Background and Aim: Business Intelligence, not as a tool of a product but as a new approach is propounded in organizations to make tough decisions in business as
To date, substantial research studies have been conducted in the field of ethical decision making in many disciplines. However, ethical efforts in the context of information technology have been limited. In this research, a focus has been put on modeling ethical decision making in cyberspace with emphasis on business intelligence scenarios. The model is comprised of six exogenous and two endogenous constructs, among them seven were delicately selected from valid and empirically tested ethical models and the eighth one is developed by the authors. After pre-testing the model by experts, reliability, convergent and discriminant validities were approved. Data were collected from 188 IT personnel in the banking industry of Iran. Results revealed that the perceived importance of an IT ethical issue, ethical judgment, ethical obligation, perceived possibility of disclosure, ego strength, and locus of control directly impact ethical intention. However, no impact from law on ethical obligation and codes of ethics on ethical intention was observed. As shown, a higher possibility on acting unethically occurs when the person feels confident that his/her actions will go unnoticed.
Nowadays organizations have perceived the importance of managing customer relationship and its potential benefits. Customer relationship management supports organizations to deliver beneficial relations with customers. Customer satisfaction and retention are the leading objectives of any organization and this cannot be done without knowing the loyalty of the customer. Accordingly, to identify the loyalty of the customers, Kmeans algorithm was applied to the bank customers' data and clustering was conducted. The customer behavior is estimated by neural network and C5.0 models. Results show that C5.0 better fits the customer behavior. In addition, estimation of customer behavior leads organizations to more successful customer management strategies.
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