Is It We or They? The Effect of Identity on Collaboration and Performance in Buyer-Supplier Relationships

In today’s scale-driven and technology-intensive global economy, collaboration becomes the supply chain’s core capability (Liker and Choi, 2004). A well-developed ability to create and sustain fruitful collaborations gives companies a significant competitive advantage (Kanter, 1994). Retailers are increasingly relying on their suppliers to reduce costs, improve quality, and develop new processes and products faster than their rivals’ vendors can. On the other hand, suppliers benefit from retailers that they are able to monitor store-level demand in real time in order to ensure the top-selling items are in-stock or the accuracy and timeliness of retailer’s demand forecast. Previous literature has shown various ways to promote collaboration in buyer-supplier relationships, but it may also have negative impacts such as deception (Gneezy, 2005), dishonesty (Mazar et al., 2008), or opportunism (John, 1984). This dissertation aims to investigate the impact of two types of identity (induced group identity and natural identity) on discretionary collaborative behaviors without any monetary incentives and supply chain performance in buyer-supplier relationships. Using social identity theory (Tajfel and Turner, 1979), Essay 1 explores the influence of buyer-supplier identification which is defined as perceived oneness of a supplier/buyer with its partner’s organization and experience of its partner’s successes and failures as its own (Ashforth and Mael, 1989) on collaboration and supply chain performance, and the foundation and formation of buyer-supplier identification. To explore the effect of natural identity, particularly, gender identity, Essay 2 addresses the impact of gender composition in buyer-supplier relationships on collaboration, and supply chain performance. It investigates whether females and males exhibit differences in trust and trustworthiness. Controlled laboratory experiments are executed for Essays 1 and 2. Together the two essays bring the concept of identity to supply chain management literature and advance our understanding of the enablers and drivers that can increase buyer-supplier collaboration and supply chain performance

Asphalt Concrete Characterization Using Digital Image Correlation: A Systematic Review of Best Practices, Applications, and Future Vision

Digital Image Correlation (DIC) is an optical technique that measures displacement and strain by tracking pattern movement in a sequence of captured images during testing. DIC has gained recognition in asphalt pavement engineering since the early 2000s. However, users often perceive the DIC technique as an out-of-box tool and lack a thorough understanding of its operational and measurement principles. This article presents a state-of-art review of DIC as a crucial tool for laboratory testing of asphalt concrete (AC), primarily focusing on the widely utilized 2D-DIC and 3D-DIC techniques. To address frequently asked questions from users, the review thoroughly examines the optimal methods for preparing speckle patterns, configuring single-camera or dual-camera imaging systems, conducting DIC analyses, and exploring various applications. Furthermore, emerging DIC methodologies such as Digital Volume Correlation and deep-learning-based DIC are introduced, highlighting their potential for future applications in pavement engineering. The article also provides a comprehensive and reliable flowchart for implementing DIC in AC characterization. Finally, critical directions for future research are presented.Comment: Journal of Testing and Evaluatio

Efficient Monotonic Multihead Attention

We introduce the Efficient Monotonic Multihead Attention (EMMA), a state-of-the-art simultaneous translation model with numerically-stable and unbiased monotonic alignment estimation. In addition, we present improved training and inference strategies, including simultaneous fine-tuning from an offline translation model and reduction of monotonic alignment variance. The experimental results demonstrate that the proposed model attains state-of-the-art performance in simultaneous speech-to-text translation on the Spanish and English translation task

Novel Nonphosphorylated Peptides with Conserved Sequences Selectively Bind to Grb7 SH2 Domain with Affinity Comparable to Its Phosphorylated Ligand

The Grb7 (growth factor receptor-bound 7) protein, a member of the Grb7 protein family, is found to be highly expressed in such metastatic tumors as breast cancer, esophageal cancer, liver cancer, etc. The src-homology 2 (SH2) domain in the C-terminus is reported to be mainly involved in Grb7 signaling pathways. Using the random peptide library, we identified a series of Grb7 SH2 domain-binding nonphosphorylated peptides in the yeast two-hybrid system. These peptides have a conserved GIPT/K/N sequence at the N-terminus and G/WD/IP at the C-terminus, and the region between the N-and C-terminus contains fifteen amino acids enriched with serines, threonines and prolines. The association between the nonphosphorylated peptides and the Grb7 SH2 domain occurred in vitro and ex vivo. When competing for binding to the Grb7 SH2 domain in a complex, one synthesized nonphosphorylated ligand, containing the twenty-two amino acid-motif sequence, showed at least comparable affinity to the phosphorylated ligand of ErbB3 in vitro, and its overexpression inhibited the proliferation of SK-BR-3 cells. Such nonphosphorylated peptides may be useful for rational design of drugs targeted against cancers that express high levels of Grb7 protein