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.     

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.     

Modularization of hybrid censoring schemes and its application to unified progressive hybrid censoring

In this paper, a structural analysis of hybrid censoring models is presented. This new modularization approach to hybrid censoring models enables a convenient derivation of distributional results. For instance, it allows to derive the exact distribution of the MLEs under an exponential assumption for very complex hybrid scenarios. In order to illustrate the benefit of this idea, we apply it to four new unified progressive hybrid censoring schemes. They are extensions of already proposed unified Type-I/II/III/IV hybrid censoring schemes to progressively Type-II censored data. The resulting analysis shows that the modularization approach provides a powerful, efficient, and elegant tool to study even more complex hybrid censoring models.     

Organizational career management practices and objective career success: A systematic review and framework

The theorization of the relationship between organizational investments in career development and individual success remains underdeveloped, and empirical tests of this relationship, which have been dispersed among several disciplinary areas, have produced inconsistent results. Addressing these issues, the purpose of this article is to propose a theoretical framework that illustrates why and how organizational career management practices translate into career success and under what circumstances the relationship is effective. Using a systematic review of empirical studies on career management practices and objective success, we identify three theoretical mechanisms - developmental, informational, and relational - and two groups of contingency factors that explain this relationship. Our framework advances the extant literature on organizational career management and provides suggestions to companies for designing effective career management systems.     

Lobbying as a potent political marketing tool for firm performance: A closer look

This study endeavors to enhance political marketing literature about the impact of lobbying on firm performance. Our sample is composed of 140 U.S. firms and spans the years 2007–2014 to encompass the 2007–2009 recession and the subsequent recovery period. Our findings indicate that lobbying expenses positively contribute to firm performance. Also, government contracts in both ways, dollar amount and number of government contracts, act as mediators between lobbying expenses and firm performance. In addition, organizational slack moderates the relationship between lobbying expenses and government contracts. The managerial implications suggest that lobbying expenses can be leveraged as a potent tool for firm performance. Firms with larger lobbying efforts acquired both, higher dollar amounts and a greater number of government contracts.     

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.