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The purpose of this empirical study is to investigate extrinsic motivations that may affect adding or acknowledging user-generated content (UGC) on business websites, which are based on voluntary crowdsourcing.

A conceptual model of extrinsic motivations for knowledge sharing in UGC-based websites was developed, suggesting reciprocity, awareness of rewards and prestige as main extrinsic motivations for adding content. The model was examined via an online survey of users of three websites that varied in the attributes of knowledge shared and reward type: The Traveler (tangible rewards), Stack Overflow (virtual rewards) and Waze (virtual rewards).

Importance of extrinsic motivations varied among websites, as it may be affected by attributes of the knowledge shared. Reciprocity positively affected recommending the website, and adding content affected acknowledging content.

Investigating extrinsic motivations is important because websites may take actions that affect them. Further research is required to reveal the potential of voluntary crowdsourcing in business contexts addressing both intrinsic and extrinsic motivations, prosumption and open innovation.

When reciprocity is a major aspect of a UGC website, badges and similar mechanisms may serve as a main extrinsic motivation to share knowledge.

The novel empirically validated model provides theoretical and practical insights for designing mechanisms for increasing extrinsic motivation for knowledge sharing according to specific characteristics of UGC websites.

The purpose of this paper is to propose an approach to detect Indirect Memory‐Corruption Exploit (IMCE) at runtime on binary code, which is often caused by integer conversion error. Real‐world attacks were evaluated for experimentation.

Current dynamic analysis detects attacks by enforcing low level policy which can only detect control‐flow hijacking attack. The proposed approach detects IMCE with high level policy enforcement using dynamic taint analysis. Unlike low‐level policy enforced on instruction level, the authors' policy is imposed on memory operation routine. The authors implemented a fine‐grained taint analysis system with accurate taint propagation for detection.

Conversion errors are common and most of them are legitimate. Taint analysis with high‐level policy can accurately block IMCE but have false positives. Proper design of data structures to maintain taint tag can greatly improve overhead.

This paper proposes an approach to block IMCE with high‐level policy enforcement using taint analysis. It has very low false negatives, though still causes certain false positives. The authors made several implementation contributions to strengthen accuracy and performance.

This research investigates the detailed mechanisms of how gamification affordances influence intrinsic and internalized extrinsic motivation and ultimately improve the quality and quantity of knowledge contribution.

Survey responses from 154 users of Stack Overflow in Portuguese were analyzed by the partial least squares–structural equation modeling approach to validate the research model.

Challenge and goal setting influence individuals to reach the flow state, which increases the quantity of knowledge contribution. Rewards enhance the quality of knowledge contribution through perceived self-worth. Social comparison increases perceived reputation, but its impact does not ultimately lead to knowledge contribution.

The current study differentiated types of motivation and dimensions of knowledge contribution when exploring the effects of gamification affordances. This perspective was proven helpful to understand the various gamification affordances' influence on knowledge contribution.

The purpose of this paper was to develop a framework of working-together relations and investigate ways to enhance working-together relations among people, organisations, communities and neighbourhoods using working-together applications. Today, people in communities, neighbourhoods and constituencies often work together in a coalition of public, private and non-profit institutions. The technology used today has enabled new forms of communications and collaboration. The rapid growth of mobile technologies and interactive, collaborative applications based on Web technologies has enabled the development of new approaches to derive and share organisational and local knowledge. Not all of these applications have succeeded; after a certain time, users tend to stop using online applications that do not assist them in developing collaborative practices with their team members.

To better understand the essential characteristics of a successful online application that effectively supports people to work together, the authors undertook an inductive analysis of related literature and existing social media application.

By combining and categorising the findings, it was possible to articulate the characteristics associated with four identified categories of working-together relations: networking, coordination, cooperation and collaboration. The study also identified essential activities that are performed in each working-together category and the factors that enable successful working-together relations: trust, risk and rewards.

Future studies will look into how applications could be further enhanced, so that, for example, an application that is currently classified as “coordination” could be improved and the required characteristics of “collaboration” could be met.

It is expected that the framework derived will assist in the design of successful online applications to support different categories of working-together relations.

The main contribution of this study is a new framework that can now be used to identify how effective an existing application can be in assisting the working-together relations.

This paper aims to share how an Asian university enhanced students’ global competence through international business study missions (BSMs). More specifically, it focuses on how the design of these BSMs enabled “deep” learning beyond industry tourism and how 21st-century competencies such as “global competence” can be acquired through participation in short-term, faculty-led study missions.

Using the case study approach, it critically analyzes the learning goals and objectives, design decisions, implementation details and learning outcomes underlying three BSMs led by three instructors from the same university to the USA (New York), Germany (Berlin and Stuttgart) and South Korea (Seoul).

The study shows that students gained global competencies related to specific fields of study such as the creative industries, urban sustainability and entrepreneurship. It shows how design choices such as destination, range of organizations, length of individual visits, range of pedagogical techniques, intensity of preparation and quality of management contribute to students’ acquisition of global competencies.

This research presents a subset of case studies that may limit the generalization of the findings; the bias that results from an unrepresentative, opportunistic sample (selection bias); and lack of quantitative causality in a qualitative evaluation.

The course design described here provides practical information for designing study abroad “deep” learning goals, objectives and outcomes focusing on global competence.

The detailed case studies of three instructors from different disciplines to achieve the country’s education vision of globally competent students.

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.

Traditional embedded operation systems are resource consuming multitask, thus they are not adapted for smart wireless sensors. This paper presents a super‐small distributed real‐time microkernel (SDREAM) dedicated to wireless sensors. SDREAM is a tuple‐based message‐driven real‐time kernel. It adopts a meta language: Kernel Modeling Language to define and describe the system primitives in abstract manner. The IPC and processes synchronization are based on the LINDA concept: the tuple model implemented by two light primitives (SND: OUT & RCV: IN). In SDREAM, tasks are classified into two categories: periodic and priority. The periodic task has the highest priority level and is responsible for capturing sensor signals or actuating control signals; the priority task has various priority levels and is suitable for time‐constraints applications. A two‐level task scheduling policy scheme, named priority‐based pre‐emptive scheduling, is used for task scheduling. SDREAM is simple and efficient. It has a flexible hardware abstraction capability that enables it to be rapidly ported into different WSN platforms and other tiny embedded devices. Currently, it has been ported and evaluated in several hardware platforms. The performance results show SDREAM requires tiny resource and is suitable and efficient for hard real‐time multitask WSN applications.

Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development.

A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016.

Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps.

The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development.

The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

Online question-and-answer (Q&A) communities serve as important channels for knowledge diffusion. The purpose of this study is to investigate the dynamic development process of online knowledge systems and explore the final or progressive state of system development. By measuring the nonlinear characteristics of knowledge systems from the perspective of complexity science, the authors aim to enrich the perspective and method of the research on the dynamics of knowledge systems, and to deeply understand the behavior rules of knowledge systems.

The authors collected data from the programming-related Q&A site Stack Overflow for a ten-year period (2008–2017) and included 48,373 tags in the analyses. The number of tags is taken as the time series, the correlation dimension and the maximum Lyapunov index are used to examine the chaos of the system and the Volterra series multistep forecast method is used to predict the system state.

There are strange attractors in the system, the whole system is complex but bounded and its evolution is bound to approach a relatively stable range. Empirical analyses indicate that chaos exists in the process of knowledge sharing in this social labeling system, and the period of change over time is about one week.

This study contributes to revealing the evolutionary cycle of knowledge stock in online knowledge systems and further indicates how this dynamic evolution can help in the setting of platform mechanics and resource inputs.

The purpose of this paper is to investigate longitudinal features of an established social question-answering (Q&A) site to study how question-answer resources and other community features change over time.

Statistical analysis and visualisation was performed on the full data dump from the Stack Overflow social Q&A site for programmers.

The timing of answers is as strong a predictor of acceptance – a proxy for user satisfaction – as the structural features of provided answers sometimes associated with quality. While many questions and answer exchanges are short-lived, there is a small yet interesting subset of questions where new answers receive community approval and which may end up being ranked more highly than early answers.

As a large-scale data oriented research study, this work says little about user motivations to find and contribute new knowledge to old questions or about the impact of the resource on the consumer. This will require complementary studies using qualitative and evaluative methods.

While content contribution to social question-asking is largely undertaken within a very short time frame, content consumption is usually over far longer periods. Methods and incentives by which content can be updated and maintained need to be considered. This work should be of interest to knowledge exchange community designers and managers.

Few studies have looked at temporal patterns in social Q&A and how time and the moderation and voting systems employed may shape resource quality.