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Pease et al., 2013)

      Neither reportedly set out to solve the problems; they made astute observations which when combined with their own knowledge led to surprise outcomes.

      • Type B. From Problem I to a Solution for Problem II

      In this variation, an individual is looking for a solution to a problem, but instead finds a solution to another problem. This is the interpretation of Solly and the literary scholars of the 19th century (and it was interpretation that was first quoted in the Oxford English Dictionary in 1913) and continues to be perhaps the most popular interpretation today.

      Examples:

      ° Fleming was growing Staphylococcus bacteria in a petri dish when it became contaminated with a spore of Penicilliusm fungus. His deep understanding of bacteria (his sagacity) led him to observe how the mold in his petri dish killed the surrounding bacteria, and thus led to one of the most important advancements in health in the early 20th century (e.g., Roberts, 1989);

      ° Art Fry was trying to develop a superglue when he accidentally devised a very weak glue that enabled two pieces of paper to be pried apart which led to development of Post-it Notes (e.g., Pease et al., 2013).

      In both these cases, the researchers were working diligently on a particular problem when an observation led them in a different direction, resulting in a novel solution to a problem that they had not initially intended to solve.

      • Type C. Unexpected Solutions

      An individual is looking for a solution to a particular problem, but the solution does not come from expected sources. The solution discovered by accident is found in an unusual or surprising way that could not have been predicted at the outset. This has also been called pseudo-serendipity (Roberts, 1989).

      Example:

      ° Goodyear was seeking a solution to the problem of rubber. In winter it hardened, while in summer it melted. As the story goes, he accidentally dropped rubber on a stove and observed on cooling that it turned into a charred leather-like substance with an elastic rim. From this unexpected event, he invented vulcanized rubber that is still in use today (e.g., Halacy, 1967).

      Serendipity Types A, B, and C share common features as illustrated in Figure 1.1. In their examination of serendipity, de Figueiredo and Campos (2001) provide a parsimonious mathematical notation to describe each. The types are typically distinguished by whether there was intent to solve a problem or find a solution to a new or existing problem (Napier and Vuong, 2013; Foster and Ford, 2003; Cunha et al, 2014; De Rond, 2014). All types emerge out of a context, which may be any work or pleasure environment, with variable starting points, and all get to a solution; if this were the only ingredients, then we would be dealing with ordinary problem-solving. What distinguishes these types from ordinary problem solving are the two key points in the process:

      • the startling, anomalous observation(s); that leads to

      • an unexpected, unpredictable outcome.

      These elements, thus, become the defining characteristics that separate the ordinary from the serendipitous.

      Figure 1.1: Three ways serendipity happens.

1.4 WHAT DO WE MEAN BY SERENDIPITY IN DIGITAL INFORMATION ENVIRONMENTS?

      A digital information environment can be any piece of digital technology that enables a “sense of place” and enables user interaction with information objects (McCay-Peet, Toms, and Kelloway, 2014). This may be any software application, from desktop to the Web. How does this environment differ from those in which we typically discuss serendipity? Most examples from science and engineering described earlier deal with human interactions with physical objects that have visually revealing characteristics, e.g., mold, burrs, floppy ears, burning rubber. When the anomalous or unexpected observation occurs in a two-dimensional digital space such as a computer, tablet or phone display, those anomalous and unexpected cues emanate from a combination of text, icons, images and sounds that make up an information object, and thus must be cognitively interpreted. There are (at present) no tangible tactile elements in a digital environment. In this volume, it is this digital environment in which we explore serendipity.

1.5 REST OF THE BOOK

      In the remainder of the volume we consider the motivation for researching serendipity, and the value and implications for doing so. In Chapter 2, we examine why it is pertinent and timely to study serendipity. In Chapter 3, we deconstruct the concept of serendipity, and consider its use and interpretation, e.g., as an event, a behavior, a process, an outcome. In Chapter 4, we consider how to facilitate it in digital environments, answering the question can we design for serendipity. In Chapters 5 and 6, we look at how various methods have been deployed to study it and how it has been or could be assessed when it has occurred. We end the book with a reflection of the research to date and a framework for explaining the concept, thus providing a basis for future research.

      ¹ This is the English translation of the comment made in French: “a Dans les champs de l’observation le hazard ne favorise que les esprits préparés.”

      CHAPTER 2

       What Drives Serendipity Research?

      Reasons for examining the phenomenon of serendipity have evolved and grown over the past 25 years. Rapid technological change has provided both impetus and inspiration for serendipity research that examines how people adopt, adapt to, use, and, in turn, influence digital environments and how to design them to better support serendipity in the context of learning, everyday life, business, scholarship, law, and leisure, to name a few. The purpose of this chapter is to gather the main drivers of serendipity research, which point to responses to the proverbial “so what?” question.

      Figure 2.1: Six main drivers of serendipity research relating to digital information environments.

      We identified six main, overlapping drivers of serendipity research relating to digital information environments (Figure 2.1).

      • Physical vs. digital: Compare serendipity-related benefits of digital and physical interactions or mimic characteristics of physical environments to support serendipity in digital environments.

      • Information overload: Develop digital environments that enable users to encounter triggers of serendipitous experiences in the face of information overload.

      • Filter bubbles: Burst “filter bubbles” through novel approaches to support serendipity.

      • User experience: Understand and foster the positive experiences associated with serendipity to benefit individuals, groups, networks, and society.

      • User strategies: Identify how users may increase opportunities for serendipity through their information behavior or frame of mind in their interactions with digital environments.

      • Understanding the phenomenon: Gain a theoretical understanding of the phenomenon of serendipity.

      Although not always explicitly stated in the research, these six drivers are tied to even broader incentives: the implications of support for serendipity reverberate politically, socially, economically, and

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