We propose a novel meta-approach to support collaborative multi-objective supplier selection and order allocation (SSOA) decisions by integrating multi criteria decision analysis and linear programming (LP). The proposed model accounts for suppliers’ performance synergy effects within a hierarchical decision structure. It incorporates both heterogeneous objective data and subjective judgments of the decision makers (DMs) representing various groups with different voting powers (VPs). We maximize the total value of purchasing (TVP) by optimizing order quantity assignment to suppliers and taking into consideration their synergies encountered in different time horizons. We apply the proposed model to a contractor selection and order quantity assignment problem in an agricultural commodity trading (ACT) company. We maximize the strategic effectiveness of both the customers and the suppliers, minimize risks, increase the degree of cooperation between trading partners on all levels of supply chain integration, enhance transparent knowledge sharing and aggregation, and support collaborative decision making.
DARIAH (Digital Research Infrastructure for the Arts and Humanities) is part of the European Strategy on Research Infrastructures. Among 38 projects originally on this roadmap, DARIAH is one of two projects addressing social sciences and humanities. According to its self-conception and its political mandate DARIAH has the mission to enhance and support digitally-enabled research across the humanities and arts. DARIAH aims to develop and maintain an infrastructure in support of ICT-based research practices. One main distinguishing aspect of DARIAH is that it is not focusing on one application domain but especially addresses the support of interdisciplinary research in the humanities and arts. The present paper first gives an overview on DARIAH as a whole and then focuses on the important aspect of technical, syntactic and semantic interoperability. Important aspects in this respect are metadata registries and crosswalk definitions allowing for meaningful cross-collection and inter-collection services and analysis.
There is evidence that survey interviewers may be tempted to manipulate answers to filter questions in a way that minimizes the number of follow-up questions. This becomes relevant when ego-centered network data are collected. The reported network size has a huge impact on interview duration if multiple questions on each alter are triggered. We analyze interviewer effects on a network-size question in the mixed-mode survey 'Panel Study 'Labour Market and Social Security'' (PASS), where interviewers could skip up to 15 follow-up questions by generating small networks. Applying multilevel models, we find almost no interviewer effects in CATI mode, where interviewers are paid by the hour and frequently supervised. In CAPI, however, where interviewers are paid by case and no close supervision is possible, we find strong interviewer effects on network size. As the area-specific network size is known from telephone mode, where allocation to interviewers is random, interviewer and area effects can be separated. Furthermore, a difference-in-difference analysis reveals the negative effect of introducing the follow-up questions in Wave 3 on CAPI network size. Attempting to explain interviewer effects we neither find significant main effects of experience within a wave, nor significantly different slopes between interviewers.