A machine learning approach to identifying decision-making styles for managing customer relationships

被引:0
作者
Ana Alina Tudoran
机构
[1] Aarhus University,School of Business and Social Sciences
[2] Fuglesangs Alle,undefined
来源
Electronic Markets | 2022年 / 32卷
关键词
Satisficers; Maximizers; Decision-making; Clickstreams; Machine learning; E-commerce; M2; M3; C38;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:351 / 374
页数:23
相关论文
共 151 条
[1]  
Anderl E(2016)Helping firms reduce complexity in multichannel online data: A new taxonomy-based approach for customer journeys Journal of Retailing 92 185-203
[2]  
Schumann JH(2003)The psychology of doing nothing: Forms of decision avoidance result from reason and emotion Psychological Bulletin 129 139-248
[3]  
Kunz W(2000)Controlling the information flow: Effects on consumers' decision making and preferences Journal of Consumer Research 27 233-34
[4]  
Anderson CJ(2018)Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making Expert Systems with Applications 111 11-662
[5]  
Ariely D(2017)If it has lots of bells and whistles, it must be the best: How maximizers and satisficers evaluate feature-rich versus feature-poor products Marketing Letters 28 651-22
[6]  
Arunachalam D(2008)clValid: An R Package Journal of Statistical Software 25 1-625
[7]  
Kumar N(2014)Decision difficulty in the age of consumer empowerment Journal of Consumer Psychology 24 608-48
[8]  
Brannon DC(2009)Click here for Internet insight: Advances in clickstream data analysis in marketing Journal of Interactive Marketing 23 35-296
[9]  
Soltwisch BW(2011)When the decision ball keeps rolling: An investigation of the Sisyphus effect among maximizing consumers Marketing Letters 22 283-216
[10]  
Brock G(2018)Unpacking decision difficulty: Testing action dynamics in Intertemporal, gamble, and consumer choices Acta Psychologica 190 199-167