Career barriers influencing career success: A focus on academics’ perceptions and experiences

Purpose – Few research has addressed the factors that undermine people’s subjective perceptions of career success. Hence, the purpose of this paper is to further illuminate the issue of career barriers in perceptions of career success for a specific group of professionals: academics. Design/methodology/approach – This study adopts an interpretative-social constructionist methodology. Complementarily, it was employed a phenomenological method in data gathering and analysis – with the use of in-depth interviews and a theme analysis. The research was undertaken with a group of 87 Portuguese academics of both sexes and in different stages of their academic careers. Findings – The findings pinpoint the existence of multi-level barriers encountered by the academics when trying to succeed in their careers. The interviewees mentioned particularly the organizational-professional career barriers pertaining to three general themes: poor collegiality and workplace relationships; the lack of organizational support and employment precariousness; and the career progression standards and expectations. At the individual life cycle level the interviewees referred to the theme of finding balance; at the same time, the gender structure was also a theme mentioned as an important career barrier in career success, particularly by the women interviewed. Research limitations/implications – One of the limitations of this research is related to the impossibility of generalizability of its findings for the general population. Nevertheless, the researcher provides enough detail that grants the reader with the ability to judge of its similarity to other research contexts. Practical implications – This research highlights the role played by distinct career barriers for a specific professional group: academics. This has implications for higher education policy-makers and for human resources managers in higher education institutions. Originality/value – The current study extends the literature on career success by offering detailed anecdotal evidence on how negative work experiences might hinder career success. This research shows that to understand career barriers to success it is useful to consider multi-level factors: organizational-level factors (e.g. poor collegiality and workplace relationships); individual-level factors (e.g. life-cycle factors such as age/career stage); and structural-level factors (e.g. gender).

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.