Search for: All records

Shifts to hybrid work prompted by the COVID-19 pandemic have the potential to substantially impact social relationships at work. Hybrid employees rely heavily on digital collaboration technologies to communicate and share information. Therefore, employees’ perceptions of the technologies are critical in shaping organizational networks. However, the dyadic-level misalignment in these perceptions may lead to relationship dissolution. To explore the social network consequences of hybrid work, we conducted a two-wave survey in a department of an industrial manufacturing firm (N = 169). Our results show that advice seekers were less likely to maintain their advice-seeking ties when they had a mismatch in ease-of-use perceptions of technology with their advisors. The effect was more substantial when advice seekers spent more time working remotely. The study provides empirical insights into how congruence in employees’ perceptions of organizational communication technologies affects how they maintain advice networks during hybrid work.

 

Despite the importance of diverse expertise in helping solve difficult interdisciplinary problems, measuring it is challenging and often relies on proxy measures and presumptive correlates of actual knowledge and experience. To address this challenge, we propose a text-based measure that uses researcher’s prior work to estimate their substantive expertise. These expertise estimates are then used to measure team-level expertise diversity by determining similarity or dissimilarity in members’ prior knowledge and skills. Using this measure on 2.8 million team invented patents granted by the US Patent Office, we show evidence of trends in expertise diversity over time and across team sizes, as well as its relationship with the quality and impact of a team’s innovation output.

 

Abstract Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations. 

Statistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary (i.e., whether a relationship exists or not), while in reality, the competition strength may vary among products. In this paper, we model the strength of the product competition by employing a statistical network model, with an emphasis on how product attributes affect which products are considered together and which products are ultimately purchased by customers. We first demonstrate how customers’ considerations and choices can be aggregated as weighted networks. Then, we propose a weighted network modeling approach by extending the valued exponential random graph model to investigate the effects of product features and network structures on product competition relations. The approach that consists of model construction, interpretation, and validation is presented in a step-by-step procedure. Our findings suggest that the weighted network model outperforms commonly used binary network baselines in predicting product competition as well as market share. Also, traditionally when using binary network models to study product competitions and depending on the cutoff values chosen to binarize a network, the resulting estimated customer preferences can be inconsistent. Such inconsistency in interpreting customer preferences is a downside of binary network models but can be well addressed by the proposed weighted network model. Lastly, this paper is the first attempt to study customers’ purchase preferences (i.e., aggregated choice decisions) and car competition (i.e., customers’ co-consideration decisions) together using weighted directed networks. 

Climate change poses a multifaceted, complex, and existential threat to human health and well-being, but efforts to communicate these threats to the public lag behind what we know how to do in communication research. Effective communication about climate change’s health risks can improve a wide variety of individual and population health-related outcomes by: (1) helping people better make the connection between climate change and health risks and (2) empowering them to act on that newfound knowledge and understanding. The aim of this manuscript is to highlight communication methods that have received empirical support for improving knowledge uptake and/or driving higher-quality decision making and healthier behaviors and to recommend how to apply them at the intersection of climate change and health. This expert consensus about effective communication methods can be used by healthcare professionals, decision makers, governments, the general public, and other stakeholders including sectors outside of health. In particular, we argue for the use of 11 theory-based, evidence-supported communication strategies and practices. These methods range from leveraging social networks to making careful choices about the use of language, narratives, emotions, visual images, and statistics. Message testing with appropriate groups is also key. When implemented properly, these approaches are likely to improve the outcomes of climate change and health communication efforts.