Artificial intelligence and knowledge management: A partnership between human and AI

Emerging artificial intelligence (AI) capabilities will likely pervade nearly all organizational contours and activities, including knowledge management (KM). This article aims to uncover opportunities associated with the implementation of emerging systems empowered by AI for KM. In doing so, we explicate the potential role of AI in supporting fundamental dimensions of KM: creation, storage and retrieval, sharing, and application of knowledge. We then propose practical ways to build the partnership between humans and AI in supporting organizational KM activities and provide several implications for the development and management of AI systems based on the components of people, infrastructures, and processes.     

人工智能发展的社会风险及监管路径研究

人工智能技术的出现和发展必然会引发一系列潜在风险,包括伦理道德危机、技术滥用隐患、极化异化风险以及军事战争灾难等。与此同时,人工智能时代的到来也使得诸多法律问题陷入困境,如法律主体资格的确定、著作权的归属、侵权责任的认定、人格权的保护以及无人驾驶汽车和替代劳工的相关问题等。对此,必须深入探究未来人工智能发展的监管路径,采取塑造多元价值目标、构建伦理规范体系、建立法律监管制度和加强国际协同合作等措施,有效规避人工智能带来的社会风险和法律挑战,从而最大程度发挥人工智能的积极作用。     

On ChatGPT and beyond: How generative artificial intelligence may affect research,teaching, and practice

How does ChatGPT, and other forms of Generative Artificial Intelligence (GenAI) affect the way we have been conducting—and evaluating—academic research, teaching, and business practice? What are the implications for the theory and practice of marketing? What are the opportunities and threats, and what are some interesting avenues for future research? This editorial aims to kick off an initial discussion and stimulate research that will help us better understand how the marketing field can fully exploit the potential of GenAI and effectively cope with its challenges.     

A Framework for Ethical Conformity in Marketing

The extant marketing literature provides little guidance for theory development or practice with regard to questions of ethical conformity and the resulting market response. To begin to bridge this research gap, we advance a theoretical framework of ethical conformity in marketing, appealing to marketing ethics, management strategy, and sociological foundations. We set the stage for our theoretical arguments by considering the role of normative expectations related to marketing practices and behaviors held by societal constituents. Against this backdrop, we propose drivers of conformity in marketing, including practices consistent with both overconformity and underconformity. The framework allows us to advance testable research propositions by which questions of ethical conformity may be explored. We conclude by suggesting additional future research needed to develop the domain, specifically in the form of empirical inquiries uncovering firm strategic decisions with ethical implications. Kelly D. Martin is Assistant Professor of Marketing at Colorado State University in Fort Collins, Colorado. Her research interests involve marketing strategy with ethical implications, interfirm relationships, the role of marketing in society, and the effects of institutions, social norms, and culture on organizations. Her work has appeared in the Academy of Management Journal, the Journal of Business Ethics, and Business & Society. Jean L. Johnson is the Gardner O. Hart Professor of Marketing at Washington State University in Pullman, Washington. Her research focuses on marketing strategy, interfirm relationships, and capabilities and learning in firms. Her work appears in the Journal of Marketing, the Academy of Management Journal, and Journal of International Business Studies, among others.     

Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers?

Artificial intelligence (AI) has emerged as a promising and increasingly available technology for managerial decision-making. With the adoption of AI-enabled software, organizations can leverage various benefits of the technology, but they also have to consider the intended and unintended consequences of using the technology for managerial roles. It is still unclear whether managers will benefit from enhancing their abilities with AI-enabled software or become powerless puppets that do more than announce AI-enabled software results. Our research has revealed distinct ways in which organizations can use AI-enabled decision-making solutions: as tools or novelties, for decision augmentation or automation, and as either a voluntary or a mandatory option. In this article, we discuss the implications of each of these combinations on the relevant managers. We consider outcomes related to managerial job design and derive practical advice for organizational designers and managers who work with AI. Our outcomes provide guidance on how to deal with the conflict-riddled relationship between managers and technology with regard to capabilities, responsibilities, and acceptance of AI-enabled software.     

Artificial intelligence as a boundary-crossing object for employee engagement and performance

The study proposes AI-powered tools and applications as boundary-crossing objects to examine how AI performance can affect employees' job engagement, service and job performance. Job security is modelled as a moderator in the boundary-crossing process. Several theories including boundary crossing, goal setting and self-regulation are drawn on to posit these relationships. The study was undertaken with Australia-based full-time employees who had experience with AI-powered tools at work. The results show that AI performance had a significant effect on job engagement, and employee service performance, which were significantly related to job performance appraisal. Job engagement and service performance exhibited significant mediation effects between AI and job performance. The moderation effect exerted by job security was significant in enhancing employees’ job engagement and service performance. The study contributes to service research and human resource management literature. The findings have implications for service marketers and human resource practitioners.     

From AI to digital transformation: The AI readiness framework

Strategies and means for selecting and implementing digital technologies that realize firms’ goals in digital transformation have been extensively investigated. The recent surge in artificial intelligence (AI) technologies has amplified the need for such investigation, as they are being increasingly used in diverse organizational practices, creating not only new opportunities for digital transformation but also new challenges for managers of digital transformation processes. In this article, I present a framework intended to assist efforts to address one of the first of these challenges: assessment of organizational AI readiness—that is, an organization’s ability to deploy AI technologies to enable digital transformation, in four key dimensions: technologies, activities, boundaries, and goals. I show that this framework can facilitate analysis both of an organization’s current sociotechnical AI status and of the prospects for the technology’s fuller value-adding, sociotechnical deployment. The AI readiness framework invites fuller theorizing of the roles that AI can—and will—play in digital transformation.     

Exploring the Role of Product Development Capability for Transforming Marketing Intelligence into Firm Performance

ABSTRACT

Purpose: This study examines the role of product development capability for transforming marketing intelligence activities into firm performance on industrial markets.

Methodology/Approach: The authors apply structural equation modeling to analyze survey data from 342 industrial manufacturing companies.

Findings: Results reveal that sales integration and holistic macroeconomic view are particularly important features of the marketing intelligence capability on industrial markets to gain additional insights in order to develop products successfully. Results also reveal that product development capability transforms the marketing intelligence activities into firm performance on industrial markets.

Research Implications: This study offers new insights on marketing theory by extending and concretizing the concept of market orientation to a broader concept of the marketing intelligence capability for industrial markets. Further, this research reveals that the product development capability serves as a mediator to business performance.

Practical Implications: This study’s systematic and tangible outline of the dimensions of marketing intelligence and its link to the product development capability helps firms to better understand how to capitalize on market orientation’s potential values.

Originality/Value/Contribution: This research (a) reveals the relevant dimensions of marketing intelligence on industrial markets and (b) shows how the product development capability tranforms marketing intelligence activities into firm performance on industrial markets.     

基于人工智能的足迹识别与特征提取

针对战场感知及侦破现场中传统人工主观经验检验与识别模式误差较大的问题,提出了一种基于人工智能的足迹识别与特征提取方法。采用三维形貌重构系统进行足迹图像采集,并将数字图像处理算法与传统足迹检验法结合,提取足迹的区域关系特征和形状长度特征,进而采用支持向量机的模式识别方法对提取的特征进行立体足迹身份鉴别对比实验。实验结果表明,所提方法准确率超过人工鉴别准确率,达到99.1%,可应用于战场感知及侦破现场足迹准确检测与识别,也可推广应用于人体身份鉴别的相关领域。     

A Framework for Assessing Immorally Manipulative Marketing Tactics

A longstanding debate exists in both academic literature and popular culture about whether non-informative marketing tactics are manipulative. However, given that we tend to believe that some marketing tactics are manipulative and some are not, the question that marketers, their critics, and consumers need to ask themselves is that of how to actually determine whether any particular marketing tactic is manipulative and whether a given manipulative tactic is, in fact, immoral. This article proposes to operationalize criteria that can be used by marketers for making such determinations and attempts to provide some clarification toward our under- standing of the concept of manipulation and the conditions for the moral acceptability of manipulative marketing practices. It argues that a marketing tactic is manipulative if it is intended to motivate by undermining what the marketer believes is his/her audience’s normal decision-making process either by deception or by playing on a vulnerability that the marketer believes exists in his/her audience’s normal decision-making process. Such a tactic is morally objectionable on several grounds, which make it morally impermissible unless outweighed by sufficient “redemptive” moral considerations.     

Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations

This research examines how individuals respond differently to recommendation options generated by ChatGPT, an AI-powered language model, in five studies. In contrast to previous research on choice overload, Studies 1 and 2 demonstrate that people tend to respond positively to a large number of recommendation options (60 options), revealing diverse consumer perceptions of AI-generated recommendations. Studies 3 and 4 further illustrate the moderating effect of recommendation agents and indicate that choice overload elicits distinct patterns of consumer reactions depending on whether the recommendations are from a human or AI agent. Lastly, Study 5 directly measures consumer preferences for recommendation agents, revealing a general preference for ChatGPT, particularly when a large number of options are available. These findings have significant implications for recommendation system design and user preferences regarding AI-powered recommendations.     

人工智能技术变革对国际贸易的影响

人工智能技术的快速发展正催生第四次工业革命,可能引发全球价值链深度重构和世界经贸格局重大变革。世界主要经济强国将发展人工智能技术作为争夺新一轮产业竞争优势的重要战略抓手。本文基于全球价值链视角研究人工智能技术变革对国际贸易的影响,我们发现人工智能技术变革可能推动国际贸易规模扩大,提升服务贸易份额,并促进国际贸易交易模式平台化、小宗化,可为中小企业创造更多参与国际贸易的机会。然而,人工智能技术变革也可能通过降低企业劳动力需求从而对我国等发展中国家的出口拉动型增长模式造成严重的潜在威胁。为应对人工智能技术变革,我国应部署并强化对人工智能产业发展的政策支持,加快培育制造业国际竞争新优势,大力推动先进制造业与现代生产性服务业深度融合发展,全面促进"中国制造"攀升全球价值链中高端。     

Conceptualization and Integration of Marketing Intelligence: The Case of an Industrial Manufacturer

Purpose: The primary goal of this article was to conceptualize a systematic marketing intelligence process for industrial manufacturers because, up until now, such concepts have only been focused on consumer goods settings. Hence, this article investigates how marketing intelligence activities are developed and managed effectively in industrial markets.

Methodology/approach: The authors conducted a case study of Maschinenfabrik Reinhausen, a German based manufacturer of regulation technology for power transformers.

Findings: We found that a systematic marketing intelligence process should be based on the resource- and market-based view of strategy as well as on the market orientation construct. When implementing marketing intelligence, the integration of the sales force within the whole process is the crucial lever for an industrial company. The formalization of the process is necessary to ensure its continuity and acceptance; however, the varying intensity allows the necessary flexibility of the process.

Research implications: The constraints of the decisive process steps of marketing intelligence have to be further enhanced for industrial markets. It is important to find out how the integration of the sales force can best be designed to incorporate reward systems and motivation structures, and how to establish a corporate marketing-minded culture throughout the organization.

Practical implications: The important steps for a well implemented marketing intelligence process are a preparation phase, followed by the gathering, analysis, and dissemination of information. This article highlights the success factors for each process step. Above all, managerial and organizational commitment is necessary for the implementation of the whole process.

Originality/value/contribution: By focusing on the industrial manufacturing business, this study provides deep insights into a neglected area of research. Light needs to be shed on marketing intelligence in industrial markets, where the lack of traditional market research has to be compensated.     

人工智能经济学的思想端倪及建构路径

人工智能技术的广泛运用正在悄然改变人类物质文明和精神文明。文章认为人工智能对社会经济、政治、文化乃至对意识形态的影响,已经展露出可建构人工智能经济学的思想端倪。基于数字经济的发展,需要解释人工智能会在哪些方面通过市场机制对厂商投资经营发生作用;厂商运用人工智能技术究竟能在多大程度上提供有效供给和预测需求,厂商在未来普遍采用大数据分析和人工智能技术进行投资经营选择时,社会经济实践会在多大范围内要求经济学家修正微观经济学基础的理性选择理论、厂商理论和产业组织理论;假若大数据分析和人工智能技术在未来果真会引发经济学革命,经济学家如何探寻人工智能经济学的建构路径等。文章拟依据微观经济学原理对以上诸问题作出分析性描述,对有可能在理论上站得住脚的人工智能经济学的建构路径展开研究,以期达到相对前卫的理论认知。     

大数据、人工智能与厂商竞争路径

经济学对市场竞争路径的学理性分析,主要集中在价格确定、产量确定、规模经济、产业组织等方面,而对科技进步引发市场竞争路径的变化并没有足够的关注。其实,市场竞争路径变化的底蕴是科技进步,只是经济学家在分析市场竞争路径时偏好于将科技因素作为外生变量处理。大数据和人工智能等的发展可谓是一场史无前例的科技革命,它对人类经济活动产生广泛而深刻的影响主要表现为：大数据及其运用怎样影响厂商投资经营,大数据与机器学习等人工智能手段相融合会在哪些方面改变厂商竞争路径,厂商如何提高数据智能化和实现网络协同化,在什么样的条件下会出现行业垄断,等等。文章的基本分析观点是：厂商竞争路径变化是贯穿于大数据、互联网和人工智能等相互融合过程的一种现象,这种现象对应于新科技进步和运用的不同层级;微观经济分析需要将新科技因素作为内生变量,通过分析大数据、机器学习与厂商竞争路径之间的关联,揭示厂商竞争路径变化机理以及由此引致的产业组织等问题。     

Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential

Artificial intelligence (AI) and machine learning (ML) may save money and improve the efficiency of business processes, but these technologies can also destroy business value, sometimes with grave consequences. The inability to identify and manage that risk can lead some managers to delay the adoption of these technologies and thus prevent them from realizing their potential. This article proposes a new framework by which to map the components of an AI solution and to identify and manage the value-destruction potential of AI and ML for businesses. We show how the defining characteristics of AI and ML can threaten the integrity of the AI system’s inputs, processes, and outcomes. We then draw from the concepts of value-creation content and value-creation process to show how these risks may hinder value creation or even result in value destruction. Finally, we illustrate the application of our framework with an example of the deployment of an AI-powered chatbot in customer service, and we discuss how to remedy the problems that arise.     

Social Commerce: A Contingency Framework for Assessing Marketing Potential

A key issue for marketers resulting from the dramatic rise of social media is how it can be leveraged to generate value for firms. Whereas the importance of social media for brand management and customer relationship management is widely recognized, it is unclear whether social media can also help companies market and sell products. Extant discussions of social commerce present a variety of perspectives, but the core issue remains unresolved. This paper aims to make two contributions. First, to address the lack of clarity in the literature regarding the meaning and domain of social commerce, the paper offers a definition stemming from important research streams in marketing. This definition allows for both a broad (covering all steps of the consumer decision process) and a narrow (focusing on the purchase act itself) construal of social commerce. Second, we build on this definition and develop a contingency framework for assessing the marketing potential that social commerce has to offer to firms. Implications for researchers and managers, based on the proposed definition and framework, are also discussed.     

Generating Competitive Intelligence in Organizations

Marketing strategy begins with customer and competitive intelligence. However, in sharp contrast to customer intelligence, there is little research on how competitive intelligence (CI) is actually generated within an organization. The absence of this knowledge makes it difficult to identify ways to improve the CI generation process. Drawing on both depth interviews with full-time personnel who conduct competitive intelligence and academic literature in related fields, the authors derive a novel conceptual framework that describes three interdependent phases of the competitive intelligence generation process: (1) organizing for competitive intelligence, (2) searching for information, and (3) sense-making. Dimensions of efficacy at each phase are also identified, and they are posited to be influenced by factors pertaining to: (1) the intelligence network, (2) the business environment, (3) the information environment, and (4) analyst characteristics. This framework departs from the existing literature by identifying core components of the competitive intelligence generation process, highlighting its iterative nature, and identifying variables germane to its success. The emergent framework's implications for managing the competitive intelligence generation process are discussed and future research directions are suggested.     

Collaborative Learning Instrument in Teaching Entrepreneurship Issues

Abstract

Due to the shortcomings associated with the largely passive learning experience currently experienced by students at the University level in Central and Eastern Europe, active learning approaches have been promoted by educationalists as a more effective method for teaching business and entrepreneurship. This paper contributes to this literature by outlining a collaborative learning instrument involving active learning that can be used to teach entrepreneurship at university level in Central and Eastern Europe. This instrument illustrates the role of entrepreneurship and proprietorship in both a well-established market economy and in the post-communist economies of Central and Eastern Europe. Students outcomes should include the following: firstly, the recognition that the emerging small business sectors of the post-communist economies have much potential as a vehicle for economic growth and for developing capitalist forms of economic production; secondly, an understanding that entrepreneurial behavior is an essential element in the development of the small business sector; thirdly, an ability to identify traits common to successful entrepreneurs; and fourthly, they will develop and practice a variety of entrepreneurial skills themselves that may make them aware of their potential as entrepreneurs.