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Technology research has long faced difficulties in consistently defining technology, establishing cause-effect relations, and generating research results that generalize across technologies, users, contexts, and time (Barley, 1998). In this essay we (1) identify three of the many contributors to these difficulties, (2) mention some ambitious responses that have been suggested in the literature, and (3) propose a smaller but more concrete first step by asking researchers and theorists to attend to both features of technologies and the contexts in which they are used, and in so doing to improve our ability as a field to accumulate knowledge in the face of these challenges.

Challenge: Technological Dynamism

  1. Top of page
  2. Challenge: Technological Dynamism
  3. Challenge: Decomposing the Whole
  4. Challenge: Integration of Features
  5. Response: Calls for More Sophisticated Theory and Research
  6. A Modest Proposal
  7. Feature-Based Variables
  8. Context-Based Variables
  9. References
  10. About the Author

Technology is a “slippery” concept (MacKenzie & Wajcman, 1985) for many reasons. Technology is not independent of context (users, situation, economics, etc.) nor is it “neutral” (Winner, 1986) in relation to those who design, implement, and use it. Furthermore, most technological artifacts continuously evolve so that technologies vary across time as well as contexts. For example, we know that the mobile phone of 2 years ago is quite a different beast from that of today, as evidenced by the proliferation of new functionalities such as mobile photo and video. Yet, our academic discourse and research reports rarely reflect these changes nor address issues of comparability. Even in those situations where functionalities change only incrementally over time, technology-mediated communication can change radically depending on a variety of factors such as how many and who have adopted a technology (e.g., critical mass, Markus, 1990, or network externalities, Kraut, Rice, Cool & Fish, 1998).

Challenge: Decomposing the Whole

  1. Top of page
  2. Challenge: Technological Dynamism
  3. Challenge: Decomposing the Whole
  4. Challenge: Integration of Features
  5. Response: Calls for More Sophisticated Theory and Research
  6. A Modest Proposal
  7. Feature-Based Variables
  8. Context-Based Variables
  9. References
  10. About the Author

Technological artifacts (“those bundles of material and cultural properties packaged in some socially recognizable form such as hardware and/or software” (Orklikowski & Iacono, 2001, p. 121) have many and varied features. Nass and Mason (1990) argued that researchers need to identify the values that a technology takes on for specific features and to compare findings based on the features rather than the artifact as a whole. Poole and DeSanctis (1990) argued that not only must research attend to features, but also that users may or may not use a particular feature or appropriate it faithfully in the spirit in which it was intended to be used. Griffith (1999) argued that features were but triggers for sense-making in communication activities. Also, features can interact. For example, the implications of feature “diffusion” may depend on whether or not the technology has feature(s) that promote interactivity (e.g., mobile phone versus handheld calculator), as Markus (1990) argued.

Challenge: Integration of Features

  1. Top of page
  2. Challenge: Technological Dynamism
  3. Challenge: Decomposing the Whole
  4. Challenge: Integration of Features
  5. Response: Calls for More Sophisticated Theory and Research
  6. A Modest Proposal
  7. Feature-Based Variables
  8. Context-Based Variables
  9. References
  10. About the Author

Nass and Mason (1990) noted that when multiple features are integrated within a single artifact, assessment of cause-effect relations at the feature level is considerably more challenging, and thus the artifact is likely to be treated holistically. Online communities, where users have a large palette of highly interlinked ways to communicate, are an example of an integrated technology. A user can even be unsure of which features are being used, as in, for example, music file sharing sites that automatically upload material from a user's device once that user logs on to download content. A key challenge is to assess which features may be influenced by or may influence communication practices.

Response: Calls for More Sophisticated Theory and Research

  1. Top of page
  2. Challenge: Technological Dynamism
  3. Challenge: Decomposing the Whole
  4. Challenge: Integration of Features
  5. Response: Calls for More Sophisticated Theory and Research
  6. A Modest Proposal
  7. Feature-Based Variables
  8. Context-Based Variables
  9. References
  10. About the Author

In the face of such challenges, technology scholars have called for greater sophistication in both theory and research. Orlikowski and Iacono (2001, p. 131), for example, suggest developing multiple theories of technological artifacts based on “distinctive cultural and computational capabilities, existing in various social, historical, and institutional contexts, understood in particular ways, and used for certain activities”. Over the years scholars also have offered various empirical desiderata, such as multisite studies (Fulk, Heino, Flanagin, Monge, & Bar, 2004); longitudinal research (Kraut, Egido, & Galegher, 1990); controlled experiments with alternative configurations of features, artifacts and context (Nass & Mason, 1990); computational modeling of the most complex relations (Palazzolo, Serb, She, Su, & Contractor, 2006); programmatic research that builds cumulatively across a variety of different studies and technologies (Steinfield & Fulk, 1990); and mining massive stored data on user communication practices on the web (Pathak, Mane, Srivastava, & Contractor, 2006). These calls point the direction but fall short of delivering on the enormous challenge of developing such theory or conducting such complex research programs.

A Modest Proposal

  1. Top of page
  2. Challenge: Technological Dynamism
  3. Challenge: Decomposing the Whole
  4. Challenge: Integration of Features
  5. Response: Calls for More Sophisticated Theory and Research
  6. A Modest Proposal
  7. Feature-Based Variables
  8. Context-Based Variables
  9. References
  10. About the Author

We offer here a proposal for a modest step forward that is within our reach and relatively concrete. We do not claim this as a comprehensive solution to the big issues so well raised by our colleagues. Our hope is that we can make steady forward progress while concurrently seeking the big prizes of complex and comprehensive theory and research programs that must be developed over time.

We call for published research to report greater detail with respect to features of technologies and the contexts in which they and their users are embedded during the time of the research. Providing more detail may seem like a small step; however, it does require researchers to attend to the technology-in-use writ large as research is designed and as data are gathered. Why types of detail are particularly relevant in the contemporary and future technological environment? In the paragraphs below we suggest several exemplary variables. This is not intended to be a comprehensive list. Rather, our goal is to stimulate thinking about the types of variables we should be considering and the interactions that occur among them. Our thinking on this topic is informed by Nass and Mason (1990), although we diverge from them in seeking only to specify variables that may be made available with evolving technology.

Feature-Based Variables

  1. Top of page
  2. Challenge: Technological Dynamism
  3. Challenge: Decomposing the Whole
  4. Challenge: Integration of Features
  5. Response: Calls for More Sophisticated Theory and Research
  6. A Modest Proposal
  7. Feature-Based Variables
  8. Context-Based Variables
  9. References
  10. About the Author

The most valuable research will identify which features are available to users, which of these features are used and how, what sense users make of these features, and how all these factors are influenced by interactions among features. Below we suggest a few examples of features for which these types of information can be reported.

Integration is the incorporation of multiple technological features within a controlled or bounded system (Nass & Mason, 1990) and the interactions that occur among these features. Facebook, for example, is a bounded technological system that incorporates a variety of different technological features such as messaging, search, and photo sharing, just to name a few. Published papers on such technologies should report the features available to users at the time of the study, what combinations of features are actually used by the research subjects, and potential interactions among them. For example, in an examination of the practice of making wall postings on Facebook it would be important to acknowledge the other different ways that users can and do communicate with one another (e.g. e-mail, instant messaging, commenting on pictures, providing status updates) because the these other communication features might influence users' wall posting behavior.

Interoperability is the ability of systems or features to exchange information or operate together effectively (Miller, 2000). Examples include the sharing of data and user identities between different systems, the ability to import contacts from one application to another, and the proliferation of open standards such as really simple syndication (RSS). For users, this can mean easy-to-use integrated platforms and effortless transitions across various communication portals; for researchers, integrated platforms and interoperable portals pose tricky challenges for understanding cause and effect. For example, the same software application (e.g., spreadsheet program) can migrate data among a local personal computer, a handheld PDA, a secure Internet database, or an open access website. The user's relation to the application may vary by device or may be influenced by the range of devices on which it is accessible. The result is that neither device nor application can be understood in isolation. Conversely, lack of interoperability may also influence users' choice in relation to technology adoption and use. Interoperability has been studied in the context of diffusion of new technologies (e.g., standards, network effects), but much less attention has been paid to its implications for the everyday experiences of technology users. It is important for research reports to describe the nature of any interoperabilities and to present cogent arguments for how each of the systems or features is implicated in communication practices.

User control deals with user agency and the extent to which a user is able to control conditions relating to the use of a particular feature, technology, or technological system. An example of a low control technology is a communication portal such as a pay phone where the user has almost no ability for customization. Moving along the control continuum, personalized homepages that users may customize are an example of a technology that provides the user with an intermediate degree of control. In contrast, the Linux operating system is an example of a high-control technology because users are able to make direct modifications to the source code if they possess the requisite skills.

User control may be conceptualized at a variety of levels of analysis, including control with respect to an entire technological system, individual technologies, and individual features of a technology. Reporting levels of user control will permit more nuanced interpretations of research findings, facilitate comparisons to other technological uses involving this key feature, and contribute to theory development about the role of user agency in technology-mediated communication practices.

Context-Based Variables

  1. Top of page
  2. Challenge: Technological Dynamism
  3. Challenge: Decomposing the Whole
  4. Challenge: Integration of Features
  5. Response: Calls for More Sophisticated Theory and Research
  6. A Modest Proposal
  7. Feature-Based Variables
  8. Context-Based Variables
  9. References
  10. About the Author

Users bring various skills, experiences, and biases. These individual differences will influence both the ways that users perceive technologies and the specific functionalities of a given technology that they actually utilize. For example, less skilled users may steer clear of complex, highly integrated technologies or may choose to use only their most basic features. We all know that users are important, so the challenge lies in finding out as much about users as possible and reporting this information in research findings. Given the potential for variation in user feature use, it is particularly important for researchers to do their best to capture which features are actually used by individual research subjects.

Location refers to the physical and cultural context in which technology is being used and may be a fixed or dynamic. The growing ubiquity of mobile devices and increased use of these devices as platforms for accessing a variety of different software-based communication technologies leaves us with many unanswered questions with respect to the role of location. Does it make a difference if I am posting videos to YouTube from a desktop computer in my home office, my laptop in a coffee shop, or from my mobile device as I am a passenger in a moving car? How might the importance of location vary from user to user? Are there certain types of users for which location may be more or less important? What is the role of the cultural context in which a technology is being used? Researchers should collect data on location, report it in papers, and consider it in interpreting results.

Complementary Technologies Users of communication technologies are increasingly engaging in simultaneous communication activities using a variety of different technologies, a process that has been termed multicommunicating (Reinsch, Turner & Tinsley, 2008). For example, an individual may be participating in a conference call while simultaneously exchanging instant messages with a colleague or composing an e-mail. These activities could all be conducted on one device or could be spread across multiple devices. The important point is that there is likely a complex interaction among these various technologies and tasks as well as with users. An interesting aspect of multicommunicating is that it represents user control over switching as opposed to technological control achieved through interoperability. As challenging as it is to tease apart this complex interplay, it is important for us to do our best to document this process.

Space constraints prevent providing detail on all relevant features and contexts. We encourage technology scholars to sharpen and expand this initial call and reviewers/editors to encourage authors to enrich their research reports with relevant feature and context information. Examples of additional features include, for example, search capabilities, networking, privacy/security, anonymity, and type and number of reputation indicators. Other context factors could include relation to social/task networks, relation to other modes of communication, degree of diffusion of the artifact, availability of complementary products (e.g., software for applications for a device), and presence of one or more substitute products (e.g., cell phone features substituting for some functions and features of the personal computer). We sincerely hope other technology scholars will contribute other features and context factors to this proposed modest reconfiguration of desiderata for research reports.

Steve Corman (2006, p. 336) recently argued that technology is not “just some new emerging research topic,” but rather is deeply implicated in virtually every communication practice in contemporary life. “Everyone who studies organizational communication writ large—including nontechies and people with gray hair—should be thinking about how to incorporate technology questions into their research,” as should, by implication, scholars in other areas of the field as well. This reality provides a compelling case for pressing forward quickly with concrete steps toward addressing these knotty challenges in studying technology.

References

  1. Top of page
  2. Challenge: Technological Dynamism
  3. Challenge: Decomposing the Whole
  4. Challenge: Integration of Features
  5. Response: Calls for More Sophisticated Theory and Research
  6. A Modest Proposal
  7. Feature-Based Variables
  8. Context-Based Variables
  9. References
  10. About the Author
  • Barley, S. (1998). What can we learn from the history of technology? Journal of Engineering and Technology Management, 15, 237255.
  • Corman, S. (2006). On being less theoretical and more technological in organizational communication. Journal of Business and Technical Communication, 20, 325339.
  • Fulk, J., Heino, R., Flanagin, A., Monge, P. & Bar, F. (2004). A test of the individual action model for organizational information commons. Organization Science, 15(5), 569586.
  • Griffith, T. (1999). Technology features as triggers for sense-making. The Academy of Management Review, 24(3), 472488.
  • Kraut, R.E., C. Egido, J. Galegher (1990). Patterns of contact and communication in scientific research collaboration. J.Galegher, R.E.Kraut, C.Egido, eds. Intellectual Teamwork: Social and Technological Foundations of Cooperative Work (pp. 149171). Hillsdale, NJ: Erlbaum.
  • MacKenzie, D., & Wajcman, J. (1985). Introductory essay. In D.MacKenzie & J.Wajcman (Eds.), Social shaping of technology: How the refrigerator got its hum (pp. 225). Philadelphia, PA: Open University Press.
  • Manross, G. & Rice, R. (1986). Don't hang up: Organizational diffusion of the intelligent telephone. Information & Management, 10(3), 161175.
  • Markus, M.L. (1990). Toward a “critical Mass” theory of interactive media. In J.Fulk & C.Steinfield (Eds.), Organizations and communication technology (pp. 174218). Newbury Park: Sage.
  • Miller, P. (2000). Interoperability: What is it and why should I want it? Ariadne, 24.
  • Nass, C. & Mason, L. (1990). On the study of technology and task: A variable-based approach. In J.Fulk & C.Steinfield (Eds.), Organizations and communication technology (pp. 4667). Newbury Park: Sage.
  • Orlikowski, W. & Iacono, S. (2001). Research commentary: Desperately seeking the “IT” in IT research- A call to theorizing the IT artifact. Information Systems Research, 12(2), 121134.
  • Palazzolo, E. T., Serb, D., She, Y., Su, C., & Contractor, N. S. (2006). Co-evolution of communication and knowledge networks as transactive memory systems: Using computational models for theoretical development. Communication Theory, 16(2), 223250.
  • Pathak, N., Mane, S., Srivastava, J., & Contractor, N. S. (2006). Knowledge perception analysis in a social nNetwork. Proceedings of the 6thSIAM—International Conference on Data Mining (SDM 06).
  • Poole, M.S. & DeSanctis, G. (1990). Understanding the use of group decision support systems : The theory of adaptive structuration. In J.Fulk & C.Steinfield (Eds.), Organizations and communication technology (pp. 173193). Newbury Park: Sage.
  • Reinsch, N.L., Turner, J.W., & Tinsley, C.H. (2008). Multicommunicating: A practice whose time has come? The Academy of Management Review, 33(2), 391403.
  • Rogers, E.M. (2003). Diffusion of innovations, 5th ed. New York: Free Press.
  • Steinfield, C.W., & Fulk, J. (1990). The theory imperative. In J.Fulk and C.Steinfield (Eds.), Organizations and communication technology (pp. 1325). Sage: Newbury Park.
  • Winner, L. (1986). The whale and the reactor: A search for limits in an age of high technology. Chicago: The University of Chicago Press.

About the Author

  1. Top of page
  2. Challenge: Technological Dynamism
  3. Challenge: Decomposing the Whole
  4. Challenge: Integration of Features
  5. Response: Calls for More Sophisticated Theory and Research
  6. A Modest Proposal
  7. Feature-Based Variables
  8. Context-Based Variables
  9. References
  10. About the Author

Janet Fulk is Professor of Communications and Professor of Management & Organization at University of Southern California. Her research examines how communication systems impact collaboration and knowledge distribution across boundaries of space, time, team, organization, and nation. Her books include Shaping Organizational Form: Communication, Connection and Community (with Gerardine DeSanctis), the award-winning Organizations and Communication Technology (with Charles Steinfield), and Policing Hawthorne (with Greg Patton and Peter Monge). Recent articles have appeared in Organization Science, Human Relations, Communication Theory, and Communication Research, and an award-winning article in Academy of Management Journal. She is a Fellow of the Academy of Management.

Jessica J. Gould (B.A.-Psychology, UC Santa Cruz) is a 3rd-year doctoral student at the Annenberg School for Communication. Her research focuses on organizational communication. Current empirical research examines processes of collaboration and knowledge sharing among members of both distributed and collocated work teams in the U.S. and China. Other ongoing projects examine online communities as formal organizations and mobile phone networks as sources of information and support for adult survivors of childhood cancer. In conjunction with the Annenberg Networks Network, Ms. Gould is analyzing the evolution and change in networks of collaboration among nongovernmental organizations in the Children's Rights sector over the past 30 years. Ms. Gould is also interested in how entrepreneurs leverage the capabilities of information and communication technologies in the development of their personal networks.