Search results

The purpose of this paper is to review behavioral explanations of the empirical observation that investment managers in mutual fund companies increase their risk taking when offered incentives based on how their performance is ranked compared to peers.

A conceptual model is proposed of how research on social comparison, competition and financial risk taking may explain increased investor risk taking induced by rank-based incentives. Research findings in each of the strands of research are reviewed.

A proposed main explanation is that an above-average bias in comparing oneself with competitors results in overconfidence that increases risk taking. A complementary proposed explanation is that an anticipated loss when lagging behind increases risk taking, and another proposed complementary explanation the belief that risk taking is a winning strategy.

The results provide a broad framework for directions of research on social comparison processes in the mutual fund industry addressing the difficulties in implementing performance evaluations.

The purpose of this paper is to investigate how social comparison and motivation to compete account for elevated risk-taking in fund management corroborated by asset market experiments when performance depends on rank-based incentives.

In two laboratory experiments, university students (n1 = 240/n2 = 120) make choices between risky and certain outcomes of hypothetical sums of money. Both experiments investigate in which direction risky choices in an individual condition (individual risk preference) are shifted when participants compare their performance to another participant's performance (social comparison), being instructed or not to outperform the other (incentive to compete).

In the absence of incentives to compete, participants tend to minimize the differences between expected outcomes to themselves and to the other, but when provided with incentives to compete, they tend to maximize these differences. An independent additional increase in risk-taking is observed when participants are provided with incentives to compete.

Original findings include that social comparison does not evoke motivation to compete unless incentives are offered and that increases in risk-taking depend both on what the other chooses and the incentives.

This study aims to build on the organizational learning theory and propose a complex strategy by combining strategic alliance with subsequent acquisitions to penetrate new product markets. The authors empirically examined whether and to what extent preacquisition alliance experience affects the short- and long-term stock performance of acquiring firms.

Data on acquisitions, in which the acquirers have experience from preacquisition alliance activities in their targets’ respective industry, were collected. Diversifying acquisitions were focused upon to ensure that preacquisition alliance experience is the major source of organizational learning. A standard event study to examine acquirers’ abnormal returns was used and a Fama-French calendar-time portfolio approach to gauge long-run abnormal stock performance was adopted. In addition, regression analysis was conducted to investigate the alliance–acquisition relationship, controlling a set of variables capturing firm and acquisition characteristics.

It has been documented that in the short run, alliance experience may not always benefit acquirers’ stock performance surrounding the acquisition announcements. In particular, for acquiring firms experiencing negative cumulative abnormal returns, investors value alliance experience negatively. However, for up to 36 months after acquisitions, acquirers with alliance experience outperform their counterparts in almost every acquisition category regardless of the short-term announcement returns.

The current study has used a large-scale representative sample to investigate the dynamic interaction between alliances and acquisitions as two organizational forms for firms to grow. Findings indicate that firms can deliberately learn from their alliance activities and, later on, enter new markets through acquisitions. More importantly, it was found that, at least for some acquirers, preacquisition alliance activities are associated with worse short-term stock price performance because of possible information spillover and lifted entry barriers. It was confirmed that short-term pain nets long-term gains for acquirers heading into new markets.

This study aims to construct a double layer heat insulation coating based on hollow glass microspheres (HGMs) and to investigate the effect of particle size on barrier property and heat insulation performance.

The waterborne double layer coating was composed of an anticorrosive epoxy ester primer and an HGM-containing silicone acrylic topcoat. With varied HGM sizes (20 μm, 40 μm, 60 μm and a 1:3 w/w mixture of 20 and 60 μm particles), the coating was immersed in 3.5 wt% NaCl solution for 28 days and was then subjected to a salt spray test for 450 h. The barrier properties of the coating were evaluated through electrochemical impedance spectroscopy. Heat insulation performance was examined using a self-made device.

The addition of HGMs decreased the barrier properties of the coating by creating particle/resin interfaces for water penetration. In the HGMs-containing coatings, the use of larger HGMs showed relatively good barrier properties because of the lower particle density. The coating with smaller particles yielded a higher heat insulating capacity as indicated by lower equilibrium temperatures.

Future work will be focused on improving the barrier properties of the coating. Field exposure tests should also be performed to assess the long-term performance of the coating.

The mechanical properties of the coatings in this study also implied that HGMs can be used to develop scratch-resistant and impact-resistant coatings. Other potential applications for further studies include the uses of HGMs for coatings with improved fire retardancy and electromagnetic interference shielding.

A double layer coating was developed to provide balanced performance on both anticorrosion and heat insulation. The effects of HGM size were particularly highlighted.

This study aims to achieve long-term prediction on a specific monotonic data series of atmospheric corrosion rate vs time.

This paper presents a new method, used to the collected corrosion data of carbon steel provided by the China Gateway to Corrosion and Protection, that combines non-linear gray Bernoulli model (NGBM(1,1) with genetic algorithm to attain the purpose of this study.

Results of the experiments showed that the present study’s method is more accurate than other algorithms. In particular, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the proposed method in data sets are 9.15 per cent and 1.23 µm/a, respectively. Furthermore, this study illustrates that model parameter can be used to evaluate the similarity of curve tendency between two carbon steel data sets.

Corrosion data are part of a typical small-sample data set, and these also belong to a gray system because corrosion has a clear outcome and an uncertainly occurrence mechanism. In this work, a new gray forecast model was proposed to achieve the goal of long-term prediction of carbon steel in China.

Due to high electromagnetic torque at low speed, vernier machines are suitable for direct-drive applications such as electric vehicles and wind power generators. The purpose of this paper is to present an exact sub-domain model for analytically predicting the open-circuit magnetic field of permanent magnet vernier machine (PMVM) including tooth tips. The entire field domain is divided into five regions, viz. magnets, air gap, slot openings, slots, and flux-modulation pole slots (FMPs). The model accounts for the influence of interaction between PMs, FMPs and slots, and radial/parallel magnetization.

Magnetic field distributions for slot and air-gap, flux linkage, back-EMF and cogging torque waveforms are obtained from the analytical method and validated by finite element analysis (FEA).

It is found that the developed sub-domain model including tooth tips is very accurate and is applicable to PMVM having any combination of slots/FMPs/PMs.

The main contributions include: accurate sub-domain model for PMVM is proposed for open-circuit including tooth-tip which cannot be accounted for in literature; the model accounts the interaction between flux modulation pole (FMP) and slot; developed sub-domain model is accurate and applicable to any slot/FMP/PM combinations; and it has investigated the influence of FMP/slot opening width/height on cogging torque.

The innovation process demands an interaction between environment agents, knowledge generators and policies of incentive for innovation and not only development by companies. Universities have gradually become the core of the knowledge production system and, therefore, their role regarding innovation has become more important and diversified. This study is aimed at identifying the mechanisms of university–industry (U–I) collaboration, as well as the operationalization steps of the U–I collaboration process.

This study is aimed at identifying, based on a systematic literature review, the mechanisms of university–industry (U–I) collaboration, as well as the operationalization steps of the U–I collaboration process.

The analysis of the 72 selected articles enabled identifying 15 mechanisms of U–I collaboration, proposing a new classification for such mechanisms and developing a framework presenting the operationalization steps of the interaction process.

In this paper, the authors screened nearly 1,500 papers and analyzed in detail 86 papers addressing U–I collaboration, mechanisms of U–I collaboration and operationalization steps of the U–I collaboration process. This paper provides a new classification for such mechanisms and developing a framework presenting the operationalization steps of the interaction process. This research contributes to both theory and practice by highlighting managerial aspects and stimulating academic research on such timely topic.

The purpose of this paper is to propose a new optical steganography framework that can be applied to public optical binary phase-shift keying (BPSK) systems by transmitting a stealth spectrum-amplitude-coded optical code-division multiple-access signal through a BPSK link.

By using high-dispersion elements, the stealth data pulses temporally stretch and the amplitude of the signal decreases after stretching. Thus, the signal can be hidden underneath the public signal and system noise. At the receiver end, a polarizer is used for removing the public BPSK signal and the stealth signal is successfully recovered by a balanced detector.

In a simulation, the bit-error rate (BER) performance improved when the stealth power increased.

The BER performance worsens when the noise power become large. Future work will consider increasing the system performance during high-noise power situation.

By properly adjusting the power of the amplified spontaneous emission noise, the stealth signal can be hidden well in the public channel while producing minimal influence on the public BPSK signal.

In conclusion, the proposed optical steganography framework makes it more difficult for eavesdroppers to detect and intercept the hidden stealth channel under public transmission, even when using a dispersion compensation scheme.

The purpose of this investigation is to identify, evaluate, integrate and summarize relevant and qualified papers through conducting a systematic literature review (SLR) on the application of recommender systems (RSs) to suggest a scholarly publication venue for researcher's paper.

To identify the relevant papers published up to August 11, 2018, an SLR study on four databases (Scopus, Web of Science, IEEE Xplore and ScienceDirect) was conducted. We pursued the guidelines presented by Kitchenham and Charters (2007) for performing SLRs in software engineering. The papers were analyzed based on data sources, RSs classes, techniques/methods/algorithms, datasets, evaluation methodologies and metrics, as well as future directions.

A total of 32 papers were identified. The most data sources exploited in these papers were textual (title/abstract/keywords) and co-authorship data. The RS classes in the selected papers were almost equally used. DBLP was the main dataset utilized. Cosine similarity, social network analysis (SNA) and term frequency–inverse document frequency (TF–IDF) algorithm were frequently used. In terms of evaluation methodologies, 24 papers applied only offline evaluations. Furthermore, precision, accuracy and recall metrics were the popular performance metrics. In the reviewed papers, “use more datasets” and “new algorithms” were frequently mentioned in the future work part as well as conclusions.

Given that a review study has not been conducted in this area, this paper can provide an insight into the current status in this area and may also contribute to future research in this field.