A burgeoning stream of sustainability research explores the role of companies’ top management team (TMT) characteristics in corporate sustainability efforts, while another stream investigates the effect of a company’s supply chain position on its likelihood of engaging in sustainability. This study shows the importance of integrating the two research streams by demonstrating that supply chain position moderates the relationship between TMT characteristics and sustainability and thus establishes boundary conditions for this relationship. By matching 758 corporate sustainability initiatives with control observations, our results show that the size of the top executive team and the average age of its members, two well-known predictors of corporate sustainability, are distinctly moderated by supply chain position. While business-to-business (B2B) companies are less likely to report a sustainability initiative compared to business-to-consumer (B2C) organizations, we found that B2B TMT size has a greater positive effect on sustainability initiative likelihood than B2C TMT size. Conversely, average B2C TMT age has greater predictive power in explaining sustainability initiative likelihood than average B2B TMT age. The implications of these findings in advancing corporate sustainability and organizational change are discussed.

Through a critical analysis of recent developments in the theory and practice of data science, including nascent feminist approaches to data collection and analysis, this commentary aims to signal the need for a transnational feminist orientation towards data science. I argue that while much needed in the context of persistent algorithmic oppression, a Western feminist lens limits the scope of problems, and thus—solutions, critical data scholars, and scientists can consider. A resolutely transnational feminist approach on the other hand, can provide data theorists and practitioners with the hermeneutic tools necessary to identify and disrupt instances of injustice in a more inclusive and comprehensive manner. A transnational feminist orientation to data science can pay particular attention to the communities rendered most vulnerable by algorithmic oppression, such as women of color and populations in non-Western countries. I present five ways in which transnational feminism can be leveraged as an intervention into the current data science canon.

Purpose: The purpose of this paper is to promote social network analysis (SNA) methodology within the humanitarian research community, surveying its current state of the art and demonstrating its utility in analyzing humanitarian operations.Design/methodology/approach: A comprehensive survey of the related literature motivates a proposed agenda for interested researchers. Analysis of two humanitarian networks in Afghanistan demonstrates the use and utility of SNA, based on secondary data. In the second case study, the use of random graphs to detect network motifs is demonstrated using Monte Carlo simulation to create the benchmark null sets.Findings: SNA is an adaptable and highly useful methodology in humanitarian research, quantifying patterns of community structure and collaboration among humanitarian organizations. Network motifs suggesting distinct affinity between particular agencies within humanitarian clusters are observed.Research limitations/implications: The authors summarize common challenges of using SNA in humanitarian research and discuss ways to alleviate them.Practical implications: Practitioners can use SNA as readily as researchers, to visualize existing networks, identify areas of concern and better communicate observations.Social implications: By making SNA more accessible to a humanitarian research audience, the authors hope its ability to capture complex, dynamic relationships will advance understanding of effective humanitarian relief systems.Originality/value: To the best of knowledge, it is the first study to conduct a systematic analysis of the application of SNA in empirical humanitarian research and outline a concrete SNA-based research agenda. This is also a currently rare instance of a humanitarian study using random graphs to assess observed SNA measures.

Background: Opioid-related deaths constitute a problem of pandemic proportions in the United States, with no clear solution in sight. Although addressing addiction—the heart of this problem—ought to remain a priority for health practitioners, examining the community-level psychological factors with a known impact on health behaviors may provide valuable insights for attenuating this health crisis by curbing risky behaviors before they evolve into addiction. Objective: The goal of this study is twofold: to demonstrate the relationship between community-level psychological traits and fatal opioid overdose both theoretically and empirically, and to provide a blueprint for using social media data to glean these psychological factors in a real-time, reliable, and scalable manner. Methods: We collected annual panel data from Twitter for 2891 counties in the United States between 2014-2016 and used a novel data mining technique to obtain average county-level “Big Five” psychological trait scores. We then performed interval regression, using a control function to alleviate omitted variable bias, to empirically test the relationship between county-level psychological traits and the prevalence of fatal opioid overdoses in each county. Results: After controlling for a wide range of community-level biopsychosocial factors related to health outcomes, we found that three of the operationalizations of the five psychological traits examined at the community level in the study were significantly associated with fatal opioid overdoses: extraversion (β=.308, P<.001), neuroticism (β=.248, P<.001), and conscientiousness (β=.229, P<.001). Conclusions: Analyzing the psychological characteristics of a community can be a valuable tool in the local, state, and national fight against the opioid pandemic. Health providers and community health organizations can benefit from this research by evaluating the psychological profile of the communities they serve and assessing the projected risk of fatal opioid overdose based on the relationships our study predict when making decisions for the allocation of overdose-reversal medication and other vital resources.

Industry and media have long represented automation as a harbinger of development and convenience in different areas of life. An anxious prospect to some, automation systems promise “progress” and profitability to others by conjuring corporate computational futures. What remains behind the scenes of these predictions and imaginaries of automation is the invisible human labor of global ghost workers caring for, maintaining, and repairing technologies. Invisible but irreplaceable, computation performed by humans in precarious conditions fills gaps that computer technologies lack skills and sensibility for. In this hybrid workshop, we ask who the “ghosts” are in the machines. The workshop will address the ghostly presence of humans and human labor in automation and its challenges to HCI research and design.