東華大學圖書館 |

Sources of Lay (Mis)Judgments on Key People Analytics Questions : = Biases in Expectations of Motivation and Performance.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sources of Lay (Mis)Judgments on Key People Analytics Questions :/
其他題名:	Biases in Expectations of Motivation and Performance.
作者:	Thornley, Nico.
面頁冊數:	1 online resource (220 pages)
附註:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
標題:	Social psychology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13905035click for full text (PQDT)
ISBN:	9781088333877

Sources of Lay (Mis)Judgments on Key People Analytics Questions : = Biases in Expectations of Motivation and Performance.
Thornley, Nico.

Sources of Lay (Mis)Judgments on Key People Analytics Questions :Biases in Expectations of Motivation and Performance. - 1 online resource (220 pages)

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--INSEAD (France and Singapore), 2019.

Includes bibliographical references

While much research has focused on understanding motivation and performance, less is known about the causes and consequences of peoples' expectations about motivation and performance. Yet, expectations drive decision making. What we believe should be done depends largely on what we expect will result of any given course of action. Therefore, understanding systematic biases in expectations of motivation and performance is critical for understanding behavior in organizations. This dissertation explores three biases that undermine the accuracy of our expectations, including an extrinsic incentives bias that distorts expectations of our own motivations in self-serving ways (Chapter I), a gender bias in compensation motivation expectations that could contribute to the gender pay gap (Chapter II), and a cognitive bias that leads us to overweight mean information and underweight variance information in performance analyses (Chapter III). Chapter I develops our understanding of the extrinsic incentives bias, where people believe others are more extrinsically motivated and less intrinsically motivated than themselves. Heath (1999) discovered this important bias, but a theoretical explanation has never been tested. Three studies examine if and how a self-serving bias could cause the extrinsic incentives bias, expanding the scope of the bias beyond workplace motivations to a more general implicit theory of motivation. First, Heath's methods are replicated with additional analyses that provide new evidence supporting a self-serving explanation. Second, we find evidence for the extrinsic incentives bias outside the boundary conditions of previous theorizing and full mediation of the extrinsic incentives bias by a self-serving bias. Third, we experimentally manipulate the admirability of extrinsic motivation and show that more positive views of extrinsic motivation attenuates the extrinsic incentives bias. Together, these studies help to build a foundation of understanding for further research on implicit theories of motivation. This area is especially important to study because it can help us to understand how and when organization may use suboptimal strategies to motivate employees. Chapter II explores individuals' stereotypes about what motivates men versus women and compares them to reality. Specifically, we examine whether people believe men are more motivated by compensation (i.e., money) than women, and whether men and women actually report and/or display different levels of compensation-motivation. In Study 1a, using a well-established measure of motivation (The Work Preferences Inventory; Amabile et al., 1994), we find that people stereotype men (versus women) as more motivated by extrinsic motivators, particularly compensation-related motivators. In Study 1b, we randomly assigned participants to either self-report their own motivation, or indicate how a stereotypical man or woman would respond. We found that perceived gender differences were significantly greater than actual differences in self-reports. In fact, while participants perceived that the compensation-related motivators were more descriptive of men than women, female participants rated these motivators as more descriptive of themselves than male participants did. In Study 2, we asked participants to predict the average self-reported "importance of pay" for 932 men and 798 women who participated in the 2013 Higher Education and Gender Survey by Pew Research Center. We found that participants overestimated the importance of pay to men, but accurately estimated the importance of pay to women. In Study 3, we extend these effects on predicted self-reports of motivation to predicted performance on an incentivized, effort-based task. We asked participants to predict how much effort men or women would exert when paid various different incentives. We found that participants expected men to increase their effort significantly more than women as incentive levels increase. This work has implications for understanding the gender pay gap, gender differences in salary negotiation outcomes, and more. Chapter III suggests that decision makers underweight information about variability when making judgments about performance, using evidence from four empirical studies, including both laboratory experiments and an archival investigation. When presented with group differences in elite achievement, participants' spontaneous explanations overwhelmingly invoke mean differences, rather than group differences in variability (Study 1). In a hiring simulation involving iterative choices between numerous job candidates, participants were more accurate at identifying top performers than reliable performers (Study 2). A longitudinal examination suggests that NBA teams overweight average performance and underweight consistency of performance when deciding players' contracts-standard deviation in player performance is a better predictor of team wins than of player salaries, whereas the reverse is true for mean player performance (Study 3). Finally, in another hiring simulation, dispersion neglect was reduced for decision makers when performance data was presented as a histogram, presumably because this visual representation of data makes relying on the distribution more intuitive (Study 4). Organizational implications of dispersion neglect are discussed.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9781088333877Subjects--Topical Terms:

520219
Social psychology.
Subjects--Index Terms:

Dispersion neglectIndex Terms--Genre/Form:

542853
Electronic books.

Sources of Lay (Mis)Judgments on Key People Analytics Questions : = Biases in Expectations of Motivation and Performance.
LDR:06859nmm a2200409K 4500 001 2364519
005 20231130105824.5
006 m o d
007 cr mn ---uuuuu
008 241011s2019 xx obm 000 0 eng d
020 $a 9781088333877
035 $a (MiAaPQ)AAI13905035
035 $a AAI13905035
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Thornley, Nico. $3 3705328
245 1 0 $a Sources of Lay (Mis)Judgments on Key People Analytics Questions : $b Biases in Expectations of Motivation and Performance.
264 0 $c 2019
300 $a 1 online resource (220 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
500 $a Advisor: Thau, Stefan;Yap, Andy J.
502 $a Thesis (Ph.D.)--INSEAD (France and Singapore), 2019.
504 $a Includes bibliographical references
520 $a While much research has focused on understanding motivation and performance, less is known about the causes and consequences of peoples' expectations about motivation and performance. Yet, expectations drive decision making. What we believe should be done depends largely on what we expect will result of any given course of action. Therefore, understanding systematic biases in expectations of motivation and performance is critical for understanding behavior in organizations. This dissertation explores three biases that undermine the accuracy of our expectations, including an extrinsic incentives bias that distorts expectations of our own motivations in self-serving ways (Chapter I), a gender bias in compensation motivation expectations that could contribute to the gender pay gap (Chapter II), and a cognitive bias that leads us to overweight mean information and underweight variance information in performance analyses (Chapter III). Chapter I develops our understanding of the extrinsic incentives bias, where people believe others are more extrinsically motivated and less intrinsically motivated than themselves. Heath (1999) discovered this important bias, but a theoretical explanation has never been tested. Three studies examine if and how a self-serving bias could cause the extrinsic incentives bias, expanding the scope of the bias beyond workplace motivations to a more general implicit theory of motivation. First, Heath's methods are replicated with additional analyses that provide new evidence supporting a self-serving explanation. Second, we find evidence for the extrinsic incentives bias outside the boundary conditions of previous theorizing and full mediation of the extrinsic incentives bias by a self-serving bias. Third, we experimentally manipulate the admirability of extrinsic motivation and show that more positive views of extrinsic motivation attenuates the extrinsic incentives bias. Together, these studies help to build a foundation of understanding for further research on implicit theories of motivation. This area is especially important to study because it can help us to understand how and when organization may use suboptimal strategies to motivate employees. Chapter II explores individuals' stereotypes about what motivates men versus women and compares them to reality. Specifically, we examine whether people believe men are more motivated by compensation (i.e., money) than women, and whether men and women actually report and/or display different levels of compensation-motivation. In Study 1a, using a well-established measure of motivation (The Work Preferences Inventory; Amabile et al., 1994), we find that people stereotype men (versus women) as more motivated by extrinsic motivators, particularly compensation-related motivators. In Study 1b, we randomly assigned participants to either self-report their own motivation, or indicate how a stereotypical man or woman would respond. We found that perceived gender differences were significantly greater than actual differences in self-reports. In fact, while participants perceived that the compensation-related motivators were more descriptive of men than women, female participants rated these motivators as more descriptive of themselves than male participants did. In Study 2, we asked participants to predict the average self-reported "importance of pay" for 932 men and 798 women who participated in the 2013 Higher Education and Gender Survey by Pew Research Center. We found that participants overestimated the importance of pay to men, but accurately estimated the importance of pay to women. In Study 3, we extend these effects on predicted self-reports of motivation to predicted performance on an incentivized, effort-based task. We asked participants to predict how much effort men or women would exert when paid various different incentives. We found that participants expected men to increase their effort significantly more than women as incentive levels increase. This work has implications for understanding the gender pay gap, gender differences in salary negotiation outcomes, and more. Chapter III suggests that decision makers underweight information about variability when making judgments about performance, using evidence from four empirical studies, including both laboratory experiments and an archival investigation. When presented with group differences in elite achievement, participants' spontaneous explanations overwhelmingly invoke mean differences, rather than group differences in variability (Study 1). In a hiring simulation involving iterative choices between numerous job candidates, participants were more accurate at identifying top performers than reliable performers (Study 2). A longitudinal examination suggests that NBA teams overweight average performance and underweight consistency of performance when deciding players' contracts-standard deviation in player performance is a better predictor of team wins than of player salaries, whereas the reverse is true for mean player performance (Study 3). Finally, in another hiring simulation, dispersion neglect was reduced for decision makers when performance data was presented as a histogram, presumably because this visual representation of data makes relying on the distribution more intuitive (Study 4). Organizational implications of dispersion neglect are discussed.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Social psychology. $3 520219
650 4 $a Dissertations & theses. $3 3560115
650 4 $a Gender differences. $3 3548331
650 4 $a Ratings & rankings. $3 3562221
650 4 $a Decision making. $3 517204
650 4 $a Bias. $2 gtt $3 1374837
653 $a Dispersion neglect
653 $a Expectations
653 $a Extrinsic incentives bias
653 $a Gendered motivation
653 $a Implicit theories
653 $a Person perception
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0703
690 $a 0451
690 $a 0454
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a INSEAD (France and Singapore). $b Organizational Behaviour. $3 3700870
773 0 $t Dissertations Abstracts International $g 81-04B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13905035 $z click for full text (PQDT)