人工智能促进绿色创新了吗

在中国致力于实现“双碳”和高质量发展目标的背景下,基于中国2007-2021 年284 个城市的面板数据,将人工智能与绿色创新纳入一个理论分析框架,借助固定效应、门槛效应和中介效应模型检验人工智能对绿色创新水平的影响、影响机制以及不同条件下人工智能对绿色创新影响的差异性问题。研究结果表明：人工智能能够赋能绿色创新,且这种赋能效果呈现边际递增到一定程度后赋能幅度下降的非线性特征。中介效应表明,人工智能通过优化人力资本结构和产业结构调整来促进绿色创新。调节效应表明,良好的制度环境和适当的政府干预正向调节人工智能对绿色创新的赋能效应。     

Reviewing half a century of measuring cross-cultural competence: Aligning theoretical constructs and empirical measures

We provide a comprehensive review of how cross-cultural competence (CCC) has been measured over the past half-century in order to more closely align theoretical constructs and empirical measures. Based on a content analysis of 68 academic and commercial CCC instruments and a supplemental survey of 160 experts, we review the approaches used in these instruments to conceptualize and quantify CCC, discuss their limitations, and recommend best practices and directions for future researchers and practitioners when selecting and using CCC instruments or developing new alternatives.     

制造业企业数智化转型测度及其影响因素研究

采用多分格主成分分析法构建数智化转型指数，对2009—2020年中国制造业上市企业的数智化转型进行量化测度，并实证分析其影响因素。研究发现：制造业企业数智化转型水平呈增长趋势，但不同类型企业间的数智化发展具有差异性；技术水平、员工人力资本和企业规模有效促进了企业数智化转型，行业竞争程度和政府支持力度却没有产生应有的影响；对于东部企业、外资企业以及技术密集型企业，技术水平和企业规模对数智化转型的促进作用更为显著。     

Brace yourself! Why managers should adopt a synthetic media incident response playbook in an age of falsity and synthetic media

Synthetic media presents looming threats to managers in a business setting. To address this issue, we first offer a short overview of the evolution of media manipulation to contextualize the new era of synthetic media. Then, we present the problems associated with synthetic media via veridicality and heuristics to illustrate how consumers have little choice but to believe what they see, read, and hear online. We outline the most likely and impactful types of synthetic media threats and attacks and present a synthetic media incident response playbook. Our aim is to inform managers about six specific phases so they can prepare, assess, detect, analyze, and recover from synthetic media incidents and coordinate their lessons learned.     

Closing the cultural intelligence skills gap in accounting students: An action research approach to cross-cultural teamwork

Cultural intelligence (CQ) is an essential feature of the skills-set demanded of accounting graduates entering the worlds of modern business and professional practice. Accounting educators are challenged to be innovative pedagogically to equip their students with this skills-set. This paper, drawing on the theoretical framework of CQ, reports on the experiences of students and faculty in accounting and taxation, in the context of an action research study which introduced cross-cultural learning across two geographies, namely Ireland and a Southern US state. Two action research cycles were designed, adapted, and reflected upon. Cycle 1 results indicate students were successful in the behaviour and cognitive dimensions of CQ only. Key changes made for cycle 2 led to overall success for students in all four dimensions of CQ. Evidence suggests the motivation dimension of CQ functions more strongly as enablers of others. Results highlight the invaluable support of an international collaborator despite challenges around grading issues. This study demonstrates how moving the cultural experience outside of a mono-cultural setting serves to energise and enhance the overall learning experience for students and faculty alike. Future research agendas can expand into other accounting sub-disciplines and/or use sufficiently large sample sizes to enable more quantitative analyses.     

The role of memorable experience and emotional intelligence in senior customer loyalty to geriatric hotels

Drawing on service encounters and experiential marketing theories, this study examined the relationship between geriatric service, memorable experience, emotional intelligence, and senior customers' attitudinal and behavioural responses. The research was conducted at geriatric hotels in Portugal. The results showed that, compared to impersonal encounters, employee service has a significantly greater effect on customer satisfaction, memorable experience, and customer loyalty. In addition, customers' memorable experiences mediated the relationship between different service encounters and organisational outcomes. However, customers’ emotional intelligence had minimal effects on these relationships. The discussion and implications of these findings are offered to researchers and practitioners.     

Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China

The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A-Share listed firms. Based on a sample of 33,544 Chinese firm-year instances from 1998 to 2017, this research respectively established one logistic regression and four ensemble learning classifiers (AdaBoost, XGBoost, CUSBoost, and RUSBoost) by 12 financial ratios and 28 raw financial data. Additionally, we divided the sample into the train and test observations to evaluate the classifiers' out-of-sample performance. In detail, we applied two metrics, namely, Area under the ROC (receiver operating characteristic) curve (AUC) and Area under the Precision-Recall curve (AUPR), to evaluate classifiers' discriminability. In the supplement test, this study put forward an algebraic fused model on the basis of the four ensemble learning classifiers and introduced the sliding window technique. The empirical results showed that the ensemble learning classifiers can detect accounting fraud for the imbalanced China A-listed firms far more effectively than the logistic regression model. Moreover, imbalanced ensemble learning classifiers (CUSBoost and RUSBoost) effectively performed better than the common ensemble learning models (AdaBoost and XGBoost) in average. The algebraic fused model in the supplement test also obtained the highest average AUC and AUPR among all the employed algorithms. Our results offer firm support for the potential role of Machine Learning (ML)-based Artificial Intelligence (AI) approaches in reliably predicting accounting fraud with high accuracy. Similarly, for the Chinese settings, our ML-based AI offers utmost advantage in forecasting accounting fraud. Finally, this paper fills the research gap on the applications of imbalanced ensemble learning in accounting fraud detection for Chinese listed firms.     

Corporate governance performance ratings with machine learning

We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity.     

人工智能对劳动收入水平和收入差距的影响——理论解读与实证检验

人工智能的发展对劳动市场产生了深远影响，但其对劳动收入水平和收入差距的效应仍处于探索阶段，且定量评估不足，缺少行业层面的经验证据。本文基于Acemoglu&Restrepo(2018)的人工智能技术模型，并引入行业人工智能发展的非对称性变动，从智能渗透和边界延展两方面分析人工智能对劳动收入水平与技能收入差距的影响及机理，并通过2003—2020年中国行业面板数据进行实证检验。结果发现：(1)人工智能对劳动收入水平和技能收入差距具有显著影响。从长期来看，人工智能的发展可以提高劳动收入水平，缩小技能收入差距。(2)人工智能对低技能劳动收入水平的冲击显著大于对高技能劳动收入水平的冲击。(3)人工智能通过提升高低技能劳动力就业水平增加劳动收入水平，缩小技能收入差距。     

Examining the context-specific reasons and adoption of artificial intelligence-based voice assistants: A behavioural reasoning theory approach

AI-based voice assistants are some of the most popular consumer products of artificial intelligence technology. This study investigates the influence of the reasoning process on overall consumer attitudes and adoption intentions towards AI-based voice assistants. Behavioural reasoning theory was applied to hypothesize relationships between values, context-specific reasons, attitudes, and adoption intention. Partial least squares structural equation modelling was used to test the research model and hypotheses using data from 550 potential consumers in India. This research suggests a positive influence of consumers' ‘reasons for’ on their attitude and adoption intentions towards AI-based voice assistants, as well as a favourable impact of consumers' values on ‘reasons for’. This study discovered that ‘reasons for’ and ‘reasons against’ are not only opposites but are entirely autonomous and have an independent impact on consumers' attitudes towards AI-based voice assistants. This research improved our understanding of AI-based product adoption by identifying the paramount role of consumers' values and the ‘reasons for’ and ‘reasons against’ in shaping their attitudes and adoption intentions towards AI-based voice assistants.