Innovation trends and industrial renewal in Finland and Sweden 1970–2013

We examine trends in innovation output for two highly ranked innovative countries: Finland and Sweden (1970–2013). Our novel dataset, collected using the LBIO (literature-based innovation output) method, suggests that the innovation trends are positive for both countries, despite an extended downturn in the 1980s. The findings cast some doubt on the proposition that the current stagnation of many developed countries is due to a lack of innovation and investment opportunities. Our data show that Finland catches up to, and passes, Sweden in innovation output in the 1990s. In per capita terms, Finland stays ahead throughout the period. We find that the strong Finnish performance is largely driven by innovation increase in just a handfull of sectors, but is not restricted to few companies. Both countries saw a rise in innovation during the dot-com era and the structural changes that followed. Since 2000 however, Sweden has outperformed Finland in terms of total innovations, especially in machinery and ICT, while the Finnish rate of innovation has stabilised. We suggest that these patterns may be explained by different paths of industrial renewal.     

A nearly perfect market?

Internet shopbots allow consumers to almost instantly compare prices and other characteristics from dozens of sellers via a single website. We estimate the magnitude of consumer search costs and benefits using data from a major shopbot for books. For the median consumer, the estimated benefit from simply scrolling down to search lower screens is $6.55. This amounts to about 60% of the observed price dispersion and suggests that consumers face significant search costs, even in this “nearly-perfect” market. Price elasticities are relatively high compared to offline markets (−7 to −10 in our base model). Furthermore, contrary to the common assumption, search intensity is not correlated with greater price sensitivity. Instead, consumers who search multiple screens put relatively more weight on non-price factors like brand.     

Does retailer CSR enhance behavioral loyalty? A case for benefit segmentation

We study the effects of consumer perceptions of four types of corporate social responsibility (CSR) activities on their behavioral loyalty toward retailers. The four activities are environmental friendliness, community support, selling locally produced products, and treating employees fairly. Behavioral loyalty is measured by share-of-wallet (SOW). We control for other retailer attributes that drive attitudes and SOW, and examine how the market is segmented in terms of consumer response. We partition the total effect of CSR on SOW into a direct effect and an indirect effect mediated through attitude towards the store. These effects differ by CSR activity and customer segment. The effects on attitude are positive and positive attitude enhances SOW, so the indirect effects on SOW are positive. While we generally find positive total effects, the total effect of one of the CSR activities, environmental friendliness, is significantly negative for one group of consumers. The magnitude of CSR's total impact on SOW is not only statistically significant but also managerially meaningful in an industry where every share point carries a substantial dollar amount. We characterize the customer segments and conclude with implications for how best a retailer can manage its CSR initiatives.     

Overcoming the “recency trap” in customer relationship management

Purchase likelihood typically declines as the length of time since the customer’s previous purchase (“recency”) increases. As a result, firms face a “recency trap,” whereby recency increases for customers who do not purchase in a given period, making it even less likely they will purchase in the next period. Eventually the customer is effectively lost to the firm. We develop and illustrate a modeling approach to target a firm’s marketing efforts, keeping in mind the customer’s recency state. This requires an empirical model that predicts purchase likelihood as a function of recency and marketing, and a dynamic optimization that prescribes the most profitable way to target customers. In our application we find that customers’ purchase likelihoods as well as response to marketing depend on recency. These results are used to show that the targeting of email and direct mail should depend on the customer’s recency and that the optimal decision policy enables the average high recency customer, who currently is virtually worthless to the firm, to become profitable.     

Benchmarking and firm heterogeneity: a latent class analysis for German electricity distribution companies

In January 2009, the German Federal Network Agency introduced incentive regulation for the electricity distribution sector based on results obtained from econometric and nonparametric benchmarking analysis. One main problem for the regulator in assigning the relative efficiency scores is unobserved firm-specific factors such as network and technological differences. Comparing the efficiency of different firms usually assumes that they operate under the same production technology, thus unobserved factors might be inappropriately understood as inefficiency. To avoid this type of misspecification in regulatory practice, estimation is carried out in two stages: in the first stage observations are classified into two categories according to the size of the network operators. Then separate analyses are conducted for each subgroup. This article shows how to disentangle the heterogeneity from inefficiency in one step, using a latent class model for stochastic frontiers. As the classification is not based on a priori sample separation criteria it delivers more robust, statistically significant, and testable results. Against this background, we analyze the level of technical efficiency of different subgroups from a sample of 200 regional and local German electricity distribution companies for a balanced panel data set (2001–2005). Testing the hypothesis if larger distributors operate under a different technology than smaller ones, we assess if a single step latent class model provides new insights to the use of benchmarking approaches within the incentive regulation schemes.