Corporate social responsibility (CSR) is defined as corporate activities and their 7 impacts on different social groups. In this paper, CSR is considered in a two-echelon supply 8 chain consisting of an upstream supplier and a downstream firm that are bound by a wholesale 9 price contract. CSR performance (the outcome of CSR conduct) of the whole supply chain is 10 gauged by a global variable and the associated cost of achieving this CSR performance is only 11 incurred by the supplier with an expectation of being shared with the downstream firm via the 12 wholesale price contract. As such, the key issue is to determine who should be allocated as the 13 responsibility holder with the right of offering the contract and how this right should be 14 appropriately restricted. Game-theoretical analyses are carried out on six games, resulting from 15 different interaction schemes between the supplier and the firm, to derive their corresponding 16 equilibriums. Comparative institutional analyses are then conducted to determine the optimal 17 social responsibility allocations based on both economic and CSR performance criteria. Main 18 results are furnished in a series of propositions and their implications to the real-world business 19 practice are discussed. The key findings are threefold: Under the current model settings, (1) the 20 optimal allocation scheme is to assign the supplier as the responsibility holder with appropriate 21 restrictions on the corresponding rights to determine the wholesale price; (2) Inherent conflict 22 exists between the economic and CSR performance criteria and, hence, the two maxima cannot 23 be achieved simultaneously; (3) Although integrative channel profit is not attainable, the system-24 wide profit will be improved by implementing optimal social responsibility allocation schemes. 25
Data mining becomes a cutting-edge information technology tool in today's competitive business world. It helps the company discover previously unknown, valid, and actionable information from various and large databases for crucial business decisions. This paper provides a promising approach of data mining to classify the credit cardholders' behavior through multiple criteria linear programming. After reviewing the history of linear discriminant analyses, we will describe first a model for classifying two-group (e.g. bad or good) credit cardholder behaviors, and then a three-group (e.g. bad, normal, or good) credit model. Besides the discussion of the modeling structure, we will utilize the well-known commercial software package SAS to implement this technology by using a real-life credit card data warehouse. A number of potential business and financial applications will be finally summarized.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.