Search results

The purpose of the study is to examine the stakeholder identification (SI) methods used in building projects procured by private corporate organisations in Southwestern Nigeria to draw a correlation between methods used for SI and project performance in the study area, thereby enhancing project performance.

The study used a census survey to purposively select 30 projects managers (who indicated from a first-stage questionnaire) that they have participated in structured stakeholder management (SM) from a list of 106 project managers and client representatives who have been involved in SM of building projects procured by private corporate organisations between 2008 and 2017 in the study area. A second-stage questionnaire was designed to elicit information from the respondents. The quantitative data that were collected were analysed using mean score (MS) analysis and Analysis of variance (ANOVA).

The study found that brainstorming in group meetings (MS = 4.33), interviews with the expert (MS = 3.20) and identification by other stakeholders (MS = 3.00) are the most frequently used SI methods in the study area. It was also found that the use of a questionnaire (MS = 0.87), Delphi method (MS = 0.80) and public hearing (MS = 0.57) was rare for SI in the study area. A further result showed that there was no significant difference in the opinion of project managers on 85% of methods used for SI in the study area irrespective of the procurement method employed.

The study examined the methods project managers used for SI on building projects procured by private corporate organisations to advocate for more robust and all-inclusive SI methods.

The study empirically examined methods used for SI and correlated them to project performance.

Gender bias in artificial intelligence (AI) should be solved as a priority before AI algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem has been identified as an established research and policy agenda, a cohesive review of existing research specifically addressing gender bias from a socio-technical viewpoint is lacking. Thus, the purpose of this study is to determine the social causes and consequences of, and proposed solutions to, gender bias in AI algorithms.

A comprehensive systematic review followed established protocols to ensure accurate and verifiable identification of suitable articles. The process revealed 177 articles in the socio-technical framework, with 64 articles selected for in-depth analysis.

Most previous research has focused on technical rather than social causes, consequences and solutions to AI bias. From a social perspective, gender bias in AI algorithms can be attributed equally to algorithmic design and training datasets. Social consequences are wide-ranging, with amplification of existing bias the most common at 28%. Social solutions were concentrated on algorithmic design, specifically improving diversity in AI development teams (30%), increasing awareness (23%), human-in-the-loop (23%) and integrating ethics into the design process (21%).

This systematic review is the first of its kind to focus on gender bias in AI algorithms from a social perspective within a socio-technical framework. Identification of key causes and consequences of bias and the breakdown of potential solutions provides direction for future research and policy within the growing field of AI ethics.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-08-2021-0452

The purpose of this paper is to present an adaptation of a program that is at the intersection of two dynamic force fields. The first relates to imperatives impinging upon and inherent in higher education. The second ties to the concept of ecosystems as spaces for aligning actors and resources to create value.

Tables I-III present pre-test and post-test means and p-values for the paired sample t-tests for the measures.

As expected, post-test means are consistently significantly higher (or lower depending on item wording) for a shift in beliefs away from self-censoring and prejudging ideas during ideation and more toward greater openness in the ideation process.

The paper examines the outcome of an educational program.

Considering the vital role of higher education institutions (HEIs) in accomplishing sustainable development goals, this study aims to examine how and when sustainable leadership (SL) influences sustainable performance by examining social innovation (SI) as a mediating mechanism and managerial discretion (MD) as a boundary condition based on upper echelon theory.

This study is cross-sectional in nature. The authors adopted a cluster-sampling approach to collect data from 500 employees of HEIs in Pakistan and China. The response rate for this study was 52.63%. As the proposed model is complex, the authors used structural equation modeling (SEM) to analyze the research hypothesis.

The empirical findings confirm the presence of SI as a competitive partial mediator between SL and sustainable performance. Nevertheless, the findings of this study do not suggest a higher positive effect of SL on SI in the presence of high MD.

The study evaluated the role of SL and SI in fostering sustainable performance from the perspective of employees in HEIs in China and Pakistan. Before the empirical evidence can be generalized, there is a need to conduct similar studies in other parts of Asia and Western countries as well.

This study presents implications for higher education leaders and policymakers at the national level to foster the sustainable performance of their institutions.

The current evidence reveals the effectiveness of SL in achieving the social goals of HEIs through SI. The recommendations presented in this study can have an impact on society, providing it with a sustainable future.

This study is the first of its kind to examine the mediating role of SI on the relationship between SL and sustainable performance. The present study also provides pioneering empirical evidence about the negative effects of MD in the context of HEIs.