A mentor plays an important role in entrepreneurial development of an individual. He guides entrepreneurs from conception of business to product development and business growth. Previous literature on entrepreneurial learning is disseminated and not properly organized; it is difficult to even find pertinent and comprehensive articles on entrepreneurial learning. The research proposed in this article helps mentors to understand and find out what type of entrepreneurs need what kind of mentoring support. This article proposes a conceptual model for mentors and discusses that an entrepreneur may need different mentoring support and skills depending on the type of entrepreneurs, personality traits, or decision-making style and phase at which entrepreneurs are at that moment. This article will also help mentors in understanding what type of skills entrepreneurs need at each stage of mentoring relationship, that is, initiation, cultivation, separation, and redefinition stage.
A knowledge map has emerged, as a powerful source of competitive advantage, and plays an important role in managing an organizational knowledge. The definition, purposes, benefits, types and principles of knowledge map have been already provided and well explored by many scholars and researchers. However, predictors for a knowledge map adoption have seldom been addressed. Hence, how to facilitate a successful adopting of a knowledge map becomes important. To address this gap this study develops a conceptual model to investigate diverse factors influencing the adoption of knowledge map in software development organizations context. The research proposed model is established on the Technological-Organizational-Environmental (TOE) framework. The model identifies thirteen variables, covering five broad categories (Technological, Organizational, Environmental, Task, and Individual) that could potentially influence knowledge map adoption. A complete analysis of the possible aspects to be considered for adopting of knowledge map in software development organizations is provided by the proposed model.
The first part of the paper provides a brief description of the Language Observatory Project (LOP) and highlights the major technical difficulties to be challenged. The latter part gives how we responded to these difficulties by adopting UbiCrawler as a data collecting engine for the project. An interactive collaboration between the two groups is producing quite satisfactory results.
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