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This paper aims to gauge developers’ perspectives regarding the participation of the public and anonymous crowd testers worldwide, with a range of varied experiences. It also aims to gather their needs that could reduce their concerns of dealing with the public crowd testers and increase the opportunity of using the crowdtesting platforms.

An online exploratory survey was conducted to gather information from the participants, which included 50 mobile application developers from various countries with diverse experiences across Android and iOS mobile platforms.

The findings revealed that a significant proportion (90%) of developers is potentially willing to perform testing via the public crowd testers worldwide. This on condition that several fundamental features were available, which enable them to achieve more realistic tests without artificial environments on large numbers of devices. The results also demonstrated that a group of developers does not consider testing as a serious job that they have to pay for, which can affect the gig-economy and global market.

This paper provides new insights for future research in the study of how acceptable it is to work with public and anonymous crowd workers, with varying levels of experience, to perform tasks in different domains and not only in software testing. In addition, it will assist individual or small development teams who have limited resources or who do not have thousands of testers in their private testing community, to perform large-scale testing of their products.

This paper aims to make it convenient for those who have only just begun their research into Community Question Answering (CQA) expert recommendation, and for those who are already concerned with this issue, to ease the extension of our understanding with future research.

In this paper, keywords such as “CQA”, “Social Question Answering”, “expert recommendation”, “question routing” and “expert finding” are used to search major digital libraries. The final sample includes a list of 83 relevant articles authored in academia as well as industry that have been published from January 1, 2008 to March 1, 2019.

This study proposes a comprehensive framework to categorize extant studies into three broad areas of CQA expert recommendation research: understanding profile modeling, recommendation approaches and recommendation system impacts.

This paper focuses on discussing and sorting out the key research issues from these three research genres. Finally, it was found that conflicting and contradictory research results and research gaps in the existing research, and then put forward the urgent research topics.

This study aims to analyze the structural characteristics of knowledge exchange in Zhihu Lives to provide feasible suggestions for improving the creative enthusiasm of knowledge providers.

This paper uses the domain classification of Zhihu Lives to construct a cross-domain knowledge exchange network.

This research makes the following findings: the small-world effect exists in the sponsorship network and is conducive to enhancing the learning willingness of knowledge providers; significant sponsorships and strong learning willingness exist among knowledge providers; the knowledge exchange is obvious among the fields of education, reading and writing, business and lifestyle and the fields of music, film, games, art, the internet, science and technology, design, financial economy and occupation; and knowledge exchange is obvious among the internal fields of education, reading and writing, and business and life style, between the internal fields of music, film, and games and art and between the internal fields of the internet, science and technology, design, financial economy and occupation.

This study can provide practical suggestions for the following development of the platform by analyzing the special phenomenon of knowledge exchange in the present stage of knowledge exchange.

Interruptions are prevalent in knowledge work, and their negative consequences have driven research to find ways for interruption management. However, these means almost always leave the responsibility and burden of interruptions with individual knowledge workers. System-level approaches for interruption management, on the other hand, have the potential to reduce the burden on employees. This paper’s objective is to pave way for system-level interruption management by showing that data about factual characteristics of work can be used to identify interrupting situations.

The authors provide a demonstration of using trace data from information and communications technology (ICT)-systems and machine learning to identify interrupting situations. They conduct a “simulation” of automated data collection by asking employees of two companies to provide information concerning situations and interruptions through weekly reports. They obtain information regarding four organizational elements: task, people, technology and structure, and employ classification trees to show that this data can be used to identify situations across which the level of interruptions differs.

The authors show that it is possible to identifying interrupting situations from trace data. During the eight-week observation period in Company A they identified seven and in Company B four different situations each having a different probability of occurrence of interruptions.

The authors extend employee-level interruption management to the system-level by using “task” as a bridging concept. Task is a core concept in both traditional interruption research and Leavitt's 1965 socio-technical model which allows us to connect other organizational elements (people, structure and technology) to interruptions.

This paper aims to support small mobile application development teams or companies performing testing on a large variety of operating systems versions and mobile devices to ensure their seamless working.

This paper proposes a “hybrid crowdsourcing” method that leverages the power of public crowd testers. This leads to generating a novel crowdtesting workflow Developer/Tester- Crowdtesting (DT-CT) that focuses on developers and crowd testers as key elements in the testing process without the need for intermediate as managers or leaders. This workflow has been used in a novel crowdtesting platform (AskCrowd2Test). This platform enables testing the compatibility of mobile devices and applications at two different levels, high-level (device characteristics) or low-level (code). Additionally, a “crowd-powered knowledge base” has been developed that stores testing results, relevant issues and their solutions.

The comparison of the presented DT-CT workflow with the common and most recent crowdtesting workflows showed that DT-CT may positively impact the testing process by reducing time-consuming and budget spend because of the direct interaction of developers and crowd testers.

To authors’ knowledge, this paper is the first to propose crowdtesting workflow based on developers and public crowd testers without crowd managers or leaders, which light the beacon for the future research in this field. Additionally, this work is the first that authorizes crowd testers with a limited level of experience to participate in the testing process, which helps in studying the behaviors and interaction of end-users with apps and obtains more concrete results.

The setting up of e-university has been slow-going. Much of e-university slow progress has been attributed to poor business models, branding, disruptive technologies, lack of organisational structure that accommodates such challenges, and failure to integrate a blended approach. One of the stumbling blocks, among many, is the handling of evaluation process. E-university models do not provide much automation compared to the original brick-and-mortar classroom model of delivery. The underlining technologies may not have been supportive; however, the conditions are changing, and more evaluation tools are becoming available for academics. The paper aims to discuss these issues.

This paper identifies the extent of current online evaluation processes. In this process, the team reviews the case study of a UK E-University using Adobe Connect learning model that mirrors much of the physical processes as well as online exams and evaluation tools. Using the Riva model, the paper compares the physical with the online evaluation processes for e-universities to identify differences in these processes to evaluate the benefits of e-learning. As a result, the models can help us to identify the processes where improvements can take place for automating the process and evaluate the impact of this change.

The paper concludes that this process can be significantly shortened and provide a fairer outcome but there remain some challenges for e-university processes to overcome.

This paper examines the vital quality assurance processes in academia as more universities move towards process automation, blended or e-university business models. Using the case study of Arden University online distance learning, the paper demonstrates, through modelling and analysis that the process of online automation of the evaluation process is achieved with significant efficiency.