeat the study with a population not as vested in technology.

A pre-assessment of the existing values, beliefs and confidence of educators involved in the change process will provide invaluable information for stakeholders on techniques and strategies vital to a successful transition.

To effectively meet the learning styles of Generation Z and those students following, educators need be able to adapt to quickly changing technology, be comfortable with students who multitask and be open to technology-rich teaching and learning environments.

This study filled a gap in the literature where little information on the humanistic challenges educators encounter when integrating technology into their learning environment providing insights into the values, beliefs and level of confidence of educators experiencing change.