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1.
《Business Horizons》2022,65(5):671-680
We live in an age of massive global disruption. Technological advancements threaten century-old business models, globalization is reordering supply chains, and people need to work with colleagues and customers who have vastly different backgrounds. On top of that, we have been in the midst of a global pandemic, and customers, employers, and investors are demanding more than just a Black Lives Matter social media post from organizations that purport to take social justice seriously. Organizations with high cultural intelligence (CQ) are able to navigate this volatility and complexity effectively. Over the last two decades, scholars from across the world have published hundreds of articles on CQ, the capability to relate and work effectively in complex, culturally diverse situations. Most of the work has examined CQ at the individual level. But what about organizations? Can organizations be culturally intelligent? The emerging research on CQ at the organizational level offers leaders and organizations critical insights for navigating today’s diverse, digital world. Organizational CQ is a firm’s capability to function effectively in a complex and unpredictable multicultural world. This article stresses the importance of the culturally intelligent organization and explains how to develop organizational CQ.  相似文献   
2.
Our study investigates how coopetition strategies can influence hotels’ competitive intelligence (CI) practices to achieve a stronger competitive advantage. In-depth interviews were conducted with 39 hoteliers from 22 hotel groups in Hong Kong. Participating hotels employed different kinds of CI activities, though they were unaware of this concept. In particular, internal customer intelligence was added to integrated intelligence to better describe CI in the service sector. Still, investing in CI can be expensive and time-consuming since it requires hoteliers to align all insights from their respective intelligence pools toward building a holistic understanding of the results. We propose coopetition as a strategic approach allowing hotels to construct collective actions around CI without losing individual competitiveness. Actual coopetition in CI was only found between sister properties. Hence, we propose a coopetition model in which hotels can collaborate and compete in CI at an inter-organizational level via focusing on sharing open-source information and knowledge.  相似文献   
3.
张萌 《科技和产业》2021,21(7):174-179
成都市先进制造业基础扎实,产业体量西部领先,但制造业智能化转型对标先进地区一直都属于落后区域.仍然存在技术研究基础薄弱、产业化程度较弱、示范效应不明显等问题.对成都市制造业智能化发展现状进行梳理并分析,总结成都市制造业发展的特点和优劣势,从而归纳出成都市制造业智能化发展的技术创新路径和产业模式发展路径.  相似文献   
4.
为了提高煤田地震勘探的精度,提出了煤矿地震数据动态解释的思路,并通过Qt编程开发了地震资料动态解释软件系统.该系统包括矿井多元数据综合管理、三维地震资料解释、解释成果编辑、三维可视化分析与地震地质成果输出等功能.工程应用表明该软件能够把煤矿三维地震勘探获得的静态勘探成果与矿井生产过程中所获得的各类地质信息相互融合,实现地震资料动态解释,实时更新地震资料解释成果,为煤矿安全高效开采提供了地质保障.  相似文献   
5.
While artificial intelligence products are widely used in the market, their anthropomorphic appearance design is becoming a frontier issue in product strategy and consumer behavior research. The aim of this study was to investigate the influence of anthropomorphic appearance on consumer behavior and brand evaluation under different AI product types. It was conducted in China, a new but rapidly-growing country in the field of Internet, AI technology and AI product consumption. This study conducted four situational experiments with a 2 (anthropomorphic design: anthropomorphic vs. non-anthropomorphic) × 2 (product type: hedonic vs. utilitarian) between subjects’ experimental design. Data was collected from 1172 Chinese “Digital Natives” by using a structured questionnaire. The findings revealed that for hedonic AI products, anthropomorphic appearance improves consumers' purchase intention and brand evaluation through perceived entertainment, and intelligence level significantly moderates the mediating effect of perceived entertainment; while for practical AI products, anthropomorphic appearance improves consumers' purchase intention and brand evaluation through perceived usefulness, and intelligence level does not significantly moderate the mediating effect of perceived usefulness. There is no significant moderating effect of intelligence level on perceived usefulness. The study contributes to development and validation of a more comprehensive understanding and theoretical foundation of anthropomorphism, and furthermore explores the impact of anthropomorphic appearance on consumer behavior and brand evaluation under different AI product types. This study also provides insights for companies to apply anthropomorphic strategies.  相似文献   
6.
Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.  相似文献   
7.
Current turnover research fails to serve the needs of an industry that is long plagued by employee turnover. Existing literature focuses more on evaluating bundles of human resource practices and fail to provide precise and clear guidance for practitioners. This study proposes that emotional intelligence (EI) unifies sufficient individual factors and organizational factors that affect employee turnover and serves as a single significant precedent for turnover. Data were collected from frontline employees at eight luxury hotels. The direct, indirect, and total impacts of employee EI on employee turnover were tested by structural equation modeling and bootstrap tests. The results suggest that EI has significant indirect impacts through the mediation of perceived organizational support, pay satisfaction and job burnout, and significant total impacts on turnover. Implication suggestions include integrating EI into the recruiting process for new employees and providing training opportunities for current employees to improve their EI.  相似文献   
8.
To overcome competition in an increasingly network dependent market, retailers are required to influence upstream channel partners while sustaining relationships. However, the contemporary supply chain literature has not sufficiently leveraged the resource and relational paradigms to examine influence. Grounded on resource dependency theory and commitment-trust theory paradigms, this study describes conceptualization and operationalization of a 12-item scale for measuring non-coercive influence on upstream channel partners in retail supply chain management (R-SCM) context. The study is based on responses from 547 retail professionals in India obtained over four successive surveys. Psychometric properties were assessed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The proposed scale demonstrates construct validity. Invariance-testing carried out over 4-levels of increasingly demanding equivalence confirmed cross-validation. Nomological validity of the scale was tested by evaluating association with suppliers’ intention to cooperate. The results indicate existence of three dimensions of non-coercive influence: collaborative intent, market intelligence dissemination, and operational support. Retailers can use the scale to assess their personnel's non-coercive influence behavior over suppliers.  相似文献   
9.
This theoretical perspective paper interprets (un)known-(un)known risk quadrants as being formed from both abstract and concrete risk knowledge. It shows that these quadrants are useful for categorising risk forecasting challenges against the levels of abstract and concrete risk knowledge that are typically available, as well as for measuring perceived levels of abstract and concrete risk knowledge available for forecasting in psychometric research. Drawing on cybersecurity risk examples, a case is made for refocusing risk management forecasting efforts towards changing unknown-unknowns into known-knowns. We propose that this be achieved by developing the ‘boosted risk radar’ as organisational practice, where suitably ‘risk intelligent’ managers gather ‘risk intelligence information’, such that the ‘risk intelligent organisation’ can purposefully co-develop both abstract and concrete risk forecasting knowledge. We also illustrate what this can entail in simple practical terms within organisations.  相似文献   
10.
The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.  相似文献   
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