Search Engine Advertising Adoption and Utilization: An Empirical Investigation of Inflectional Factors

© Taylor & Francis Group, LLC. Search engine advertising (SEA) is a prominent source of revenue for search engine companies, and also a solution for businesses to promote their visibility on the web. However, there is little academic research available about the factors and the extent to which they may influence businesses’ decision to adopt SEA. Building on Theory of Planned Behavior, Technology Acceptance Model, and Unified Theory of Acceptance and Use of Technology, this study develops a context-specific model for understanding the factors that influence the decision of businesses to use SEA. Using structural equation modeling and survey data collected from 142 businesses, this research finds that the intention of businesses to use SEA is directly influenced by four factors: (i) attitude toward SEA, (ii) subjective norms, (iii) perceived control over SEA, and (iv) perceived benefits of SEA in terms of increasing web traffic, increasing sales and creating awareness. Furthermore, the research we discover six additional factors that have an indirect influence: (i) trust in search engines, (ii) perceived risk of SEA, (iii) ability to manage keywords and bids, (iv) ability to analyze and monitor outcomes, (v) advertising expertise, and (vi) using external experts

Economic development, demographic characteristics, road network and traffic accidents in Zhongshan, China: gradient boosting decision tree model

This paper explores the joint effects of economic development, demographic characteristics and road network on road safety. Although extensive efforts have been undertaken to model safety effects of various influential factors, little evidence is provided on the relative importance of explanatory variables by accounting for their mutual interactions and non-linear effects. We present an innovative gradient boosting decision tree (GBDT) model to explore joint effects of comprehensive factors on four traffic accident indicators (the number of traffic accidents, injuries, deaths, and the economic loss). A total of 27 elaborated influential factors in Zhongshan, China during 2000–2016 are collected. Results show that GBDT not only presents high prediction accuracy, but can also handle the multicollinearity between explanatory variables; more importantly, it can rank the influential factors on traffic accidents. We also investigate the partial effects of key influential factors. Based on key findings, we highlight the practical insights for planning practice

Provider Feedback Information and Customer Choice Decisions on Crowdsourcing Marketplaces: Evidence from Two Discrete Choice Experiments

Crowdsourcing marketplaces are increasingly becoming popular for the online transactions of services. On these marketplaces, profile information of providers, especially feedback left by previous customers, is the main information source for choice decisions of prospective customers. In the study reported in this paper, we examined the impacts of various feedback information components on provider profiles on the decisions of customers. We conducted two fractional factorial discrete choice experiments, one in a controlled laboratory setting and one online on a crowdsourcing marketplace. We found that the feedback information components "number of reviews" and "average weighted rating" have the largest impacts on the decisions of customers. We also found that "positive ratings" and "positive comments" have significant impacts on customers' decision-making, especially when they appear on the first feedback page. We also found in the lack of highly visible feedback components on the subsequent feedback pages, "negative comments" become a significant determinant of customers' decisions. We also showed the significant impact of information consistency on customers' decision-making, through the synergistic interaction effects between different feedback components. Finally, we found evidence that the cost of evaluating a feedback information component has a negative impact on the likelihood of customers evaluating that information component. The article concludes with implications of the findings of the study for theory and practice

Using genetic programming on GPS trajectories for travel mode detection

The widespread and increased use of smartphones, equipped with the global positioning system (GPS), has facilitated the automation of travel data collection. Most studies on travel mode detection that used GPS data have employed hand-crafted features that may not have the capabilities to detect all complex travel behaviours since their performance is highly dependent on the skills of domain experts and may limit the performance of classifiers. In this study, a genetic programming (GP) approach is proposed to select and construct features for GPS trajectories. GP increased the macro-average of the F1-score from 77.3 to 80.0 in feature construction when applied to the GeoLife dataset. It could transform the decision tree into a competitive classifier with support vector machines (SVMs) and neural networks that are both able to extract high-level features. Simplicity, interpretability, and a relatively lower risk of overfitting allow the proposed model to be readily used for passive travel data collection even on smartphones with limited computational capacities. The model is validated by a second dataset from Australia and New Zealand, which indicated that a decision tree with the GP constructed features as its input has a considerably higher transferability than SVMs and neural networks

Transport-related walking among young adults: When and why?

Background: The existing smartphones' technology allows for the objective measurement of a person's movements at a fine-grained level of geographic and temporal detail, and in doing so, it mitigates the issues associated with self-report biases and lack of spatial details. This study proposes and evaluates the advantages of using a smartphone app for collecting accurate, fine-grained, and objective data on people's transport-related walking. Methods: A sample of 142 participants (mostly young adults) was recruited in a large Australian university, for whom the app recorded all their travel activities over two weekdays during August-September 2014. We identified eight main activity nodes which operate as transport-related walking generators. We explored the participants' transport-related walking patterns around and between these activity nodes through the use of di-graphs to better understand patterns of incidental physical activity and opportunities for intervention to increase incidental walking. Results: We found that the educational node - in other samples may be represented by the workplace - is as important as the residential node for generating walking trips. We also found that the likelihood of transport-related walking trips is larger during the daytime, whereas at night time walking trips tend to be longer. We also showed that patterns of transport-related walking relate to the presence of 'chaining' trips in the afternoon period. Conclusions: The findings of this study show how the proposed data collection and analytic approach can inform urban design to enhance walkability at locations that are likely to generate walking trips. This study's insights can help to shape public education and awareness campaigns that aim to encourage walking trips throughout the day by suggesting locations and times of the day when engaging in these forms of exercise is easiest and least intrusive