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The purpose of this paper is to propose a comprehensive theoretical framework to explain the different diffusion dynamics of a technology realized under an open source (OS) regime vs a technology realized under a closed-proprietary (CP) regime.

Following a systematic combining approach, theoretical predictions derived from literature on diffusion of innovation and on open innovation were matched with empirical observations derived from a case study of two mobile operation systems, which were released under different technological regimes.

This paper proposes a theoretical framework and a set of original theoretical propositions that can help to explain the differences in the diffusion dynamics of technologies that are released under different regimes (i.e. CP vs OS).

This paper provides managers a better understanding of the diffusion dynamics of technologies released according to an open innovation strategy. In addition, the empirical case study improves the understanding of an important industry (the MOS industry) that has not been fully investigated from the innovation diffusion perspective. Nevertheless, the efficacy and generalizability of the theoretical framework proposed in this paper require future empirical tests.

This paper makes an original contribution to the open innovation and innovation diffusion literature by linking, in a conceptual model, three antecedents of the diffusion of a new technology that previous literature has considered only separately: the regime under which the technology is released (OS vs CP), the contingencies related to the characteristics of the technology and to the network of its adopters, and the resulting barriers to adoption.

The purpose of this paper is to propose an approach that helps in acquisition of live data as well as data stored in the internal/external memory of android mobile device considering that the data on the device are not much altered during the extraction process. Also, the emphasis is laid on testing the validity of existing forensic tools against the data obtained manually and by using this approach. Smartphones have spurred the mobile computing technology, and Android is widely used as an Operating System in these devices. These days, users store most of their personal information like emails, images, contacts etc., on Phones/Tablets as their data would be readily accessible and thus convenient for them.

Android Operating System is built on the Linux Kernel and scripts to extract data from Android Mobile Device with the use of Android Debugging Bridge have been written. The approach is more focused on the logical acquisition of data from devices rather than acquisition using physical methods.

Live data of the Facebook application running on the device can be extracted. Also, the password of the LuksManager application (used to create an encrypted volume on the device), which is stored in the internal memory, is also extracted and identified.

The study has been conducted in an academic environment, thereby limiting external validity. Another limitation is the limited edition of some of the software forensics tools that are used. The full access to these software tools are restricted by Law enforcement and Investigation policies. The research provides a different approach which could aid in criminal investigation activities on mobile devices.

The devices which have the latest versions of Android not only store messages and mails, but a lot of information about GPS, as well as information about popular applications like Facebook, WhatsApp, etc. This could practically help a lot in criminal investigation.

This study is important because very few works have been done on recent versions (Jellybean and Kitkat) of Android. The proposed approach could extract large amounts of information as compared to earlier approaches with the newer versions of Android having larger memory and new features.

The purpose of the paper is to introduce standardization as a neutral arena for open innovation. The aim is to show that different policies towards open membership in standardization initiatives lead to different open innovation processes.

Two cases are used to illustrate the differences in open innovation processes. The cases are the Android mobile operating system and the service platform developed by the Open Mobile Alliance. The core process types introduced by Gassmann and Enkel are used to show the different open innovation approaches.

Both cases use open innovation to create standards. Open membership leads to a coupled process, while a more restricted membership gives separate inside‐out and outside‐in processes. The case lead by established firms in the industry has a process where radical innovations are introduced early in the process, while the case lead by newcomers has a process where radical innovations are introduced late in the process. The two cases have different approaches towards commercialization of the products. Android relies on third‐party developers, while the Open Mobile Alliance relies on their own members.

The cases are from the telecommunication sector and based on standardization of large technical platforms. The findings might not be the same for other sectors.

The paper establishes open innovation as a neutral arena for open innovation outside the domain of any single firm. It shows how the openness towards membership influences the choice of open innovation processes.

This paper aims to discuss the privacy and security concerns that have risen from the permissions model in the Android operating system, along with two shortcomings that have not been adequately addressed.

The impact of the applications’ evolutionary increment of permission requests from both the user’s and the developer’s point of view is studied, and finally, a series of remedies against the erosion of users’ privacy is proposed.

The results of this work indicate that, even though providing access to personal data of smartphone users is by definition neither problematic nor unlawful, today’s smartphone operating systems do not provide an adequate level of protection for the user’s personal data. However, there are several ideas that can significantly improve the situation and mitigate privacy concerns of users of smart devices.

The proposed approach was evaluated through an examination of the Android’s permission model, although issues arise in other operating systems. The authors’ future intention is to conduct a user study to measure the user’s awareness and concepts surrounding privacy concerns to empirically investigate the above-mentioned suggestions.

The proposed suggestions in this paper, if adopted in practice, could significantly improve the situation and mitigate privacy concerns of users of smart devices.

The recommendations proposed in this paper would strongly enhance the control of users over their personal data and improve their ability to distinguish legitimate apps from malware or grayware.

This paper emphasises two shortcomings of the permissions models of mobile operating systems which, in authors’ view, have not been adequately addressed to date and propose an inherent way for apps and other entities of the mobile computing ecosystem to commit to responsible and transparent practices on mobile users’ privacy.

The purpose of this paper is to conduct a forensic analysis of Google Allo messenger on an Android-based mobile phone. The focus was on the analysis of the data stored by this application in the internal memory of the mobile device, with minimal use of third-party applications. The findings were compared with the already existing works on this topic. Android is the most popular operating system for mobile devices, and these devices often contain a massive amount of personal information about the user such as photos and contact details. Analysis of these applications is required in case of a forensic investigation and makes the process easier for forensic analysts.

Logical acquisition of the data stored by these applications was performed. A locked Android device was used for this purpose. Some scripts are presented to help in data acquisition using Android Debug Bridge (ADB). Manual forensic analysis of the device image was performed to see whether the activities carried out on these applications are stored in the internal memory of the device. A comparative analysis of an existing mobile forensic tool was also performed to show the effectiveness of the methodology adopted.

Forensic artifacts were recovered from Allo application. Multimedia content such as images were also retrieved from the internal memory.

As this study was conducted for forensic analysis, it assumed that the mobile device used already has USB debugging enabled on it, although this might not be the applicable in some of the cases. This work provides an optimal approach to acquiring artifacts with minimal use of third-party applications.

Most of the mobile devices contain messaging application such as Allo installed. A large amount of personal information can be obtained from the forensic analysis of these applications, which can be useful in any criminal investigation.

This is the first study which focuses on the Google Allo application. The proposed methodology was able to extract almost as much as the data obtained using earlier approaches, but with minimal third-party application usage.

With rapid increase of Android devices and application systems, there is a strong demand for Android application programmers. A lot of schools are offering Android programming courses to meet this demand. However, Android programming can be different from the conventional one because it needs interactive functions through interfaces with users, which makes the study more difficult. This paper aims to propose an Android Programming Learning Assistance System, namely, APLAS, to assist the Java-based Android programming study and education.

By adopting the test-driven development method, APLAS is designed to achieve independent learning without the presence of teachers. Using JUnit and Robolectric, the answers from the students are automatically marked in APLAS. To cover extensive materials in Android programming, APLAS offers four stages where each stage involves several topics.

To evaluate the effectiveness of APLAS, we implemented the Unit Converter assignment that covers the first two topics, namely, Basic user interface in the first stage and basic activity in the second stage. Through solving the assignment, it is expected to learn basic application development. Forty novice students of an IT department in Indonesia were asked to solve both topics separately.

The results show that APLAS is useful and helpful for the self-study of Android programming, as they could complete codes with good execution performances.

This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East.

A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems.

The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft.

Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future.

Measures to improve security practices of smartphone users are needed urgently. The findings indicate that such measures should be broadly effective and relatively costless for users to implement.

Personal smartphones are joining enterprise networks through the acceptance of Bring-Your-Own-Device computing. Users’ laxity about smartphone security thus puts organizations at risk.

The paper highlights the key factors influencing smartphone security and compares the situation for the three leading operating systems in the smartphone market.

With the rapid development of the digital economy, an increasing number of digitalized two-sided platforms have deployed the tying strategy to leverage their market power from the core two-sided product to other two-sided products in the competitive market, which transforms the competition among single platforms into that among platform ecological networks. To clarify the mechanism of the formation of the digital platform ecological networks, this paper aims to analyze the expansion and stability of platform ecology by exploring the impacts of network externalities and sellers’ heterogeneity on the tying strategy of two-sided platforms.

This paper develops a game model of two-sided platforms based on Choi and Jeon (2021), which highlights the decisive influence of non-negative price constraints (NPC) on platforms’ tying motivation. Taking the operating systems market as an example, we expand from the perspective of platform service differences to relax the NPC and explore the internal logic of platform ecosystem expansion.

Platforms have an incentive to charge lower prices or even subsidize buyers when the network externalities on the sellers’ side are relatively strong. When the product is highly differentiated and heterogenous, platforms are motivated to tie to capture more buyers with a lower price and grab excess profits from sellers. Eventually, tying is able to consolidate the two-sided platform ecological networks by excluding competitors, capturing user value and deterring entry.

In order to describe the characteristics of platform ecological network more generally, this paper extends the research based on the analyses of Choi and Jeon (2021) by (1) allowing horizontal differences between tied products and (2) relaxing the NPC. Unlike Choi and Jeon (2021), this paper allows platforms to charge users of two-sided platforms at negative prices (or to subsidize them). (3) Setting simultaneous pricing in two-sided platforms. Classical two-sided market theory stresses that the presence of cross-network externalities can give rise to a “chicken and egg” problem.

This paper aims to apply a methodology that is capable to classify and localize mechanical, electrical and plumbing (MEP) elements to assist facility managers. Furthermore, it assists in decreasing the technical complexity and sophistication of different systems to the facility management (FM) team.

This research exploits artificial intelligence (AI) in FM operations through proposing a new system that uses a deep learning pre-trained model for transfer learning. The model can identify new MEP elements through image classification with a deep convolutional neural network using a support vector machine (SVM) technique under supervised learning. Also, an expert system is developed and integrated with an Android application to the proposed system to identify the required maintenance for the identified elements. FM team can reach the identified assets with bluetooth tracker devices to perform the required maintenance.

The proposed system aids facility managers in their tasks and decreases the maintenance costs of facilities by maintaining, upgrading, operating assets cost-effectively using the proposed system.

The paper considers three fire protection systems for proactive maintenance, where other structural or architectural systems can also significantly affect the level of service and cost expensive repairs and maintenance. Also, the proposed system relies on different platforms that required to be consolidated for facility technicians and managers end-users. Therefore, the authors will consider these limitations and expand the study as a case study in future work.

This paper assists in a proactive manner to decrease the lack of knowledge of the required maintenance to MEP elements that leads to a lower life cycle cost. These MEP elements have a big share in the operation and maintenance costs of building facilities.