Identifying Effective Indicators in the Assessment of Organizational Readiness for Accepting Social CRM

The main objective of this study is to provide a model for assessing organizational readiness for accepting social customer relationship management (SCRM). The dilemma, the organizations are faced with is that they go ahead directly toward the preparation and implementation of new technologies, especially the technology without determining their level of readiness and this causes that they fail at the implementation stage or they do not benefit from the investment and spending they have done. In this research, after studying various resources, in particular, resources related to the assessment of organizational readiness in accepting information systems and social networks-based systems for using in organizations businesses and the proposed models by the mentioned resources, components and sub-components affecting the organization's readiness in accepting SCRM have been identified and extracted. We categorized them in four main categories and dimensions, naming organizational, technological, human and environmental factors. Each of these main components includes sub-components that are mentioned in this research

The Identification and Ranking of Effective Indicators in Assessing Organization Readiness to Accept Social CRM Using FANP

For today's organizations, customers have become important resources and communications with them have been considered as valuable assets and investments. Therefore, the effective management of customer relationship has become a preferred task and an approach for managing in many organizations. The dilemma, the organizations are faced with is that they go ahead directly toward the preparation and implementation of new technologies, especially the technology without determining their level of readiness and this causes that they fail at the implementation stage or they do not benefit from the investment and spending they have done. This study wants to investigate the indicators involved in successful acceptance of this product and the degree of importance of these indicators compared to other indicators. After identifying these indicators, according to the research background and internal experts' opinion, the status of these indicators has been assessed and they were ranked using multi-criteria decision-making model known as fuzzy analytic network process in Iran's Mellat Bank. Finally, by examining the fuzzy average of the studied organization data, a general consensus has been achieved about the importance of each indicator as well as their ranking based on the weight of each indicator compared to other indicators in assessing Iranian organization readiness to accept social customer relationship management. It is worth mentioning that this research paid more attention to negotiations and meetings with chief technology officers, customer relationship experts, strategic managers, project managers and human resource managers

Measuring Customers Satisfaction of E-Commerce Sites Using Clustering Techniques: Case Study of Nyazco Website

Today the use of modern technologies in the daily life for satisfying the needs is unavoidable. Follow the news and searching through the internet has affected organizations to provide platform on the Internet for availability of information for the customers. With the development of e-commerce, online shopping plays an increasingly important role in people’s life. With the use of data mining technique prospect, managers of this site can analyze preferences and purchasing patterns of online customers in order to custom product recommendations. Data mining helps to provide services in accordance with customers’ requirements. The aim of this research is to identify the customers’ requirements in online shopping and cluster these customers based on independent attributes such as gender, product classification, recency, frequency and monetary. For this purpose, the data related to Nyazco website that is an e-commerce website with a variety of products, were examined as a case study in the period of 7 months. The authors of this paper will define four clusters by using k-means algorithm and RFM model by IBM SPSS Modeler 14.2 software. Customers in the third cluster and fourth cluster will be identified as the most important customers. Therefore, providing the demands of these customers should be prioritized

The Effect of Drivers\u27 Demographic Characteristics on Road Accidents in Different Seasons Using Data Mining

According to World Health Organization, each year, over 1.2 million people die on roads, and between 20 and 50 million suffer non-fatal injuries. Based on international reports, Iran has a high death rate caused by road accidents. The objective of this study was to extract implicit knowledge from road accident data sets on roads of Iran through data mining. In this regard, three useful data mining techniques were combined: clustering, classification and rule extraction. Following the preparation stage, data were segmented via three clustering algorithms; Kohonen, K-Means and Twostep. Two-step cluster analysis is a one-pass-through data approach which generates a fairly large number of pre-clusters. Next, the optimized algorithm and cluster were identified, after which, in the classification level and by adding the drivers\u27 demographic features through C5.0, a classification algorithm was employed so as to make the decision tree. Ultimately, the effects of these demographic features were investigated on road accidents. The characteristics such as age, job, driving license duration and gender proved to be more important factors in accident analysis. Certain rules of accidents were then extracted in each season of the year.</p

Forensically ready digital identity management systems, issues of digital identity life cycle and context of usage

Collecting necessary digital and network forensics to prove the identity of an individual who is responsible for a crime, or suspected of a malicious attack, or has used a device during an incident, with minimum doubt to the court or other legitimate organisations based on the digital forensic investigation model is one of the most important legal and security issues of digital identity management systems (DIMSs). Without a good understanding and identification of the most important parameters of DIMS based on the digital forensic investigation model, it is not possible to do digital forensic investigation and provide required evidence. Therefore, the main goal of this paper is to identify and prioritise DIMS parameters by considering a user’s digital identity lifecycle, the contexts of usage challenges, and constraints that should be considered in a digital forensic readiness model

Assessing the Evaluation Models of Business Intelligence Maturity and Presenting an Optimized Model

The main purpose of this study is to present a new Business Intelligence Maturity Model according to the prior models and their available components to review the level of Business Intelligence maturity in organizations. The business maturity helps all organizations to get safe and effective operations without extra troubles and, executive expenses and trial & error through reporting and data analyzing. Today we can strongly claim that applying the business intelligence solution in an organization makes it more powerful and discriminates it from the others by the increase in competitiveness. This solution causes organizations to use competitive advantages and pioneer through available information. This is a practical research in which we use a survey descriptive method and matter. The result of the study is to create a new model in order to study the level of business intelligence maturity in the banking industry which has maturity levels including initial, immature, controlled, managed and mature, and effective infrastructures on BI system which contains technology, organizational culture, and rules

Marine data users clustering using data mining technique

The objective of this research is marine data users clustering using data mining technique. To achieve this objective, marine organizations will enable to know their data and users requirements. In this research, CRISP-DM standard model was used to implement the data mining technique. The required data was extracted from 500 marine data users profile database of Iranian National Institute for Oceanography and Atmospheric Sciences (INIOAS) from 1386 to 1393. The TwoStep algorithm was used for clustering. In this research, patterns was discovered between marine data users such as student, organization and scientist and their data request (Data source, Data type, Data set, Parameter and Geographic area) using clustering for the first time. The most important clusters are: Student with International data source, Chemistry data type, “World Ocean Database” dataset, Persian Gulf geographic area and Organization with Nitrate parameter. Senior managers of the marine organizations will enable to make correct decisions concerning their existing data. They will direct to planning for better data collection in the future. Also data users will guide with respect to their requests. Finally, the valuable suggestions were offered to improve the performance of marine organizations

The Effect of Drivers' Demographic Characteristics on Road Accidents in Different Seasons Using Data Mining

According to World Health Organization, each year, over 1.2 million people die on roads, and between 20 and 50 million suffer non-fatal injuries. Based on international reports, Iran has a high death rate caused by road accidents. The objective of this study was to extract implicit knowledge from road accident data sets on roads of Iran through data mining. In this regard, three useful data mining techniques were combined: clustering, classification and rule extraction. Following the preparation stage, data were segmented via three clustering algorithms; Kohonen, K-Means and Twostep. Two-step cluster analysis is a one-pass-through data approach which generates a fairly large number of pre-clusters. Next, the optimized algorithm and cluster were identified, after which, in the classification level and by adding the drivers' demographic features through C5.0, a classification algorithm was employed so as to make the decision tree. Ultimately, the effects of these demographic features were investigated on road accidents. The characteristics such as age, job, driving license duration and gender proved to be more important factors in accident analysis. Certain rules of accidents were then extracted in each season of the year