Technovation最新文献

As algorithms powered by Artificial Intelligence (AI) are increasingly involved in the management of organizations, it becomes imperative to conduct human-centered AI management research and understand people's feelings and behaviors when machines gain power over humans. The two mainstream methods – vignette studies and case studies – reveal important but inconsistent insights. Here we discuss the respective limitations of vignette studies (affective forecasting errors, biased media coverage, and question substitution) and case studies (social desirability biases and lack of random assignment and control conditions), which may lead them to overrate negative and positive reactions to AI management, respectively. We further discuss the advantages of a third method for mitigating these limitations: field experiments on crowdsourced marketplaces. A proof-of-concept study on Amazon Mechanical Turk (Mturk; as a world-leading crowdsourcing platform) showed unique human reactions to AI management, which were not perfectly aligned with those in vignette or case studies. Participants (N = 504) did not differ significantly under AI versus human management, in terms of performance, intrinsic motivation, fairness perception, and commitment. We suggest that crowdsourced marketplaces can go beyond human research subject pools and become models of AI-managed workplaces, facilitating timely behavioral research and robust predictions on human-centered work designs and organizations.

This study explores relationships among various aspects of Innovation Intermediaries' (IIs) business models and their intention to provide digital and data-enabled services to their members/customers. Using mixed research methods with data collected from Danish IIs, we find that IIs' current use level of digital resources has a positive relationship with their intention of offering digital and data services, while the level of existing digital services demotivates or prevents IIs from providing them. We also find that the breadth of IIs' customers and partnerships is not directly associated with their intention to offer more digital services in the future. These findings are enriched with nuanced insights from a follow-up qualitative study showing that strategic partners do not provide the required support for intermediaries to develop and provide digital offerings. Instead, IIs rely on system integrators to equip themselves with the needed digital capacity to support their members/customers. The study contributes to the understanding of IIs’ business models linked to their service catalogue, particularly on digital offerings and their function as digitalization champions in a shifting paradigm of industrial digital transformation. Furthermore, it adds knowledge about open innovation by researching service offerings and relevant resources and capabilities of IIs as network organizations.

Artificial Intelligence (AI) stands ready to impact all aspects of business, from optimizing operations to personalizing services and enhancing customer value. However, many organizations grapple with implementing AI solutions due to a lack of necessary infrastructure and mechanisms. In short, many companies are not adequately prepared to adopt AI. To make matters worse, the literature does not offer sufficient insights into this issue. To help address this issue, in this article, the authors explore what it means to become ‘AI-ready.’ Specifically, this study identifies the various dimensions of AI readiness through in-depth semi-structured interviews with top- and middle-level managers from 52 multinational corporations in Southeast Asia, primarily in India. This study employed a qualitative data analysis approach to construct a grounded theory model focusing on AI readiness. The methodology involved systematic examination and coding of data to identify key themes and patterns, enabling the development of a comprehensive theoretical framework. The findings suggest that AI readiness can be categorized into eight dimensions: informational, environmental, infrastructural, participants, process, customers, data, and technological readiness. This study makes a significant contribution to marketing, management, and information systems by conceptualizing the AI readiness construct and identifying its key dimensions.

Data has become a powerful source of competitive advantage in contemporary business-to-business (B2B) markets, enabling vendors to create enhanced and completely new value propositions. However, while many firms can generate a lot of data from their businesses, they struggle to translate it into sellable offerings. To address this issue, this study examines how firms can sell and monetize data in B2B markets. Based on an extensive multiple case study, this study identifies four archetypical data-driven value propositions (data as a product, data-enhanced products, data-driven services, and data-enabled performance outcomes) and illuminates the key data-related capabilities and challenges related to each value proposition. The findings contribute to the digital servitization and data monetization literatures by illustrating how industrial firms can harness, package, and monetize data in B2B markets. For managers, this study offers important insights into the various strategies and capabilities needed to sell and monetize data in B2B markets.

Innovation is key to the economic performance of firms. However, several types of risks and uncertainties often lead to high failure rates in innovation projects, to the extent that failure is now considered a typical step in an innovation process. If innovation failure was provoking stress in the past, the current socio-economic contexts makes if it something to regularly deal with and learning from. However, there still is a dilemma on whether innovation failure should be considered as an opportunity or a trauma for the firm. Exploring the three steps of an innovation failure, that is identifying, analyzing and experimenting, the present research offers new insights on the nature of innovation failure at an individual and team levels. In particular, via an inductive approach this study provides fours key approaches to understanding innovation failure. First, we have uncovered a wide range of approaches to organizational management and ‘Spaghetti’ organizational models of innovation that perceive failure as an opportunity for the innovation process. Secondly, our study identified what seems to be a widespread perception of innovation failure as a trauma, which encourages the culture of openness to failure and to share knowledge of failing innovations. Such views apply not only at organizational but also at individual and team levels. Third, our results serve to encourage individuals, teams and organizations to find new ways to learn from failures in innovation projects, and to have a less centralized management and control over innovation. Fourth, in our perception of the domain, failure could become the ‘new normal’ and a mean to generate new knowledge. This stimulates new directions in the decision-making process, towards novel pathways to innovation.

Discontinuance usage of mobile banking application has been a great concern for banks. Yet, little attention in existing literature is directed toward understanding the underlying reasons why some customers stop using or rarely use mobile banking. Meanwhile, banking research shows that mobile banking is a highly welcomed innovation which has received substantial investment and has numerous benefits. This study argues that customers, in the quest to fulfilling their control beliefs, perceptions of social expectations, and attitudinal perceptions, tend to discontinue using mobile banking. Moreover, their intrinsic needs for stimulation through exploratory behaviors on mobile banking application determine the degree of fulfilling these beliefs and perceptions, leading to Mobile Banking Discontinuance Usage Intentions (DUI). The study adopts the Theory of Planned Behavior (TPB) with respect to discontinuance and the Optimum Stimulation Level (OSL) theory for the investigation. Covariance-based structural equation modeling technique is applied using web-based survey data, collected from 628 bank customers between April 2022 and June 2022 in a Far East Emerging Market. The results show that TPB predictors have positive significant relationships with DUI. Moreover, significant and non-significant moderating impacts of OSL on these relationships are revealed through the lenses of high and low OSL customers. Specifically, increased perceived behavioral control leads to stronger DUI among high OSL customers, increased subjective norms lead to stronger DUI for both high and low OSL customers, and unfavorable attitude toward mobile banking positively influences DUI regardless of customers’ OSL. The empirical findings offer key theoretical contributions and relevant practical implications for overcoming the dilemma confronting this technology, revamping mobile banking, and management of a range of major mobile financial services.

This study examines the role of technological change in affecting the evolution of the links across sectoral systems. Although the extant literature has investigated various aspects of the links among actors and institutions within sectoral systems, how a rather isolated sectoral system can evolve into a sectoral system vertically related with other ones still remains an unexplored terrain. This study shows how radical changes in knowledge and technology may open new links and connect previously unconnected sectoral systems. During this process, a previously isolated sector may drastically change and expand its links with the upstream and downstream sectors. Consequently, sectoral systems may evolve in becoming highly interconnected in terms of firms, non-firm actors, and institutions. We illustrate this process with a case study of the evolution of the mobile communications sector, which has witnessed increasing vertical linkages with two key related sectors: upstream (e.g., semiconductors) and downstream (e.g., automobiles). The focus on changing links across sectoral systems calls for a multi-sectoral analytical framework for policymakers and industry practitioners in an era of technological convergence, interdependence, and interdisciplinarity.

In view of the valuable and heterogeneous roles of science in technological development, we investigate the extent to which the cognitive distance and geographical boundary-spanning search of scientific knowledge affect the value of inventions in this study. In particular, we examine the diverse influencing mechanisms of scientific and technological knowledge as well as their interplay in the recombination process of invention. By analyzing the pharmaceutical patents granted by the United States Patent and Trademark Office from 2010 to 2019, alongside their citations of scientific publications, we reveal that the cognitive distance of scientific knowledge has an inverted U-shaped impact on the value of inventions in terms of both their technological impacts and economic value, whereas the cognitive distance of technological knowledge has a positive and an inverted U-shaped effect on the technological impacts and the economic value of inventions, respectively. Moreover, the cognitive distances of these two types of knowledge exhibit a complementary relationship with regard to the technological impacts of inventions while displaying a weakly substitutive relationship concerning the economic value of inventions. Notably, combining moderately distant scientific knowledge and distant technological knowledge obtains the highest value of inventions in terms of both technological impacts and economic value. Additionally, geographical boundary-spanning search of science significantly raises both the technological impacts and the economic value of inventions.

The purpose of this study is to conceptualize the role of growth hacking, a data-driven iterative experimentation process, in minimizing the likelihood of innovation failure within firms. Drawing upon existing literature on innovation and growth hacking, we provide a conceptual background to frame our research. To investigate this phenomenon, we employ a qualitative approach that combines the Gioia method and phenomenography. Our primary data source consists of in-depth interviews conducted with managers and practitioners who possess extensive experience in innovation management and growth hacking. Through a systematic inductive concept development approach and a multilevel analysis, we develop a novel conceptualization that illustrates how growth hacking strategies can be effectively implemented across four levels of analysis: market, organization, project, and product. Our findings highlight the importance of adopting growth hacking practices to minimize the likelihood of innovation failure in each of these domains. From a practical perspective, we offer recommendations on the strategies that companies should employ to effectively learn from the challenges associated with innovation. By leveraging these insights, firms can enhance their ability to overcome potential obstacles and optimize their innovation processes.