Strategic CSR for innovation in SMEs: Does diversity matter?

Both corporate social responsibility and diversity influence firms' innovation, yet their relationship and links to innovation remain uncertain, especially among small to medium-sized enterprises. Relying on strategic and institutional CSR perspectives and a value-in-diversity approach, this study examines the mediating roles of gender and nationality diversity on the CSR–innovation link at the organizational level. With a sample of 1348 SMEs from Luxembourg, the results show that strategic CSR can promote both types of diversity, but only nationality diversity triggers technological innovation. Nationality diversity emerges as a partial mediator of the relationship between CSR and SMEs’ technological innovation. Thus, strategic CSR, through the genuine pursuit of such diversity, can help SMEs attain positive returns on their product or process innovation. These results have important theoretical and managerial implications.     

Positive and negative preferences in human mate selection

This paper focuses on preferences for specific characteristics in a potential partner using data from 1993 to 1999 provided by a French marriage bureau. We perform an econometric analysis of the various traits either sought or rejected in a potential partner, respectively by men and women. Our results are consistent with investment in marriage. On the one hand, men tend to reject vulgar and unfaithful women, meaning that they are likely to suffer serious fitness costs from infidelity. On the other hand, women dread meeting potential partners who are alcoholic, selfish or violent.     

DEMOCRATIC PEACE AND ELECTORAL ACCOUNTABILITY

Democracies rarely engage in conflicts with one another, though they are not averse to fighting autocracies. We exploit the existence in many countries of executive term limits to show that electoral accountability is the key reason behind this “democratic peace” phenomenon. We construct a new dataset of term limits for a sample of 177 countries over the 1816–2001 period, and combine this information with a large dataset of interstate conflicts. Our empirical analysis shows that, although democracies are significantly less likely to fight each other, democracies with leaders who face binding term limits are as conflict prone as autocracies. The study of electoral calendars confirms the importance of re‐election incentives: in democracies with two‐term limits, conflicts are less likely to occur during the executive's first mandate than in the last one. Our findings support the Kantian idea that elections act as a discipline device, deterring leaders from engaging in costly conflicts.     

Positional Independence in Preference Aggregation: A Remark

In a recent article, Quesada (Soc Choice Welfare 20 363–370, 2003) showed that if, for strict preferences, a unique strongly unanimous Choice Function (CF) is used to aggregate preferences position-wise then the resulting Social Welfare Function (SWF) is dictatorial. We show that this result is still true if one allows the use of different choice functions, one for each position. We also show that allowing choice functions not to be unanimous leads to SCW that are perm-dictatorial or imposed. We also show a way to set a dual problem to the one exposed in Que03, namely only perm-dictatorial and imposed SWFs can be decomposed into several Dual Choice Functions.An anonymous referee is thanked for his useful remarks.     

Influence of a Community Health Worker Intervention on Mental Health Outcomes among Low-Income Latino and African American Adults with Type 2 Diabetes

This study investigated the influence of a community health worker (CHW) diabetes lifestyle intervention on mental health outcomes. Our study was guided by the principles of community-based participatory research. Data were collected from 164 African American (N = 94) and Hispanic adults (N = 70) participating in a randomized, 6-month delayed intervention group design for improving glycemic control. The intervention time periods were baseline to 6 months for the treatment group and 6–12 months for the delayed group. Linear mixed models were used to conduct longitudinal analyses of the Problem Areas in Diabetes (PAID) and Patient Health Questionnaire (PHQ) scales. In the model adjusted for demographics, the PAID dropped significantly from pre-intervention to post-intervention within both the treatment and delayed groups (p < 0.05) with an average intervention effect of ?6.4 (p < 0.01). The PAID dropped even further within the immediate group from 6 to 12 months. Although the PHQ did not change significantly, the PHQ-2 had an average intervention effect of ?0.3 (p < 0.05) in the model adjusted for demographics. This study contributes to an understanding of how a CHW-led diabetes intervention can result in positive mental health outcomes for Latinos and African Americans with Type 2 diabetes. It also highlights the importance of further exploring what factors may contribute to racial/ethnic variation in mental health outcomes for African Americans and Latinos with diabetes and the role CHWs can play.     

Maximum likelihood estimator in a multi-phase random regression model

We consider a random regression model with several-fold change-points. The results for one change-point are generalized. The maximum likelihood estimator of the parameters is shown to be consistent, and the asymptotic distribution for the estimators of the coefficients is shown to be Gaussian. The estimators of the change-points converge, with n ^?1 rate, to the vector whose components are the left end points of the maximizing interval with respect to each change-point. The likelihood process is asymptotically equivalent to the sum of independent compound Poisson processes.     

Lessons learned from Toulouse and Buncefield disasters: from risk analysis failures to the identification of atypical scenarios through a better knowledge management

The recent occurrence of severe major accidents has brought to light flaws and limitations of hazard identification (HAZID) processes performed for safety reports, as in the accidents at Toulouse (France) and Buncefield (UK), where the accident scenarios that occurred were not captured by HAZID techniques. This study focuses on this type of atypical accident scenario deviating from normal expectations. The main purpose is to analyze the examples of atypical accidents mentioned and to attempt to identify them through the application of a well-known methodology such as the bow-tie analysis. To these aims, the concept of atypical event is accurately defined. Early warnings, causes, consequences, and occurrence mechanisms of the specific events are widely studied and general failures of risk assessment, management, and governance isolated. These activities contribute to outline a set of targeted recommendations, addressing transversal common deficiencies and also demonstrating how a better management of knowledge from the study of past events can support future risk assessment processes in the identification of atypical accident scenarios. Thus, a new methodology is not suggested; rather, a specific approach coordinating a more effective use of experience and available information is described, to suggest that lessons to be learned from past accidents can be effectively translated into actions of prevention.     

EWMA Control Charts for Monitoring High Yield Processes

The main objective of this article is to scrutinize the efficiency and verify the performance superiority of the one-sided EWMA control chart on high-yield processes. The proposed control chart is designed to detect both upward and downward shifts of the fraction of non conforming products and is developed based on non transformed geometric counts. Its algorithmic function is theoretically established and numerous performance measures are extracted using analytical methods based on the Markov modeling of the chart. Comparisons with traditional high yield control charts are conducted. Optimality tables and nomograms are included to help graphical determination of the optimal chart parameters.     

Spatial Regression Models Using Inter-Region Distances in a Non-Random Context

This article considers spatial data z( s ₁), z( s ₂),…, z( s _n ) collected at n locations, with the objective of predicting z( s ₀) at another location. The usual method of analysis for this problem is kriging, but here we introduce a new signal-plus-noise model whose essential feature is the identification of hot spots. The signal decays in relation to distance from hot spots. We show that hot spots can be located with high accuracy and that the decay parameter can be estimated accurately. This new model compares well to kriging in simulations.