Ashish Sood

Technological Evolution and Radical Innovation

Technological change is perhaps the most powerful engine of growth in markets today. To harness this source of growth, firms need answers to key questions about the dynamics of technological change: (1) How do new technologies evolve? (2) How do rival technologies compete? and (3) How do firms deal with technological evolution? Currently, the literature suggests that a new technology seems to evolve along an S-shaped path, which starts below that of an old technology, intersects it once, and ends above the old technology. This belief is based on scattered empirical evidence and some circular definitions. Using new definitions and data on 14 technologies from four markets, the authors examine the shape and competitive dynamics of technological evolution. The results contradict the prediction of a single S-curve. Instead, technological evolution seems to follow a step function, with sharp improvements in performance following long periods of no improvement. Moreover, paths of rival technologies may cross more than once or not at all.

Do Innovations Really Pay Off? Total Stock Market Returns to Innovation

Critics often decry an earnings-focused short-term orientation of management that eschews spending on risky, long-term projects such as innovation to boost a firms stock price. Such critics assume that stock markets react positively to announcements of immediate earnings but negatively to announcements of investments in innovation that have an uncertain long-term pay off. Contrary to this position, we argue that the markets true appreciation of innovation can be estimated by assessing the total market returns to the entire innovation project. We demonstrate this approach via the Fama-French 3-factor model (including Carharts momentum factor) on 5,481 announcements from 69 firms in five markets and 19 technologies between 1977 and 2006. The total market returns to an innovation project are

Functional Regression: A New Model for Predicting Market Penetration of New Products

643 million, more than 13 times the

Demystifying Disruption: A New Model for Understanding and Predicting Disruptive Technologies

49 million from an average innovation event. Returns to negative events are higher in absolute value than those to positive events. Returns to initiation occur 4.7 years ahead of launch. Returns to development activities are the highest and those to commercialization the lowest of all activities. Returns to new product launch are the lowest among all eight events tracked. Returns are higher for smaller firms than larger firms. Returns to the announcing firm are substantially greater than those to competitors across all stages. We discuss the implications of these results.

Predicting the Path of Technological Innovation: SAW vs. Moore, Bass, Gompertz, and Kryder

The Bass model has been a standard for analyzing and predicting the market penetration of new products. We demonstrate the insights to be gained and predictive performance of functional data analysis (FDA), a new class of nonparametric techniques that has shown impressive results within the statistics community, on the market penetration of 760 categories drawn from 21 products and 70 countries. We propose a new model called Functional Regression and compare its performance to several models, including the Classic Bass model, Estimated Means, Last Observation Projection, a Meta-Bass model, and an Augmented Meta-Bass model for predicting eight aspects of market penetration. Results (a) validate the logic of FDA in integrating information across categories, (b) show that Augmented Functional Regression is superior to the above models, and (c) product-specific effects are more important than country-specific effects when predicting penetration of an evolving new product.

Innovation Does Pay Off-If You Measure Correctly

The failure of firms in the face of technological change has been a topic of intense research and debate, spawning the theory (among others) of disruptive technologies. However, the theory suffers from circular definitions, inadequate empirical evidence, and lack of a predictive model. We develop a new schema to address these limitations. The schema generates seven hypotheses and a testable model relating to platform technologies. We test this model and hypotheses with data on 36 technologies from seven markets. Contrary to extant theory, technologies that adopt a lower attack (“potentially disruptive technologies”) (1) are introduced as frequently by incumbents as by entrants, (2) are not cheaper than older technologies, and (3) rarely disrupt firms; and (4) both entrants and lower attacks significantly reduce the hazard of disruption. Moreover, technology disruption is not permanent because of multiple crossings in technology performance and numerous rival technologies coexisting without one disrupting the other. The proposed predictive model of disruption shows good out-of-sample predictive accuracy. We discuss the implications of these findings.

Analyzing Client Profitability across Diffusion Segments for a Continuous Innovation

Competition is intense among rival technologies, and success depends on predicting their future trajectory of performance. To resolve this challenge, managers often follow popular heuristics, generalizations, or “laws” such as Moores law. We propose a model, Step And Wait (SAW), for predicting the path of technological innovation, and we compare its performance against eight models for 25 technologies and 804 technologies-years across six markets. The estimates of the model provide four important results. First, Moores law and Kryders law do not generalize across markets; neither holds for all technologies even in a single market. Second, SAW produces superior predictions over traditional methods, such as the Bass model or Gompertz law, and can form predictions for a completely new technology by incorporating information from other categories on time-varying covariates. Third, analysis of the model parameters suggests that (i) recent technologies improve at a faster rate than old technologies; (ii) as the number of competitors increases, performance improves in smaller steps and longer waits; (iii) later entrants and technologies that have a number of prior steps tend to have smaller steps and shorter waits; but (iv) technologies with a long average wait time continue to have large steps. Fourth, technologies cluster in their performance by market.

Do Disruptive Visions Pay Off? The Impact of Disruptive Entrepreneurial Visions on Venture Funding: Do Disruptive Visions Pay Off?

Innovation is probably one of the most important forces fueling the growth of new products, sustaining incumbents, creating new markets, transforming industries, and promoting the global competitiveness of nations. However, some critics assert that an earnings-focused, short-term orientation on boosting stock price may undercut investments in innovation that typically have a long payoff. These critics assume that stock markets respond positively to announcements of immediate earnings but negatively to announcements of investment in innovation that have an uncertain long-term payoff. As a consequence, many firms may not be investing enough in innovation, and some analysts fear that the U.S. may be losing its competitive edge as a result. Firms may under-invest in R&D because of the high costs, the long delay in reaping market returns, if any, the uncertainty of those returns, and the difficulty of adequately measuring them. Therefore, accurately assessing the market returns to innovation may be critical to understanding how markets respond to innovation and motivating firms to invest in innovation. Assessing Innovation Returns The event study method is one of the best means of assessing the true returns to an innovation project ( 1,2 ). The basic assumption underlying the method is the effi- cient market hypothesis, which states that a stock price at a particular point in time fully reflects all available information up to that point. Thus, any change in price of a stock due to arrival of new information reflects the present value of all expected current and future profits from that new information. The method has been widely used in the finance, accounting, economics, management, and marketing literatures to assess the market response to new information. However, past research has focused on isolated events of an innovation project (e.g., alliances, patents or new product launch) to estimate the returns to innovation, instead of the entire project. This approach may lead to a substantial underestimation of the total returns. If the returns to the entire innovation project could be estimated from a single, target event during the project, then returns for other events would not be significantly different from zero. That target event would be critical, with important implications for firms and investors. On the other hand, if firms continue to experience incremental returns to various events over the innovation project, ignoring certain events would result in underestimating the total returns to innovation. Considering All Events We propose that the total returns to innovation can only be estimated if all events of the innovation project are included in the analysis. We include eight important events spanning the entire innovation project: alliances, funding, expansion, prototypes, patents, pre-announcements, launch, and awards. The total returns to innovation are the sum of returns to all events in an innovation project. In addition to completeness, the benefit of considering all events in an innovation project is that it compensates for sub-optimal or strategic announcements of the firm. For example, if the firm under-promises in early stages of an innovation project and over-delivers in later stages, the possibly low market returns in early stages will be compensated by high returns in later stages. Conversely, if a firm over-promises and then under-delivers, taking all events into consideration will compensate for possibly too-high returns in earlier stages. Methodology For this study, we defined an innovation project as the total of a firms activities in researching, developing and introducing any new product based on a new technology, from the initiation of the technology to about a year after introduction of the new product. We defined a technology as a distinct principle or platform for producing products to serve a consumer need ( 3 ). For example, neon lamps are based on fluorescence technology, which produces light by the distinct scientific principle of fluorescence. …

Demystifying Disruption: On the Hazard of Being Replaced by New Technology

While a time-based segmentation approach to customer segmentation for new products allows firms to identify consumers in the innovator and early adopter segments, this study adds a profitability-based perspective to generate new insights. Using six years of data on the adoption of technology services over three generations from a large technology manufacturer–service provider across seven countries, the authors provide empirical evidence that the short-term and long-term profitability per period of clients in the early majority segment is the highest, followed by the late majority, the innovators, the early adopters, and the laggards, respectively. While a time-based segmentation approach enables firms to identify consumers who are likely to adopt new products sooner than others, a profitability-based perspective can complement their targeting strategy and enhance overall profits. Managers can make informed decisions on investments required to develop new markets with better estimates of the profitability of consumers from later segments. Our study offers managers the necessary insights to develop a road map for identifying and targeting the most profitable clients.

Challenges of Technological Evolution in Contemporary Markets

Entrepreneurs often articulate a vision for their venture that purports to fundamentally change, disturb, or re‐order the ways in which organizations, markets, and ecosystems operate. We call these visions disruptivevisions. Neglected in both the disruption and the impression management literature, disruptive visions are widespread in business practice. We integrate real options and impression management theories to hypothesize that articulating a disruptive vision increases the likelihood of the venture receiving funding but reduces the amount of funding obtained. A novel dataset of Israeli start‐ups shows that a standard deviation increase in disruptive vision communication increases the odds of receiving a first round of funding by 22 per cent, but reduces amounts of funds received by 24 per cent. A randomized online experiment corroborates these findings and further demonstrates that the expectation of extraordinary returns is the key mechanism driving investors’ sensemaking.