Designing Operations to Inspire Trust
Designing Operations to Inspire Trust
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151359
- ISBN
- 9798382783468
- DDC
- 640
- 서명/저자
- Designing Operations to Inspire Trust
- 발행사항
- [Sl] : Harvard University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 177 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
- 주기사항
- Advisor: Ferreira, Kris.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2024.
- 초록/해제
- 요약In this dissertation I study trustworthy operations across three chapters. These span two streams. In the first - corresponding to Chapters 1 and 2 - I seek to understand how to inspire consumer trust in companies through building socially responsible operations. In Chapter 1 we examine when organizations should make statements on sociopolitical issues to best appeal to consumers. We find that consumers express more positive sentiment and greater purchasing intentions toward firms that react more quickly to sociopolitical issues. In Chapter 2 we examine how consumers perceive transparency into an operation's workforce diversity and we find that consumers perceive firms that disclose their workforce diversity data to be more committed to DEI initiatives, view disclosing firms more positively, and are more likely to choose their offerings over those of non-disclosing firms.In my second stream of research - corresponding to Chapter 3 - I study the calibration of employee trust in algorithms for more effective human-algorithm collaboration. In Chapter 3 we hypothesize that people are biased towards following a naive advice weighting (NAW) heuristic when overriding algorithms: they take a weighted average between their own prediction and the algorithm's, with a constant weight across prediction instances, regardless of whether they have valuable private information. This leads to humans over-adhering to the algorithm's predictions when their private information is valuable and under-adhering when it is not. We further design interventions to get users to move away from NAW, leading to improved human-algorithm collaboration in predictions.
- 일반주제명
- Home economics
- 기타저자
- Harvard University Business Administration
- 기본자료저록
- Dissertations Abstracts International. 85-12A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■1001 ▼aBalakrishnan, Maya.▼0(orcid)0000-0002-7823-1808
■24510▼aDesigning Operations to Inspire Trust
■260 ▼a[Sl]▼bHarvard University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a177 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: A.
■500 ▼aAdvisor: Ferreira, Kris.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2024.
■520 ▼aIn this dissertation I study trustworthy operations across three chapters. These span two streams. In the first - corresponding to Chapters 1 and 2 - I seek to understand how to inspire consumer trust in companies through building socially responsible operations. In Chapter 1 we examine when organizations should make statements on sociopolitical issues to best appeal to consumers. We find that consumers express more positive sentiment and greater purchasing intentions toward firms that react more quickly to sociopolitical issues. In Chapter 2 we examine how consumers perceive transparency into an operation's workforce diversity and we find that consumers perceive firms that disclose their workforce diversity data to be more committed to DEI initiatives, view disclosing firms more positively, and are more likely to choose their offerings over those of non-disclosing firms.In my second stream of research - corresponding to Chapter 3 - I study the calibration of employee trust in algorithms for more effective human-algorithm collaboration. In Chapter 3 we hypothesize that people are biased towards following a naive advice weighting (NAW) heuristic when overriding algorithms: they take a weighted average between their own prediction and the algorithm's, with a constant weight across prediction instances, regardless of whether they have valuable private information. This leads to humans over-adhering to the algorithm's predictions when their private information is valuable and under-adhering when it is not. We further design interventions to get users to move away from NAW, leading to improved human-algorithm collaboration in predictions.
■590 ▼aSchool code: 0084.
■650 4▼aHome economics
■653 ▼aBehavioral operations management
■653 ▼aOperational transparency
■653 ▼aCorporate social responsibility
■653 ▼aHuman-algorithm interaction
■653 ▼aWorkforce diversity
■690 ▼a0796
■690 ▼a0310
■690 ▼a0454
■690 ▼a0703
■690 ▼a0386
■71020▼aHarvard University▼bBusiness Administration.
■7730 ▼tDissertations Abstracts International▼g85-12A.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161456▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
