532 research outputs found
The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan
The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings
Multi-sensor driver drowsiness monitoring
A system for driver drowsiness monitoring is proposed, using multi-sensor data
acquisition and investigating two decision-making algorithms, namely a fuzzy inference system
(FIS) and an artificial neural network (ANN), to predict the drowsiness level of the driver.
Drowsiness indicator signals are selected allowing non-intrusive measurements. The experimental
set-up of a driver-drowsiness-monitoring system is designed on the basis of the soughtafter
indicator signals. These selected signals are the eye closure via pupil area measurement,
gaze vector and head motion acquired by a monocular computer vision system, steering wheel
angle, vehicle speed, and force applied to the steering wheel by the driver. It is believed that, by
fusing these signals, driver drowsiness can be detected and drowsiness level can be predicted.
For validation of this hypothesis, 30 subjects, in normal and sleep-deprived conditions, are
involved in a standard highway simulation for 1.5 h, giving a data set of 30 pairs. For designing a
feature space to be used in decision making, several metrics are derived using histograms and
entropies of the signals. An FIS and an ANN are used for decision making on the drowsiness
level. To construct the rule base of the FIS, two different methods are employed and compared
in terms of performance: first, linguistic rules from experimental studies in literature and,
second, mathematically extracted rules by fuzzy subtractive clustering. The drowsiness levels
belonging to each session are determined by the participants before and after the experiment,
and videos of their faces are assessed to obtain the ground truth output for training the
systems. The FIS is able to predict correctly 98 per cent of determined drowsiness states
(training set) and 89 per cent of previously unknown test set states, while the ANN has a correct
classification rate of 90 per cent for the test data. No significant difference is observed between
the FIS and the ANN; however, the FIS might be considered better since the rule base can be
improved on the basis of new observations
- …