Drivers of RFID adoption in the automotive industry

The automotive industry is confronted with increasing competition, leading to higher cost pressures and the demand to optimize production processes and value chains. Here the RFID technology promises to improve a range of processes in logistics and manufacturing. Despite its promising potential in the automotive industry, RFID has not yet made a decisive step from pilots to real-life implementations in the supply chain. Building on existing models of technology adoption, we analyze RFID adoption dynamics in the automotive industry. Building on existing IOS adoption models tailored to RFID specifics and based on ten semi-structured interviews with OEMs and suppliers, we evaluate main drivers of RFID adoption in the automotive industry. Our key findings are that the use of a coercive approach by the OEM could be redundant because of the market-driven RFID adoption among many suppliers. Furthermore, suppliers implementing RFID can now gain an early mover competitive advantage by developing higher trust in their relationship with the OEM as well as accumulating unique expertise in this area.

Determination of Δ9-tetrahydrocannabinolic acid A (Δ9-THCA-A) in whole blood and plasma by LC–MS/MS and application in authentic samples from drivers suspected of driving under the influence of cannabis

Delta-9-tetrahydrocannabinolic acid A (THCA-A) is the biosynthetic precursor of delta-9-tetrahydrocannabinol (THC) in cannabis plants, and has no psychotropic effects. THCA-A can be detected in blood and urine, and several metabolites have been identified. THCA-A was also shown to be incorporated in hair by side stream smoke to a minor extent, but incorporation via blood stream or sweat seems unlikely. The detection of THCA-A in biological fluids may serve as a marker for differentiating between the intake of prescribed THC medication – containing only pure THC – and cannabis products containing THC besides THC-acid A and other cannabinoids. However, the knowledge about its usefulness in forensic cases is very limited. The aim of the present work was the development of a reliable method for THCA-A determination in human blood or plasma using LC–MS/MS and application to cases of driving under the influence of drugs. Fifty eight (58) authentic whole blood and the respective plasma samples were collected from drivers suspected of driving under the influence of cannabis from the region of Bern (Switzerland). Samples were first tested for THC, 11-OH-THC and THC-COOH, and then additionally for THCA-A. For this purpose, the existing LC–MS/MS method was modified and validated, and found to be selective and linear over a range of 1.0 to 200 ng/mL (the correlation coefficients were above 0.9980 in all validation runs). Limit of detection (LOD) and limit of quantification (LOQ) were 0.3 ng/mL and 1.0 ng/mL respectively. Intra- and inter-assay accuracy were equal or better than 90% and intra- and inter-assay precision were equal or better than 11.1%. The mean extraction efficiencies were satisfactory being equal or higher than 85.4%. THCA-A was stable in whole blood samples after 3 freeze/thaw cycles and storage at 4 °C for 7 days. Re-injection (autosampler) stability was also satisfactory. THC was present in all blood samples with levels ranging from 0.7 to 51 ng/mL. THCA-A concentrations ranged from 1.0 to 496 ng/mL in blood samples and from 1.4 to 824 ng/mL in plasma samples. The plasma:blood partition coefficient had a mean value of 1.7 (±0.21, SD). No correlation was found between the degree of intoxication or impairment stated in the police protocols or reports of medical examinations and the detected THCA-A-concentration in blood.

Explaining car drivers' intention to prevent road-traffic noise: An application of the Norm Activation Model

Strengthening car drivers’ intention to prevent road-traffic noise is a first step toward noise abatement through voluntary change of behavior. We analyzed predictors of this intention based on the norm activation model (i.e., personal norm, problem awareness, awareness of consequences, social norm, and value orientations). Moreover, we studied the effects of noise exposure, noise sensitivity, and noise annoyance on problem awareness. Data came from 1,002 car drivers who participated in a two-wave longitudinal survey over 4 months. Personal norm had a large prospective effect on intention, even when the previous level of intention was controlled for, and mediated the effect of all other variables on intention. Almost 60% of variance in personal norm was explained by problem awareness, social norm, and biospheric value orientation. The effects of noise sensitivity and noise exposure on problem awareness were small and mediated by noise annoyance. We propose four communication strategies for strengthening the intention to prevent road-traffic noise in car drivers.