Social bundling: A novel method to enhance consumers’ intention to purchase online bundles

Bundling in retail has been argued to improve sales volume and speed, which can improve retailers’ operation performance. However, recent research finds that the purchase quantity requirement in traditional bundling deters non-buyers from becoming buyers. This paper proposes “social bundling,” as a novel method that alleviates the quantity requirement while satisfying the bundling benefits for consumers and retailers. We empirically test a theoretical model that explains the advantages and disadvantages of social bundling vis-à-vis traditional bundling in influencing consumers’ intentions to purchase in bundles. We conclude that social bundling outperforms traditional bundling in driving intention to purchase in bundles.     

Machine learning for enterprises: Applications,algorithm selection,and challenges

Machine learning holds great promise for lowering product and service costs, speeding up business processes, and serving customers better. It is recognized as one of the most important application areas in this era of unprecedented technological development, and its adoption is gaining momentum across almost all industries. In view of this, we offer a brief discussion of categories of machine learning and then present three types of machine-learning usage at enterprises. We then discuss the trade-off between the accuracy and interpretability of machine-learning algorithms, a crucial consideration in selecting the right algorithm for the task at hand. We next outline three cases of machine-learning development in financial services. Finally, we discuss challenges all managers must confront in deploying machine-learning applications.     

Deepfakes: Trick or treat?

Although manipulations of visual and auditory media are as old as media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by the latest technological advances in artificial intelligence and machine learning, deepfakes offer automated procedures to create fake content that is harder and harder for human observers to detect. The possibilities to deceive are endless—including manipulated pictures, videos, and audio—and organizations must be prepared as this will undoubtedly have a large societal impact. In this article, we provide a working definition of deepfakes together with an overview of its underlying technology. We classify different deepfake types and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to ensure deniability, Expose deepfakes early, Advocate for legal protection, and Leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter deepfake tricks as we appreciate deepfake treats.     

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 data to action: How marketers can leverage AI

Artificial intelligence (AI) is at the forefront of a revolution in business and society. AI affords companies a host of ways to better understand, predict, and engage customers. Within marketing, AI’s adoption is increasing year-on-year and in varied contexts, from providing service assistance during customer interactions to assisting in the identification of optimal promotions. But just as questions about AI remain with regard to job automation, ethics, and corporate responsibility, the marketing domain faces its own concerns about AI. With this article, we seek to consolidate the growing body of knowledge about AI in marketing. We explain how AI can enhance the marketing function across nine stages of the marketing planning process. We also provide examples of current applications of AI in marketing.     

Rulers of the world,unite! The challenges and opportunities of artificial intelligence

A decade ago, we published an article in Business Horizons about the challenges and opportunities of social media with a call to action: “Users of the world, unite!” To celebrate its anniversary, we look at artificial intelligence and the need to create the rules necessary for peaceful coexistence between humanity and AI. Hence, we now are urging: “Rulers of the world, unite!” In this article, we outline six debates surrounding AI in areas like artificial superintelligence, geographical progress, and robotics; in doing so, we shed light on what is fact and what is utopia. Then, using the PESTEL framework, we talk about the six dilemmas of AI and its potential threat and use. Finally, we provide six directions on the future of AI regarding its requirements and expectations, looking at enforcement, employment, ethics, education, entente, and evolution. Understanding AI’s potential future will enable governments, corporations, and societies at large (i.e., the rulers of this world) to prepare for its challenges and opportunities. This way, we can avoid a scenario in which we return in 10 years to write the article: “Dreamers of the world, unite!”     

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.     

Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel

The B2B sales process is undergoing substantial transformations fueled by advances in information and communications technology, specifically in artificial intelligence (AI). The premise of AI is to turn vast amounts of data into information for superior knowledge creation and knowledge management in B2B sales. In doing so, AI can significantly alter the traditional human-centric sales process. In this article, we describe how AI affects the B2B sales funnel. For each stage of the funnel, we describe key sales tasks, explain the specific contributions AI can bring, and clarify the role humans play. We also outline managerial considerations to maximize the contributions from AI and people in the context of B2B sales.     

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 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.     

Impacts of Asynchronous Learning Networks on Individual and Group Problem Solving: A Field Experiment

An Asynchronous Learning Network (ALN) is a Computer-Mediated Communication System designed to support "anytime/anywhere" interaction among students and between students and instructors. A field experiment compared groups and individuals solving an ethical case scenario, with and without an ALN, to determine the separate and joint effects of communication medium and teamwork. Dependent variables include quality and length of the reports, and subjective perceptions of learning and satisfaction. The results indicate that that an ALN enhances the quantity and quality of the solutions to an ethical case scenario. The combination of teamwork with ALN-support increases the students' perception of learning. Although the perception of collaborative learning was similar between ALN-supported and unsupported groups, participants in computer-mediated groups reported lower perceptions of discussion quality than participants in manual groups.