Revisiting Employee Perceptions on Work-related E-Mail

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Revisiting Employee Perceptions on Work-related E-Mail: An Assessment of Post-adoption Usage Introduction Computer-mediated communication is the major medium for organizational communication, and electronic mail (e-mail) is an indispensible medium for that purpose. As a medium of managerial choice (Markus, 1994; Whittaker & Sidner, 1996; Bontis, Fearon, & Hishon, 2003; Tassabehji & Vakola, 2005), e-mail is a mission critical platform to interact with internal and external colleagues, in combination with other media (Watson-Manheim & Belanger, 2007). Although originally designed for asynchronous communication activities, email is currently utilized for a variety of work-related operational activities, which are indirectly related to communication, e.g., document management, archiving, task delegations and tracking, scheduling and reminders, and storing and maintaining personal data (Whittaker & Sidner, 1996; Watson-Manheim & Belanger, 2007). Moreover, it is utilized as a medium for social exchange of information, knowledge, and experiences at the workplace (Tassabehji & Vakola, 2005). In business settings, e-mail has replaced conventional communication platforms, such as face-to-face meetings and phone. Consequently, there is an increased reliance on e-mail for most of the communication and information-sharing activities. A practitioner survey reported that in 2006, the average corporate e-mail user received 126 e-mail messages per day, representing an increase of 55% since 2003.1 One of the reasons of this elevated usage is the emergence of geographically-distributed, intra- and interorganizational virtual teams that communicate across time and space (Whittaker & Sidner, 1996). Hence, the lack of physical collocation and mobility results in the extensive use of e-mail for communication and 1

Radicati Group white paper survey, 2007

2 collaboration (Higa, Sheng, Shin, & Figueredo, 2000; Watson-Manheim & Belanger, 2007). However, even the collocated workers tend to frequently utilize e-mail for coordination and documentation of exchanges (Watson-Manheim & Belanger, 2007). Accordingly, e-mail has become a company standard and mandatory social norm in communication. E-mail is known to enhance communication, enable efficient teamwork and knowledge sharing, increase individual and organizational productivity, and contribute to the growth of distributed organizations (Sproull & Kiesler, 1986; Fulk & Boyd, 1991; Straub, 1994; Whittaker & Sidner, 1996; Lucas, 1998; Kock, 2000; Dawley & Anthony 2003). Despite these organizational-level benefits, increased e-mail traffic might cause individuallevel adverse implications such as increase of administrative challenges (Whittaker & Sidner, 1996; Radicati Group, 2007), fatigue and overload (Whittaker & Sidner, 1996; Dawley & Anthony 2003). This overload might outweigh the productivity gains at the individual level (Tassabehji & Vakola, 2005). Consequently, depending on how the employees perceive these challenges, they might alter the level of e-mail use, albeit its status as a communication norm. Therefore, understanding employees‟ perceptions in detail is important to increase the efficiency and productivity of work-related e-mail usage. In IS domain, rooted in the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975), the technology acceptance model (TAM) (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989) has presented individual-level perceptions on technology as the driver of the attitude towards usage. In turn, the attitude impacts the actual usage of the technology. In a parsimonious model, TAM identifies these perceptions as perceived ease of use (PEU) and perceived usefulness (PU). As an established medium, i.e. a medium adopted and utilized for a substantial time period, e-mail is recognized for its ease of use and usefulness. However, the employees might show different post-adoption usage levels and TAM is not sufficient to explain this behavior. In fact, researchers criticize TAM as being oversimplified or ignoring

3 other dimensions (Karahanna & Limayem, 2000; Hubona & Burton-Jones, 2002). Hence, there is a need to revisit the employee perception specific to the post-adoption usage of email at workplace. The main motivation of this research is to re-evaluate and re-identify the perceptions on work related e-mail at post-adoption phase. Moreover, there is a highly likelihood that these perceptions differ by individual characteristics such as gender and education. Thus, this study is motivated to illuminate the individual differences in perceptions, which shape the attitude towards the use of technology. Equally important, the study has practical implications for managers. Understanding employee perceptions, managers can support the employees to decrease the overload and increase the productivity of e-mail. Managers can redesign corporate e-mail usage by creating new communication practices, deploying policies and even reconfiguring the e-mail systems accordingly. For this purpose, this study seeks to answer the following research questions: 

What are the individual-level perceptions impacting the use of work-related e-mail?

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Do these perceptions change by gender and education level? The remainder of this paper is organized as follows: Next section discusses the

theoretical foundation of this research and the research model along with the hypotheses. Afterwards, Methods section presents the empirical study in terms of sample, operationalization of the variables, data analysis and its results. The paper concludes with a Discussion section, including implications for research and practice, limitations, and suggestions on future research.

Theoretical Foundation Organizational Implications of e-mail Usage E-mail is an asynchronous, low in richness, and fast store-and-forward application, which can be utilized both as a point-to-point and a broadcast medium (Sproull & Kiesler,

4 1986; Markus, 1994; Lucas, 1998). A key social dimension of e-mail is its relative formality (Lucas, 1998); hence, it is used as the legitimate authoring tool for business correspondence, i.e. communication and information sharing (Tassabehji & Vakola, 2005). As a communication medium, it has a limited capacity to transfer tacit knowledge due to its low richness; however, as a vehicle of explicit knowledge flow, it is used to create and disseminate codified organizational knowledge that amplifies individual knowledge to organizational and interorganizational levels (Whittaker & Sidner, 1996; Bontis et al., 2003). Although it was originally designed as a communication platform; in time, it has become the integral part of how people conduct their businesses (Markus, 1994; Bontis et al., 2003; Tassabehji & Vakola, 2005). Currently, in addition to communication, it is utilized as a production facility and a cabinet for human-computer interaction and artificial intelligence (Ducheneaut & Watts; 2005). Examples of the activities managed via corporate e-mail are document management, archiving, task delegations and tracking, scheduling and maintaining personal data (Whittaker & Sidner, 1996; Watson-Manheim & Belanger, 2007). Usage of work-related e-mail has substantial organizational implications. Research studies report that e-mail enhances the ease and speed of communication inside the organizations (Lucas, 1998) by reducing the number of meetings and phone conversations (Whittaker & Sidner, 1996). It increases knowledge sharing and changes patterns of information distribution in organizations (Sproull & Kiesler, 1986) and may reduce the functional complexity of managerial coordination (Fulk & Boyd, 1991). Moreover, it contributes to the growth of distributed organizations by providing absence availability and enabling asynchronous and distributed interaction among temporarily and spatially dispersed teams; thus, it supports emergence of on-line communities (Whittaker & Sidner, 1996; Kock, 2000). Hence, its usage improves teamwork and facilitates information flow across individuals, teams and organizations. In summary, e-mail at workplace increases personal

5 productivity and organizational effectiveness (Fulk & Boyd, 1991; Straub, 1994; Dawley & Anthony 2003). Despite organizational-level advantages, increased e-mail traffic might have individual-level disadvantages. With sheer amount of e-mails, managing e-mail (e.g. classifying, storing, archiving, setting reminders) requires additional time and effort (Radicati Group, 2007). Employees might be confronted with administrative challenges such as reading and replying e-mails in a timely manner, filtering the emails and finding information easily (Whittaker & Sidner, 1996). Furthermore, e-mail usage has increased the accessibility tremendously and the organization has been flattened across hierarchies. This increased accessibility might cause fatigue for some employees as well (Whittaker & Sidner, 1996; Dawley & Anthony 2003). Due to these negative implications, employees might reduce email usage unless it is mandatory, or the overload might outweigh productivity gains at the individual level (Tassabehji & Vakola, 2005). Therefore, to effectively and efficiently utilize e-mail at workplace, the determinants of the usage should be identified correctly.

Determinants of e-mail usage There is a diverse body of research on determinants of e-mail usage at workplace accumulated over 35 years. In communication domain, several contingency theories (e.g., media trait and social influence theories) have been presented to explain how and when different communication media are used (Carlson & Davis, 1998; Carlson & Zmud, 1999). Media trait theories assume that media have static and objective characteristics, and media choice is objectively rational. However, other studies report that depending on the situational or contextual factors, same medium can be perceived as rich or lean (Lee, 1994; Carlson & Davis, 1998; Carlson & Zmud, 1999). Capturing these perceptual differences, the channel expansion theory extends the media trait theories (Carlson & Zmud, 1999). According to this theory, an individual‟s

6 experience with media, topic, organizational context and a communication partner influences his perceptions and thus, the media choice. Research studies introduce several contextual factors that influence this choice, such as urgency of the communication event (Trevino, Lengel, Bodensteiner, Gerloff, & Muir, N.K., 1990), characteristics of the decision and the user‟s role in decision making (Jones, Saunders, & McLeod, 1989), institutional factors (Saunders & Jones, 1990), familiarity with technology and task (Fulk, 1993; Carlson & Zmud 1999), the level of job pressure and task routineness (Fulk, 1993). Contrasting the media trait theories, social influence theories underline the role of a social actor in media use (Fulk, Steinfield, Schmitz, & Power, 1987). This research stream posits that not only objective media characteristics and contextual factors but also information derived from the social environment impact an individual‟s perceptions and behaviors towards media choice and usage. Analyzing e-mail as a managerial communication medium, Markus (1994) attributes usage to social behavior instead of the capabilities of the media. The researcher argues that adoption and usage of media are shaped by social processes in the organizations, such as sponsorships and social control. In other words, not the media per se but the social processes determine the usage patterns. To summarize, these theories highlight the role of media perceptions on attitude towards media that influence the actual usage behavior. The perceptions have been studied in detail in social psychology domain. According to TRA (Fishbein & Ajzen, 1975), an individual‟s perceptions and feelings are the cognitive responses to some external stimulus. These responses do not shape directly the behaviors; instead, external variables influence an individual‟s beliefs and perceptions, which in turn shape attitudes towards performing a behavior. Attitude is defined as the individual's positive or negative feelings about performing a behavior. It is determined through the assessment of beliefs in terms of consequences and their desirability. TRA posits that attitude together with

7 the subjective norm impacts intention to perform the behavior, thus ultimately influences the behavior itself. Figure 1 illustrates TRA. *****Insert Figure 1 about here**** Drawn on the fundamental premises of TRA, research studies in IS domain have analyzed e-mail medium from technology acceptance and usage perspectives. This research stream is rooted in TAM (Davis, 1989; Davis et al., 1989) and its extensions: TAM2 (Venkatesh & Davis, 2000), and Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003). TAM explains the attitude towards technology usage, which in turn, is used to predict the adoption and the usage of a technology. The theory condenses the beliefs comprised in TRA into two main constructs: PEU and PU. PEU is the extent the user believes that he/she spends less effort and time while using the system whereas PU indicates the job-related advantages through the use of the system. In this theoretical model, PEU has been identified as an antecedent of PU, which has relatively greater impact on IS usage behavior. On the other hand, PEU is more salient to a newly adopted technology, but for post-adoption stage it is relatively insignificant (Venkatesh & Davis, 2000). Therefore, PEU might not be relevant for e-mail usage at post-adoption stage. Figure 2 shows TAM. *****Insert Figure 2 about here**** TAM2 incorporates the antecedents of PU such as social influence (e.g. subjective norm, voluntariness and image) and cognitive instrumental processes (e.g. job relevance, output quality, and result demonstrability) (Venkatesh & Davis, 2000). The impact of subjective norms subsides with increased experience; as users gain more experience with a technology, they tend to rely less on social information, but more on PU (Venkatesh & Davis, 2000; Karahanna & Limayem, 2000). On another note, subjective norms have significant direct impact on usage intention for mandatory settings. Finally, derived from previous

8 theories, UTAUT expands the existing model by adding new variables: performance expectancy, effort expectancy, social influence, and facilitating conditions as direct determinants of usage intention and behavior (Venkatesh et al., 2003). In summary, these theoretical models underline the influence of an individual‟s perceptions on technology usage mediated by attitude towards the technology. At one end of the theory spectrum, TAM presents a parsimonious model with two perception categories, PU and PEU. However, for post-adoption technologies, the theory is oversimplified or ignores other dimensions of perceptions (Karahanna & Limayem, 2000; Hubona & Burton-Jones, 2002). These dimensions emerge when users gather experience via repetitive usage of the technology and thus, can assess it thoroughly. At the other end of the spectrum, UTAUT combines an extensive set of variables (Venkatesh et al., 2003), which might be highly correlated with each other. In the next section, I discuss a refined research model to analyze employee perceptions that impact the post-adoption usage of a mature communication technology.

Research Model Employee Perceptions on Work-related E-mail TAM explains technology acceptance and usage behaviors on new technologies from usefulness and ease of use perspectives (Davis, 1989; Davis et al., 1989). In this study, I analyze post-adoption usage of work-related e-mail; to reflect the mature character of the email, I adapt TAM‟s perception base. First, PEU is excluded from the new research model. As a perception, ease of use is more salient for a new technology at the early stages of usage, but for post-adoption stage, it is found to be insignificant (Adams, Nelson, & Todd, 1992). Currently, there are various email platforms available for users (e.g. Lotus Notes, Microsoft applications, Gmail etc.); however, most of the functionalities are common. Additionally, since e-mail is embedded in

9 daily communication outside of work, the vast majority of users have already accumulated sufficient experience with the technology. Therefore, I argue that the users can adapt to different e-mail platforms easily and perceptions on ease of use are no more salient to usage of e-mail at workplace. Instead of PEU, I include perceived load (PL) into the model to represent the employees‟ cognitive responses on extended use of work-related e-mail. PL conceptualizes the perceived level of additional work effort while using e-mail. With the sheer amount of email and increased speed of communication, employees face additional administrative challenges in managing e-mails (such as filtering and prioritizing the e-mails, replying in a timely manner, searching for information embedded in the e-mails) (Whittaker & Sidner, 1996). Consequently, this extra burden might outweigh productivity advantages (Tassabehji & Vakola, 2005). The employees might decrease e-mail usage to avoid fatigue or if they perceive the load as less, their attitude towards e-mail usage will be more positive. Hence, at the post-adoption stage, PL is a more salient cognitive response to technology usage than PEU. Hypothesis 1: Perceived load (PL) of work-related e-mail is negatively associated with attitude towards e-mail. Second, PU is modified to reflect the extent of the employees‟ experience with the medium. Considering the organizational implications of e-mail usage, such as the impact on personal productivity and organizational effectiveness (Fulk & Boyd, 1991; Straub, 1994; Dawley & Anthony 2003), I elaborate usefulness into two dimensions: effectiveness and necessity. Perceived effectiveness of e-mail (PE) represents the extent that the employees choose e-mail as the most effective medium for communicative tasks compared to traditional synchronous media, e.g., face-to-face-meeting and phone. On the other hand, perceived necessity (PN) measures employees' perceptions on how necessary e-mail is for them to

10 perform work-related tasks. I posit that both PE and PN have positive influence on the attitude towards e-mail usage at workplace. Hypothesis 2: Perceived effectiveness (PE) of work-related e-mail is positively associated with attitude towards e-mail. Hypothesis 3: Perceived necessity (PN) of work-related e-mail is positively associated with attitude towards e-mail. Finally, I add perceived positive and negative impacts on communication into the model. As e-mail has become an indispensable communication component at the workplace, its usage shapes the interactions within and across the teams and the individuals. It changes communication patterns and behaviors by enhancing the ease and speed of communication (Lucas, 1998), increasing the number of persons in communication and extending availability for co-workers (Whittaker & Sidner, 1996; Dawley & Anthony 2003). Thus, employees might perceive that e-mail usage impacts work-related communication positively. However, for the same reasons, they might develop negative beliefs related to e-mail‟s impacts on communication as well. For example, they might feel distracted or overwhelmed in communication. Moreover, the lack of cues or asynchronous nature of e-mail might cause misunderstandings in communicating sensitive issues. Eventually, these cognitive responses cause different level of e-mail usage for corporate communication. To measure these perceptions, I introduce perceived positive and negative impacts on communication (PPIC and PNIC respectively). Hypothesis 4: Perceived positive impacts on communication (PPIC) of work-related e-mail are positively associated with attitude towards e-mail. Hypothesis 5: Perceived negative impacts on communication (PNIC) of work-related e-mail are negatively associated with attitude towards e-mail.

11 As it is posited in TRA and TAM, the above-mentioned perceptions impact employees‟ attitude towards work-related e-mail, which in turn, mediates the relationship between perceptions and usage behavior. Hence, Hypothesis 6: Mediated partially by attitude, perceptions on work-related e-mail influences its usage.

Individual Differences An individual‟s perceptions are shaped by personal attributes such as gender, age, and education. Research posits that these personal attributes determine how individuals approach to technology adoption (Daft, 1978, Fulk et al, 1987). For example, senior people may find it harder to adopt new technologies than younger people because of the substantial learning effort required (Fulk, 1993). Similarly, senior people who are used to conventional communication media may feel that e-mail‟s asynchronous and text-based characteristics are not rich enough to satisfy their communication needs (Markus, 1994). On the other hand, people who have more experience with IT may adopt new technologies more easily (Higa et al., 2000). However, age and experience are more salient to new technologies. For technologies at post-adoption stage, individuals have considerable experience with the medium; thus, the learning curve has been completed. Therefore, I posit that age and experience do not have a strong influence on perceptions, attitude and usage of e-mail at workplace. Rather, I focus on individual differences in terms of gender and education. According to socio-psychological research, gender is a socio-cultural factor influencing both perceptions and behaviors (Gefen & Straub, 1997). For instance, in terms of communications, men and women tend to use language differently and they have different social norms in conversation. Therefore, same communication model might be differently perceived or same communication medium might be differently utilized by different gender. This perspective is also valid for e-mail; empirical findings suggest that perceptions and

12 attitudes towards e-mail vary with gender (Gefen & Straub, 1997; Tassabehji & Vakola, 2005). Women perceive social pressure and ease of use of e-mail to be higher than men do (Gefen & Straub, 1997; Venkatesh et al., 2000). The effect of subjective norm is more salient for women in the early stages of experience, though it decreases over time (Venkatesh et al., 2000). On the other hand, men are more likely to feel confident with technology (Tassabehji & Vakola, 2005). Applying the gender differences perspective on the new perception dimensions, I argue that female employees rate perceived effectiveness, positive and negative impacts of work-related e-mail on communication higher than male employees do. Similarly, attitude towards work-related e-mail is influenced by gender; however, the usage is not. Hypothesis 7a: Perceived effectiveness of work-related e-mail varies with gender. Women rate perceived effectiveness of work-related e-mail higher than men do. Hypothesis 7b: Perceived positive impacts of work-related e-mail on communication vary with gender. Women rate perceived positive impacts of work-related e-mail higher than men do. Hypothesis 7c: Perceived negative impacts of work-related e-mail on communication vary with gender. Women rate perceived negative impacts of work-related e-mail higher than men do. Hypothesis 7d: Attitude towards work-related e-mail varies with gender. Women have more positive attitude than men have. Hypothesis 7e: Usage of e-mail in quantitative terms does not vary with gender. Education has been included into information systems acceptance models as an external factor. Empirical findings suggest that education level significantly affects

13 perceptions (Hubona & Burton-Jones, 2002). Employees equipped with higher level of education might assess the technology more easily, and thus, use it more effectively, but not necessarily more frequently. Hence, higher level of education might lead to higher level of perceived effectiveness and perceived impacts on communication. Similarly, employees with higher level of education might develop a more positive attitude towards e-mail. However, the usage in terms of numbers and frequency is not influenced by higher level of education. Hypothesis 8a: Perceived effectiveness of work-related e-mail varies with education. Higher level of education is associated with higher level of perceived effectiveness. Hypothesis 8b: Perceived positive impacts of work-related e-mail on communication vary with education. Higher level of education is associated with higher level of perceived positive impacts. Hypothesis 8c: Perceived negative impacts of work-related e-mail on communication vary with education. Higher level of education is associated with higher level of perceived negative impacts. Hypothesis 8d: Attitude towards work-related e-mail varies with education. Higher level of education is associated with more positive attitude towards e-mail. Hypothesis 8e: Usage of e-mail in quantitative terms does not vary with education. The proposed research model depicts the relationships as hypothesized above (Figure 3). *****Insert Figure 3 about here****

Methods Sample For empirical analysis, I utilized data from a secondary data source (Pew Internet and American Life project by Pew Research Center). Pew data were collected by Princeton

14 Survey Research Associates (PSRA) via a phone survey between April 9 and May 17, 2002 among a sample of 2447 Internet users who were 18 and older. The sample was randomly selected using random digit sample of phone numbers in the continental United States. The response rate to survey was 39%. To focus on work-related e-mail, I eliminated the participants who did not use e-mail at work, reducing sample size to 1003 respondents. In the sample, 53% of the respondents were male and 47% was female. The average age of the respondents was 39. 48 % of the respondents had high-school or lesser education whereas 52% had higher education degrees. 68% of the respondents were working for private, 11% for state-owned, 14% for educational and 6% for non-profit institutions. Table 1 illustrates the demographics of survey respondents. *****Insert Table 1 about here****

Operationalization and Scale Development The perceptions introduced in the research models are new constructs; thus, for the operationalization, I created and validated scales for PPIC, PNIC and PL via Exploratory Factor Analysis (EFA). The survey items entered into EFA are presented in Appendix 1. In EFA, I applied primary component analysis and VARIMAX rotation, and eliminated the items with factor loadings less than +/- 0.4 and with loadings on multiple factors. Furthermore, to check the consistency of the outcomes, I segregated the dataset randomly in two sub-sets, and performed EFA on both sub-sets in addition to the main dataset. Those three EFA procedures produced consistent and similar results. The factor loadings indicated that the individual items representing a construct loaded appropriately on their factors, and the loadings within these constructs were higher than those across other constructs. Hence, convergent and divergent validities are satisfied. Table 2 shows the EFA results of the main dataset. *****Insert Table 2 about here****

15 After the elimination of missing cases and cases with “DK/Refused” answer options, PPIC, PNIC and PL scales were based on 922, 936 and 932 responses. These numbers were deemed to be sufficient for empirical analysis. All scales indicated high level of reliability, i.e., Cronbach‟s alpha values (0.822, 0.708, and 0.689 for PPIC, PNIC and PL respectively). Moreover, the inter-item correlations were high, marking the coherence in the scale as well. The scale items and statistics are presented in Tables 3, 4.1, 4.2 and 4.3. *****Insert Table 3 about here**** *****Insert Table 4.1 about here**** *****Insert Table 4.2. about here**** *****Insert Table 4.3 about here**** To measure PN, I utilized question 34 (Appendix 1) on necessity of work-related email (measured by a 10-point scale) as a continuous variable. For PE, I developed a composite index variable based on question 45 (Appendix 1). This question asked for the most effective medium to perform specific communication tasks, such as arrangement of meetings, editing or reviewing documents, inquiring work-related issues, dealing with sensitive issues, and solving problems with a supervisor. Media alternatives were e-mail, telephone and face-to-face. I created a binary variable to mark e-mail usage for each task and summed the scores of tasks as a composite index that represents perceived effectiveness. The distribution of the variable was close to normal. Attitude was measured by a single-item scale (question 49). Single-item scales might be criticized as a limitation; however, Baroudi and Orlikowski (1988) argue that the singleitem measure can be conveniently utilized if the researcher is interested in an overall indication of the construct rather than particular dimensions of it. Thus, for the purpose of this research, usage of a single-item scale is legitimate. Previous studies measure usage of information systems either by self-reported

16 quantitative usage statistics (such as frequency, extent, and volume) or by behavioral scales indicating the approach towards usage, such as dependency to the system. For e-mail usage particularly, Davis (1989) suggests the time spent whereas Adams et al. (1992) utilizes the number of messages sent and received for measurement. Although researchers argue that self-reports can be biased and usage should be factored down to computer-recorded statistics (Straub et al., 1995), there is not sufficient evidence to support their claims. In fact, Taylor and Todd (1995) show that self-reported usage values correlate well with actual usage values. Hence, I utilized question 36 (time spent) to measure e-mail usage; this measurement complies with original TAM measures as well. Table 5 shows the descriptive statistics for variables Attitude, PN, PE and Usage. *****Insert Table 5 about here**** Previous studies frequently use individual, organizational and situational characteristics as control variables in hypothesis testing. Hence, I included individual (gender, age and education level), organizational (company type), and situational (perceived formality level of the company) characteristics to control the research model. Dummy variables were created to measure these characteristics, except that age was measured as a continuous variable. In the second part of the analysis, gender and education were introduced as determinants of perceptions and attitudes in the model, rather than control variables.

Data Analysis In the first part of the analysis, the independent variables were Perceived Positive Impact on Communication (PPIC), Perceived Negative Impact on Communication (PNIC), Perceived Load (PL), Perceived Necessity (PN) and Perceived Effectiveness (PE) of workrelated e-mail; Attitude was the dependent variable. Controlling for the individual (gender and education level), organizational (company type), and situational (perceived formality level of the company), I performed multiple regression to test the hypotheses 1-5. According

17 to the results, PPIC, PE and PN were positively whereas PNIC and PL were negatively correlated with Attitude. All independent variables (PPIC, PNIC, PE, PL and PN) had statistically significant correlations with the dependent variable at 5% of significance level. Regarding the control variables, the correlations indicated mixed results; gender and postgraduate education had positive and statistically significant correlations with attitude, whereas age had a negative and statistically significant correlation. Table 6 shows the correlations between independent and dependent variables and their significance levels. *****Insert Table 6 about here**** The residual analysis indicated close to normal distribution and the scatter plot of unstandardized residuals revealed that the error terms were not correlated among cases. Hence, the main assumptions of multiple regression on residuals were met. The statistics revealed that the level of multicollinearity between independent variables was low. The standardized betas suggested that all independent variables (PPIC, PNIC, PL, PN and PE) had statistically significant associations with the dependent variable (Attitude). In terms of directions, PPIC, PN and PE were positively whereas PNIC and PL were negatively associated with attitude, as hypothesized. Individual control variables such as age, gender and education had significant associations with Attitude. Age indicated negative whereas gender and education revealed positive relationships with Attitude. However, organizational and situational control variables had no significant relationship with the dependent variable. In terms of the magnitude of the associations PPIC was the highest and PL was the lowest. In summary, Hypotheses 1, 2, 3, 4 and 5 were supported by the analysis. Table 7 shows the coefficient statistics. *****Insert Table 7 about here**** To test the mediation of perceptions by the attitude, I applied the procedure suggested by Baron and Kenny (1986). All three models were statistically significant. However, in the

18 second model where usage was regressed on the perceptions, PE was not statistically significant any more at 5% significance level. Similarly, in the third model where usage was regressed on both the perceptions and the mediator, i.e., Attitude, the same independent variable had no statistically significant relationship to the dependent variable. Furthermore, Attitude also seemed not to have a statistically significant relationship with usage. These mixed results were not enough to support the Hypothesis 6. As an alternative hypothesis, I explored the direct impact of perceptions on usage, i.e., usage was regressed on same perception variables with the same control variables in the original model. This time, the analysis showed that all perception variables in the model except PE had statistically significant direct relationships with usage. For the second part of the analysis, I tested the impact of individual characteristics on the perceptions in the model. Gender and education were introduced as determinants and age as a control variable into the model. I performed univariate General Linear Model (GLM) with perception variables (PPIC, PNIC, and PE), attitude towards usage and usage itself. In general, this procedure is sensitive to the cell sizes; however, for non-experimental studies, the unequal cell sizes might not be problematic. Nevertheless, the analysis of cell sizes indicated that both genders were equally represented. For education, cell sizes deviated from being equal. Since there was no bias in sampling, this issue did not pose any threat for the study. The GLM procedure assumes homogeneity of error variances across the cells, measured by Levene‟s test. Levene‟s test was not significant for all variables except PPIC, meaning that the null hypothesis was not rejected and the error variances of the dependent variable were equal across groups. Hence, GLM assumptions were met. For PPIC, non-linear tests can be performed for further analysis. The tests of between-subject effects showed mixed results for the main effects; the interaction effects between gender and education were not statistically significant. From a

19 practical significance perspective, for all cases partial Eta-square values were small indicating lower contributions to the model. Table 8 reveals the results of the between-subject effects with both statistical and practical significance levels. According to these analyses, gender was related to PE and attitude, thus the hypotheses 7a and 7d were supported. Contrarily, there was not enough evidence to support the hypotheses 7b and 7c on gender effects. The analysis suggested that there were significant relationships between education and all perceptions and attitude. Thus, the hypotheses 8a, b, c and d were supported. Both gender and education had no statistically significant relationships to usage; thus, hypotheses 7e and 8e were also supported. *****Insert Table 8 about here**** Moreover, to understand the differences among educational levels, I performed posthoc analyses for statistically significant variables. In terms of attitude, for all educational levels, descriptive statistics indicated that women had higher mean scores with lower standard deviation than men had. Post-hoc tests (Tukey‟s test for equal variances) suggested that at 5% significance level there was a statistically significant difference in attitudes towards e-mail between persons with high school and graduate degrees. Employees with higher level of education showed relatively more positive attitude towards work-related email. Other pair-wise comparisons were not statistically significant. For PE, descriptive statistics revealed that women had higher mean scores than men had across all educational levels. Post-hoc tests, Tukey‟s and Tamhane‟s statistics produced similar results; namely there were statistically significant differences among education levels. This time, the employees with undergraduate degree had relatively more positive perceptions on effectiveness than the employees with graduate and high school degrees. For PPIC and PNIC, descriptive statistics showed that in general women had higher mean scores than men had across all educational levels. For PPIC, there was no statistically significant difference

20 between educational levels. On the other hand, in terms PNIC, Tukey‟s test suggested that there was a statistically significant difference across educational levels: the higher the level of education, the higher the perception of negative impacts on communication. In terms of the validity concerns, this research study was bounded by the Pew survey instrument and the data. The PSRA claimed that their methodology for instrument creation fulfilled content validity requirements. They stated that the face validity of the survey instrument assessed during the pretest. Since the aim of the study was to present the direction and magnitude of the associations between constructs, causality was not a concern. On the other hand, applying EFA procedure applied across three different sets resulted consistently in reliable scales. Moreover, the theoretical framework of TAM provided support for internal and construct validity. External validity was assured because of random sampling methodology, wide sample size and variety in terms of participant characteristics, and the study‟s independence from organizational settings and e-mail applications. Finally, as the participants had different job positions at different type of companies and they used different type of e-mail applications, the differential validity of the empirical study was fulfilled as well. To summarize, the empirical context supported the generalizability of the results.

Discussion E-mail has become a crucial communication medium for organizations of all types and sizes (Markus, 1994; Whittaker & Sidner, 1996; Bontis et al., 2003; Tassabehji & Vakola, 2005; Watson-Manheim & Belanger, 2007). There is evidence that e-mail usage at the workplace contributes positively to teamwork, enables exchange of information, knowledge and experiences among employees and external partners, and supports cultivation of corporate culture (Sproull & Kiesler, 1986; Fulk & Boyd, 1991; Straub, 1994; Whittaker & Sidner, 1996; Lucas, 1998; Kock, 2000; Dawley & Anthony 2003). Nevertheless, its usage is not limited to communication. Employees utilize e-mail platforms for tasks indirectly related

21 to communication such as document management and tracking, task delegation and monitoring, meeting scheduling and reminders, and storage of personal communication data (Whittaker & Sidner, 1996; Bontis et al., 2003; Tassabehji & Vakola, 2005; WatsonManheim & Belanger, 2007). Consequently, e-mail facilitates virtual collaboration among temporally and spatially-dispersed teams (Whittaker & Sidner, 1996; Kock, 2000). Hence, it is considered as an indispensible medium for communication, collaboration, and information sharing at the workplace. Differentiated by personal characteristics, employee perceptions play an important role in technology adoption and usage (Daft, 1978, Fulk et al., 1987; Fulk, 1993; Markus, 1994; Gefen & Straub, 1997; Higa et al., 2000; Venkatesh et al., 2000). Depending on how employees perceive the advantages and the disadvantages of using e-mail at the workplace, they alter the level and the extent of the usage. For example, technological features of e-mail increase accessibility of an employee for other colleagues. As a negative consequence, ease of access might cause information overload and fatigue for some employees (Whittaker & Sidner, 1996; Dawley & Anthony 2003). On the other hand, the misusage of e-mail by peers might impact some employees negatively and cause them to develop negative attitudes towards e-mail. Overall, these individual perceptions drive the attitude towards usage, which in turn, affects behavior, i.e., the actual usage. This perception-attitude-behavior chain has been introduced by TRA (Fishbein & Ajzen, 1975) and applied to various applications as a theoretical framework to understand acceptance and usage of technologies. Rooted in TRA, TAM (Davis, 1989, Davis et al., 1989) focuses on two main perceptions, perceived ease of use and perceived usefulness, as the determinants of attitude. The theory has been applied to many technologies in adoption and diffusion phases, including e-mail. However, TAM is more salient to newly introduced technologies at acceptance and adoption phases (Adams et al., 1992). On the contrary, e-mail

22 is a mature medium currently utilized at post-adoption phase. Hence the perceptions in TAM do not represent employees‟ beliefs on work-related e-mail correctly. In this paper, I contribute to the discourse by revisiting and re-identifying TAM‟s perceptions. This research study introduces new perceptions on a technology at post-adoption phase where the users possess sufficient experience with the medium to effectively assess it and modify its usage. They are perceived effectiveness, perceived necessity, perceived load and perceived positive and negative impacts on communication of work-related e-mail. The empirical study provides support for the hypotheses on the relationships between newlyintroduced perceptions and attitude towards e-mail. Contrasting TAM, the mediating impact of attitude on actual usage reveals mixed results. The partial mediation is not fully supported; on the other hand, an alternative test on direct relationship between perceptions and usage points statistical significance. This result might reflect the fact that attitudes towards a mature communication technology are already established and perceptions directly influence the usage behavior. Alternatively, the operationalization of usage might have an impact on this outcome. If the usage is operationalized as a behavioral variable, i.e., usage style is measured rather than volume, the results for mediation effects might be different. Another area of contribution is the empirical test of relationships between individual characteristics (such as gender and education) and the main constructs in the model. In previous research, level of education has been introduced as external stimulus to the models (Hubona & Burton-Jones, 2002). Following this approach, the empirical analysis suggests that new perceptions and attitude vary by the level of education, but there is no direct effect on usage. In terms of gender, research posits that gender might impact the beliefs, but has no influence on usage (Gefen & Straub, 1997). In this model, some perceptions are found to vary with gender, some not. Hypotheses on attitude and usage are supported by the data. This result means that gender and education level do play a significant role on how individuals

23 shape their perceptions and attitudes, but not their behaviors. This research study has limitations mainly in terms of operationalization of the constructs. The operationalization and the measurement of variables are bounded by the Pew survey instrument. Usage of secondary data has challenged adoption and/or adaptation of the previously validated scales. Therefore, I constructed the scales for perceptions applying EFA. This procedure has produced scales with fewer items such as perceived load as a two-item scale, and attitude as a single-item scale. Eventually, these factors might have caused lower R2 values in multiple regression models. However, my aim is not to explain the dependent variable, i.e., usage, comprehensively, but to introduce new perceptions and to test their impacts on usage behavior. Thus, I argue that lower R2 values do not pose a threat for the purposes of the study. As far as the future research is concerned, the individual perspective on work-related e-mail needs to be further elaborated to understand the role of gender, education and other personal characteristics not included in the model. Moreover, the study does not differentiate employee perceptions according to their roles, e.g., sender and receiver. Depending on the intensity of the incoming and outgoing e-mail traffic, employees might change the usage levels. Therefore, future studies might explore this dimension. Additionally, since gender influences perceptions, it might also affect the style of using e-mail at the workplace. To understand this phenomenon, researchers might develop measurements for usage style or elaborate the usage purposes. In this research model, a missing component is the social influence occurring within the organization. Although company type and level of formality are included as control variables into the model, they don‟t reflect the organizational context sufficiently. Future research can contribute to the theory by exploring the impacts of the social norms on the newly-introduced employee perceptions. A similar future research stream may be interested

24 in analyzing the managerial impact on employee perceptions and usage, or the e-mail usage by managers who are not involved in operational activities and utilize e-mail for managerial responsibilities. For practice, the study provides feedback to managers on work-related e-mail so that they can take actions to change negative perceptions and attitude towards e-mail, decrease the overload and increase both individual and organizational productivity. Using this feedback, managers can redesign corporate e-mail usage by creating new communication practices, deploying policies and even reconfiguring e-mail systems. By the same token, managers might play a role model for employees and promote effective and creative usage of email at the workplace. Depending on differences by gender and education level, managers might consider to tailor individual training programs on e-mail usage to empower employees in communication. Finally, this research can assist managers to motivate employees to find, apply and promote new and innovative ways of e-mail usage. Figure 1. TRA (Fishbein & Ajzen, 1975)

Attitude Towards Act or Behavior

Behavioral Intention

Behavior

Subjective Norm

Figure 2. TAM (Davis et al., 1989)

Perceived Ease of Use (PEU)

Attitude Towards Usage

Perceived Usefulness (PU)

Intention to Use

Actual System Usage

25 Figure 3. Proposed Research Model

Individual Differences Perceptions on E-mail at Workplace Perceived Positive Impact on Communication (PPIC)

Perceived Negative Impact on Communication (PNIC)

Perceived Effectiveness (PE)

Gender

Education

(+)

(-)

Attitude Towards Workrelated E-mail

(+)

(+)

Perceived Necessity (PN) (-)

Perceived Load (PL)

Table 1. Respondent Demographics Gender (%) Male Female Education (%) High school or less Undergraduate Postgraduate Employed company type (%) Private State Education Non-profit Other Average age

53 47 48 31 21 68 11 14 6 1 39

E-mail Usage At Workplace

26 Table 2. Factor Loadings (rotated) 2 Factor Loadings 1 Formality q27 q27b q33 q37a q37b q37c q37d q46a q46b q46c q46d q46e q47a q47c q47d q47e q47f q47g q50a q50b

2

2

3

4

5

6

7

8

0.0787

0.0024

0.0942

0.0604

0.0684

0.0507

0.9165

-0.0312

0.0719

0.0757

0.0455

0.8592

0.1328

0.0329

-0.0239

0.0625

0.1134

0.0446

0.046

0.8804

-0.0073

-0.0264

0.0781

-0.0065

0.0939

-0.0113

-0.0084

0.0556

-0.0403

-0.0307

-0.0347

0.9582

0.0745

0.807

0.0289

0.0444

0.0555

0.0878

0.0021

0.0297

0.0596

0.732

0.0426

0.0557

-0.0191

0.0018

0.0168

0.0497

0.0907

0.73

0.02

0.0065

0.0367

-0.1056

0.016

-0.0141

0.08

0.8431

-0.001

0.0226

0.0833

0.0444

-0.0368

-0.0822

0.7063

0.1048

0.1167

0.1723

0.1452

0.0193

-0.0558

-0.1094

0.7825

0.0277

0.0248

-0.0148

0.1084

-0.0407

0.0609

0.0144

0.7814

0.0731

0.0202

0.0241

0.081

-0.0096

0.099

0.112

0.7626

0.0744

-0.0156

0.0981

-0.004

-0.0531

-0.1219

-0.0389

0.7441

0.0895

0.1578

-0.0201

0.0237

0.0107

0.1438

0.1456

0.073

-0.0126

0.5884

-0.0504

0.1584

-0.0771

0.2769

-0.0284

0.2154

0.0593

0.1288

0.015

0.7468

-0.0379

0.1714

0.1587

0.0642

0.0007

0.1674

0.0793

0.6967

0.1313

0.0028

-0.1755

0.0876

0.0284

0.8266

-0.027

-0.0766

0.036

0.1196

-0.0098

0.0504

0.0601

0.7103

0.0885

0.2279

0.1238

-0.1459

0.0454

0.0503

-0.0186

0.6198

0.1761

0.3891

0.1744

-0.0659

-0.0191

0.0007

0.0908

0.1326

-0.0108

0.0848

0.8461

-0.0127

-0.0327

-0.0698

-0.0703

0.0257

0.0162

0.0449

0.8711

0.0531

-0.0018

Factor loadings greater than 0.4 and less than - 0.4 are bolded.

27 Table 3 Scale Items and Reliability Values Construct

Scale Items

Reliability

Perceived Workplace Impact positive aspects (PPIC)

q46 Now I‟d like to ask some questions about how using email for work-related tasks have changed your work life. First, how much, if at all, has using email at work... 1 Not at all 2 Only a little 3 Some 4 All

0.822

q46a Expanded the number of people you communicate with q46b Improved teamwork q46c Made it easier to stay current with events at work q46d Saved time q46e Made me more available to my coworkers Perceived Workplace Impact negative aspects (PNIC)

Now I‟d like to ask some more questions about how using email for work-related tasks have changed your work life. First, how much, if at all, has using email at work... 1 Not at all 2 Only a little 3 Some 4 All

0.708

q47b Make it impossible to get away from work q47e Caused misunderstandings q47f Been distracting q47g Added new sources of stress

Perceived Load (PL)

q50 Has using email changed the amount of time you spend or not? If yes: Has the Internet increased or decreased the amount of time you spend doing this? 1 Decreased 2 Not changed 3 Increased q50a Working overall q50b Specifically working at the office

0.689

Table 4.1 Scale Statistics / PPIC Item Statistics

Inter-Item Correlation Matrix

Mean q46a

3.1096

STD 1.0912

N

q46a

q46b

922

q46a

1

q46c

q46d

q46e

q46b

2.6815

1.1306

922

q46b

0.4345

1

q46c

3.2353

1.001

922

q46c

0.4626

0.5409

1

922

q46d

0.4513

0.4999

0.4846

1

q46e

0.4705

0.5215

0.5157

0.4424

q46d

3.059

q46e Scale

1.0284

2.8298

1.186

922

14.9152

4.1636

5

Table 4.2 Scale Statistics / PNIC Item Statistics Mean q47b

1.4561

STD 0.8741

N

Inter-Item Correlation Matrix q47b q47e

936

q47b

1

q47f

q47g

q47e

1.6721

0.9219

936

q47e

0.2687

1

q47f

1.823

0.9782

936

q47f

0.3462

0.3813

1

q47g

0.3988

0.3831

0.4774

q47g

1.681

0.9699

936

Scale

6.633

2.7368

4

1

1

28 Table 4.3 Scale Statistics / PL Item Statistics

Inter-Item Correlations

Mean

STD

N

q50a

2.0669

0.4642

q50b

2.0231

Scale

4.0912

q50a

q50b

932

q50a

1

0.4685

932

q50b

0.5249

0.8145

2

Table 5 Descriptive Statistics for PN, PE, Attitude and Usage PN N

PE

Attitude

Usage

945

828

929

724

Minimum

1

0

1

2

Maximum

10

5

4

8

7.178

1.815

3.04

4.128

0.08

0.038

0.022

0.063

8

2

3

4

2.465

1.087

0.658

1.694

-0.824

-0.055

-1.207

0.661

0

-0.614

3.157

-0.416

Mean Std. Error of Mean Median Std. Deviation Skewness Kurtosis

Table 6 Correlations Variable

Attitude

Gender (Male / Female)

0.108

Age

-0.064 (p