Employer Branding Applied to SMEs: A Pioneering Model Proposal for Attracting and Retaining Talent

Most business enterprises are small and medium-sized enterprises (SMEs), and many of them are without a human resource and recruitment department. Thus, one of the challenges that organizations currently face is to find a strategy to retain and attract talent. To overcome this difficulty, enterprises must invest in employer branding policies and be aware of the factors that differentiate them from others when attracting employees. This study aims to develop an employer branding model applicable to SMEs, to increase and enhance the attraction and retention of talents. An exploratory approach based on a quantitative perspective was adopted to develop an employer branding model applied to SMEs, with two major reference employer branding models and frameworks used as the main support. The model of employer branding was applied to SMEs regarding four dimensions, whereby essential questions are asked, namely (1) organizational culture (e.g., do employees have a job description aligned with the corporate culture?), (2) company strategy (e.g., what is the strategy if business volume decreases?), (3) company reputation (e.g., how do you perceive and treat negative feedback?), and (4) reward systems (e.g., do you feel that your employees are motivated intrinsically or extrinsically or both?), ordered by layers based on a logical sequence. The new proposed model is expected to serve as a useful strategic tool and as a basis for attracting, retaining and managing talent, specifically in the SMEs context. This new model provides a set of strategic and competitiveness benefits for SMEs, while contributing to making enterprises more profitable. The model also contributes to SMEs having a better image and reputation, enabling them to stand out from others in the war for talent.info:eu-repo/semantics/publishedVersio

Users' trust in information resources in the Web environment: a status report

This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

A Direct Reputation Model for VO Formation

We show that reputation is a basic ingredient in the Virtual Organisation (VO) formation process. Agents can use their experiences gained in direct past interactions to model other’s reputation and deciding on either join a VO or determining who is the most suitable set of partners. Reputation values are computed using a reinforcement learning algorithm, so agents can learn and adapt their reputation models of their partners according to their recent behaviour. Our approach is especially powerful if the agent participates in a VO in which the members can change their behaviour to exploit their partners. The reputation model presented in this paper deals with the questions of deception and fraud that have been ignored in current models of VO formation

Flow-based reputation: more than just ranking

The last years have seen a growing interest in collaborative systems like electronic marketplaces and P2P file sharing systems where people are intended to interact with other people. Those systems, however, are subject to security and operational risks because of their open and distributed nature. Reputation systems provide a mechanism to reduce such risks by building trust relationships among entities and identifying malicious entities. A popular reputation model is the so called flow-based model. Most existing reputation systems based on such a model provide only a ranking, without absolute reputation values; this makes it difficult to determine whether entities are actually trustworthy or untrustworthy. In addition, those systems ignore a significant part of the available information; as a consequence, reputation values may not be accurate. In this paper, we present a flow-based reputation metric that gives absolute values instead of merely a ranking. Our metric makes use of all the available information. We study, both analytically and numerically, the properties of the proposed metric and the effect of attacks on reputation values

Reconsidering online reputation systems

Social and socioeconomic interactions and transactions often require trust. In digital spaces, the main approach to facilitating trust has effectively been to try to reduce or even remove the need for it through the implementation of reputation systems. These generate metrics based on digital data such as ratings and reviews submitted by users, interaction histories, and so on, that are intended to label individuals as more or less reliable or trustworthy in a particular interaction context. We suggest that conventional approaches to the design of such systems are rooted in a capitalist, competitive paradigm, relying on methodological individualism, and that the reputation technologies themselves thus embody and enact this paradigm in whatever space they operate in. We question whether the politics, ethics and philosophy that contribute to this paradigm align with those of some of the contexts in which reputation systems are now being used, and suggest that alternative approaches to the establishment of trust and reputation in digital spaces need to be considered for alternative contexts