Leveling the playing field: factors influencing trade show success for small companies

Recent research indicates that trade shows are a place to find prospects and close sales. Yet, many requisites of success found in prior studies may not be available to companies with limited resources. A study that compares the activities of successful vs. unsuccessful exhibitors from small companies was conducted. Results indicate similar budgets, yet very different results. Factors influencing success include strategic factors such as centering responsibility in one position, as well as tactical decisions such as those regarding pre-show promotion.     

山西省平原绿化再上新台阶的战略选择

山西省在实现平原绿化达标之后 ,以服务农业、富裕农民、美化农村为主导思想 ,用生态经济学和系统工程学理论作指导 ,开展了高标准平原绿化建设 ,把建设高标准农田林网、建设生态经济园林村、建立平原林业产业 ,作为平原绿化的新台阶攀登。为此 ,山西省将高标准平原绿化建设纳入县域经济发展的总体规划中 ,典型引路 ,整体推进 ,大力培育优种壮苗 ,切实加大科技投入 ,使本地区的平原绿化逐步走向具有复合效益的平原林业     

Firm heterogeneity, productivity and spatially bounded knowledge externalities

In this paper, we argue that conceptually disentangling the ‘context versus composition’ aspects of regional growth is a multilevel issue. By applying multilevel models (also called random-effects models), we show (1) the importance of considering firm-specific characteristics simultaneously with region-specific characteristics, as we find that a large part of what is traditionally assigned to the impact of the region should be assigned to firm-specific characteristics and (2) that existing single-level methodologies can be problematic, as they are vulnerable to the charge of estimating significance levels that are too liberally assigned and promote exaggerations. This is illustrated empirically by showing that single-level approaches would lead to the conclusion that innovation spillovers are highly significant in a setting of Dutch urban growth differentials, while multilevel analyses shows less liberally assigned significance levels. We conclude that multilevel-effect models better fit research questions that combine firm and spatial characteristics simultaneously, especially because they allow firm-specific characteristics to be differently linked to their regional contexts.     

New evidence on the efficiency of Italian airports: A bootstrapped DEA analysis

A bootstrapped DEA procedure is used to estimate technical efficiency of 18 Italian airports during the period 2000-2004. Departing from previous studies, we separate the efficiency related to ability to manage airside activities (operational) from that related to the management of all business activities (financial). In general, Italian airports operate at poor levels of efficiency, with slightly better performance in terms of their financial activities. In the current study, selected intrinsic and environmental characteristics are considered as possible drivers of Italian airport performance. In particular, we found that: (i) the airport dimension does not allows for operational efficiency advantages, (ii) on the other hand, the airport dimension allows for financial efficiency advantages for the case of hubs and disadvantages for the case of the smallest airports (iii) the type(s) of concession agreement(s) might be considered as important source of technical efficiency differentials for those airports running marginal commercial activities; (iv) the introduction of a dual-till price cap regulation might create incentives which lead to the increase of financial efficiency at the detriment of the operational performance. Lastly, the development of a second hub (Milano Malpensa), has negatively affected the performance of the country’s national hub (Roma Fiumicino).     

A new method for measuring congestion in data envelopment analysis

In data envelopment analysis (DEA), there are two principal methods for identifying and measuring congestion: Those of Färe et al. [Färe R, Grosskopf S. When can slacks be used to identify congestion. An answer to W. W. Cooper, L. Seiford, J. Zhu. Socio-Economic Planning Sciences 2001;35:1–10] and Cooper et al. [Cooper WW, Deng H, Huang ZM, Li SX. A one-model approach to congestion in data envelopment analysis. Socio-Economic Planning Sciences 2002;36:231–8]. In the present paper, we focus on the latter work in proposing a new method that requires considerably less computation. Then, by proving a selected theorem, we show that our proposed methodology is indeed equivalent to that of Cooper et al.     

EKC-type transitions and environmental policy under pollutant uncertainty and cost irreversibility

Previous studies have suggested that some pollutant levels first increases due to the economic growth and then start decreasing, the pattern being called the “environmental Kuznets curve” (EKC). We examine EKC-type transitions of pollutant levels not with respect to economic growth but more generally in time. Assuming that each policy maker optimally executes the two switching options of regulation and unregulation for pollution, the switching dynamics of environmental policy can be described by an alternating renewal process. It is shown that the double Laplace transform of transition density of a pollutant level can be obtained by a novel application of renewal theory. The expected level of overall pollutants is then calculated numerically and found to exhibit either a Λ‐shaped or an N-shaped pattern in time. Our results present a simple explanation for the EKC-type transitions of pollutant levels within a real options framework.     

A review of experience curve analyses for energy demand technologies

Transitioning towards a sustainable energy system requires the large-scale introduction of novel energy demand and supply technologies. Such novel technologies are often expensive at the point of their market introduction but eventually become cheaper due to technological learning. In order to quantify potentials for price and cost decline, the experience curve approach has been extensively applied to renewable and non-renewable energy supply technologies. However, its application to energy demand technologies is far less frequent. Here, we provide the first comprehensive review of experience curve analyses for energy demand technologies. We find a widespread trend towards declining prices and costs at an average learning rate of 18 ± 9%. This finding is consistent with the results for energy supply technologies and for manufacturing in general. Learning rates for individual energy demand technologies are symmetrically distributed around the arithmetic mean of the data sample. Absolute variation of learning rates within individual technology clusters of 7 ± 4%-points and between technology clusters of 7 ± 5%-points both contribute to the overall variability of learning rates. Our results show that technological learning is as important for energy demand technologies as it is for energy supply technologies. Applying the experience curve approach to forecast technology costs involves, however, unresolved uncertainties, as we demonstrate in a case study for the micro-cogeneration technology.