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From a corporate social responsibility perspective, there are many reasons to promote teleconference use as an alternative to business travel. The present study examines psychosocial and organizational factors relevant to teleconference use. We tested an extended Theory of Planned Behavior model of teleconference use among office workers of four organizations. Results indicate that intention was the strongest direct predictor of teleconference use. Habit and perceived norm, in turn, were the strongest predictors of intention to use teleconference. In contrast, attitude was only weakly predictive and perceived control not predictive at all of intention to use teleconference. We also examined how this model was influenced by the organizational context by comparing organizations from two different regions, and organizations from the private vs. the public sector. Most teleconference-related beliefs differed between regions and organizational sectors. The relevance of specific attitudinal and normative beliefs to the overall attitude and perceived norm also differed between organizational sectors. Implications for practice and future research are discussed.

Purpose. Nutrition education and labeling may help consumers to eat less fat. The purpose of this study is to assess the effect of nutrition education with and without shelf labeling on reduced fat intake in Dutch supermarkets. Methods. The design consisted of a randomized, pretest-posttest, experimental control group design. In total, 2203 clients of 13 supermarkets were included in the sample. Total fat intake of clients and behavioral determinants of eating less fat were measured by a questionnaire. A mixed-effect regression model was used for the analysis. Results. No significant effects were found for the educational intervention, alone or with the labeling, on total fat intake and the psychosocial determinants of eating less fat. Conclusion. Nutrition education and labeling of low-fat food products in supermarkets did not prove to be effective strategies. The fact that the supermarket is a highly competitive environment may have accounted for this lack of effect.

Hazard Banding (HB) is a process of allocating chemical substances in bands of increasing health hazard based on their hazard classifications. Recent Control Banding (CB) tools use the classifications of the United Nations Global Harmonized System (UN GHS) or the European Union Classifications, Labelling and Packaging (EU CLP) which are grouped over 5 HBs. The use of CB is growing worldwide for the risk control of substances without an Occupational Exposure Limit Value (OELV). Well-known CB-tools like HSE-COSHH Essentials, BAuA-Einfaches Maßnahmenkonzept Gefahrstoffe (EMKG), and DGUV-IFA-Spaltenmodell (IFA) use however different GHS/CLP groupings which may lead to dissimilar HBs and control regimes for individual substances. And as the choice for a CB tool seems to be determined by geography and/or local status these differences may hamper a global, aligned HSE approach. Therefore, the HB-engines of the three public CBs and an in-company (Solvay) CB called 'Occupational Exposure Banding' (S-OEB) were compared mutually and ranked in their relation with the OELV as the 'de facto' standard. This was investigated graphically and using a 5 strength indicator, statistical method. A data set of 229 substances with high-quality GHS/CLP classifications and OELVs was used. HB concentration ranges, as linked to S-OEB and COSHH, were validated against the corresponding OELV distributions. The four HB-engines allocate between 23 and 64% of the 229 substances in the same bands. The remaining substances differ at least one band, with IFA placing more substances in a higher hazard band, EMKG doing the opposite and COSHH and S-OEB in between. The overall strength scores of S-OEB, IFA, and EMGK HB-engines are higher than COSHH, with S-OEB having the highest overall strength score. The lower ends of the concentration ranges defined for the 3 'highest' hazard bands of S-OEB were in good agreement with the 10th percentiles of the corresponding OELV distributions obtained from the substance data set. The lower ends of the COSHH ...

Hazard Banding (HB) is a process of allocating chemical substances in bands of increasing health hazard based on their hazard classifications. Recent Control Banding (CB) tools use the classifications of the United Nations Global Harmonized System (UN GHS) or the European Union Classifications, Labelling and Packaging (EU CLP) which are grouped over 5 HBs. The use of CB is growing worldwide for the risk control of substances without an Occupational Exposure Limit Value (OELV). Well-known CB-tools like HSE-COSHH Essentials, BAuA-Einfaches Maßnahmenkonzept Gefahrstoffe (EMKG), and DGUV-IFA-Spaltenmodell (IFA) use however different GHS/CLP groupings which may lead to dissimilar HBs and control regimes for individual substances. And as the choice for a CB tool seems to be determined by geography and/or local status these differences may hamper a global, aligned HSE approach. Therefore, the HB-engines of the three public CBs and an in-company (Solvay) CB called 'Occupational Exposure Banding' (S-OEB) were compared mutually and ranked in their relation with the OELV as the 'de facto' standard. This was investigated graphically and using a 5 strength indicator, statistical method. A data set of 229 substances with high-quality GHS/CLP classifications and OELVs was used. HB concentration ranges, as linked to S-OEB and COSHH, were validated against the corresponding OELV distributions. The four HB-engines allocate between 23 and 64% of the 229 substances in the same bands. The remaining substances differ at least one band, with IFA placing more substances in a higher hazard band, EMKG doing the opposite and COSHH and S-OEB in between. The overall strength scores of S-OEB, IFA, and EMGK HB-engines are higher than COSHH, with S-OEB having the highest overall strength score. The lower ends of the concentration ranges defined for the 3 'highest' hazard bands of S-OEB were in good agreement with the 10(th) percentiles of the corresponding OELV distributions obtained from the substance data set. The lower ends of the COSHH ...

This article describes a test of Karasek's Job Demand-Control (JD-C) Model using both group and individual level assessments of job characteristics. By group assessments, we mean aggregated individual data. A random sample from general hospitals and nursing homes included 16 institutions, 64 units, and 1489 health care workers (82% response). Because of the hierarchically nested data structure (i.e., institutions, units, and individuals) the research questions and hypothesis were tested in multilevel regression analyses (VARCL). The results revealed both group level and individual level effects with regard to psychological outcomes, and stressed the usefulness of multilevel techniques. Karasek's JID-C Model was partly confirmed by finding two interaction effects at group level and at individual level with regard to job satisfaction and work motivation, respectively. The discussion focuses on theoretical, methodological, and practical implications of multilevel modeling with respect to the JD-C Model.