Business level strategy and performance: the moderating effects of environment and structure

Purpose – This study aims to examine the moderating effects of external environment and organisational structure in the relationship between business-level strategy and organisational performance. Design/methodology/approach – The focus of the study is on manufacturing firms in the UK belonging to the electrical and mechanical engineering sectors, and respondents were CEOs. Both objective and subjective measures were used to assess performance. Non-response bias was assessed statistically and appropriate measures taken to minimise the impact of common method variance (CMV). Findings – The results indicate that environmental dynamism and hostility act as moderators in the relationship between business-level strategy and relative competitive performance. In low-hostility environments a cost-leadership strategy and in high-hostility environments a differentiation strategy lead to better performance compared with competitors. In highly dynamic environments a cost-leadership strategy and in low dynamism environments a differentiation strategy are more helpful in improving financial performance. Organisational structure moderates the relationship of both the strategic types with ROS. However, in the case of ROA, the moderating effect of structure was found only in its relationship with cost-leadership strategy. A mechanistic structure is helpful in improving the financial performance of organisations adopting either a cost-leadership or a differentiation strategy. Originality/value – Unlike many other empirical studies, the study makes an important contribution to the literature by examining the moderating effects of both environment and structure on the relationship between business-level strategy and performance in a detailed manner, using moderated regression analysis.

Generic strategies and performance: evidence from manufacturing firms

Purpose – The purpose of this study is to examine the relationship between business-level strategy and organisational performance and to test the applicability of Porter's generic strategies in explaining differences in the performance of organisations. Design/methodology/approach – The study was focussed on manufacturing firms in the UK belonging to the electrical and mechanical engineering sectors. Data were collected through a postal survey using the survey instrument from 124 organisations and the respondents were all at CEO level. Both objective and subjective measures were used to assess performance. Non-response bias was assessed statistically and it was not found to be a major problem affecting this study. Appropriate measures were taken to ensure that common method variance (CMV) does not affect the results of this study. Statistical tests indicated that CMV problem does not affect the results of this study. Findings – The results of this study indicate that firms adopting one of the strategies, namely cost-leadership or differentiation, perform better than “stuck-in-the-middle” firms which do not have a dominant strategic orientation. The integrated strategy group has lower performance compared with cost-leaders and differentiators in terms of financial performance measures. This provides support for Porter's view that combination strategies are unlikely to be effective in organisations. However, the cost-leadership and differentiation strategies were not strongly correlated with the financial performance measures indicating the limitations of Porter's generic strategies in explaining performance heterogeneity in organisations. Originality/value – This study makes an important contribution to the literature by identifying some of the gaps in the literature through a systematic literature review and addressing those gaps.

The relationship between challenging parenting behaviour and childhood anxiety disorders

Background: This research investigates the relationship between challenging parenting behaviour and childhood anxiety disorders proposed by Bögels and Phares (2008). Challenging parenting behaviour involves the playful encouragement of children to go beyond their own limits, and may decrease children’s risk for anxiety (Bögels & Phares, 2008). Method: Parents (n = 164 mothers, 144 fathers) of 164 children aged between 3.4 and 4.8 years participated in the current study. A multi-method, multi-informant assessment of anxiety was used, incorporating data from diagnostic interviews as well as questionnaire measures. Parents completed self-report measures of their parenting behaviour (n = 147 mothers, 138 fathers) and anxiety (n = 154 mothers, 143 fathers). Mothers reported on their child’s anxiety via questionnaire as well as diagnostic interview (n = 156 and 164 respectively). Of these children, 74 met criteria for an anxiety disorder and 90 did not. Results: Fathers engaged in challenging parenting behaviour more often than mothers. Both mothers’ and fathers’ challenging parenting behaviour was associated with lower report of child anxiety symptoms. However, only mothers’ challenging parenting behaviour was found to predict child clinical anxiety diagnosis. Limitations: Shared method variance from mothers confined the interpretation of these results. Moreover, due to study design, it is not possible to delineate cause and effect. Conclusions: The finding with respect to maternal challenging parenting behaviour was not anticipated, prompting replication of these results. Future research should investigate the role of challenging parenting behaviour by both caregivers as this may have implications for parenting interventions for anxious children.

Equatorial waves in the lower stratosphere. I: A novel detection method

Wavenumber-frequency spectral analysis and linear wave theory are combined in a novel method to quantitatively estimate equatorial wave activity in the tropical lower stratosphere. The method requires temperature and velocity observations that are regularly spaced in latitude, longitude and time; it is therefore applied to the ECMWF 15-year re-analysis dataset (ERA-15). Signals consistent with idealized Kelvin and Rossby-gravity waves are found at wavenumbers and frequencies in agreement with previous studies. When averaged over 1981-93, the Kelvin wave explains approximately 1 K-2 of temperature variance on the equator at 100 hPa, while the Rossby-gravity wave explains approximately 1 m(2)s(-2) of meridional wind variance. Some inertio-gravity wave and equatorial Rossby wave signals are also found; however the resolution of ERA-15 is not sufficient for the method to provide an accurate climatology of waves with high meridional structure.

A jackknife variance estimator for unequal probability sampling

The jackknife method is often used for variance estimation in sample surveys but has only been developed for a limited class of sampling designs.We propose a jackknife variance estimator which is defined for any without-replacement unequal probability sampling design. We demonstrate design consistency of this estimator for a broad class of point estimators. A Monte Carlo study shows how the proposed estimator may improve on existing estimators.

Variance estimation with highly stratified sampling designs with unequal probabilities

It is common practice to design a survey with a large number of strata. However, in this case the usual techniques for variance estimation can be inaccurate. This paper proposes a variance estimator for estimators of totals. The method proposed can be implemented with standard statistical packages without any specific programming, as it involves simple techniques of estimation, such as regression fitting.

Variance estimation with Chao's sampling scheme

We show that the Hájek (Ann. Math Statist. (1964) 1491) variance estimator can be used to estimate the variance of the Horvitz–Thompson estimator when the Chao sampling scheme (Chao, Biometrika 69 (1982) 653) is implemented. This estimator is simple and can be implemented with any statistical packages. We consider a numerical and an analytic method to show that this estimator can be used. A series of simulations supports our findings.

A method for estimating the turbulent kinetic energy dissipation rate from a vertically pointing Doppler lidar, and independent evaluation from balloon-borne in situ measurements

A method of estimating dissipation rates from a vertically pointing Doppler lidar with high temporal and spatial resolution has been evaluated by comparison with independent measurements derived from a balloon-borne sonic anemometer. This method utilizes the variance of the mean Doppler velocity from a number of sequential samples and requires an estimate of the horizontal wind speed. The noise contribution to the variance can be estimated from the observed signal-to-noise ratio and removed where appropriate. The relative size of the noise variance to the observed variance provides a measure of the confidence in the retrieval. Comparison with in situ dissipation rates derived from the balloon-borne sonic anemometer reveal that this particular Doppler lidar is capable of retrieving dissipation rates over a range of at least three orders of magnitude. This method is most suitable for retrieval of dissipation rates within the convective well-mixed boundary layer where the scales of motion that the Doppler lidar probes remain well within the inertial subrange. Caution must be applied when estimating dissipation rates in more quiescent conditions. For the particular Doppler lidar described here, the selection of suitably short integration times will permit this method to be applicable in such situations but at the expense of accuracy in the Doppler velocity estimates. The two case studies presented here suggest that, with profiles every 4 s, reliable estimates of ϵ can be derived to within at least an order of magnitude throughout almost all of the lowest 2 km and, in the convective boundary layer, to within 50%. Increasing the integration time for individual profiles to 30 s can improve the accuracy substantially but potentially confines retrievals to within the convective boundary layer. Therefore, optimization of certain instrument parameters may be required for specific implementations.

A method to diagnose boundary-layer type using Doppler lidar

A new technique for objective classification of boundary layers is applied to ground-based vertically pointing Doppler lidar and sonic anemometer data. The observed boundary layer has been classified into nine different types based on those in the Met Office ‘Lock’ scheme, using vertical velocity variance and skewness, along with attenuated backscatter coefficient and surface sensible heat flux. This new probabilistic method has been applied to three years of data from Chilbolton Observatory in southern England and a climatology of boundary-layer type has been created. A clear diurnal cycle is present in all seasons. The most common boundary-layer type is stable with no cloud (30.0% of the dataset). The most common unstable type is well mixed with no cloud (15.4%). Decoupled stratocumulus is the third most common boundary-layer type (10.3%) and cumulus under stratocumulus occurs 1.0% of the time. The occurrence of stable boundary-layer types is much higher in the winter than the summer and boundary-layer types capped with cumulus cloud are more prevalent in the warm seasons. The most common diurnal evolution of boundary-layer types, occurring on 52 days of our three-year dataset, is that of no cloud with the stability changing from stable to unstable during daylight hours. These results are based on 16393 hours, 62.4% of the three-year dataset, of diagnosed boundary-layer type. This new method is ideally suited to long-term evaluation of boundary-layer type parametrisations in weather forecast and climate models.

A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method

In this paper a modified algorithm is suggested for developing polynomial neural network (PNN) models. Optimal partial description (PD) modeling is introduced at each layer of the PNN expansion, a task accomplished using the orthogonal least squares (OLS) method. Based on the initial PD models determined by the polynomial order and the number of PD inputs, OLS selects the most significant regressor terms reducing the output error variance. The method produces PNN models exhibiting a high level of accuracy and superior generalization capabilities. Additionally, parsimonious models are obtained comprising a considerably smaller number of parameters compared to the ones generated by means of the conventional PNN algorithm. Three benchmark examples are elaborated, including modeling of the gas furnace process as well as the iris and wine classification problems. Extensive simulation results and comparison with other methods in the literature, demonstrate the effectiveness of the suggested modeling approach.

An optimal inverse method using Doppler lidar measurements to estimate the surface sensible heat flux

Inverse methods are widely used in various fields of atmospheric science. However, such methods are not commonly used within the boundary-layer community, where robust observations of surface fluxes are a particular concern. We present a new technique for deriving surface sensible heat fluxes from boundary-layer turbulence observations using an inverse method. Doppler lidar observations of vertical velocity variance are combined with two well-known mixed-layer scaling forward models for a convective boundary layer (CBL). The inverse method is validated using large-eddy simulations of a CBL with increasing wind speed. The majority of the estimated heat fluxes agree within error with the proscribed heat flux, across all wind speeds tested. The method is then applied to Doppler lidar data from the Chilbolton Observatory, UK. Heat fluxes are compared with those from a mast-mounted sonic anemometer. Errors in estimated heat fluxes are on average 18 %, an improvement on previous techniques. However, a significant negative bias is observed (on average −63%) that is more pronounced in the morning. Results are improved for the fully-developed CBL later in the day, which suggests that the bias is largely related to the choice of forward model, which is kept deliberately simple for this study. Overall, the inverse method provided reasonable flux estimates for the simple case of a CBL. Results shown here demonstrate that this method has promise in utilizing ground-based remote sensing to derive surface fluxes. Extension of the method is relatively straight-forward, and could include more complex forward models, or other measurements.

A Variance-Minimizing Filter for Large-Scale Applications

A truly variance-minimizing filter is introduced and its per for mance is demonstrated with the Korteweg– DeV ries (KdV) equation and with a multilayer quasigeostrophic model of the ocean area around South Africa. It is recalled that Kalman-like filters are not variance minimizing for nonlinear model dynamics and that four - dimensional variational data assimilation (4DV AR)-like methods relying on per fect model dynamics have dif- ficulty with providing error estimates. The new method does not have these drawbacks. In fact, it combines advantages from both methods in that it does provide error estimates while automatically having balanced states after analysis, without extra computations. It is based on ensemble or Monte Carlo integrations to simulate the probability density of the model evolution. When obser vations are available, the so-called importance resampling algorithm is applied. From Bayes’ s theorem it follows that each ensemble member receives a new weight dependent on its ‘ ‘distance’ ’ t o the obser vations. Because the weights are strongly var ying, a resampling of the ensemble is necessar y. This resampling is done such that members with high weights are duplicated according to their weights, while low-weight members are largely ignored. In passing, it is noted that data assimilation is not an inverse problem by nature, although it can be for mulated that way . Also, it is shown that the posterior variance can be larger than the prior if the usual Gaussian framework is set aside. However , i n the examples presented here, the entropy of the probability densities is decreasing. The application to the ocean area around South Africa, gover ned by strongly nonlinear dynamics, shows that the method is working satisfactorily . The strong and weak points of the method are discussed and possible improvements are proposed.