A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers

Ballestar, María Teresa; Doncel, Luis Miguel; Sáinz, Jorge; Ortigosa-Blanch, A.

doi:10.1016/j.techfore.2019.119756

Cited by 22 publications

(16 citation statements)

References 44 publications

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“…There is plenty of new opportunities for practitioners and academics. Mixed methods of cross section and longitudinal research, real time applications or the constructions of new and more robust algorithms, tested through triangulation are only a few of the new opportunities [1], [2], [19].…”

Section: Discussionmentioning

confidence: 99%

“…The second comprises the development of methods that are able to segment customers in a dynamic longitudinal view over time (using a data sample collected over time). By adding the time variable to segmentation, these methods not only show the classification of the customers into groups at a specific moment in time, but are able also to depict their evolution over time drawing the customer journey between one segment to the next one in their path from acquisition to being an advocate of the brand [1]. Hence, the more advanced the segmentation methods the more benefits are provided for business decision making.…”

Section: Big Data Machine Learning and Customer Segmentationmentioning

confidence: 99%

“…This data correctly processed and analysed using data science methods such as machine learning, allows for the development of different data strategies that include novel dynamic multivariate segmentations. The development of the traditional segmentation methods provides companies with a longitudinal perspective in the evolution of the segments of customers, as it adds the variable of time to the models [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Editorial: Segmenting the Future of E-Commerce, One Step at a Time

Ballestar

2021

J. theor. appl. electron. commer. res.

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Section: Discussionmentioning

confidence: 99%

Section: Big Data Machine Learning and Customer Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Editorial: Segmenting the Future of E-Commerce, One Step at a Time

Ballestar

2021

J. theor. appl. electron. commer. res.

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“…They studied the impact of information technology (IT) productivity on bank efficiencies and verified that information technology has a significant impact on bank performances, with the fact that almost 80% IT efficient banks are overall efficient. Ballestar et al (2019) developed a novel machine learning approach for evaluating the productivity of university researchers. Among studies applying Bayesian models, a preeminent one is the study by Tsionas and Mallick (2019), who computed latent dynamic stochastic productivity, performed a Bayesian analysis, and developed a model to measure productivity with flexible frontiers.…”

Section: Research Areas and Methodologiesmentioning

confidence: 99%

Data science and productivity: A bibliometric review of data science applications and approaches in productivity evaluations

Shi

Zhu

Charles

2021

Journal of the Operational Research Society

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This paper provides a comprehensive review of the applications of data science techniques and methodologies in productivity. The paper is structured as a combination of a bibliometric analysis and an empirical review. In the bibliometric analysis, the sources, authorship, and documents are reviewed and discussed. Visualisation aids, including summative tables and figures, are incorporated. In the empirical review, the corpus of 533 articles identified are reviewed based on the application areas of data science approaches and the primary methodology of the papers, and the selected most impactful and relevant papers in each methodological category are discussed in detail. The objective of this paper is to provide an overview of the current predominant trends and patterns in data science and productivity, explore how the interplay has been manifested, and provide an outlook on future research orientations.

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“…For example, Suominen et al [30] used Latent Dirichlet Allocation (LDA) to show the evolution of the knowledge profiles of firms in the telecommunication industry. Ballestar et al [31] applied Automated Nested Longitudinal Clustering (ANLC) and neural networks to evaluate the effect of a new employment policy on the productivity of university researchers in Madrid. Recently, Kim and Sohn [32] used semantic analysis to identify convergence patterns of new technology from patent data.…”

Section: Introductionmentioning

confidence: 99%

Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach

et al. 2021

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This paper seeks to study work-related and geographical conditions under which innovativeness is stimulated through the analysis of individual and regional data dating from just prior to the smartphone age. As a result, by using the ISSP 2005 Work Orientations Survey, we are able to examine the role of work flexibility, among other work-related conditions, in a relatively more traditional context that mostly excludes modern, smartphone-driven, remote-working practices. Our study confirms that individual freedom in the work place, flexible work hours, job security, living in suburban areas, low stress, private business activity, and the ability to take free time off work are important drivers of innovation. In particular, through a spatial econometric model, we identified an optimum level for weekly work time of about 36 h, which is supported by our findings from tree-based ensemble models. The originality of the present study is particularly due to its examination of innovative output rather than general productivity through the integration of person-level data on individual work conditions, in addition to its novel methodological approach which combines machine learning and spatial econometric findings.

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A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers

Cited by 22 publications

References 44 publications

Editorial: Segmenting the Future of E-Commerce, One Step at a Time

Editorial: Segmenting the Future of E-Commerce, One Step at a Time

Data science and productivity: A bibliometric review of data science applications and approaches in productivity evaluations

Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach

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