Innovation analytics: Leveraging artificial intelligence in the innovation process

Artificial intelligence (AI) is about imbuing machines with a kind of intelligence that is mainly attributed to humans. Extant literature—coupled with our experiences as practitioners—suggests that while AI may not be ready to completely take over highly creative tasks within the innovation process, it shows promise as a significant support to innovation managers. In this article, we broadly refer to the derivation of computer-enabled, data-driven insights, models, and visualizations within the innovation process as innovation analytics. AI can play a key role in the innovation process by driving multiple aspects of innovation analytics. We present four different case studies of AI in action based on our previous work in the field. We highlight benefits and limitations of using AI in innovation and conclude with strategic implications and additional resources for innovation managers.     

Affective Computing in Marketing: Practical Implications and Research Opportunities Afforded by Emotionally Intelligent Machines

After years of using AI to perform cognitive tasks, marketing practitioners can now use it to perform tasks that require emotional intelligence. This advancement is made possible by the rise of affective computing, which develops AI and machines capable of detecting and responding to human emotions. From market research, to customer service, to product innovation, the practice of marketing will likely be transformed by the rise of affective computing, as preliminary evidence from the field suggests. In this Idea Corner, we discuss this transformation and identify the research opportunities that it offers.

     

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 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.     

Cooperative communication: Computerware and humanware

This article emphasizes the combined requirements of computer systems and humanistics. In cooperative computing, negotiations can be used as a basic paradigm by which different roles and their requisites can be identified—the facilitator, the mediator, and the negotiator. The negotiation‐cooperation process has a logical sequence of agreements, definition of terms, objectives, mode of operation, common security measures concerning integrity and liability, handling protocols, etc. The cooperation is based on models of the subject and the partners—i.e., a minimum of three models should be matched. The usual methods of human negotiations supported by metacommunication should have a computer‐realizable substitute. All these subjects are outgrowths of recent research in artificial intelligence (knowledge‐based systems) and cognitive psychology; some experiences are reported in the field. However, the main task is human‐oriented—education of people for this new powerful means of coexistence.     

Cloud computing capability: its technological root and business impact

Cloud computing can help organizations create business value for long-term viability and sustainability by providing flexibility and versatility. We report a systematic analysis of the central role of cloud computing capability in bridging the information technology (IT) features of cloud computing and its business value. We posit that the IT features of cloud computing lead to measureable increase in business value on both dimensions of performance benefit and collaboration benefit through cloud computing capability. Survey data collected from 174 firms largely support our hypotheses. This study offers fine-grained insights into the mechanisms of how the IT features of cloud computing influence the business value stemming from cloud computing. Firms should focus more on cultivating organizational capabilities to effectively deploy cloud computing in order to harvest the benefits promised by cloud computing.     

Embedded ethics: How complex systems and structures guide ethical outcomes

This article introduces a new concept, embedded ethics, to explain the subtle impact that complex systems and structures have on ethical outcomes. We define embedded ethics as the entrenched complex of networked structural indicators that subtly and silently direct actions in the form of normalized industrial, organizational, and/or functional-role behavior. We then describe two examples—one from the legal systems (corporate governance) and one from business (shareholder value)—to demonstrate the usefulness of this concept in helping to identify opportunities to improve unethical outcomes in systems in which actors otherwise are understood as just doing their job. The concept of embedded ethics is especially critical in our too-big-to-fail corporate environment and Post-Internet Age of technological innovation.     

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.     

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.     

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.     

Consumer value of virtual music festivals

Virtual music festivals (VMFs) are a great opportunity for the music industry to improve high quality digital events; as an alternative consumption phenomenon, VMFs can reach hybrid audiences and help in the challenge of digitalization. This study aims to investigate the multidimensional structure of consumer value in VMFs (intra-variable perspective), as well as the effects of value on cognitive versus affective satisfaction and loyalty (inter-variable perspective). Using a qualitative–quantitative approach—focus group and survey with festival attendees (n = 246)—, a value structure of VMF is tested with partial least square–SEM as a reflective-formative-formative third-order model. Benefits and sacrifices are second-order constructs, while five positives (escape, novelty, enjoyment, musical content, and socialization) and two negatives (monetary and nonmonetary costs) are first-order ones. Results show VMFs value as a multidimensional trade-off, where socialization is not contributing to value, but a balance between cognitive (musical content) and affective (enjoyment, and less escape and novelty) exists. Furthermore, consumer value intensifies both cognitive and affective satisfaction, but just the former affects loyalty. These findings provide new insights to better understand the decision-making processes of virtual festival attendees.     

Künstliche Intelligenz und der Faktor Arbeit

Artificial Intelligence (AI) and its potential impact on the demand for human labour has become a fashionable topic — particularly among economic policy advisors. However, most papers that warn about high substitution rates of conventional jobs use questionable data and methodologies. Future economic policy advice ought to include a company perspective — and discover that disruption will be much more gradual and less dramatic than proclaimed by the current topical papers. As this AI revolution is just at its very beginning, economic policy makers are advised to accompany and support digital change instead of actively intervening in digital value chains with taxes and/or subsidies.     

Social anxiety as a moderator in consumer willingness to accept AI assistants based on utilitarian and hedonic values

This study develops a comprehensive research model to explain user willingness to accept AI assistants, and the acceptance path pertaining to this process. User data was used to test how the advantages of AI assistant (accuracy, responsiveness, compatibility, anthropomorphism, & affinity) influence consumer utilitarian and hedonic value, and explore how their willingness to accept AI assistants is affected by their value perceptions. This research also examines whether social anxiety moderates the relationship between AI assistant advantages and utilitarian/hedonic value. The study reveals that AI assistant advantages are important factors affecting the utilitarian/hedonic value perceived by users, which further influence user willingness to accept AI assistants. The relationships between AI assistant advantages and utilitarian and hedonic value are affected differently by social anxiety. Marketers and managers in the AI context can refer to the study methods to help improve AI assistants and develop more effective marketing strategies for product promotion.     

Rags to riches? Entrepreneurs' social classes,resourceful time allocation,and venture performance

Social classes shape entrepreneurial pursuits in that entrepreneurs from lower social class groups face more resource deficiencies compared to those from higher social class groups. In this study, we theorize that being resourceful with a particular resource—time—helps ventures run by lower-class entrepreneurs achieve better performance. However, we further argue that the extent to which entrepreneurs use time resourcefully is affected by the cognitive schemas stamped on them by their social class backgrounds. Our empirical analysis of 8663 Chinese private entrepreneurs between 2006 and 2010 lends robust support to these arguments. By revealing both material and cognitive constraints stemming from entrepreneurs' social classes, our study contributes to research on social classes and entrepreneurial resourcefulness and has important implications for understanding the persistence of inequality in entrepreneurship.     

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.     

Search reduction in hierarchical distributed problem solving

Knoblock and Korf have determined that abstraction can reduce search at a single agent from exponential to linear complexity (Knoblock 1991; Korf 1987). We extend their results by showing how concurrent problem solving among multiple agents using abstraction can further reduce search to logarithmic complexity. We empirically validate our formal analysis by showing that it correctly predicts performance for the Towers of Hanoi problem (which meets all of the assumptions of the analysis). Furthermore, a powerful form of abstraction for large multiagent systems is to group agents into teams, and teams of agents into larger teams, to form an organizational pyramid. We apply our analysis to such an organization of agents and demonstrate the results in a delivery task domain. Our predictions about abstraction's benefits can also be met in this more realistic domain, even though assumptions made in our analysis are violated. Our analytical results thus hold the promise for explaining in general terms many experimental observations made in specific distributed AI systems, and we demonstrate this ability with examples from prior research.This research has been sponsored, in part, by the National Science Foundation under grants IRI-9015423 and IRI-9010645, by the University of Michigan Rackham Graduate School, and by a Bell Northern Research Postgraduate Award.     

A Multi-Agent View of Strategic Planning Using Group Support Systems and Artificial Intelligence

The strategic planning process is dynamic and complex. Including a Group Support System (GSS) in the problem-solving process can improve the content quality of the strategic plan by allowing increased participation by more members of the organization. However, it can also add to the complexity of the problem by increasing the quantity of textual information that can result from group activity. Added complexity increases cognitive overload and frustrations of those participants negotiating the contents of the strategic plan. This article takes a multi-agent view of the strategic planning process. It considers group participants as multiple agents concerned with the content quality of the strategic plan. The facilitator agent is responsible for guiding groups in the strategic plan construction process as well as for solving process problems such as cognitive overload. We introduce an AI Concept Categorizer agent, a software tool that supports the facilitator in addressing the process problem of cognitive overload associated with convergent group activities by synthesizing group textual output into conceptual clusters. The implementation of this tool reduces frustrations which groups encounter in the process of classifying textual output and provides more time for discussion of the concepts themselves. Because of the large amount of convergent activity necessary for strategic planning, the addition of the AI Concept Categorizer to the strategic planning process should increase the quality of the strategic plan and the buy-in of the participants in the strategic planning process.     

Keep it,shave it,cut it: A closer look into consumers’ video viewing behavior

The convergence of communications, information, entertainment, commerce, and computing—combined with a rising number of new services—has caused changes in the way people access and consume media. In recent months, the phenomenon of viewing audiences shifting wholly or partially from cable TV providers to over-the-top (OTT) media, also known as cord cutting or cord shaving respectively, has gained much attention. Despite anecdotal evidence of cord cutting and cord shaving presented in the trade press and industry conferences, there has been little rigorous examination of the true effect that cord cutting or cord shaving may have on TV networks and the media industry at large. In this article, we use behavioral data from a leading cable operator in the U.S. to identify and describe the key viewer segments. We also conduct simulations to examine the effects and implications of cord cutting and cord shaving from a customer lifetime value perspective for content providers, content distributors, and advertisers.     

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.