These days, a wave of artificial intelligence applications is arriving in the way we manage innovation. But where are we now, and how can one use AI practically in innovation management? Against this background, we first extend an earlier literature review in text classification to demonstrate that the field remains scarce. We then address the current research gap by successfully conducting a machine learning project to evaluate ideas using text classification with transformer models. For this, we semi-automatically extracted customer needs in the field of smart speakers by downloading a Twitter dataset containing 473,301 tweets. We classified 107,738 tweets using a machine learning model, identified 12,641 potential need-containing tweets, and manually coded the first 1000. Our results can assist innovation managers and researchers in implementing high-performing machine learning models to support their innovation management tasks in different ways. We documented the design choices for this machine learning project in detail, deriving guidelines for creating text classification projects tailored to the domain of innovation management.
Leveraging the exogenous shock of the implementation of China's Environmental Protection Tax Law, this study empirically examines the impact of green tax reform on corporate R&D manipulation. Using the sample of Chinese listed firms and employing a difference-in-differences approach, we find that green tax reform significantly curbs corporate R&D manipulation, specifically by reducing abnormal R&D expenditures. This conclusion remains robust after a series of tests. Mechanism analyses reveal that this effect operates primarily through three channels: the optimization of environmental information disclosure, enhanced supervisory governance by external stakeholders, and the reduction of transaction costs associated with regulatory compliance. Further analysis suggests that by mitigating R&D manipulation, green tax reform ultimately enhances both the quality and efficiency of corporate innovation. Our findings contribute to the growing literature on environmental regulation and corporate innovation by highlighting the governance role of green taxation, and provide important policy implications for designing market-based environmental instruments to foster genuine and sustainable innovation.
Business model innovation (BMI) is a key decision faced by entrepreneurial firms. Yet, a firm's entrepreneurial team can hold diverse cognitive views of business models, leading to an incomplete understanding of how entrepreneurial teams can actually leverage these diverse cognitive resources for BMI. Drawing on the cognitive view of BMI, we explore how domain-specific schemas-a key aspect of managerial cognition gleaned from prior industry experience-may influence BMI. Rather than assuming the influence of schemas is uniform across situations, we conceptualize it as contingent on two salient situated factors-namely, team faultlines and industry competition logic. We posit that while the sharing of schemas in entrepreneurial teams is critical for BMI, whether those teams are able to bring about actual innovation depends on the industry context in which their new venture operates. Empirical analysis of 799 IT-based firms in China provides general support for our arguments. Our study contributes to the cognitive view of BMI and deepens our understanding of the role of situated context in BMI.
Generative AI (GenAI) is reshaping firm innovation through advanced content generation. While prior studies have emphasized GenAI's contributions to both market-facing and operational innovation, most remain fragmented observations, lacking theoretical integration and large-scale empirical validation. To address this gap, this study integrates the Dynamic Capability View and Institutional-Based View to propose and test a conceptual framework that includes demand-related (product/marketing) and supply-related (process/organizational) innovations. Analyzing survey data from 335 Chinese manufacturing and Information and Communication Technology firms using PLS-SEM, we find that GenAI routine use enhances market agility, thereby facilitating product and marketing innovation, and improves operational agility, which in turn boosts process and organizational innovation. Crucially, the moderating effect of environmental dynamism is negative and uniquely observed among state-owned enterprises. These findings underscore GenAI's dual innovation value and highlight organizational agility as a critical pathway linking technology embeddedness to ambidextrous innovation. They also suggest that realizing the full potential of GenAI requires alignment between a firm's underlying dynamic capabilities and the dynamism of its external environment.
This study investigates how CEOs' political ideology affects corporate decisions to sue for patent infringement. Integrating upper-echelons and behavioral-agency perspectives, we theorize that conservative-leaning CEOs-marked by heightened threat sensitivity and low tolerance for ambiguity-frame infringement as a looming loss and therefore favor litigation as the most rule-bound, controllable response. Liberal CEOs, by contrast, are less likely to initiate litigation. We further contend that recent firm performance amplifies this ideological imprint: when results exceed aspirations, the ideology-litigation relationship steepens, with conservative CEOs becoming more likely to file and liberal CEOs less likely to do so; when performance weakens, this divide narrows. These propositions are tested on an unbalanced panel of 1275 US firm-year observations, obtained by linking political-donation records with patent, financial and litigation data. Results confirm that conservative CEOs significantly raise the odds of initiating a patent suit; the effect strengthens at high performance levels and weakens when performance falls below industry benchmarks. By exposing the ideological microfoundations of patent enforcement, the study extends competitive-dynamics research, demonstrates how personal values shape nonmarket strategies and competitive tactics. Consequently, we alert boards, R&D managers and investors to a behavioral driver of costly patent disputes.
