A differential kinetic spectrophotometric method for determination of three sulphanilamide artificial sweeteners with the aid of chemometrics

A simple and sensitive spectrophotometric method for the simultaneous determination of acesulfame-K, sodium cyclamate and saccharin sodium sweeteners in foodstuff samples has been researched and developed. This analytical method relies on the different kinetic rates of the analytes in their oxidative reaction with KMnO4 to produce the green manganate product in an alkaline solution. As the kinetic rates of acesulfame-K, sodium cyclamate and saccharin sodium were similar and their kinetic data seriously overlapped, chemometrics methods, such as partial least squares (PLS), principal component regression (PCR) and classical least squares (CLS), were applied to resolve the kinetic data. The results showed that the PLS prediction model performed somewhat better. The proposed method was then applied for the determination of the three sweeteners in foodstuff samples, and the results compared well with those obtained by the reference HPLC method.

Simultaneous kinetic-spectrophotometric determination of maltol and ethyl maltol in food samples by using chemometrics

A fast and accurate procedure has been researched and developed for the simultaneous determination of maltol and ethyl maltol, based on their reaction with iron(III) in the presence of o-phenanthroline in sulfuric acid medium. This reaction was the basis for an indirect kinetic spectrophotometric method, which followed the development of the pink ferroin product (λmax = 524 nm). The kinetic data were collected in the 370–900 nm range over 0–30 s. The optimized method indicates that individual analytes followed Beer’s law in the concentration range of 4.0–76.0 mg L−1 for both maltol and ethyl maltol. The LOD values of 1.6 mg L−1 for maltol and 1.4 mg L−1 for ethyl maltol agree well with those obtained by the alternative high performance liquid chromatography with ultraviolet detection (HPLC-UV). Three chemometrics methods, principal component regression (PCR), partial least squares (PLS) and principal component analysis–radial basis function–artificial neural networks (PC–RBF–ANN), were used to resolve the measured data with small kinetic differences between the two analytes as reflected by the development of the pink ferroin product. All three performed satisfactorily in the case of the synthetic verification samples, and in their application for the prediction of the analytes in several food products. The figures of merit for the analytes based on the multivariate models agreed well with those from the alternative HPLC-UV method involving the same samples.

Multicomponent kinetic spectrophotometric determination of pefloxacin and norfloxacin in pharmaceutical preparations and human plasma samples with the aid of chemometrics

A spectrophotometric method for the simultaneous determination of the important pharmaceuticals, pefloxacin and its structurally similar metabolite, norfloxacin, is described for the first time. The analysis is based on the monitoring of a kinetic spectrophotometric reaction of the two analytes with potassium permanganate as the oxidant. The measurement of the reaction process followed the absorbance decrease of potassium permanganate at 526 nm, and the accompanying increase of the product, potassium manganate, at 608 nm. It was essential to use multivariate calibrations to overcome severe spectral overlaps and similarities in reaction kinetics. Calibration curves for the individual analytes showed linear relationships over the concentration ranges of 1.0–11.5 mg L−1 at 526 and 608 nm for pefloxacin, and 0.15–1.8 mg L−1 at 526 and 608 nm for norfloxacin. Various multivariate calibration models were applied, at the two analytical wavelengths, for the simultaneous prediction of the two analytes including classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), radial basis function-artificial neural network (RBF-ANN) and principal component-radial basis function-artificial neural network (PC-RBF-ANN). PLS and PC-RBF-ANN calibrations with the data collected at 526 nm, were the preferred methods—%RPET not, vert, similar 5, and LODs for pefloxacin and norfloxacin of 0.36 and 0.06 mg L−1, respectively. Then, the proposed method was applied successfully for the simultaneous determination of pefloxacin and norfloxacin present in pharmaceutical and human plasma samples. The results compared well with those from the alternative analysis by HPLC.

Visitor relationship orientation of destination marketing organisations

The proposition underpinning this study is engaging in meaningful dialogue with previous visitors represents an efficient and effective use of resources for a destination marketing organization (DMO), compared to above the line advertising in broadcast media. However there has been a lack of attention in the tourism literature relating to destination switching, loyalty and customer relationship management (CRM) to test such a proposition. This paper reports an investigation of visitor relationship marketing (VRM) orientation among DMOs. A model of CRM orientation, which was developed from the wider marketing literature and a prior qualitative study, was used to develop a scale to operationalise DMO visitor relationship orientation. Due to a small sample, the Partial Least Squares (PLS) method of structural equation modelling was used to analyse the data. Although the sample limits the ability to generalise, the results indicated the DMOs’ visitor orientation is generally responsive and reactive rather than proactive.

Photochemistry and chemometrics : an overview

Photochemistry has made significant contributions to our understanding of many important natural processes as well as the scientific discoveries of the man-made world. The measurements from such studies are often complex and may require advanced data interpretation with the use of multivariate or chemometrics methods. In general, such methods have been applied successfully for data display, classification, multivariate curve resolution and prediction in analytical chemistry, environmental chemistry, engineering, medical research and industry. However, in photochemistry, by comparison, applications of such multivariate approaches were found to be less frequent although a variety of methods have been used, especially with spectroscopic photochemical applications. The methods include Principal Component Analysis (PCA; data display), Partial Least Squares (PLS; prediction), Artificial Neural Networks (ANN; prediction) and several models for multivariate curve resolution related to Parallel Factor Analysis (PARAFAC; decomposition of complex responses). Applications of such methods are discussed in this overview and typical examples include photodegradation of herbicides, prediction of antibiotics in human fluids (fluorescence spectroscopy), non-destructive in- and on-line monitoring (near infrared spectroscopy) and fast-time resolution of spectroscopic signals from photochemical reactions. It is also quite clear from the literature that the scope of spectroscopic photochemistry was enhanced by the application of chemometrics. To highlight and encourage further applications of chemometrics in photochemistry, several additional chemometrics approaches are discussed using data collected by the authors. The use of a PCA biplot is illustrated with an analysis of a matrix containing data on the performance of photocatalysts developed for water splitting and hydrogen production. In addition, the applications of the Multi-Criteria Decision Making (MCDM) ranking methods and Fuzzy Clustering are demonstrated with an analysis of water quality data matrix. Other examples of topics include the application of simultaneous kinetic spectroscopic methods for prediction of pesticides, and the use of response fingerprinting approach for classification of medicinal preparations. In general, the overview endeavours to emphasise the advantages of chemometrics' interpretation of multivariate photochemical data, and an Appendix of references and summaries of common and less usual chemometrics methods noted in this work, is provided. Crown Copyright © 2010.

Simultaneous voltammetric determination of three herbicides in food and water samples with the aid of chemometrics

Differential pulse stripping voltammetry method(DPSV) was applied to the determination of three herbicides, ametryn, cyanatryn, and dimethametryn. It was found that their voltammograms overlapped strongly, and it is difficult to determine these compounds individually from their mixtures. With the aid of chemometrics, classical least squares(CLS), principal component regression(PCR) and partial least squares(PLS), voltammogram resolution and quantitative analysis of the synthetic mixtures of the three compounds were successfully performed. The proposed method was also applied to the analysis of some real samples with satisfactory results.

Validating the is-impact model in the Malaysian public sector

This research is one of several ongoing studies conducted within the IT Professional Services (ITPS) research programme at Queensland University of Technology (QUT). In 2003, ITPS introduced the IS-Impact model, a measurement model for measuring information systems success from the viewpoint of multiple stakeholders. The model, along with its instrument, is robust, simple, yet generalisable, and yields results that are comparable across time, stakeholders, different systems and system contexts. The IS-Impact model is defined as “a measure at a point in time, of the stream of net benefits from the Information System (IS), to date and anticipated, as perceived by all key-user-groups”. The model represents four dimensions, which are ‘Individual Impact’, ‘Organizational Impact’, ‘Information Quality’ and ‘System Quality’. The two Impact dimensions measure the up-to-date impact of the evaluated system, while the remaining two Quality dimensions act as proxies for probable future impacts (Gable, Sedera & Chan, 2008). To fulfil the goal of ITPS, “to develop the most widely employed model” this research re-validates and extends the IS-Impact model in a new context. This method/context-extension research aims to test the generalisability of the model by addressing known limitations of the model. One of the limitations of the model relates to the extent of external validity of the model. In order to gain wide acceptance, a model should be consistent and work well in different contexts. The IS-Impact model, however, was only validated in the Australian context, and packaged software was chosen as the IS understudy. Thus, this study is concerned with whether the model can be applied in another different context. Aiming for a robust and standardised measurement model that can be used across different contexts, this research re-validates and extends the IS-Impact model and its instrument to public sector organisations in Malaysia. The overarching research question (managerial question) of this research is “How can public sector organisations in Malaysia measure the impact of information systems systematically and effectively?” With two main objectives, the managerial question is broken down into two specific research questions. The first research question addresses the applicability (relevance) of the dimensions and measures of the IS-Impact model in the Malaysian context. Moreover, this research question addresses the completeness of the model in the new context. Initially, this research assumes that the dimensions and measures of the IS-Impact model are sufficient for the new context. However, some IS researchers suggest that the selection of measures needs to be done purposely for different contextual settings (DeLone & McLean, 1992, Rai, Lang & Welker, 2002). Thus, the first research question is as follows, “Is the IS-Impact model complete for measuring the impact of IS in Malaysian public sector organisations?” [RQ1]. The IS-Impact model is a multidimensional model that consists of four dimensions or constructs. Each dimension is represented by formative measures or indicators. Formative measures are known as composite variables because these measures make up or form the construct, or, in this case, the dimension in the IS-Impact model. These formative measures define different aspects of the dimension, thus, a measurement model of this kind needs to be tested not just on the structural relationship between the constructs but also the validity of each measure. In a previous study, the IS-Impact model was validated using formative validation techniques, as proposed in the literature (i.e., Diamantopoulos and Winklhofer, 2001, Diamantopoulos and Siguaw, 2006, Petter, Straub and Rai, 2007). However, there is potential for improving the validation testing of the model by adding more criterion or dependent variables. This includes identifying a consequence of the IS-Impact construct for the purpose of validation. Moreover, a different approach is employed in this research, whereby the validity of the model is tested using the Partial Least Squares (PLS) method, a component-based structural equation modelling (SEM) technique. Thus, the second research question addresses the construct validation of the IS-Impact model; “Is the IS-Impact model valid as a multidimensional formative construct?” [RQ2]. This study employs two rounds of surveys, each having a different and specific aim. The first is qualitative and exploratory, aiming to investigate the applicability and sufficiency of the IS-Impact dimensions and measures in the new context. This survey was conducted in a state government in Malaysia. A total of 77 valid responses were received, yielding 278 impact statements. The results from the qualitative analysis demonstrate the applicability of most of the IS-Impact measures. The analysis also shows a significant new measure having emerged from the context. This new measure was added as one of the System Quality measures. The second survey is a quantitative survey that aims to operationalise the measures identified from the qualitative analysis and rigorously validate the model. This survey was conducted in four state governments (including the state government that was involved in the first survey). A total of 254 valid responses were used in the data analysis. Data was analysed using structural equation modelling techniques, following the guidelines for formative construct validation, to test the validity and reliability of the constructs in the model. This study is the first research that extends the complete IS-Impact model in a new context that is different in terms of nationality, language and the type of information system (IS). The main contribution of this research is to present a comprehensive, up-to-date IS-Impact model, which has been validated in the new context. The study has accomplished its purpose of testing the generalisability of the IS-Impact model and continuing the IS evaluation research by extending it in the Malaysian context. A further contribution is a validated Malaysian language IS-Impact measurement instrument. It is hoped that the validated Malaysian IS-Impact instrument will encourage related IS research in Malaysia, and that the demonstrated model validity and generalisability will encourage a cumulative tradition of research previously not possible. The study entailed several methodological improvements on prior work, including: (1) new criterion measures for the overall IS-Impact construct employed in ‘identification through measurement relations’; (2) a stronger, multi-item ‘Satisfaction’ construct, employed in ‘identification through structural relations’; (3) an alternative version of the main survey instrument in which items are randomized (rather than blocked) for comparison with the main survey data, in attention to possible common method variance (no significant differences between these two survey instruments were observed); (4) demonstrates a validation process of formative indexes of a multidimensional, second-order construct (existing examples mostly involved unidimensional constructs); (5) testing the presence of suppressor effects that influence the significance of some measures and dimensions in the model; and (6) demonstrates the effect of an imbalanced number of measures within a construct to the contribution power of each dimension in a multidimensional model.

Prediction of the wash-off of traffic related semi- and non-volatile organic compounds from urban roads under climate change influenced rainfall characteristics

Traffic generated semi and non volatile organic compounds (SVOCs and NVOCs) pose a serious threat to human and ecosystem health when washed off into receiving water bodies by stormwater. Climate change influenced rainfall characteristics makes the estimation of these pollutants in stormwater quite complex. The research study discussed in the paper developed a prediction framework for such pollutants under the dynamic influence of climate change on rainfall characteristics. It was established through principal component analysis (PCA) that the intensity and durations of low to moderate rain events induced by climate change mainly affect the wash-off of SVOCs and NVOCs from urban roads. The study outcomes were able to overcome the limitations of stringent laboratory preparation of calibration matrices by extracting uncorrelated underlying factors in the data matrices through systematic application of PCA and factor analysis (FA). Based on the initial findings from PCA and FA, the framework incorporated orthogonal rotatable central composite experimental design to set up calibration matrices and partial least square regression to identify significant variables in predicting the target SVOCs and NVOCs in four particulate fractions ranging from >300-1 μm and one dissolved fraction of <1 μm. For the particulate fractions range >300-1 μm, similar distributions of predicted and observed concentrations of the target compounds from minimum to 75th percentile were achieved. The inter-event coefficient of variations for particulate fractions of >300-1 μm were 5% to 25%. The limited solubility of the target compounds in stormwater restricted the predictive capacity of the proposed method for the dissolved fraction of <1 μm.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

IS service quality as a multi-dimensional formative constuct

This study is motivated by, and proceeds from, a central interest in the importance of evaluating IS service quality and adopts the IS ZOT SERVQUAL instrument (Kettinger & Lee, 2005) as its core theory base. This study conceptualises IS service quality as a multidimensional formative construct and seeks to answer the main research questions: “Is the IS service quality construct valid as a 1st-order formative, 2nd-order formative multidimensional construct?” Additionally, with the aim of validating the IS service quality construct within its nomological net, as in prior service marketing work, Satisfaction was hypothesised as its immediate consequence. With the goal of testing the above research question, IS service quality and Satisfaction were operationalised in a quantitative survey instrument. Partial least squares (PLS), employing 219 valid responses, largely evidenced the validity of IS service quality as a multidimensional formative construct. The nomological validity of the IS service quality construct was also evidenced by demonstrating that 55% of Satisfaction was explained by the multidimensional formative IS service quality construct.

Evaluating the success of large-scale, integrated information systems through the lens of IS-impact and IS-support

This study proceeds from a central interest in the importance of systematically evaluating operational large-scale integrated information systems (IS) in organisations. The study is conducted within the IS-Impact Research Track at Queensland University of Technology (QUT). The goal of the IS-Impact Track is, "to develop the most widely employed model for benchmarking information systems in organizations for the joint benefit of both research and practice" (Gable et al, 2009). The track espouses programmatic research having the principles of incrementalism, tenacity, holism and generalisability through replication and extension research strategies. Track efforts have yielded the bicameral IS-Impact measurement model; the ‘impact’ half includes Organisational-Impact and Individual-Impact dimensions; the ‘quality’ half includes System-Quality and Information-Quality dimensions. Akin to Gregor’s (2006) analytic theory, the ISImpact model is conceptualised as a formative, multidimensional index and is defined as "a measure at a point in time, of the stream of net benefits from the IS, to date and anticipated, as perceived by all key-user-groups" (Gable et al., 2008, p: 381). The study adopts the IS-Impact model (Gable, et al., 2008) as its core theory base. Prior work within the IS-Impact track has been consciously constrained to Financial IS for their homogeneity. This study adopts a context-extension strategy (Berthon et al., 2002) with the aim "to further validate and extend the IS-Impact measurement model in a new context - i.e. a different IS - Human Resources (HR)". The overarching research question is: "How can the impacts of large-scale integrated HR applications be effectively and efficiently benchmarked?" This managerial question (Cooper & Emory, 1995) decomposes into two more specific research questions – In the new HR context: (RQ1): "Is the IS-Impact model complete?" (RQ2): "Is the ISImpact model valid as a 1st-order formative, 2nd-order formative multidimensional construct?" The study adhered to the two-phase approach of Gable et al. (2008) to hypothesise and validate a measurement model. The initial ‘exploratory phase’ employed a zero base qualitative approach to re-instantiating the IS-Impact model in the HR context. The subsequent ‘confirmatory phase’ sought to validate the resultant hypothesised measurement model against newly gathered quantitative data. The unit of analysis for the study is the application, ‘ALESCO’, an integrated large-scale HR application implemented at Queensland University of Technology (QUT), a large Australian university (with approximately 40,000 students and 5000 staff). Target respondents of both study phases were ALESCO key-user-groups: strategic users, management users, operational users and technical users, who directly use ALESCO or its outputs. An open-ended, qualitative survey was employed in the exploratory phase, with the objective of exploring the completeness and applicability of the IS-Impact model’s dimensions and measures in the new context, and to conceptualise any resultant model changes to be operationalised in the confirmatory phase. Responses from 134 ALESCO users to the main survey question, "What do you consider have been the impacts of the ALESCO (HR) system in your division/department since its implementation?" were decomposed into 425 ‘impact citations.’ Citation mapping using a deductive (top-down) content analysis approach instantiated all dimensions and measures of the IS-Impact model, evidencing its content validity in the new context. Seeking to probe additional (perhaps negative) impacts; the survey included the additional open question "In your opinion, what can be done better to improve the ALESCO (HR) system?" Responses to this question decomposed into a further 107 citations which in the main did not map to IS-Impact, but rather coalesced around the concept of IS-Support. Deductively drawing from relevant literature, and working inductively from the unmapped citations, the new ‘IS-Support’ construct, including the four formative dimensions (i) training, (ii) documentation, (iii) assistance, and (iv) authorisation (each having reflective measures), was defined as: "a measure at a point in time, of the support, the [HR] information system key-user groups receive to increase their capabilities in utilising the system." Thus, a further goal of the study became validation of the IS-Support construct, suggesting the research question (RQ3): "Is IS-Support valid as a 1st-order reflective, 2nd-order formative multidimensional construct?" With the aim of validating IS-Impact within its nomological net (identification through structural relations), as in prior work, Satisfaction was hypothesised as its immediate consequence. The IS-Support construct having derived from a question intended to probe IS-Impacts, too was hypothesised as antecedent to Satisfaction, thereby suggesting the research question (RQ4): "What is the relative contribution of IS-Impact and IS-Support to Satisfaction?" With the goal of testing the above research questions, IS-Impact, IS-Support and Satisfaction were operationalised in a quantitative survey instrument. Partial least squares (PLS) structural equation modelling employing 221 valid responses largely evidenced the validity of the commencing IS-Impact model in the HR context. ISSupport too was validated as operationalised (including 11 reflective measures of its 4 formative dimensions). IS-Support alone explained 36% of Satisfaction; IS-Impact alone 70%; in combination both explaining 71% with virtually all influence of ISSupport subsumed by IS-Impact. Key study contributions to research include: (1) validation of IS-Impact in the HR context, (2) validation of a newly conceptualised IS-Support construct as important antecedent of Satisfaction, and (3) validation of the redundancy of IS-Support when gauging IS-Impact. The study also makes valuable contributions to practice, the research track and the sponsoring organisation.

Extending and validating the is-impact model in Saudi Arabia : accounting for computer network quality

Organizations from every industry sector seek to enhance their business performance and competitiveness through the deployment of contemporary information systems (IS), such as Enterprise Systems (ERP). Investments in ERP are complex and costly, attracting scrutiny and pressure to justify their cost. Thus, IS researchers highlight the need for systematic evaluation of information system success, or impact, which has resulted in the introduction of varied models for evaluating information systems. One of these systematic measurement approaches is the IS-Impact Model introduced by a team of researchers at Queensland University of technology (QUT) (Gable, Sedera, & Chan, 2008). The IS-Impact Model is conceptualized as a formative, multidimensional index that consists of four dimensions. Gable et al. (2008) define IS-Impact as "a measure at a point in time, of the stream of net benefits from the IS, to date and anticipated, as perceived by all key-user-groups" (p.381). The IT Evaluation Research Program (ITE-Program) at QUT has grown the IS-Impact Research Track with the central goal of conducting further studies to enhance and extend the IS-Impact Model. The overall goal of the IS-Impact research track at QUT is "to develop the most widely employed model for benchmarking information systems in organizations for the joint benefit of both research and practice" (Gable, 2009). In order to achieve that, the IS-Impact research track advocates programmatic research having the principles of tenacity, holism, and generalizability through extension research strategies. This study was conducted within the IS-Impact Research Track, to further generalize the IS-Impact Model by extending it to the Saudi Arabian context. According to Hofsted (2012), the national culture of Saudi Arabia is significantly different from the Australian national culture making the Saudi Arabian culture an interesting context for testing the external validity of the IS-Impact Model. The study re-visits the IS-Impact Model from the ground up. Rather than assume the existing instrument is valid in the new context, or simply assess its validity through quantitative data collection, the study takes a qualitative, inductive approach to re-assessing the necessity and completeness of existing dimensions and measures. This is done in two phases: Exploratory Phase and Confirmatory Phase. The exploratory phase addresses the first research question of the study "Is the IS-Impact Model complete and able to capture the impact of information systems in Saudi Arabian Organization?". The content analysis, used to analyze the Identification Survey data, indicated that 2 of the 37 measures of the IS-Impact Model are not applicable for the Saudi Arabian Context. Moreover, no new measures or dimensions were identified, evidencing the completeness and content validity of the IS-Impact Model. In addition, the Identification Survey data suggested several concepts related to IS-Impact, the most prominent of which was "Computer Network Quality" (CNQ). The literature supported the existence of a theoretical link between IS-Impact and CNQ (CNQ is viewed as an antecedent of IS-Impact). With the primary goal of validating the IS-Impact model within its extended nomological network, CNQ was introduced to the research model. The Confirmatory Phase addresses the second research question of the study "Is the Extended IS-Impact Model Valid as a Hierarchical Multidimensional Formative Measurement Model?". The objective of the Confirmatory Phase was to test the validity of IS-Impact Model and CNQ Model. To achieve that, IS-Impact, CNQ, and IS-Satisfaction were operationalized in a survey instrument, and then the research model was assessed by employing the Partial Least Squares (PLS) approach. The CNQ model was validated as a formative model. Similarly, the IS-Impact Model was validated as a hierarchical multidimensional formative construct. However, the analysis indicated that one of the IS-Impact Model indicators was insignificant and can be removed from the model. Thus, the resulting Extended IS-Impact Model consists of 4 dimensions and 34 measures. Finally, the structural model was also assessed against two aspects: explanatory and predictive power. The analysis revealed that the path coefficient between CNQ and IS-Impact is significant with t-value= (4.826) and relatively strong with â = (0.426) with CNQ explaining 18% of the variance in IS-Impact. These results supported the hypothesis that CNQ is antecedent of IS-Impact. The study demonstrates that the quality of Computer Network affects the quality of the Enterprise System (ERP) and consequently the impacts of the system. Therefore, practitioners should pay attention to the Computer Network quality. Similarly, the path coefficient between IS-Impact and IS-Satisfaction was significant t-value = (17.79) and strong â = (0.744), with IS-Impact alone explaining 55% of the variance in Satisfaction, consistent with results of the original IS-Impact study (Gable et al., 2008). The research contributions include: (a) supporting the completeness and validity of IS-Impact Model as a Hierarchical Multi-dimensional Formative Measurement Model in the Saudi Arabian context, (b) operationalizing Computer Network Quality as conceptualized in the ITU-T Recommendation E.800 (ITU-T, 1993), (c) validating CNQ as a formative measurement model and as an antecedent of IS Impact, and (d) conceptualizing and validating IS-Satisfaction as a reflective measurement model and as an immediate consequence of IS Impact. The CNQ model provides a framework to perceptually measure Computer Network Quality from multiple perspectives. The CNQ model features an easy-to-understand, easy-to-use, and economical survey instrument.

In pursuit of tourism destination brand loyalty : visitor relationship management orientation of destination marketing organisations

The proposition underpinning this study is engaging in meaningful dialogue with previous visitors represents an efficient and effective use of resources for a destination marketing organization (DMO), compared to above the line advertising in broadcast media. However there has been a lack of attention in the tourism literature relating to destination switching, loyalty and customer relationship management (CRM) to test such a proposition. This paper reports an investigation of visitor relationship marketing (VRM) orientation among DMOs. A model of CRM orientation, which was developed from the wider marketing literature and a prior qualitative study, was used to develop a scale to operationalise DMO visitor relationship orientation. Due to a small sample, the Partial Least Squares (PLS) method of structural equation modelling was used to analyse the data. Although the sample limits the ability to generalise, the results indicated the DMOs’ visitor orientation is generally responsive and reactive rather than proactive.

Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models

Determining the properties and integrity of subchondral bone in the developmental stages of osteoarthritis, especially in a form that can facilitate real-time characterization for diagnostic and decision-making purposes, is still a matter for research and development. This paper presents relationships between near infrared absorption spectra and properties of subchondral bone obtained from 3 models of osteoarthritic degeneration induced in laboratory rats via: (i) menisectomy (MSX); (ii) anterior cruciate ligament transaction (ACL); and (iii) intra-articular injection of mono-ido-acetate (1 mg) (MIA), in the right knee joint, with 12 rats per model group (N = 36). After 8 weeks, the animals were sacrificed and knee joints were collected. A custom-made diffuse reflectance NIR probe of diameter 5 mm was placed on the tibial surface and spectral data were acquired from each specimen in the wavenumber range 4000–12 500 cm− 1. After spectral acquisition, micro computed tomography (micro-CT) was performed on the samples and subchondral bone parameters namely: bone volume (BV) and bone mineral density (BMD) were extracted from the micro-CT data. Statistical correlation was then conducted between these parameters and regions of the near infrared spectra using multivariate techniques including principal component analysis (PCA), discriminant analysis (DA), and partial least squares (PLS) regression. Statistically significant linear correlations were found between the near infrared absorption spectra and subchondral bone BMD (R2 = 98.84%) and BV (R2 = 97.87%). In conclusion, near infrared spectroscopic probing can be used to detect, qualify and quantify changes in the composition of the subchondral bone, and could potentially assist in distinguishing healthy from OA bone as demonstrated with our laboratory rat models.

Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra

The conventional mechanical properties of articular cartilage, such as compressive stiffness, have been demonstrated to be limited in their capacity to distinguish intact (visually normal) from degraded cartilage samples. In this paper, we explore the correlation between a new mechanical parameter, namely the reswelling of articular cartilage following unloading from a given compressive load, and the near infrared (NIR) spectrum. The capacity to distinguish mechanically intact from proteoglycan-depleted tissue relative to the "reswelling" characteristic was first established, and the result was subsequently correlated with the NIR spectral data of the respective tissue samples. To achieve this, normal intact and enzymatically degraded samples were subjected to both NIR probing and mechanical compression based on a load-unload-reswelling protocol. The parameter δ(r), characteristic of the osmotic "reswelling" of the matrix after unloading to a constant small load in the order of the osmotic pressure of cartilage, was obtained for the different sample types. Multivariate statistics was employed to determine the degree of correlation between δ(r) and the NIR absorption spectrum of relevant specimens using Partial Least Squared (PLS) regression. The results show a strong relationship (R(2)=95.89%, p<0.0001) between the spectral data and δ(r). This correlation of δ(r) with NIR spectral data suggests the potential for determining the reswelling characteristics non-destructively. It was also observed that δ(r) values bear a significant relationship with the cartilage matrix integrity, indicated by its proteoglycan content, and can therefore differentiate between normal and artificially degraded proteoglycan-depleted cartilage samples. It is therefore argued that the reswelling of cartilage, which is both biochemical (osmotic) and mechanical (hydrostatic pressure) in origin, could be a strong candidate for characterizing the tissue, especially in regions surrounding focal cartilage defects in joints.