Cultural adaptation in Chinese-Western supply chain partnerships: dyadic learning in an international context

Article"This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited."Purpose: Inter-firm learning, or dyadic learning, has been studied extensively in recent years however very little attention has been devoted to extending the concept to an international context and no formal definition exists. We propose ‘cultural adaptation’ as a special form of international dyadic learning and link it to supply relationship performance. Design/methodology/approach: Case studies in four Chinese-Western buyer-supplier relationships, providing cross-case replication, employing qualitative and quantitative methods. Data are triangulated by questionnaires, semi-structured interviews, and documentation. Findings: Qualitative and quantitative evidence shows that cultural adaptation can lead to mutual benefits (relationship rents) and inbound spillover rents for both parties in a supply relationship. Research limitations/implications: Using four cases and a small sample of key informants completing the questionnaire limits generalisability of findings. Practical implications: 1. We develop the causal relationship between cultural adaptation and mutual benefits motivating managers to adapt culturally. 2. We emphasize that the current relationship performance measures should include guanxi quality in order to adapt to the Chinese context. Originality/value: Building on Extended Resource Based Theory, stating that strategic resources may lie beyond a firm’s boundary and that relational and inbound spillover rents may be obtained from the relationship, the research contributes to dyadic or inter-organisational learning literature by empirically building causal relationships between cultural adaptation (as a form of international dyadic learning) and associated mutual benefits (relational and inbound spillover rents), using multiple data sources and methods and tentatively redefining the dyadic learning concept

Dyadic Reinforcement Learning

Mobile health aims to enhance health outcomes by delivering interventions to individuals as they go about their daily life. The involvement of care partners and social support networks often proves crucial in helping individuals managing burdensome medical conditions. This presents opportunities in mobile health to design interventions that target the dyadic relationship -- the relationship between a target person and their care partner -- with the aim of enhancing social support. In this paper, we develop dyadic RL, an online reinforcement learning algorithm designed to personalize intervention delivery based on contextual factors and past responses of a target person and their care partner. Here, multiple sets of interventions impact the dyad across multiple time intervals. The developed dyadic RL is Bayesian and hierarchical. We formally introduce the problem setup, develop dyadic RL and establish a regret bound. We demonstrate dyadic RL's empirical performance through simulation studies on both toy scenarios and on a realistic test bed constructed from data collected in a mobile health study

THE EFFECTIVENESS OF DYADIC ESSAY TECHNIQUE IN TEACHING WRITING VIEWED FROM STUDENTS’ CREATIVITY

This article refers to an experimental study on teaching writing using Dyadic Essay Technique at High School in Bojonegoro. Dyadic Essay Technique is a kind of collaborative learning technique. This technique encourage students to actively involvedin the learning process since they have to compose an esay question before they start writing. The objective of this activity is to check students’ understanding about the material. Two samples of the research were X-2 class as control class and X-3 class as experimental class. Each class consists of 30 students. In collecting the data, several steps were applied: (1) tried out creativity test to non-sample class; (2) held creativity test; (3) implemented teaching techniques to control and experimental class; (4) conducted post-test; (5) analyzed students’ writing. The data were gained from students’ creativity test and students’ writing test. Meanwhile, the data were analyzed by ANOVA and Tukey Test.The result of the research were; (1) students who are taught by using Dyadic Essay Technique have better writing skills than by using Mind MappingTechnique. It means that the implementation of Dyadic Essay technique is more effective than Mind Mapping Technique; (2) students who have high creativity have better writing skill than students who have low creativity; (3) there is an interaction between teaching techniques and students’ creativity in teaching writing for tenth grade of SMAN 1 Bojonegoro

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

A two-step learning approach for solving full and almost full cold start problems in dyadic prediction

Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic prediction, a setting that occurs when both objects in an out-of-sample dyad have not been observed during training, or if one of them has been observed, but very few times. A popular approach for addressing this problem is to train a model that makes predictions based on a pairwise feature representation of the dyads, or, in case of kernel methods, based on a tensor product pairwise kernel. As an alternative to such a kernel approach, we introduce a novel two-step learning algorithm that borrows ideas from the fields of pairwise learning and spectral filtering. We show theoretically that the two-step method is very closely related to the tensor product kernel approach, and experimentally that it yields a slightly better predictive performance. Moreover, unlike existing tensor product kernel methods, the two-step method allows closed-form solutions for training and parameter selection via cross-validation estimates both in the full and almost full cold start settings, making the approach much more efficient and straightforward to implement