The CIO role expectations instrument: validation and model testing

The validation of IS instruments has not been given the attention that it deserves. This study uses component-based structural equation modelling (PLS/SEM) to investigate the psychometric properties and possible modelling of the CIO role expectations instrument based on data obtained from 174 Australian CIOs. Results show that the CIO role expectation instrument has exhibited solid validity and reliability indices despite some minor weaknesses. The results also demonstrate the possibility to model the constructs of this instrument in different null and hierarchical models, and the validity of this instrument to measure the CIO role in different types of industries not just the healthcare sector in which it was developed. The results provide support for CIO role theory on two central issues: (1) CIOs are fulfilling a configuration of roles not just one specific role (2) the CIO roles can be grouped into two major categories: supply side roles and demand side roles

Automated neural network-based instrument validation system

In a complex control process, instrument calibration is periodically performed to maintain the instruments within the calibration range, which assures proper control and minimizes down time. Instruments are usually calibrated under out-of-service conditions using manual calibration methods, which may cause incorrect calibration or equipment damage. Continuous in-service calibration monitoring of sensors and instruments will reduce unnecessary instrument calibrations, give operators more confidence in instrument measurements, increase plant efficiency or product quality, and minimize the possibility of equipment damage during unnecessary manual calibrations. In this dissertation, an artificial neural network (ANN)-based instrument calibration verification system is designed to achieve the on-line monitoring and verification goal for scheduling maintenance. Since an ANN is a data-driven model, it can learn the relationships among signals without prior knowledge of the physical model or process, which is usually difficult to establish for the complex hon-linear systems. Furthermore, the ANNs provide a noise-reduced estimate of the signal measurement. More importantly, since a neural network learns the relationships among signals, it can give an unfaulted estimate of a faulty signal based on information provided by other unfaulted signals; that is, provide a correct estimate of a faulty signal. This ANN-based instrument verification system is capable of detecting small degradations or drifts occurring in instrumentation, and preclude false control actions or system damage caused by instrument degradation. In this dissertation, an automated scheme of neural network construction is developed. Previously, the neural network structure design required extensive knowledge of neural networks. An automated design methodology was developed so that a network structure can be created without expert interaction. This validation system was designed to monitor process sensors plant-wide. Due to the large number of sensors to be monitored and the limited computational capability of an artificial neural network model, a variable grouping process was developed for dividing the sensor variables into small correlated groups which the neural networks can handle. A modification of a statistical method, called Beta method, as well as a principal component analysis (PCA)-based method of estimating the number of neural network hidden nodes was developed. Another development in this dissertation is the sensor fault detection method. The commonly used Sequential Probability Ratio Test (SPRT) continuously measures the likelihood ratio to statistically determine if there is any significant calibration change. This method requires normally distributed signals for correct operation. In practice, the signals deviate from the normal distribution causing problems for the SPRT. A modified SPRT (MSPRT) was developed to suppress the possible intermittent alarms initiated by spurious spikes in network prediction errors. These methods were applied to data from the Tennessee Valley Authority (TVA) fossil power plant Unit 9 for testing. The results show that the average detectable drift level is about 2.5% for instruments in the boiler system and about 1% in the turbine system of the Unit 9 system. Approximately 74% of the process instruments can be monitored using the methodologies developed in this dissertation

INSTRUMENT VALIDATION OF WEBSITE QUALITY TO ATTRACT DONATION

Background and Purpose: With fund scarcity eminent in Malaysia, soliciting donations effectively is essential to any non-profit organisation (NPO). Many NPOs struggle to raise funds to execute a beneficial project for society. The dimensions to identify relevant online factors influencing the NPO website user decision-making process slowly evolved in a local context due to limited interest in exploring this field. This research was conducted to identify relevant variables related to NPO website user interest to donate. However, a proper instrument must be developed to meet the research aim. Thus, this research was conducted to identify relevant variables and items in the research instrument.   Methodology: The study collected 269 responses from ten popular NPO website users. Their responses were recorded using the five-point Likert Scale questionnaire ranging from strongly agree to strongly disagree. At first, the study conducted a content validity test and a pre-test. The sample size is determined based on the SEM requirement. Finally, the responses are analysed using pooled confirmatory factor analysis to determine the instrument reliability and validity.   Findings: Only seven variables are retained after the validity and reliability analysis, with 43 items out of 74 items. The result indicated only information, system, service quality, perceived ease of use, and trust are relevant for the NPO to meet website user satisfaction and influence their decision to donate.   Contributions: The finding is essential as a guideline to develop a website that meets user preferences. The result also contributed to the website quality literature for the non-profit sector.   Keywords: Website quality, trust, satisfaction, non-profit organisation, technology acceptance model. Cite as: Ibrahim, M. S. (2023). Instrument validation of website quality to attract donation. Journal of Nusantara Studies, 8(1), 446-468. http://dx.doi.org/10.24200/jonus.vol8iss1pp446-46

Development of Digital Identification Instruments for Mentally Retarded Children

This study aims to develop a digital identification instrument-based Decision Support System (DSS) for mentally disabled children. First-year research consisted of three stages, namely (a) instrument need analysis, (b) instrument prototype development, (c) instrument validation. The study involved 20 special schools taken by purposive sampling from eight districts/cities in Central Java. The method of collecting data at the needs analysis stage uses a questionnaire given to 32 respondents (teachers and principals). At the stage of prototype development, data collection is done by using a web-based DSS development technique. Furthermore, the instrument validation stage is done through professional judgment with Focus Group Discussion (FGD) techniques involving special education experts, information and technology experts, psychometric experts, language experts, psychologists, and users/teachers. Data analysis was performed with quantitative descriptive and qualitative descriptive techniques. The results of the study at the needs analysis stage showed that 75% of respondents had difficulty in compiling identification instruments for mentally disabled children; all respondents (100%) needed the development of identification instruments for mentally disabled children, and 50% of respondents chose identification instruments in digital form. At the instrument development stage, mentally disabled children's characteristics based on the previous DSM-V criteria are described in the indicators. This digital instrument's identification results are still limited to indications, so that it needs to be continued with more in-depth assessment by authorized professionals. Expert validation results at the instrument validation stage indicate that this digital identification instrument for mentally disabled children meets the eligibility criteria with improvements in language, web appearance, and the instrument's technical operation. Furthermore, the instrument prototype is repaired and will be continued with small-scale and wide-scale trials

Microclimate monitoring in the Carcer Tullianum: temporal and spatial correlation and gradients evidenced by multivariate analysis; first campaign

Too often microclimate studies in the field of cultural heritage are published without any or scarce information on sampling design, sensors (type, number, position) and instrument validation. Lacking of this fundamental information does not allow an open discussion in the scientific community. This work aims to be an invitation for a different approach

Addressing Barriers to Medication Adherence: An Evidence-Based Screening Instrument Validation Study

Adherence to a prescribed medication regimen is often critical to successful disease management. Cancer diagnoses often further complicate control of the comorbid diseases. Older cancer patients with multiple comorbidities receiving chemotherapy treatment are at increased risk for adverse health outcomes from uncontrolled disease when nonadherent to their medication regimen. The intent of this pilot study was to test the validity of an evidence-based screening instrument designed to identify patients at risk for medication nonadherence and uncontrolled illness. The W-BMA (Washburn-Barrier to Medication Adherence) screening criteria were applied to retrospective data of cancer patients with multiple co-morbidities. SPSS was used to analyze the data using classification trees to compare the W-BMA screen with the current screens used in the clinic alone. The W-BMA identified a significantly larger number of patients with barriers than the current screens alone. Barriers found by the W-BMA screening instrument are strongly related to uncontrolled illness, and, these barriers are often multi-layered, impacting adherence and the health of the patient. Incidentally, there was strong evidence that patients who have barriers addressed by oncology support services (nurse navigation and social work) often fare much better than patients who do not. The instrument studied in this pilot project requires additional analysis and refinement, however, there is strong evidence that proper use of the W-BMA screening instrument used as part of a comprehensive medication adherence program may improve adherence and lower risk of uncontrolled illness and adverse events