以“客房之声”制定运营战略

客户之声的管理方法自20世纪90年代早期进入管理主流以来,现已大大推动了无数产品的设计。越来越多的企业正从其客户那里收集一手资料,然后将这些信息应用于产品开发决策和流程之中,通过这种努力,这些公司获得了决定性的竞争优势。然而,目前只有少数企业将客户之声的信息广泛应用于业务与运营战略的其它方面。     

The Gender Pay Gap in EU Countries — New Evidence Based on EU-SES 2014 Data

Intereconomics - Gender differences in wages are a persistent pattern in most European countries. This study analyses the earnings divide between men and women and the driving forces behind it in...     

The Complementarity between Tax Avoidance and Manager Diversion: Evidence from Tax Haven Firms

We investigate whether tax avoidance and manager diversion are complementary when the costs of diversion are low by comparing dividend payouts, performance, and overinvestments of tax haven firms versus other multinational firms based in countries with weak and strong investor protections. Desai and Dharmapala (2006, 2009a, b) and Desai et al. (2007) set forth a theory of tax avoidance within an agency framework (the D&D theory) based on the assumption that tax avoidance and manager diversion are complementary when the corporate governance system is “ineffective” (i.e., the manager's expected costs of diversion are low). Tax haven firms are corporate groups whose parent firms are incorporated in tax haven countries that are not the countries where the groups’ headquarters or primary operations are located (i.e., their “base” countries). We argue that tax haven incorporation potentially lowers the costs of diversion for managers of firms based in countries with weak investor protections. Using a sample from 28 base countries, we provide evidence that manager diversion and tax avoidance are complementary for tax haven firms based in countries with weak investor protections but not for tax haven firms based in countries with strong investor protections. Our results are consistent with the complementarity assumption underlying the D&D model and provide additional insights into the potential impact of the decentralization of the global firm.     

Do not swipe the small stuff: A randomized evaluation of rules of thumb-based financial education

We perform the first rigorous test of a rules of thumb-based approach to financial education on consumer behavior and outcomes. We test two rules of thumb that are targeted at reducing credit card revolving and deliver them in a randomized fashion via e-mail, online banner, and physical mailer. Using monthly administrative data and pre and postintervention credit data on almost 14,000 consumers, we find that the “Do not swipe the small stuff” rule of thumb reduces participants' targeted credit card balance by an average of 2% at a cost of around $0.50 per person. The “Credit keeps charging” rule shows a decline as well but the impact is not significant.     

National information and euro area monetary policy: a generalized ordered choice approach

We empirically analyze the influence of inflationary pressure originating from persistent national misalignments on the ECB’s interest rate decisions between 2000 and mid-2010. To do so, we introduce an indicator that summarizes the threat to euro area price stability originating from self-reinforcing expected inflation differentials. The indicator is computed based on persistent deviations of national expected inflation and GDP growth rates from the corresponding euro area aggregate. It thereby captures area-wide excess demand pressure on the euro area inflation rate. In order to determine the information content of this indicator, we add it to an empirical monetary policy reaction function. We then analyze this reaction function in the framework of a generalized ordered choice model that fits the data a lot better than its commonly used, more restricted counterpart. Within this empirical framework, we find that after controlling for several area-wide aggregates, national information does not provide additional information that is indicative of the ECB’s policy rate decision.     

The Role of Dialectical Interrogation in Review Studies: Theorizing from What We See Rather Than What We Have Already Seen

Review-centric works receive increasing attention for generating insightful contributions to management and organization studies. Despite this, the literature on theory building has taken little note of their place in the theorizing process. This deserves attention, however, given the challenges reviews face in theorizing in the absence of new empirical observations. Accordingly, these works run the risk of merely summarizing ‘what we have already seen’, instead of ‘maximizing what we see’. Drawing on the strategies of theorizing from similarities and theorizing from anomalies, we propose dialectical interrogation as a critical step in theorizing through which review scholars imaginatively engage in a back and forth inquiry between the phenomenal world of a given field and existing theory. By analysing selected review studies from top management journals, we reveal that theorizing outcomes occur through two ways of dialectical interrogation (consolidative and disruptive). We contribute by demonstrating that review scholars can enter into powerful theorizing through the consolidative or disruptive interrogation of the review data with extant theory to detect emergence and novelty alongside puzzles, conflicts and paradoxes. Dialectical interrogation can address the shortcomings of current theorizing in review-centric works and bears potential for advancing theories of management and organization studies.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

Durability Versus Concentration as an Explanation for Price Inflexibility

We document the extent of price rigidity across United States manufacturing industries in the 1980s and early 1990s and compare rigidity across different phases of the business cycle. We measure price rigidity in three ways – each under four different sets of assumptions. We take an approach that relies on disaggregated data; we look at price patterns for over 4000 individual manufactured commodities. Both durability and seller concentration are found to be important factors explaining differences in price rigidity across industrial product classes. Using our data, we replicate the regression results found in Carlton (1986) that were based on actual transaction prices from the 1960s.     

A Spatial Autocorrelation Approach for Examining the Effects of Urban Greenspace on Residential Property Values

This paper presents spatially explicit analyses of the greenspace contribution to residential property values in a hedonic model. The paper utilizes data from the housing market near downtown Los Angeles. We first used a standard hedonic model to estimate greenspace effects. Because the residuals were spatially autocorrelated, we implemented a spatial lag model as indicated by specification tests. Our results show that neighborhood greenspace at the immediate vicinity of houses has a significant impact on house prices even after controlling for spatial autocorrelation. The different estimation results from non-spatial and spatial models provide useful bounds for the greenspace effect. Greening of inner city areas may provide a valuable policy instrument for elevating depressed housing markets in those areas.