What a Difference a DV Makes ... The Impact of Conceptualizing the Dependent Variable in Innovation Success Factor Studies

The quest for the "success factors" that drive a company's innovation performance has attracted a great deal of attention among both practitioners and academics. The underlying assumption is that certain critical activities impact the innovation performance of the company or the project. However, the findings of success factor studies lack convergence. It has been speculated that this may be due to the fact that extant studies have used many different measures of the dependent variable "innovation performance". Our study is the first to analyze this issue systematically and empirically: we analyze the extent to which different conceptualizations of the dependent variable (a firm's innovation performance) lead to different innovation success factor patterns. In order to do so, we collected data from 234 German firms, including well-established success factors and six alternative measures of innovation performance. This allowed us to calculate whether or not success factors are robust to changes in the measurement of the dependent variable. We find that this is not the case: rather, the choice of the dependent variable makes a huge difference. From this, we draw important conclusions for future studies aiming to identify the success factors in companies' innovation performance

Understanding customers' holistic perception of switches in automotive human–machine interfaces

For successful new product development, it is necessary to understand the customers' holistic experience of the product beyond traditional task completion, and acceptance measures. This paper describes research in which ninety-eight UK owners of luxury saloons assessed the feel of push-switches in five luxury saloon cars both in context (in-car) and out of context (on a bench). A combination of hedonic data (i.e. a measure of ‘liking’), qualitative data and semantic differential data was collected. It was found that customers are clearly able to differentiate between switches based on the degree of liking for the samples' perceived haptic qualities, and that the assessment environment had a statistically significant effect, but that it was not universal. A factor analysis has shown that perceived characteristics of switch haptics can be explained by three independent factors defined as ‘Image’, ‘Build Quality’, and ‘Clickiness’. Preliminary steps have also been taken towards identifying whether existing theoretical frameworks for user experience may be applicable to automotive human–machine interfaces

The direct and indirect effects of education policy on school and post school outcomes

Successive British governments since the early 1980s have introduced a host of educational policy reforms in an attempt to raise pupil performance at school. One of the most important educational policies in the secondary education sector was the specialist schools policy, which was introduced in 1994. Using data from the YCS for pupils who left school in either 2002 or 2004, a period of rapid expansion of the specialist schools programme, we seek to evaluate the effects of the policy. Unlike most previous work in this area we investigate the effects of the policy on test scores and truancy for pupils at school, but also assess whether the policy had direct and/or indirect effects on post-school outcomes, such as labour market status, wages and A-Level scores. We show that specialist schools did raise test scores during compusory schooling, and that the policy had a positive and statistically significant effect in raising the probability of employment. The evidence on A-level scores suggests a negative effect and, due to data limitations, no effect on wages is apparent. Although we stop short of claiming that our findings are causal, they do imply that policy makers need to take a more comprehensive view of the effects of education policies when trying to address whether they deliver value for money to the taxpayer

Customer perception of switch-feel in luxury sports utility vehicles

Successful new product introduction requires that product characteristics relate to the customer on functional, emotional, aesthetic and cultural levels. As a part of research into automotive human machine interfaces (HMI), this paper describes holistic customer research carried out to investigate how the haptics of switches in luxury sports utility vehicles (SUVs) are perceived by customers. The application of these techniques, including an initial proposal for objective specifications, is addressed within the broader new product introduction context, and benefits described. One-hundred and one customers of SUVs assessed the feel of automotive push switches, completing the tasks both in, and out of vehicles to investigate the effect of context. Using the semantic differential technique, hedonic testing, and content analysis of customers’ verbatim comments, a holistic picture has been built up of what influences the haptic experience. It was found that customers were able to partially discriminate differences in switch-feel, alongside considerations of visual appearance, image, and usability. Three factors named ‘Affective’, ‘Robustness and Precision’, and ‘Silkiness’ explained 61% of the variance in a principle components analysis. Correlations of the factors with acceptance scores were 0.505, 0.371, and 0.168, respectively

Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such controllers need to be robust with the respect to a series of exogenous sources of uncertainty. In this paper, we develop an open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment. With this framework, we investigate how deep RL-based adaptive traffic controllers perform under different scenarios, namely under demand surges caused by special events, capacity reductions from incidents and sensor failures. We extract several key insights for the development of robust deep RL algorithms for traffic control and propose concrete designs to mitigate the impact of the considered exogenous uncertainties.Comment: 8 page

Persistence and Activation of Right-Wing Political Ideology

We investigate the persistence of right-wing ideology in Germany. The “Alternative for Germany” (AfD), founded as a party espousing fiscal conservatism, has turned to an openly nationalist and anti-immigrant platform since 2015. We document this rhetorical change with quantitative text analysis. We further show that municipalities that voted more for the AfD after 2015 also exhibited higher support for the Nazi party in the 1920s and 30s. The historical correlation we observe is positive, significant, and large. In our preferred specification, a one standard deviation increase in historical support for the Nazi party is associated with a 0.15 standard deviations larger change in votes towards the AfD. Our results are robust to controlling for a large set of historical and contemporary covariates, especially relating to unemployment and the recent inflow of refugees from the Middle East