Recent research suggests that rational choice and by Juan Pardo

Recent research suggests that rational choice and social influence theories provide complementary explanations for individual selection and use of communication media in organizations. Focusing specifically on e-mail selection and use, our study builds on this research by investigating the determinative role of individual differences. We find that individual differences influence e-mail use directly, as well as influence the relation between other predictor variables and e-mail use. Specifically, favorable attitudes toward innovation and change, computer self-efficacy, and computer experience directly and positively influence e-mail use, and in addition, attitudes toward innovation and change influence (moderate) the relationship between social context and e-mail selection and use. These findings point to the need for a more comprehensive and complex model of the process determining the selection and use of e-mail.

Members Use E-Mail: The Role of Individual Differences in Channel Choice

Why Faculty

Barbara D. Minsky Daniel B. Marin Louisiana State

lectronic

University, Baton Rouge

mail is ubiquitous in contemporary organizations. E-mail lib-

erates the communicator from the time and space constraints of other media, allowing communication between two or more people widely

separated geographically, each of whose responded to when convenient.

messages may be received and

The breadth of current research on electronic communication argues the embeddedness of electronic communication, particularly e-mail, in the continuing evolution of organizational life. For example, researchers have explored attitudinal responses to voice mail at Syncrude Canada Ltd. (Beswick & Reinsch, 1987); challenges posed for e-mail research by ethical and intellectual property issues (Howard, 1993); the organization-shaping force of the genre repertoire developed by a community of computer language designers in their e-mail communications (Orlikowski & Yates, 1994); the efficacy of technology-use mediation in helping to adapt new communication technology to its organizational context (Orlikowski, Yates, Okamura, & Fujimoto, 1995); the privacy, accuracy, and intellectual property issues raised by technological advances in business communication (Herschel & Andrews, 1997); and the virtues of computer-mediated versus face-to-face communication (Bordia, 1997). In 1995, Organization Science devoted an entire issue to electronic communication. Researchers have documented the significant organizational opportunities e-mail presents. Rapid transmission of large files increases communication velocity, supports collaborative work, and sustains both strong and weak ties among communicators (Wellman, Salaff, Dimitrova, Garton, Gulia, & Haythornthwaite, 1996). E-mail makes it easier for organizations to access and process information. E-mail encourages

195 increased participation, more egalitarian participation, and less centralized leadership (Hollingshead, McGrath, & O’Connor, 1993). More equal participation improves the quality of ideas (Finholt, Sproull, & Kiesler, 1990; Wellman et al., 1996). E-mail facilitates cross-organizational communication and improves customer service. In an era of down-sizing, email may help managers handle broader spans of control. Although e-mail does not have the same effect in every organization, researchers agree that e-mail is significantly changing life in organizations. Much research has focused on the determinants of communication media use in organizations. What factors influence a person’s choice to use and how widely to use particular media? Why does one person choose to use e-mail at least once a week, while another uses it seldom, if ever? Two major explanatory theories seem applicable: rational choice theory and social influence theory. Rational choice theory posits that individuals choose communication media by matching the medium’s inherent objective characteristics and the objective requirements of the communication task (Fulk, Schmitz, & Steinfield, 1990). Social influence theory, in contrast, argues that channel choice is a function not only of objective characteristics of the medium or the task, but also, and perhaps even more so, of individual perceptions conditioned by the social context of media and task (Webster & Trevino, 1995). Both theories have contributed greatly to our understanding of media selection and use in general and of e-mail selection and use in particular. However, both theories focus on factors external to the individual communicator: rational choice on the characteristics of the communication task and the communication medium, social influence on the social context in which the communication occurs. Neither theory purports to explain why in a particular set of circumstances one person may use a particular communication medium more frequently than another person uses it. This question is our research question: Why in the same circumstances might one person use e-mail frequently while another person uses it infrequently, or not at all? Reviewing theoretical developments in organizational communication, Fulk and Boyd (1991) suggested, &dquo;Beyond rational and social influence factors, other important forces operate at both the individual and organizational level&dquo; (p. 413). Their suggestion is consistent with literature focusing on technological innovation and its diffusion (Nelson & White, 1990; Rogers, 1983), which claims that the way people perceive and use new technologies may be explained, at least in part, by individual differences: the configurations of traits, including personality traits and inclinations and demographic factors, particular to the individual. Accordingly, we propose adding individual differences to those factors specified by rational choice and social influence theories to more fully explain the decision to use e-mail. We have attempted to build upon extant rational choice and social

196 influence research by investigating the role of individual differences in the choice and use of e-mail. Our findings indicate that individual differences do directly influence choice and use of e-mail and, in addition, may moderate the influences of both rational choice and social context. In this paper we will (a) briefly review the business communication literature on e-mail and some prominent rational choice and social influence theories, (b) draw upon the innovation literature to propose and discuss individual difference factors, (c) present our hypotheses, (d) present and discuss the results of our investigation, (e) indicate limitations of the study, and (f) assess the contribution of our results to an understanding of the determinants of e-mail use in organizations and indicate some implications for future research. What emerges, as one considers in succession rational choice, social influence, and individual difference, is a progressively more comprehensive and more complex view of the process of selecting and using communication media in organizations.

Literature Review Rational choice and social influence theories have been used by researchers to explain channel choice. Researchers have suggested a role for individual differences in the dissemination and adoption of innovation.

Rational Choice Theories Rational choice theory posits that individuals choose communication media by matching the mediumâ&#x20AC;&#x2122;s inherent objective characteristics and the objective requirements of the communication task (Fulk, Schmitz, & Steinfield, 1990). One of the earliest models posited that choice depended on the social presence of the medium. Short, Williams, and Christie (1976) proposed a continuum based upon the degree of social presence each medium provides; they suggested that the medium providing the degree of social presence required by the particular communication task would then be chosen. Trevino, Lengel, and Daft (1987) ranged media along a continuum according to information richness, a measure of the opportunity the medium affords communicators to interpret equivocal messages. Face-to-face contact is the richest medium because face, voice, and body cues allow interpretation of equivocal message content. Much as does social presence, information richness declines along a continuum from face-to-face through telephone, e-mail, and various other kinds of written documents. By ranging the various communication media along a single continuum, both the social presence model and the information richness model propose unidimensional, single-criterion, concepts of human rationality in the choice and use of communication media. Fulk, Schmitz, and Steinfield (1990) noted that although the social presence model received &dquo;moderate support&dquo; in laboratory tests, it explained &dquo;only a small proportion of the variance in media-related behav-

197 ior&dquo; and was not readily generalizable (p. 118); they noted the information richness model has received only weak empirical support. Researchers’ inability to find strong empirical support for these models suggests that their unidimensionalality and objectivist perspective lead to an insufficiently complex model. Social Influence Theories Social influence theories set aside the assumptions of objectivity and full cognition and look to the social context to identify factors that may affect a person’s perception, choice, and use of communication media (Webster & Trevino, 1995). Social influence theories do not deny that choosing whether and how often to use a medium may be rational and reasonable given communicators’ perceptions, whether objectively correct or not, of the characteristics of communication media and tasks. Gutek (1990) found that work groups requiring communication within the work group or between the work group and other work groups might collaborate more effectively if they used a computer system that fitted their task structure. This finding opens the prospect of a rationality, but not necessarily the narrowly conceived objectivity of rational choice theories, in the fitting of technology to task in the determination of communication technology. Further, Hunter and Allen (1992) found that people were more likely to continue to use e-mail when the system was easy to use and when people perceived benefits of using it. Insofar as the perceived benefits of using e-mail may be social or political, this finding also points to social influence and a more broadly conceived rationality. Sproull and Kiesler (1986) found that geographic, organizational, and situational variables-features of the communication situation, including the relationships among senders and receivers, the topic of the communication, the social norms-influenced perceived social context. Perceived social context, in turn, might influence the choice of communication medium through cognitive interpretations and behavior (Fulk, 1993). Thus, situational variables such as a supervisor’s use of e-mail, a peer group’s use, or a group norm of using e-mail, which might be functions of geographic location, could influence an e-mail. Under social influence theories, &dquo;media perceptions are, in part, subjective and socially constructed&dquo; (Fulk, Schmitz, & Steinfield, 1990, p. 121). Social influence theories thus present us with a more complex model of channel choice. The communicator is not merely matching communication task and media, but is influenced, consciously or unconsciously, by social relationships, organizational structures, and local norms in the selection and use of communication media. The social influence theory of technology use (Fulk, 1993; Webster & Trevino, 1995) draws on social learning theory and social information pro-

employee

use

198

cessing theory. Fulk (1993) pointed out that technology-related behaviors and attitudes, including media choice, may result from processes of social learning in the work place. Bandura (1977) suggested that one learns and uses behaviors based upon what one sees modeled within social groupings. Observed behaviors of others influence the observer to emulate those behaviors. Rogers (1983) found that similarities between the potential user and the observed person as well as the potential user’s view of this person affect the potential user’s willingness to adopt the new behaviors. In the case of e-mail, this might mean that behaviors of co-workers and superiors affect perception and hence use of e-mail through social modeling. Social information processing theory (Salancik & Pfeffer, 1978) posits mechanisms by which peers influence behavior and attitudes. Social information consists of comments and observations made by people whose views an individual considers relevant. Thus, co-workers’ overt statements may influence media choice. Social information may bear on media choice and use as aspects of the environment become more or less salient or as environmental clues are interpreted by co-workers. Social information helps a person identify what other people in organizations consider important. Fulk (1993) suggested that social information influences both attitudes towards and use of communication media. Webster and Trevino (1995) argued that rational choice and social influence theories &dquo;are complementary, not competing&dquo; (p. 1564). Social influence theories, we propose, do not deny rationality but provide a multidimensional and therefore more complete, and perhaps more realistic, view of the processes of choosing and using communication media by individual members of organizations. However, some researchers have indicated the need to move &dquo;beyond rational and social influence factors [to] other important forces&dquo; to account for the observation attributed to Rice and Case (1983) that &dquo;some media are favored regardless of circumstances&dquo; (Fulk & Boyd, 1991, p. 413). The Role of Individual Differences in

Innovation Social influence theories regard perception as a social construct. However, a person’s perception of a particular communication medium may be a function not only of social context and rationality but of the combined influences of these and of traits intrinsic to the person, such as personality traits, inclinations, and demographic factors. The behavior flowing from this confluence, moreover, may be inconsistent with the behavior predicted by rational choice and social influence theories. A person characterized by a strong disinclination to change may refuse to adopt e-mail despite demonstrable benefit and social pressure, preferring instead to talk face-to-face even about trivial matters amenable to e-mail. Some such possibility, at least, is urged by Staw and Ross’ (1985) investigation of a &dquo;dispositional approach to job attitudes&dquo; (p. 469). They observed not only

Adopting

199 &dquo;a much stronger case for dispositional effects than has been presented for many years in organizational psychology&dquo; but also &dquo;not ... nearly as strong situational effects as one would predict from the social information

processing

job design perspectives&dquo; (p. 477).

The innovation literature supports the theory that individual differences matter. Rogers (1983) found favorable attitudes toward change positively associated with favorable attitudes toward innovation. He suggested that early adopters of innovation tend to be people who are more favorably inclined towards change and science, who possess greater ability to deal with uncertainty and risk, and who are less fatalistic. Nelson and White (1990) found that peopleâ&#x20AC;&#x2122;s own attitudes and values, in addition to those of their peers, leaders, and supervisors, influence their adoption of innovation. The diversity of factors they identified argues the complexity of adopting an innovation. Attitudes toward change in general and toward working with computers in particular were either positively or negatively related to a variety of factors such as faith in management, time pressure, role conflict, responsibility for people, career progress, job overload, job scope, stress, group openness, group morale, In addition group pressure, group goal clarity, and group cohesion. Nelson and White observed that computer anxiety tends to diminish and more

ence

positive attitudes toward computers develop with computers. Research

people gain experi-

Hypotheses

social influence theory, with its broader conception of rationality, may explain the weaknesses in empirical support for rational choice theories, so individual differences such as an inherent disinclination to change might account for defects in the explanatory force of social influence. Such predispositions may turn out to be relatively stable (Staw & Ross, 1985). If so, we would expect individual differences to interact with some of the expected effects of both rational choice and social context. A personâ&#x20AC;&#x2122;s general disinclination to change would work against using a new technology, the dictates of rational choice and social influence notwithJust

standing. Although individual differences have,

as we suggest above, received the attention of researchers in the fields of job attitudes and dissemination of innovation, their impact on e-mail use has received only limited research attention (Fulk, Schmitz, & Steinfield, 1990). We propose that insofar as perceptions, bearing on the choice to use e-mail, may be a function of individual differences as well as of external social influences, individual differences will play a role in the determination of e-mail use. We believe that e-mail presents an especially likely case of the importance of individual differences to adopting innovations. Arguably, techno-

logical innovation, especially computer-based technology, purports

help

200

people be more effective and efficient in a variety of tasks. E-mail is more than an innovative technology; it affects communication mode or style, which becomes a part of the person, an aspect of personal image. Thus, we can reasonably expect not only rational choice and social influence but also intrinsic individual differences to play roles in determining whether, to what extent, and in what circumstances a person will use e-mail. Such a model may explain behavior that would be regarded as anomalous by rational choice and social influence theories. Our first two hypotheses recognize the role of a broadly conceived rationality, not necessarily grounded in the assumption of an objective reality, as a determinant of e-mail use by people in organizations. If use of a system seems to be beneficial, it is rational to use it. If the system is easy to use, it is rational to use it. These hypotheses flow from the work noted above of Gutek (1990) and of Hunter and Allen (1992). H 1:

Perception of the benefits of using e-mail is positively related of e-mail. perception that it is easy to use an e-mail system is tively related to the use of that system. to the

H2:

use

The

posi-

Our third hypothesis examines the influence of social context on e-mail use, based on the findings by Sproull and Kiesler (1986) and Fulk (1993). A groupâ&#x20AC;&#x2122;s leaderâ&#x20AC;&#x2122;s use of e-mail provides a social context which encourages the use of e-mail by group members. H3:

The use of e-mail by people within a group is to the use of e-mail by the group leader.

positively related

The rest of our hypotheses concern the effects of individual differences both alone and interacting with the effects of rational choice and social influence. The next two hypotheses, prompted by the innovation literature of Rogers (1983) and Nelson and White (1990), concern the impact of attitudes toward science and change on e-mail use. People with an intrinsic inclination for science, we suspect, will be more at ease with the computer technology associated with e-mail. The same, we suspect, will be true for people with a favorable attitude toward change and innovation, since, relatively speaking, e-mail does represent change and innovation. H4:

Working in the sciences is positively related

H5:

mail. A favorable attitude toward tively related to e-mail use.

change

to the

use

and innovation is

posi-

our sixth hypothesis, we propose that attitudes toward change act only directly but also indirectly, moderating the effects of rational choice and social influence. This is an important distinction, we think, because it opens the possibility of explaining why in some cases a person may use e-mail infrequently even though it is an appropriate medium for

not

201 the communication task and is socially validated. The moderating effects of individual difference might help to explain such an anomaly. A favorable attitude toward change and innovation is positively associated with intensification of the positive effects of rational choice and social influence on the use of e-mail.

H6:

Two additional hypotheses are prompted by Rogersâ&#x20AC;&#x2122; (1983) work on the diffusion of innovation, specifically his observation, noted above, concerning the inclination of less fatalistic people toward change and innovation. Here, as our discussion above proposes, we translate Rogersâ&#x20AC;&#x2122; idea into the concept of locus of control and apply it directly (H7) and indirectly (H8) to the issue of e-mail use. H7: H8:

An internal locus of control is positively related to e-mail use. Locus of control positively or negatively moderates the effects of social influence on e-mail use. That is, for example, we expect an internal locus of control to intensify positive social influence effects and diminish negative social influence effects on e-mail use.

Related to these hypotheses concerning locus of control is the following in the premise that people characterized by an external locus of control and hence a strong desire to fit in, as indicated by the social desirability construct, will be more affected by social context.

hypothesis, grounded

A high score on ated with e-mail

H9:

social desirability scale is positively associin social contexts which favor its use.

use

Our last two hypotheses concern another individual difference investigated by Nelson and White (1990): the relation between experience with computers and attitudes toward computers. Our focus is the attitude, specifically the degree of confidence vis-a-vis computers, as a direct (H10) and indirect (H 11) influence on e-mail use. We suppose that people characterized by high computer self-efficacy will be more likely to use e-mail and that, for example, a person with high computer self-efficacy will be more likely to use e-mail even in a social context inimical to its use. H 10:

High computer self-efficacy is positively associated with e-mail use.

H 11:

High computer self-efficacy positively

negatively moderates

the effects of social influence and rational choice

e-mail

use.

Method Our sample

163 faculty members in two colleges in a large state 60% from the College of Arts and Sciences and 40% from the university: of College Basic Science. Of these, 73% were male, 27% female; 57% were between the ages of 30 and 50, 41% were over 50, and only 2% were under was

202 30. Almost all, 96%, had desktop computers in their offices, and over 86% had computers at home. Length of time that e-mail was available in the college varied by department from approximately 6 months to over a year. We chose these two groups assuming faculty in the College of Basic Science (which at this university includes the hard sciences, such as chemistry, physics, and biology) were more &dquo;science-oriented&dquo; than faculty in the College of Arts and Sciences, which includes humanities such as history, philosophy, foreign language, and speech and social sciences, such as anthropology, psychology, and sociology. Also we hoped that these two groups might provide a particularly wide range of individual inclinations and hence of e-mail selection and use outcomes. (Of course, we acknowledge the fact that research methodology in the social sciences and even in humanities such as history is or is becoming more quantitative and more like the methodology in the natural sciences.) We delivered surveys to the associate dean of each of the Colleges. Each dean wrote a cover letter to department chairs and mailed the surveys to the chairs for distribution to faculty members. The surveys were returned directly to the researcher to lessen the possibility that individuals would feel peer and/or supervisor pressures in responding to the survey questions. A total of 188 surveys were mailed to College of Basic Science faculty; 51 were returned. A second request resulted in the return of 14 more, for a response of 34% from Basic Science. A total of 292 surveys were mailed to the College of Arts and Science; 98 were returned for a 34% response from Arts and Science. Nonrespondents can be compared to late respondents (Armstrong & Overton, 1977). In our survey the second request respondents were demographically similar to the other respondents, as well as representative with respect to age and gender of the entire faculty. A second request was not sent to the College of Arts and Sciences.

Measures To ascertain an individual’s level of e-mail use, we adapted Ku’s (1996) scale of social and nonsocial uses of electronic messaging systems in organizations (a .8871). Responses on a 5-point Likert scale (1 Never, 2 Seldom, 3 Sometimes, 4 Often, 5 Always) tapped frequency of e-mail use for nine activities, such as exchanging routine information, scheduling meetings, coordinating project activities, and negotiating. (See Appendix A, section II, for the full list.) To measure general e-mail use, we adapted Ku’s (1996) measure of electronic messaging systems. Respondents answered &dquo;Yes&dquo; or &dquo;No&dquo; to 15 questions (e.g., &dquo;Do you use e-mail at least once a day?&dquo; &dquo;Do you like using e-mail?&dquo;). We used a combination of items as surrogate measures for social context and rational choice. Social context was measured with the following items: &dquo;Do you use e-mail to keep in touch with others?&dquo; &dquo;Does =

203 your chair and/or dean use e-mail on a regular basis?&dquo; &dquo;Does e-mail facilitate your committee work?&dquo; Rational choice was measured with these

items: &dquo;Is e-mail an efficient method of communication?&dquo; &dquo;Is e-mail a convenient method of communication?&dquo; &dquo;Do you have access to more information with e-mail?&dquo; (See Appendix A, Section III for the full prompt.) We used Campeau and Higgins’ (1995) 10-item scale to assess computer confidence. The measure tapped magnitude and strength by asking individuals to first respond &dquo;Yes&dquo; or &dquo;No&dquo; and then to indicate their level of confidence in their ability on a 10-point Likert scale (1 Not at all confident to 10 Totally confident). The survey stated, &dquo;I could use the software package in my teaching/administrative duties ...&dquo; and then posed 10 scenarios such as &dquo;... if there were no one around to tell me what to do as I go&dquo; or &dquo;if someone had helped me get started.&dquo; (See Section IV, Appendix A for the full prompt.) Ettlie and O’Keefe (1982) determined that the best way to predict innovative behavior is to assess attitudes toward change in general as well as those toward a particular innovation. We adapted their scale (a .865). A 5-point Likert scale (1 Strongly agree to 5 Strongly disagree) with 15 items measured attitudes towards change and innovation. Sample items included &dquo;I try new ideas and new approaches to problems.&dquo; &dquo;People who depart from the accepted university routine should not be punished.&dquo; (See Appendix A, Section V for the full list.) We adapted Rotter’s (1966) scale to measure locus of control (a .70). People with an external locus of control might be more easily influenced by the social context. Respondents indicated which of each of seven paired statements was truer. (See Appendix A, Section VII.) For example, respondents were asked which of this pair was truer: &dquo;Without the right breaks, one cannot be an effective leader&dquo; or &dquo;Capable people who fail to become leaders have not taken advantage of their opportunities.&dquo; We used the seven items suggested by Fischer and Fick (1993; a = .792) from the Marlowe-Crowne Social Desirability Scale to assess individual differences. This dual-use scale measures need for approval and situational demands. Sample items in this True/False scale include &dquo;I like to gossip at times&dquo; and &dquo;I’m always willing to admit when I make a mistake.&dquo; (See Appendix A, Section VIII.) A high score indicates high need for approval, which would increase the influence of social context. For example, if one’s peers and leader used e-mail, an individual scoring high in social desirability would be even more likely to use e-mail. =

Data

Analysis

We created correlation matrices to determine if any of the variables of interest were significantly correlated with one another. Hypotheses 1-3 and 5-11 were tested by analyzing the correlation matrix: Is there a relationship between e-mail use on the one hand and attitudes toward change

204 and innovation, locus of control, social desirability, computer self-efficacy, leader e-mail use, ease of learning, or recognition of benefits on the other? Hypotheses 6 and 11 were also tested with multiple regression. We used moderated regression to test the moderator hypotheses: Will a favorable attitude toward change, or locus of control, or computer self-efficacy moderate the effects of rational choice and social influence? As suggested by Baron and Kenny (1986), we first entered the main effects and then entered the interaction term. The two models were then analyzed to determine whether the model with the interaction term explained significantly more variance. We performed one-way ANOVA to test hypothesis #4: Does working in the sciences presage e-mail use?

Results and Discussion

Hypotheses 1 and 2 were supported: Analysis of the correlation matrix people who perceive e-mail as beneficial and easy to learn to .37 respectively. See .46 and r use are more likely to use e-mail (r shows that

Table 1 for the means, standard deviations, and correlations of the variables. Table 2 indicates which hypotheses were supported.) Although our survey data do not allow us say whether these perceptions are objectively correct, our results do suggest that rationality-broadly conceived-plays a positive role in the decision to use e-mail. Hypothesis 3 was supported: Group members are more likely to use email when the group’s leaders also use it (r .23). Thus, social context, as well as rationality, appears to play a role in determining e-mail use. Arguably, an effect here assigned to social context may be attributable to rationality broadly conceived. If the group’s leader is an e-mail user, it makes sense (is rational) for an individual member to use it. Following the leader, as it were, is rational. Social influence and rationality converge. These results support Webster and Trevino’s (1995) argument that rational choice and social influence theories provide complementary explanations for media choice. Rational choices and social influences are sometimes mutually reinforcing. On the other hand, were a person to perceive e-mail as easy to use, but still not use it, the explanation might lie in social influences militating against its use. Hypothesis 4 was supported: ANOVA indicated that college was significant for both computer self-efficacy and e-mail usage. Insofar as predilection for science is signaled by college, individuals with a predilection for science were more likely to use e-mail. Hypotheses 5 and 10 were supported: A favorable attitude toward change and innovation (r .38) and high computer self-efficacy (r .35) were positively associated with e-mail use, as revealed by analysis of the correlation matrix. This result is intuitively appealing: such individual differences may underlie perceptual differences which lead to variant interactions with new technologies; people favorably disposed toward innova=

205

111.

= x a (A

d ,-1&dquo;0

o 3

~9’&dquo;a .2

°§A

s o on

..... 1.0 0 0

0 0

(2) w

-5 -5

.......... ce ..........

r . r.

ce ce Q

= 5

*a a rJ’.J rJ’.J ~_ ~_ e e o

~’S-t~ ’? Q)Q) S S 11

206 Table 2

Hypotheses Which Were Supported

tion and change and confident as well as experienced in their engagements with computers are likely to be more disposed to use e-mail. Here individual difference factors suggested by innovation literature were associated with e-mail use, opening the prospect of a more comprehensive and complex model of the determination process that may explain individual behavior that seems anomalous under the theories of rational choice and social influence. Hypothesis 6 was partially supported: The regression model including attitude toward change and innovation, the social context, and rational choice explained a significant amount of the variance (adjusted R2 .40; F 29.823; p .000) in e-mail use. A favorable attitude toward change and innovation moderated the influence of social context but did not moderate that of rational choice on e-mail use. This finding, interestingly, suggests that a personâ&#x20AC;&#x2122;s favorable regard of change and innovation may strengthen (or in some cases override) the effects of social context, although it does not alter more rationally determined behavior. Perhaps another way of putting this is to say that, in the presence of favorable regard of change and innovation, rationality is more compelling than social context in the determinative process. However, Hypotheses 7, 8, 9, and 11 were not supported. Neither internal locus of control nor high social desirability scores seemed to be positively related to e-mail use and neither internal locus of control nor high computer self-efficacy seemed to moderate the effects of rational choice or social influence respectively on e-mail use. The regression model including locus of control, social context, and rational choice explained a significant amount of variance (RZ .37, F 20.291, p .000) in e-mail =

207 locus of control did not play a significant role in the equation. Locus of control did not moderate the relationship between e-mail use and social context. The regression model including computer self-efficacy, social context, and rational choice did explain a significant amount of the variance in e-mail use, but computer self-efficacy did not moderate the relationship between e-mail use and rational choice or that between e-mail use and social influence. An interesting case here is that of locus of control, which, prompted by Rogers (1983), we expected to directly, as well as indirectly, affect email use. Our negative results may signal a distinction to be drawn, as we suggest above, between e-mail use as a personal communication medium and other technological innovations. Conceivably an internal locus of control in conjunction with a disinclination toward change and innovation would work against e-mail use, because mode of communication constitutes part of a person’s self-image, which one’s internal locus of control dictates is to be preserved against external social forces. Such an explanation would be consistent with Staw and Ross’ (1985) observation that disposition (i.e., prior attitude) was a stronger predictor than some aspects of social context. However, our present data do not allow us to investigate this interesting possibility. While neither locus of control nor social desirability were directly related to e-mail use, we found, though we had not predicted it, that locus of control moderated the influence of rational choice. Perhaps people with an external locus of control are more alert to their social environments, which may include favorable perceptions of e-mail, and consequently are more likely to base a rational decision to use e-mail on those perceptions and people with an internal locus of control, being less alert to their social environment, are less likely find rational grounds for e-mail use in that environment. However, though not predicted, locus of control did moderate the influence of rational choice on e-mail use (the change in F is significant, p .015). The regression model including computer self-efficacy, the social context, and rational choice did explain a significant amount of the variance (adjusted R2 .37, F 22.689, p .000) in e-mail use. But computer neither the relationship between e-mail use and moderated self-efficacy rational choice nor that between e-mail use and social influence. use, but

Limitations

First, the nature of our sample

means

that

cannot

bright and

generalize to corare often early

porate populations. University faculty adopters of computer technology (Rice & Williams, 1984). Almost all participants had computers both at their offices and at their homes. The are

sample turned

out to be

examination of the

scores on

homogenous than

we desired. However, an revealed various scales the they fell within

208

normal curves, thus suggesting that range restriction was not a problem. In addition, although some of the moderating effects hypothesized were not revealed, we think it is possible that because our sample was a highly educated group of university professors, the two personality scales may have proved to be too transparent and may have distorted the result. Second, all of the variables investigated were assessed using single-source self-reports. We attempted to ameliorate some of these effects by using varied rating scales, etc. A factor analysis suggested that common method variance was not an issue.

Implications

for Future Research and Conclusions

Our findings suggest that the explanations provided by the theories of rational choice and social influence can be enhanced through consideration of the effects of individual differences. Our results support the rational choice model in the sense that individuals seem to make rational choices to use e-mail based upon their perceptions of e-mail and of the benefits it offers. However, our results neither justify nor discourage acceptance of the objectivist perspective associated with the rational choice model. Nelson and White (1990) found individuals with an internal locus of control had more positive attitudes towards computers than those with an external locus of control. We have suggested that attitudes towards e-mail may not be quite the same as attitudes towards computer technology in general. Computers can be used to assist people in being more effective and efficient, while e-mail, a means of communication, capable of bearing on interpersonal relationships, may thereby engage individual personality attributes. Our investigation has just begun to tap the importance of individual differences in this particular context. Kersten and Phillips (1992) suggested numerous behaviors demonstrated by e-mail users were also impression management behaviors: ingratiation, self-promotion, intimidation, exemplification, and supplication. Hence the use of e-mail may play a role in the development, maintenance, and distribution of power in an organization. We suggest that as computer technology becomes more common, research should investigate the ways people in organizations may reinvent technologies in accordance with their unique individual personalities to reach general social goals. Our somewhat perplexing results regarding the personality attributes of locus of control and social desirability reinforce the need for future research along these lines. Other individual difference factors need to be identified and investigated to determine their effect on e-mail use. It is possible-we think likely-that some of these might have both a main effect on e-mail use and a moderator effect on the relationships between e-mail and social context and rational choice. Webster and Trevino (1995) recommended that &dquo;system designers may need to look beyond general job level to specific work people do and the

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type of messages they need to send&dquo; (p. 1565). Our investigation prompts us to recommend that system designers may also need to consider individual characteristics of the users as well as those of the media, communication task, and social context. Despite what appear to us as limitations in our research, as indicated above, we propose that our results do point to the interaction of individual differences with rational choice and social influence factors and hence to a new and rewarding direction for future research. Perhaps even more significant revelations will flow from the interactions between innovation research and the rational choice and social influence models. Electronic communication media have historically been associated with the evolution of the modern corporation. Given its accelerating rate of evolution, electronic communication promises to be perpetually innovative. This prompts the further observation that the contributions of innovation research can be expected to be particularly important to the advancement of our knowledge in this area. In short, our investigation suggests the desirability of a more comprehensive and complex model of the determining process of e-mail selection and use by individuals in organizations, of advance from the simple, unidimensional (social presence, information richness) models proposed by rational choice theories and the more broadly conceived multidimensional, but still rationalistic, models proposed by social influence theories to models comprehensive and complex enough to accommodate a range of apparently anomalous human behaviors, without denying force to rationality and social influence in the determination of human behavior. NOTES Barbara D. Minsky is a doctoral candidate in Business Administration majoring in Organizational Behavior/Human Resource Management in the William W. & Catherine M. Rucks Department of Management, E.J. Ourso College of Business Administration at Louisiana State University. Her research interests are related to the area of perceptual differences and include leadership/LMX, career issues, and values. Daniel B. Marin is Assistant Professor in the William W. & Catherine M. Rucks Department of Management, E.J Ourso College of Business Administration at Louisiana State University. He has published fiction, nonfiction, poetic translation, literary criticism, and articles on organizational evolution and business communication. His current research interests are business ethics and business communication. An earlier version of this paper was presented at the Southern Management Association Meeting in November, 1998, in New Orleans, LA. Send correspondence to Daniel B. Marin, Louisiana State University, William W. & Catherine M. Rucks Department of Management, E.J. Ourso College of Business Administration, Baton Rouge, LA 70803-6312 <bamari@lsuvm.sncc.lsu.edu>. REFERENCES

Armstrong, J. S., & Overton, T.S. (1977, August). Estimating nonresponse bias mail surveys. Journal of Marketing Research, 14 (3), 396-402.

210

Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall. Baron, R. M., & Kenny, D. A. (1986, December). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51 (6), 11731182.

R. W., & Reinsch, N. L., Jr. (1987, Summer). Attitudinal responses to voice mail. The Journal of Business Communication, 24 (3), 23-35. P. Face-to-face versus Bordia, (1997, January). computer-mediated communication: A synthesis of the experimental literature. The Journal of Business Communication, 34 (1), 99-120.

Beswick,

Campeau, O. R., & Higgins, C. A. (1995, June). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19 (2), 189-211. D. J. & R. Innovative Ettlie, E., O’Keefe, (1982, April). attitudes, values, and intentions in organizations. Journal of Management Studies, 19 (2), 163-182. Finholt, T., Sproull, L., & Kiesler, S. (1990). Communication and performance in ad hoc task groups. In J. Galegher, R. E. Kraut, & C. Egido (Eds.), Intellectual teamwork: Social and technological foundations of cooperative work (pp. 291325). Hillsdale, NJ: Lawrence Erlbaum. Fischer, O. G., & Fick, C. (1993, Summer). Measuring social desirability: Short forms of the Marlowe-Crowne social

(2): chological Measurement, 53

desirability scale. Educational

and

Psy-

417-424.

Fulk, J. (1993, October). Social construction of communication technology. Acad(5), 921-950. emy of Management Journal, 36 Fulk, J., & Boyd, B. (1991, June). Emerging theories of communication in organizations. Journal of Management, 17 (2), 407-446. Fulk, J., Schmitz, J. A., & Steinfield, C. W. (1990). A social influence model of technology use. In J. Fulk & C. Steinfield (Eds.), Organizations and communication technology (pp. 117-142). Newbury Park, CA: Sage. Gutek, B. A. (1990). Work group structure and information technology: A structural contingency approach. In J. Galegher, R. E. Kraut, & C. Egido (Eds.), Intellectual teamwork: Social and technological foundations of cooperative work (pp. 63-78). Hillsdale, NJ: Lawrence Erlbaum. Herschel, R. T. & Andrews, P. H. (1997, April). Ethical implications of technolog ical advances

business communication. The Journal of Business Communi-

cation, 34 (2), 160-170. Hollingshead, A. B., McGrath, J. E., & O’Connor, K. M. (1993, August). Group task performance and communication technology: A longitudinal study of com(3), puter-mediated versus face-to-face work groups. Small Group Research, 24 307-333.

Howard, T. (1993, June). The property issue in e-mail research. The Bulletin, (2), 40-41. 56 Hunter, J., & Allen, M. (1992, August). Adaptation to electronic mail. Journal of (3), 254-274. Applied Communication Research, 2 Kersten, L., & Phillips, S. R. (1992). Electronic identities: The strategic use of email for impression management. Paper presented at the Annual Meeting of the Western Speech Communication Association. Ku, L. (1996, July). Social and nonsocial uses of electronic messaging systems in (3), 297-325. organizations. The Journal of Business Communication, 33 Nelson, D. L., & White, M. A. (1990, Fall). Management of technological innova-

211

attitudes, stress, and work group attributes. The Journal of (2), 137-148. High Technology Management Research, 1 Orlikowski, W., & Yates, J. (1994). Genre repertoire: The structuring of communicative practices in organizations. Administrative Science Quarterly, 39 (4), tion: Individual

541-574.

Orlikowski, W., Yates, J., Okamura, K. , & Fujimoto, M. (1995). Shaping electronic communication: The metastructuring of technology in the context of use. Organization Science, 6 (4), 423-444. Rice, R. E., & Case, D. (1983, Winter). Electronic message systems in the uni(1), 153versity : A description of use and utility. Journal of Communication, 33 161.

Rice, R. E., & Williams, F. (1984). Theories old and new: The study of new media. In Rice and Associates (Eds.), The new media (pp. 55-80). Beverly Hills, CA:

Sage.

Rogers,

E. M. (1983). Diffusion of innovations. New York: The Free Press. Rotter, J. B. (1966, January). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs, 80 (1), 1-28. J. A social G. & information June). Salancik, Pfeffer, (1978, R., processing approach to job attitudes and task design. Administrative Science Quarterly, (2), 224-253. 23 Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications. London: Wiley. Sproull, L., & Kiesler, S. (1986, November). Reducing social context cues: Electronic mail in organizational communication. Management Science, 32 (11), 1492-1512.

Staw, B. M., & Ross, J. (1985, August). Stability in the midst of change: A dispositional approach to job attitudes. Journal of Applied Psychology, 70 (3), 469480.

Trevino, L. K., Lengel, R. H., & Daft, R. L. (1987, October). Media symbolism, media richness, and media choice in organizations. Communication Research, (5), 553-574. 14 Webster, J., & Trevino L. K. (1995, December). Rational and social theories as

complementary explanations of communication media choices: Two policy cap(6), 1544-1572. turing studies. Academy of Management Journal, 38 Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., & Haythornthwaite, C. (1996). Computer networks as social networks: Collaborative work, telework, and virtual community. In J. Hagan and K. S. Cook, (Eds.), Annual Review of Sociology, 22, 213-239.