An empirical analysis of behavioral maintenance for organizational change in Ethiopia through machine learning techniques

Abstract

Behavior maintenance for organizational change is the continuous behavior performance following an initial intentional change. This research examines the importance of factors that influence behavioral maintenance for organizational change. This study proposes a research model incorporating self-determination, regular-fit, self-concept, and habit theories to identify potential influencing factors of behavioral maintenance for organizational change in Ethiopia and quantify the importance level of these factors using ML techniques. A survey study was carried out in Addis Ababa, Ethiopia, with 310 valid responses. The comparison of five different ML techniques shows that Naive Bayes (GaussianNB) outperforms the other classification model.  Naive Bayes (GaussianNB) model-based feature importance analysis shows that perceived competency, perceived enjoyment, and perceived autonomy are the most prominent contributor to behavioral maintenance for organizational change. The results confirmed that the quality of individuals' motivation affects the extent to which individuals will engage in, and persist with, behaviors.

Keywords: {'name': 'Behavioral maintenance'}, {'name': 'organizational change'}, {'name': 'machine learning'}, {'name': 'quantitative study'}, {'name': 'Naive Bayes (GaussianNB)'}

Authors & affiliations

Abay Kidane
Affiliation not supplied

Zhao Xuefeng
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

AlAmeri, B. (2017). The implications of implementing Electronic Human Resource Management in Abu Dhabi. Liverpool John Moors University.

DOI: 10.1016/j.wasman.2015.09.034

DOI: 10.1177/0145445502238696

DOI: 10.1016/j.ejor.2015.06.032

DOI: 10.1037/0021-9010.91.4.954

DOI: 10.21315/mjms2018.25.4.12

DOI: 10.1080/00313831.2012.656281

DOI: 10.1186/1748-5908-8-117

DOI: 10.1037/t73076-000

Dai, Z., & Heckel, R. (2019). Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients. http://arxiv.org/abs/1907.09539

DOI: 10.1089/apc.2005.19.384

DOI: 10.1177/1359105309358115

DOI: 10.1177/18479790211016273

DOI: 10.20491/isarder.2021.1164

Goodman, P.S., & Dean, J.W. (1982). Change In Organizations. Chapter 6. Creating Long-Term Organizational Change.

DOI: 10.1186/s12966-019-0787-4

DOI: 10.1016/j.jbusres.2019.11.069

DOI: 10.1016/j.lrp.2013.01.001

Hasan, N. (2022). Rethinking End-user’s Adoption and Post-adoption Behavior of Mobile Health in a Least Developed Country: Technical Concepts and Mixed-methods.

DOI: 10.1007/s11747-014-0403-8

Higgins, E. T. (2008). Regulatory fit. In J. Y. Shah & W. L. Gardner (Eds.), Handbook of motivation science (pp. 356–372). The Guilford Press.

DOI: 10.1016/j.jretconser.2020.102259

DOI: 10.1108/03090590210422058

DOI: 10.1037/h0079104

Keith, L. K., & Bracken, B. A. (1996). Self-concept instrumentation: A historical and evaluative review. In B. A. Bracken (Ed.), Handbook of self-concept: Developmental, social, and clinical considerations (pp. 91–170). John Wiley & Sons.

DOI: 10.4236/psych.2019.104027

Kline, R.B. (2011) Principles and Practice of Structural Equation Modeling. Guilford Press, New York.

Kotter, J. (1996). John Kotter ’ s Eight Step Change Model. Management Study Guide.

DOI: 10.1080/17437199.2016.1151372

DOI: 10.1073/pnas.1219716110

DOI: 10.2991/assehr.k.200407.010

DOI: 10.1037/rev0000033

DOI: 10.3390/su11113159

DOI: 10.13075/ijomeh.1896.01197

DOI: 10.1037/0022-3514.35.2.63

DOI: 10.3389/fpsyg.2018.01157

DOI: 10.1080/01431161.2018.1433343

DOI: 10.3390/admsci12020060

DOI: 10.1177/1363459308094417

DOI: 10.3389/fpsyg.2022.962150

DOI: 10.1002/widm.1301

DOI: 10.1108/LODJ-07-2014-0129

DOI: 10.1001/jama.292.14.1711

DOI: 10.1016/j.scitotenv.2021.147481

DOI: 10.1037/0003-066X.55.1.68

DOI: 10.1006/pmed.1999.0545

DOI: 10.1080/10447318.2020.1841423

DOI: 10.1007/s00477-015-1021-9

DOI: 10.3390/s18010018

DOI: 10.3389/fpsyg.2018.01946

DOI: 10.1016/S0140-6736(00)98191-1

DOI: 10.2307/20650284

DOI: 10.1109/ACCESS.2020.2992058

DOI: 10.1007/s10869-016-9453-x