Contribution behaviors in distributed environments

Olivera et al./Contribution Behavior in Distributed Environments RESEARCH ARTICLE CONTRIBUTION BEHAVIORS IN DISTRIBUTED ENVIRONMENTS1 By: Fernando Olivera Richard Ivey School of Business University of Western Ontario London, ON N6A 3K7 CANADA folivera@ivey.uwo.ca Paul S. Goodman Tepper School of Business Carnegie Mellon University Pittsburgh, PA 15213 U.S.A. pg14+@andrew.cmu.edu Sharon Swee-Lin Tan Department of Information Systems National University of Singapore Singapore 117543 SINGAPORE tansl@comp.nus.edu.sg Abstract In this paper, we develop a framework for understanding contribution behaviors, which we define as voluntary acts of helping others by providing information. Our focus is on why and how people make contributions in geographically distributed organizations where contributions occur primarily 1 Carol Saunders was the accepting senior editor for this paper. Sandy Staples was the associate editor. Peter Gray and Pam Hinds served as reviewers. A third reviewer chose to remain anonymous. through information technologies. We develop a model of contribution behaviors that delineates three mediating mechanisms: (1) awareness; (2) searching and matching; and (3) formulation and delivery. We specify the cognitive and motivational elements involved in these mechanisms and the role of information technology in facilitating contributions. We discuss the implications of our framework for developing theory and for designing technology to support contribution behaviors. Keywords: Contribution behaviors, knowledge sharing, knowledge management Introduction The objective of this paper is to develop a model of why and how people make contributions in distributed organizations where contributions often occur through information technologies. We define contributions as voluntary acts of helping others by providing information. Our focus is on contributions in organizations that have multiple, geographically distributed work sites, such as professional services firms (e.g., accounting, consulting, law) that operate sites around the world. Individuals who work in these organizations engage in similar activities and are likely to possess knowledge or expertise that could be useful to others in the firm (Hansen 1999). They also have access to various communication technologies, including e-mail, voice mail, and corporate databases, through which they can make contributions (Olivera 2000). There are, however, several impediments to making contributions in distributed organizations (Goodman and Darr 1998). MIS Quarterly Vol. 32 No. 1, pp. 23-42/March 2008 23 Olivera et al./Contribution Behavior in Distributed Environments Consider the following situation: An employee of a large, global consulting firm receives an electronic mail (e-mail) from someone she has never met, who works at a different site of the same organization, and with whom she has no job interdependencies. The message asks for advice on a problem in an area in which she has some expertise. As usual, other messages in the employee’s mailbox demand her attention, as do a variety of immediate tasks to be accomplished that day. Several questions arise from this situation: Will she become aware of this request? Will she devote time and effort to finding a solution to the problem and formulating a response? Will she ultimately reply? What role will information technologies play in facilitating the contribution? In this paper, we develop a framework for addressing these questions. Our approach to explaining contribution behaviors focuses on specifying the mediating mechanisms that constitute the act of contribution. We theorize the effects of information technology on contributions by delineating the effects of technology on these mediating mechanisms. There are several motivations for this paper. First, contribution behaviors can improve organizational effectiveness (Goodman and Darr 1998). These behaviors leverage expertise within the organization and can reduce the costs of developing solutions for recurring problems. Understanding how and why individuals make contributions can help us develop recommendations for designing and implementing systems that facilitate contribution behaviors. Constant et al. 1996; Wasko and Faraj 2005). The approach we propose here focuses on understanding the mediating mechanisms that are involved in the contribution act. We explain these mechanisms in terms of both their cognitive and motivational elements. The rationale for our approach is that theorizing about mediating mechanisms can help us better understand why and how contributions occur, and better specify information technology’s role in facilitating or inhibiting contributions. Finally, this paper deals with the practice of knowledge management. In the past decade, knowledge management practices have been recognized as a source of strategic competitive advantage (Davenport and Prusak 1998). Information systems researchers have theorized about the role of information technology in knowledge management (Alavi and Leidner 2001; Stein and Zwass 1995) and developed technologies to support knowledge management practices (e.g., Ackerman and McDonald 2000). Although many large organizations have invested heavily in knowledge-sharing technologies, few systems have met their expectations or objectives (Cross and Baird 2000; Davenport and Prusak 1998; McDermott 1999). A detailed understanding of contribution behaviors can inform the development of future technologies and practices to improve knowledge management. Second, although there has been extensive research on individuals’ helping behaviors toward peers in their immediate work environment (e.g, Anderson and Williams 1996; Kraut et al. 2002; Perlow and Weeks 2002; Settoon and Mossholder 2002), our focus is on helping behaviors in settings where individuals work for the same organization but don’t have work interdependencies or personal ties and where helping occurs primarily through information technologies. Other research has explored contributions in the context of electronic discussion groups (Constant et al. 1996), networks of practice (Wasko and Faraj 2005), open source software projects (von Krogh and von Hippel 2006), and online communities (e.g., Beenen et al. 2004; Rashid et al. 2006). We build upon this research while noting that our framework focuses on contributions that occur within organizations, among employees who are subject to standard operating procedures and pay, promotion, and evaluation systems. We focus our analysis in several ways. First, we address situations where the contribution act is voluntary; that is, where individuals choose to contribute and thus engage in a decision-making process about whether, what, and how to contribute. Second, we assume that contributors intend to help, regardless of whether the recipient finds the information useful. We specify the motivations of the contributor, rather than the perceptions of the recipient. Third, we focus on contributions of what others have characterized as explicit knowledge (Polanyi 1967); that is, knowledge that can be communicated orally or in written form. This type of knowledge is also sometimes referred to in the literature as information (e.g., Davenport and Prusak 1998). Thus, we use the terms information or information objects to refer to that which is communicated orally or in writing. Finally, our analysis addresses contributions that are made in response to requests for help (e.g, through e-mail, instant messaging, or organizational electronic discussion forums and bulletin boards), while recognizing that there are other modes of contribution, such as to corporate databases. Third, despite the growing research on knowledge sharing, little work has been done on understanding the contribution act in detail. Prior research has focused primarily on identifying factors, such as individual motivations, that are associated with contributions (e.g., Beenen et al. 2004; The paper is structured as follows: We first present a conceptual framework for contribution behaviors. We then illustrate its applicability by developing theoretical propositions regarding the antecedents of contributions that result from direct requests for help. Then we examine the role that 24 MIS Quarterly Vol. 32 No.1/March 2008 Olivera et al./Contribution Behavior in Distributed Environments information technologies play in facilitating or inhibiting contributions. Finally, we discuss the theoretical and practical implications of our framework. A Model of Contribution Behaviors The central element of our model of contribution behaviors is the act of contributing. Two theoretical bases are critical to understanding this behavior: human problem solving and cognitive motivation theories. Theories of problem solving (e.g., Newell and Simon 1972) help specify the cognitive activities that individuals must perform to make a contribution. Cognitive motivation theories (Kanfer 1990) explain why individuals decide to allocate time and effort to the contribution act. Contributions involve a variety of cognitive activities. We draw from theories of problem solving (Newell and Simon 1972) to identify the cognitive tasks that must be carried out to make a contribution. Theories of problem solving posit that, to solve a problem, individuals must first develop an internal, cognitive representation of the problem. In the context of contribution behaviors, then, individuals need to develop cognitive representations of opportunities to help others. We refer to this cognitive activity as awareness. Once the problem solver has developed a representation of the problem, an iterative process ensues to search for and identify solutions. We refer to this activity as searching and matching. Finally, the problem solver must articulate the solution. We refer to this as formulation and delivery of the contribution. Theories of decision making also specify principles and biases that tend to govern cognition. We use theories of satisficing (Simon 1957), escalation of commitment (Staw and Ross 1987), and estimation of costs and benefits (Anderson 2003) to explain contribution behaviors. Motivational forces are required to initiate, sustain, and carry to completion the activities that are necessary for making a contribution. Motivation is a resource-allocation process through which individuals make decisions about how to allocate their time and effort across tasks (Kanfer and Ackerman 1989; Naylor et al. 1980; Pritchard and Payne 2003). This resource allocation perspective assumes that (1) individuals have a limited amount of energy (or resource capacity), (2) individuals make decisions about how to allocate this energy across competing activities, and (3) these decisions are determined by individuals’ perceptions of whether their efforts will result in desired outcomes (perceived costs and benefits). These resource-allocation decisions, combined with the perceived benefits of allocating resources to a specific task, constitute a motivational force determining the direction, intensity, and persistence of effort. In essence, the contribution act reflects a decision to allocate time and effort to the contribution and away from other tasks. These two theories of problem solving and motivation are complementary, and we combine them to develop a framework of contribution behaviors. The framework builds on prior research on contributions but also represents a significant theoretical redirection. It emphasizes the mediating mechanisms that relate antecedents (such as a request for help) to contributions. Theories of motivated behavior acknowledge the importance of specifying mediating mechanisms to understand the effects of stimuli on behavior (Locke and Latham 1990; Mitchell 1997; Mitchell and Daniels 2003). Focusing on mediating mechanisms helps better specify the theoretical relationship between independent and dependent variables (Baron and Kenny 1986; Fichman 1999) and better account for how different independent variables jointly explain a dependent variable (e.g., Todd and Benbasat 1999). Most prior research on contributions has used a bivariate approach relating predictor variables to contributions. These studies develop theoretical arguments for why an independent variable, such as expertise or personal motivations, should correlate with contributions. The recent MIS Quarterly special issues on knowledge management (2005, Volume 29 Numbers 1 and 2) included several empirical studies adopting this approach. For example, Wasko and Faraj (2005) theorized a link between a variety of independent factors (such as individual motivations, personal characteristics, and exchange expectations) and contributions. They found that some associations were statistically significant (e.g., perceived reputation enhancement, having relevant experience, being part of a network) and some were not (e.g., enjoyment in helping others, expectations of reciprocity, commitment). Similarly, Kankanhalli, Tan, and Wei (2005) theorized that costs (loss of power, codification effort), extrinsic benefits (organizational rewards, image, reciprocity), and intrinsic benefits (self efficacy, enjoyment in helping others) would predict contributions as moderated by contextual factors (norms). In contrast to Wasko and Faraj, however, they found no support for the argument that enhanced public image motivates contributions. They also found a significant association between enjoyment and contributions. The bivariate approach has generated some interesting and useful findings, as well as a long list of predictors and, on occasion, contradictory findings. In contrast to the bivariate approach, however, the present approach focuses on understanding the mechanisms through which predictors affect con- MIS Quarterly Vol. 32 No. 1/March 2008 25 Olivera et al./Contribution Behavior in Distributed Environments tributions. Understanding these mechanisms can help integrate prior research and reconcile contradictory findings by specifying how antecedents affect contributions and the conditions under which other individual or contextual factors are likely to moderate these effects. We theorize how various features of information technologies can have differential effects on each of the mediating mechanisms and hence affect contribution behaviors. There are both cognitive and motivational accounts for contribution behaviors. Prior research has focused primarily on motivational explanations, for example, reputation enhancement, reciprocity, extrinsic rewards, and so forth. Some studies have explored contextual factors, such as organizational norms (Constant et al. 1994; Jarvenpaa and Staples 2001; Kankanhalli et al. 2005) or climate (Bock et al. 2005), conceptualized as motivational explanations (i.e., one behaves according to norms to avoid the negative consequences of violating the norm). Some authors have acknowledged the role of cognitive processes (e.g., Hinds and Pfeffer 2003), but have not integrated cognitive and motivational explanations of contributions. Our view is that it is necessary to integrate both cognitive and motivational theories to explain why individuals make contributions. Finally, our framework specifies the role of technology in facilitating or inhibiting contributions. Most prior studies are conducted in the context of a specific technology, such as knowledge repositories or discussion forums, where the technology is a constant and its effects are seldom theorized. In our framework, we elaborate on the role of technology in supporting the mediating mechanisms of contributions. Consistent with frameworks of technology–task fit (Zigurs and Buckland 1998), we theorize the role of information technologies as facilitating information processing and communication tasks. Specifically, we propose that information technology affects contribution behaviors by its impact (positive or negative) on contribution-related activities. We characterize information technologies in terms of their key features, such as communication channels and bandwidth, and relate these to the activities of awareness, searching and matching, and formulation and delivery. Although we address objective features of technology, we recognize that it is the subjective understanding of technology that ultimately affects its use (Orlikowski 1992). The model in Figure 1 depicts the key elements of the framework. Contributions are the dependent variable. Features of requests for help and information technology are independent variables. Three activities (awareness, searching and matching, and formulation and delivery) function as mediating mechanisms. These activities generate cognitive and moti- 26 MIS Quarterly Vol. 32 No.1/March 2008 vational states that represent both outputs of each activity as well as inputs to subsequent activities. Mediating Mechanisms in Contributions Awareness, searching and matching, and formulation and delivery represent three distinct activities that jointly constitute contribution behaviors. These activities operate as mechanisms that mediate the effects of technology and requests for help on contributions. These activities may not be exhaustive, but they are essential for contribution behaviors. The outcomes of these activities are cognitive and motivational states that serve as inputs for subsequent activities. Below, we elaborate on the cognitive and motivational features of each mediating mechanism. Awareness is a cognitive activity through which a person recognizes an opportunity to contribute. It involves attending to and interpreting requests for help. Awareness is critical because contributions will not occur unless individuals recognize an opportunity to contribute (e.g., Bendapudi et al. 1996). Two cognitive and motivational states result from awareness. The first is a mental representation of the request for help, including its source (i.e., who is requesting the contribution) and content (i.e., topic domain, concreteness, and specificity). The second is some level of motivation to contribute. Motivation to contribute is a necessary precondition for searching and matching: if no motivation is elicited during awareness, then no contribution will occur. Searching and matching is a cognitive activity through which individuals determine whether and how the knowledge domain of the help request matches their own personal knowledge. Gray and Meister (2001) noted that knowledge sharing involves identifying matches between personal knowledge and the situations described by those who request help. This matching process reconciles personally held knowledge with the external request and may require an individual to search both his or her internal memory and external memory aids, such as files and databases (Corbett 2000). This activity may result in an exact match, an approximate or partial match, or no match. The match itself may be quite concrete, such as a solution to a problem or a pointer to where a solution may be located, or it may be quite general, involving a broad domain of knowledge (Browne et al. 2007). We assume the search occurs sequentially, and the contributor adopts a satisficing approach (Simon 1957). Thus, individuals adopt the first acceptable solution rather than engaging in optimal search behavior (Todd and Benbasat 1999). This searching and matching activity requires cognitive effort (Newell et al. Olivera et al./Contribution Behavior in Distributed Environments Request Characteristics Sender Status Mediating Mechanisms Cognitive and Motivational States Sender Affiliation Cognitive representation of the request Request Domain Awareness Motivation to engage in searching and matching Request Concreteness Request Specificity Technology Characteristics Cognitive representation of match with request Searching and Matching Motivation to engage in formulation and delivery Social Presence Synchronicity Cognitive representation of the contribution Formulation and Delivery Quality of Search, Indexing, and Retrieval Motivation to contribute Communication Channel Number and Accessibility Contribution Quality of Authoring Tools Figure 1. Model of Contribution Behavior 2004). If matches are not initially identified, the search costs increase, and unless there are some countervailing motivations, the search activity will end and no contribution will occur. The two cognitive and motivational states that result from searching and matching are a cognitive representation of the match and a level of motivation to continue with formulation and delivery of the contribution. Formulation and delivery is a cognitive and behavioral activity through which the contribution is articulated and communicated. Formulation represents the cognitive activity of determining what specifically should be communicated (Clark and Brennan 1991; Goodman and Darr 1998). Delivery may take many forms, including oral communications, e-mail, or posting to a discussion forum or corporate database. Formulation and delivery may be separate activities; for example, when a solution to a problem is written down and subsequently communicated in electronic form. Or, formulation and delivery may occur simultaneously, such as during a telephone conversation. The steps of formulation MIS Quarterly Vol. 32 No. 1/March 2008 27 Olivera et al./Contribution Behavior in Distributed Environments and delivery require effort (Clark and Brennan 1991; Goodman and Darr 1998; Kankanhalli et al. 2005). Research suggests that individuals have difficulty articulating what would otherwise be tacit knowledge (Polanyi 1967), or knowledge that is highly contextual (Tyre and von Hippel 1997), in a way that others can undersand (Markus 2001), particularly when the recipients of this knowledge are novices (Ackerman 1994; Hinds and Pfeffer 2003). The two resulting states from formulation and delivery are a cognitive representation of the contribution and a level of motivation to complete the contribution. These three mediating mechanisms or activities have important theoretical features. First, they are analytically independent. Becoming aware of a request for a contribution is different from either searching and matching knowledge or formulating and delivering a response. Each mechanism is a necessary and sufficient condition in the temporal sequence of activities. That is, awareness must occur for searching and matching to begin. Finding a match means formulation and delivery can be initiated. However, there may also be reciprocal causation. For example, if an individual finds it difficult to formulate and deliver a contribution, they may undertake more searching and matching and review their understanding of the request. Second, all three mechanisms involve cognitive activities that shape the mental representation of the contribution. In awareness, individuals develop a representation of the request for help; in searching and matching, they recognize the overlap between the request and their knowledge; and in formulation and delivery, they develop a representation of their planned contribution. These representations are antecedents to subsequent activities. For example, the representation generated from awareness is the input for searching and matching. Third, the three mechanisms are analytically independent, but if we could capture real protocols of the cognitive activities (Simon and Ericcson 1984), the actual processing time may be mere seconds in some cases, but much longer in others if interruptions delay the contribution act. This sequence of cognitive activities may manifest as a discrete set of events or, in other cases, as overlapping activities. Finally, all three mechanisms require effort and thus motivational forces are needed for individuals to be willing to carry out these activities. Motivational Forces in Contributions For a contribution to occur, motivational forces must offset the effort required by each of the mediating mechanisms (e.g., Pritchard and Payne 2003; Vroom 1964). The cognitive demands of contributions are costs that can be offset by the benefits of contributing (Payne et al. 1993). Consistent with 28 MIS Quarterly Vol. 32 No.1/March 2008 theories of decision making, we assume that individuals strive to minimize the amount of cognitive effort exerted to accomplish tasks (Todd and Benbasat 1999). The greater the amount of effort required to make a contribution, the greater the necessary motivational force that is needed. If the effort required is higher than the motivational forces, no contribution will occur. This theoretical perspective is consistent with how other researchers have explained contributions to electronic forums (e.g., Butler 2001; Holingshead et al. 2002; Kollock 1999; Monge et al. 1998; Thorn and Connolly 1987). Motivation is both a consequence of and an antecedent to the mediating mechanisms. For instance, the motivational force generated by awareness serves as input for searching and matching. The searching and matching activity may deplete the motivational force, in which case the contribution act would terminate, or it may generate further motivation. The resulting level of motivation will then serve as input for formulation and delivery. We draw upon literature on why people act in altruistic or prosocial ways in organizations (Batson and Powell 2003) to specify the motivations that drive contribution behaviors. Three distinct types of motivational explanations are particularly relevant for contribution behaviors: self-enhancement, exchange, and instrumental. Self-enhancement motivations are aimed at developing and maintaining positive beliefs about oneself (Baumeister 1998; Cialdini and Goldstein 2004). For example, individuals may help out to express their expertise (Constant et al. 1996; Orr 1996) and reinforce their own identity as an expert (Milton and Westphal 2005). They may also help to maintain their self-concept as “a helper” or “a good citizen” (Lakhani and von Hippel 2000). Helping may also provide opportunities for learning (von Hippel and von Krogh 2003; Wasko and Faraj 2000) or may be enjoyable in itself (Constant et al. 1996; Wasko and Faraj 2000). In all of these cases, helping is self-enhancing for the contributor. Exchange motivations stem from reciprocity and equity concerns. Individuals strive to maintain equitable relations “in which the ratio of outcomes to inputs is equal for the relating individuals” (Batson and Powell 2003, p. 469). Individuals who receive help will want to reciprocate in a similar way (Gouldner 1960). Also, helping others generates an expectation of future reciprocity (Cialdini and Goldstein 2004). Exchanges may occur directly between individuals, but they may also be indirect (Blau 1964; Clark and Mills 1978; Kankanhalli et al. 2005). Constant and his colleagues (1996), for example, reported that 17 percent of respondents indicated they helped others because they expected help from others in the future (see also Kankanhalli et al. 2005). Olivera et al./Contribution Behavior in Distributed Environments Exchange motivations are thus driven by a desire to initiate reciprocal exchanges or reduce perceived inequities generated by prior exchanges. Instrumental motivations stem from a desire to obtain external rewards. Similar to other theories of motivated behavior, the assumption is that individuals strive to obtain rewards from others (Vroom 1964). In the context of contributions, these rewards may be tangible, such as monetary compensation or other resources (Goodman and Darr 1998; Markus 2001), or intangible, such as public recognition (Ardichvili et al. 2003; Constant et al. 1996; Kollock 1999). Instrumental motivations may also inhibit contributing, such as when contributions result in loss of power or personal utility (Gray 2001; Hollingshead et al. 2002; Thorn and Connolly 1987). These three classes of motivators—self-enhancement, exchange, and instrumental—represent the benefits of contributing, and are necessary to offset its costs. Determinants of Contributions In the following sections, we develop propositions related to the three mediating mechanisms (awareness, searching and matching, formulation and delivery). These propositions consider the role of motivational forces (stemming from selfenhancement, exchange, and instrumental motivations) and information technology in offsetting the cognitive effort involved in making contributions. We first present determinants of the mechanisms and then explain the specific role of information technology in each mechanism. These propositions relate antecedents to the states that result from each mediating mechanism. They are meant to illustrate the applicability of the framework rather than provide a comprehensive list of all antecedents of contributions. We focus on contributions that occur in response to a direct request for help. In the discussion section, we describe how the model can be applied to other contribution situations, such as contributions to corporate databases where no direct request for help may have been made, and how other predictors of contributions can be incorporated into the model. Table 1 summarizes the theoretical framework and propositions. Awareness Awareness refers to the recognition of an opportunity to contribute. The two resulting states of awareness are a cognitive representation of the request for help and a level of motivation to engage in searching and matching. The request itself is a trigger of awareness. Requests for help can be thought of as interruptions (Perlow 1999; Speier et al. 2003) that momentarily distract recipients from their regular activities. The cognitive tasks for someone who receives a request for help are to attend to and interpret the request. The message itself is likely to provide the information necessary to form a cognitive representation of what the requester needs (e.g., Petty and Cacioppo 1986). Requests for help have two main properties: a sender and content (e.g., Dabbish et al. 2005). These elements are likely to (1) trigger motivational responses and (2) shape the recipient’s cognitive representation of the request (Bendapudi et al. 1996). Sender characteristics. Senders typically convey information about themselves in their requests, either in the body of their message (e.g., “I am a project manager in the Advanced Technologies Division in Atlanta”) or in their signature (e.g., John Smith, MSc., Project Manager, Advanced Technologies Division, Atlanta Office). Although senders have many individual characteristics, their status and affiliations are important organizational attributes that are likely to motivate contribution behaviors. E-mail messages, for instance, often convey status differences; for example, high-status individuals are more likely to attach signatures to the messages they send to lower-status individuals than vice-versa (Panteli 2002). We expect that requests from senders of higher status will trigger instrumental motivations to respond. When receiving a request from someone of higher status, individuals are likely to consider the potential benefits of providing help in terms of praise, recommendations, or future opportunities (Cialdini and Goldstein 2004; French and Raven 1959). Conversely, individuals may want to avoid the potential negative consequences of not providing help to someone of higher status. This motivational response is less likely to occur when the request comes from someone of equal or lower status. We thus propose that Proposition 1a: Requests from senders of status higher than the recipient will be more likely to result in a cognitive representation of the request and will generate a higher motivation to engage in searching and matching than will requests from senders of equal or lower status. A sender’s affiliation can also trigger a motivational response. Affiliation may be conveyed in terms of an organizational function (e.g., Engineering), location (e.g., the Atlanta office), or group (e.g., the Advanced Technologies Division or the New Products Initiative). The sender’s affiliation can elicit exchange motivations related to reciprocity. Consider the MIS Quarterly Vol. 32 No. 1/March 2008 29 Olivera et al./Contribution Behavior in Distributed Environments Table 1. Key Elements of Contributions and Summary of Propositions Awareness Searching and Matching Formulation and Delivery Cognitive activity Developing a representation of the request for help Identifying a solution that addresses the information request Articulating and communicating the contribution Role of motivation Motivational force generated by characteristics of the sender (P1a, b) and the request (P2) Motivational force needed to sustain effort in searching and matching Motivational force needed to sustain effort in formulation and delivery Specificity and concreteness of request increase motivation (P3a-c) Motivational force generated by searching and matching (P6) Likelihood of completing the contribution higher for concrete than abstract requests (P10) Costs increase as searching and matching moves from internal to external memory systems (P7) Cognitive/ motivational phenomena Specific requests generate higher motivation than general requests (P8) Facilitating role of technology Use of media high in social presence increases motivation (P4) Use of effective search, indexing, and retrieval technologies increases motivation (P9) Use of synchronous media increases motivation (P5) case where an individual has benefitted from prior exchanges with a specific organizational group. For example, if an individual is helped by someone in the Advanced Technology Division, they will feel obliged to exchange with that division in the future (see Liao-Troth and Griffith 2002). Although the requester may be a stranger, his or her affiliation should evoke feelings of reciprocity, and the receiver will be motivated to respond to the request for help. These exchange motivations are aimed at carrying out indirect rather than direct exchanges (Blau 1964; Clark and Mills 1978). Specifically, we propose that Proposition 1b: Requests from senders with affiliations with which the recipient has had prior, positive exchanges will be more likely to result in a cognitive representation of the request and will generate a higher motivation to engage in searching and matching than requests from senders with affiliations with which the recipient has had no prior positive exchanges. The above propositions are not exhaustive of all possible motivational responses to sender characteristics. For 30 MIS Quarterly Vol. 32 No.1/March 2008 Escalation induced by high investment in searching and matching (P11) Perception of effort costs associated with follow-up requests (P12) Access to multiple communication channels increases likelihood of contribution (P13) Use of authoring tools increases likelihood of contribution (P14) example, a request from a high status individual may generate both instrumental and exchange motivations. The strength of the motivational force is likely to increase as the request elicits more motivators. That is, the individual is more likely to decide to devote time and effort to making a contribution if he or she perceives that the contribution will generate more benefits (Pritchard and Payne 2003). Request characteristics. The content of the message, such as its topic domain, is also likely to affect the recipient’s motivational response. Topic domain refers to the general knowledge area addressed by the request, for example, accounting procedures or software implementation. The topic of the request may trigger a motivational response when it overlaps domains that the recipient considers to be his or her areas of expertise and sees as important to a sense of personal identity (Milton and Westphal 2005). Demonstrating expertise is an important motivator for contributing (Constant et al. 1996; Kollock 1999; Wasko and Faraj 2000). It contributes to the individual’s self-image and is thus a self-enhancement motivator. If there is no clear overlap between the domain of the request and the recipient’s expertise, or there is overlap but the area of expertise is not central to the recipient’s Olivera et al./Contribution Behavior in Distributed Environments identity, we expect that the request will not trigger a motivational response. We expect that Proposition 2: Requests in a topic domain in which the recipient has expertise that is central to his or her identity will be more likely to result in a cognitive representation of the request and will generate a higher motivation to engage in searching and matching than requests in topic domains in which the recipient has little expertise or that are peripheral to their identity. Request characteristics can also affect the perceived and actual costs of contributing. There are immediate costs associated with attending to and interpreting a request. There are also anticipated costs (Anderson 2003), in terms of the amount of time and effort that will be required for searching and matching and for formulation and delivery. For example, individuals may anticipate high costs associated with finding the required information or writing an elaborate response (e.g., Goodman and Darr 1998). Cost perceptions relate to the nature of what is being requested. Perceptions of anticipated costs are important because they inform individuals’ assessment of the cost-to-benefit ratio of contributing and thus their decision to continue the contribution activity. We focus on two specific aspects of a request that map onto the anticipated costs of searching and matching and formulation and delivery: (1) the level of specificity (from specific to general) and (2) the level of concreteness (from concrete to abstract) of what is requested. Specific requests set out exactly what information is needed. General requests are open-ended regarding the information that may help the requester. Concrete requests ask for information objects that are readily available or for knowledge that can be easily codified (i.e., written down). Abstract requests ask for knowledge that is not easy to codify. Figure 2 uses the example of software implementation to illustrate the specific–general and concrete–abstract request dimensions. These request characteristics make salient the anticipated costs of making a contribution. Specific requests facilitate searching and matching because they set clear boundaries around the search domain, hence limiting the number of potential matches (see Browne et al. 2007). In contrast, general requests involve higher searching and matching costs because there is a wider search domain and a higher number of potential matches from which an appropriate match must be specified (e.g., Campbell 1988; Newell and Simon 1972). Similarly, concrete requests are likely to require less effort to formulate and deliver than will abstract requests, which involve codifying expertise or tacit knowledge (Goodman and Darr 1998; Hinds and Pfeffer 2003; Kankanhalli et al. 2005). In sum, message specificity influences the anticipated costs of searching and matching, while message concreteness influences the anticipated costs of formulation and delivery. Proposition 3a: Requests that are specific will generate lower anticipated effort costs and in turn a higher motivation to engage in searching and matching than requests that are general. Proposition 3b: Requests that are concrete will generate lower anticipated effort costs and in turn a higher motivation to engage in searching and matching than requests that are abstract. We expect that the dimensions of specificity and concreteness will have an additive effect on motivation. For example, in Figure 2 we expect that requests that are specific and concrete will generate the lowest levels of perceived anticipated effort costs. Hence, even at low (but some) levels of motivation, individuals will engage in searching and matching when the request is specific and concrete. In contrast, requests that are general and abstract will generate the highest levels of perceived anticipated effort costs. In this case, individuals are likely to continue their contribution efforts only when motivation is high. Requests that are specific and abstract and general and concrete thus represent moderate levels of anticipated effort costs, requiring a motivational force that is higher than specific–concrete requests but less than general– abstract requests. However, these two cases differ in that, for general–concrete requests, the anticipated costs are immediate—arising from the searching and matching activities, which will be perceived as more difficult due to the general request. The perceived anticipated costs of specific–abstract requests are likely to be less salient because they relate to the more distant activity of formulation and delivery. This notion is consistent with research findings that individuals are more sensitive to immediate costs than to future costs (Lowenstein and Elster 1992). Thus, we expect that Proposition 3c: Requests that are specific and abstract will generate lower perceived anticipated effort costs and thus a higher motivation to engage in searching and matching than requests that are general and concrete. Information technology effects on awareness. Information technology can affect awareness in at least two ways. First, the medium in which the request is being delivered is likely to affect the recipient’s motivation to attend to the request. Researchers have studied how the social presence of media affects interpersonal communication (Miranda and Saunders MIS Quarterly Vol. 32 No. 1/March 2008 31 Olivera et al./Contribution Behavior in Distributed Environments Specific “I need a document outlining the steps for implementing the ABC software system.” “I need advice on how to effectively implement the ABC software system.” General “I need materials on how to implement software systems.” “I need to know about implementing software systems.” Concrete Abstract Figure 2. Examples of Requests Along Specificity and Concreteness Dimensions 2003; Short et al. 1976; Straub and Karahanna 1998). The communicator’s presence is more psychologically felt through face-to-face, videoconferencing, or phone interaction (Short et al. 1976) than through e-mail, instant messaging, or discussion forums. Drawing attention to a help request can be thought of as an interpersonally involving task, much like persuasion (Cialdini and Goldstein 2004). The requester has to generate sufficient motivation for the recipient to pay attention to the request. Media that are high in social presence can communicate urgency and other socioemotional concerns (such as sincerity or respect) better than media that are low in social presence (Miranda and Saunders 2003). Hence we propose that Proposition 4: Requests communicated through media that are high in social presence will be more likely to result in a cognitive representation of the request and will generate a higher motivation to engage in searching and matching, than requests communicated through media that are low in social presence. Second, information technology can directly affect the costs of developing a cognitive representation of the request. Communication research suggests that the use of synchronous media (such as phone, video-conferencing, and instant messaging) can improve understanding and accuracy of communication (Clark and Brennan 1991). This is in part, because these media make it relatively easy for communicators to ask clarifying questions. As we noted above, requests that are general (or low in specificity) are more likely to require clarification than requests that are specific. Hence, we expect that synchronous media will facilitate understanding and the development of a cognitive representation of general requests. In the case of specific requests, either synchronous or asynchronous media are likely to generate similar levels of motivation to contribute. 32 MIS Quarterly Vol. 32 No.1/March 2008 Proposition 5: General requests communicated through synchronous media will be more likely to result in a cognitive representation of the request and will generate a higher motivation to engage in searching and matching, than general requests communicated through asynchronous media. Searching and Matching Searching and matching is the cognitive activity through which individuals determine whether and how the knowledge domain of a contribution opportunity matches their personal knowledge. This activity involves searching through personal experiences or external memory systems, such as files, databases, and social networks (Corbett 2000; Olivera 2000) for knowledge that addresses the requester’s needs. The states that result from searching and matching are a cognitive representation of the match between the contribution opportunity and the contributor’s knowledge domain and a certain level of motivation to formulate and deliver the contribution. Searching and matching generally involves a focused cognitive effort. In some circumstances, the match may be almost immediate and require little effort, in which case the contribution will continue without evoking much motivational force. However, when the match is not obvious to the contributor, motivational forces are needed to sustain the effort involved in searching and matching. One possible scenario is that there is no match (i.e., there is no recognized overlap between the contributor’s knowledge domain and the requester’s need), in which case the contribution is likely to end. There may also be some match, in terms of substantive knowledge (such as the answer to a question) or pointers to where the knowledge may be found (such as the name of someone who may know the answer). Olivera et al./Contribution Behavior in Distributed Environments We noted earlier that one outcome of awareness is a certain level of motivation to engage in searching and matching. We expect the motivational force evoked during awareness to serve as input for searching and matching. For example, a request stressing that the recipient is an expert in the field is likely to trigger self-enhancement motivations. A request from someone affiliated with a group with whom there have been prior, positive exchanges may generate exchange motivations based on reciprocity. A request from a high status individual may evoke instrumental motivations. These motivations will initially offset the costs associated with searching and matching. The searching and matching activity itself also may generate a motivational response. Research on contributions to intranets has found that contributors often report that they enjoy solving problems (e.g., Lakhani and von Hippel 2000; von Hippel and von Krogh 2003; Wasko and Faraj 2000). Individuals may enjoy finding solutions to problems from their repertoire of experiences. Also, partial matches may reinforce individuals’ beliefs that they are able to help (Locke and Latham 1990), thus generating further selfenhancement motivations. We propose that Proposition 6: The searching and matching activity may itself generate self-enhancement motivations that will increase the likelihood of developing a cognitive representation of a match between the content of the request and personal knowledge and the motivation to engage in formulation and delivery. We also expect that motivation may decrease during searching and matching. Individuals are likely to begin searching and matching with the least effortful approach and move to more effortful approaches only if a match is not identified (Newell et al. 2004; O’Reilly, 1982; Todd and Benbasat 1999). Searching and matching will become more costly as it moves from internal to external searches. Internal search involves reflecting on personal experiences and drawing knowledge from personal memory. In the absence of a match, individuals may search their personal memory aids, such as files. The next stage may be to use other external sources, such as databases and one’s social network. Efforts made at these stages incur increasing searching and matching costs. Note that asking colleagues for advice on how to help someone else involves initiating new exchanges, which require future reciprocity (Cialdini and Goldstein 2004). Thus, the motivation to continue may reduce as costs escalate, potentially ending the contribution. Proposition 7: As searching and matching moves from internal to external memory systems, the likelihood of developing a cognitive representation of a match between the content of the request and personal knowledge will decrease, as will the motivation to engage in formulation and delivery. As noted earlier, request characteristics are key determinants of awareness. In terms of searching and matching, the extent to which the request is specific versus general will influence the amount of effort that is required to find a match. Consider the case where someone in the company asks for information about a forecasting model. The request about what they want to learn about the model may be specific (e.g., the formula that underlies it or the results of a forecast) or general (e.g., broad information about the model). The costs of searching and matching are likely to be higher for general than for specific requests, and thus generate less motivation to formulate and deliver the contribution. General requests are likely to generate multiple matches during the searching and matching activity because there may be multiple ways to interpret a general request. The individual then needs to identify an appropriate match from this broad set of possible matches (Campbell 1988), which adds to the time and effort involved in searching and matching. We propose that Proposition 8: Searching and matching for specific requests will be more likely to result in a cognitive representation of a match between the content of the request and personal knowledge and generate a higher level of motivation to engage in formulation and delivery, than searching and matching for general requests. Information technology effects on searching and matching. Searching and matching is likely to involve external memory aids, such as files and databases. Information technology can affect searching and matching by facilitating both the electronic storage of personal and organizational documents, as well as the search through individual and organizational memory systems (Corbett 2000; Olivera 2000; Zigurs and Buckland 1998). Search and retrieval mechanisms are essential components of knowledge management systems (Alavi and Leidner 2001). Effective indexing, storing, searching, and retrieval technologies reduce the effort of finding relevant information during searching and matching. We argued above that the costs of searching and matching increase as the search moves from personal to external memory systems. The technological context may enhance or diminish this effect. The volume of documents in organizational memory systems is likely to be significantly greater than that in personal files. The likelihood of finding infor- MIS Quarterly Vol. 32 No. 1/March 2008 33 Olivera et al./Contribution Behavior in Distributed Environments mation will thus depend on the quality of indexing. Individuals are likely to have a good understanding of how their personal files are indexed. However, using indexing systems developed by others can pose several challenges. For example, different people may use different words to refer to similar concepts (Markus 2001; Marwick 2001), which makes it difficult to find specific information. A related problem is that queries may return a large proportion of irrelevant documents (Marwick 2001) that the user needs to sort though to find the relevant information. Researchers have proposed several techniques to improve indexing and facilitate searching, including natural language queries (Lewis and Jones 1996), searching by themes and threads (Ackerman and McDonald 1996), machine-learned pattern matching and recognition (Stein and Zwass 1995), and taxonomy and semantic network-based indexing (Goodman and Darr 1998; Marwick 2001). We propose that Proposition 9: Using effective search, indexing, and retrieval technologies will reduce the costs of searching through external memory systems and thus increase the likelihood of developing a cognitive representation of a match between the content of the request and personal knowledge and the motivation to engage in formulation and delivery. Formulation and Delivery Formulation and delivery is the activity through which the contribution is articulated and communicated to the recipient. We can think of these as two interrelated activities. In formulation, the contributor reviews the information identified through searching and matching, and selects and organizes information that may be meaningful to the individual who made the request. Delivery is the act of communicating this information via electronic and other media. The states resulting from formulation and delivery are a cognitive representation of the contribution and a level of motivation to conclude the contribution. This section focuses on the factors that determine whether formulation and delivery will result in a contribution. Note that at this stage the contributor is aware of the opportunity, understands the match between their personal knowledge and the domain of the contribution opportunity, and is motivated to continue the contribution. Formulation and delivery, however, will require further time and effort. The question is whether the person will actually complete the contribution. Formulating a contribution is an effortful task (Clark and Brennan 1991). Formulation requires articulating knowledge in a way that can be understood by others, which can be 34 MIS Quarterly Vol. 32 No.1/March 2008 difficult even for experts (Hinds and Pfeffer 2003). Consistent with theories of sensemaking (Weick 1995), we expect that individuals will become more cognizant of the effort required to articulate a contribution during formulation than during the awareness or searching and matching mechanisms. That is, the costs of formulation become more salient when individuals actually begin formulating the contribution. The motivational force that results from searching and matching serves as input for formulation and delivery and needs to be strong enough to sustain the formulation activity. As was the case in searching and matching, it is conceivable that formulating a response will be enjoyable and motivating (i.e., a self-enhancement motivation). However, these motivational forces may not be sufficient to sustain a highly effortful formulation and delivery activity, for example, when the request is for abstract knowledge. We argued above that the extent to which a request is concrete versus abstract will determine the amount of effort required in formulation and delivery. Requests that are very abstract require individuals to articulate their expertise and tacit knowledge, which is likely to require high levels of effort (Goodman and Darr 1998; Hinds and Pfeffer 2003). Proposition 10: Formulation and delivery of the contribution are more likely to be completed when the request is concrete than when the request is abstract. The motivation for formulation and delivery may also be affected by the amount effort exerted during searching and matching. Consistent with theories of escalation of commitment (Staw and Ross 1987), we expect that individuals who invested significant effort in searching and matching will be motivated to continue the contribution in spite of increased effort costs in formulation. High levels of commitment in searching and matching may drive formulation and delivery as a way of justifying the earlier efforts. Note that the escalation reasoning is independent of the types and levels of motivators present. We propose that Proposition 11: A high investment of time and effort in searching and matching will increase one’s likelihood of completing the formulation and delivery activity. We also expect that, during formulation and delivery, individuals will become aware of how the recipient will react to the contribution. The role of the contributor during formulation and delivery is of a communication sender, so we expect that cognitions about the recipients will become Olivera et al./Contribution Behavior in Distributed Environments salient. As individuals consider the recipient of the contribution, they are likely to consider further anticipated costs (Anderson, 2003). For instance, as they formulate their response, the contributor may realize that the contribution is likely to generate further follow-up questions or clarification. These requests represent additional future costs of time and cognitive effort. We thus propose that Proposition 12: The likelihood of completing formulation and delivery of the contribution will decrease to the extent that the contributor anticipates follow-up requests as a result of the contribution. Information technology effects on formulation and delivery. Information technology can facilitate these activities by providing communication channels that reduce the costs of articulating and communicating the contribution. Consider the case of a contribution that requires articulating abstract knowledge. It may be less costly to formulate the contribution verbally, rather than in writing. Conversely, a report may be more easily communicated as an e-mail attachment than over the phone. An elaborate procedure may be best communicated through video. Current technologies make it relatively easy to transfer text and graphs and, with sufficient bandwith, video and audio files. Telephone and videoconferencing systems (via telephone lines or internet) make it possible to engage in synchronous communication, which facilitates verbal elaboration of a response (Clark and Brennan 1991) and reduces the potential costs associated with receiving additional clarifying questions about a contribution. Access to multiple communication channels gives contributors the opportunity to choose the format or configuration of media that best suits the communication (Watson-Manheim and Bélanger 2007), thus decreasing the perceived and actual costs of contributing. We propose that Proposition 13: The greater the number of communication channels within the organization, and the more accessible they are, the greater the likelihood that individuals who use them will complete the contribution. Information technology can also directly reduce the costs of formulating the contribution. Authoring or formulating a contribution can be an effortful task. Contributors need to collect, cull, organize, and distill materials identified during the searching and matching phase (Ackerman and McDonald 1996). They also need to organize and articulate the contribution in ways that will be useful to others (Markus 2001). Document authoring technologies can facilitate this activity in a variety of ways, including automatically generating content (Woukeu et al. 2004) and creating electronic document formats that are easily shared via the Web, e-mail, or databases (Marwick 2001). In addition, authoring systems may employ artificial intelligence to help users customize their contribution for novices or experts, using prompts to check for completeness, accuracy, and appropriateness of materials for the intended recipient. Authoring tools could also help sift through the materials identified during searching and matching, retrieving only what is useful. For example, the WiCKEd authoring tool (Woukeu et al. 2004) incorporates a context-based knowledge retrieval mechanism that retrieves knowledge according to the location of concepts within the document. A name typed in the “Introduction” section of a document would retrieve a summary of the person’s work; the same name typed in the “References” section would retrieve a list of relevant publications. Finally, the authoring tools could have delivery capabilities that prompt the user to indicate the mode of delivery and recipient. The system would then automatically generate the appropriate content and deliver it to the target destination. In sum, we propose that Proposition 14: Using high-quality authoring tools will reduce the costs of formulation and delivery, thus increasing the likelihood of completing the contribution. Discussion Contribution behaviors are a natural way to increase organizational effectiveness. Ideas and solutions derived in one setting can often solve problems at another. However, there is empirical evidence (e.g., Goodman and Darr 1998; Szulanski 1996) of several impediments to contribution behaviors. Limitations in technology, the complexity of the object to be communicated, and individual motivations often work against contributing. What seems to be a process for increasing organizational effectiveness is, therefore, often not an organizational reality. Our approach to this problem is to develop a fine-grained understanding of why and how people make contributions. If we understand the activities that constitute contributions, we are better positioned to build theory and inform information system design. Our theoretical approach differs from others in the following ways. First, we focus on three independent but critical mediating mechanisms: awareness, searching and matching, and formulation and delivery. We explain contribution behaviors in terms of these three mechanisms, centering on MIS Quarterly Vol. 32 No. 1/March 2008 35 Olivera et al./Contribution Behavior in Distributed Environments cognitive activities and the underlying motivational forces that are required to sustain them. We noted that antecedents of one mechanism (e.g., awareness) are not necessarily the precursors to another mechanism (e.g., formulation and delivery). The cognitive representations and motivations that result from each mechanism serve as inputs to subsequent activities. Developing the linkages from antecedents to mechanisms and the relationships among mechanisms is the critical consideration in building theory about contributions. A second distinguishing feature of our theoretical development is that it integrates cognitive and motivational theories. Most prior research has focused on motivational explanations, although some has acknowledged the importance of cognitive processes (e.g., Hinds and Pfeffer 2003). Our framework relates specific cognitive activities to the motivational forces required to sustain them. Further, we use theoretical constructs at the intersection of cognition and motivation, such as satisficing (Simon 1957) and escalation of commitment (Staw and Ross 1987) to understand why people make contributions. The third feature is that the framework explicitly develops the role of technology in contribution behaviors. We specify how technology can increase or decrease the amount of cognitive effort required by each of the mediating mechanisms. Consider how technologies such as telephone and email differ in the extent to which they facilitate developing a mental representation of a request for help. The former is likely to be less cognitively demanding. Technology can also facilitate searching and matching to the extent that it allows information to be effectively stored, indexed, and retrieved. Also, technology can facilitate formulation and delivery by, for example, providing multiple communication channels or authoring tools. Contextual factors may moderate the effects of technology on contributions. For instance, access to synchronous communication channels may not facilitate awareness in environments where individuals are already constantly engaged in multiple synchronous interactions. In such environments a request for help transmitted through synchronous media, such as instant messaging, may go unnoticed. Also, synchronous media may not facilitate formulation and delivery when its use is infeasible due to differing time zones. The general principle, however, is that, to the extent that technologies reduce or increase the amount of cognitive effort required by the three mechanisms, they will promote or discourage contributions. contributions that are made in response to direct requests for help. Consider contributions to knowledge repositories where, for example, a person may decide to contribute a document summarizing best practices or lessons learned from a recently completed project to a corporate database of best practices. This common form of contribution in organizations is enabled by information technologies, and it has received significant attention in the literature (e.g., Markus 2001). Contributions to knowledge repositories are similar to contributions that result from direct requests in several ways. In both cases, the individual attempts to solve the problem of providing helpful information to others. The main activities that constitute contributions also apply: Individuals need to be aware of the opportunity to contribute, engage in searching and matching to identify how their knowledge relates to what others need, and formulate and deliver the contribution. The individual may be motivated to contribute by self-enhancement, exchange opportunities, or instrumental rewards (e.g., Constant et al. 1996; Goodman and Darr 1998; Kankanhalli et al. 2005). Technology may increase or decrease the effort necessary to carry out the activities required to contribute. Implications for Theory There are also some important differences between these two contribution modes. Contributions to databases are proactive and there is no request to help determine the content, recipient, or communication mode of the contribution. As a result, contributions to databases are likely to require significantly more effort than responses to requests. Awareness requires determining whether one has generated information that is worth sharing, with whom it should be shared, and how it should be communicated. Searching and matching involves determining whether the intended contribution will match the needs of others in the organization. The contributor may ask: Who may benefit from the contribution? What should the contribution contain to be of use to others? This is likely to be an iterative process in which the what and who of the contribution are aligned. Formulation and delivery will require building a contribution for multiple potential recipients or “whos” (Markus 2001), determining how the information will be delivered (e.g., through knowledge repositories, personal distribution lists, etc.), and actually delivering the contribution. The latter may involve additional administrative costs, such as obtaining authorization to post a resource, filling out forms describing the contribution, or having the contribution examined for quality (Olivera 2000). In sum, each of the mediating mechanisms is likely to require significant cognitive effort. A model of contributions to databases should thus consider that any of these activities can offset the motivation to contribute. Our framework can be used to further develop theory regarding an array of contribution types. We focused on Theorizing about the role of technology in contributions to knowledge repositories requires specifying how technology 36 MIS Quarterly Vol. 32 No.1/March 2008 Olivera et al./Contribution Behavior in Distributed Environments affects each of the mediating mechanisms. Awareness may be facilitated by features of the knowledge repositories, such as whether they provide information about questions that others in the organization have asked and examples of contributions that have been acknowledged as useful or valuable (Rashid et al. 2006; Resnick and Varian 1997; Snowdon and Gasso 2002). Similar to responses to direct requests for help, high-quality search, indexing, and retrieving technologies should facilitate searching and matching in the case of knowledge repository contributions. Authoring technologies can reduce the costs of collecting and organizing the documents that will be part of the contribution (Ackerman and McDonald 1996) as well as the costs of articulating the contribution in ways that will be useful to others (Markus 2001). Thus, these technologies can reduce the costs of formulation and delivery. Technology may also guide users through the contribution process (Ackerman and McDonald 1996), reducing the administrative costs of contributing. Our framework can also help specify how contextual factors affect contributions. A host of organizational variables may affect contribution behaviors, including culture, norms, reward systems, and training systems (Bock et al. 2005; Constant et al. 1994; Jarvenpaa and Staples 2001; Kankanhalli et al. 2005; Quigley et al. 2007). Consider the role of organizational norms. Researchers have argued that organizational norms for sharing stimulate contributions by reducing individuals’ perceived costs of contributing (Kankanhalli et al. 2005; Quigley et al. 2007). In our framework, the effects of contextual variables such as organizational norms need to be theorized in terms of their effects on the mediating mechanisms. For instance, strong norms for sharing may operate by eliciting instrumental motivations when individuals receive requests for help. These instrumental motivations may be strong enough to support the subsequent activities (searching and matching and formulation and delivery), and we would therefore expect more contributions from organizational units with strong sharing norms. However, it is possible that organizational norms increase the likelihood of awareness, which requires relatively low cognitive effort, but are not sufficient to offset the costs of searching and matching. In this case, norms would interact with other factors, such as quality of searching technologies, to predict contributions. The fundamental principle is that theorizing about the effects of contextual factors on contributions requires specifying how contextual factors affect each of the activities that constitute contribution behaviors. This approach to theorizing should result in better-specified models of how contextual factors affect contributions. This approach to theorizing about contribution behaviors is a departure from the more common bivariate approach. We have argued that understanding mediating mechanisms helps us better specify theoretical models of contributions. Although this approach has common elements with process theorizing (Mohr 1982), it differs in some important ways. Processes are defined in terms of stages, where transition from one stage to another is binary. Although we have specified some temporal sequence in the mechanisms we propose (e.g., searching and matching follows awareness), the emergent states from these mechanisms are not binary (e.g., cognitive representations or motivations are either present or absent). Rather, the outcomes are continuous (e.g., motivation may range from very low to very high) and these outcomes may change as individuals engage in the activities themselves (e.g., searching and matching may generate additional motivation). We conceptualize the relationship between the mediating mechanisms as dynamic, where, for example, the act of formulation and delivery may require further searching and matching. Although the mechanisms are discrete and have unique properties that differentiate them, the interactions among them may be frequent and nonlinear. Finally, we acknowledge that individuals develop perceptions of the costs associated with the mediating mechanisms prior to actually engaging in them. For example, individuals may form perceptions of the costs that will be associated with searching and matching or formulation and delivery, and these perceptions can affect their decision to continue the activity. Process models do not lend themselves to this type of theorizing. Limitations We now review some of the possible limitations of our approach. First, we focused on contribution behaviors within organizations. This constraint helped us to limit the scope of our analysis. However, we recognize that contributions often occur among individuals from different organizations, such as in online communities. The proposed theory applies even if we relax this definitional constraint. The three mechanisms would still need to be played out in responding to a request posted on an electronic discussion forum or posting a resource to an online community. We expect, however, that features of the online community will affect the types of motivations that will be salient to contributors. For example, Cosley et al. (2005) found that quality and quantity of contributions to an online community increased when the contributions were peer reviewed. Peer reviews likely affect motivation by making salient the selfenhancement and instrumental benefits of contributing. Features of the interface itself may also affect the costs MIS Quarterly Vol. 32 No. 1/March 2008 37 Olivera et al./Contribution Behavior in Distributed Environments associated with awareness, searching and matching, and formulation and delivery. Online communities, however, also have unique properties that are not explicitly accounted for in our framework. For instance, there is evidence that social dynamics among community members, such as their newcomer status and history of participation, affect contribution behaviors (Arguello et al. 2006). The linkages between online community dynamics and the resulting motivational forces to contribute are beyond the scope of our framework. We also constrained our definition to geographically dispersed organizations, where there may be no personal ties or work interdependencies between requesters and contributors. However, we can also use this framework to explore why individuals contribute to people within their local work environment and with whom they have personal ties, such as friends or close colleagues. The theoretical framework we proposed would hold, but some of the motivational forces, such as exchange motivations, may be stronger. Also, the costs of developing cognitive representations (of the request, of the match with personal knowledge, and of the contribution) are likely to be reduced by the shared cognitive schemas that typically result from interaction within a shared context (Wasko and Faraj 2005). Finally, we limited our discussion to the individual level of analysis. A request for help may be made on behalf of a group or organization. Also, contributions may be made by groups, such as project teams, not exclusively by individuals. We see potential in applying the mediating mechanisms to the group level, although extending our framework to other levels of analysis is beyond the scope of this paper. Doing so will require theorizing about how groups develop mental representations of opportunities to help (Cannon-Bowers and Salas 2001) and how the activities that constitute contributions are likely to be distributed among group members (Kozlowski and Klein 2000). It will also require theorizing about how motivational forces operate at the group level, a relatively unexplored topic (Kozlowski and Bell 2003). Implications for Practice Our theoretical framework also has important implications for managers and information technology developers. The framework provides a diagnosis tool for managers wanting to understand or enhance the level of contribution behaviors in their organizations. Rather than assume that low contributions result from low motivation, managers may assess, for instance, whether the barriers to contributions relate to difficulties in awareness, searching and matching, or formulation and delivery and determine specific ways to address 38 MIS Quarterly Vol. 32 No.1/March 2008 these difficulties through training or information system design. Training can be aimed at developing skills associated with each of the mediating mechanisms. For example, training on how to write requests for help may facilitate awareness for recipients and help requesters get the information they are seeking. Training on how to effectively use the organization’s search tools should increase the individual’s effectiveness in searching and matching. Alternatively, training could be aimed at helping people learn how to effectively formulate responses, thus reducing the costs of formulation and delivery. This type of training could be part of formal management training or could potentially be presented in an online, self-directed form. In terms of information system design, our model specifies the costs that prevent contribution behavior and the role of information systems in reducing these costs. We have proposed that several features of information technologies can increase or reduce cognitive effort, including social presence, communication capabilities (such as synchronous versus asynchronous communications), and memorybuilding capabilities (including indexing, searching, and updating). Other possible design considerations stem from the framework. With respect to awareness, tools that prioritize requests (by importance or potential match with the recipient’s knowledge domain) may facilitate awareness. Given that individuals often postpone attending to messages in their inbox (Dabbish et al. 2005), technology can remind users to respond to requests at a time when the user has free time in his or her calendar. In terms of searching and matching, technology’s indexing, searching, and matching capabilities can reduce the cognitive effort of sifting through personal or organizational memory systems. For example, indexes based on taxonomies that are sufficiently detailed and based on semantic-networks are likely to be more user friendly than those that are general and based on keywords (Goodman and Darr 1998). In terms of formulation and delivery, communication capabilities can facilitate interaction and transfer of knowledge objects between contributor and recipient. Access to different media, bandwidth, and channel switching makes communication more flexible so individuals can find the best communication match for the content and target of their contribution. Finally, the use of common (or compatible) software platforms throughout the organization can reduce the costs of formulation and delivery by making it possible for contributors to use authoring tools, embed comments in documents, and send files as e-mail attachments knowing that the recipient will be able to retrieve the information. Olivera et al./Contribution Behavior in Distributed Environments This paper provides a guide to the critical mechanisms for effective contribution behaviors. A deep understanding of these elements within specific organizational settings can result in better diagnosis of roadblocks, the development of effective training tools, and information system design to facilitate contributions in organizations. Acknowledgments We thank Debbie Compeau, Denise Rousseau, Senior Editor Carol Saunders, Associate Editor Sandy Staples, and the three reviewers for their helpful comments on previous versions of this paper. References Ackerman, M. 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K. 1998. “A Theory of Task/Technology Fit and Group Support Systems Effectiveness,” MIS Quarterly (22:3), pp. 313-334. About the Authors Fernando Olivera is an associate professor of Organizational Behavior at the Richard Ivey School of Business, University of Western Ontario. He holds a Ph.D. in Organizational Behavior and Theory and an M.S. in Industrial Administration from Carnegie Mellon University. His research focuses on learning processes at the individual and group levels and the effects of technology on learning processes. Recent publications include “Error Reporting in Organizations” (Academy of Management Review, 2006) and “Group-to-Individual Transfer of Learning: Cognitive and Social Factors” (Small Group Research, 2004). 42 MIS Quarterly Vol. 32 No.1/March 2008 Paul S. Goodman holds the Richard M. Cyert Professorship at Carnegie Mellon University. He was educated at Trinity College (BA), the Amos Tuck School at Dartmouth College (MBA), and has a Ph.D. from Cornell University in Organizational Psychology. His research interests focus on designing effective work groups, learning in distributed work groups, and organizational change and effectiveness. His most recent books are Missing Organizational Linkages (Sage, 2000) and Technology Enhanced Learning (Erlbaum, 2001). Professor Goodman is a Fellow in the American Psychological Society and the Academy of Management. He is also a film producer. Sharon Tan is an assistant professor in the Department of Information Systems at The National University of Singapore (NUS). She received her Ph.D. and M.Sc. degrees in Industrial Administration from Carnegie Mellon University, and her M.Sc. and B.Sc. Honours degrees in Information Systems from NUS. Her research interests include knowledge contribution and seeking behaviors in organizations, work interruption and its impact on work performance and emotional outcomes, and the use of information technology in healthcare organizations. Her work has been published in the proceedings of the International Conference of Information Systems, the ACM Conference on Computer Supported Cooperative Work, and the America’s Conference on Information Systems. She was a runner-up for the Best Paper Award of the 2002 International Conference on Information Systems.