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The corporate digital responsibility (CDR) calculus: How and why organizations reconcile digital and ethical trade-offs for growth 企业数字责任 (CDR) 计算:企业如何以及为何协调数字与道德之间的权衡,以实现增长
IF 2 4区 管理学 Q2 BUSINESS Pub Date : 2024-04-01 DOI: 10.1016/j.orgdyn.2024.101056
Nicole Hartley , Werner Kunz , James Tarbit

In this digital age, organizations are increasingly reliant on technology to drive business growth and innovation. With this reliance, comes an impetus for firms to responsibly and ethically manage their digital assets and data. In response, Corporate Digital Responsibility (CDR) has been espoused as a means by which firms can address the competing demands of digital innovation and transformation and ethical responsibility. Ultimately, good CDR practices have the potential to safeguard not only firms, but the various stakeholders they interact with across their digital ecosystems. However, it is recognized that the resource investment required to do so, can come at a significant cost. The CDR Calculus has been developed to assist organizations in evaluating the risks and benefits associated with digital and data practices and to determine whether they align with the organization’s CDR objectives. This paper provides a roadmap of how firms can take steps to establish a culture of CDR practice in light of these tensions. We also recognize that external regulation is required, if not needed, to enforce and cement positive CDR practices as firms respond to growing digital demands.

在这个数字时代,企业越来越依赖技术来推动业务增长和创新。伴随着这种依赖,企业需要以负责任和合乎道德的方式管理其数字资产和数据。为此,企业数字责任(CDR)被推崇为企业应对数字创新和转型与道德责任之间竞争需求的一种手段。归根结底,良好的 CDR 实践不仅有可能保护企业,还能保护企业在数字生态系统中与之互动的各利益相关方。不过,我们也认识到,为此所需的资源投入可能会付出巨大代价。CDR 计算法的开发旨在帮助企业评估与数字和数据实践相关的风险和收益,并确定它们是否符合企业的 CDR 目标。本文提供了一个路线图,说明企业如何采取措施,根据这些矛盾建立 CDR 实践文化。我们还认识到,在企业应对日益增长的数字化需求时,外部监管即使不是必需的,也是必要的,以执行和巩固积极的 CDR 实践。
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引用次数: 0
Implementing corporate digital responsibility (CDR): Tackling wicked problems for the digital era: Pilot study insights 履行企业数字化责任(CDR):解决数字时代的棘手问题:试点研究的启示
IF 2 4区 管理学 Q2 BUSINESS Pub Date : 2024-04-01 DOI: 10.1016/j.orgdyn.2024.101040
Karen Elliott, Jehana Copilah-Ali
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引用次数: 0
“With great power comes great responsibility”: Exploring the role of Corporate Digital Responsibility (CDR) for Artificial Intelligence Responsibility in Retail Service Automation (AIRRSA) "能力越大,责任越大":探索企业数字责任(CDR)在零售服务自动化人工智能责任(AIRRSA)中的作用
IF 2 4区 管理学 Q2 BUSINESS Pub Date : 2024-04-01 DOI: 10.1016/j.orgdyn.2024.101030
Daniele Scarpi , Eleonora Pantano

Scholars conceptualized the role of Corporate Digital Responsibility (CDR) to emphasize the ethical issues unique to the digital context by pushing all the actors involved in developing and integrating digital technologies and related data processing to act ethically and responsibly. However, how to apply AI services more ethically and develop AI equipped with moral/ethical intelligence are still open questions.Accordingly, this paper aims to understand the managerial applications and tools of Corporate Digital Responsibility (CDR) in AI retail service automation to identify the CDR value for consumers and retail managers. We link the five AI intelligence types (verbal-linguistic, logic-mathematical, visual-spatial, social, and speed-processing) to CDR strategies to produce value for retailers and consumers. Specifically, we define intelligent retail service automation, emphasizing actions, tools, and (privacy) concerns, and discuss the role of CDR in AI for intelligent retail service by developing the new concept of Artificial Intelligence Responsibility in Retail Service Automation (AIRRSA). Finally, we provide implications for scholars, managers, and policy-makers while proposing future challenges.

学者们将 "企业数字责任"(Corporate Digital Responsibility,简称 CDR)概念化,以强调数字环境下特有的伦理问题,推动所有参与数字技术开发和整合以及相关数据处理的行为者以道德和负责任的方式行事。因此,本文旨在了解企业数字责任(CDR)在人工智能零售服务自动化中的管理应用和工具,以确定企业数字责任对消费者和零售管理者的价值。我们将五种人工智能智能类型(语言-语言、逻辑-数学、视觉-空间、社交和速度-处理)与 CDR 战略联系起来,从而为零售商和消费者创造价值。具体而言,我们定义了智能零售服务自动化,强调了行动、工具和(隐私)问题,并通过提出 "零售服务自动化中的人工智能责任"(AIRRSA)这一新概念,讨论了 CDR 在智能零售服务人工智能中的作用。最后,我们为学者、管理者和政策制定者提供了启示,同时提出了未来的挑战。
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引用次数: 0
Responsible algorithmic decision-making 负责任的算法决策
IF 2 4区 管理学 Q2 BUSINESS Pub Date : 2024-04-01 DOI: 10.1016/j.orgdyn.2024.101031
Christoph F. Breidbach

Algorithmic decision-making - the use of computational methods that enable machines to automatically complete tasks and/or make decisions - is emerging as a critical source of competitive advantage for organizations. However, despite many benefits, there is an inherent dark side associated with it that can manifest issues ranging from a loss of privacy for individuals to societal power imbalance. Managers and policymakers alike need to be able to understand potentially unethical consequences that can arise from algorithmic decision-making before they can fully manifest. This article aims to support this undertaking by identifying, analysing, and explaining the challenges that can arise from algorithmic decision-making, and by contributing a seven-step roadmap to those wanting to responsibly implement and benefit from algorithms today.

算法决策--使用计算方法使机器自动完成任务和/或做出决策--正在成为组织竞争优势的重要来源。然而,尽管它有很多好处,但也有其固有的阴暗面,可能会表现出从个人隐私丧失到社会权力失衡等各种问题。管理者和政策制定者都需要在算法决策可能产生的不道德后果完全显现之前,就能够了解这些后果。本文旨在通过识别、分析和解释算法决策可能带来的挑战,并为那些希望以负责任的方式实施算法并从中获益的人们提供一份七步路线图,从而为这项工作提供支持。
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引用次数: 0
Principles of responsible digital implementation: Developing operational business resilience to reduce resistance to digital innovations 负责任的数字化实施原则:发展业务运营复原力,减少数字创新阻力
IF 2 4区 管理学 Q2 BUSINESS Pub Date : 2024-04-01 DOI: 10.1016/j.orgdyn.2024.101043
Zixuan Mia Cheng , Francesca Bonetti , Anouk de Regt , Joel Lo Ribeiro , Kirk Plangger

Organizations are readily implementing innovative technological solutions, including artificial intelligence (AI) and robotics, to remain competitive. However, these implementations often disrupt the existing routines and practices of stakeholders that are critical for organizational performance and success. If stakeholders are not part of the implementation decision process, the technological disruption may induce stakeholder resistance that may potentially lead to organization-wide turbulence. Addressing this scenario, this paper conceptualizes six principles of responsible digital implementations to develop operational resilience facilitated by discursive channels between organizational leadership and stakeholders. We close by outlining an action plan that provides guidance for managers considering implementing digital technologies, as well as suggest some potential fruitful future areas of research.

为了保持竞争力,各组织都在积极实施创新技术解决方案,包括人工智能(AI)和机器人技术。然而,这些实施往往会破坏利益相关者的现有常规和做法,而这些常规和做法对组织的绩效和成功至关重要。如果利益相关者没有参与实施决策过程,技术颠覆可能会引起利益相关者的抵制,从而可能导致整个组织的动荡。针对这种情况,本文构思了负责任的数字化实施的六项原则,通过组织领导层与利益相关者之间的话语渠道来促进业务弹性的发展。最后,我们概述了一项行动计划,为考虑实施数字技术的管理者提供指导,并提出了一些潜在的富有成效的未来研究领域。
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引用次数: 0
Principles for advertising responsibly using generative AI 使用生成式人工智能负责任地发布广告的原则
IF 2 4区 管理学 Q2 BUSINESS Pub Date : 2024-04-01 DOI: 10.1016/j.orgdyn.2024.101042
Sean Sands , Colin Campbell , Carla Ferraro , Vlad Demsar , Sara Rosengren , Justine Farrell
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引用次数: 0
Artificial intelligence and performance management 人工智能和绩效管理
IF 2 4区 管理学 Q2 BUSINESS Pub Date : 2024-01-01 DOI: 10.1016/j.orgdyn.2024.101037
Arup Varma , Vijay Pereira , Parth Patel

Artificial Intelligence (AI) enabled tools have increasingly becoming popular in our societies and are increasingly being used by students and practitioners, among others. Within corporations, numerous different applications have been identified where AI-enabled tools have been applied with different levels of success. In this article, we explore the pros and cons of using AI in performance management (PM). We draw upon the practitioner literature to summarize the current status of AI and AI-enabled tools. We also interviewed 8 HR professionals from around the world to learn about their experience(s) with the tools and to gain an insight into the future. In doing so, we explore the various components of performance management systems (PMS) and discuss how each might be impacted by the use of AI. Finally, we discuss the pros and cons of such usage and make recommendations for organizations that are considering using AI or AI enabled tools in their PMSs.

人工智能(AI)工具在我们的社会中越来越受欢迎,学生和从业人员等也越来越多地使用这些工具。在企业内部,人工智能工具已被广泛应用,并取得了不同程度的成功。在本文中,我们将探讨在绩效管理(PM)中使用人工智能的利弊。我们借鉴了从业人员的文献,总结了人工智能和人工智能工具的现状。我们还采访了来自世界各地的 8 位人力资源专业人士,了解他们在使用这些工具方面的经验,并对未来进行展望。在此过程中,我们探讨了绩效管理系统(PMS)的各个组成部分,并讨论了人工智能的使用可能对每个组成部分产生的影响。最后,我们将讨论使用人工智能的利弊,并为考虑在其绩效管理系统中使用人工智能或人工智能工具的组织提出建议。
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引用次数: 0
Artificial intelligence, algorithms, and compensation strategy: Challenges and opportunities 人工智能、算法和薪酬战略:挑战与机遇
IF 2 4区 管理学 Q2 BUSINESS Pub Date : 2024-01-01 DOI: 10.1016/j.orgdyn.2024.101039
Janet H. Marler

Compensation strategy plays a crucial role in attracting, motivating, and retaining strategic human capital. Amping up the advantages of being strategic about compensation are advances in technology such as cloud computing and storage along with digitized big data that make the sheer amount of information available and analyzed electronically, a huge competitive opportunity. The good news is these advances have unleashed a tsunami of technology solutions that promise to solve all compensation challenges. In this paper, I synthesize and summarize the literature on artificial intelligence and compensation management and describe four key challenges that companies face in using AI to manage compensation strategically. The first challenge is when and how to use AI to automate and augment compensation tasks and decisions. The second challenge is how to use AI effectively to improve fairness and equity in compensation practices. The third challenge is explaining how AI recommended changes in compensation practices are derived. The fourth challenge is how to actually be strategic using AI solutions. In describing these four challenges, I identify issues, opportunities, gaps, and current limitations of existing AI applications in supporting the strategic management of compensation in organizations.

薪酬战略在吸引、激励和留住战略人力资本方面发挥着至关重要的作用。云计算和存储等技术的进步以及数字化大数据使大量信息可以通过电子方式获取和分析,从而带来了巨大的竞争机遇。好消息是,这些进步引发了技术解决方案的海啸,有望解决所有薪酬挑战。在本文中,我将对有关人工智能和薪酬管理的文献进行归纳和总结,并描述公司在使用人工智能对薪酬进行战略性管理时所面临的四大挑战。第一个挑战是何时以及如何使用人工智能来自动化和增强薪酬任务和决策。第二个挑战是如何有效利用人工智能来提高薪酬实践中的公平性和公正性。第三个挑战是如何解释人工智能在薪酬实践中建议的变化是如何产生的。第四个挑战是如何真正利用人工智能解决方案实现战略目标。在描述这四个挑战时,我指出了现有人工智能应用在支持组织薪酬战略管理方面存在的问题、机遇、差距和当前的局限性。
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引用次数: 0
Will artificial intelligence radically change human resource management processes? 人工智能会从根本上改变人力资源管理流程吗?
IF 2 4区 管理学 Q2 BUSINESS Pub Date : 2024-01-01 DOI: 10.1016/j.orgdyn.2024.101034
Dianna L. Stone , Kimberly M. Lukaszewski , Richard D. Johnson

Today artificial intelligence (AI) is being employed to streamline and transform many business processes including those in human resource management (HR). AI has and will continue to revolutionize the way that organizations attract talented applicants, hire qualified employees, train workers, manage their performance, and develop compensation and reward systems. Many analysts believe AI will help organizations gain a competitive edge in attracting, motivating, and retaining talented employees. These new AI systems have several key benefits including reduced transaction times, decreased costs, improved employee service, and streamlined administrative processes (e.g., screening applications). Despite the growing use of AI in organizations, many managers have indicated that they do not fully understand how AI will transform HR practices. Thus, the primary purpose of this article is to highlight how AI will help organizations modify HR processes and practices so that they can meet their HR goals.

如今,人工智能(AI)正被用于简化和改造许多业务流程,包括人力资源管理(HR)流程。人工智能已经并将继续彻底改变组织吸引优秀求职者、雇用合格员工、培训员工、管理员工绩效以及制定薪酬和奖励制度的方式。许多分析师认为,人工智能将帮助企业在吸引、激励和留住优秀员工方面获得竞争优势。这些新的人工智能系统有几个主要优势,包括缩短交易时间、降低成本、改善员工服务和简化行政流程(如筛选申请)。尽管人工智能在企业中的应用越来越广泛,但许多管理者表示,他们并不完全了解人工智能将如何改变人力资源实践。因此,本文的主要目的是强调人工智能将如何帮助企业修改人力资源流程和实践,从而实现其人力资源目标。
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引用次数: 0
Ethical considerations of generative AI-enabled human resource management 人工智能生成式人力资源管理的伦理考量
IF 2 4区 管理学 Q2 BUSINESS Pub Date : 2024-01-01 DOI: 10.1016/j.orgdyn.2024.101032
Pierre Andrieux , Richard D. Johnson , Jalal Sarabadani , Craig Van Slyke

This paper examines critical ethical considerations linked to making human resources management (HRM) decisions based on the potential capabilities (affordances) offered by generative artificial intelligence (GAI). We first provide a broad overview of the status quo surrounding the use of GAI in the HRM context. Then, we introduce the concept of “affordance” and explain how it provides a useful perspective for human resource (HR) managers to use when evaluating potential benefits and/or harm resulting from the implementation of a potential GAI-based capability to support HRM processes decisions. We discuss concrete examples of how GAI HRM affordances could be implemented in different HRM functions and the ethical questions that arise from their use. Finally, we present an ethics-based framework, the Two-Rule Method, along with ethics-specific recommendations to guide HR managers through the complex issues that arise because of the use of GAI-enabled HR tools.

本文探讨了与基于生成式人工智能(GAI)提供的潜在能力(可负担性)做出人力资源管理(HRM)决策相关的重要伦理考虑因素。我们首先概述了在人力资源管理中使用 GAI 的现状。然后,我们介绍 "承受能力 "的概念,并解释它如何为人力资源(HR)管理者提供一个有用的视角,用于评估实施基于生成式人工智能的潜在能力以支持人力资源管理流程决策所带来的潜在利益和/或损害。我们讨论了如何在不同的人力资源管理职能中实施GAI人力资源管理能力的具体实例,以及使用这些能力所产生的伦理问题。最后,我们提出了一个基于伦理的框架--"双规则法",以及针对伦理的建议,以指导人力资源管理者解决因使用GAI支持的人力资源工具而产生的复杂问题。
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引用次数: 0
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Organizational Dynamics
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