Machines augmenting entrepreneurs: Opportunities (and threats) at the Nexus of artificial intelligence and entrepreneurship

Artificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to flexibly adapt to tasks to achieve specific outcomes. This paper briefly explains AI and looks into the future to highlight some of AI's broader and longer-term societal implications. We propose that AI can be combined with entrepreneurship to represent a super tool. Scholars can research the nexus of AI and entrepreneurship to explore the possibilities of this potential AI-entrepreneurship super tool and hopefully direct its use to productive processes and outcomes. We focus on specific entrepreneurship topics that benefit from AI's augmentation potential and acknowledge implications for entrepreneurship's dark side. We hope this paper stimulates future research at the AI-entrepreneurship nexus.Executive summaryArtificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to flexibly adapt to tasks to achieve specific outcomes. Machine learning is the most common form of AI and largely relies on supervised learning—when the machine (i.e., AI) is trained with labels applied by humans. Deep learning and adversarial learning involve training on unlabeled data, or when the machine (via its algorithms) clusters data to reveal underlying patterns.AI is simply a tool. Entrepreneurship is also simply a tool. How they are combined and used will determine their impact on humanity. While researchers have independently developed a greater understanding of entrepreneurship and AI, these two streams of research have primarily run in parallel. To indicate the scope of current and future AI, we provide examples of AI (at different levels of development) for four sectors—customer service, financial, healthcare, and tertiary education. Indeed, experts from industry research and consulting firms suggest many AI-related business opportunities for entrepreneurs to pursue.Further, we elaborate on several of these opportunities, including opportunities to (1) capitalize on the “feeling economy,” (2) redistribute occupational skills in the economy, (3) develop and use new governance mechanisms, (4) keep humans in the loop (i.e., humans as part of the decision making process), (5) expand the role of humans in developing AI systems, and (6) expand the purposes of AI as a tool. After discussing the range of business opportunities that experts suggest will prevail in the economy with AI, we discuss how entrepreneurs can use AI as a tool to help them increase their chances of entrepreneurial success. We focus on four up-and-coming areas for entrepreneurship research: a more interaction-based perspective of (potential) entrepreneurial opportunities, a more activities-based micro-foundation approach to entrepreneurial action, a more cognitively hot perspective of entrepreneurial decision making and action, and a more compassionate and prosocial role of entrepreneurial action. As we discuss each topic, we also suggest opportunities to design an AI system (i.e., entrepreneurs as potential AI designers) to help entrepreneurs (i.e., entrepreneurs as AI users).AI is an exciting development in the technology world. How it transforms markets and societies depends in large part on entrepreneurs. Entrepreneurs can use AI to augment their decisions and actions in pursuing potential opportunities for productive gains. Thus, we discuss entrepreneurs' most critical tasks in developing and managing AI and explore some of the dark-side aspects of AI. Scholars also have a role to play in how entrepreneurs use AI, but this role requires the hard work of theory building, theory elaboration, theory testing, and empirical theorizing. We offer some AI topics that we hope future entrepreneurship research will explore. We hope this paper encourages scholars to consider research at the nexus of AI and entrepreneurship.     

Artificial intelligence: Building blocks and an innovation typology

The range of topics and the opinions expressed on artificial intelligence (AI) are so broad that clarity is needed on the the field’s central tenets, the opportunities AI presents, and the challenges it poses. To that end, we provide an overview of the six building blocks of artificial intelligence: structured data, unstructured data, preprocesses, main processes, a knowledge base, and value-added information outputs. We then develop a typology to serve as an analytic tool for managers grappling with AI’s influence on their industries. The typology considers the effects of AI-enabled innovations on two dimensions: the innovations’ boundaries and their effects on organizational competencies. The typology’s first dimension distinguishes between product-facing innovations, which influence a firm’s offerings, and process-facing innovations, which influence a firm’s operations. The typology’s second dimension describes innovations as either competence-enhancing or competence-destroying; the former enhances current knowledge and skills, whereas the latter renders existing skills and knowledge obsolete. This framework lets managers evaluate their markets, the opportunities within them, and the threats arising from them, providing valuable background and structure to important strategic decisions.     

Artificial Intelligence and Marketing: Pitfalls and Opportunities

This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations.     

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.     

Sustainable Supply Chains in the Age of AI and Digitization: Research Challenges and Opportunities

Sustainability has become a global corporate mandate with implementation impacted by two key trends. The first is recognition that global supply chains have a profound impact on sustainability which requires “greening” the entire supply chain. The second is technology—digitization, artificial intelligence (AI), and “big data”—which have become ubiquitous. These technologies are impacting every aspect of how companies organize and manage their supply chains and have a powerful impact on sustainability. In this essay, we synthesize current dominant themes in research on sustainable supply chains in the age of digitization. We also highlight potential new research opportunities and challenges and showcase the papers in our STF.     

How intelligent is Watson? Enabling digital transformation through artificial intelligence

Due to its intrinsic characteristics, artificial intelligence (AI) can be considered a general-purpose technology (GPT) in the digital era. Most studies in the field focus on the ex-post recognition and classification of GPT but in this article, we look at a GPT design ex-ante by reviewing the extreme and inspiring example of IBM’s Watson. Our objective is to shed light on how companies can create value through AI. In particular, our longitudinal case study highlights the strategic decisions IBM took to create value in two dimensions: internal development and external collaborations. We offer relevant implications for practitioners and academics eager to know more about AI in the digital world.     

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.     

Evolutionary Business Information Systems

This article reflects on existing and emerging future challenges arising in the area of “evolutionary business information systems”, a class of systems that demand an evolutionary software development process and which support secondary design of various conceptual layers. We place both existing contributions and future research opportunities in context by referring to an idealized, preliminary system architecture. Finally, we emphasize our pluralistic perspective on the research object and the resulting need for methodological flexibility in the sense of interdisciplinary configurations of research methods.     

Customer Lifetime Value as the Basis of Customer Segmentation

SUMMARY

In this article, we examine current trends in customer life-time value and customer segmentation models and identify key issues for future research. CLV-based segmentation is a segmentation approach that groups customers into meaningful segments based upon customer lifetime value and (potentially) other factors. In the article, we discuss the extent to which CLV-based segmentation meets the criteria for effective segmentation. We also identify six areas for future research: (1) models and management of “micro-segments,” (2) using CLV-based segmentation to improve the efficiency of marketing programs, (3) the need for more dynamic CLV-based segmentation models, (4) applying CLV-based customer segmentation to new products and new customers, (5) challenges associated with implementing CLV-based segmentation, and (6) the need for new models that enable firms to segment customers by response to marketing activities and CLV at different points in the customer decision process.     

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.     

The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types

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.     

Trends in international business,thought, and literature: The past and future of political analysis for international business

The literature on political risks (and opportunities) in international business has expanded far beyond its initial preoccupation with expropriation and instability in “third world,” developing countries. The literature has thus become more concerned with other types of government policies and with political conditions in “first world” and “second world” countries as well. In those respects, the literature has developed analytically and become of more widespread relevance to managerial issues in international business. This article reviews the topical coverage of the political risk literature of international business, and in addition it considers key analytical issues concerning the levels of analysis, theoretical content, and analytical methods that have been evident, as well as those that have been neglected. © 1993 John Wiley & Sons, Inc.     

Kenya

Arguably, Kenya is the “darling” of tourists who are brave enough to “explore” Africa and to find out for themselves the myths and reality of life on a continent that always receives negative media report overseas. Other tourists are attracted to Kenya because Hollywood movies based on Africa and set in Kenya (e.g., Out of Africa) have depicted the romanticized life of Europeans/White settlers/expatriates in the “deep” and “remote” African frontier. Thus, those with a stereotypical picture of Africa acquired from Western television screens expect to find an extremely undeveloped country. But arrival at Jomo Kenyatta International airport in Nairobi reveals a relatively well‐developed city that is very much integrated into the world capitalist system/economy. With a relatively strong economy Kenya has been successful in attracting both tourists and foreign investors. This article attempts to provide a clearer picture of the business climate in the country. Among other issues, it highlights the fact that the country has enjoyed peace and political stability while many of its neighbors remain deeply entrenched in wars and ethnic conflicts. This has resulted in some degree of economic prosperity that has eluded many countries in sub‐Saharan Africa. While highlighting the numerous investment opportunities especially in the telecommunications sector, the article also sheds light on some of the challenges that confront foreign investors and how to tackle them. © 2003 Wiley Periodicals, Inc.     

Siri,Siri, in my hand: Who’s the fairest in the land? On the interpretations,illustrations, and implications of artificial intelligence

Artificial intelligence (AI)—defined as a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation—is a topic in nearly every boardroom and at many dinner tables. Yet, despite this prominence, AI is still a surprisingly fuzzy concept and a lot of questions surrounding it are still open. In this article, we analyze how AI is different from related concepts, such as the Internet of Things and big data, and suggest that AI is not one monolithic term but instead needs to be seen in a more nuanced way. This can either be achieved by looking at AI through the lens of evolutionary stages (artificial narrow intelligence, artificial general intelligence, and artificial super intelligence) or by focusing on different types of AI systems (analytical AI, human-inspired AI, and humanized AI). Based on this classification, we show the potential and risk of AI using a series of case studies regarding universities, corporations, and governments. Finally, we present a framework that helps organizations think about the internal and external implications of AI, which we label the Three C Model of Confidence, Change, and Control.     

Re-conceptualizing intellectual property regimes in international business research: Foreign-friendliness paradoxes facing MNCs in China

This article seeks to better explain the complex challenges that intellectual property (IP) regimes pose to foreign multinational corporations (MNCs). It draws on in-depth research into the appropriability and entrepreneurial risks as well as transaction costs that China’s IP regime has posed to foreign MNCs to date, and forecasts how these risks and costs may evolve in the future. I find that, contrary to conventional wisdom, IP regimes are not always best conceptualized as either “weak” or “strong”. Instead, I illustrate that complex “foreign-friendliness paradoxes” are possible in IP regimes, show how they evolve, and explain them with a more robust framework than previously available. These findings help re-conceptualize IP regimes in IB research.     

Will AI ever sit at the C-suite table? The future of senior leadership

As the sophistication of artificial intelligence (AI) systems develop and AI becomes a key element of organizational strategy across a wide spectrum of industries, new demands are being placed on senior leaders. To understand the growing challenges leaders will face in the age of AI, we conducted interviews with 33 senior leaders in several countries across a wide range of industries. Our research highlights key capabilities and skills that leaders will require. Underlying these capabilities is a mindset oriented toward continuous learning and self-development, which will enable ongoing and rapid adaptation to change. Our findings identified the following key capabilities: digital know-how, data-driven focus, networking, ethics, and agility. To successfully navigate the coming era, senior leaders will need to focus on reskilling the workforce, recruiting and retaining highly skilled talent, building an intrapreneurial culture, and managing unprecedented changes in technologies and the nature of work.     

In the heat of the game: Analogical abduction in a pragmatist account of entrepreneurial reasoning

We draw on Searle's philosophy of language to distinguish between “opportunities” as intentional content directed towards a preferred future that entrepreneurs aim to fulfill and opportunities as conditions to be met for their satisfaction. We maintain that studying the former requires adopting a player stance rather than the analyst stance that prevails in the current literature. We build on pragmatist conceptions of truth and imagination to elaborate on the player stance and propose analogical abduction as a mechanism for conceiving and fulfilling “opportunities”. We develop a pragmatist process model of entrepreneurial reasoning that balances the two stances, and derive action principles for entrepreneurs from it.     

Understanding Programmatic Creative: The Role of AI

In this article, we expand the concept of programmatic advertising to include programmatic creative as a vital component. While artificial intelligence (AI) has already automated the media buying process, the advertising creative process still requires extensive human efforts. Such discrepancy calls for AI to transform the advertising creative process. We provide a framework for understanding and investigating programmatic creative by drawing evidence from the advertising industry in China. We specifically discuss how big data and machine learning algorithms underpin programmatic advertising. We argue that AI will integrate programmatic buying and programmatic creative in the future. We also discuss the technological, regulatory, and legal challenges faced by programmatic creative. We argue that new theories and methods are needed to conduct research in this area and provide guidance for the advertising industry.     

Four visions of e‐commerce in Latin America in the year 2010

Excitement is growing around the world about the Internet's potential to enable a global electronic commerce. The reality, however, differs a great deal depending on what part of the world we consider. In Latin America, the e‐commerce theme is often found in the popular press portraying it as a solution for multiple national problems and as a catalyst to propel the region to a stronger position in the world economy. Unfortunately, many of the “new” capabilities required to harness the economic benefit of e‐commerce, which emphasize value‐adding steps performed through and with information, are very scarce in the region. This article raises questions about Latin America's ability to exploit the Internet, and to implement and capitalize on e‐commerce applications. It underscores the region's need to engage in a future planning discourse about the adoption of e‐commerce within organizations, communities, and across traditional boundaries of competition and national borders. To start the dialogue, four scenarios are presented as “future snapshots” written from the vantage point of the year 2010. Each scenario considers issues regarding technological adoption and potential social responses, and discusses some of the critical assumptions about patterns observed in the region today and their implications for the future. © 2001 John Wiley & Sons, Inc.     

Deconstructing the Franchise as a Legal Entity: Practice and Research in International Franchise Law

This article canvasses practice and research in international franchising law. The franchisor law's key concepts are introduced. I then identify aspects of franchising practice that are poorly accommodated by the law. These aspects offer opportunities for productive research. I identify these aspects as follows: franchising law's reliance on contracts to regulate the relationship through all its phases, the risk that a “franchisee” is an employee, good faith, governance, and insolvency. I continue with suggestions as to why these challenges exist. The article concludes with emerging themes in franchise practice and research: e-commerce, natural disasters, sustainability, micro-franchising, and social franchising.