Warum Kunden in Online-Communities innovieren: Ergebnisse einer Motivanalyse

For companies, online communities (OCs) have become a potent means of rapidly and easily identifying user needs as a result of the social and technological changes within the Web 2.0. Some OCs even are suited ideally for integration into NPD as they frequently have innovative members. Despite their growing relevance, however, user innovation activities within OCs still are underexplored. The members’ motivations to innovate and contribute to OCs in particular are part of a young line of research requiring further investigation. This research provides an in-depth netnographic analysis of innovative, privately operated OCs dedicated to tangible consumer products. Most fundamentally, we differentiate 1) motives to join OCs, 2) motives to innovate, and 3) motives to publish innovations in OCs. This is the first study to categorize the motives of innovative OC members depending on the stages of their membership as well as situational factors. Our results support companies in understanding and classifying the members’ motives in independent customer OCs. This is a precondition for the development of specific incentives that stimulate innovative user activities in OCs and contribute to customer integration.     

Knowledge transfer and R&D in pharmaceutical companies: A case study

This paper analyzes the consequences for the knowledge transfer and the organization of R&D of pharmaceutical companies after the acquisition of biotech companies. Based on four in-depth case studies, this paper comes to the conclusion that there is no systematic biotechnological know-how transfer from the biotech to the pharmaceutical company after the acquisition. Instead, the biotech companies remain independent and take over the role of centers of excellence for R&D within the pharmaceutical companies because that is the only way of preserving the innovative capabilities of the biotech company.     

Psychological Weil-Being Among Unemployed and Employed People After a Company Closedown: A Longitudinal Study

In a three-year longitudinal study, we examined the effects of unemployment and the fear of becoming unemployed on the psychological well-being of 1153 employees at a shipyard that was closed down, and a control group of 441 employees from another, operative shipyard. On all three questionnaires, unemployed people were significantly lower in psychological well-being than employed people. Change to or from employment was significantly associated with changes in psychological well-being, whereas remaining employed or unemployed did not lead to any systematic changes in psychological well-being. Among employed people, the fear of unemployment was strongly associated with reduced psychological well-being. Thus, the health-related consequences of unemployment affect not only the unemployed but also employed people who have little job security. Consequently, we stress the need to create new workplaces and to implement reforms that can reduce job insecurity in the labor market.     

Welfare consequences of the Swedish disability service system

It has been frequently discussed whether the Swedish disability service system is efficient and whether it meets the demands of equity. The costs of the system for support and service were recently estimated to be USD 11 billion. This article describes the welfare situation for disabled people compared with the general population in Sweden. The Swedish disability service system has managed to provide disabled people with a disposable income over the subsistence minimum in Sweden, but also in the same range as for the general population. In other welfare parameters, disabled people appear to have great welfare losses compared with the general population. Evaluations of handicap technologies (such as service and support) aiming to reduce or compensate for these welfare losses have to meet the requirements of equity and efficiency of the Swedish disability service system.     

Weighted modified first order regression procedures for estimation in linear models with missingX-observations

This paper considers the estimation of coefficients in a linear regression model with missing observations in the independent variables and introduces a modification of the standard first order regression method for imputation of missing values. The modification provides stochastic values for imputation and, as an extension, makes use of the principle of weighted mixed regression. The proposed procedures are compared with two popular procedures—one which utilizes only the complete observations and the other which employs the standard first order regression imputation method for missing values. A simulation experiment to evaluate the gain in efficiency and to examine interesting issues like the impact of varying degree of multicollinearity in explanatory variables is proceeded. Some work on the case of discrete regressor variables is in progress and will be reported in a future article to follow.     

A class of shrinkage estimators in linear regression

We consider the problem of using shrinkage estimators that shrink towards subspaces in linear regression, in particular subspaces spanned by principal components. This is especially important when multicollinearity is present and the number of predictors is not small compared to the sample size. New theoretical results about Stein estimation are used to get estimators with lower theoretical risk than standard Stein estimators used by Oman (1991). Application of the techniques to real data is largely successful.     

Mixing of MCMC algorithms

We analyse MCMC chains focusing on how to find simulation parameters that give good mixing for discrete time, Harris ergodic Markov chains on a general state space X having invariant distribution π. The analysis uses an upper bound for the variance of the probability estimate. For each simulation parameter set, the bound is estimated from an MCMC chain using recurrence intervals. Recurrence intervals are a generalization of recurrence periods for discrete Markov chains. It is easy to compare the mixing properties for different simulation parameters. The paper gives general advice on how to improve the mixing of the MCMC chains and a new methodology for how to find an optimal acceptance rate for the Metropolis-Hastings algorithm. Several examples, both toy examples and large complex ones, illustrate how to apply the methodology in practice. We find that the optimal acceptance rate is smaller than the general recommendation in the literature in some of these examples.     

Finite-Sample Properties of Some Alternative GMM Estimators

We investigate the small-sample properties of three alternative generalized method of moments (GMM) estimators of asset-pricing models. The estimators that we consider include ones in which the weighting matrix is iterated to convergence and ones in which the weighting matrix is changed with each choice of the parameters. Particular attention is devoted to assessing the performance of the asymptotic theory for making inferences based directly on the deterioration of GMM criterion functions.