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This paper aims to propose a numerical definition-based systematic design approach (NDSDA) to generate an explicit numerical definition of the product form profile and to establish the correlation between the product form features and the corresponding consumers’ image perceptions.

To illustrate the feasibility of the proposed method, this study considers the design of a two-dimensional automobile profile for illustration purposes and commences by developing a detailed numerical definition of an automobile profile using Bézier curves. A series of automobile image evaluations are conducted to examine the relationship between the characteristics of an automobile profile and its associated consumer image perception. Finally, the evaluation results are analyzed statistically, and the statistical results are used to construct mathematical models formalizing the correlations between the automobile profile design variables and the consumers’ perceptions of the product image.

The results of the evaluation trials are used to construct mathematical models capable of predicting the likely consumer response to any automobile profile designed in accordance with the numerical definition. Furthermore, the evaluation data enable the critical design variables and form features to be determined such that the efficacy of the design process in creating automobiles which better meet the consumers’ image aspirations can be improved.

The major contribution of the NDSDA methodology presented in this study is the ability it gives to designers to specify the product form in a detailed yet highly efficient manner. Furthermore, the numerical definition of the product form not only provides a suitable basis for identifying the critical design variables and form features of the product but also provides an understanding of how these variables should be manipulated to obtain or reinforce the desired product image.

In this study, the authors extend upon Brockman et al. (2008), who provide evidence that managers opportunistically accelerate bad news prior to share repurchases, but provide limited evidence that managers withhold good news until after repurchases. The authors examine management forecasts surrounding share repurchases in periods when companies must disclose detailed repurchase information. The authors argue these disclosures increase managers' legal and reputation risks of accelerating bad news, but have a lesser effect on delaying good news.

First, the authors examine whether managers alter the information released to the market before buying back shares by comparing managerial forecasts made within 30 days before the beginning of a repurchasing period with those made outside of this window. Second, the authors examine whether managers are more likely to provide good news forecasts, in terms of both magnitude and frequency, after buying back shares. Lastly, the authors examine the impact of CEO stock ownership on managerial forecasting behavior surrounding share buybacks.

Consistent with the authors’ hypotheses and contrary to Brockman et al. (2008), the authors find limited evidence that the likelihood or magnitude of bad news forecasts is greater in the period before share buybacks. Instead, the authors document that the frequency and magnitude of good news forecasts increase in periods following share buybacks and that these associations are positively moderated by managerial equity incentives. The authors also find that the withholding of good news is associated with lower average repurchase prices and greater repurchase volume. The authors further show that, when litigation risk is greater, managers are less likely to accelerate bad news prior to repurchases and more likely to withhold good news until after. Overall, the study results are consistent with managers balancing the benefits of opportunistic repurchase behavior with the costs.

This study contributes to the management forecast and share repurchase literatures by providing evidence consistent with managers opportunistically releasing earnings forecasts in the period after buying back shares. Most importantly, the authors show that after the rule revision, managers refrain from actively disclosing bad news that carry higher legal costs. Instead, they opt for the omission of good news to repurchase stocks at lower prices. The study results reconcile the conflicting evidence of Brockman et al. (2008) and Ge and Lennox (2011).

The purpose of this paper is to investigate the state of logistics management research in Asia. The study focuses on the research agenda, the topics of interest, and the extent of research collaboration in logistics theory building and knowledge specific to Asia.

This study uses a mixed methods approach namely, content analysis drawn from the articles found in six well-recognized peer-reviewed logistics management related journals from 2003 to 2013, followed by social network analysis which is applied on the selected articles to provide a structure of the collaboration relationship.

Initial findings suggest that there are some scholars in Asia who are instrumental in research collaboration and in building a body of knowledge on logistics management focused on Asia. More co-production of knowledge from deeper and tightly knit industry-academic collaboration is needed to progress this domain. Most of the published work use an empirical instrument drawn from the resource-based view to explore firm level supply chain collaboration and strategy. This suggests a positivist research tradition within logistics. There is a shortage of studies conducted on the supply chain as a network of enterprises.

The review of the articles is limited to six logistics specific journals and the authors only concentrate on logistics management research focused on Asia. The contributions from the other journals may have been missed. More collaboration at the institutional, national, and international levels is called for especially on cross-collaboration between practice and theory.

Though the analysis is restricted to 260 articles found in six journals, this paper can shed light on the research needs from different perspectives and facilitate the progress of logistics management research in Asia.

This is the first paper to discuss the state of logistics management research collaboration in Asia, and provides an overview of the research issues, topics, and approaches undertaken thus far. Through this work, this study hopes that it will encourage greater research collaboration between industry and academia, and academics themselves.

Peer-to-Peer (P2P) lending, which makes borrowers and investors meet directly through online platforms bypassing traditional financial institutions, is an emerging financing market after the traditional financial institutions crushed during the global financial crisis from 2007 to 2009. P2P lending platforms meet the credit demand more efficiently and play a vital role for the credit market and economic activity. This study sheds light on whether the credit spread of P2P lending is well predictive of economic activity compared to the bond credit spread which has been fully investigated in prior studies. Our findings show that the P2P credit spread performs similarly in predicting the economic activity as bond credit spread only during the financial crisis. However, the predictive power of P2P credit spread becomes inverse during the noncrisis periods since P2P lending platforms provide an alternative and easier financing channel to individuals who hardly borrow money for refinancing from traditional financial institutions. This study highlights the alternative role of P2P lending platform in financing and provides the evidence of different predictive powers of P2P credit spread on economic activity in different time periods.

This study empirically verified employee engagement (EE) as an outcome of organizational communication and confirmed that the formation of EE is strengthened when smartphone use (SU) is at a higher level.

A quantitative approach was used in this research, whereby 408 valid samples were collected with an online survey. The hypotheses of direct effects were tested using the structural equation modeling (SEM) procedure, and the moderating effects were tested using the unconstrained product indicator method and the PROCESS macro.

The results showed that EE was significantly influenced by person–organizationvalue fit (POVF), transformational leadership (TFL) and job autonomy (JA), and the effects of POVF and TFL were moderated significantly by SU. Although the influence of social support (SS) on EE was insignificant in the full model, SU moderated the effect of SS. The evidence also showed that work–family conflict (WFC) had no negative impact on EE.

The participants of this study were restricted to a local area.

Organizations should develop job designs via two-way communication to bring up EE and SU can facilitate the process.

Previous research has identified EE as an outcome of organizational communication, but this concept has not yet been empirically verified. This research provides evidence to verify the above-mentioned concept and additionally confirms the moderating role of SU.

This study aims to examine whether banks are compliant with the Financial Accounting Standards Board’s standard Accounting Standards Update (ASU) 2010-06 requiring disaggregated fair value hierarchy information. It also identifies institutional and firm-specific factors that are associated with compliance or non-compliance.

Using quarterly reports of banks for the first quarters of 2009 (pre- ASU 2010-06) and 2010 (post- ASU 2010-06), we hand-collect information on disclosures about fair values from the footnotes. Using a logistic regression with compliance/non-compliance as the dependent variable, we examine factors associated with compliance/non-compliance.

Results show that 23 per cent of banks do not comply with ASU 2010-06 and that the non-compliant banks tend to be small, lack effective internal controls and are more likely to be audited by non-specialist auditors.

This study only considers one type of non-compliance with ASU 2010-06, i.e. whether or not firms provide disaggregated fair value hierarchy information. There may be other forms of non-compliance that the authors do not examine because of the difficulties involved in objectively defining non-compliance.

The findings suggest firms may need to increase training for internal personnel and hire high-quality auditors for ensuring compliance with fair value accounting rules. The authors also suggest that smaller firms may find compliance to be onerous and recommend additional research to examine whether smaller firms should be exempted from some or all of the fair value rules.

This study provides some of the first evidence on the level of compliance with mandated fair value disclosures.

The purpose of this paper is to propose a fault diagnosis method for rolling bearings, in which the fault feature extraction is realized in a two-dimensional domain using scale invariant feature transform (SIFT) algorithm. This method is different from those methods extracting fault feature directly from the traditional one-dimensional domain.

The vibration signal of rolling bearings is first transformed into a two-dimensional image. Then, the SIFT algorithm is applied to the image to extract the scale invariant feature vector which is highly distinctive and insensitive to noises and working condition variation. As the extracted feature vector is high-dimensional, kernel principal component analysis (KPCA) algorithm is utilized to reduce the dimension of the feature vector, and singular value decomposition technique is used to extract the singular values of the reduced feature vector. Finally, these singular values are introduced into a support vector machine (SVM) classifier to realize fault classification.

The experiment results show a high fault classification accuracy based on the proposed method.

The proposed approach for rolling bearing fault diagnosis based on SIFT-KPCA and SVM is highly effective in the experiment. The practical value in engineering application of this method can be researched in the future.

The key purpose of this paper is to overview the many issues related to the integration of social sensing and pervasive sensing in the support of adaptive context-aware services.

From the analysis of existing proposals and prototypes, the authors found out that the process of integrating social and pervasive sensing can follow a limited number of approaches, which enables the authors to properly frame the proposals existing in the literature (and/or available as prototype infrastructures) according to a simple taxonomy, which is very useful to make the survey much more effective than a simple list of systems and proposals.

The taxonomy shows that, when integrating social sensing with pervasive sensing, it is possible, at one extreme, to exploit social network as a mere source of information and have such information flow towards the infrastructure supporting the execution of pervasive computing services. At the other extreme, it is possible exploiting a social network as an infrastructure for the integration, by having data from pervasive devices flow towards social networks. In between the extremes, different means can consider to have social networks and pervasive infrastructures converge towards each other to enable the integration of social and pervasive sensing.

Besides introducing the main concepts related to social sensing and framing the key approaches that can be undertaken to pursue the integration with traditional pervasive sensing, the authors go further discussing open issues and key research challenges behind their seamless integration.

In real-time entertainment processing applications, processing of the multiple data streams demands high efficient multiple transfers, which leads to the computational overhead for system-on-chip (SoC), which runs the artificial intelligence algorithms. High-performance direct memory access controller (DMAC) is incorporated in SoC to perform the multiple data transfers without the participation of main processors. But achieving the area-efficient and power-aware DMAC suitable for streaming the multiple data remains to be a daunting challenge among the researchers.

The purpose of this paper to provide the DMA operations without intervention of central processing unit (CPU) for bulk video data transmissions.

The proposed DMAC has been developed based on the hybrid advanced extensible interface (AXI)-PCI bus subsystem to handle the multiple data streams from the video sources. The proposed model consists of bus selector module, user control signal, status register, DMA-supported address and AXI-PCI subsystems to achieve better performance in analysing the video frames.

The extensive experimentation is carried out with Xilinx Zynq SoC architecture using Very High Speed integrated circuit hardware description language (VHDL) programming, and performance metrics such as utilization area and power are calculated and compared with the other existing DMA controllers such as Scatter-DMA, Gather-DMA and Enhanced DMA. Simulation results demonstrate that the proposed DMAC has outperformed other existing DMAC in terms of less area, less delay and power, which makes the proposed model suitable for streaming multiple video streams.