Why adopt now? Multiple case studies and survey studies comparing small,medium and large firms

While a number of factors have been highlighted in the innovation adoption literature, little is known about whether different factors are related to innovation adoption in differently sized firms. We used preliminary case studies of small, medium and large firms to ground our hypotheses, which were then tested using a survey of 94 firms. We found that external stakeholder pressure and non-financial readiness were related to innovation adoption in SMEs; but that for large firms, adoption was related to the opportunity to innovate. It may be that the difficulties of adopting innovations, including both the financial cost and the effort involved, are too great for SMEs to overcome unless there is either a compelling need (external pressure) or enough in-house capability (non-financial readiness). This suggests that SMEs are more likely to have innovation “pushed” onto them while large firms are more likely to “pull” innovations when they have the opportunity.     

The Value of Changes in Deer Season Length: An Application of the Nested Multinomial Logit Model

Increasing deer populations can be controlled through manipulatingharvest limits or season length. While such actions often result in benefitsto hunters, both motorists and the agricultural sector also benefit as alower deer population leads to fewer incidences of harmful human-deerencounters. Traditional recreation demand models are often employed toexamine the welfare implications of changes in daily hunting bag limits.Studies measuring the effects of changes in season length, however, arenoticeably absent from the literature. This study uses a nested randomutility model to examine hunter choice over site and season selection toderive the values of changes in season length.     

Two-stage DEA: <Emphasis Type=Italic>caveat emptor</Emphasis>

This paper examines the wide-spread practice where data envelopment analysis (DEA) efficiency estimates are regressed on some environmental variables in a second-stage analysis. In the literature, only two statistical models have been proposed in which second-stage regressions are well-defined and meaningful. In the model considered by Simar and Wilson (J Prod Anal 13:49–78, 2007), truncated regression provides consistent estimation in the second stage, where as in the model proposed by Banker and Natarajan (Oper Res 56: 48–58, 2008a), ordinary least squares (OLS) provides consistent estimation. This paper examines, compares, and contrasts the very different assumptions underlying these two models, and makes clear that second-stage OLS estimation is consistent only under very peculiar and unusual assumptions on the data-generating process that limit its applicability. In addition, we show that in either case, bootstrap methods provide the only feasible means for inference in the second stage. We also comment on ad hoc specifications of second-stage regression equations that ignore the part of the data-generating process that yields data used to obtain the initial DEA estimates.     

One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient,root-<Emphasis Type="Italic">n</Emphasis> consistent StoNEZD method

Understanding the effects of operational conditions and practices on productive efficiency can provide valuable economic and managerial insights. The conventional approach is to use a two-stage method where the efficiency estimates are regressed on contextual variables representing the operational conditions. The main problem of the two-stage approach is that it ignores the correlations between inputs and contextual variables. To address this shortcoming, we build on the recently developed regression interpretation of data envelopment analysis (DEA) to develop a new one-stage semi-nonparametric estimator that combines the nonparametric DEA-style frontier with a regression model of the contextual variables. The new method is referred to as stochastic semi-nonparametric envelopment of z variables data (StoNEZD). The StoNEZD estimator for the contextual variables is shown to be statistically consistent under less restrictive assumptions than those required by the two-stage DEA estimator. Further, the StoNEZD estimator is shown to be unbiased, asymptotically efficient, asymptotically normally distributed, and converge at the standard parametric rate of order n ^−1/2. Therefore, the conventional methods of statistical testing and confidence intervals apply for asymptotic inference. Finite sample performance of the proposed estimators is examined through Monte Carlo simulations.     

Low interest rate policy and the use of reserve requirements in emerging markets

This paper sheds light on the link between the interest rate policy in large advanced economies with international funding and reserve currencies (the United States and the euro area) and the use of reserve requirements in emerging markets. Using reserve requirement data for 28 emerging markets from 1998 to 2012, we provide evidence that emerging market central banks tend to raise reserve requirements when interest rates in international funding markets decline or financial inflows accelerate, most likely to preserve financial stability. In contrast, when global liquidity risk rises and funding from the large advanced economies dries up, emerging markets lower reserve requirements.     

Agricultural Leadership Education: Past History,Present Reality,and Future Directions

The article explores the past, present, and projected future of agricultural leadership education. Beginning with roots in youth leadership development and grounded in land‐grant universities, agricultural leadership education has undergone a shift from working primarily with rural youth to a new focus on also educating undergraduate and graduate students to empower community members and create change. The renewed focus has encouraged recent growth and in the last 10 years, the profession has added three leadership majors, six leadership minors, seven leadership focus areas, seven undergraduate leadership programs, and four graduate leadership programs. Research exploring the student impact of agricultural leadership education revealed that nationwide a total of 7,904 students are exposed to 208 agricultural leadership education courses. As the discipline continues to mature, recommendations are made for continued program development. Discussion highlights prior research examining potential courses to include types of experiences, program objectives, and career connectedness relating to agricultural leadership education. Programs have ample opportunity for future growth and recommendations are made to continue the development of innovative leadership opportunities and consistent pedagogical practices across institutions.     

Signalling Theory and Equilibrium in Strategic Management Research: An Assessment and a Research Agenda

Actors within organizations commonly must make choices armed with incomplete and asymmetrically distributed information. Signalling theory seeks to explain how individuals are able to do so. This theory's primary predictive mechanism is ‘separating equilibrium’, which occurs when a signal's expectations are confirmed through experience. A content analysis finds that most strategic management signalling theory studies have not fully leveraged separating equilibrium. This presents two possible paths for future research. First, some researchers may wish to incorporate separating equilibrium. We illustrate how doing so can uncover new relationships, generate novel insights, and fortify the theory's application. Others who want to theorize about signals, but not examine separating equilibrium, could integrate ideas from signalling theory with other information perspectives. Here a signal becomes one stimulus among many that corporate actors interpret and act upon. We provide research agendas so strategy scholars can apply signalling theory most effectively to meet their research objectives.     

TESTS OF EQUAL FORECAST ACCURACY FOR OVERLAPPING MODELS

This paper examines the asymptotic and finite‐sample properties of tests of equal forecast accuracy when the models being compared are overlapping in the sense of Vuong (Econometrica 1989; 57 : 307–333). Two models are overlapping when the true model contains just a subset of variables common to the larger sets of variables included in the competing forecasting models. We consider an out‐of‐sample version of the two‐step testing procedure recommended by Vuong but also show that an exact one‐step procedure is sometimes applicable. When the models are overlapping, we provide a simple‐to‐use fixed‐regressor wild bootstrap that can be used to conduct valid inference. Monte Carlo simulations generally support the theoretical results: the two‐step procedure is conservative, while the one‐step procedure can be accurately sized when appropriate. We conclude with an empirical application comparing the predictive content of credit spreads to growth in real stock prices for forecasting US real gross domestic product growth. Copyright © 2013 John Wiley & Sons, Ltd.