An Explorative Study of the Effectiveness of Mobile Advertising

This study examines factors related to the effectiveness of mobile advertising. Using a large data set with 115, 899 records of ad tap through from a mobile advertising company, we identify that the influencing factors for ad tap through are application type, mobile operators, scrolling frequency, and the regional income level. We use a logit model to analyze how the probability of ad tap through is related to the identified factors. The results show that application type, mobile operators, scrolling frequency, and the regional income level all have significant effects on the likelihood whether users would tap on certain types of advertising. Based on the findings, we propose strategies for mobile advertisers to engage in effective and targeted mobile advertising

Digitizing Offline Shopping Behavior Towards Mobile Marketing

The proliferation of mobile technologies makes it possible for mobile advertisers to go beyond the real-time snapshot of the static location and contextual information about consumers. In this study, we propose a novel mobile advertising strategy that leverages full information on consumers’ offline moving trajectories. To evaluate the effectiveness of this strategy, we design a large-scale randomized field experiment in a large shopping mall in Asia based on 83,370 unique user responses for two weeks in 2014. We found the new mobile trajectory-based advertising is significantly more effective for focal advertising store compared to several existing baselines. It is especially effective in attracting high-income consumers. Interestingly, it becomes less effective during the weekend. This indicates closely targeted mobile ads may constrict consumer focus and significantly reduce the impulsive purchase behavior. Our finding suggests marketers should carefully design mobile advertising strategy, depending on different business contexts

Competing Forces Framework of Technology Assimilation: An Investigation into a Group of Mobile Device Users

Despite evidence that competing forces shape adoption and assimilation of technologies, there is currently no comprehensive model available that explains how such forces impact individually and socially oriented usage of technology. We distinguish between exploration versus exploitation forces and individual versus social forces and posit that these play key roles in shaping assimilation behaviors and usage outcomes. On this basis, we develop the Competing Forces Framework (CFF) of technology assimilation and validate it by analyzing how a group of fifteen iPhone users assimilated mobile services over a period of seven months. In doing so, we draw on data about the antecedent conditions at the time of iPhone adoption, about interactions within the group and its wider social network, and about how individual usage patterns developed over the considered time period. Based on the analysis, we describe and explain how the iPhone was assimilated into the group. As a result, we offer two distinct contributions to the literature. First, we present the CFF to support further investigation of how assimilation behaviors and usage outcomes are shaped as social groups adopt new technologies. Second, we offer new insight into the forces that shape assimilation of mobile devices into a social group of users. At present the analysis is forthcoming

Operator-based approaches to harm minimisation in gambling: summary, review and future directions

In this report we give critical consideration to the nature and effectiveness of harm minimisation in gambling. We identify gambling-related harm as both personal (e.g., health, wellbeing, relationships) and economic (e.g., financial) harm that occurs from exceeding one’s disposable income or disposable leisure time. We have elected to use the term ‘harm minimisation’ as the most appropriate term for reducing the impact of problem gambling, given its breadth in regard to the range of goals it seeks to achieve, and the range of means by which they may be achieved. The extent to which an employee can proactively identify a problem gambler in a gambling venue is uncertain. Research suggests that indicators do exist, such as sessional information (e.g., duration or frequency of play) and negative emotional responses to gambling losses. However, the practical implications of requiring employees to identify and interact with customers suspected of experiencing harm are questionable, particularly as the employees may not possess the clinical intervention skills which may be necessary. Based on emerging evidence, behavioural indicators identifiable in industryheld data, could be used to identify customers experiencing harm. A programme of research is underway in Great Britain and in other jurisdiction

Quality modeling in electronic healthcare: a study of mHealth Service

Information and communication technologies (ICTs) have the potential to radically transform health services in developing countries. Among various ICT driven health platforms, mobile health is the most promising one because of its widespread penetration and cost effective services. This paper aims to examine Quality Modeling in Electronic Healthcare by using PLS based SEM

FraudDroid: Automated Ad Fraud Detection for Android Apps

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (93%) and recall (92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure

The Study of Typology of Competitive Actions in Digital Environment: An Empirical Investigation of Mobile Instant Messaging

The highly competitive and increasingly transparent characteristics of the digital environment have led inter-firm rivalry more frequently. Competitive actions and competitor’s responses together determine firms’ value creation. The key to obtain competitive advantages is to stop or delay competitor’s response. Therefore, our research question is which type of actions is the most effective in digital environment. Grounded in the framework of explorative/exploitative in the organizational learning literature, we organize competitive actions from two dimensions: resources based (strategic versus tactical) and innovation based (innovative versus efficient). This paper studies the competitive actions in mobile instant messaging industry and use structured content analysis to capture firms’ competitive actions. Finally, 113 matched competitive actions and responses were collected. Then, we compare the effects of different types of competitive actions from three aspects, that is the number of responses, response time and response quality. The results show that innovative-strategic action is the most effective action in digital environment. Our action-level study of MIM (mobile instant messaging) promotes better understanding of how firms interact with each other in digital environment. Moreover, the new typology of competitive action helps us identify competitive actions in digital environment more precisely and help managers to better understand industry dynamics thus developing appropriate strategies to compete in the industry