Search results

With the development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more autonomous and smart. Therefore, there is a growing demand for developing a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method.

This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents.

By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment.

This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.

The multiple factors of intelligence measurement are critical in intelligent science. The intelligence measurement is typically built as a model based on multiple factors. The different agent is generally difficult to measure because of the uncertainty between multiple factors. The purpose of this paper is to solve the problem of uncertainty between multiple factors and propose an effective method for universal intelligence measurement for the different agents.

In this paper, the authors propose a universal intelligence measurement method based on meta-analysis for crowd network. First, the authors get study data through keywords in the database and delete the low-quality data. Second, they compute the effect value by odds ratio, relative risk and risk difference. Then, they test the homogeneity by Q-test and analyze the bias by funnel plots. Third, they select the fixed effect and random effect as a statistical model. Finally, through the meta-analysis of time, complexity and reward, the weight of each factor in the intelligence measurement is obtained and then the meta measurement model is constructed.

This paper studies the relationship among time, complexity and reward through meta-analysis and effectively combines the measurement of heterogeneous agents such as human, machine, enterprise, government and institution.

This paper provides a universal intelligence measurement model for crowd network. And it can provide a theoretical basis for the research of crowd science.

The originality of the crowd cyber system lies in the fact that it possesses the intelligence of multiple groups including intelligence of people, intelligence of objects and intelligence of machines. However, quantitative analysis of the level of intelligence is not sufficient, due to many limitations, such as the unclear definition of intelligence and the inconformity of human intelligence quotient (IQ) test and artificial intelligence assessment methods. This paper aims to propose a new crowd intelligence measurement framework from the harmony of adaption and practice to measure intelligence in crowd network.

The authors draw on the ideas of traditional Confucianism, which sees intelligence from the dimensions of IQ and effectiveness. First, they clarify the related concepts of intelligence and give a new definition of crowd intelligence in the form of a set. Second, they propose four stages of the evolution of intelligence from low to high, and sort out the dilemma of intelligence measurement at the present stage. Third, they propose a framework for measuring crowd intelligence based on two dimensions.

The generalized IQ operator model is optimized, and a new IQ algorithm is proposed. Individuals with different IQs can have different relationships, such as cooperative, competitive, antagonistic and so on. The authors point out four representative forms of intelligence as well as its evolution stages.

The authors, will use more rigorous mathematical symbols to represent the logical relationships between different individuals, and consider applying the measurement framework to a real-life situation to enrich the research on crowd intelligence in the further study.

Intelligence measurement is one of foundations of crowd science. This research lays the foundation for studying the interaction among human, machine and things from the perspective of crowd intelligence, which owns significant scientific value.

The purpose of this paper is to examine the influence of the emotional intelligence (EI) of the person in charge of making human resource management (HRM) decisions on the adoption of high-performance human resource (HR) practices in small- and medium-sized enterprises (SMEs).

This study takes evidences from 157 HR decision makers in SMEs who autonomously make the decisions in the HR area and were responsible for the HR practices in their firm. The authors used multiple linear regression analysis to test the hypotheses.

Results show that both the EI and the different EI competencies of which it is comprised affect the adoption of various HR practices. Thus, the main theoretical contribution of this work stems from the incorporation of a psychological variable (EI) as an antecedent of HRM. Managers of the SME will find guidance about which emotional competencies are the most important for them to be more successful in their roles and for improving HRM.

First, the sample of firms the authors studied is limited to a specific geographic area in one country – Spain (Canary Islands) – that will necessarily limit generalisation of the results obtained to other populations of SMEs. Researchers should replicate the current model in other geographic areas. Second, and with regard the methodology, researchers could explore other tools to measure EI and emotional competencies. It would be interesting to measure this construct using qualitative analytical techniques, with 360 – or 180 – degree tools. Finally, the current study is cross-sectional in nature, which limits our ability to draw causal inferences from the data. This cross-sectional design prevents us, for example, from analysing EI’s influence on the continued development of high-performance HR practices over time. Future research using longitudinal methodologies to study these variables could provide additional advances in this area. This work makes important contributions to both the literature and the business world. With regard to the theoretical implications, results confirm that EI as a whole, as well as in terms of its specific emotional competencies, affects the decision making related to the adoption of high-performance HR practices, which is known to contribute to the organisational performance.

With regard its practical implications, SMEs’ owners-managers and HR practitioners may find our results and conclusions interesting. Indeed, recommendations in business management have often been accompanied by new approaches in HRM (Kent, 2005), as this study proposes. In particular, managers will find evidence of how a decision-maker’s higher EI propitiates the adoption of high-performance HR practices, thus being able to improve HRM in their SMEs. Moreover, managers will obtain guidance on which emotional competencies are the most important for adopting each HR practice, and so find greater success in their HRM roles. SMEs could organise programmes to develop the HR decision-maker’s emotional competencies, as large firms do for their executives.

Thus, the main theoretical contribution of this work stems from the incorporation of a psychological variable (EI) as an antecedent of HRM. Managers of the SME will find guidance about which emotional competencies are the most important for them to be more successful in their roles and for improving HRM.