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.     

Technologies in caregiving: professionals’ strategies for engaging with new technology

This article explores the adoption of new technology in organisations that provide senior citizen care. Inspired by Niklas Luhmann’s systems theory, we study how technology reduces complexity by identifying client needs and ensuring predictability in service delivery. However, how technologies are adopted in practice is not determined by technology since it is also structured by care-workers' continuous decision-making. Against this backdrop, we explore how technologies alter the conditions for decision-making in two settings of elderly care, and we describe how care workers seek to adapt technologies to their practical needs as well as conception of care ethics. Developing a systems theory approach, the article eschews a priori assumptions of technological constraint on care-workers’ professional autonomy, offering a more open-ended exploration of diversified strategies for coping with new technology. Our case studies show that employees develop diversified strategies for technology adoption, including both non-usage, heated resistance, excessive embrace, and creative adaption.     

Climate for conflict management,exposure to workplace bullying and work engagement: a moderated mediation analysis

The present study investigates a potential preventive factor in relation to workplace bullying. Specifically, we examine how climate for conflict management (CCM) may be related to less bullying, increased work engagement, as well as whether CCM is a moderator in the bullying engagement relationship. The study was based on a cross-sectional survey among employees in a transport company (N = 312). Hypotheses were tested simultaneously in a moderated mediation analysis which showed that bullying and job engagement were related (H1), CCM was related to less reports of bullying (H2), CCM was related to work engagement (H3) and that CCM was indirectly related to job engagement through bullying (H4), but only when CCM was weak (H5). That is, CCM moderated the relationship between bullying and work engagement in that this relationship only existed when CCM was low. The present study contributes to theory within this research field by showing that organizational measures may not only prevent bullying, but may also affect how employees react when subjected to bullying. Furthermore, the effect of climate in relation to bullying may be down to the narrow bandwidth facet of CCM. The study informs employers how they may act to prevent bullying while also reducing the potential negative outcomes of those cases of bullying that inevitably will show up from time to time.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

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.