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Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher & Puckett, 2007; Puckett et al., 2007; Puckett & Hensher, 2008), one significant empirical constraint was difficult in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Bliemer & Rose, 2006).

The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.

Over the past 50 years, air travel in the United States has increased from approximately 33 million passengers in 1960 to over 607 million passengers in 2007 (National Transportation Statistics, 2011, Table 1–40). This is over an 18-fold increase in air travel in the past five decades. Over that same time period, the number of airports increased modestly, from 15,161 in 1980 to 19,750 in 2009. The number of those airports serving public commercial traffic is even smaller, and has declined from 730 airports in 1980 to 559 in 2009 (National Transportation Statistics, 2011, Table 1–3). Together, these two facts point to phenomenal growth among airports (measured by the number of passenger trips).

Despite the relevance of firm size in the analysis of corporate social responsibility (CSR) engagement, there is still much to know about the specific impact of firm size on CSR formalisation. Moreover, in order to better understand such a relation, the interaction effects of development strategies on which companies may base its growth, namely diversification and internationalisation, will be also taken into account. Specifically, this work contributes to shed light on these issues by combining theories related to external and internal drivers of CSR. Using a sample of Spanish listed firms, the results show that firm size affects positively CSR formalisation, and that this effect is stronger in the case of adopting a diversification strategy, while no evidence was found for the moderating effect of internationalisation strategy.

This study applies asymmetric rather than conventional symmetric analysis to advance theory in occupational psychology. The study applies systematic case-based analyses to model complex relations among conditions (i.e., configurations of high and low scores for variables) in terms of set memberships of managers. The study uses Boolean algebra to identify configurations (i.e., recipes) reflecting complex conditions sufficient for the occurrence of outcomes of interest (e.g., high versus low financial job stress, job strain, and job satisfaction). The study applies complexity theory tenets to offer a nuanced perspective concerning the occurrence of contrarian cases – for example, in identifying different cases (e.g., managers) with high membership scores in a variable (e.g., core self-evaluation) who have low job satisfaction scores and when different cases with low membership scores in the same variable have high job satisfaction. In a large-scale empirical study of managers (n = 928) in four (contextual) segments of the farm industry in New Zealand, this study tests the fit and predictive validities of set membership configurations for simple and complex antecedent conditions that indicate high/low core self-evaluations, job stress, and high/low job satisfaction. The findings support the conclusion that complexity theory in combination with configural analysis offers useful insights for explaining nuances in the causes and outcomes to high stress as well as low stress among farm managers. Some findings support and some are contrary to symmetric relationship findings (i.e., highly significant correlations that support main effect hypotheses).

This paper explores the relationship between size, book-to-market, beta, and expected stock returns in the U.S. Information Technology sector over the July 1990–June 2001 period. Two models, the multivariate model and the three-factor model, are employed to test these relationships. The risk-return tests confirm the relationship between size, book-to-market, beta and stock returns in IT stocks is different from that in other non-financial stocks. However, the sub-period results (the periods before and after the technology crash in April 2000) show that the nature of the relationship between stock returns, size, book-to-market, and market factors, or the magnitude of the size, book-to-market, and market premiums, is on average unchanged for both sub-periods. This result suggests the technology stock crash in April 2000 was not a correction of stock prices.

This article estimates the loan spread equation taking into account the endogenous matching between banks and firms in the loan market. To overcome the endogeneity problem, I supplement the loan spread equation with a two-sided matching model and estimate them jointly. Bayesian inference is feasible using a Gibbs sampling algorithm that performs Markov chain Monte Carlo (MCMC) simulations. I find that medium-sized banks and firms tend to be the most attractive partners, and that liquidity is also a consideration in choosing partners. Furthermore, banks with higher monitoring ability charge higher spreads, and firms that are more leveraged or less liquid are charged higher spreads.

Purpose – To assess the following question: Do large Protestant congregations in the United States exert social and political influence simply as a function of their size, or do other characteristics amplify their influence?

Methodology/Approach – Using the U.S.-based National Congregations Study and the General Social Survey, the chapter employs a multivariate regression model to control for other factors related to church size.

Findings – Larger congregations contain a larger proportion of regular adult participants living in high-income households and possessing college degrees, and a smaller proportion of people living in low-income households. In congregations located in relatively poor census tracts, the relationship between high socioeconomic status (SES) and congregation size remains significant. Across Protestant groups, size and proportion of the congregation with high SES are correlated. Individual-level analyses of linked data from the General Social Survey confirm the positive relationship between the size of congregation the respondent attends with both high household income and possessing a college degree. These analyses also reveal a negative relationship between size and low household income.

Social implications – Size is an important factor when considering the social impact of congregations.

Originality/Value of chapter – This chapter identifies a systematic difference between churches of different sizes based on SES. This relationship has not been previously identified in a nationally representative sample.