Perceived usefulness of RFID-enabled information services – a systematic approach

Even though RFID technology is currently gaining importance mainly in logistics, usage areas, such as shopping or after-sales enhancements beyond the supply chain are envisioned. Yet, while RFID hits the street it is questioned if it may undermine one’s privacy while providing few customer benefits. Meeting this criticism this paper investigates RFID-enabled information services and the drivers of their usefulness for consumers. The article claims that the more risk one associates with a product the more benefit from RFID-enabled information services is perceived. We show empirically that the nature of product risk provides a useful framework to decide on the types of RFID information services a marketer should offer to create RFID usefulness perceptions and increase technology acceptance.

Linked Services for Enabling Interoperability in the Sensing Enterprise

In future, the so called “sensing enterprise”, as part of the Future Internet, will play a crucial role in the success or the failure of an enterprise. We present our vision of an enterprise interacting with the physical world based on a retail scenario. One of the main challenges is the interoperability not only between the enterprise IT systems themselves, but also between these systems and the sensing devices. We will argue that semantically enriched service descriptions, the so called linked services will ease interoperability between two or more enterprises IT systems, and between enterprise systems and the physical environment.

"Deal of the day” platforms: what drives consumer loyalty?

Maintaining a loyal customer base is challenging for “Deal of the Day” (DoD) platforms. DoD providers market and sell deals on products and services, yet it is the merchants who ultimately deliver those to consumers. Low entry and switching costs drive competition in this market. However, research on the determinants of user loyalty in the DoD context is limited. This study uses Grounded Theory and Structural Equation Modeling to explore the phenomenon of DoD platform loyalty. Particularly, monetary benefits, signal-to-noise ratio, perceived risk, and service friendliness during a merchant encounter emerge as powerful determinants of loyalty in this novel context.

The Meta in Meta-object Architectures

Behavioral reflection is crucial to support for example functional upgrades, on-the-fly debugging, or monitoring critical applications. However the use of reflective features can lead to severe problems due to infinite metacall recursion even in simple cases. This is especially a problem when reflecting on core language features since there is a high chance that such features are used to implement the reflective behavior itself. In this paper we analyze the problem of infinite meta-object call recursion and solve it by providing a first class representation of meta-level execution: at any point in the execution of a system it can be determined if we are operating on a meta-level or base level so that we can prevent infinite recursion. We present how meta-level execution can be represented by a meta-context and how reflection becomes context-aware. Our solution makes it possible to freely apply behavioral reflection even on system classes: the meta-context brings stability to behavioral reflection. We validate the concept with a robust implementation and we present benchmarks.

JExample: Exploiting Dependencies Between Tests to Improve Defect Localization

To quickly localize defects, we want our attention to be focussed on relevant failing tests. We propose to improve defect localization by exploiting dependencies between tests, using a JUnit extension called JExample. In a case study, a monolithic white-box test suite for a complex algorithm is refactored into two traditional JUnit style tests and to JExample. Of the three refactorings, JExample reports five times fewer defect locations and slightly better performance (-8-12\%), while having similar maintenance characteristics. Compared to the original implementation, JExample greatly improves maintainability due the improved factorization following the accepted test quality guidelines. As such, JExample combines the benefits of test chains with test quality aspects of JUnit style testing.

Flow-Centric, Back-In-Time Debugging

Conventional debugging tools present developers with means to explore the run-time context in which an error has occurred. In many cases this is enough to help the developer discover the faulty source code and correct it. However, rather often errors occur due to code that has executed in the past, leaving certain objects in an inconsistent state. The actual run-time error only occurs when these inconsistent objects are used later in the program. So-called back-in-time debuggers help developers step back through earlier states of the program and explore execution contexts not available to conventional debuggers. Nevertheless, even back-in-time debuggers do not help answer the question, ``Where did this object come from?'' The Object-Flow Virtual Machine, which we have proposed in previous work, tracks the flow of objects to answer precisely such questions, but this VM does not provide dedicated debugging support to explore faulty programs. In this paper we present a novel debugger, called Compass, to navigate between conventional run-time stack-oriented control flow views and object flows. Compass enables a developer to effectively navigate from an object contributing to an error back-in-time through all the code that has touched the object. We present the design and implementation of Compass, and we demonstrate how flow-centric, back-in-time debugging can be used to effectively locate the source of hard-to-find bugs.

PyGirl: Generating Whole-System VMs from High-Level Prototypes Using PyPy

Virtual machines (VMs) emulating hardware devices are generally implemented in low-level languages for performance reasons. This results in unmaintainable systems that are difficult to understand. In this paper we report on our experience using the PyPy toolchain to improve the portability and reduce the complexity of whole-system VM implementations. As a case study we implement a VM prototype for a Nintendo Game Boy, called PyGirl, in which the high-level model is separated from low-level VM implementation issues. We shed light on the process of refactoring from a low-level VM implementation in Java to a high-level model in RPython. We show that our whole-system VM written with PyPy is significantly less complex than standard implementations, without substantial loss in performance.

Learning Software-Maintenance Tasks in the Transition Phase of Offshore Outsourcing Projects: Two Learning-Theoretical Perspectives

The increasing practice of offshore outsourcing software maintenance has posed the challenge of effectively transferring knowledge to individual software engineers of the vendor. In this theoretical paper, we discuss the implications of two learning theories, the model of work-based learning (MWBL) and cognitive load theory (CLT), for knowledge transfer during the transition phase. Taken together, the theories suggest that learning mechanisms need to be aligned with the type of knowledge (tacit versus explicit), task characteristics (complexity and recurrence), and the recipients’ expertise. The MWBL proposes that learning mechanisms need to include conceptual and practical activities based on the relative importance of explicit and tacit knowledge. CLT explains how effective portfolios of learning mechanisms change over time. While jobshadowing, completion tasks, and supportive information may prevail at the outset of transition, they may be replaced by the work on conventional tasks towards the end of transition.

Self-disclosure and privacy calculus on social networking sites: the role of culture

Social Networking Sites (SNSs) have become extremely popular around the world. They rely on user-generated content to offer engaging experience to its members. Cultural differences may influence the motivation of users to create and share content on SNS. This study adopts the privacy calculus perspective to examine the role of culture in individual self-disclosure decisions. The authors use structural equation modeling and multi-group analysis to investigate this dynamics. The findings reveal the importance of cultural dimensions of individualism and uncertainty avoidance in the cognitive processes of SNS users.