Gerard J. Tellis

The Incumbent’s Curse? Incumbency, Size, and Radical Product Innovation

A common perception in the field of innovation is that large, incumbent firms rarely introduce radical product innovations. Such firms tend to solidify their market positions with relatively incremental innovations. They may even turn away entrepreneurs who come up with radical innovations, though they themselves had such entrepreneurial roots. As a result, radical innovations tend to come from small firms, the outsiders. This thesis, which we term the “incumbents curse,” is commonly accepted in academic and popular accounts of radical innovation. This topic is important, because radical product innovation is an engine of economic growth that has created entire industries and brought down giants while catapulting small firms to market leadership. Yet a review of the literature suggests that the evidence for the incumbents curse is based on anecdotes and scattered case studies of highly specialized innovations. It is not clear if it applies widely across several product categories. The authors reexamine the incumbents curse using a historical analysis of a relatively large number of radical innovations in the consumer durables and office products categories. In particular, the authors seek to answer the following questions: (1) How prevalent is this phenomenon? What percentage of radical innovations do incumbents versus nonincumbents introduce? What percentage of radical innovations do small firms versus large firms introduce? (2) Is the phenomenon a curse that invariably afflicts large incumbents in current industries? Is it driven by incumbency or size? and (3) How consistent is the phenomenon? Has the increasing size and complexity of firms over time accentuated it? Does it vary across national boundaries? Results from the study suggest that conventional wisdom about the incumbents curse may not always be valid.

Strategic Bundling of Products and Prices: A New Synthesis for Marketing

Bundling is pervasive in todays markets. However, the bundling literature contains inconsistencies in the use of terms and ambiguity about basic principles underlying the phenomenon. The literature also lacks an encompassing classification of the various strategies, clear rules to evaluate the legality of each strategy, and a unifying framework to indicate when each is optimal. Based on a review of the marketing, economics, and law literature, this article develops a new synthesis of the field of bundling, which provides three important benefits. First, the article clearly and consistently defines bundling terms and identifies two key dimensions that enable a comprehensive classification of bundling strategies. Second, it formulates clear rules for evaluating the legality of each of these strategies. Third, it proposes a framework of 12 propositions that suggest which bundling strategy is optimal in various contexts. The synthesis provides managers with a framework with which to understand and choose bundling strategies. It also provides researchers with promising avenues for further research.

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.

Best Value, Price-Seeking, and Price Aversion: The Impact of Information and Learning on Consumer Choices

When information on product quality is not perfect, theories in the areas of consumer rationality, inference, and risk-aversion suggest at least three consumer choice strategies: best value, price-...

Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance

This study examines whether user-generated content (UGC) is related to stock market performance, which metric of UGC has the strongest relationship, and what the dynamics of the relationship are. We aggregate UGC from multiple websites over a four-year period across 6 markets and 15 firms. We derive multiple metrics of UGC and use multivariate time-series models to assess the relationship between UGC and stock market performance. Volume of chatter significantly leads abnormal returns by a few days (supported by Granger causality tests). Of all the metrics of UGC, volume of chatter has the strongest positive effect on abnormal returns and trading volume. The effect of negative and positive metrics of UGC on abnormal returns is asymmetric. Whereas negative UGC has a significant negative effect on abnormal returns with a short “wear-in” and long “wear-out,” positive UGC has no significant effect on these metrics. The volume of chatter and negative chatter have a significant positive effect on trading volume. Idiosyncratic risk increases significantly with negative information in UGC. Positive information does not have much influence on the risk of the firm. An increase in off-line advertising significantly increases the volume of chatter and decreases negative chatter. These results have important implications for managers and investors.

How Well Does Advertising Work? Generalizations from Meta-Analysis of Brand Advertising Elasticities

The authors conduct a meta-analysis of 751 short-term and 402 long-term direct-to-consumer brand advertising elasticities estimated in 56 studies published between 1960 and 2008. The study finds several new empirical generalizations about advertising elasticity. The most important are as follows: the average short-term advertising elasticity is .12, which is substantially lower than the prior meta-analytic mean of .22; there has been a decline in the advertising elasticity over time; and advertising elasticity is higher (1) for durable goods than nondurable goods, (2) in the early stage than the mature stage of the life cycle, (3) for yearly data than quarterly data, and (4) when advertising is measured in gross rating points than monetary terms. The mean long-term advertising elasticity is .24, which is much lower than the implied mean in the prior meta-analysis (.41). many of the results for short-term elasticity hold for long-term elasticity, with some notable exceptions. The authors discuss the implications of these findings.

What to Say When: Advertising Appeals in Evolving Markets

The authors study how ad cues affect consumer behavior in new versus well-established markets. The authors use theoretical insights from consumer information processing to argue that the same ad cues can have different effects on consumer behavior, depending on whether the market is new or old. The authors then test these hypotheses in the context of a toll-free referral service, using a highly disaggregate econometric model of advertising response. The results indicate that argument-based appeals, expert sources, and negatively framed messages are particularly effective in new markets. Emotion-based appeals and positively framed messages are more effective in older markets than in new markets.

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

Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation

643 million, more than 13 times the

A Critical Review of Marketing Research on Diffusion of New Products

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.