The most important insight provided by some of this newer workis that contributors act for different reasons, and that theories based on a sin-gle uniform motivational model are likely to mischaracterize the motivationaldynamics.

In particular, these neweraccounts have focused on social status, peer effects, prosocial altruism, groupidentification, and related social psychological dimensions of group behavior

hat said, a growing number of stud-ies also suggest that these motives interact with each other in unpredictableways and, as a result, are vulnerable to “crowding out” when the introduc-tion of extrinsic incentives undermines intrinsic motivation

Other foundational research on motivation in peer production by Lernerand Schankerman (2010) and others has explored why organizations, firmsand governments, rather than individual users, choose to participate in opensource software.

Despite their differences in emphasis,scope, and genre, all of these surveys support the claim that motivations inpeer production are diverse and heterogeneous.

Frequently cited motivations in foundational work by theseauthors, von Hippel and von Krogh (2003), von Krogh (2003), and others in-cluded: the use value of the software to the contributing developer; the hedo-nic pleasure of building software; the increased human capital, reputation,or employment prospects; and social status within a community of peers.Other early accounts analyzing examples of peer production beyond FLOSSsuggested additional motivations. For example, Kollock (1999) emphasizedreciprocity, reputation, a sense of efficacy, and collective identity as salientsocial psychological drivers of contribution to online communities and fo-rums.

A second quality of peer production that challenged conventional economictheories of motivation and cooperation was the absence of clear extrinsic in-centives like monetary rewards. Traditional economic explanations of behav-ior rely on the assumption of a fundamentally self-interested actor mobilizedthrough financial or other incentives. In seeking to explain how peer produc-tion projects attract highly skilled contributors without money, much of theliterature on peer production has focused on questions of participant moti-vation.

A newer wave of work has stepped back from this approachand sought to explain how multiple motivational “vectors” figure in the cre-ation of common pool resources online – an approach that underscores a coreadvantage of peer production in its capacity to enable action without requir-ing translation into a system of formalized, extrinsic, carrots and sticks.

Future workcan also begin to address questions of whether, how, and why peer produc-tion systems have transformed some existing organizational fields more pro-foundly than others. An empirically-informed understanding of when andwhere organizational practices drawn from peer production provide efficientand equitable means to produce, disseminate, and access information can pro-vide social impact beyond the insights available through the study of any in-dividual community

By structuring design changes as exper-iments, these studies make credible causal claims about the relationship oforganizational structure and project outcomes that previous work struggledto establish. By intervening in real communities, these efforts achieve a levelof external validity that lab-based experiments cannot

Similar cross-organization studies in other areas of peer production, orstudies comparing across differenttypesof peer production, have remainedchallenging and rare. One difficulty with comparative work across organiza-tions, in general, is designing research capable of supporting inference intothe causes of organizational success and failure

Although some of the earliest theories of the organization of peer pro-duction celebrated the phenomena as non-hierarchical, more recent work hasquestioned both the putative lack of hierarchy and its purported benefits (e.g.,Kreiss et al., 2011).

More recent work on organizational aspects of peer production has begunto question the “stylized facts” that prevailed in earlier research.

This research built on earlier work showing that contrib-utors to bulletin boards, newsgroups, forums, and related systems adopteddurable “social roles” through their patterns of contribution (e.g., Fisher et al.,2006) – an approach that was subsequently applied to Wikipedia by Welseret al. (2011) and McDonald et al. (2011)

A large body of descriptive work has also sought to characterize organi-zational dimensions of Wikipedia and the practices of its contributors.

Much of the earliest empirical research inductively sought to describe thestructure and organization of FLOSS communities

Other foundational accounts, like Moglen (1999) and Weber (2004),attended to the emergence of informal hierarchies and governance arrange-ments within communities.

Peer productioncould, Benkler argued, outperform traditional organizational forms underconditions of widespread access to networked communications technologies,a multitude of motivations driving contributions, and non-rival informationcapable of being broken down into granular, modular, and easy-to-integrate

Initial scholarship on the organization of peer production emphasized the-oretical distinctions between peer production, bureaucracy, and transactioncost explanations of firms and markets.

Indeed, peer production com-munities perform all of the “classical” organizational functions like coordina-tion, division of labor, recruitment, training, norm creation and enforcement,conflict resolution, and boundary maintenance – but do so in the absence ofmany of the institutions associated with more traditional organizations.

In the rest of this chapter, we describe the development of the academicliterature on peer production and collective intelligence in three areas – or-ganization, motivation, and quality. In each area, we introducefoundationalworkconsisting primarily of earlier scholarship that sought to describe peerproduction and establish its legitimacy. Subsequently, we characterize work,usually more recent, that seeks to pursuenew directionsand to derive morenuanced analytical insights. Because FLOSS and Wikipedia have generatedthe majority of the peer production research to date, we focus on researchanalyzing those efforts. For each theoretical area, we briefly synthesize bothfoundational work and new directions and describe some of the challenges forfuture scholarship. Neither our themes nor our periodization are intended toencompass the complete literature on peer production. Instead, they reflectcore areas of research that speak most directly to the literature on collectiveintelligence and locate peer production within that broader phenomenon. Weconclude with a discussion of several issues that traverse our themes and impli-cations of peer production scholarship for research on collective intelligencemore broadly.

Although peer production is central to social scientific and legal researchon collective intelligence, not all examples of collective intelligence created inonline systems are peer production. First, (1) collective intelligence can in-volve centralized control over goal-setting and execution of tasks.

Peer production is the most significant organizational in-novation that has emerged from Internet-mediated social practice, among themost visible and important examples of collective intelligence, and a centraltheoretical frame used by social scientists and legal scholars of collective intel-ligence.

Following Benkler (2013), we define peer produc-tion as a form of open creation and sharing performed by groups online that:set and execute goals in a decentralized manner; harness a diverse range ofparticipant motivations, particularly non-monetary motivations; and sepa-rate governance and management relations from exclusive forms of propertyand relational contracts (i.e., projects are governed as open commons or com-mon property regimes and organizational governance utilizes combinationsof participatory, meritocratic and charismatic, rather than proprietary or con-

Consistent with this exam-ple, foundational social scientific research relevant to understanding collec-tive intelligence has focused on three central concerns: (1) explaining the or-ganization and governance of decentralized projects, (2) understanding themotivation of contributors in the absence of financial incentives or coerciveobligations, and (3) evaluating the quality of the products generated throughcollective intelligence systems.

Historically,researchers in diverse fields such as communication, sociology, law, and eco-nomics have argued that effective human systems organize people through acombination of hierarchical structures (e.g., bureaucracies), completely dis-tributed coordination mechanisms (e.g., markets), and social institutions ofvarious kinds (e.g., cultural norms). However, the rise of networked systemsand online platforms for collective intelligence has upended many of the as-sumptions and findings from this earlier research.

mporal features. We have then to consider how organizational participants are affected by situations containing temporal features, but also how these actors shape, by their behavior and beliefs, local context according to their needs.

s a means for coordinating action among groups of users (e.g., Bardram, 2005, this volume)

The hierarchical structure of the Boer et al. adaptation of the Activity Theory model can help to reconcile the differences in granularity and the difficulties of supporting collaboration identified in our work; future activity-centered user interfaces might take advantage of the zoomable user interface paradigm or feature control over the level of detail (LOD) represented in the interface to more accurately reflect the depth at which a given user conceptualizes their own tasks or the tasks of their colleagues.

Activities also span place; that is, it is common for work to take place outside of the immediate office environment. However, current office technologies sometimes present a very different view of information across different physical and virtual settings.

The idea that activities may exist at different levels of granularity is not a new one. Boer, van Baalen & Kumar (2002) provide a model explaining how an activity at one level of analysis may be modeled as an action—a component of an activity—at another. This holds true for individual users, as in the example provided above, but is even more pronounced when a single activity is viewed from multiple participants’ perspectives.

Additionally, activities need to be represented in such a way that their contents can be shared, with the caveats that individual participants in an activity may have very different perceptions of the activity, they may bring different resources to play over the course of the activity, and, particularly for large activities in which many individual users participate, users themselves may come and go over the life of the activity.

Recognizing the mediating role of the digital work environment in enabling users to meaningfully collaborate is a critical step to ensuring the success of these systems.

At present, information and communication technologies con-tinue to reshape temporal experience and collective time consciousness (Nowotny, 1989b)

Despite the apparent diversity of themes, certain common patterns can be discerned in empirical studies dealing with time. They bear the imprint of the ups and downs of research fashions as well as the waxing and waning of influences from neighbouring disciplines. But they all acknowledge 'time as a problem' in 'time-compact' societies (Lenntorp, 1978), imbued with the pressures of time that come from time being a scarce resource.

Another strategy in dealing with sui generis time consists in juxtaposing clock time to the various forms of 'social time' and considers the latter as the more 'natural' ones, i.e. closer to subjective perceptions of time, or to the temporality that results from adaptations to seasons or other kinds of natural (biological, environmental) rhythm. This strategy, often couched also in terms of an opposition between 'linear' clock time and 'cyclical' time of natural and social rhythms devalues, or at least ques-tions, the temporality of formal organizations which rely heavily on clock time in fulfilling their coordinative and integrative and controlling functions (Young, 1988; Elchardus, 1988).

Sorokin and Merton (1937) may be said to have provided the 'definitive' classic statement on the distinction between social and natural time. They associate the physical time of diurnal and seasonal cycles with clock time and define this time as 'purely quantitative, shorn of qualitative variation' (p. 621). 'All time systems', Sorokin and Merton suggest further, 'may be reduced to the need of providing means for synchronising and co-ordinating the activities and observations of the constituents of groups' (p. 627).

Brown and Eisenhardt studied change and project management in com­puter firms and found that firms with less successful project portfolios demon­strated very low amounts of communication across projects. This was part of the context in which projects were planned, divided into small tasks, and then executed in a “structured sequence of steps” (1997, p. 14). A structured sequence is, of course, a monochronic strategy, and the low amount of communication is consistent with the proposition that monochronic strategies generate less awareness of other activities and tasks. One of the managers in their study re­marked, “Most people only look at their part” (p. 14); another, “The work of everyone else doesn’t really affect my work” (p. 14). These responses contrasted with the pattern of work in the companies that managed their portfolios of projects more successfully, which Brown and Eisenhardt characterized as “it­erative” (p. 14). Iterative (repetitive) patterns are suggestive of the back and forth flow of polychronic strategies

in a more polychronic culture, people would stand closer to each other while talking. So time and space are related in the social as well as the physical world.

Leshed and Sengers’s research reminds us that calendars are not just tools for the management of time, but are also sites of identity work where people can project to themselves and others the density of their days and apparent ‘success’ at doing it all[26]. These seemingly innocuous artifacts can thus perpetuate deeper normative logics a

. When creating tools for schedulingandcoordination, it is crucial to provide ways for people to take into account not just the multiplicity of (potentially dissonant) rhythms [22, 46], but also the differential affective experiencesof time rendered by such rhythms.

This point ties into the conceptualisation of time as collec-tive [29] and entangled [43]. The infrastructure that sup-ports a 24/7 society is one that relies on people as well as technologies, the conventional nine-to-five work rhythm, for example, being underpinned by people working shifts outside of these hours.

Notably, a practice-oriented treatment of digital time does open up avenues for research and design, one that resonates with Kuutti and Bannon’s [23] recent account of a practice perspective forming a new paradigm for HCI. They propose that a central issue in a practice-based research agenda is the need to develop the capability to transform practices through technology. Essential to this is understanding the role of computer artefacts in the emergence and transfor-mation of practices, and the possibilities for influencing these by changing the artefacts themselves.

How can we design for time as collective and interdependent, rather than individualised on the one hand, or explicitly scheduled on the other? What does it mean to position collective time not as something that is achieved when people come together, but as a set of relationships through which they are connected? Both Sharma and Mazmanian and Erickson raise this challenge while highlighting the difficulty in addressing it; neither offer a solution.

Designing for an alternative temporal experience means understanding the ways in which multiple temporali-ties intersect, whether these frame a person’s working day, or allow a family to spend time together. While scheduling technologies do of course have a role to play here [see e.g. 31], many of the temporal structures that frame everyday life are not so much scheduled as unfold in a way that isunremarkable [54], or are so firmly established that they are no longer seen as alterable.

In his analysis of the concept, Southerton [45] identifies quality time as a contemporary concern, and uses it alongside an analysis of diaries written in 1937 and 2000 to examine the impression that everyday life is speeding up. His findings lead him to argue that the feeling of time pressure that seems inherent to modern life is due to difficulties in coor-dinating practices, rather than the sheer density of events that need to be accomplished.

While the Printer Clock focused on emphasising the embodied and situated nature of time, pointing to the mesh of activities and characters that come together to create time, the TimeBots drew attention to personal rhythms and how they played out within the context of the classroom

Thus in developing a theoretical framework which could support an understanding of time as multiple, heterogeneous and deeply entangled within various social formations (which may be discrete or overlapping), work in the social sciences, particularly anthropology and sociology, has proven to be more useful. Such approaches enable us to ask different questions about what time is and how it works. Rather than seeing time as a flow between past, present and future (whether this be linear or nonlinear), it becomes possible to ask how time operates as a system for social collaboration (Sorokin and Merton 1937), how it legitimates some and ‘manages’ others (Greenhouse 1996), or how it works within systems of exclusion (Fabian 1983). We thus move from time as flow to time as s

The Triple Feedback Loop offers a compass by aligning the information flows in a framework for operating a network of disparate players with different goals who work together on an overarching goal.

On average, each cash - using household pays $1 49 to card - using households and each card - using h ousehold receives $1, 133 from cash users every year. Because credit card spending and rewards are positively correlated with household income, the payment instrument transfer also induces a regressive transfer from low - income to high - income households in g eneral