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The paper deals with the design and creation of an intelligent user interface augmenting the user experience in everyday environments, by providing an immersive audio environment. We highlight the potential of augmenting the visual real environment in a personalized way, thanks to context modeling techniques. The LISTEN project, a system for an immersive audio augmented environment applied in the art exhibition domain, provides an example of modeling and personalization methods affecting the audio interface in terms of content and organization. In addition, the different evolution steps of the system and the outcomes of the accompanying user tests are here reported.

Purpose – Marketers are under increasing pressure to demonstrate the financial return associated with marketing expenditures. Concurrently, more attempts at measuring return on investment from marketing as well as achieving other long-term goals such as building brand equity and increasing shareholder value have been made. As a result of this emphasis, the degree to which advertising budgets are spent efficiently and the impact of these expenditures on the bottom line are an important topic to study.

Methodology/approach – This study applies data envelopment analysis (DEA) to a group of large firms to assess the degree to which companies spend advertising dollars efficiently and to examine the impact of advertising efficiency on investor behavior and, ultimately, stock prices.

Findings – The analysis reveals that firms that advertise more efficiently are rewarded by investors by positive stock returns.

Research limitations/implications (if applicable) – The study is limited to large enterprises with strong brands within a time frame of only four years.

Practical implications (if applicable) – The results imply that it is advisable for marketing managers not to limit their focus to increasing market-based assets at any cost. The efficiency of their efforts can send a positive signal to investors and contribute to shareholder value enhancement.

Originality/value of the chapter – The chapter finds investors to pay attention not only to the effectiveness of advertising activities but also to their efficiency. The study also demonstrates how DEA and stock return response modeling can be combined to investigate the link between advertising efficiency and investor behavior.

“Garbage in, garbage out” is a common expression that academics and practitioners use to emphasize that empirical analysis is only as good as the basis on which it relies. Although the importance of sound data and valid measures has long been acknowledged, it is nevertheless often problematic to follow required quality standards in concrete research situations. Potential sources of error are usually unknown, methods to ensure data quality are unavailable, and existing methods for scale development, index construction, data collection, and data analysis are insufficient or erroneously applied. This is especially true of international marketing research, which often makes great demands on the data and measures used, as well as on the research methodology applied. Against this background, this volume addresses issues pertaining to measurement and research methodology in an international marketing context. Thanks to the efforts of authors and reviewers, we are pleased to present nine articles that deal with cutting-edge topics such as formative measurement, response-bias in cross-cultural research, marketing efficiency measurement, and segmentation methods.

As the center of the fourth industrial revolution, artificial intelligence (AI) has marked its presence in various disciplines including the education field in the form of AI-powered learning applications. The purpose of this study is to build a research model capturing the relationships among use contexts, user gratification, attitude, learning performance and continuous intention to use an AI-powered English learning application.

Using the use and gratification theory, use contexts and the belief-attitude-intention theory, this paper uses a quantitative approach based on a survey method for data collection and structural equation modeling for analysis. A total of 478 students from an international university in Guangdong, China, participated in the survey after using Liulishuo for two weeks.

The results showed that perceived use contexts affected all variables associated with gratifications-obtained and gratification-opportunities. With the exception of social integrativeness, all other gratification-based factors significantly affected attitude. The attitude in turn significantly influenced learning performance and continuous use intention.

Mobile AI-powered learning applications are at the center of research on technology-enhanced learning in the age of media and technology convergence. The study is timely and contributes to the discussion of the roles of use context and gratifications on technology users’ attitudes and behavioral intentions.

This paper aims to focus on contextualized features for mobile language learning apps. The scope of this paper is to explore students’ perceptions of contextualized mobile language learning.

An extended Technology Acceptance Model was developed to analyze the effect of contextual app features on students’ usage intention. The suggested app concept applies context-triggered push notifications to initiate learning sessions based on a location-aware vocabulary. Partial least squares structural equation modeling (PLS-SEM) was used for an empirical validation of the proposed research model.

The results of the analysis revealed, that students perceived the proposed app as beneficial for their learning endeavors. The location-aware feature is essentially relevant to improve the perceived usefulness of the system, as it may increase the learning effectiveness of the app in their everyday life.

The study was conducted in quite a homogenous population. The sample size of the survey was rather small (n = 45). Further research is necessary to confirm the promising results of the research.

The results give some first evidence that the integration of innovative contextual features in mobile language learning apps may increase the usage intention and motivation to engage in a learning activity.

The paper aims to resolve three major issues in location-based applications (LBA) known as heterogeneity, user privacy, and context-awareness by proposing an elastic and open design platform named OpenLS privacy-aware middleware (OPM) for LBA.

The paper analyzes relevant approaches ranging from both academia and mobile industry community and insists the importance of heterogeneity, user privacy, and context-awareness towards the development of LBA.

The paper proposes the OPM by design. As a result, the OPM consists of two main component named application middleware and location middleware, which are cooperatively functioned to achieve the above goals. In addition, the paper has given the implementation of the OPM as well as its experiments. It is noted that two privacy-preserving techniques at two different levels are integrated into the OPM, including Memorizing algorithm at the application level and Bob-tree at the database level. Last but not least, the paper shows further discussion about other problems and improvements that might be needed for the OPM.

Each issue has its sub problems that cause more influences to the OPM. Besides, each of the issues requires more investigations in depth in order to have better solutions in detail. Therefore, more overall experiments should be conducted to assure the OPM's scalability and effectiveness.

The paper hopefully promotes and speeds up the development of LBA when providing the OPM with suitable application programming interfaces and conforming the OpenLS standard.

This paper shows its originality towards location-based service (LBS) providers to develop their applications and proposes the OPM as a unified solution dealing with heterogeneity, user privacy, and context-awareness in the world of LBS.

This study aims to show the relevance of augmented reality (AR) in mobile learning for the 21st century. With AR, any real-world environment can be augmented by providing users with accurate digital overlays. AR is a promising technology that has the potential to encourage learners to explore learning materials from a totally new perspective. Besides, the advancements made in information technology further broaden the scope for educational AR applications. Furthermore, the proliferation of wireless mobile devices such as smartphones and tablets is also introducing AR into the mobile domain.

This discussion paper gives an insight of the different potential fields of application of AR and eventually proposes an AR application that will give a completely different learning experience for learners. This AR mobile application will not only provide learners with supplementary information but will also assist lecturers in their teaching process. There are certain concepts in computer science at the tertiary level that are at times difficult for learners to understand using the traditional classroom approach. Through this AR application developed, the learners are able to see what is happening and experience a different form of learning where the focus is more on “learning by doing” and on the ability of visualizing the complete set of steps involved for a particular operation. Finally what is proposed is a generic framework/process for the development of AR applications for learning purposes.

The AR application developed and tested has proved to be helpful in understanding complex concepts of computer science that average students have much difficulty in understanding. Through AR, learning has been brought to a new dimension where the students can easily visualize what is happening and easily understand complex concepts. This low-cost system that has been proposed can track and detect both markerless and marker-based images. A number of experiments have also been carried out to determine a set of best practices for the development and use of such AR applications.

Learners have been able to have a more interactive and enriching learning experience through two-dimensional and three-dimensional digital augmentations. The AR mobile application has been enhancing the cognitive skills of learners through enabling them to scan images from printed materials with their smartphones. Then, informative digital augmentation has been overlaid in real time on the mobile screen with the image preview still in the background.

This paper aims to present a methodology for defining and modeling context-awareness and describing efficiently the interactions between systems, applications and their context. Also, the relation of modern context-aware systems with distributed computation is investigated.

On this purpose, definitions of context and context-awareness are developed based on the theory of computation and especially on a computational model for interactive computation which extends the classical Turing Machine model. The computational model proposed here encloses interaction and networking capabilities for computational machines.

The definition of context presented here develops a mathematical framework for working with context. Also, the modeling approach of distributed computing enables us to build robust, scalable and detailed models for systems and application with context-aware capabilities. Also, it enables us to map the procedures that support context-aware operations providing detailed descriptions about the interactions of applications with their context and other external sources.

A case study of a cloud-based context-aware application is examined using the modeling methodology described in the paper so as to demonstrate the practical usage of the theoretical framework that is presented.

The originality on the framework presented here relies on the connection of context-awareness with the theory of computation and distributed computing.

This paper aims to propose an automated mobile app labeling framework based on a novel app classification scheme that is aligned with users’ primary motivations for using smartphones. The study addresses the gaps in incorporating the needs of users and other context information in app classification as well as recommendation systems.

Based on a corpus of mobile app descriptions collected from Google Play store, this study applies extensive text analytics and topic modeling procedures to profile mobile apps within the categories of the classification scheme. Sufficient number of representative and labeled app descriptions are then used to train a classifier using machine learning algorithms, such as rule-based, decision tree and artificial neural network.

Experimental results of the classifiers show high accuracy in automatically labeling new apps based on their descriptions. The accuracy of the classification results suggests a feasible direction in facilitating app searching and retrieval in different Web-based usage environments.

As a common challenge in textual data projects, the problem of data size and data quality issues exists throughout the multiple phases of experiments. Future research will extend the data collection scope in many aspects to address the issues that constrained the current experiments.

These empirical experiments demonstrate the feasibility of textual data analysis in profiling apps and user context information. This study also benefits app developers by improving app descriptions through a better understanding of user needs and context information. Finally, the classification framework can also guide practitioners in customizing products and services beyond mobile apps where context information and user needs play an important role.

Given the widespread usage and applications of smartphones today, the proposed app classification framework will have broader implications to different Web-based application environments.

While there have been other classification approaches in the literature, to the best of the authors’ knowledge, this framework is the first study on building an automated app labeling framework based on primary motivations of smartphone usage.