Internal market orientation and market-oriented behaviours

Purpose: The purpose of this empirical paper is to investigate internal marketing from a behavioural perspective. The impact of internal marketing behaviours, operationalised as an internal market orientation (IMO), on employees’ marketing and other in-role behaviours (IRB) were examined. ---------- Design/methodology/approach: Survey data measuring IMO, market orientation and a range of constructs relevant to the nomological network in which they are embedded were collected from the UK retail managers. These were tested to establish their psychometric properties and the conceptual model was analysed using structural equations modelling, employing a partial least squares methodology. ---------- Findings: IMO has positive consequences for employees’ market-oriented and other IRB. These, in turn, influence marketing success. Research limitations/implications – The paper provides empirical support for the long-held assumption that internal and external marketing are related and that organisations should balance their external focus with some attention to employees. Future research could measure the attitudes and behaviours of managers, employees and customers directly and explore the relationships between them. ---------- Practical implications: Firm must ensure that they do not put the needs of their employees second to those of managers and shareholders; managers must develop their listening skills and organisations must become more responsive to the needs of their employees. ---------- Originality/value: The paper contributes to the scarce body of empirical support for the role of internal marketing in services organisations. For researchers, this paper legitimises the study of internal marketing as a route to external market success; for managers, the study provides quantifiable evidence that focusing on employees’ wants and needs impacts their behaviours towards the market.

Geometry‐specified troposphere decorrelation for subcentimeter real‐time kinematic solutions over long baselines

Real‐time kinematic (RTK) GPS techniques have been extensively developed for applications including surveying, structural monitoring, and machine automation. Limitations of the existing RTK techniques that hinder their applications for geodynamics purposes are twofold: (1) the achievable RTK accuracy is on the level of a few centimeters and the uncertainty of vertical component is 1.5–2 times worse than those of horizontal components and (2) the RTK position uncertainty grows in proportional to the base‐torover distances. The key limiting factor behind the problems is the significant effect of residual tropospheric errors on the positioning solutions, especially on the highly correlated height component. This paper develops the geometry‐specified troposphere decorrelation strategy to achieve the subcentimeter kinematic positioning accuracy in all three components. The key is to set up a relative zenith tropospheric delay (RZTD) parameter to absorb the residual tropospheric effects and to solve the established model as an ill‐posed problem using the regularization method. In order to compute a reasonable regularization parameter to obtain an optimal regularized solution, the covariance matrix of positional parameters estimated without the RZTD parameter, which is characterized by observation geometry, is used to replace the quadratic matrix of their “true” values. As a result, the regularization parameter is adaptively computed with variation of observation geometry. The experiment results show that new method can efficiently alleviate the model’s ill condition and stabilize the solution from a single data epoch. Compared to the results from the conventional least squares method, the new method can improve the longrange RTK solution precision from several centimeters to the subcentimeter in all components. More significantly, the precision of the height component is even higher. Several geosciences applications that require subcentimeter real‐time solutions can largely benefit from the proposed approach, such as monitoring of earthquakes and large dams in real‐time, high‐precision GPS leveling and refinement of the vertical datum. In addition, the high‐resolution RZTD solutions can contribute to effective recovery of tropospheric slant path delays in order to establish a 4‐D troposphere tomography.

A Modified inverse integer Cholesky decorrelation method and the performance on ambiguity resolution

One of the research focuses in the integer least squares problem is the decorrelation technique to reduce the number of integer parameter search candidates and improve the efficiency of the integer parameter search method. It remains as a challenging issue for determining carrier phase ambiguities and plays a critical role in the future of GNSS high precise positioning area. Currently, there are three main decorrelation techniques being employed: the integer Gaussian decorrelation, the Lenstra–Lenstra–Lovász (LLL) algorithm and the inverse integer Cholesky decorrelation (IICD) method. Although the performance of these three state-of-the-art methods have been proved and demonstrated, there is still a potential for further improvements. To measure the performance of decorrelation techniques, the condition number is usually used as the criterion. Additionally, the number of grid points in the search space can be directly utilized as a performance measure as it denotes the size of search space. However, a smaller initial volume of the search ellipsoid does not always represent a smaller number of candidates. This research has proposed a modified inverse integer Cholesky decorrelation (MIICD) method which improves the decorrelation performance over the other three techniques. The decorrelation performance of these methods was evaluated based on the condition number of the decorrelation matrix, the number of search candidates and the initial volume of search space. Additionally, the success rate of decorrelated ambiguities was calculated for all different methods to investigate the performance of ambiguity validation. The performance of different decorrelation methods was tested and compared using both simulation and real data. The simulation experiment scenarios employ the isotropic probabilistic model using a predetermined eigenvalue and without any geometry or weighting system constraints. MIICD method outperformed other three methods with conditioning improvements over LAMBDA method by 78.33% and 81.67% without and with eigenvalue constraint respectively. The real data experiment scenarios involve both the single constellation system case and dual constellations system case. Experimental results demonstrate that by comparing with LAMBDA, MIICD method can significantly improve the efficiency of reducing the condition number by 78.65% and 97.78% in the case of single constellation and dual constellations respectively. It also shows improvements in the number of search candidate points by 98.92% and 100% in single constellation case and dual constellations case.

Information sharing with key suppliers : a transaction cost theory perspective

Purpose: The purpose of this paper is to explain variations in discretionary information shared between buyers and key suppliers. The paper also aims to examine how the extent of information shared affects buyers’ performance in terms of resource usage, output, and flexibility. ----- ----- Design/methodology/approach: The data for the paper comprise 221 Finnish and Swedish non-service companies obtained through a mail survey. The hypothesized relationships were tested using partial least squares modelling with reflective and formative constructs.----- ----- Findings: The results of the study suggest that (environmental and demand) uncertainty and interdependency can to some degree explain the extent of information shared between a buyer and key supplier. Furthermore, information sharing improves buyers’ performance with respect to resource usage, output, and flexibility.----- ----- Research limitations/implications: A limitation to the paper relates to the data, which only included buyers.Abetter approach would have been to collect data from both, buyers and key suppliers. Practical implications – Companies face a wide range of supply chain solutions that enable and encourage collaboration across organizations. This paper suggests a more selective and balanced approach toward adopting the solutions offered as the benefits are contingent on a number of factors such as uncertainty. Also, the risks of information sharing are far too high for a one size fits all approach.----- ----- Originality/value: The paper illustrates the applicability of transaction cost theory to the contemporary era of e-commerce. With this finding, transaction cost economics can provide a valuable lens with which to view and interpret interorganizational information sharing, a topic that has received much attention in the recent years.

Prediction model of the buildup of volatile organic compounds on urban roads

A model to predict the buildup of mainly traffic-generated volatile organic compounds or VOCs (toluene, ethylbenzene, ortho-xylene, meta-xylene, and para-xylene) on urban road surfaces is presented. The model required three traffic parameters, namely average daily traffic (ADT), volume to capacity ratio (V/C), and surface texture depth (STD), and two chemical parameters, namely total suspended solid (TSS) and total organic carbon (TOC), as predictor variables. Principal component analysis and two phase factor analysis were performed to characterize the model calibration parameters. Traffic congestion was found to be the underlying cause of traffic-related VOC buildup on urban roads. The model calibration was optimized using orthogonal experimental design. Partial least squares regression was used for model prediction. It was found that a better optimized orthogonal design could be achieved by including the latent factors of the data matrix into the design. The model performed fairly accurately for three different land uses as well as five different particle size fractions. The relative prediction errors were 10–40% for the different size fractions and 28–40% for the different land uses while the coefficients of variation of the predicted intersite VOC concentrations were in the range of 25–45% for the different size fractions. Considering the sizes of the data matrices, these coefficients of variation were within the acceptable interlaboratory range for analytes at ppb concentration levels.

Study of the interaction between 10-hydroxycamptothecine and DNA with the use of ethidium bromide dye as a fluorescence probe

The interaction of 10-hydroxycamptothecine (HCPT) with DNA under pseudo-physiological conditions (Tris-HCl buffer of pH 7.4), using ethidium bromide (EB) dye as a probe, was investigated with the use of spectrofluorimetry, UV-vis spectrometry and viscosity measurement. The binding constant and binding number for HCPT with DNA were evaluated as (7.1 ± 0.5) × 104 M-1 and 1.1, respectively, by multivariate curve resolution-alternating least squares (MCR-ALS). Moreover, parallel factor analysis (PARAFAC) was applied to resolve the three-way fluorescence data obtained from the interaction system, and the concentration information for the three components of the system at equilibrium was simultaneously obtained. It was found that there was a cooperative interaction between the HCPT-DNA complex and EB, which produced a ternary complex of HCPT-DNA-EB. © 2011 Elsevier B.V.

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.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

Use of virtual index for measuring efficiency of innovation systems : a cross-country study

In this study we propose a virtual index for measuring the relative innovativeness of countries. Using a multistage virtual benchmarking process, the best and rational benchmark is extracted for inefficient ISs. Furthermore, Tobit and Ordinary Least Squares (OLS) regression models are used to investigate the likelihood of changes in inefficiencies by investigating country-specific factors. The empirical results relating to the virtual benchmarking process suggest that the OLS regression model would better explain changes in the performance of innovation- inefficient countries.

The rademacher complexity of coregularized kernel classes

In the multi-view approach to semisupervised learning, we choose one predictor from each of multiple hypothesis classes, and we co-regularize our choices by penalizing disagreement among the predictors on the unlabeled data. We examine the co-regularization method used in the co-regularized least squares (CoRLS) algorithm, in which the views are reproducing kernel Hilbert spaces (RKHS's), and the disagreement penalty is the average squared difference in predictions. The final predictor is the pointwise average of the predictors from each view. We call the set of predictors that can result from this procedure the co-regularized hypothesis class. Our main result is a tight bound on the Rademacher complexity of the co-regularized hypothesis class in terms of the kernel matrices of each RKHS. We find that the co-regularization reduces the Rademacher complexity by an amount that depends on the distance between the two views, as measured by a data dependent metric. We then use standard techniques to bound the gap between training error and test error for the CoRLS algorithm. Experimentally, we find that the amount of reduction in complexity introduced by co regularization correlates with the amount of improvement that co-regularization gives in the CoRLS algorithm.

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.

Information sharing with key suppliers : a contingency approach

This paper focuses on information sharing with key suppliers and seeks to explore the factors that might influence its extent and depth. We also investigate how information sharing affects a company’s performance with regards to resource usage, output, and flexibility. Drawing from transaction cost- and contingency theories, several factors, namely environmental uncertainty, demand uncertainty, dependency and, the product life cycle stage are proposed to explain the level of information shared with key suppliers. We develop a model where information sharing mediates the (contingent) factors and company performance. A mail survey was used to collect data from Finnish and Swedish companies. Partial Least Squares analysis was separately performed for each country (n=119, n=102). There was consistent evidence that environmental uncertainty, demand uncertainty and supplier/buyer dependency had explanatory power, whereas no significance was found for the product life cycle stage. The results also confirm previous studies by providing support for a positive relationship between information sharing and performance, where output performance was found to be the most strongly related