Going beyond technology: Knowledge sharing as a tool for enhancing customer-oriented attitudes

Numerous articles, books and essays have shown that ongoing efforts to meet customers’ needs characterize market leaders. For a firm to successfully develop customer-oriented attitudes throughout the entire organization, employees at all levels and sections need to be committed to providing a high level of service. It is therefore essential to understand what drives employees – especially those who do not interact daily with customers – to adopt a customer-oriented attitude. The aim of this study is to shed some light on one facet of this relationship, namely the mechanism of knowledge sharing that may impact employee customer orientation. The underlying notion is that technology, in its various advanced forms of CRM software, enhances and enables client “knowing.” In other words, it leads to a greater ability on the part of employees to share knowledge, which brings about real customer orientation.This paper provides empirical evidence, based on a sample of 345 Hi-Tech workers from a telecommunication company, of a link between sharing explicit knowledge and tacit knowledge to enhance customer orientation. The findings indicate that sharing tacit knowledge has a positive effect on increasing employee customer orientation and increases sharing of explicit knowledge, while also strengthening communication about customers in the organization. It is concluded that firms should encourage tacit knowledge sharing and its transformation by technology into explicit knowledge to augment employees’ customer-oriented attitudes.     

Levels of thinking in computer science: Development in bachelor students’ conceptualization of algorithm

How do we know if our students are beginning to think like computer scientists? In a first study we defined four levels of abstraction in computer science students’ thinking about the concept of algorithm. We constructed a list of questions about algorithms to measure the answering level as an indication for the thinking level. This list was presented to various groups of Bachelor computer science students. The mean answering level increased between successive year groups as well as within year groups during the year, mainly from the second to the third level. Student-level estimations provided by teachers fell in the same range as the level measurements, but level growth was not detected in their estimations; level estimation appeared very difficult for lecturers. The reliability of the instrument proved to be satisfactory. To investigate the validity, a follow-up study was done with a small heterogeneous group of Bachelor students. They answered the same questions and were successively interviewed to check whether they understood the terms they used. Their understanding proved to be satisfactory, sustaining the validity of the instrument. In the first study little relation was found between thinking levels and regular test results on algorithm-oriented courses. Supposedly, besides levels on the dimension of abstraction, levels on concretizing, analyzing and synthesizing are also relevant. A broader framework for future research is being proposed.