An Organizational Analysis on Apple

In this article, Apple’s bureaucratic, political, contingency and cultural aspects were analyzed. Primary data is collected through an interview with an APPLE employee and this is compared with secondary data to get a broader perceptive.Apple’s bureaucratic analysis reveals that the organization has a flat, organic structure centralized from the top. The organization shows a good structural fit to technology and environment to deal with contingencies. Apple culture is unique with the elements of accountability, secrecy, and innovation and embraces the paradox of “command and control”. The high amount of power imbalance exists in the organization which affects in open communication and decision making authority.Apple is famous for its secrecy, and for this reason, internal organizational information is not publicized widely, which is a limitation of this article. Nevertheless, the secondary data collected from journals, articles, reports are used for this article. As part of the conclusion, a word of recommendations is also given. Keywords: Apple, Culture, Organization, Technolog

Impact of Supervisory Communication Skills on Employee Job Satisfaction: A Case Study on KIA Motors

Communication within an organization plays a very crucial role in motivating the employees in order to achieve their personal as we well as company goals. Hence communication skills are very important especially between the supervisors and the team members in order to improve their job performance and also for the organization success.  This study was conducted to show the impact of effective supervisory communication skills on employee satisfaction in an automobile company named KIA MOTORS in Dubai. A survey was conducted with 20 employees working full-time with the company and four interviews were made face to face which included both supervisors and subordinates. The survey indicated that verbal and written responses given by the supervisors created a much more positive impact on employee job satisfaction compared to immediate responses such as emails, messages etc. The results indicated that supervisors’ communication skills had a positive correlation with job satisfaction. Supervisory Communication Skills also include feedback clarity and also interpersonal communication skills. Specifically, overall 66.67 % of the employees who took part in the survey agreed that supervisory communication contributed to their job satisfaction. However this study is subject to limitations in terms of the size of data collected (number of employees) and unit of analysis (only one company).Hence we cannot generalize our findings to other industries but a similar study done with larger number of employees from different companies will provide a better picture regarding the impact of supervisory communication on job satisfaction and will help us perhaps reduce the barriers of effective communication if any. Keywords: motivation, job satisfaction, communication, supervisory communication method, communication skill, workplace communicatio

Context-Based Entity Matching for Big Data

In the Big Data era, where variety is the most dominant dimension, the RDF data model enables the creation and integration of actionable knowledge from heterogeneous data sources. However, the RDF data model allows for describing entities under various contexts, e.g., people can be described from its demographic context, but as well from their professional contexts. Context-aware description poses challenges during entity matching of RDF datasets—the match might not be valid in every context. To perform a contextually relevant entity matching, the specific context under which a data-driven task, e.g., data integration is performed, must be taken into account. However, existing approaches only consider inter-schema and properties mapping of different data sources and prevent users from selecting contexts and conditions during a data integration process. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and a context-based similarity metric to map contextually equivalent RDF graphs. COMET follows a two-fold approach to solve the problem of entity matching in RDF graphs in a context-aware manner. In the first step, COMET computes the similarity measures across RDF entities and resorts to the Formal Concept Analysis algorithm to map contextually equivalent RDF entities. Finally, COMET combines the results of the first step and executes a 1-1 perfect matching algorithm for matching RDF entities based on the combined scores. We empirically evaluate the performance of COMET on testbed from DBpedia. The experimental results suggest that COMET accurately matches equivalent RDF graphs in a context-dependent manner