A clustering algorithm for ipsative variables

Moreno, Jessica Rubiano; Malaver, Carlos Alonso; Nucamendi-Guillén, Samuel; López-Hernández, Carlos

doi:10.15446/dyna.v86n211.77835

Cited by 1 publication

(1 citation statement)

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“…In the second step, after the clusters had been identified, the score obtained in the centroid for each characteristic was used to characterize the profiles considering their sociodemographic variables and, if possible, describe the preferences of each group (Rubiano-Moreno et al, 2019). This way, it was possible to define the profile of each cluster.…”

Section: Clustering Proceduresmentioning

confidence: 99%

Work Motivation Profiles of the Millennial Generation

Rubiano-Moreno,

Alonso-Malaver,

Nucamendi-Guillén

et al. 2023

Rev. CEA

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Purpose: This study aimed to determine characteristic profiles of the Millennial generation based on their sociodemographic features and motivational preferences regarding work. It contributes to the literature on Millennial motivation and provides insights for organizations seeking to better understand and manage said generation. Design/Methodology: The study was conducted on a sample of 197 questionnaire responses from individuals in the Millennial generation who had work experience. The sampling was non-probabilistic and did not consider aspects related to socioeconomic or education levels to broaden the coverage of the study. The data were collected through an online survey in Guadalajara, Jalisco, Mexico. Said data were examined using an analytical procedure—which involves a clustering algorithm to determine the optimal number of clusters—and logistic regression analysis—to identify significant variables that can explain the behavior of each group. Findings: Two distinct motivational profiles were found among Millennials: (1) a group motivated by achievement and power and (2) another one inspired by affiliation and supervision group. It was also found that these two profiles are related to certain sociodemographic features, such as age and main breadwinner. Conclusions: Understanding the motivational profiles of Millennials can help organizations better tailor their management practices and work environments to meet the needs of this generation. Likewise, organizations may need to provide several kinds of incentives and rewards to motivate different groups of Millennials. Future research in this area could explore the relationship between these motivational profiles and other outcomes, such as job satisfaction and turnover. Originality: This study contributes to the literature on Millennial motivation by introducing a quantitative methodology to identify different motivational profiles and explore their relationship with sociodemographic features. The use of a clustering algorithm and regression analysis also contributes to the methodological approaches employed in this area of research. Focused on the Mexican context, this paper also provides insights into the unique cultural and economic factors that may influence Millennial motivation in this region.

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Section: Clustering Proceduresmentioning

confidence: 99%