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In the controlled environment of a professional business seminar, the paper collects data on the willingness of participants to strategically default on a mortgage that is underwater by varying degrees. By providing the participants with fabricated exogenous strong and weak information signals, the paper is able to examine the effect of the signals on their responses. The purpose of this paper is to find evidence suggesting that gender, moral opposition, level of susceptibility to information, and information signal strength influence herding in business professionals.

The paper adopts the Hirshleifer and Hong Teoh (2003) definition of herding as “any behavior similarity/dissimilarity brought about by the interaction of individuals.” In the controlled environment of a professional business seminar, the paper collects data on the willingness of participants to strategically default on a mortgage that is underwater by varying degrees. By providing the participants with fabricated exogenous strong and weak information signals, the paper is able to examine the effect of the signals on their responses.

The major contribution is that the paper finds evidence suggesting that signal strength does indeed matter. The paper finds that a weak signal is more likely to produce herding when respondents answer questions relating their own decisions and strong signals produce more herding when respondents provide advice to others. The paper also finds that women are less likely to herd and people who report they are susceptible to information influences are more likely to herd. Not surprisingly, participants who are morally opposed to strategic default are less likely to herd in most scenarios.

No other study of which the authors are aware looks specifically at signal strength in a financial setting or uses a sample of business professionals to examine herding of a financial nature.

Drawing on signaling theory, the purpose of this study is to investigate the effects of the strength and framing of firm signals sent to repair relationships following relationship violations.

Three 2 × 2 scenario-based experiments (total n = 527) manipulate signal strength × violation type (Study 1); signal frame × violation type (Study 2); and signal strength × brand familiarity (Study 3) to examine their dynamic impacts on relationship recovery efforts.

Stronger signals are more effective at relationship repair and are especially important following integrity (vs competence) violations. Signals framed as customer gains (vs firm costs) lead to more favorable relationship outcomes. Finally, brands that are less (vs more) familiar see greater benefits from strong signals.

The three experiments were scenario-based, which may not replicate real-life behavior or capture participants’ actual emotions following a violation, thus future research should extend into real-world recovery efforts.

Managers should send strong signals (communicating the level of resources invested in the recovery efforts) framed as benefits to the customer, rather than costs to the firm. Strong signals are especially important when brand familiarity is low or an integrity violation has occurred.

This is the first research to directly apply signaling theory to the relationship recovery process and contributes to theory by examining the role of signal strength; framing of the signal as a customer gain vs firm cost; and the interplay of signal strength and brand familiarity on the relationship recovery effort.

A cell phone that behaves in a manner reflective of the current context has been a goal for the pervasive and ubiquitous research communities for a long time. This paper aims to demonstrate how two aspects of context – location and activity – can be inferred using GSM data present on standard cell phones.

A background knowledge of GSM networks is provided, followed by an assessment of the stability of signal strength levels in order to establish their usefulness in inferring aspects of context. A qualitative location system using GSM signals is presented and how to infer the current activity of the cell phone carrier is demonstrated.

The paper shows that by using the patterns of signal strength fluctuations and changes to the current serving cell and monitored neighbouring cells it is possible to distinguish between various states of movement such as walking, driving a car and remaining stationary.

The paper focuses on the practical aspects of deploying and managing location based services in dynamic outdoor environments.

The increasing number of context aware services, which depend on various multimodal sensing, processing and actuating techniques, technologies and formats ask for a physical framework that is able to handle their heterogeneity. Thereto, we propose a context model bridging the semantic gaps between context aware services. In addition we propose a simple system architecture of Distribution Servers and Transformation Servers that bridge semantic gaps among context aware services. Applying our framework we solve the heterogeneity problem existing for location services. Location is typically a form of context where heterogeneity is a problem.

The purpose of this paper is to address the use of passive RFID technology for the development of an autonomous surveillance robot. Passive RFID tags can be used for labelling both valued objects and goal‐positions that the robot has to reach in order to inspect the surroundings. In addition, the robot can use RFID tags for navigational purposes, such as to keep track of its pose in the environment. Automatic tag position estimation is, therefore, a fundamental task in this context.

The paper proposes a supervised fuzzy inference system to learn the RFID sensor model; Then the obtained model is used by the tag localization algorithm. Each tag position is estimated as the most likely among a set of candidate locations.

The paper proves the feasibility of RFID technology in a mobile robotics context. The development of a RFID sensor model is first required in order to provide a functional relationship between the spatial attitude of the device and its responses. Then, the RFID device provided with this model can be successfully integrated in mobile robotics applications such as navigation, mapping and surveillance, just to mention a few.

The paper presents a novel approach to RFID sensor modelling using adaptive neuro‐fuzzy inference. The model uses both Received Signal Strength Indication (RSSI) and tag detection event in order to achieve better accuracy. In addition, a method for global tag localization is proposed. Experimental results prove the robustness and reliability of the proposed approach.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The purpose of this paper is to present a novel localization scheme using infrared identification (IRID) fused with encoder information.

IRID emitters are mounted on the ceiling in order to divide the floor workspace into sectors. Encoding the IRID signal then allows the mobile robot to identify which sector it is in. The sector information is fused with the dead‐reckoning results for estimation of the robot configuration based on the fact that IRID has highly deterministic characteristics.

Fusing the dead‐reckoning result and the IRID information bounds and in some cases reduces the size of the uncertainty. This enables one to more accurately estimate the robot's configuration.

A new artificial landmark, IRID is developed for mobile robot localization. This paper also demonstrates a framework that fuses the IRID information (deterministic) and dead‐reckoning result (stochastic).

This study explores whether institutional investors can distinguish an undervalued share repurchase from a falsely signaled share repurchase. This study also aims to determine what information institutions use when investing in repurchase stocks.

This study uses unique Taiwanese data and concentrates on foreign institutions because they are the most sophisticated investors in Taiwan.

The results show that foreign institutional trading in open market repurchase (OMR) stocks will earn both positive concurrent and post-OMR excess returns. In addition, there is a significant positive relationship between pre-OMR insider trading and foreign institutional trading during the OMR period; that is, foreign institutions follow insiders to trade their OMR stocks.

This study finds that foreign institutions use publicly available data on insider trading to choose OMR stocks and create excess returns. This encourages individual investors without private information, who can also earn a positive return if they diligently study available public information.

This study contributes to the international investment literature by determining the price impacts associated with foreigner trading in the firm-level returns of the host country. In addition, this study finds that foreign institutions choose OMRs based on insider trading information, which fills the gap in existing studies on share repurchasing. Moreover, this study enriches the insider literature by showing how foreign institutions can benefit by using insider trading information.

The importance of mobile services in our everyday life is growing while at the same time new interoperability issues arise due to hardware and software heterogeneity. Therefore, new architectural paradigms and models are needed to enhance software engineering methodologies with regard to platform independence and interoperability. This paper describes an UML pattern based approach for developing reconfigurable autonomous mobile services. Through the analysis of an mcommerce project, the relevance of our proposed architecture will be explained. Our focus lays on a generic reconfiguration mechanism based on profile matching from software modules. This profiling part will be further described and discussed. Finally, the applicability of our approach is investigated within a project about reconfigurable indoor navigation computers and a project about robot assisted sensor networks.

An Improved Rank Criterion-based NLOS node Detection Mechanism (IRC-NLOS-DM) is proposed based on the benefits of a reputation model for effective localization of NLOS nodes during the dynamic exchange of emergency messages in critical situations.

This proposed IRC-NLOS-DM scheme derives the benefits of a reputation model that influentially localizes the NLOS nodes under dynamic exchange of emergency messages. This proposed IRC-NLOS-DM scheme is an attempt to resolve the issues with the routing protocols that aids in warning message delivery of vehicles that are facing NLOS situations with the influence of channel contention and broadcast storm. It is developed for increasing the warning packet delivery rate with minimized overhead, delay and channel utilization.

The simulation results of the proposed IRC-NLOS-DM scheme confirmed the excellence of the proposed IRC-NLOS-DM over the existing works investigated based on the channel utilization rate, neighborhood prediction rate and emergency message forwarding rate.

It is proposed for reliable warning message delivery in Vehicular Ad hoc Networks (VANETs) which is referred as the specialized category of mobile ad hoc network application that influences Intelligent Transportation Systems (ITS) and wireless communications. It is proposed for implementing vehicle safety applications for constructing a least cluttered and a secure environment on the road.

It is contributed as a significant mechanism for facilitating reliable dissemination of emergency messages between the vehicular nodes, which is essential in the critical environment to facilitate a risk-free environment. It also aids in creating a reliable environment for accurate localization of Non-Line of Sight (NLOS) nodes that intentionally introduces the issues of broadcasting storm and channel congestion during the process of emergency message exchanges.