The role of context and motivation variables in mobile commerce usage: A further perspective on Chong (2013)

We comment on a recent article by Chong (2013) on the roles of demographic and motivation variables in mobile commerce usage. Drawing on the recent research on the service-dominant logic, socioemotional selectivity theory, and data from a first empirical study, we argue that a broader discussion on the value relevance of mobile commerce activities for customers and the consideration of consumers' future time perspectives would provide a richer, potentially more appropriate picture of the drivers of mobile commerce usage. Furthermore, using data from a second empirical study, we highlight several validity issues of the used scales. We hope to motivate a replication and extension of Chong's study and also provide recommendations for future research on this area.

Pedestrian Crashes in Ethiopia: Identification of contributing factors through modelling of exposure and road environment variables

Pedestrian safety is a critical issue in Ethiopia. Reports show that 50 to 60% of traffic fatality victims in the country are pedestrians. The primary aim of this research was to examine the possible causes of and contributing factors to crashes with pedestrians in Ethiopia, and improve pedestrian safety by recommending possible countermeasures. The secondary aim was to develop appropriate pedestrian crash models for two-way two-lane rural roads and roundabouts in the capital city of Ethiopia. This research uses quantitative methods throughout the process of the investigation. The research has applied various statistical methods. The results of this research support the idea that geometric and operational features have significant influence on pedestrian safety and crashes. Accordingly, policies and strategies are needed to safeguard pedestrians in Ethiopia.

Adverse weather effects on bus ridership

This study focuses on the effects of weather on daily bus ridership in Brisbane, given the dominance of buses in that city. The weather pattern of Brisbane varies by season according to its subtropical climate characteristics. Bus operation is affected by inclement weather conditions, as buses share the road system with general traffic. Moreover, bus stops generally offer little, or sometimes no, protection from adverse weather. Hence, adverse weather conditions such as rain are thought to directly impact on daily travel behaviour patterns. There has been limited Australian research on the impact of weather on daily transit ridership. This study investigates the relationship between rainy days and daily bus ridership for the period 2010 to 2012. Overall, rainfall has a negative effect, with varying impacts on different transit groups. However, this analysis confirmed a positive relationship between consecutive rainy days (rain continuing for 3 or more days). A possible explanation could be that people switch their transport mode to bus to avoid high traffic congestion and higher accident potentiality on rainy days. Also, Brisbane’s segregated busway corridor works favourably towards this mode choice. The findings of our study enhance the fundamental understanding of traveller behaviour, particularly mode-choice behaviour, under adverse weather conditions.

Growth curves with time-dependent explanatory variables

A class of growth models incorporating time-dependent factors and stochastic perturbations are introduced. The proposed model includes the existing growth models used in fisheries as special cases. Particular attention is given to growth of a population (in average weight or length) from which observations are taken randomly each time and the analysis of tag-recapture data. Two real data sets are used for illustration: (a) to estimate the seasonal effect and population density effect on growth of farmed prawn (Penaeus monodon) from weight data and (b) to assess the effect of tagging on growth of barramundi (Lates calcarifer)

Growth curves with explanatory variables and estimation of the effect of tagging

The von Bertalanffy growth model is extended to incorporate explanatory variables. The generalized model includes the switched growth model and the seasonal growth model as special cases, and can also be used to assess the tagging effect on growth. Distribution-free and consistent estimating functions are constructed for estimation of growth parameters from tag-recapture data in which age at release is unknown. This generalizes the work of James (1991, Biometrics 47 1519-1530) who considered the classical model and allowed for individual variability in growth. A real dataset from barramundi (Lates calcarifer) is analysed to estimate the growth parameters and possible effect of tagging on growth.

Using rowers’ perceptions of on-water stroke success to evaluate sculling catch efficiency variables via a boat instrumentation system

Aim An effective catch in sculling is a critical determinant of boat velocity. This study used rowers’ performance-based judgments to compare three measures of catch slip efficiency. Two questions were addressed: (1) would rower-judged Yes strokes be faster than No strokes? and (2) which method of quantifying catch slip best reflected these judgements? Methods Eight single scullers performed two 10-min blocks of sub maximal on-water rowing at 20 strokes per minute. Every 30 s, rowers reported either Yes or No about the quality of their stroke at the catch. Results It was found that Yes strokes identified by rowers had, on average, a moderate effect advantage over No strokes with a standardised effect size of 0.43. In addition, a quicker time to positive acceleration best reflected the change in performance; where the standardised mean difference score of 0.57 for time to positive acceleration was larger than the scores of 0.47 for time to PowerLine force, and 0.35 for time to 30% peak pin force catch slip measures. For all eight rowers, Yes strokes corresponded to time to positive acceleration occurring earlier than No strokes. Conclusion Rower judgements about successful strokes was linked to achieving a quicker time to positive acceleration, and may be of the most value in achieving a higher average boat velocity.

Meta-analyses of the determinants and outcomes of belief in climate change

Recent growth in the number of studies examining belief in climate change is a positive development, but presents an ironic challenge in that it can be difficult for academics, practitioners and policy makers to keep pace. As a response to this challenge, we report on a meta-analysis of the correlates of belief in climate change. Twenty-seven variables were examined by synthesizing 25 polls and 171 academic studies across 56 nations. Two broad conclusions emerged. First, many intuitively appealing variables (such as education, sex, subjective knowledge, and experience of extreme weather events) were overshadowed in predictive power by values, ideologies, worldviews and political orientation. Second, climate change beliefs have only a small to moderate effect on the extent to which people are willing to act in climate-friendly ways. Implications for converting sceptics to the climate change cause—and for converting believers’ intentions into action—are discussed.

The use of metabolomics to monitor simultaneous changes in metabolic variables following supramaximal low volume high intensity exercise

High Intensity Exercise (HIE) stimulates greater physiological remodeling when compared to workload matched low-moderate intensity exercise. This study utilized an untargeted metabolomics approach to examine the metabolic perturbations that occur following two workload matched supramaximal low volume HIE trials. In a randomized order, 7 untrained males completed two exercise protocols separated by one week; 1) HIE150%: 30 x 20s cycling at 150% VO2peak, 40s passive rest; 2) HIE300%: 30 x 10s cycling at 300% VO2peak, 50 s passive rest. Total exercise duration was 30 minutes for both trials. Blood samples were taken at rest, during and immediately following exercise and at 60 minutes post exercise. Gas chromatography-mass spectrometry (GC-MS) analysis of plasma identified 43 known metabolites of which 3 demonstrated significant fold changes (HIE300% compared to the HIE150% value) during exercise, 14 post exercise and 23 at the end of the recovery period. Significant changes in plasma metabolites relating to lipid metabolism [fatty acids: dodecanoate (p=0.042), hexadecanoate (p=0.001), octadecanoate (p=0.001)], total cholesterol (p=0.001), and glycolysis [lactate (p=0.018)] were observed following exercise and during the recovery period. The HIE300% protocol elicited greater metabolic changes relating to lipid metabolism and glycolysis when compared to HIE150% protocol. These changes were more pronounced throughout the recovery period rather than during the exercise bout itself. Data from the current study demonstrate the use of metabolomics to monitor intensity-dependent changes in multiple metabolic pathways following exercise. The small sample size indicates a need for further studies in a larger sample cohort to validate these findings.