Appropriability of innovation benefit as a predictor of the source of innovation

It has been empirically observed that, in some industries product users are the most frequent sources of product innovations while, in other industries, product manufacturers are. I hypothesize that such differences are caused by differences in the ability of these two “functional” categories of innovators to appropriate innovation benefit. I explore this hypothesis by examining the real-world effectiveness of mechanisms (such as patents and lead time) used for the appropriation of innovation benefit and the dependence of this effectiveness on the functional relationship between innovator and innovation.     

User community vs. producer innovation development efficiency: A first empirical study

In this paper we report upon a first empirical exploration of the relative efficiency of innovation development by product users vs. product producers. In a study of over 50 years of product innovation in the whitewater kayaking field, we find users in aggregate were approximately 3× more efficient at developing important kayaking product innovations than were producers in aggregate. We speculate that this result is driven by what we term “efficiencies of scope” in problem-solving. These can favor an aggregation of many user innovators, each spending a little, over fewer producer innovators benefitting from higher economies of scale in product development. We also note that the present study explores only one initial point on what is likely to be a complex efficiency landscape.     

Behavioral innovation: Pilot study and new big data analysis approach in household sector user innovation

Innovation researchers have begun to look beyond how users develop tangible objects or product innovations and moved to investigate the existence and impact of intangible user-developed innovations in techniques and services in the household sector . In this paper, to incorporate technique and service innovations and other varieties of intangible innovations not yet described in the literature into an efficient and encompassing typology, we propose the new concept of intangible Behavioral Innovation as an overarching category that stands in contrast to tangible product innovation. Behavioral innovation is defined as consisting of one or a connected sequence of intangible problem-solving activities that provide a functionally novel benefit to its user developer relative to previous practice. We demonstrate in a pilot study using a relatively novel big data-gathering and semantic analysis approach that behavioral innovation exists and can be identified in user-generated content posted openly online in peer-to-peer discussion forums relating to household sector activities such as parenting. The preponderance (N = 138) of the 168 user innovations captured in our samples of discussion comments were intangible behavioral innovations, most of which were developed by women. The majority of behavioral innovations identified were diffused by their user developers in response to specific requests for help or advice from peers in their online community. Thus, incorporating the new concept of intangible behavioral innovation into studies of user innovation's scope and significance in the household sector can serve to clarify which users innovate in our communities of interest, what and how they innovate, why they are triggered to diffuse their innovations peer-to-peer, and how their innovative activities might impact social welfare.     

A customer-active paradigm for industrial product idea generation

New product need assessment and idea generation methodologies, developed and used successfully in the consumer product arena, are seldom used to aid in the design of new industrial products. It is suggested that the ‘fault’ lies in large part with the ‘manufacturer-active’ paradigm underlying these methodologies, which prescribes that the product manufacturer has the role of assessing customer needs and developing a responsive product idea. A new ‘customer-active’ paradigm is proposed in which the customer develops the new product idea and takes the initiative to transfer it to an interested manufacturer.The hypothesis that the customer-active paradigm offers a better fit to industrial product idea generation practice than does the manufacturer-active paradigm is tested against the available empirical data (eight studies are reviewed) and found supported. Speculative reasoning is then offered in support of the notion that the customeractive paradigm provides a good fit to the inherent requirements of industrial product idea generation as well as to current practice. Implications for research and practice are discussed.     

Cooperation between rivals: Informal know-how trading

“Informal” know-how trading is the extensive exchange of proprietary know-how by informal networks of process engineers in rival (and non-rival) firms. I have observed such know-how trading networks to be very active in the US steel minimill industry and elsewhere, and they appear to represent a novel form of cooperative R&D.When one examines informal know-how trading in the framework of a “Prisoner's Dilemma”, real-world conditions can be specified where this behavior both does and does not make economic sense from the point of view of participating firms. Data available to date on the presence and absence of such trading seem to be roughly in accordance with the predictions of this simple model.Although presently documented only as a firm-level phenomenon involving the trading of proprietary technical knowhow, informal know-how trading seems relevant to (and may currently exist in) many other types of situation. Indeed, it may be applicable to any situation in which individuals or organizations are involved in a competition where possession of proprietary know-how represents a form of competitive advantage.     

New Confidence Intervals and Bias Comparisons Show That Maximum Likelihood Can Beat Multiple Imputation in Small Samples

When analyzing incomplete data, is it better to use multiple imputation (MI) or full information maximum likelihood (ML)? In large samples ML is clearly better, but in small samples ML’s usefulness has been limited because ML commonly uses normal test statistics and confidence intervals that require large samples. We propose small-sample t-based ML confidence intervals that have good coverage and are shorter than t-based confidence intervals under MI. We also show that ML point estimates are less biased and more efficient than MI point estimates in small samples of bivariate normal data. With our new confidence intervals, ML should be preferred over MI, even in small samples, whenever both options are available.     

Need-solution pair recognition by household sector individuals: Evidence,and a cognitive mechanism explanation

Problem-solving by everyday individuals is thought to occur as a two-step process. First, an individual identifies or formulates a problem, followed by entering into a subsequent search to find the best solution. Here, however, we consider an alternative process that everyday individuals may use for solution finding first theorized by von Hippel and von Krogh (2016). Specifically, von Hippel and von Krogh proposed that everyday individuals may sometimes discover a solution and the need it satisfies simultaneously without the need for apriori problem formation, a cognitive process they called “need-solution pair recognition”. Utilizing a rich literature from psychology and neuroscience, we propose that seemingly spontaneous discoveries found by need-solution pair recognition are natural products of the object recognition system and its underlying mechanisms. This view asserts that on encountering an object and reasoning how it might be used (i.e. functional object understanding), an individual's perception of an object may culminate in recognizing the object as a solution, and in some cases, as a solution to a problem previously unknown to him or her, thus bypassing formal problem-formulation and active solution searching entirely. To empirically test this view, we manipulated the ability of everyday individuals to functionally reason about objects while we examined the spontaneous occurrence of solutions found by either need-solution pair recognition or traditional problem-first problem-solving. Consistent with our hypothesized mechanism, our results indicate that need-solution pair recognition occurs more frequently when constraints on functional object understanding are reduced. That is, we found that needsolution pair discoveries outpaced solutions found from traditional problem solving, in environments with unfamiliar objects, where participants were not directed to solve specific problems. Our results provide clear evidence that everyday individuals in the household sector do not always innovate through traditional problem-solving processes, but instead may arrive at solutions as they recognize and reason about objects. Implications for research and practice in household innovation, and for innovation more generally are considered.     

How open source software works: “free” user-to-user assistance

Research into free and open source software development projects has so far largely focused on how the major tasks of software development are organized and motivated. But a complete project requires the execution of “mundane but necessary” tasks as well. In this paper, we explore how the mundane but necessary task of field support is organized in the case of Apache web server software, and why some project participants are motivated to provide this service gratis to others. We find that the Apache field support system functions effectively. We also find that, when we partition the help system into its component tasks, 98% of the effort expended by information providers in fact returns direct learning benefits to those providers. This finding considerably reduces the puzzle of why information providers are willing to perform this task “for free.” Implications are discussed.