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Over the past five centuries, advances in Western understanding of and control over the material world have strongly influenced European responses to non-Western peoples and cultures. InMachines as the Measure of Men, Michael Adas explores the ways in which European perceptions of their scientific and technological superiority shaped their interactions with people overseas. Adopting a broad, comparative perspective, he analyzes European responses to the cultures of sub-Saharan Africa, India, and China, cultures that they judged to represent lower levels of material mastery and social organization. Beginning with the early decades of overseas expansion in the sixteenth century, Adas traces the impact of scientific and technological advances on European attitudes toward Asians and Africans and on their policies for dealing with colonized societies. He concentrates on British and French thinking in the nineteenth century, when, he maintains, scientific and technological measures of human worth played a critical role in shaping arguments for the notion of racial supremacy and the \"civilizing mission\" ideology which were used to justify Europe's domination of the globe. Finally, he examines the reasons why many Europeans grew dissatisfied with and even rejected this gauge of human worth after World War I, and explains why it has remained important to Americans. Showing how the scientific and industrial revolutions contributed to the development of European imperialist ideologies,Machines as the Measure of Menhighlights the cultural factors that have nurtured disdain for non-Western accomplishments and value systems. It also indicates how these attitudes, in shaping policies that restricted the diffusion of scientific knowledge, have perpetuated themselves, and contributed significantly to chronic underdevelopment throughout the developing world. Adas's far-reaching and provocative book will be compelling reading for all who are concerned about the history of Western imperialism and its legacies. First published to wide acclaim in 1989,Machines as the Measure of Menis now available in a new edition that features a preface by the author that discusses how subsequent developments in gender and race studies, as well as global technology and politics, enter into conversation with his original arguments.

Would you read this book if a computer wrote it? Would you even know? And why would it matter? Today's eerily impressive artificial intelligence writing tools present us with a crucial challenge: As writers, do we unthinkingly adopt AI's time-saving advantages or do we stop to weigh what we gain and lose when heeding its siren call? To understand how AI is redefining what it means to write and think, linguist and educator Naomi S. Baron leads us on a journey connecting the dots between human literacy and today's technology. From nineteenth-century lessons in composition, to mathematician Alan Turing's work creating a machine for deciphering war-time messages, to contemporary engines like ChatGPT, Baron gives readers a spirited overview of the emergence of both literacy and AI, and a glimpse of their possible future. As the technology becomes increasingly sophisticated and fluent, it's tempting to take the easy way out and let AI do the work for us. Baron cautions that such efficiency isn't always in our interest. As AI plies us with suggestions or full-blown text, we risk losing not just our technical skills but the power of writing as a springboard for personal reflection and unique expression. Funny, informed, and conversational, Who Wrote This? urges us as individuals and as communities to make conscious choices about the extent to which we collaborate with AI. The technology is here to stay. Baron shows us how to work with AI and how to spot where it risks diminishing the valuable cognitive and social benefits of being literate.

This paper identifies three stages in the radical technological innovation process, namely formation process in niches, breaking out of niches and entering regimes, and new regime formation. It then adopts Multi-level Perspective (MLP) to explore the formation process, operating mechanism, breakthrough path, and impact factors of radical technological innovation. A three-phase model, which includes formation of radical innovation, breakout of radical innovation, and new regimes construction, is proposed to analyze radical technological innovation. The model is adopted in a case study to analyze the leapfrogging development of technologies in China’s mobile communication industry. This paper enriches technological innovation theory and provides supports for policy making and guidance for industries/enterprises practices regarding technological innovation in emerging economies.

This paper empirically demonstrates the productivity exhaustion entailed by the continuous accumulation of fixed capital in technological advancement, as part of the imitation strategy adopted by countries. It investigates the relationship between fixed capital per employee and the distance to the technological frontier in terms of Total Factor Productivity (TFP), using panel data from 118 countries during the period 1955-2019. The results reveal a diminishing marginal relationship between physical capital and technical progress in developed countries, especially in the top ten largest advanced economies in the world. These findings suggest the existence of a limit on the amount of capital per worker, beyond which no further technical progress would occur, thus indicating the erosion of the imitative strategy in the long term. KCI Citation Count: 0

Chatbot identity disclosure negatively affects customer purchases because customers perceive the disclosed bot as less knowledgeable and less empathetic. Empowered by artificial intelligence (AI), chatbots are surging as new technologies with both business potential and customer pushback. This study exploits field experiment data on more than 6,200 customers who are randomized to receive highly structured outbound sales calls from chatbots or human workers. Results suggest that undisclosed chatbots are as effective as proficient workers and four times more effective than inexperienced workers in engendering customer purchases. However, a disclosure of chatbot identity before the machine–customer conversation reduces purchase rates by more than 79.7%. Additional analyses find that these results are robust to nonresponse bias and hang-ups, and the chatbot disclosure substantially decreases call length. Exploration of the mechanisms reveals that when customers know the conversational partner is not a human, they are curt and purchase less because they perceive the disclosed bot as less knowledgeable and less empathetic. The negative disclosure effect seems to be driven by a subjective human perception against machines, despite the objective competence of AI chatbots. Fortunately, such negative impact can be mitigated by a late disclosure timing strategy and customer prior AI experience. These findings offer useful implications for chatbot applications, customer targeting, and advertising in conversational commerce.

The technology readiness (TR) index aims to better understand people’s propensity to embrace and use cutting-edge technologies. The initial TR construct considers four dimensions—innovativeness, optimism, insecurity, and discomfort—that collectively explain technology usage. The present meta-analysis advances understanding of TR by reexamining its dimensionality, and investigating mediating mechanisms and moderating influences in the TR–technology usage relationship. Using data from 193 independent samples extracted from 163 articles reported by 69,263 individuals, we find that TR is best conceptualized as a two-dimensional construct differentiating between motivators (innovativeness, optimism) and inhibitors (insecurity, discomfort). We observe strong indirect effects of these dimensions on technology usage through mediators proposed by the quality–value–satisfaction chain and technology acceptance model. The results suggest stronger relationships for motivators than for inhibitors, but also that these TR dimensions exert influence through different mediators. Further, the moderator results suggest that the strength of TR–technology usage relationships depends on the technology type (hedonic/utilitarian), examined firm characteristics (voluntary/mandatory use; firm support), and country context (gross domestic product; human development). Finally, customer age, education, and experience are related to TR. These findings enhance managers’ understanding of how TR influences technology usage.

Why do some new technologies emerge and quickly supplant incumbent technologies while others take years or decades to take off? We explore this question by presenting a framework that considers both the focal competing technologies as well as the ecosystems in which they are embedded. Within our framework, each episode of technology transition is characterized by the ecosystem emergence challenge that confronts the new technology and the ecosystem extension opportunity that is available to the old technology. We identify four qualitatively distinct regimes with clear predictions for the pace of substitution. Evidence from 10 episodes of technology transitions in the semiconductor lithography equipment industry from 1972 to 2009 offers strong support for our framework. We discuss the implication of our approach for firm strategy.

Recognizing the rapid advances in sales digitization and artificial intelligence technologies, we develop concepts, priorities, and questions to help guide future research and practice in the field of personal selling and sales management. Our analysis reveals that the influence of sales digitalization technologies, which include digitization and artificial intelligence, is likely to be more significant and more far reaching than previous sales technologies. To organize our analysis of this influence, we discuss the opportunities and threats that sales digitalization technologies pose for (a) the sales profession in terms of its contribution to creating value for customers, organizations, and society and (b) sales professionals, in terms of both employees in organizations and individuals as self, seeking growth, fulfillment, and status in the functions they serve and roles they live. We summarize our discussion by detailing specific research priorities and questions that warrant further study and development by researchers and practitioners alike.

The development of novel digital technologies connected to the Internet of Things, along with advancements in artificial intelligence and automation, is enabling a new wave of manufacturing innovation. “Smart factories” will leverage industrial equipment that communicates with users and with other machines, automated processes, and mechanisms to facilitate real-time communication between the factory and the market to support dynamic adaptation and maximize efficiency. Smart factories can yield a range of benefits, such as increased process efficiency, product quality, sustainability, and safety and decreased costs. However, companies face immense challenges in implementing smart factories, given the large-scale, systemic transformation the move requires. We use data gathered from in-depth studies of five factories in two leading automotive manufacturers to analyze these challenges and identify the key steps needed to implement the smart factory concept. Based on our analysis, we offer a preliminary maturity model for smart factory implementation built around three overarching principles: cultivating digital people, introducing agile processes, and configuring modular technologies.

We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor—the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production—due to automation and new tasks—can be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades.