The need to stimulate entrepreneurial skills in graduates as a strategy for tackling graduate unemployment has spurred the introduction of entrepreneurship education programs. The effectiveness of such entrepreneurship education programs from an African context is the focus of this paper. A modified model for evaluating the effectiveness of entrepreneurship education was derived from Fayolle, Gaily, and Lassa-Clerc; and was tested via structural equation modeling. Data were collected from randomly selected 750 participants who had undergone at least one compulsory entrepreneurship module at the university level. It was found that entrepreneurship education which is not well aligned with contextual peculiarities may not optimally yield the desired outcome. This paper, therefore, underscores the need for a thoroughly contextualized curriculum that encapsulates national, local, and very importantly, institutional factors. RÉSUMÉLa n ecessit e de stimuler les comp etences entrepreneuriales parmi les diplôm es, en tant que strat egie pour aborder le chômage des diplôm es, a stimul e l'introduction de programmes de formation a l'entrepreneuriat. Cet article se concentre sur l'efficacit e de tels programmes dans un contexte africain. Un mod ele modifi e d' evaluation de l'efficacit e de l' education a l'entrepreneuriat a et e d eriv e des travaux de Fayolle, Gaily et Lassa-Clerc; et a et e test e par mod elisation d' equation structurelle. Les donn ees ont et e collect ees aupr es de 750 participants s electionn es au hasard qui avaient suivi au moins un module obligatoire sur l'entrepreneuriat au niveau universitaire. L' etude montre que la formation a l'entrepreneuriat qui n'est pas bien adapt ee aux particularit es contextuelles peut ne pas compl etement donner le r esultat escompt e. Par cons equent, cet article souligne la n ecessit e d'un programme d' etudes contextualis e qui englobe les facteurs nationaux, locaux et, non moins importants, institutionnels.

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Purpose The purpose of this paper is to test and explain the context where motivation to learn (MTL) reduces innovative behavior in the organizational context. Design/methodology/approach The authors used questionnaire survey to collect data in a field study. In order to test the moderating effect of transfer climate, MTL on the relationship between MTL and innovative behavior, a sample of 606 employees was analyzed to examine the theoretical expectation by using multiple regression and bootstrapping. Findings The authors found employees motivated to learn showed less innovative behavior when perceived transfer climate is less favorable. The authors further revealed that motivation to transfer mediates the moderating effect of transfer climate for the relationship between MTL and innovative behavior. Research limitations/implications One suggestion for further research is to investigate the relationship among the four constructs by using multi-source, multi-wave and multi-level method. Practical implications This study provides several useful guidance of how organization and manager avoid the negative effects of MTL through encouraging employees to learn new knowledge and skills, and providing employee opportunities to use their acquired knowledge and skills. Originality/value The authors contribute to the motivational literature by taking a step further to understand the effect of MTL. The authors propose and confirm that employee MTL can lead to negative outcomes when individuals perceived transfer climate is low. The results offer new insight beyond previous findings on positive or non-significant relationship between MTL and innovative behavior. The results further show that this interactive effect is induced by motivation to transfer. Particularly, low transfer climate reduces individuals’ motivation to transfer, and individuals with high MTL have low innovative behavior when they are less motivated to transfer.

At present, the explosive growth of data and the mass storage state have brought many problems such as computational complexity and insufficient computational power to clustering research. The distributed computing platform through load balancing dynamically configures a large number of virtual computing resources, effectively breaking through the bottleneck of time and energy consumption, and embodies its unique advantages in massive data mining. This paper studies the parallel k-means extensively. This article first initializes random sampling and second parallelizes the distance calculation process that provides independence between the data objects to perform cluster analysis in parallel. After the parallel processing of the MapReduce, we use many nodes to calculate distance, which speeds up the efficiency of the algorithm. Finally, the clustering of data objects is parallelized. Results show that our method can provide services efficiently and stably and have good convergence.