Methodology: Experimental/Quasi Experimental最新文献

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Black Box Analytics and Ethical Decision Making 黑箱分析和道德决策

Methodology: Experimental/Quasi Experimental Pub Date : 2019-04-30 DOI: 10.2139/ssrn.3318717

Michael J. Davern, Pujawati Mariestha (Estha) Gondowijoyo, P. Murphy

{"title":"Black Box Analytics and Ethical Decision Making","authors":"Michael J. Davern, Pujawati Mariestha (Estha) Gondowijoyo, P. Murphy","doi":"10.2139/ssrn.3318717","DOIUrl":"https://doi.org/10.2139/ssrn.3318717","url":null,"abstract":"Using an experiment with participants having management experience, we examine sales target setting decisions using an analytics-based forecasting system in a situation involving an ethical dilemma. Specifically, participants have private information that the forecast significantly underestimates likely sales, making the suggested target easily achievable. We explore the extent to which participants act unethically by not adjusting the sales target upwards. We employ a 2x2 between-subjects design, manipulating forecasting system transparency (opaque vs transparent) and accountability both as a measured continuous variable and with the use of a prompt either before (pre-prompt) or after (post-prompt) the adjustment decision. We find that participants make less ethical decisions when the system is opaque and more ethical decisions when they feel greater accountability. The effect of accountability is greatest when the system is opaque. We also examine reasons provided for less ethical decisions and find that the least ethical participants use more rationalizations than those whose decisions are not as unethical. Our results suggest that organizations should endeavor to make data analytics systems transparent to decision making users. However, when they cannot, they should ensure that decision makers feel accountable for their decisions; for example, with a prompt or decision aid.","PeriodicalId":231436,"journal":{"name":"Methodology: Experimental/Quasi Experimental","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130553216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Mitigating the Negative Effects of Causal Models: Encouraging a Hypothesis Testing Mindset and Managers’ Quantitative Knowledge 减轻因果模型的负面影响:鼓励假设检验思维与管理者的量化知识

Methodology: Experimental/Quasi Experimental Pub Date : 2018-12-05 DOI: 10.2139/ssrn.3314669

Kun Huo, K. Kelly, Alan Webb

{"title":"Mitigating the Negative Effects of Causal Models: Encouraging a Hypothesis Testing Mindset and Managers’ Quantitative Knowledge","authors":"Kun Huo, K. Kelly, Alan Webb","doi":"10.2139/ssrn.3314669","DOIUrl":"https://doi.org/10.2139/ssrn.3314669","url":null,"abstract":"The causal model of a firm may change as its competitive environment changes. We use an experiment to examine how providing an initially accurate causal model that subsequently becomes inaccurate affects managerial learning after the accuracy of the model changed. We predict and find a negative effect of providing a causal model on learning. However, we predict and find a positive interaction effect on learning from encouraging a hypothesis-testing mindset (versus not doing so) and providing a causal model, such that encouraging a hypothesis-testing mindset mitigates the negative effect of providing a causal model. Similarly, we predict and find a positive interaction effect on learning from having more quantitative knowledge and providing a causal model, with more quantitative knowledge mitigating some of the negative effect of providing a causal model. Lastly, we find that encouraging a hypothesis testing mindset and having more quantitative knowledge are substitutes in terms of mitigating the negative effect of a causal model, in that there is significant positive interaction effect of quantitative knowledge and causal model only for participants who have not been encouraged to adopt a hypothesis-testing mindset whereas there is no significant interaction effect for participants who have been encouraged with a hypothesis-testing mindset. Our results help companies understand the potential negative implications of providing a causal model which potentially changes over time, and the possible mechanisms to mitigate those negative effects.","PeriodicalId":231436,"journal":{"name":"Methodology: Experimental/Quasi Experimental","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130779588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0