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Article
Publication date: 4 December 2023

Despoina Ioakeimidou, Dimitrios Chatzoudes, Symeon Symeonidis and Prodromos Chatzoglou

This study aims to develop and test an original conceptual framework that examines the role of various factors borrowed from three theories (i.e. Institutional Theory…

Abstract

Purpose

This study aims to develop and test an original conceptual framework that examines the role of various factors borrowed from three theories (i.e. Institutional Theory, Resource-Based View and Diffusion of Innovation) in adopting Human Resource Analytics (HRA).

Design/methodology/approach

A new conceptual framework (research model) is developed based on previous research and coherent theoretical arguments. Its factors are classified using the Technology–Organization–Environment (TOE) framework. Research hypotheses are tested using primary data collected from 152 managers of Greek organizations. Empirical data are analyzed using the “Structural Equation Modelling” (SEM) technique.

Findings

The technological and organizational context proved extremely important in enhancing Organizational Analytics Maturity (OAM) and HRA adoption, while the environmental context did not. Relative advantage and top management support were found to significantly impact the adoption of HRA, while Information Technology (IT) infrastructure, human resource capabilities and top management support are crucial for increasing OAM. Overall, the latter is the most important factor in enhancing HRA adoption.

Originality/value

This study contributes to the limited published research on HRA adoption while at the same time it can be used as a guideline for future research. The novel findings offer insights into the factors impacting OAM and HRA adoption.

Details

International Journal of Manpower, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-7720

Keywords

Article
Publication date: 14 May 2018

Georgios Kalamatianos, Symeon Symeonidis, Dimitrios Mallis and Avi Arampatzis

The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. This paper aims to focus on the Greek…

Abstract

Purpose

The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. This paper aims to focus on the Greek language and the microblogging platform Twitter, investigating methods for extracting emotion of individual tweets as well as population emotion for different subjects (hashtags).

Design/methodology/approach

The authors propose and investigate the use of emotion lexicon-based methods as a mean of extracting emotion/sentiment information from social media. The authors compare several approaches for measuring the intensity of six emotions: anger, disgust, fear, happiness, sadness and surprise. To evaluate the effectiveness of the methods, the authors develop a benchmark dataset of tweets, manually rated by two humans.

Findings

Development of a new sentiment lexicon for use in Web applications. The authors then assess the performance of the methods with the new lexicon and find improved results.

Research limitations/implications

Automated emotion results of research seem promising and correlate to real user emotion. At this point, the authors make some interesting observations about the lexicon-based approach which lead to the need for a new, better, emotion lexicon.

Practical implications

The authors examine the variation of emotion intensity over time for selected hashtags and associate it with real-world events.

Originality/value

The originality in this research is the development of a training set of tweets, manually annotated by two independent raters. The authors “transfer” the sentiment information of these annotated tweets, in a meaningful way, to the set of words that appear in them.

Details

Journal of Systems and Information Technology, vol. 20 no. 2
Type: Research Article
ISSN: 1328-7265

Keywords

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