Developing a conceptual model for the internal data source to measure customer satisfaction

Traditional CSM approach is performed at certain frequencies. The gap between such events can be termed as a ‘blind period’, because customer satisfaction is left unobserved and unmanaged. The blind period may sometimes accelerate the growth of customer dissatisfaction. One way to eliminate the impact of the blind period is to reduce the gap between CSM events. The initial assessment indicates that conducting CSM more frequently, may weaken the accuracy of measurement, and increase the cost of the programme. The authors believe that the reason behind these limitations is the use of the external data source, collecting data directly from customer, therefore suggests using the internal data source, as an alternative to measure customer satisfaction. The purpose of this paper is to develop a conceptual model for the internal data source to measure customer satisfaction. To achieve this objective, a conceptual model need to be developed based on three determined steps: define the formation of customer satisfaction value, identify the CSM factors and dimensions, and mirror the CSM instruments to identify the internal performance values. The paper indicates that internal data source could provide researchers with an alternative data source to measure customer satisfaction with minimum limitations on frequency of implementation, accuracy and cost

DSP-based ionospheric radiolink using DS-CDMA and on-line channel estimation

In this paper, a new blind multiuser detection algorithm is presented. It can both cancel multiuser interference and estimate the multipath channel response in a blind way. The method has been specially conceived for low coherence bandwidth channels such as the ionospheric channel and exhibits very low computational requirements. Real-time measurements from a fully digital HF radio-link are presented that confirm the reliability of the method for the ionospheric channel.Peer ReviewedPostprint (published version

BMICA-independent component analysis based on B-spline mutual information estimator

The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. Its estimation however using B-Spline has not been used before in creating an approach for Independent Component Analysis. In this paper we present a B-Spline estimator for mutual information to find the independent components in mixed signals. Tested using electroencephalography (EEG) signals the resulting BMICA (B-Spline Mutual Information Independent Component Analysis) exhibits better performance than the standard Independent Component Analysis algorithms of FastICA, JADE, SOBI and EFICA in similar simulations. BMICA was found to be also more reliable than the 'renown' FastICA

Marker effects and examination reliability: a comparative exploration from the perspectives of generalizability theory, Rasch modelling and multilevel modelling

This study looked at how three different analysis methods could help us to understand rater effects on exam reliability. The techniques we looked at were: generalizability theory (G-theory) item response theory (IRT): in particular the Many-Facets Partial Credit Rasch Model (MFRM) multilevel modelling (MLM) We used data from AS component papers in geography and psychology for 2009, 2010 and 2011 from Edexcel.</p

BIM: a technology acceptance model in Peru

The purpose of this paper is to empirically study factors that facilitate the adoption of building information modelling (BIM) among practitioners using the unified theory of technology acceptance model (TAM). The factors identified in the TAM were examined using a quantitative approach. The empirical investigation has been conducted using a survey questionnaire. The data set has been obtained from 73 architects and engineers in Peru. Results show that Perceived Usefulness (PU) is the most important determinant of Behavioural Intention (BI), while Perceived Ease of Use (PEOU) is found to have no significant effect on BI. The findings provide an excellent backdrop in the development of policy and a roadmap for BIM implementation in Peru. The original contribution and value of the paper is the use of TAM to provide empirical evidence on factors that facilitate BIM adoption in Peru

Extraction of the underlying structure of systematic risk from non-Gaussian multivariate financial time series using independent component analysis: Evidence from the Mexican stock exchange

Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.Peer ReviewedPostprint (published version

Disparity between General Symptom Relief and Remission Criteria in the Positive and Negative Syndrome Scale (PANSS): A Post-treatment Bifactor Item Response Theory Model.

Objective: Total scale scores derived by summing ratings from the 30-item PANSS are commonly used in clinical trial research to measure overall symptom severity, and percentage reductions in the total scores are sometimes used to document the efficacy of treatment. Acknowledging that some patients may have substantial changes in PANSS total scores but still be sufficiently symptomatic to warrant diagnosis, ratings on a subset of 8 items, referred to here as the "Remission set," are sometimes used to determine if patients' symptoms no longer satisfy diagnostic criteria. An unanswered question remains: is the goal of treatment better conceptualized as reduction in overall symptom severity, or reduction in symptoms below the threshold for diagnosis? We evaluated the psychometric properties of PANSS total scores, to assess whether having low symptom severity post-treatment is equivalent to attaining Remission. Design: We applied a bifactor item response theory (IRT) model to post-treatment PANSS ratings of 3,647 subjects diagnosed with schizophrenia assessed at the termination of 11 clinical trials. The bifactor model specified one general dimension to reflect overall symptom severity, and five domain-specific dimensions. We assessed how PANSS item discrimination and information parameters varied across the range of overall symptom severity (θ), with a special focus on low levels of symptoms (i.e., θ&lt;-1), which we refer to as "Relief" from symptoms. A score of θ=-1 corresponds to an expected PANSS item score of 1.83, a rating between "Absent" and "Minimal" for a PANSS symptom. Results: The application of the bifactor IRT model revealed: (1) 88% of total score variation was attributable to variation in general symptom severity, and only 8% reflected secondary domain factors. This implies that a general factor may provide a good indicator of symptom severity, and that interpretation is not overly complicated by multidimensionality; (2) Post-treatment, 534 individuals (about 15% of the whole sample) scored in the "Relief" range of general symptom severity, but more than twice that number (n = 1351) satisfied Remission criteria (37%). 2 in 3 Remitted patients had scores that were not in a low symptom range (corresponding to Absent or Minimal item scores); (3) PANSS items vary greatly in their ability to measure the general symptom severity dimension; while many items are highly discriminating and relatively "pure" indicators of general symptom severity (delusions, conceptual disorganization), others are better indicators of specific dimensions (blunted affect, depression). The utility of a given PANSS item for assessing a patient depended on the illness level of the patient. Conclusion: Satisfying conventional Remission criteria was not strongly associated with low levels of symptoms. The items providing the most information for patients in the symptom Relief range were Delusions, Preoccupation, Suspiciousness Persecution, Unusual Thought Content, Conceptual Disorganization, Stereotyped Thinking, Active Social Avoidance, and Lack of Judgment and Insight. Lower scores on these items (item scores ≤2) were strongly associated with having a low latent trait θ or experiencing overall symptom relief. The inter-rater agreement between Remission and Relief subjects suggested that these criteria identified different subsets of patients. Alternative subsets of items may offer better indicators of general symptom severity and provide better discrimination (and lower standard errors) for scaling individuals and judging symptom relief, where the "best" subset of items ultimately depends on the illness range and treatment phase being evaluated