From the blogosphere into the parliament: The role of digital technologies in organizing social movements

In this paper, we present a process model exploring the roles played by digital technologies (DTs) in the organization of collective action (CA) of social movements (SMs) at different points in their lifecycles. The process model, which is based on an exploratory case study of the Italian Five Star Movement, relates the environmental conditions to the working logic, structure and use of DTs at three different stages of the SMs. We explain how these choices are adopted at each stage to address internal and environmental challenges and how they create further challenges to be addressed to pass to the next stage. We further explore the dynamics between the logic of connective and collective action and reflect on the growing need for structures and control. By so doing, our work addresses the need for a better understanding of the coevolution between DTs and organizational structures and of the ways in which DTs are used to mobilize people to sustain CA along the SM lifecycle.     

Evolution of FDI flows in the global network: 2003–2012

As one type of international capital flow, FDI maintains its important role in globalization. This article attempts to investigate the evolution of the FDI flows from a network perspective. Based on the bilateral FDI flows data between countries from 2003 to 2012, we construct the global FDI flows network for each year and thus quantify network measures (such as flow volumes and connections); further by analysing the tendency and changes of these network measures during the past 10 years, we delineate the features and dynamics of the FDI flows in the global network. We have the following findings: (a) the flows network changes during and after the crisis, i.e. flow volume fallen down and recovered, and flow connection restructured with more diversity; (b) the global FDI flows network is getting more loosely connected; (c) individual countries vary in different patterns.     

Effects of individual resources and team-member exchange on service quality

While workplace resources are generally viewed as positively affecting performance, some studies indicate that not all resources increase performance. This study addresses the controversial disparate effects of workplace resources on performance by exploring functionally classified workplace resources in self-managing service teams and their relative impacts on team service quality. Considering membership dynamics and consequences between members, a field experiment was conducted in a cafeteria, with data collected through a working diary for workplace resources and a customer survey to evaluate service quality. Results suggest that efficacy-resources in self-managing teams should be controlled with caution as they may adversely affect team service quality. Supporting the person–situation interactionism perspective, workplace resources interact dynamically with individual employees and with situations. Esteem-resources were found to increase team service quality, while team-member exchange not only improved team service quality but also moderated the impact of esteem-resources on team service quality.     

Does ethics improve stock market resilience in times of instability?

This paper compares the resilience of ethical (Islamic and socially responsible) indexes among five developed (US, UK, Japan, Canada, Australia) and three emerging markets (Brazil, India, South Africa) during the period following the 2008 subprime crisis. It relies on a multivariate CAPM-EGARCH model that accounts for sudden changes in volatility through the application of an iterated cumulative sums of squares (ICSS) algorithm on daily data over the sample period 2008–2014 to model time-varying volatility and ensure reliable estimates. The study confirms the lower systemic risk associated with Islamic indexes during the bearish period and reports that SRI, despite being more subject to systemic risk, offered higher alphas in highly integrated markets, while Islamic indexes performed better in less integrated ones. The evidence also reveals a very limited increase in the models’ predictability power from the integration of sudden changes in volatility into the EGARCH models during the full sample period. This limit is more marked during the bearish sub-period. Our findings have important implications for international investment and portfolio diversification perspectives in times of financial downturn.     

Resource Dependence and Network Relations: A Test of Venture Capital Investment Termination in China

This study examines how venture capital (VC) firms terminate investments in an emerging economy context. We contend that due to the weak institutional environment, it is appropriate to draw on insights from power and social relation perspectives for a better understanding of the phenomenon. Specifically, we argue that a termination decision hinges on not only the dependence relationship between a VC firm and its portfolio companies, but also the social relationships among VC firms. Event history analyses of approximately 12,000 VC deals made in China between 2001 and 2012 reveal that when a VC firm has a greater number of investments in an industry, it is more likely to terminate investments on a portfolio company in that industry. Moreover, such effect on termination is moderated by the focal VC's embeddedness with its syndicate partners and collaboration opportunities with other VC firms outside the immediate access of the syndicate partners. Our study sheds light on research on VC decision making in emerging markets by integrating insights from resource dependence relationships and interorganizational network characteristics.     

Risk assessment for public–private partnership projects: using a fuzzy analytic hierarchical process method and expert opinion in China

Public–private partnerships (PPP) have been widely used in China to procure public facilities and services. Complicated problems in PPP projects in China arise because of a variety of risk factors. A proper risk assessment model is needed to identify risks and provide risk response strategies for future Chinese PPP projects. The fuzzy analytic hierarchy process (F-AHP) method provides the ability to solve complex risk assessment of PPP projects. Current risk assessment models are limited to PPP projects in specific countries and do not consider unique risks in China, especially political, economic, social, and legal risks. This paper designed a risk evaluation index system for PPP projects based on the Delphi expert investigation method, and then established an optimized risk evaluation model for PPP projects in China using the F-AHP method. The risks identified are confirmed by interviewing experts from Chinese local government departments, private enterprise, third-party intermediary consulting and regulatory agencies, and academic organizations. The results show that the risks that ranked among the top ten are closely related to China’s political and economic policies and relationships among stakeholders. It can be concluded that government authorities play a critical role in providing a favorable political, social, and economic environment and an effective institutional framework for PPP projects. Furthermore, it is also important to deal with relationships among stakeholders based on the public–private ‘win–win’ principle. This study provides risk response strategies, addressing key issues from eight aspects: an impeccable legal and regulatory framework; a central coordinating and regulating PPP authority; supportive governmental authorities; institutional capacity-building; favorable economic conditions and viability; community, partner, and moral accountability; clear division of responsibilities through contracts; and effective advisory management. These effective measures may be useful in reducing the adverse effects of risk for PPP projects in China.     

Assessing the Reliability of Self‐reported Income Information in Informal Small Business Lending through a Bogus Pipeline Experiment

Coping with asymmetric information plays a major role in successful small business lending. Our purpose is to determine if small business applicants report their income information correctly when requesting a loan. We use a randomised controlled trial bogus pipeline experiment, established during a typical cash‐flow analysis of a bank for small businesses in the Philippines. The bogus pipeline approach is commonly applied in social science and aims to increase the rate of truth telling by informing participants that answers will be verified by a lie detector. The experimental data, which include 243 observations of credit clients that are mainly from the agricultural and food value chain, served to identify asymmetric information. Additionally, debtors’ repayment behaviour for approved loans was observed by the bank. Our results indicate that loan applicants of the treatment group report lower incomes, an effect which is most pronounced in lower income quantile. Our analyses also reveal higher loan delinquencies in the control group.     

A dynamic perspective: local interactions driving the spread of social networks

ABSTRACT

This study takes a micro standpoint to quantitatively verify the extent to which local interactions affect the expansion of dynamic social networks. By reviewing and synthesizing the theoretical literature in this article, we seek to establish a pragmatic analytical framework to capture the underlying functioning of local interactions. It then aims to produce findings on seven distinct marketing campaigns in which the real-world virtual communities offer intact time-series connections. In our considered cases, the firm-to-consumer interactivity plays a pivotal role in social media marketing while the customer-driven influence advances individual information transmission. The significant positive relationships demonstrate features of interactivity within subgroups including reciprocal edges and directed 3-cliques associated with the evolution of the virtual community by electronic word-of-mouth (WOM) of both firm-to-consumer and consumer-to-consumer interactions. More importantly, the attenuation of local interactions to a large extent leads to the cut-off point of the growth of the dynamic social network.     

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.     

Agricultural productivity growth in Brazil: Large and small farms excel

The long-standing debate on the relationship between farm size and productivity has been generally limited by the range of farm sizes evaluated and the definition of productivity. In this paper we use data from three Brazilian agricultural censuses to address these issues. In particular, we introduce a wider distribution of farm sizes than presently available from the literature and we employ total factor productivity (TFP) as our performance measure. In doing so, we test which farm size class had the highest TFP levels in 1985 and 2006, how factor productivity growth varied within and across farm size classes between those years, and which policy or factor had the greatest productivity enhancing effect. When examining TFP growth, we move beyond the common decomposition into technical and efficiency changes by identifying the complete distribution of farm productivity performances. We find that by 2006 a U-shaped distribution of productivity over farm sizes had emerged. Considerable 1985–2006 TFP growth differences are prevalent; positive rates for the majority accompany stagnant or negative rates for some. Public education investments were associated with faster productivity growth regardless of farm size, while technical assistance’s positive effect and credit’s negative effect were associated with larger farm sizes. The role of specialization varied by size.