Using a method that combines the Experience Sampling Method (Kubey, et al., 1996) and the diary survey method, we surveyed frequent Internet users about their online activities, along with their interest, confidence, and satisfaction in regard to these activities. A link to an online survey was sent to respondents five times a day for three consecutive days. The results reported here are based on 2,656 diary forms submitted by 417 respondents. Through inductive analysis of respondents’ open–ended accounts of their activities, we identified four information activity dimensions — information object, information behavior, goal, and intention. The results reveal that younger respondents were more likely than older respondents to mention that they engage in online activities with the intention of sharing or evaluating information, while older respondents more frequently mentioned the intentions of gathering data and keeping up to date. Respondents reported spending more time on traditional types of online activities (such as reading) and were more confident in their ability to conduct these types of activities. However, they also reported spending considerable amounts of time on more participatory types of activities, such as creating content and commenting on content. Furthermore, they often rated their interest and satisfaction levels higher when their goals and intentions for their activities were more social in nature and thus more characteristic of Web 2.0 activities, such as connecting with people, self–expression, and sharing. Respondents’ goals and intentions for their activities, as well as their interest, confidence, and satisfaction with various types of online activities, along with the relative amount of time they spent on various types of online activities and the locations from which they conducted these activities, all proved to be important factors to consider when attempting to reach a better understanding of people’s online activities. The contribution of this study lies in the unique data collection and analysis methods that we used in order to reach a better understanding of their online activities across multiple information activity dimensions.
Characteristics of respondents
Information activity dimensions by respondent age
Time and location of online activities
Interest, confidence, and satisfaction with regard to online activities
Discussion and conclusion
With rapid changes taking place within the online environment, people are increasingly using the Internet as an everyday tool to engage in various types of information behaviors, using a variety of Web sites. People’s goals and intentions in relation to their information activities have also become more diverse. This paper examines online information activities across multiple dimensions, observing the types of information objects with which individuals are interacting, types of information behaviors in which they are engaging, and their goals and intentions behind these behaviors. The purpose of this paper is to provide a detailed picture of what people are doing online and why. We analyze the amount of time individuals are spending on different types of online activities, along with the places from which they are conducting these activities. Furthermore, for each activity, we examine perceptions and self–evaluations in terms of the extent of their interest in specific activities, their confidence in their ability to conduct these activities, and their overall satisfaction.
There have been numerous studies about online activities and pursuits. Predominantly using large–scale survey studies, researchers have questioned users about how they spend their time online. Some of these studies have yielded general findings about online activities. For example, Nielsen Wire (2010) reports that the top three online activities in terms of proportion of time spent are social networking, online gaming, and e–mailing. More specifically, Americans reported spending 22.7 percent of their time online on social networking sites and blogs, 10.2 percent on online games, and 8.3 percent on e–mail. This study further found that Americans are spending approximately 35 percent of their time online communicating and networking with other users through social networking sites, blogs, e–mail messages, and instant messaging.
Other studies have found gender and/or age differences in terms of what Internet users are doing online. For example, Fallows (2005) found that men and younger people (18– to 29–year–olds) were more likely to use search engines and to have more confidence in their search abilities. Based on a survey of Chinese and British students, Li and Kirkup (2007) found that men were more likely than women to use e–mail and chat rooms and to play video games, and that they were more self–confident about their computer skills. A recent study by the Pew Research Center (2010) found that social networking sites were more popular among women while men were more likely than women to post videos of themselves online. In general, study findings indicate that women tend to use the Internet for communication while men tend to use it for entertainment (Fallows, 2004; Li and Kirkup, 2007; Pew Research Center, 2010).
In addition to gender differences, some studies have also found age–related differences in terms of Internet users’ online activities. The Pew Research Center (2010) found that Internet users of various generations were engaging in different types of online activities. For example, 75 percent of Millennials (people born after 1980) had created a profile on a social networking site. This figure falls to 50 percent for Gen X’ers (people born between 1965 and 1980), 30 percent for Boomers (people born between 1946 and 1964), and six percent for Silents (people born between 1928 and 1945). Playing video games was similarly much more popular among the younger generations. While 28 percent of Millennials reported that they had played a video game within the past 24 hours, this figure drops to 14 percent for Gen X’ers, 15 percent for Boomers, and six percent for Silents. In contrast, the percentage of Internet users who had sent or received an e–mail message within the past 24 hours was fairly steady across the three younger generations (Millennials: 56 percent; Gen X’ers: 57 percent; Boomers: 54 percent; Silents: 26 percent). Based on a survey of European Internet users, Brandtzæg, et al. (2011) found that younger users tended to engage in a greater breadth of activities online and that they were likely to be “Entertainment Users” who tended to use the Internet to access videos, games, and music. In contrast, older users tended to be “Instrumental Users” who used the Internet to search for information and to use e–commerce sites.
In this study, we examine people’s online activities using a data collection method that obtains information about their actual, concurrent online activities in situ. We employed a method that blends a diary survey method and the Experience Sampling Method (Kubey, et al., 1996) in that we surveyed people about their online activities five times a day for three consecutive days. The diary survey included both open–ended and multiple–choice questions. The purpose of employing this method was three–fold: (1) to capture data about respondents’ online activities that they were conducting at various times throughout the day from morning to late night across multiple days; (2) to collect data about respondents’ activities that they were conducting within various information use environments, including their homes, workplaces, schools, and other public places; and, (3) to have respondents describe, in their own words, their online information activities along with their purposes for conducting these activities, instead of requiring respondents to choose their activities and purposes from predetermined categories or lists of choices generated by the researchers.
This study’s results are based on 2,656 diary forms submitted by 417 unique individuals living in the state of Michigan. The survey was administered between April and June of 2009. The sample for this study does not represent general Internet users. Rather, this study focuses on frequent Internet users — that is, adult users who spend at least one hour in total on the Web every day, excluding time spent solely on e–mail. We chose to focus on this particular population because we wanted to concentrate on users who have adopted the Internet as a tool which they use in their everyday life, embracing online activities as part of their daily routines.
The specific research questions driving this study are as follows:
- What types of activities are frequent Internet users conducting online?
- What are their goals and intentions for engaging in these activities?
- How do frequent Internet users’ activities, goals, and intentions vary depending on their age?
- How interested, confident, and satisfied are frequent Internet users with their various types of online activities? Are there any gender differences regarding users’ confidence levels in their ability to conduct different types of online activities?
To collect data about the online activities in which respondents were engaging at various times throughout the day within their everyday context, we used a method that blended a diary method (e.g., Hilligoss and Rieh, 2008) and the Experience Sampling Method (Kubey, et al., 1996). We e–mailed respondents links to online activity diary forms five times per day for a period of three consecutive days, including one weekend day and two weekdays. The diary form first asked respondents to report all of the online activities in which they had participated during the previous three hours, indicating both the nature of their activities as well as the types of Web site(s) that they had used for each of these activities. It then asked respondents to describe the one activity on which they had spent the most time during the past three hours. It also asked respondents to describe what they were trying to accomplish in conducting this activity. These latter two questions were open–ended, asking respondents to describe their one activity and their reasons(s) for conducting this activity in detail. Additional questions collected information about the location from which respondents conducted their one activity, the amount of time they spent on this activity, their interest in this activity, their confidence in their ability to conduct this activity, and their degree of satisfaction with the way this activity went. The last four questions on the survey asked about respondents’ assessments of the credibility of the information they encountered during this activity. Results relating to these credibility–related questions are reported elsewhere (Rieh, et al., 2010).
Diary surveys were administered during the months of April through June of 2009. Using a Random Digital Dialing (RDD) method, we called the telephone numbers of over 9,000 Michigan residents. We screened into the study respondents who met all of the following criteria: (1) are at least 18 years of age; (2) have Internet access at both home and work/school (as applicable); (3) go on the Web every day, including weekends, for purposes other than just e–mail; and, (4) spend at least one hour, in total, on the Web every day (excluding time spent on e–mail).
Each respondent who was screened into the study first received an e–mail message that included a background questionnaire along with an informed consent form. The respondent then received links to diary forms throughout each of the three days of the study (Sunday, Monday, and Tuesday). Links to diary forms were e–mailed to respondents at 9:00 AM, 12:30 PM, 4:00 PM, 7:00 PM, and 10:00 PM on each of these three days. Respondents were asked to fill out a diary form only if they had actually used the Internet for some purpose other than e–mail during the previous three hours. Respondents had to fill out at least one diary form per day in order to continue to receive links to subsequent diary forms. For instance, if respondents did not submit at least one diary on Sunday, they were not sent links on Monday or Tuesday.
After removal of incomplete records, the final data set included 2,656 diary forms submitted by a total of 417 unique individuals. Respondents include those who filled out the diary form for all three days from Sunday to Tuesday as well as those who submitted diary forms only on Sunday or only on Sunday and Monday. The number of diaries submitted by each respondent ranged from 1 to 15, averaging six diaries per respondent.
Respondents submitted diaries fairly steadily across the three days of the study. Out of the total 2,656 diaries entries, 912 of the diaries (34.3 percent) were submitted on a Sunday, 901 (33.9 percent) were submitted on a Monday, and 843 (31.7 percent) were submitted on a Tuesday. Also, respondents submitted their diaries fairly equally across four of the five time periods — 582 (21.9 percent) were submitted in response to the 9:00 AM e–mail; 571 (21.5 percent) were submitted in response to the 12:30 PM e–mail; 557 (21.0 percent) were submitted in response to the 4:00 PM e–mail; and, 586 (22.1 percent) were submitted in response to the 7:00 PM e–mail. However, just 360 (13.6 percent) of the diaries were submitted in response to the 10:00 PM e–mail across all three days. Comparing response patterns for weekdays versus weekend days shows that diary submissions increased steadily up through the 7:00 PM e–mail on Sunday while the submissions on Monday and Tuesday did not show any particular pattern. Figure 1 shows the number of online activity diaries submitted in response to each e–mail we sent out.
Figure 1: Number of online activity diaries submitted.
We analyzed both quantitative and qualitative data from the background questionnaires and the diary forms. We conducted content analysis of the two open-ended questions from the diary form: (1) respondents’ descriptions of the one activity on which they had spent the most time during the previous three hours; and, (2) respondents’ descriptions of what they were trying to accomplish in conducting this activity. As a result, we identified the following four dimensions of information activities: information object, information behavior, goal, and intention. The definition for each dimension is as follows:
- Information object: Genre of the Web site with which an individual interacts while engaging in a particular information behavior
- Information behavior: Human behavior in which an individual engages while interacting with an information object in order to achieve his/her goals and intentions
- Goal: A user’s personal goal that motivates them to conduct a particular information activity (Rieh, 2004; Xie, 2008)
- Intention: Subgoal that a user has to achieve while engaging in an information activity (Xie, 2008)
These dimensions are shown in Tables 1 and 2 below, along with the major groupings we identified for each dimension as well as the specific types falling under each of the major groupings. We identified 17 types of information objects, classifying them into six groups: (1) general Web sites; (2) news Web sites; (3) e–commerce Web sites; (4) search engines; (5) multimedia; and, (6) social media. We also identified 14 different types of behaviors, classifying them into five groups: (1) reading & searching; (2) multimedia use; (3) shopping/financial transactions; (4) content mediation; and, (5) content creation. The first group of behaviors, ‘reading & searching,’ involves the lowest level of user participation on the Web while the last group, ‘content creation,’ involves the most active participation from users. Table 1 lists the different types of information objects that we identified and Table 2 lists the different types of information behaviors that we identified.
Table 1: Types of information objects. Information objects Group Types General Web sites Application/form
General Web site
News Web sites News Web sites E–commerce Web sites E–commerce Web sites Search engine Search engine Multimedia Game
Search media Blog
Social networking site
Table 2: Types of information behaviors. Information behaviors Group Types Reading & searching Fill out
Multimedia use Download/upload
Shopping/financial transactions Perform financial transaction
Content mediation Comment/post
Content creation Content creation
We also identified 11 different goals and nine different intentions based on respondents’ open–ended descriptions of what they were trying to accomplish when engaging in their online activities. These are shown in Table 3 below.
Table 3: Information goals and intentions. Goals Intentions Buy
Connect with people
Help other people
Maintain household and electronics
Perform school–related task
Perform work–related task
Plan for future
Keep up to date
Manage personal information
Demographic characteristics of respondents
The sample of 417 respondents was diverse with regard to a number of demographic characteristics. The sample was mixed in terms of gender — roughly three–fifths female (n=253; 61 percent) and two–fifths male (n=164; 39 percent). The sample also included people from a wide range of age groups, with nearly half (n=203) of our respondents falling between the ages of 35 and 54. People aged 55 and up constituted 31 percent (n=129) while respondents aged 18 to 34 constituted 21 percent (n=85). Figure 2 shows the distribution of our sample by age range.
Figure 2: Respondents by age range.
Our sample was also diverse in terms of educational background, although our sample contained a much higher percentage of college graduates (47.5 percent) than the state of Michigan (21.8 percent) (U.S. Census Bureau, 2000). Figure 3 shows the distribution of our sample by educational attainment.
Figure 3: Respondents by educational attainment.
Respondents filled out their occupation in an open–ended format. We then classified their responses using SRC 2–Digit Occupation Codes , as well as some codes that were created specifically for this study. Table 4 shows respondents’ occupations classified into 14 major groups. The responses of over one–quarter of the respondents fit into the Professional, Technical and Kindred Workers classification, which includes physicians, accountants, teachers, engineers, scientists, technicians, and lawyers. The next three most common occupational categories were Retired (13.7 percent), Clerical and Kindred Workers (13.2 percent), and Managers, Officials, and Proprietors (11.8 percent).
Table 4: Occupations of respondents (based on SRC 2–Digit Occupation Codes). Occupation N Percent Professional, Technical and Kindred Workers 112 26.9 Retired 57 13.7 Clerical and Kindred Workers 55 13.2 Managers, Officials and Proprietors 49 11.8 Housewives/Homemakers/Stay–at–home Moms 38 9.1 Craftsmen, Foremen and Kindred Workers 19 4.6 Service Workers 17 4.1 Unemployed/Laid Off 17 4.1 Sales Workers 14 3.4 Students 13 3.1 Operatives and Kindred Workers 10 2.4 Disabled 8 1.9 Laborers and Farm Foremen 5 1.2 Occupation NA 3 0.7 Total 417 100.0
Self–rated ability to conduct online activities
Respondents were asked to rate their ability to conduct each of seven different types of online activities using a seven–point Likert scale where ‘1’ was labeled ‘Novice’, ‘4’ was labeled ‘Average’, and ‘7’ was labeled ‘Expert’. Table 5 shows the mean ratings given by respondents regarding their ability to conduct each type of online activity. Overall, respondents rated their ability to conduct more traditional types of online activities such as navigating information on the Web (M=5.43), finding information online using a search engine (M=5.62), and purchasing a product or service online (M=5.54) quite highly. In contrast, their ratings tended to be lower for the more participatory Web 2.0 types of online activities, such as rating a product, service, or person online (M=4.77), sharing documents, photographs, videos, or music online (M=4.56), and using online social or professional networking sites (M=4.22). Respondents rated their ability to create Web pages or blogs the lowest of all (M=2.52).
Table 5: Self–ratings of ability to conduct online activities. Online activity M SD Finding information online using a search engine 5.62 1.21 Purchasing a product or service online 5.54 1.44 Navigating information on the Web 5.43 1.25 Rating a product, service, or person online 4.77 1.60 Sharing documents, photographs, videos, or music online 4.56 1.66 Using online social or professional networking sites 4.22 1.83 Creating Web pages or blogs 2.52 1.84 All activities 4.70 1.84
Figure 4 depicts the mean ability self–ratings of males and females for each of the various online activities. For nearly all types of activities, males rated their abilities higher than females; however only one of these differences was statistically significant — sharing documents, photographs, videos, or music online (t=2.04, df=408, p=.042).
Figure 4: Self–ratings of ability to conduct online activities by gender.
Figure 5 shows the mean ability self–ratings across three age groups — 18 through 34, 35 through 54, and 55 and up. Not surprisingly, younger people rated their abilities to conduct each of the online activities higher than did older people. On all activities except one (purchasing a product or service online), the youngest group rated their abilities the highest, the middle group rated their abilities the next highest, and the oldest group rated their abilities the lowest. All of the differences between the ratings given by the youngest group (age 18–34) and those given by the oldest group (age 55 and up) and all of the differences between the ratings given by the middle group (age 35–54) and those given by the oldest group (age 55 and up) were statistically significant. However, the differences between the ratings given by the youngest group (age 18–34) and those given by the middle group (age 35–54) were statistically significant on only three online activities: Using online social or professional networking sites (t=4.62, df=278, p=.000); Sharing documents, photographs, videos, or music online (t=3.04, df=284, p=.003); and, Creating Web pages or blogs (t=3.54, df=268, p=.000).
Figure 5: Self–ratings of ability to conduct online activities by age group.
We were also interested in analyzing the four information activity dimensions in regard to differing respondent age groups. Figure 6 below shows the distribution of information object types across respondent age groups. There were clearly differences across the respondent age groups in terms of the types of information objects respondents reported using. The activity descriptions submitted by the youngest respondents (ages 18 through 34) were much more likely to mention multimedia and social media, while those submitted by older respondents were more likely to mention news, e–commerce, and general Web sites. To some degree, younger respondents were also more likely than older respondents to report using search engines.
Figure 6: Information objects by respondent age group.
Figure 7 shows that across the various respondent age groups, information behaviors varied quite a bit as well. Reading & searching and shopping were more prevalent among the older respondents, while multimedia use and content mediation were more prevalent among the younger respondents. Content creation, however, was rarely reported by respondents; In fact, the percent of diaries describing content creation behaviors is quite similar across all of the age groups, ranging from 0.5 percent to 1.1 percent.
Figure 7: Information behaviors by respondent age group.
An analysis of the two other information activity dimensions we identified — goals and intentions — by respondent age group also yielded some interesting findings. Figures 8 and 9 below show the distribution of respondents’ goals and intentions, respectively, by age group. Figure 8 shows that the goal of entertain was prevalent among the diaries submitted by the youngest age group (18– to 24–year–olds), constituting nearly 55 percent of the goals we identified from these respondents’ diaries. Similarly, the goal of connect with people was prevalent among the diaries submitted by the second youngest age group (25– to 34–year–olds), constituting over one–third of the goals we identified from these respondents’ diaries. The activity descriptions provided by respondents in either of the two middle–age groups (35– to 44–year–olds and 45– to 54–year–olds) had much more diversity in terms of goals. The most commonly mentioned goal by 35– to 44–year–olds was entertain, constituting just over one–fourth of the goals identified for this age group. The most common goal mentioned by 45– to 54–year–olds was buy, constituting just over one–fifth of the goals we identified for this age group. For the two oldest age groups (55– to 64–year–olds and those 65 and over), the goals of entertain and buy were, by far, the most prevalent. In fact, over one–half of the goals described by respondents in the 55–64 age group had to do with these two goals. For the oldest age group (65 and over), this figure rises to nearly 75 percent. The goals of perform school–related task and self–expression, while not commonly mentioned overall, were much more likely to be mentioned by respondents in the one of the two youngest age groups (that is, respondents aged 18 to 34). The goals of get employed and perform work–related task were most commonly mentioned by respondents in one of the four middle age groups (that is, ages 25 to 64). The goals of plan for future and help other people were most commonly mentioned by respondents in either the 45–54 age group or the 55–64 age group. The goals of sell and maintain household and electronics were seldom mentioned; however, they were much more likely to be mentioned by respondents aged 65 and over. Overall, the prevalence of the goal ‘buy’ increased steadily with increasing respondent age, ranging from a low of 12.6 percent for 18– to 24–year–olds to a high of 41.5 percent for respondents aged 65 and up. In contrast, the prevalence of the goal ‘connect with people’ decreased steadily with increasing respondent age, ranging from a high of 33.5 percent for 25– to 34–year–olds to a low of 2.8 percent for respondents aged 65 and up.
Figure 8: Goals by respondent age group.
Figure 9 shows that the intentions of ‘keep up to date’ and ‘gather data’ were the most commonly mentioned overall. The intentions of evaluate and share were more commonly associated with diaries submitted by younger respondents (18– to 34–year–olds). The intention of learn was most commonly mentioned by 18– to 24–year–olds; however, respondents in the 25–34, 55–64, and 65+ age groups also described conducting a considerable number of activities with this intention. The intention of ‘keep up to date’ accounted for more than one–half of the diaries submitted by respondents aged 35 to 44. Interestingly, the intention of ‘produce’, though rarely mentioned overall, was only associated with diaries submitted by respondents between the ages of 35 and 64.
Figure 9: Intentions by respondent age group.
Time spent on online activities
After respondents were asked to describe the one activity on which they had spent the most time during the three hours preceding their receipt of the diary form, they were then asked to answer a series of questions about that particular activity. One of these questions pertained to the amount of time they had spent on the one activity they described. Respondents were provided with a seven–point response scale, with ‘1’ indicating ‘0-10 minutes’ and ‘7’ indicating ‘more than one hour’. Table 6 presents the amount of time respondents reported spending on their one online activity. The median response category was ‘4,’ 31 to 40 minutes. However, the largest number of diaries (n=558, 21.1 percent) reported that the respondent had spent more than an hour on this one activity.
Table 6: Time spent on one online activity. Category Time spent N Percent 1 0–10 minutes 222 8.4 2 11–20 minutes 501 18.9 3 21–30 minutes 500 18.9 4 31–40 minutes 353 13.3 5 41–50 minutes 232 8.8 6 51–60 minutes 284 10.7 7 More than an hour 558 21.1 Total 2,650 100.0 Blank 6 0.2 Median 4.0 Mode 7
We analyzed the time spent on online activities with respect to the different types of information objects respondents used. As seen in Table 7, we found that respondents spent the most time (51 minutes or more) when they used information relating to an online course or when they played online games. They spent the least amount of time (21–30 minutes) when they filled out an online application or form. When they read news or used social networking sites, they spent about 21–30 minutes. When working with blogs, wikis, e–commerce sites, and search engines, they reported that they spent about 31–40 minutes on their one information activity.
Table 7: Time spent on one online activity by information object. Information object N Median Mode Online course 24 6.5 7 Game 72 6.0 7 TV/radio/podcast 28 5.0 7 Video 84 5.0 7 Blog 53 4.0 7 General Web site 656 4.0 7 Search engine 94 4.0 7 Unspecified format 75 4.0 7 Wiki 15 4.0 5 E–commerce site 411 4.0 3 Forum 70 4.0 3 Music 72 4.0 3 Photos 28 3.0 3 Social networking site 238 3.0 3 Article/e–book 16 3.0 2 News site 494 3.0 2 Application/form 14 3.0 1 All information objects 2,444 4.0 7
Table 8 shows an analysis of the time spent on activities involving each type of information behavior. Not surprisingly, respondents spent the longest time (51–60 minutes) when they played online. Other types of information behaviors on which respondents spent a relatively longer time (41–50 minutes) include organizing as well as downloading/uploading. When respondents engaged in creating content, listening/watching/viewing, searching and shopping, they were likely to spend more time (31 to 40 minutes) than when they were commenting/posting, reading, or monitoring (21 to 35 minutes). Respondents spent the least amount of time (11 to 20 minutes) when performing financial transactions or voting/rating.
Table 8: Time spent on one online activity by information behavior. Information behavior N Median Mode Play 71 6.0 7 Download/upload 67 5.0 7 Organize 9 5.0 7 Create 12 4.0 7 Listen/watch/view 170 4.0 7 Search 770 4.0 7 Shop 150 4.0 3 Comment/post 108 3.5 2 Read 596 3.5 2 Fill out 14 3.0 3 Monitor 276 3.0 1 Perform financial transaction 30 2.0 2 Vote/rate 5 2.0 2 All behaviors 2,278 4.0 7
We also examined how the amount of time people reported spending on their activities varied depending on their goals and intentions for engaging in the activity. As presented in Table 9, respondents answered that they tended to spend a longer time (between 45–60 minutes) when they conducted online activities with the goals of getting employed, performing work-related task, and performing school–related task. It was interesting to find that they tend to spend a longer time conducting online activities with the purpose of entertain or help other people (about 41–50 minutes) than did they for self-expression (31–40 minutes), connect with people (21–30 minutes), and plan for future (21–30 minutes).
Table 9: Time spent on one online activity by user goal. Goal N Median Mode Get employed 68 6.0 7 Perform work–related task 169 6.0 7 Perform school–related task 40 5.5 7 Entertain 362 5.0 7 Help other people 40 5.0 7 Maintain household and electronics 24 4.0 7 Sell 14 4.0 7 Self–expression 15 4.0 4 Buy 288 4.0 3 Connect with people 257 3.0 3 Plan for future 84 3.0 2 All goals 1,361 4.0 7
When examining time spent with respect to respondent intentions for their one online activity, we found a small variance across the different types of intentions (see Table 10). In general, respondents reported that they tend to spend about 31–40 minutes when they gather data, learn, share, and evaluate information while they spend about 21–30 minutes on average when they produce content, decide, keep up to date, manage personal information, and verify information.
Table 10: Time spent on one online activity by intention. Intention N Median Mode Gather data 532 4.0 7 Learn 118 4.0 7 Share 29 4.0 7 Evaluate 91 4.0 3 Produce 11 3.0 7 Decide 38 3.0 3 Keep up to date 614 3.0 2 Manage personal information 66 3.0 2 Verify 22 3.0 2 All intentions 1,521 3.0 2
Locations from which respondents conducted their one online activity
An analysis of user goals and intentions with respect to the place from which respondents conducted their one online activity yielded some interesting findings. Figures 10 and 11 show the distribution of respondents’ goals and intentions, respectively, for their one online activity by the place from which they reported conducting this activity. Although the vast majority of activities that respondents reported were conducted from home, it was interesting to note that respondents engaged in online activities from their workplaces with the goals of planning for future and helping other people. They never engaged in online activities to sell products in their workplaces; however, they did buy products even though they were at their workplaces. Not surprisingly, over two–thirds of the activities (67.5 percent) with the goal of perform work–related task were conducted from respondents’ workplaces. In general, respondents rarely reported online activities that they conducted in public places such as a restaurant, coffee shop, or bar; however, when their goal for their online activity was to connect with other people, they sometimes conducted this activity in a public place such as a restaurant, coffee shop, or bar. When respondents conducted online activities from school or campus, their goal was to perform school–related task, help other people, maintain household and electronics, or entertain.
Figure 10: User goals by place from which one online activity was conducted.
While respondents rarely reported conducting their one online activity from a restaurant, coffee shop, or bar, the intention of respondents who did report conducting their online activity from these locations was to share or produce content (see Figure 11). Over 40 percent of the online activities that respondents conducted with the intention of verifying information were conducted from workplaces compared to just 15.5 percent of activities across all intentions.
Figure 11: User intentions by place from which one online activity was conducted.
As part of each diary form, we asked respondents to rate how interesting they found their reported activity to be, how confident they were in their ability to conduct this activity, and how satisfied they were with the way this activity went. They were provided with a seven–point scale, where ‘1’ represented ‘Not at all’, ‘4’ represented ‘Somewhat’, and ‘7’ represented ‘Very’. Analyzing the responses to these three questions based on the various dimensions of respondents’ information activities (i.e., information object, information behavior, goal, and intention) yielded interesting findings. Table 11 shows the averages of respondents’ interest, confidence, and satisfaction ratings based on information object classification. The results show that respondents found multimedia (M=6.06) and social media (M=5.74) to be the most interesting to use and search engines (M=5.32) and e–commerce Web sites (M=5.14) to be the least interesting to use. They were most confident in their ability to use news Web sites (M=6.73) and social media (M=6.66), while they were least confident in their ability to use e–commerce Web sites (M=6.43), general Web sites (M=6.43), and search engines (M=6.13). They were the most satisfied when using news Web sites (M=6.25), multimedia (M=6.20), and social media (M=6.19). They were least satisfied while using e–commerce Web sites (M=5.61) and search engines (M=5.40).
Table 11: Interest, confidence, and satisfaction ratings by information object. Information object N Interest Confidence Satisfaction M
Rank Search engines 94 5.32
6 General Web sites 792 5.51
4 News Web sites 494 5.65
1 Multimedia 298 6.06
2 E–commerce Web sites 410 5.14
5 Social media 383 5.74
3 All information objects 2,471 5.57
Table 12 shows differences in respondents’ reported levels of interest, confidence, and satisfaction with respect to the types of information behavior in which they engaged. ‘Listen/watch/view’ (M=6.12), ‘play’ (M=5.87), and ‘comment/post’ (M=5.76) were rated as the most interesting behaviors while ‘fill out’ (M=4.86), ‘perform financial transaction’ (M=3.87), and ‘organize’ (M=3.33) were rated the least interesting. Respondents’ confidence levels were fairly high across all behaviors; however, they were least confident about their ability to search (M=6.42), shop (M=6.37), create (M=6.33), and organize (M=6.00). Their satisfaction levels were highest for ‘fill out’ (M=6.40), ‘comment/post’ (M=6.34), and ‘perform financial transaction’ (M=6.33). Their satisfaction levels were lowest for search (M=5.73), shop (M=5.46), create (M=5.25), and organize (M=5.00).
Table 12: Interest, confidence, and satisfaction ratings by behavior. Behavior N Interest Confidence Satisfaction M
Rank Search 770 5.51
10 Read 596 5.74
9 Monitor 276 5.32
5 Fill out 14 4.86
1 Organize 9 3.33
13 Listen/watch/view 170 6.12
5 Download/upload 67 5.52
8 Play 71 5.87
7 Shop 150 5.30
11 Perform financial transaction 30 3.87
3 Comment/post 107 5.76
2 Vote/rate 5 5.00
4 Create 12 5.08
12 All behaviors 2,277 5.56
Respondents’ average interest, confidence, and satisfaction ratings by goal and by intention are shown in Tables 13 and 14. Respondents’ interest ratings were highest when their goals were ‘entertain’ (M=5.88), ‘connect with people’ (M=5.72), and ‘self–expression’ (M=5.60). Their interest ratings were lowest when their goals were ‘perform work–related task’ (M=5.05), ‘perform school–related task’ (M=4.73), and ‘maintain household and electronics’ (M=4.21). Respondents expressed the most confidence in their abilities to conduct online activities when their goals were ‘sell’ (M=6.85), ‘connect with people’ (M=6.66), and ‘entertain’ (M=6.59). They expressed the least confidence when their goals were ‘perform school–related task’ (M=6.15), ‘self–expression’ (M=6.13) and ‘maintain household and electronics’ (M=5.92). Respondents’ satisfaction ratings were highest when their goals were ‘sell’ (M=6.54), ‘self–expression’ (M=6.47), and ‘connect with people’ (M=6.25). Their satisfaction ratings were lowest when their goals were ‘buy’ (M=5.53), ‘get employed’ (M=4.99), and ‘maintain household and electronics’ (M=4.75).
Table 13: Interest, confidence, and satisfaction ratings by respondent goal. Goal N Interest Confidence Satisfaction M
Rank Buy 288 5.33
9 Connect with people 257 5.72
3 Entertain 362 5.88
5 Get employed 68 5.51
10 Help other people 40 5.10
7 Maintain household and electronics 24 4.21
11 Perform school–related task 40 4.73
8 Perform work–related task 169 5.05
6 Plan for future 84 5.54
4 Self–expression 15 5.60
2 Sell 13 5.46
1 All goals 1,360 5.50
Table 14 shows that respondents expressed most interest in their online activities when their intentions were ‘share’ (M=6.31), ‘learn’ (M=6.18), and ‘produce’ (M=6.00). They expressed the least interest when their intentions were ‘verify’ (M=5.18), ‘evaluate’ (M=5.09), and ‘manage personal information’ (M=4.18). Respondents were most confident in their online activities when their intentions were ‘keep up to date’ (M=6.72), ‘share’ (M=6.66), and ‘manage personal information’ (M=6.55). They were least confident when their intentions were ‘gather data’ (M=6.41), ‘produce’ (M=6.36), and ‘decide’ (M=6.32). Respondents expressed the most satisfaction with their online activities when their intentions were ‘share’ (M=6.72), ‘verify’ (M=6.41), and ‘manage personal information’ (M=6.35). They expressed the least satisfaction when their intentions were ‘gather data’ (M=5.78), ‘decide’ (M=5.50), and ‘evaluate’ (M=5.35).
Table 14: Interest, confidence, and satisfaction ratings by respondent intention. Intention N Interest Confidence Satisfaction M
Rank Decide 38 5.61
8 Evaluate 91 5.09
9 Gather data 532 5.70
7 Keep up to date 614 5.69
4 Learn 118 6.18
5 Manage personal information 66 4.18
3 Produce 11 6.00
6 Share 29 6.31
1 Verify 22 5.18
2 All intentions 1,521 5.64
This study yields several findings that support those of earlier studies, as well as a number of novel findings. We found that respondents were more confident about their abilities to conduct traditional types of online activities than about their abilities to conduct more participatory types of activities, such as rating a product, service, or person online, sharing documents, photographs, videos, or music online, using online social or professional networking sites, and creating Web pages or blogs. Similar to the findings from earlier studies, we found gender– and age–related differences in people’s confidence in their abilities to carry out different types of online activities, as well as in the various dimensions of their information activities. Analogous to Fallows’ (2005) earlier findings specifically in regard to the use of search engines, we found that male and younger respondents expressed more confidence in their abilities to conduct nearly all types of online activities. In fact, our data show that for nearly all types of online activities, the youngest group (18– to 34–year–olds) expressed more confidence in their abilities than the middle group (35– to 54–year–olds), who in turn expressed more confidence in their abilities than the oldest group (55+). The only online activity for which these findings did not hold was purchasing a product or service online. Female respondents were slightly more confident than male respondents in regard to their ability to conduct this particular activity and the younger age groups (18– to 34–year–olds and 35– to 54–year–olds) expressed equal amounts of confidence in regard to this activity.
A novel contribution of this study is our identification of four information activity dimensions — information objects, information behaviors, goals, and intentions. We identified these dimensions through inductive analysis of respondents’ open–ended accounts of their activities and their descriptions of what they were trying to accomplish by conducting these activities. Some age–related differences were found among these dimensions. Consonant with the findings from an earlier Pew Research Center (2010) report, we found that younger respondents were more likely to describe using multimedia and social media Web sites. Also, like Fallows (2005), we found that younger respondents were more likely to report using search engines. Regarding older respondents, we found that they were more likely to describe using news and e–commerce sites. Our findings are in line with Brandtzæg, et al.’s (2011) findings that younger users tended to engage in a greater breadth of online activities and that they tended to be “Entertainment Users,” while older users tended to be “Instrumental Users,” using the Internet more for information–related purposes and for e–commerce.
Regarding the second information activity dimension that we identified, information behaviors, we found that younger respondents were more likely to describe activities involving multimedia use and content mediation while older respondents were more likely to describe less participatory types of activities such as reading, searching, and shopping.
An additional novel contribution of this study is our elicitation of people’s goals and intentions for conducting their online activities. Overall, we found that the incidence of the goal ‘buy’ increased steadily with respondent age, while the incidence of the goal ‘connect with people’ decreased steadily with respondent age. Regarding respondents’ intentions, we found that the intentions of ‘share’ and ‘evaluate’ were somewhat more likely to be mentioned in diary entries completed by younger respondents, while the intention of ‘gather data’ was somewhat more likely to be mentioned in diary entries submitted by older respondents. In fact, more than 75 percent of the diary entries submitted by respondents in the two middle–age groups (i.e., respondents between the ages of 35 and 54) described either an intention of ‘keep up to date’ or ‘gather data’.
Our results regarding the amount of time respondents are spending on various types of online activities partially supported previous findings. Consistent with Nielsen Wire’s (2010) findings, we found that respondents were spending a great deal of time on online games and blogs. However, our respondents reported spending more time on online courses than on online games, and more time watching videos and TV/radio/podcast broadcasts than using blogs. In contrast with Nielsen Wire’s (2010) findings, our respondents reported spending relatively little time on social networking sites. In fact, the only information objects with which respondents reported spending less time than social networking sites were articles/e–books, news sites, and applications/forms.
In regard to time spent on various types of information behaviors, we found that respondents reported spending the most time playing and downloading/uploading and the least time performing financial transactions and voting/rating. Interestingly, they reported spending more time on playing, downloading/uploading, organizing, creating, listening/watching/viewing, searching, shopping, and commenting/posting than on reading. Also, they reported spending more time creating than searching.
Although the most commonly described goals were entertain, buy, and connect with people, respondents reported spending more time on online activities when their goals were work– or school–related. Somewhat surprisingly, respondents reported spending the least time when their goal was ‘plan for future.’ Regarding respondents’ intentions, ‘gather data’ and ‘keep up to date’ were, by far, the most commonly described. Respondents reported spending the most time on online activities when their intentions were ‘gather data’ or ‘learn’ and the least time on activities when their intentions were ‘manage personal information’ or ‘verify’.
Our analysis of the locations from which respondents were conducting their online activities yielded a couple of interesting findings. First, respondents reported quite a wide range of goals and intentions for online activities they were conducting from work. Second, it appears that there may be a relationship between a person’s goals and intentions for their online activities and the location from which they conduct these activities. For example, respondents who reported conducting their online activity from a restaurant, coffee shop, or bar — a much more social location than home or work — most frequently described a goal having to do with connecting with people and intentions having to do with sharing or producing content. However, it must be noted that the number of respondents who reported conducting their online activity from a restaurant, coffee shop, or bar was quite low.
An analysis of respondents’ interest, confidence, and satisfaction levels for each information activity dimension revealed the most interesting findings of this study. Respondents were most interested in information activities involving multimedia and social media and least interested in using search engines and e–commerce sites. They were the most confident and satisfied when using news Web sites, social media, and multimedia Web sites and the least confident and satisfied when using search engines.
Regarding information behaviors, respondents reported the highest interest levels in their online activities when they were ‘listening/watching/viewing’, ‘playing’, and ‘commenting/posting’ and the lowest interest levels when they were ‘filling out’, ‘performing a financial transaction’, and ‘organizing’. They reported the highest confidence levels when ‘monitoring’, ‘reading’, and ‘listening/watching/viewing’ and the lowest confidence levels when ‘searching’, ‘shopping’, ‘creating’, and ‘organizing’. They reported the greatest satisfaction when ‘filling out’, ‘commenting/posting’, and ‘performing financial transactions’ and the least satisfaction when ‘searching’, ‘shopping’, ‘creating’, and ‘organizing’.
Similar findings were reached based on an analysis of respondents’ interest, confidence, and satisfaction levels depending on their goals and intentions for conducting their online activities. Respondents were most interested in activities they conducted with a goal of ‘entertain’, ‘connect with people’, or ‘self–expression’, while they were least interested in activities conducted with a goal of ‘perform work—related task’, ‘perform school–related task’, or ‘maintain household and electronics’. They were the most confident in their online activities when their goal was to ‘sell’, ‘connect with people’ or ‘entertain’; however, they were least confident when their goal was ‘perform school–related task’, ‘self–expression’, or ‘maintain household and electronics’. They were the most satisfied when their goal was ‘sell’, ‘self–expression’, or ‘connect with people’, while they were the least satisfied when their goal was ‘buy’, ‘get employed’, or ‘maintain household and electronics’.
Regarding respondent intentions, activities involving sharing, learning, or producing were deemed the most interesting, while those involving verifying, evaluating, or managing personal information were deemed the least interesting. Respondents reported the highest confidence levels when their intentions for their online activities were ‘keep up to date’, ‘share’, or ‘manage personal information’. Their confidence levels were lowest when their intentions were ‘gather data’, ‘produce’, or ‘decide’. Respondents indicated the greatest satisfaction when their intentions were ‘share’, ‘verify’, or ‘manage personal information’ and the least satisfaction when their intentions were ‘gather data’, ‘decide’, or ‘evaluate’.
An overall analysis of the age–related differences within each of the information activity dimensions yielded findings that lend support to the idea that younger Internet users are more actively engaging in Web 2.0 activities and are more likely to use the Internet for more social purposes. Our younger respondents were far more likely to describe using multimedia and social media Web sites. In fact, over 56 percent of the information objects described in the diaries submitted by 18– to 24–year–olds fell within one of these two categories. This figure remains high for the second youngest age group (25– to 34–year–olds) at 37.4 percent. Multimedia use and content mediation were similarly popular among these two age groups. Over 37 percent of the information behaviors described by the 18– to 24–year–old respondents and over 25 percent of the information behaviors described by the 25– to 34–year–old respondents fit within one of these two categories. The goals and intentions of these younger respondents also reflect a bias toward Web 2.0 activities, relative to older respondents. For the youngest age group (18–24), the goals of entertain, connect with people, and self–expression constituted 72 percent of all of their goals. For the second–youngest age group (25–34), this figure remains high, topping out at over 55 percent. The related intention of ‘share’ was also most commonly mentioned by respondents in one of these two younger groups.
The findings seem to reflect the fact that there is a significant transition taking place in terms of the types of online activities in which Internet users are engaging and their reasons for engaging in these activities. Respondents generally expressed more confidence about their ability to engage in more traditional types of Internet activities and were much more likely to report engaging in these types of activities. However, they also reported spending considerable amounts of time conducting more participatory types of activities and reported relatively high levels of interest and satisfaction in regard to these activities. In fact, our respondents often tended to provide higher interest and satisfaction ratings when their goals and intentions for their activities were more characteristic of Web 2.0 types of activities, such as ‘entertain’, ‘connect with people’, ‘self–expression’, and ‘share’. Respondents’ high levels of interest and satisfaction with Web 2.0 types of activities, along with the age–related differences that we identified across the various information activity dimensions, suggest that this transition will only tend to accelerate and intensify.
About the authors
Beth St. Jean is an Assistant Professor at the University of Maryland College of Information Studies (Maryland’s iSchool). Her research interests include information behavior, relevance and credibility, information literacy, and institutional repositories and open access. Her dissertation research entailed a longitudinal investigation into the consumer health information behavior of people with type 2 diabetes. From 2008 through 2011, Beth was a research assistant for the MacArthur Foundation–funded Credibility 2.0 Project led by Professor Soo Young Rieh at the University of Michigan School of Information.
E–mail: bstjean [at] umd [dot] edu
Soo Young Rieh is an Associate Professor in the School of Information at the University of Michigan. She is the Principal Investigator of the Credibility Assessment in the Participatory Web Environment Project funded by the MacArthur Foundation. Her research focuses on user assessments of information credibility and cognitive authority in the process of information seeking and use. She has published research on credibility judgments of online information, credibility assessment in everyday life information activities, mental effort in Web searching, and perceptions of institutional repositories.
E–mail: rieh [at] umich [dot] edu
Yong–Mi Kim is a Post–Doctoral Associate in the Center for Technology, Entertainment and Media at Duke University’s Fuqua School of Business, where she is conducting research on the use of social media in large corporations. Yong–Mi completed her Ph.D. at the University of Michigan, where she was a Research Assistant for the MacArthur Foundation–funded Credibility 2.0 Project led by Professor Soo Young Rieh. Yong–Mi’s research interests include information behavior in social media, and use and evaluation of interactive information retrieval systems. Yong–Mi has conducted research on credibility assessment of user–generated content, incentives for participating in Q&A communities, and online searching behavior.
E–mail: yongmi [dot] kim [at] duke [dot] edu
Ji Yeon Yang is a doctoral candidate at the University of Michigan School of Information, as well as a Research Assistant for the MacArthur Foundation–funded Credibility 2.0 Project led by Professor Soo Young Rieh. Ji Yeon’s research interests include information behavior in organizations, information credibility, cognitive authority, and workplace social media. Her dissertation research examines information mediation between colleagues in the workplace.
E–mail: jiyeon [at] umich [dot] edu
Support for the Credibility 2.0 (Credibility Assessment in the Participatory Web Environment) Project (http://credibility.si.umich.edu/) is provided by the John D. and Catherine T. MacArthur Foundation. We would like to thank Peter Batra and other staff members of the University of Michigan Institute for Social Research for their assistance in recruitment of participants. We would also like to thank the many Michigan residents who participated in this study.
1. Nolte and Servais, 2010, pp. 8–9.
Petter Bae Brandtzæg, Jan Heim, and Amela Karahasanovic, 2011. “Understanding the new digital divide — A typology of Internet users in Europe,” International Journal of Human–Computer Studies, volume 69, number 3, pp. 123–138.http://dx.doi.org/10.1016/j.ijhcs.2010.11.004
Deborah Fallows, 2005. “Search engine users: Internet searchers are confident, satisfied and trusting — but they are also unaware and naïve,” at http://www.pewinternet.org/~/media//Files/Reports/2005/PIP_Searchengine_users.pdf.pdf, accessed 18 July 2011.
Deborah Fallows, 2004. “The Internet and daily life: Many Americans use the Internet in everyday activities, but traditional offline habits still dominate,” at http://www.pewinternet.org/~/media/Files/Reports/2004/PIP_Internet_and_Daily_Life.pdf.pdf, accessed 18 July 2011.
Brian Hilligoss and Soo Young Rieh, 2008. “Developing a unifying framework of credibility assessment: Construct, heuristics, and interaction in context,” Information Processing and Management, volume 44, number 4, pp. 1,467–1,484.
Robert Kubey, Reed Larson, and Mihaly Csikszentmihalyi, 1996. “Experience sampling method applications to communication research questions,” Journal of Communication, volume 46, number 2, pp. 99–120.http://dx.doi.org/10.1111/j.1460-2466.1996.tb01476.x
Nai Li and Gill Kirkup, 2007. “Gender and cultural differences in Internet use: A study of China and the U.K.,” Computers & Education, volume 48, number 2, pp. 301–317.http://dx.doi.org/10.1016/j.compedu.2005.01.007
Nielsen Wire. 2010. “What Americans do online: Social media and games dominate activity” (2 August), at http://blog.nielsen.com/nielsenwire/online_mobile/what-americans-do-online-social-media-and-games-dominate-activity/, accessed 13 July 2011.
Michael A. Nolte and Marita A. Servais, 2010. “Occupation and industry coding in HRS/AHEAD (Health and Retirement Study Documentation Report DR–021),” at http://hrsonline.isr.umich.edu/sitedocs/dmgt/dr-021.pdf, accessed 19 June 2011.
Pew Research Center, 2010. “Millennials: A portrait of generation next: Confident. Connected. Open to change” (24 February), at http://pewresearch.org/pubs/1501/millennials-new-survey-generational-personality-upbeat-open-new-ideas-technology-bound, accessed 13 July 2011.
Soo Young Rieh, 2004. “On the Web at home: Information seeking and Web searching in the home environment,” Journal of the American Society for Information Science and Technology, volume 55, number 8, pp. 743–753.http://dx.doi.org/10.1002/asi.20018
Soo Young Rieh, Yong–Mi Kim, Ji Yeon Yang, and Beth St. Jean, 2010. “A diary study of credibility assessment in everyday life information activities on the Web: Preliminary findings,” Proceedings of the American Society for Information Science and Technology, volume 47, number 1, pp. 1–10.http://dx.doi.org/10.1002/meet.14504701182
U.S. Census Bureau, 2000. “Michigan DP–2. Profile of Selected Social Characteristics: 2000,” at http://www.census.gov/census2000/states/mi.html, accessed 15 November 2011.
Iris Xie, 2008. Interactive information retrieval in digital environments. Hershey, Pa.: IGI Publishing.
Received 17 November 2011; accepted 2 February 2012.
Copyright © 2012, First Monday.
Copyright © 2012, Beth St. Jean, Soo Young Rieh, Yong–Mi Kim, and Ji Yeon Yang. All rights reserved.
An analysis of the information behaviors, goals, and intentions of frequent Internet users: Findings from online activity diaries
by Beth St. Jean, Soo Young Rieh, Yong–Mi Kim, and Ji Yeon Yang
First Monday, Volume 17, Number 2 - 6 February 2012
A Great Cities Initiative of the University of Illinois at Chicago University Library.
© First Monday, 1995-2016.