Search results

Notes the increasing importance of option‐adjusted spread analysis for pricing in the mortgage‐backed securities market and develops a partial differentiation equation method (PDE) for calculation, as an alternative to the Monte Carlo method. Discusses the mathematical theory involved and illustrates its use with a numerical example. Claims PDE is more accurate and cheaper than the Monte Carlo method and promises a further article on using it for horizon analysis and risk management.

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.

We contend that the international strategy adopted by and the international experience of top executives in parent firms, as well as the embeddedness of foreign subsidiaries in host countries, moderate the impact of institutional distance between home and host countries on the divergence of isomorphic pressures experienced by foreign subsidiary managers. We further suggest that diverging isomorphic pressures are more likely to spur foreign subsidiary managers to deinstitutionalize organizational routines from parent firms when these managers possess knowledge‐based power, the subsidiary’s performance is declining, or social controls are lacking from the parent firm. Implications for research and practice are discussed.

This study empirically investigates the long-run and interactive effect of Chinese foreign direct investment (CFDI) on Africa's industrialization process.

The authors employed industry and manufacturing value-added (% GDP) as the dependent variables and applied the two-step GMM and panel-corrected standard errors' (PCSE) techniques involving a panel of 49 African countries from 2003 to 2020.

The industry value-added (% GDP) results show that the presence of CFDI propels industrial productivity by contributing to value-addition in the short and long run. Moreover, the study shows that the magnitude of the CFDI effect on industrialization is pronounced in the short-run when it is associated with labor and natural resources. This result reveals efficiency-seeking behavior of CFDI and the CFDI-Africa industrialization nexus is not primarily resource-driven. More importantly, the authors found human capital, electricity and political stability, as primary factors that magnify CFDI's effect on industrialization in the short and long run.

This study is the first to use macro-level data to empirically investigate and find the significant effect of CFDI on Africa's industrialization in the long run. More importantly, the authors investigated channels through which CFDI magnifies industrialization in Africa in the short and long run.

In this article we compare the performance of the traditional CAPM with the multi factor model of Fama and French (1996) for equities listed in the Shanghai Stock Exchange. We also investigate the explanatory power of idiosyncratic volatility and respond to the claim that multi factor model findings can be explained by the turn of the year effect. Our results show that firm size, book to market equity and idiosyncratic volatility are priced risk factors in addition to the theoretically well specified market factor. As far as the turn of the year effect is concerned we reject the claim that the findings are driven by seasonal factors.

Webrooming (i.e. searching information online and making the final purchase in a physical store) has become a popular shopping practice, but remains insufficiently studied. To address this, a research framework encompassing online and offline channel attributes (i.e. online review diagnosticity, online search convenience, expected price loss, offline purchase effort and offline after-sales service convenience), consumer traits (i.e. anticipated regret) and shopping experience (i.e. smart-shopping perception) as determinants of webrooming continuance intention is proposed.

The proposed model was validated by conducting a questionnaire-based survey that yielded 354 useable responses. The data was subjected to partial least squares structural equation modelling and importance-performance map analysis.

According to the obtained results, online review diagnosticity, offline after-sales service convenience and anticipated regret are the vital antecedents of webrooming continuance intention, while smart-shopping perception acts as the mediator.

The current study adds significantly to the body of knowledge about webrooming by validating the inter-relationships between online review diagnosticity, after-sales service convenience, anticipated regret, smart-shopping perception and webrooming continuance intention.

Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner.

Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words.

The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach.

By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.

The objective of this study is to develop a model for analyzing the relative importance of facilitating and constraining factors that determine changes in the structure of distribution channels as a country progresses along the path of economic development.

Data on channel structure were collected on national, regional and municipal levels. The hypotheses in this study were tested by using time series and cross‐sectional data. The ordinary least squares method was used for multiple regression and correlation analyses.

The most significant findings from the above research are that channel structure is determined by economic development and, to some extent, by government policy. Both of these factors work hand‐in‐hand to affect distribution reform. First, economic development provides the need for more efficient channels, first as more privately‐owned intermediaries enter the distribution system, and later as the number of channel intermediaries contracts, partly stemming from vertical integration and from a shakeout of less efficient enterprises. Second, government policy can be formulated to encourage these developments. In order to promote distribution reform, the government's “open policy”, which allows for market access by foreign firms as well as local ones, should be continued.

More cross‐sectional data for China are needed in order to better analyze the impact of different economic sectors (at different levels of development) on channel evolution. As more data become available, these analyses will be possible.

Very little is known about the factors that shape a nation's channel system. Improvements in distribution systems are crucial to upgrading marketing systems in developing countries. Understanding how improvements in distribution systems contribute to economic development can aid decision makers in developing countries to improve resource allocation. This study has contributed to the theory of channel evolution in developing countries.

This study aims to propose and test a research model outlining the chain effects of social media marketing activities (SMMA) on brand equity, encompassing the potential mediators of self-congruity, consumer empowerment and brand experience.

An online survey was conducted, and 241 valid responses were acquired. The data was submitted to Partial Least Squares Structural Equation Modelling (PLS-SEM), complemented by the artificial neural network (ANN) analysis.

The results revealed that SMMA can foster the development of self-congruity and consumer empowerment. These two psychological responses represent the key drivers to reinforce the positive brand experience and ultimately lead to brand equity. The sequential mediating effect was confirmed. The ANN analysis offered further insights into the ranking of variable importance.

The present study presents a breakthrough by taking into account the roles of self-congruity, consumer empowerment, brand experience simultaneously and assesses their sequential mediating roles in the linkage between SMMA and brand equity.

The purpose of this work is (1) to supply a framework of actors in sport sponsorship and articulate the service relationships that support these partnerships and (2) to propose research questions in this space that are unaddressed and forward-looking.

Sponsorship is part of a complex network of actors and service relationships found in sport. The sports team, activity, or event is a sport property, often with long-term and dynamic service relationships. The authors consider how a sponsor's relationship with the sport property intersects with organizing bodies, venues, communities and society. The authors identify clusters of actors that interact with and influence other clusters (e.g. governing bodies, media, host community and venue/teams/fans) within an ecosystem, paying special attention to aspects of co-creation and co-destruction and the feedback loops that cause them.

Through this analysis, the authors identify areas of needed research at the intersection of sport sponsorship and service. The model synthesizes the literature from service-dominant logic, sports, sponsorship, systems thinking and co-creation/co-destruction research areas. Using the model and relevant cases, the authors can better understand the complexities of sport service relationships and advance research at the intersection of sport sponsorship and service.

This is the first sport sponsorship service ecosystem model. It is also the first integration of systems thinking with constructs in sport sponsorship and services.