Assessing information integration processes: a comparison of findings obtained with between-subjects designs versus within-subjects designs

Quality & Quantity - Within-subject designs (WSDs) remain unpopular in psychology. Social psychologists have argued that they create “demand” and “order” effects....     

Capital gains lock-in and governance choices

Differences in accrued gains and investors’ tax-sensitivity induce variation in a capital gains lock-in effect across mutual funds even for the same stock at the same time. Exploiting this variation, we show this effect influences funds’ governance decisions: higher capital gains decrease the likelihood a fund exits prior to contentious votes and increase the likelihood a fund votes against management. Consistent with tax motivation, these findings are concentrated among funds with tax-sensitive investors. Further, high aggregate capital gains across funds holding a stock predict a higher likelihood management loses a vote and a lower likelihood a contentious vote is proposed.     

Prácticas de compra predatorias en las cadenas mundiales de suministro de la industria de la confección: tensión en las relaciones laborales en la India

Desde hace tiempo se viene documentando en múltiples estudios la vulneración de los derechos de los trabajadores en las cadenas mundiales de suministro de la industria de la confección; sin embargo, se ha prestado menos atención al reciente empeoramiento de algunos déficits de trabajo decente y sus causas, que el presente artículo asocia a las prácticas de compra predatorias derivadas de las crecientes asimetrías de poder. Estas prácticas, ilustradas aquí a través de las empresas indias del sector del vestido orientadas a la exportación, ejercen presiones salariales a la baja y favorecen la proliferación de formas atípicas de empleo y la banalización de la violencia física y verbal contra los trabajadores, al tiempo que aumentan las exigencias de productividad. Para frenar esta evolución, es necesario establecer mecanismos de fijación de precios que tengan en cuenta el costo que entraña el cumplimiento del principio del trabajo decente, adoptar una reglamentación estatal adecuada y contar con la participación de los trabajadores.     

Environmental Policy,Sustainable Development,Governance Mechanisms and Environmental Performance

We investigate the effects of environmental policy (Climate Change Act – CCA), sustainable development frameworks (Global Reporting Initiative – GRI; UN Global Compact – UNGC) and corporate governance (CG) mechanisms on environmental performance (carbon reduction initiatives – CRIs; actual carbon performance – GHG emissions) of UK listed firms. We use the generalized method of moments (GMM) estimation technique to analyse data consisting of 2245 UK firm‐year observations over the 2002–2014 period. First, we find that the CCA has a positive effect on CRIs, and this effect is stronger in better‐governed firms. Second, we find that the GRI‐based framework is positively associated with CRIs. Third, we find that firms with poor CG structures have lower actual carbon performance compared with their better‐governed counterparts. Overall, our evidence suggests that firms can symbolically conform to environmental policy (CCA) and sustainable development frameworks (GRI, UNGC) by engaging in CRIs without necessarily improving actual environmental performance (GHG emissions) substantively. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment     

Blockchain in Additive Manufacturing and its Impact on Supply Chains

Additive manufacturing (AM) appears to be a particularly attractive use case for blockchain. This research combines inductive in‐depth interviews with the Delphi method to explore what potentials blockchain technology in AM creates, which adoption barriers firms need to overcome, and how supply chains will be affected by the integration of these two potentially disruptive technologies. The results suggest opportunities that are related to intellectual property (IP) rights management, the monitoring of printed parts throughout their lifecycle, process improvements, and data security. The most important barriers for blockchain adoption in AM are an absence of blockchain‐skilled specialists on the labor market, missing governance mechanisms, and a lack of firm‐internal technical expertise. By addressing important limitations of AM, blockchain is expected to improve the competitiveness of AM in parts’ production, catalyzing the trend toward more decentralized manufacturing resulting in more agile, resilient, and flexible supply chains and reduced logistics costs. Beyond that, blockchain‐based AM platforms are expected to enhance supply chain visibility, drive supply chain digitalization, support supply chain finance, and contribute to the emergence of shared factory systems.     

Outward foreign direct investment by Polish state-owned multinational enterprises: is ‘stateness’ an asset or a burden?

State-owned enterprises (SOEs) are commonly associated with undue advantages due to preferential treatment by the state. Simultaneously they are often quoted as handicapped given the notorious state interference, management problems and agency tensions. They used to be regarded as a mainly domestic issue but in the context of globalisation and the fact that states enter treaties with new obligations, SOEs’ performance ceased to be solely a domestic problem, increasingly so as state-owned multinational enterprises (SOMNEs) emerge. This article presents the results of research on Polish SOEs’ outward foreign direct investment (OFDI). It offers an overview of overseas activities of nine major Polish firms with a state stake and aims to contribute to the conceptual literature on foreign investments conducted by SOMNEs. We distinguish between FDI by SOMNEs as specific – privileged (facilitated) or discriminated (hampered) – investments subject to the home country’s state power and the host country’s state perception.     

Testing the asymmetric effects of exchange rate pass‐through in BRICS countries: Does the state of the economy matter?

This paper investigates not only the question of whether there is exchange rate pass‐through (ERPT) but also the extent to which the pass‐through is asymmetric or state‐dependent in the BRICS countries. Using monthly data from 1999:M1 to 2019:M12 and non‐linear smooth transition vector autoregressive (STVAR) model, our results provide evidence of period‐specific ERPT between the upper and lower regime periods, governed by the selected transition variables. The results further suggest that the pass‐through of exchange rate is higher when the economy is experiencing large appreciations and expansions as well as large depreciations and recessions. Theimplication for these findings is that ERPT is strongly affected by the state of the economy.     

Bitcoin price and its marginal cost of production: support for a fundamental value

This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.