Investing in conflict zones:a firm-level analysis

The purpose of this paper is to examine, using panel data econometric techniques, the determinants of a firm’s strategy to invest in a conflict location. To the best of our knowledge this has not been done before. We use a large database of firm-level data that includes 2858 multinational firms that have a subsidiary in a developing country (during 1999-2006). Out of these firms 290 are classified as having a subsidiary in a conflict location. The choice of a conflict location is based on data from the Inter Country Risk Guide (ICRG). We start with the population of multinationals who have chosen to invest in low income countries with weak institutions. Our analysis then proceeds to explain the decision of those firms to invest in conflict locations. We have four hypotheses: (1) Firms with concentrated ownership are more likely to invest in a conflict region; (2) Firms from countries with weaker institutions are more likely to invest in conflict regions; (3) Firms and Countries with less concern over corporate social responsibility are more likely to invest in conflict countries; and (4) that there is large sector level differences in the propensity to invest in a conflict region. The results suggest that all of these hypotheses can be confirmed.

Costs and efficiency of higher education institutions in England:a DEA analysis

As student numbers in higher education in the UK have expanded during recent years, it has become increasingly important to understand its cost structure. This study applies Data Envelopment Analysis (DEA) to higher education institutions in England to assess their cost structure, efficiency and productivity. The paper complements an earlier study that used parametric methods to analyse the same panel data. Interestingly, DEA provides estimates of subject-specific unit costs that are in the same ballpark as those provided by the parametric methods. The paper then extends the previous analysis and finds that further student number increases of the order of 20–27% are feasible through exploiting operating and scale efficiency gains and also adjusting student mix. Finally the paper uses a Malmquist index approach to assess productivity change in the UK higher education. The results reveal that for a majority of institutions productivity has actually decreased during the study period.

Effective forms of market orientation across the business cycle:a longitudinal analysis of business-to-business firms

Macroeconomic developments, such as the business cycle, have a remarkable influence on firms and their performance. In business-to-business (B-to-B) markets characterized by a strong emphasis on long-term customer relationships, market orientation (MO) provides a particularly important safeguard for firms against fluctuating market forces. Using panel data from an economic upturn and downturn, we examine the effectiveness of different forms of MO (i.e., customer orientation, competitor orientation, interfunctional coordination, and their combinations) on firm performance in B-to-B firms. Our findings suggest that the impact of MO increases especially during a downturn, with interfunctional coordination clearly boosting firm performance and, conversely, competitor orientation becoming even detrimental. The findings further indicate that both the role of MO and its most effective forms vary across industry sectors, MO having a particularly strong impact on performance among B-to-B service firms. The findings of our study provide guidelines for executives to better manage performance across the business cycle and tailor their investments in MO more effectively, according to the firm's specific industry sector.

Bagging model with cost sensitive analysis on diabetes data

Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.