The impact of entrepreneurship orientation on project performance: A machine learning approach

Sabahi, Sima; Parast, Mahour Mellat

doi:10.1016/j.ijpe.2020.107621

Cited by 61 publications

(46 citation statements)

References 120 publications

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“…This is a main feature of the study that contributes to its novelty, as applications of machine learning techniques on the topic of entrepreneurship are very rare. 1 Two recent comprehensive studies that examine the topic are by Montebruno et al (2020) and Sabahi and Parast (2020). Montebruno et al (2020) classify individuals whose entrepreneurial status were unregistered in the British censuses through the period 1851-1881 into the categories of ''entrepreneur '' and ''non-entrepreneur'' (or ''worker'') based on training on newer census data using a wide range of ML algorithms.…”

Section: The Benefits Of Machine Learning Methods In View Of the Complexity Of Entrepreneurshipmentioning

confidence: 99%

“…While this information is also present in LiTS III (for the respondent and the parents), the large number of missing observations for our restricted sample renders it unusable. 2 Sabahi and Parast (2020), on the other hand, take a different approach and establish a clear connection between project performance and individual entrepreneurial orientation, by considering entrepreneurship as an explanatory feature is a series of ML algorithms among other predictors. Clearly, studies by Montebruno et al (2020) and Sabahi and Parast (2020) are important illustrations on the suitability of ML methods to understand the underlying mechanisms of entrepreneurial activity, particularly given the high chance that the outcome is subject to ''ambiguous functional forms'' (Gu et al, 2018;Sabahi & Parast, 2020).…”

Section: The Benefits Of Machine Learning Methods In View Of the Complexity Of Entrepreneurshipmentioning

confidence: 99%

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A machine learning approach to rural entrepreneurship

Celbiş

2021

Papers in Regional Science

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This study offers a novel approach to understand the mechanisms of rural entrepreneurship by applying five alternative machine learning techniques on data obtained from the Life in Transition Survey III. Results highlight how capital constraints, age, factors related to trust and over-trust, awareness of current trends, the use of various media tools, a competitive character, institutional factors, and education are associated with the success and failure of potential entrepreneurs in rural areas who attempt to set up a business. The final predictions are achieved with accuracies ranging from seventy-two to ninety-two percent.

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Section: The Benefits Of Machine Learning Methods In View Of the Complexity Of Entrepreneurshipmentioning

confidence: 99%

Section: The Benefits Of Machine Learning Methods In View Of the Complexity Of Entrepreneurshipmentioning

confidence: 99%

A machine learning approach to rural entrepreneurship

Celbiş

2021

Papers in Regional Science

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“…The impact of entrepreneurship orientation and attitude on the project performance has been examined by Sabahi and Parast (2020). The outcomes of the study revealed that entrepreneurship orientation and entrepreneurial attitude are the most important predictors of project performance.…”

Section: Entrepreneurial Leadership and Job Performancementioning

confidence: 99%

Impact of Managerial Competency and Learning Orientation on Job Performance

Niazi

Faraz

Arshad³

et al. 2020

JAFEE

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The study empirically investigates the impact of managerial competency and learning orientation on job performance of individuals working in the software industry of Pakistan. Social skills have been introduced as a moderator alongside entrepreneurial leadership which acts as a mediator in both relationships. The data from 384 respondents, collected through survey, was analyzed using Structural Equation Modeling (SEM) through SmartPLS. The results suggested that managerial competency and learning orientation are positively related to job performance, and entrepreneurial leadership partially mediated the relationship of managerial competency and job performance, and learning orientation and job performance. Social skills moderate the relationship of managerial competency and job performance, while the moderating role of social skills was not found for the association of learning orientation and job performance. To the best of our knowledge, this is the first investigation of its kind, incorporating technical and behavioral aspects (social skills) in a single framework. The study contributes to the existing literature by analyzing the impact of aforementioned relationships in the Pakistani organizational context. The study may present vital insights for organizations to prioritize development of managerial competencies, learning orientation, entrepreneurial leadership and social skills for improved job performance.

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“…Bilal and Oyedele (2020) presented guidelines for Applied Machine Learning (AML) in the construction industry from training to operationalizing models, which are drawn from our experience of working with construction folks to deliver Construction Simulation Tool (CST). Sabahi and Parast (2020) used predictive analytics by proposing a machine learning approach to predict individuals' project performance based on measures of several aspects of entrepre-neurial orientation and entrepreneurial attitude of the individuals.…”

Section: Forecasting Techniques In Construction Industry: Earned Valumentioning

confidence: 99%

Forecasting techniques in construction industry: earned value indicators and performance models

Jaber

Al-Zwainy

Jasim

2020

srees

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Machine Learning Regression Techniques (MLRT) as a shrewd method can be utilized in this study being exceptionally fruitful in demonstrating non-linear and the interrelationships among them in problems of construction projects such as the earned value indexes for tall buildings projects in Republic of Iraq. Three forecasting models were developed to foresee Schedule Performance Index (SPI) as first model, Cost Performance Index (CPI) as a second model, and the third model is To Complete Cost Performance Indicator (TCPI) in Bismayah New City was chosen as a case study. The methodology is mainly impacted by the deciding various components (variables) which impact on the earned value analysis, six free factors (X1: BAC, Budget at Completion; X2: AC, Actual Cost; X3: A%, Actual Percentage; X4: EV, Earned Value; X5: P%, Planning Percentage, and X6: PV, Planning Value) were self-assertively assigned and agreeably depicted for per tall buildings projects. It was found that the MLRT showed good results of estimation in terms of correlation coeffi cient (R) generated by MLR models for SPI and CPI and TCPI where the R were 85.5%, 89.2%, and 86.3% respectively. At long last, a result tends to be presumed that these models show a brilliant concurrence with the genuine estimations.

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The impact of entrepreneurship orientation on project performance: A machine learning approach

Cited by 61 publications

References 120 publications

A machine learning approach to rural entrepreneurship

A machine learning approach to rural entrepreneurship

Impact of Managerial Competency and Learning Orientation on Job Performance

Forecasting techniques in construction industry: earned value indicators and performance models

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