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.     

Environmental performance,sustainability, governance and financial performance: Evidence from heavily polluting industries in China

This study seeks to contribute to the existing business strategy and the environment literature by examining the effect of governance structures on Chinese firms' environmental performance, and consequently ascertain the extent to which the financial performance–environmental performance nexus is moderated by governance mechanisms. Using a sample of Chinese companies from heavily polluting industries over a 5-year period, our baseline findings suggest that, on average, board size and governing board meetings are positively associated with Chinese firms' environmental performance, whilst board independence and gender diversity have positive, but insignificant association with firms' environmental performance. Our evidence suggests further that the examined internal governance mechanisms have a mixed moderating effect on the link between financial performance and environmental performance. Our findings have important implications for company executives, environmental activists, policy-makers, and regulators. Our results support insights drawn from agency, resource dependence, stakeholder, and legitimacy theories.     

Navigating compromise: How founder authenticity affects venture identification amidst organizational hybridity

Founders of hybrid ventures encounter organizational tensions that can compel compromise in both their organizations' and their own personal values. Such compromises may, in turn, undermine founders' identification with their ventures. In a multi-case study analysis we examine why social entrepreneurs differ in their responses to organizational tensions, both at the firm- and individual-level, and how such differences relate to their venture identification. Specifically, our findings reveal that strategic decisions made in the context of values-based complexity are often accompanied by concerns regarding founder authenticity—that is, judgments about the alignment between founders' actions and the commitments or responsibilities associated with their identities as entrepreneurs. Yet, because founders differ in the basis from which they seek to maintain such alignment, these differences shape both hybridity management and subsequent venture identification. By unpacking such differences, our findings contribute new theory, bridging recent scholarship on founder authenticity with longstanding research on organizational identification and hybrid organizing.     

Long-term reversals in the corporate bond market

Long-term reversals in corporate bonds are economically and statistically significant in a comprehensive sample spanning the period 1977 to 2017. Such reversals are stronger for bonds with high credit risk and more binding regulatory, capital, and funding liquidity constraints. Bond long-term reversal is not a manifestation of the equity counterpart and is mainly driven by long-term losers. A long-term reversal factor carries a sizable premium and is not explained by long-established equity and bond market factors. Thus, past returns capture investors’ ex-ante risk assessment and the degree of institutional constraints they face, so losing bonds command higher expected returns.     

Mega‐Regional Agreements and Their Impact on Australia

This paper estimates the effects of emerging mega‐regional agreements on the economy of Australia using a Computable General Equilibrium model. Scenarios considered include the 11‐member Comprehensive and Progressive Agreement for Trans‐Pacific Partnership (CPTPP) agreement, its possible enlargement to include five additional members (‘TPP16’) and the Regional Comprehensive Economic Partnership (RCEP) currently under negotiation. The study finds that Australia stands to enjoy real income and trade gains along both the CPTPP and RCEP paths. However, since Australia is already benefiting from its own liberal trade policies and many prior trade agreements, benefits are relatively modest, typically below one per cent of real income.     

Sharing versus collaborative economy: how to align ICT developments and the SDGs in tourism?

Great hopes have been placed in the sharing economy to provide a new business model based on peer-to-peer (P2P) exchanges of underutilized assets. As a model, the sharing economy has been expected to make significant contributions to sustainability, providing new opportunities for entrepreneurship, more sustainable use of resources, and consumer co-operation in tight economic networks. However, in recent years, digital platforms have turned into the most important actors in the global sharing economy, turning global corporations, such as AirBnB, Booking, or TripAdvisor into intermediaries controlling and profiting from most transactions. Focused on accommodation, this paper conceptualizes the sharing economy in comparison to the wider collaborative economy, and discusses its social, economic, environmental, and political impacts in comparison to the sustainable development goals. It concludes that the sharing economy has great potential to make very significant contributions to sustainability, though the model is increasingly being replaced by the collaborative economy, which performs as an extension and acceleration of neoliberal economic practices.     

“Going natural”: Black women’s identity project shifts in hair care practices

Hair care, a seemingly mundane consumption practice, is packed with sociocultural meanings and constitutes an important realm of identity work for consumers. Drawing upon existing literature on consumer identity projects, this paper investigates the experiences of urban, Black women in Kenya as they disengage from the normative practice of altering their natural hair texture using chemical straighteners (a practice that conforms to Eurocentric beauty ideals of straight, flowing hair), and adopt the non-normative hair care practice of “going natural,” in which they embrace their natural hair texture and hairstyles. This paper traces the historical, sociocultural, and political events that underpin the normative ideology associated with the identity marker of natural Black hair, and how this ideology influences the women’s “going natural” experience. Findings reveal tensions that emerge in the women’s identity project disengagement and reconstruction process, and how the women navigate their position as they reimagine their embodied identity quest.     

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.     

Trading off learning and performance: Exploration and exploitation at work

Employees are increasingly given control over how they learn, and their choices for training are diverse and varied, yet employees must balance competing demands. On one hand, they are expected to be increasingly efficient in their current job duties – on the other hand, they are expected to develop new skills and competencies that enable them to adapt and respond to changing job demands. Drawing from the organizational learning literature, we propose a model of worker and work characteristics that inform choices between two mindsets related to learning at work. The first mindset is exploration, or the pursuit of learning outside one’s current knowledge domain; the second mindset is exploitation, the refinement/deepening of one’s existing knowledge stock focusing on the task at hand. We further propose that these strategic choices, or trade-offs, influence employee learning and performance in unique ways, with different implications for both routine and adaptive performance. Finally, we incorporate the notions of feedback loops and risk assessments that influence ongoing decisions between exploration and exploitation mindsets. Recommendations for future research and extensions of the theoretical model are also proposed.