Speaking vs. listening? Balance conversation attributes of voice assistants for better voice marketing

Artificial Intelligence (AI) is shaping marketing in an unprecedented way. Empowered by AI, voice assistants are increasingly capable of speaking and listening like humans, offering a great opportunity for a new marketing approach - voice marketing. This research examines how conversation attributes of voice assistants determine consumer trust and intention to engage in voice shopping. Using a sequential mixed-method design, three studies consistently show that consumers perceive the speaking attribute of voice assistants as more human-like than the listening attribute. We find that such incongruency between the two conversation attributes can undermine consumers' trust in voice assistants, leading to reduced willingness to accept product recommendations from voice assistants and shop via voice assistants, which would hamper the development of voice marketing. Accordingly, this research suggests that AI giants with strong technological strength and capital support should distribute more resources to advance the underlying technologies enabling human-like listening (e.g., natural language understanding and voice recognition). But for AI startups with limited financing ability and technical talents, they may consider appropriately reducing investments in the underlying technologies enabling human-like speaking (e.g., natural language generation and voice synthesis) to enhance the congruency level between the conversation attributes of voice assistants.     

数字经济背景下工业智能化对就业的影响——以广东省为例

随着智能化进程的加快，引发了“机器换人”的担忧和争论。基于这一背景，采用广东省2009—2019年的面板数据，研究智能化发展对就业的影响。结果显示：广东省各城市的就业水平和工业智能化水平存在着明显的空间依赖性；工业智能化的发展减少了制造业的就业份额，增加了服务业就业份额及其细分的生产性服务业和高端服务业的就业份额，这有助于促进产业结构的高级化，从而实现高质量就业。     

智能化发展对就业结构的影响研究北大核心

本文基于2001~2020年省级面板数据实证检验了智能化发展对中国就业结构的影响,结果表明:智能化发展整体上促进中国就业,在第一产业减少就业,在第二产业和第三产业增加就业。工资水平较高的地区和智能化水平较高的地区,智能化对第二产业就业的创造效应不再显著。最后根据研究结论提出了相应的对策建议。     

Accounting and auditing with blockchain technology and artificial Intelligence: A literature review

This paper surveys the published work on how blockchain technology will impact accounting in general, but AI-enabled auditing specifically. The purpose is to investigate how blockchain technology can improve transparency and trust in accounting practice and how professionals can use blockchain data to improve decision-making, based on the qualities of immutability, append-only, shared, verified, and agreed-upon (i.e., consensus-driven) blockchain data. The multi-party validation of blockchain protocols adds real-time trusted data for the AI systems used by auditors to improve assurance and efficiency. This review summarizes four themes emerging from the literature focusing on how blockchain technology has changed record-keeping in accounting: event approach to accounting; real-time accounting; triple entry-accounting and continuous auditing. The research interprets the findings using agency theory and stakeholder theory to advance how using blockchain to mitigate information asymmetry and improve stakeholder collaborations is understood. The investigation also summarizes the challenges and clarifies organizations’ reasons to be cautious about adopting blockchain. Lastly, the study suggests that future researchers use this study in two ways that enrich blockchain literature: first, to apply the themes and answer the questions identified within this review to improve the business methods of practitioners and policymakers; and second, to encourage stakeholders such as practitioners, system designers/developers, and policymakers to collaborate in designing blockchain ecosystems that suit accounting and auditing as they transform digitally.     

Ethical implications of text generation in the age of artificial intelligence

We are at a turning point in the debate on the ethics of Artificial Intelligence (AI) because we are witnessing the rise of general-purpose AI text agents such as GPT-3 that can generate large-scale highly refined content that appears to have been written by a human. Yet, a discussion on the ethical issues related to the blurring of the roles between humans and machines in the production of content in the business arena is lacking. In this conceptual paper, drawing on agenda setting theory and stakeholder theory, we challenge the current debate on the ethics of AI and aim to stimulate studies that develop research around three new challenges of AI text agents: automated mass manipulation and disinformation (i.e., fake agenda problem), massive low-quality content production (i.e., lowest denominator problem) and the creation of a growing buffer in the communication between stakeholders (i.e., the mediation problem).     

Mind the gender gap: Inequalities in the emergent professions of artificial intelligence (AI) and data science

The emergence of new prestigious professions in data science and artificial intelligence (AI) provide a rare opportunity to explore the gendered dynamics of technical careers as they are being formed. In this paper, we contribute to the literature on gender inequality in digital work by curating and analysing a unique cross-country data set. We use innovative data science methodology to investigate the nature of work and skills in these under-researched fields. Our research finds persistent disparities in jobs, qualifications, seniority, industry, attrition and even self-confidence in these fields. We identify structural inequality in data and AI, with career trajectories of professionals differentiated by gender, reflecting the broader history of computing. Our work is original in illuminating gendering processes within elite high-tech jobs as they are being configured. Paying attention to these nascent fields is crucial if we are to ensure that women take their rightful place at forefront of technological innovation.