Social media and Web 2.0 for knowledge sharing in product design

Working collaboratively with internal and external partners (suppliers, customers and internal stakeholders) has been at the epicentre of product design. Knowledge sharing has been well recognised in this context. However, there is limited research that has addressed the role of social media/Web 2.0 in facilitating knowledge sharing for sense- and decision-making within product design. To address this gap, this study draws on the resource-based view (RBV) of the firm and two vignettes that relate to ‘collaborative co-design’ and ‘collaborative design-to-order’. We illustrate the role of social media/Web 2.0 in building knowledge sharing capabilities for sense- and decision-making for internal and external partners during product design. Limitations and further research into the use of social media/Web 2.0 are also discussed.     

Performance of ridge estimator in inverse Gaussian regression model

The presence of multicollinearity among the explanatory variables has undesirable effects on the maximum likelihood estimator (MLE). Ridge estimator (RE) is a widely used estimator in overcoming this issue. The RE enjoys the advantage that its mean squared error (MSE) is less than that of MLE. The inverse Gaussian regression (IGR) model is a well-known model in the application when the response variable positively skewed. The purpose of this paper is to derive the RE of the IGR under multicollinearity problem. In addition, the performance of this estimator is investigated under numerous methods for estimating the ridge parameter. Monte Carlo simulation results indicate that the suggested estimator performs better than the MLE estimator in terms of MSE. Furthermore, a real chemometrics dataset application is utilized and the results demonstrate the excellent performance of the suggested estimator when the multicollinearity is present in IGR model.     

Focused estimation for noisy and small data sets: a Bayesian minimum expected loss estimator approach

Central to many inferential situations is the estimation of rational functions of parameters. The mainstream in statistics and econometrics estimates these quantities based on the plug‐in approach without consideration of the main objective of the inferential situation. We propose the Bayesian Minimum Expected Loss (MELO) approach focusing explicitly on the function of interest, and calculating its frequentist variability. Asymptotic properties of the MELO estimator are similar to the plug‐in approach. Nevertheless, simulation exercises show that our proposal is better in situations characterised by small sample sizes and/or noisy data sets. In addition, we observe in the applications that our approach gives lower standard errors than frequently used alternatives when data sets are not very informative.     

On classical and Bayesian order restricted inference for multiple exponential step stress model

In this article, we consider the multiple step stress model based on the cumulative exposure model assumption. Here, it is assumed that for a given stress level, the lifetime of the experimental units follows exponential distribution and the expected lifetime decreases as the stress level increases. We mainly focus on the order restricted inference of the unknown parameters of the lifetime distributions. First we consider the order restricted maximum likelihood estimators (MLEs) of the model parameters. It is well known that the order restricted MLEs cannot be obtained in explicit forms. We propose an algorithm that stops in finite number of steps and it provides the MLEs. We further consider the Bayes estimates and the associated credible intervals under the squared error loss function. Due to the absence of explicit form of the Bayes estimates, we propose to use the importance sampling technique to compute Bayes estimates. We provide an extensive simulation study in case of three stress levels mainly to see the performance of the proposed methods. Finally the analysis of one real data set has been provided for illustrative purposes.     

Selecting better process based on difference statistic using double sampling plan

Supplier management plays an important role in the quality improvement process. Recently, supplier selection has received much attention in many production industries. In this paper, we propose a double sampling plan for selecting a better supplier comparing two suppliers with linear profiles. The difference test statistic based on the process yield index S_pkA is used to determine the optimal parameters of the proposed double sampling plan. The efficiency of the proposed sampling plan is compared with the existing single sampling plan.     

Bayesian inverse problems with heterogeneous variance

We consider inverse problems in Hilbert spaces under correlated Gaussian noise, and use a Bayesian approach to find their regularized solution. We focus on mildly ill-posed inverse problems with fractional noise, using a novel wavelet-based vaguelette–vaguelette approach. It allows us to apply sequence space methods without assuming that all operators are simultaneously diagonalizable. The results are proved for more general bases and covariance operators. Our primary aim is to study posterior contraction rate in such inverse problems over Sobolev classes and compare it to the derived minimax rate. Secondly, we study effect of plugging in a consistent estimator of variances in sequence space on the posterior contraction rate. This result is applied to the problem with error in forward operator. Thirdly, we show that empirical Bayes posterior distribution with a plugged-in maximum marginal likelihood estimator of the prior scale contracts at the optimal rate, adaptively, in the minimax sense.     

the Block–Block Bootstrap: Improved Asymptotic Refinements

The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the join‐point problem. In this paper, we propose a method of solving this problem. The idea is not to alter the block bootstrap. Instead, we alter the original sample statistics to which the block bootstrap is applied. We introduce block statistics that possess join‐point features that are similar to those of the block bootstrap versions of these statistics. We refer to the application of the block bootstrap to block statistics as the block–block bootstrap. The asymptotic refinements of the block–block bootstrap are shown to be greater than those obtained with the block bootstrap and close to those obtained with the nonparametric iid bootstrap and parametric bootstrap.     

Adaptive Local Polynomial Whittle Estimation of Long‐range Dependence

The local Whittle (or Gaussian semiparametric) estimator of long range dependence, proposed by Künsch (1987) and analyzed by Robinson (1995a), has a relatively slow rate of convergence and a finite sample bias that can be large. In this paper, we generalize the local Whittle estimator to circumvent these problems. Instead of approximating the short‐run component of the spectrum, ϕ(λ) , by a constant in a shrinking neighborhood of frequency zero, we approximate its logarithm by a polynomial. This leads to a “local polynomial Whittle” (LPW) estimator. We specify a data‐dependent adaptive procedure that adjusts the degree of the polynomial to the smoothness of ϕ(λ) at zero and selects the bandwidth. The resulting “adaptive LPW” estimator is shown to achieve the optimal rate of convergence, which depends on the smoothness of ϕ(λ) at zero, up to a logarithmic factor.     

Incorporating Price and Replacement Purchases in New Product Diffusion Models for Consumer Durables*

The article presents theoretical and empirical research findings which incorporate price and replacement purchases in new product diffusion models. On the theoretical side, this paper characterizes, qualitatively, optimum pricing policies for new products. Possible entry of rivals is not considered, but repeat sales, cost learning dynamics and discounting of future profit streams are allowed. Theoretical research findings suggest that the inclusion of repeat purchases in the diffusion model significantly changes the derived optimal pricing policy even if replacements were not price dependent. On the empirical side, alternative first purchase and repeat purchase models have been estimated and compared using nonlinear procedures. The diffusion data analyzed is related to nine consumer durables. Empirical research findings suggest that, for the considered product categories, diffusion is basically an imitative process, price can affect first and replacement purchases, and unit production cost is a decreasing function of cumulative first purchases. Managerial implications of the research findings are also discussed.     

Product Positioning and Pricing Under Production Cost Considerations*

A framework in a competitive environment is proposed that incorporates production cost and economies of scale in the problem of positioning a product for a market segment. The model facilitates the existence of a Nash equilibrium in prices and product positions. As such, firms can simultaneously choose prices and product positions for the segment. This result improves the traditional theory on equilibria points in prices and product positions where firms choose their product positions first and then set their prices. A sensitivity analysis demonstrates the effects of changes in the unit savings derived from economies of scale or the cost of furnishing a product with its attributes by one firm on the product positions, prices, and profits of all competing firms. More important, the paper examines the effect on prices and profits of competing firms when one of the firms repositions its product closer to the segment's ideal point. It is shown that under certain conditions, the profit of a firm may actually decrease as it redesigns its product closer to the segment's ideal point. These conditions assist management to identify the product design beyond which enhancements of the product would lead to lower profits because of increasing production costs. It is also shown that the price of this firm increases. Past research supports the idea that positioning a brand closer to the ideal point, given fixed product positions of competing firms, would lead to greater buyer preferences and eventually higher profits. The price and profits of the competing firm may increase or decrease. Conditions are derived under which a movement towards the segment's ideal point by one firm would lead to higher profits by the competing firm.