Knowledge that Transforms

Abstract Our study, grounded in the multimarket competition, knowledge management, and technological change literature, investigates how multimarket contacts among multinational enterprises—i.e., the extent to which rival multinational enterprises meet simultaneously in multiple countries—shape their innovation and patent litigation decisions in host countries. We propose that the degree of multimarket contacts a multinational enterprise has with its competitors in a host country may restrain it from aggressively launching product and technological innovations in that country due to a fear of cross-country retaliation. However, increased multimarket contacts also facilitate knowledge diffusion, heightening the risk of imitation, and consequently enhance the multinational enterprise’s patent litigation intensity—a means to protect knowledge-based resources—in that country. Moreover, we examine how two features of the host-country technological environment—technological ferment (an era of intense technical variation following a technological discontinuity until a dominant design emerges) and home–host country differences in intellectual property protection—moderate these relationships. We test our hypotheses using a dataset of 85 mobile phone vendors, which includes data on their products and technological innovations and litigation cases in 46 countries between 2003 and 2015. This study sheds light on the interplay among multinational enterprises’ multimarket contacts, innovation, and patent litigation.

Abstract Managers’ identities in large matrixed MNE structures are complex and pliable. While research has examined the identities of individual actors located in headquarters (HQ) or local subsidiaries, the unique identity challenges faced by those operating outside these conventional units are less predictable. Our study explores how managers experience and manage a ‘liminal identity’—feeling ‘betwixt and between’ multiple identity positions. Focusing on US MNEs in the life sciences sector, we interviewed managers who assumed global responsibilities from corporate HQ while remaining situated in their local (Irish) subsidiary. We find that operating between corporate HQ, a global network, and the local subsidiary forged a liminal identity. Straddling this local–corporate–global nexus was a profoundly ambiguous, precarious, and destabilizing experience. Yet, individuals also sought to leverage their liminal identity by adopting multiple identities, depending on the audience. This involved ‘switching’ between renouncing a local identity, assimilating a corporate identity and co-constructing a global identity. However, this produced an intense identity struggle, eroding a clear sense of self and belonging. Our study cautions firms to be attentive when supporting managers in these ‘in-between’ spaces to avoid the trap of ‘identity limbo’, where individuals risk becoming disconnected and isolated.

The theory of platform envelopment rests on network effects as the key mechanism for value creation, which nonetheless receives mixed support for its efficacy in determining competitive outcomes. We argue that the value of network effects depends on matching quality, which is a function of platform-specific algorithm technology and market-level data-driven learning. In formalizing these conceptualizations, we analyze a model that demonstrates how an entrant with a superior algorithm technology may outcompete an incumbent possessing a user base advantage, a strategy we call “algorithm envelopment.” By considering specific characteristics of data-driven learning, our analysis leads to propositions regarding the entry barriers for the enveloper, illuminating how learning may overshadow or interact with network effects in impacting the enveloper’s market selection decisions. We also show that market selection may be contingent on whether algorithm envelopment is instituted through competition or mergers, suggesting an interdependence between “where to enter” and “how to enter.” Finally, we explore the welfare effects of algorithm envelopment. We extend the recent debate on “data network effects” and show how teasing apart network effects, data-driven learning, and algorithm technology in envelopment attacks can generate novel implications for incumbency advantages, yield insights into platform diversification, and inform antitrust policymaking.

Since Shane and Venkataraman's (2000) seminal paper, entrepreneurship research has been preoccupied with the exploitation of entrepreneurial opportunities.Lewis, Bruton and Shepherd (2024) (LBS hereafter) fruitfully shift focus from "which opportunities are exploited to which opportunities are not" (p.11).Trying to answer why-not questions is critical to advancing theoretical understanding of how nonactual but desirable futures can be realized.As importantly, understanding why the needs of marginalized consumers are not served is imperative for building more inclusive capitalist societies.LBS address this lacuna by providing several conceptual innovations.They explain that market inefficiencies are grounded in structural disadvantage resulting from social closure, segregation, and stereotyping of marginalized consumers.This insight is not only radical for

We use newspapers to create Equity Market Volatility (EMV) trackers at daily and monthly frequencies. Our headline EMV tracker moves closely with the VIX and the S&P500 returns volatility in and out of sample. We exploit the volume of newspaper text to construct forty category-specific EMV trackers. News about commodity markets, interest rates, real estate markets, aggregate activity, and inflation figure prominently in EMV articles. Policy news is another major source of market volatility: 30 % of EMV articles discuss tax policy, 30 % discuss monetary policy, and 25 % refer to some form of regulation. Combining our newspaper-based trackers with textual analysis of 10-K filings, we obtain monthly firm-level risk exposure measures. These measures help explain the cross-sectional structure of realized volatilities and its evolution over time, even after conditioning on firm and time fixed effects.

We study the equity, cash, and corporate bond risk premium expectations of asset managers, investment consultants, wealth advisors, public pension funds, and professional forecasters. Subjective risk premia vary one-to-one with objective risk premia that are available in real time and countercyclical. Despite their significant time-series variation, several subjective equity premia vary more in the cross-section of institutions than in the time series. This heterogeneity persists both over time and across asset classes. We tie the heterogeneity in subjective equity return expectations to heterogeneous expectations about long-term equity valuations: some institutions believe that the price–earnings ratio behaves like a random walk, whereas others believe in varying degrees of mean reversion.

Policy uncertainty can undermine the power of government subsidies to stimulate environmentally friendly research and development. We show that Chinese firms’ green R&D falls as the uncertainty of environmental subsidies rises: Exogenous, weather-driven air pollution variability induces subsidies to fluctuate, and firms in areas with high weather-driven subsidy variability undertake less green R&D and hire fewer technical employees, controlling for the average level of subsidies. Heavy emitters and environmental technology firms are more affected. The results also illustrate how policy uncertainty can arise when policymakers are influenced by conditions that are salient but with causes that are difficult to disentangle.

We study the nature and effects of cultural biases in choice under risk and uncertainty by comparing peer-to-peer loans the same individuals ( lenders ) make alone and after observing robo-advised suggestions. When unassisted, lenders are more likely to choose co-ethnic borrowers, facing 8% higher defaults and 7.3pp lower returns. Robo-advising does not affect diversification but reduces lending to high-risk co-ethnic borrowers. Lenders in locations with high inter-ethnic animus drive the results, even when borrowers reside elsewhere. Biased beliefs explain these results better than a conscious taste for discrimination: lenders rarely override robo-advised matches to ethnicities they discriminated against when unassisted.

This paper studies the transmission of monetary policy to the stock market through investors’ discount factors. To isolate this channel, we investigate the effect of US monetary policy surprises on the ratio of prices of the same stock listed simultaneously in Hong Kong and Mainland China. We identify a strong discount rate channel driven exclusively by cycle-amplifying surprises, defined as rate cuts during easing cycles and surprise hikes during tightening cycles. A 100 basis point of such cycle-amplifying surprise induces a 30 basis point change in the price ratio within five days.

Disagreement about macroeconomic fundamentals accounts for only part of the disagreement about future interest rates, creating a “disagreement correlation” puzzle. This puzzle arises because standard equilibrium models with belief differences predict a strong link between asset return disagreement and fundamental disagreement, a link not supported by the data. We address this puzzle by introducing a model where disagreement about future demand for savings—driven by disagreement over the prevalence of patient versus impatient investors in the economy—generates asset return disagreement. Our mechanism produces stochastic yield volatility, time-varying bond risk premia, and an upward-sloping yield curve. Empirically, we construct a proxy for demand disagreement by isolating the component of yield disagreement unrelated to disagreement about macro-fundamentals. This proxy is positively related to yields and their volatilities, and predicts future bond risk premia, consistent with the predictions of our demand disagreement model.