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This paper examines the short and long‐term comovements among UK regional property markets over the period 1976‐2001. The markets examined are London, Outer South East, East Anglia, South West, East Midlands, West Midlands, Yorkshire and Humberside, North and North West. Multivariate cointegration procedures, Granger non‐causality tests, level VAR and generalised variance decomposition analyses based on error‐correction and vector autoregressive models are conducted to analyse relationships among these markets. The results indicate that there is a stationary, long‐term relationship and a number of long‐term causal linkages between the various UK property markets. In terms of the percentage of variance explained, other regional markets are generally more important than innovations in a given region, though this is not the case for the Outer South East. The Outer South East market is segmented from the other regional markets, though also extremely influential in explaining forecast variance in these markets. The overall suggestion is that opportunities exist for portfolio diversification in the UK regional property market, and the Outer South East market should be seen as containing valuable information for forecasting performance in the regional markets.

The relative benefit of sector and regional diversification is a topic of continuing interest to academics, however, the issue has not previously been investigated in Italy. Additionally, previous studies have used geographically defined regions, rather than economically functional areas, when performing the analysis even though most would argue that it is the economic structure of the area that will lead to differences in demand and hence property performance. Therefore the purpose of this paper is to use the economically defined regions of Italy to test the relative benefits of regional diversification versus sector diversification within the Italian real estate portfolio.

The paper uses the dummy variable methodology of Heston and Rouwenhorst on the sector and regional affiliation of 27 cities in Italy using annual data over the period 1989 to 2007 for three property‐type: residential, retail and offices and four economically defined regions: the North West, the North East, the Centre of Italy and the South and Islands.

In contrast, to previous studies it is found that the sector and regional factors effect the returns of properties in Italy in almost equal measure, which is probably a result of using the diverse economic regions of Italy rather than arbitrary geographically locations. Nonetheless, the results show that the sector factor has started to dominate the regional effect in Italy since 1997.

This is the first paper to study the relative benefits of sector and regional diversification in Italy. Additionally, this is the first paper to use regions which are defined on an economic functional basis rather than a geographical basis. The results suggest that, unlike managers in other countries, Italian real estate managers need to monitor both the regional and sector composition of their portfolios.

This study investigates the level of variance in the real time demand for bagged cement, induced in response to the climatic sequence of the humid tropics, to support best practice calls for a weather-responsive supply chain strategy.

Data on the consumption of cement and site works for 100 ongoing building construction sites were gathered for a period of 12 months. The variance partitioning capabilities of the Ordinary Least Squares and Hierarchical Linear Modelling forms of regression analysis are comparatively used to evaluate the sensitivity of cement demand to the meteorological profile of wet-humid climate

The study outcome provides statistical evidence demonstrating that the meteorological profile of wet-humid climate induces a significantly high percentage of the variance in the real-time demand for bagged cement on construction sites. However, nested within this variance, are the fixed effects of the cement footprint of the building architecture inherent in the locality. Particularly, positive changes to reduce the wet trade composition of buildings or compensating changes in technological bias, are necessary to combat weather interference in the humid tropics.

The findings are exploratory, and not for the purposes of holistically forecasting cement demand, and can therefore only form part of a more comprehensive decision support system, bespoke to the study area.

The study outcome provides a back-end view to climatic adaptation in wet humid settings, making a compelling case for localized climate-risk adaptive supply chain strategies and policies geared towards sustainability in cement usage.

The study delineates the confounding impact of weather, distinct from local building architecture and technological bias, thus creating a methodological platform for replication and comparative productivity studies in diverse geographical areas.

The New Zealand (NZ) dairy industry faces the challenge of increasing productivity and dealing with public concerns over nutrient pollution. The effective policy needs to address regional differences in productivity and fertilizer use. The purpose of this paper is to investigate how spatial effects influence the relationship between dairy yields and intensive farming practices across regions in NZ.

This paper employs spatial panel data models to establish whether unobserved spatial effects exist in the relationship between dairy yields and nutrient inputs regionally and nationally using 2002, 2007 and 2012 data from Statistics NZ and DairyNZ.

The results show positive spatial spillovers for most intensive inputs. The high level of effluent use and estimated negative yield response to nitrogen suggests that an opportunity exists for greater use of effluent as a substitute for nitrogenous fertilizer. Substitution has the potential to reduce dependence on fertilizer and contribute to a reduction in the nutrient pollution.

This paper is the first empirical application of spatial econometric methods to examine the spatial relevance of dairy yields and intensive farming in NZ. In particular, the spatial panel data model accounts for cross-sectional dependence and controls for heterogeneity. The results contribute to an understanding of how farmers can improve their management of intensive inputs and contribute to the formation of regional environmental policy that recognizes regional heterogeneity.

The purpose of this paper is to examine how regional factors affect customer satisfaction in the food service sector. This is achieved through investigating the moderating effects on the most important attributes of restaurants.

Hierarchical linear models, comprising customer observations nested within regions, were developed to explore whether there is any variance in customer satisfaction with the restaurants of an international chain (hereafter known as K Restaurants) across 52 regions in China and which regional factors contribute to understanding such variance.

The results indicate that there is an apparent difference in customer satisfaction across regions. Regional consumption level can positively (negatively) moderate the relationship between food taste (physical environment) and customer satisfaction. Economic condition and population density have a negative moderating effect on the relationship between the physical environment and customer satisfaction. Education level, however, does not have any regional effect on satisfaction.

Multinational companies or chain corporations must account for regional influences when evaluating their performance based on customer satisfaction surveys. Standardization across a nation is not necessarily the best approach because the various regions of a nation may differ in terms of socioeconomic condition.

This pioneering study examines the moderating effects of regional factors on the relationships between the attribute qualities of a restaurant and customer satisfaction.

This paper compares alternative estimation procedures for multi-level factor models which imply blocks of zero restrictions on the associated matrix of factor loadings. We suggest a sequential least squares algorithm for minimizing the total sum of squared residuals and a two-step approach based on canonical correlations that are much simpler and faster than Bayesian approaches previously employed in the literature. An additional advantage is that our approaches can be used to estimate more complex multi-level factor structures where the number of levels is greater than two. Monte Carlo simulations suggest that the estimators perform well in typical sample sizes encountered in the factor analysis of macroeconomic data sets. We apply the methodologies to study international comovements of business and financial cycles.

This chapter examines herd behavior across national borders. A dynamic latent factor model with Gibbs sampling is used to decompose the national herd behavior into the world, regional, and country-specific components. Testing the daily data from 2000 through 2014 for 47 countries, we find that the impact of world factor on national herd behavior is short-lived. This study indicates that world and regional factors play a significant role in explaining the variations of national herd behavior, constituting 33% of the herding variability. The significance of world and regional components is likely to produce a biased herding estimator.

The purpose of this paper is to assess the benefits to investors of international diversification among only Islamic funds. Compared to conventional investors who are not restricted in their choice of funds, Islamic investors are restricted to investing in shari’a-compliant funds, thus giving up some diversification benefits. The possibility of international diversification among only Islamic funds may thus help Islamic investors to invest in accordance to their religious beliefs and still benefit from diversification.

The paper assesses the benefits of diversification by analyzing the extent of co-integration among four regional Islamic funds and by estimating the short-term and long-term structural dynamics of and among these funds. The paper uses an Autoregressive-Distributed Lag (ARDL) approach to testing the long-run relationships among these funds and use variance decomposition and impulse response functions to examine the structural dynamics of the relationship between these funds. These methods can also be used for predictive purposes and represent, in authors opinion, a useful approach that complements the traditional methodology of static covariance matrix to find the efficient frontier at a given moment in time.

The results indicate that international diversification can help reduce risk if Asia Pacific Islamic funds and MENA region Islamic funds are invested contemporaneously and/or Asia Pacific Islamic funds and North America Islamic funds, and/or Europe funds and MENA funds. The paper also finds that investors would benefit from investing in North American funds and MENA funds both in the long run and in the short run. Conversely, the paper finds that Europe funds and North American funds are co-integrated in the long-run precluding the opportunity for substantial diversification benefits from these particular portfolio mixes.

The long-run analysis helps passive fund managers and investors in composing their portfolio by providing evidence that some portfolio mixes of different regional Islamic funds lead to better risk return performance than one regional Islamic fund portfolios. The short-run analysis however helps the active fund managers and investors as it suggests that diversifying in the short run and reviewing their portfolio on a regular basis would be beneficial as well.

This analysis justifies the promotion of Islamic finance as the negative correlation between several Islamic funds across the regions studied suggests better opportunities of investments via international diversification making Islamic funds more desirable.

The purpose of this paper is to investigate the relationship between culture and entrepreneurial activity at both the national and regional levels of analyses. While there has been significant progress in investigating the effects of culture on entrepreneurial activity, most work overlooks the effects that time-orientation may have on national or regional entrepreneurial activity. Specifically, this study argues for the connection between long-term orientation (LTO) and subsequent levels of entrepreneurship such that the more a nation or region is long-term oriented, the higher the subsequent entrepreneurial activity will be.

Data from the World Value Survey (WVS), which is a global project that measures individuals’ values across 62 countries (World Value Survey, 2011), were used for this project. The final sample consisted of 36,652 individual observations across 29 nations and 262 regions and was analyzed using ecological factor analyses and multilevel modeling.

The findings suggest that LTO as a cultural dimension does influence entrepreneurship activity levels. The findings also suggest that the effects of LTO at the regional and national levels vary widely. Specifically, the authors find LTO to be positively related to entrepreneurship at the regional, but not national, level of analysis.

The findings reveal important nuances about the implications that the understudied cultural factor of LTO has on entrepreneurial activity across multiple levels of analysis.

The purpose of this study is to investigate the influence that national levels of social capital have on entrepreneurial activity. Specifically, we argue that national and regional level social capital positively influences the ability of entrepreneurs to mobilize and access important resources thereby positively impacting the rate of entrepreneurship within nations and regions.

We advance a multilevel and multidimensional conceptualization of social capital. Then based on a dataset of 68 nations and 665 within-nation regions, we empirically evaluate the effects of social capital at the national and regional level in explaining differences in entrepreneurial activity across nations and regions using a combination of regression analysis and multilevel hierarchical linear modeling (HLM).

Our findings emphasize the importance of formulating a multilevel conceptualization of social capital for entrepreneurship research. We discuss the results, provide implications for public policy and suggest avenues for future research.

The overwhelming majority of entrepreneurship research focused on investigating the implications of social capital reside at the individual level of analysis. Our unique inquiry is an inaugural effort to consider this important implications at the macro and meso-level of analysis by examining both regional and national-level effects.