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Many studies have shown that women use the Internet more often for health-related information searches than men, but we have limited knowledge about the underlying reasons. We also do not know whether and how women and men differ in their current use of the Internet for communicating with their general practitioner (GP) and in their future intention to do so (virtual patient-physician relationship).
This study investigates (1) gender differences in health-related information search behavior by exploring underlying emotional, motivational, attitudinal as well as cognitive variables, situational involvement, and normative influences, and different personal involvement regarding health-related information searching and (2) gender differences in the virtual patient-physician relationship.
Gender differences were analyzed based on an empirical online survey of 1006 randomly selected German patients. The sample was drawn from an e-panel maintained by GfK HealthCare. A total of 958 usable questionnaires were analyzed. Principal component analyses were carried out for some variables. Differences between men (517/958) and women (441/958) were analyzed using t tests and Kendall’s tau-b tests. The survey instrument was guided by several research questions and was based on existing literature.
Women were more engaged in using the Internet for health-related information searching. Gender differences were found for the frequency of usage of various Internet channels for health-related information searches. Women used the Internet for health-related information searches to a higher degree for social motives and enjoyment and they judged the usability of the Internet medium and of the information gained by health information searches higher than men did. Women had a more positive attitude toward Web 2.0 than men did, but perceived themselves as less digitally competent. Women had a higher health and nutrition awareness and a greater reluctance to make use of medical support, as well as a higher personal disposition of being well-informed as a patient. Men may be more open toward the virtual patient-physician relationship.
Women have a stronger social motive for and experience greater enjoyment in health-related information searches, explained by social role interpretations, suggesting these needs should be met when offering health-related information on the Internet. This may be interesting for governmental bodies as well as for the insurance and the pharmaceutical industries. Furthermore, women may be more easily convinced by health awareness campaigns and are, therefore, the primary target group for them. Men are more open to engaging in a virtual relationship with the GP; therefore, they could be the primary target group for additional online services offered by GPs. There were several areas for GPs to reinforce the virtual patient-physician relationship: the fixing of personal appointments, referral to other doctors, writing prescriptions, and discussions of normal test results and doctor’s notes/certificates of health.
The Internet is one of the most important sources of health information, no longer only for a small segment of Internet users, but now also for the “general public” [
Furthermore, the second part of the paper deals with whether and how men and women differ with regard to the virtual patient-physician relationship. In our paper, we define the virtual patient-physician relationship as communication between a patient and the physician (or the physician’s surgery or office) via the Internet. Examples include emailing, making an appointment online to see the doctor, and a virtual meeting with the doctor (eg, via Skype). We address current communication as well as future intention to communicate with the general practitioner (GP) via the Internet in general and with regard to different areas of treatment (eg, routine treatments, acute disorders, discussion of health test results, referrals to other physicians).
There are many approaches and models that aim at explaining why individuals search for information. For instance, Marton and Choo [
An extension of the theory of reasoned action (TRA) [
Based on the TRA [
According to Dutta-Bergman [
Insights into gender differences in the virtual patient-physician relationship can also be drawn from the consumer behavior literature. According to Solomon [
Based on the aforementioned concepts, the objectives of the paper are as follows:
Investigate differences in health-related information searching on the Internet in part 1 of the paper, especially by investigating gender differences in using the Internet for health-related information searching. This will be done by (1) analyzing gender differences in feelings toward the Internet and Web 2.0 for health-related information searching (emotional perspective); (2) analyzing gender differences in perceived behavioral control, which we conceptualize as perceived digital competence (cognitive perspective); (3) analyzing gender differences in the underlying motives for using the Internet for health-related information searching (motivational perspective); (4) analyzing gender differences in health and nutrition awareness (attitudinal perspective); (5) analyzing gender differences in the personal disposition of being well-informed as a patient (personal involvement perspective); and (6) analyzing gender differences in the importance of situational circumstances, which foster the usage of Internet health information searching as well as differences in the importance of normative pressure on the usage of the Internet for health-related information searching (situational involvement and/or a normative perspective).
Analyze gender differences in the present and future virtual patient-physician relationship in part 2 of the paper.
An online survey of 1006 German patients was conducted in September 2012. The term “patients” in this paper refers to individuals who visited a physician at least once in the previous 3 months. The sample was drawn from an e-panel maintained by GfK HealthCare, a leading survey research company in Nuremberg, Germany. It was based on a randomly generated set of users who had visited a GP at least once during the 3 months before the beginning of the survey. Originally, 1561 individuals were contacted; 555 persons could not participate because they did not fulfill this criterion. The recruitment rate was 64.45% (1006/1561) [
The survey was designed by the researchers based on the existing literature and was guided by the research questions. All items apart from categorical variables (sociodemographic variables) and ordinal variables (frequency variables) were measured with 7-point rating scales. Most of the items had a “no answer” category as an alternative. Existing scales and items from the literature were used where applicable. Data were analyzed using SPSS version 22 (IBM Corp, Armonk, NY, USA).
Age (D2_1), gender (D1), the highest educational level attained (D4), family status (D5), household size (D6_1), and the categorical monthly household net income were measured (D8).
Feelings toward the Internet and other Web-based applications in general were included in the questionnaire and measured by an item derived from Porter and Donthu [
Digital literacy is the ability to effectively and critically use a range of digital technologies. Literate individuals are able to make responsible choices and to access information and ideas in the digital world and to share information with others. In-line with previously published studies, digital literacy was measured with an item based on Norman and Skinner [
Respondents were asked about their daily Internet use, especially how many hours they spent on the Internet for private purposes on average on a daily, weekly, or monthly basis (total private use) (F3_1 to F3_3), and on average searching for health-related information (total private use for health-related information) (F4_1 to F4_3). We then calculated the total private Internet use and the total private Internet use for health-related information for each respondent on a daily basis.
For the purpose of this investigation, the importance of different sources (family, friends, physician, pharmacist, insurance agent, Internet, books/journals, other sources) was examined using items adopted from Moorman and Matulich [
For the purpose of investigating different search methods in the use of the Internet for health-related information, participants were asked to indicate how often they used the following channels on the Internet for health-related information searches: search engines (eg, Google), wikis (eg, Wikipedia), electronic databases and electronic papers as well as scientific papers and studies (eg, www.bmj.com), email, social networks/microblogs/networks (eg, Facebook), health forums (eg, www.imedo.de), podcasts (eg, YouTube), instant messaging/chat (eg, Skype, ICQ), and apps [
Concerning the motives of using the Internet for health-related information searching, different items from literature were used (F11_1 to F11_18). Perceived ease of use and perceived usefulness of the Internet to gain health-related information were measured by existing multi-item scales derived and adapted from Davis et al [
According to Cacioppo and Petty [
Attitude is conceptualized by Solomon [
Eight additional items were developed and integrated into the questionnaire in recognition of the fact that using the Internet could not only be due to a reason lying in the respondent himself or herself, but rather because of normative or situational reasons (F12_1 to F12_8). Therefore, after literature reviews, some complementary items measuring situational and normative influences were derived and adapted from the TAM and the TPB [
For the purpose of investigating the present usage and future intention to communicate with the GP on the Internet and to partially replace personal communication with and treatment by a GP by the Internet, some additional items were developed by the researchers as shown in
A comparison of the sample used in the current study and German Internet users in 2012 (the German online population) [
Characteristics of study sample compared to the German Internet population in 2012.
Variable and category | Female |
Male |
Total |
German Internet usersa
|
|
|
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|
|
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|
|
Men | 0 | 517 (100.00) | 517 (53.97) | 29,553,000 (51.81) |
|
Women | 441 (100.00) | 0 | 441 (46.03) | 27,492,000 (48.20) |
|
41.21 (13.39) | 45.88 (12.40) | 43.73 (13.04) |
|
|
|
Age range (years) | 18-70 | 18-70 | 18-70 | >10 |
|
441 (100.00) | 517 (100.00) | 958 (100.00) |
|
|
|
<24 | 56 (12.70) | 25 (4.84) | 81 (8.45) | 12,552,000 (22.00) |
|
25-44 | 192 (43.54) | 198 (38.30) | 390 (40.71) | 20,344,000 (35.60) |
|
45-64 | 177 (40.14) | 254 (49.13) | 431 (44.99) | 18,799,000 (32.96) |
|
>65 | 16 (3.64) | 40 (7.74) | 56 (5.85) | 5,348,000 (9.38) |
|
437 (100.00) | 514 (100.00) | 951 (100.00) | 52,589,000 (100.00)b | |
|
Without school qualification | 2 (0.46) | 2 (0.39) | 4 (0.42) |
|
|
Secondary general school | 8 (1.83) | 5 (0.97) | 13 (1.37) | 9,487,000 (18.04)c |
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Polytechnic secondary school | 43 (9.84) | 77 (14.98) | 120 (12.62) |
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|
Intermediate secondary school | 142 (32.49) | 127 (24.71) | 269 (28.28) | 29,467,000 (56.03)d |
|
Matura examination or higher | 242 (55.38) | 303 (58.95) | 545 (57.31) | 13,635,000 (25.93)e |
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439 (100.00) | 517 (100.00) | 956 (100.00) |
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|
|
1 | 90 (20.50) | 117 (22.63) | 207 (21.65) |
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|
2 | 169 (38.49) | 194 (37.52) | 363 (37.97) |
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3 | 87 (19.82) | 113 (21.86) | 200 (20.92) |
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|
4 | 83 (18.91) | 72 (13.93) | 155 (16.21) |
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>4 | 10 (2.28) | 21 (4.06) | 31 (3.24) |
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|
439 (100.00) | 509 (100.00) | 948 (100.00) |
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|
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Single | 92 (20.95) | 108 (21.22) | 200 (21.10) |
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Close-partnered | 110 (25.06) | 105 (20.63) | 215 (22.68) |
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Married | 194 (44.19) | 266 (52.26) | 460 (48.52) |
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Divorced | 36 (8.20) | 28 (5.50) | 64 (6.75) |
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Widowed | 7 (1.60) | 2 (0.39) | 9 (0.95) |
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347 (100.00) | 429 (100.00) | 776 (100.00) |
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<1500 | 77 (22.19) | 52 (12.12) | 129 (16.63) |
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1500-2500 | 97 (27.95) | 105 (24.47) | 202 (26.03) |
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2501-3500 | 94 (27.09) | 134(31.24) | 228 (29.38) |
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3501-4500 | 53 (15.28) | 68 (15.85) | 121 (15.59) |
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>4500 | 26 (7.49) | 70 (16.32) | 96 (12.37) |
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a Rounded to 1000 people. Projected number of Germans who have used the Internet in the last 3 months. Age limit for questions concerning education and occupation: 16 years.
b For the German Internet users, low education corresponds with levels 0, 1, and 2 of the ISCED classification system (up to secondary general school), medium education corresponds with levels 3 and 4 of the ISCED classification system (up to university entrance qualification), and high education corresponds with levels 5 and 6 of the ISCED classification system (higher than matura examination respectively university entrance qualification).
c low education
d medium education
e high education
There was a significant difference between the 2 groups in terms of their perceived digital competence (
To do justice to the relatively large sample size, which lead to a higher probability of differences becoming significant between the 2 groups, we added the effect size of Hedges’
Gender differences in the specified psychographic variables relating to health-related information searching are reported in the next section. Because of the large number of subsequent psychographic variables, we decided to summarize the motivational, attitudinal, and personal involvement items that might contribute to the explanation of gender differences in health-related information searching. Therefore, for each group of psychographic variables (motivational, attitudinal, and personal involvement processes underlying Internet health information searching) and the group of normative and situational influences, exploratory factor analyses (EFAs) were calculated for the total sample. Only those subsets of variables were factor analyzed, which were measured on an interval scale level (statistical precondition) and which could be assigned to a specific psychographic construct or to the group of normative and situational influences. This procedure was chosen to reduce the complexity versus the alternative of a large number of group differences on a single item level. The number of factors for each of the subscales was determined by the eigenvalue criterion; principal component analyses were used with a subsequent varimax rotation with Kaiser normalization. Items with low loadings and with loadings greater than 0.45 on more than 1 factor were removed. The variances extracted were reported only for the purified scales. The factor loadings of the purified scales were used for subsequent calculation of weighted means of factor sum scores. One advantage of this method is that items with the highest loadings on the factor have the largest effect on the factor score [
Gender differences in Internet health information search behavior, emotions, and cognitions influencing Internet health-related information searching.
Variables | Female |
Male |
Total |
|
Kendall’s tau-b |
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Hedges’ |
||||
|
n | Mean (SD) |
n | Mean (SD) |
n | Mean (SD) |
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Feelings toward the Internet and other Web-based applications in generala | 431 | 5.75 (1.04) | 514 | 5.80 (1.16) | 954 | 5.78 (1.11) | 0.63 (943) |
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.53 | 0.05 | |
Perceived digital competenceb | 441 | 5.72 (1.11) | 517 | 5.99 (1.0) | 958 | 5.87 (1.06) | 3.91 (899) |
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<.001 | 0.26 | |
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Total private use | 441 | 3.18 (2.52) | 517 | 3.02 (2.07) | 958 | 3.10 (2.29) | –1.05 (853) |
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.30 | –0.07 |
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Total private use for health-related information | 441 | 0.53 (2.05) | 517 | 0.35 (0.86) | 958 | 0.43 (1.53) | –1.76 (572) |
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.08 | –0.12 |
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Family | 437 | 4.85 (1.71) | 511 | 4.85 (1.73) | 948 | 4.85 (1.72) | 0.04 (946) |
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.97 | 0.00 |
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Friends | 436 | 4.36 (1.70) | 510 | 4.01 (1.74) | 946 | 4.17 (1.73) | –3.08 (944) |
|
.002 | –0.20 |
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Physician | 440 | 6.44 (0.90) | 515 | 6.41 (0.98) | 955 | 6.42 (0.95) | –0.40 (953) |
|
.69 | 0.03 |
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Pharmacist | 432 | 5.15 (1.54) | 506 | 4.89 (1.59) | 938 | 5.01 (1.57) | –2.52 (936) |
|
.012 | –0.17 |
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Insurance agent | 405 | 1.75 (1.34) | 486 | 1.80 (1.34) | 891 | 1.78 (1.34) | 0.63 (889) |
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.53 | 0.04 |
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Internet | 437 | 4.73 (1.44) | 516 | 4.51 (1.43) | 953 | 4.61 (1.44) | –2.36 (951) |
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.02 | –0.15 |
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Books/journals | 425 | 4.44 (1.64) | 497 | 4.15 (1.70) | 922 | 4.29 (1.68) | –2.64 (920) |
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.009 | –0.17 |
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Other sources | 280 | 3.02 (1.8) | 352 | 2.81 (1.79) | 632 | 2.90 (1.79) | –1.5 (630) |
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.14 | –0.12 |
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Search engines | 441 | 3 | 517 | 4 | 958 |
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–0.06 | .045 |
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Wikis online encyclopedia | 441 | 4 | 517 | 4 | 958 |
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–0.02 | .41 |
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Electronic databases/journals | 441 | 5 | 517 | 5 | 958 |
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0.03 | .39 |
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441 | 5 | 517 | 5 | 958 |
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0.03 | .38 |
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Social network/microblogging | 441 | 6 | 517 | 6 | 958 |
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–0.03 | .27 |
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Health forums/blogs | 441 | 5 | 517 | 5 | 958 |
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–0.06 | .03 |
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Podcasts | 441 | 6 | 517 | 6 | 958 |
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–0.03 | .35 |
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Videoconferences | 441 | 6 | 517 | 6 | 958 |
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0.02 | .55 |
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Instant messaging/chat | 441 | 6 | 517 | 6 | 958 |
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–0.04 | .24 |
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Apps | 441 | 6 | 517 | 6 | 958 |
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0.07 | .02 |
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a 1=very negative, 7=very positive.
b 1=not literate at all, 7=very literate.
c 1=not important at all, 7=very important.
d 1=daily, 2=weekly, 3=less often than weekly, 4=monthly, 5=less often than monthly, 6=never.
Strong evidence was found for the existence of different motives when using the Internet for health-related information searching. Because the same procedure for the EFA was executed for all the groups of variables (attitudinal, personal involvement, situational, and normative perspective), it is only described in detail for the EFA 1. Detailed information for the other EFAs are included in the respective tables in
Women used the Internet to a greater extent than men did due to a social motive and enjoyment of Internet health information searching (
Gender differences of weighted means of factor sum scores for motives influencing Internet health information searching on an aggregate level.
Factors | Female |
Male |
Total |
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|
Hedges’ |
|||
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n | Mean (SD) | n | Mean (SD) | n | Mean (SD) |
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Social motive and joyousness of Internet health information searching | 387 | 4.50 (1.50) | 450 | 4.27 (1.48) | 837 | 4.37 (1.49) | –2.31 (835) | .02 | –0.15 |
Perceived usefulness of the Internet for health information searching | 417 | 6.08 (0.93) | 494 | 5.94 (1.12) | 911 | 6.0 (1.04) | –1.94 (908.55) | .05 | –0.14 |
Usefulness of the information gained from Internet health information searching | 434 | 5.37 (1.17) | 510 | 5.12 (1.26) | 944 | 5.23 (1.22) | –3.16 (942) | .002 | –0.21 |
An EFA of the 9 items measuring the attitudinal influences deriving from different health and nutrition awareness and proneness to use medical support lead to a 2-factor solution for the purified scale explaining 61.14% of variance (see Table B in
As is shown in
Gender differences of weighted means of factor sum scores for attitudes influencing Internet health information searching on an aggregate level.
Factors | Female |
Male |
Total |
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|
Hedges’ |
|||
|
n | Mean (SD) | n | Mean (SD) | n | Mean (SD) |
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Health and nutrition awareness | 440 | 5.24 (1.08) | 515 | 5.02 (1.15) | 955 | 5.12 (1.12) | –3.07 (953) | .002 | –0.20 |
Reluctance to make use of medical support | 439 | 4.79 (1.56) | 514 | 4.52 (1.52) | 953 | 4.64 (1.54) | –2.58 (951) | .010 | –0.18 |
An EFA of the 9 items measuring the personal disposition of being well-informed as a patient lead to a single factor solution explaining 52.93% of variance (see Table C of
Gender differences of weighted factor sum scores for the personal disposition of being well-informed influencing Internet health information search behavior on an aggregate as well as on a basis level.
Factors/Variables | Female |
Male |
Total |
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|
Hedges’ |
||||
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n | Mean (SD) | n | Mean (SD) | n | Mean (SD) |
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Personal disposition of being well-informed as a patient | 413 | 4.05 (1.41) | 486 | 3.94 (1.37) | 899 | 3.99 (1.39) | –1.19 (897) | .24 | –0.08 | |
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It is important to me to be well-informed when consulting a physician. | 436 | 4.70 (1.70) | 514 | 4.72 (1.72) | 955 | 4.71 (1.71) | 0.12 (953) | .91 | 0.01 |
|
When I obtain health-related information from the Internet, I need to talk about this information with my physician. | 436 | 3.99 (1.90) | 514 | 4.12 (1.82) | 950 | 4.06 (1.86) | 1.11 (948) | .27 | 0.07 |
|
When a therapy is prescribed for me, I look for alternative therapies on the Internet. | 439 | 4.30 (1.92) | 514 | 4.33 (1.85) | 953 | 4.32 (1.88) | 0.23 (951) | .82 | 0.02 |
|
Sometimes I have the feeling that I am better informed about my medical condition than my physician. | 439 | 3.78 (2.01) | 513 | 3.55 (1.92) | 952 | 3.66 (1.96) | –1.79 (950) | .08 | –0.12 |
|
If the patient is informed, the communication with the physician is improved. | 437 | 4.74 (1.70) | 510 | 4.68 (1.72) | 947 | 4.71 (1.71) | –0.51 (945) | .61 | –0.04 |
|
I only decide whether a consultation with a physician is really necessary, once I have conducted some health information searches on the Internet. | 437 | 3.18 (1.97) | 512 | 2.93 (1.88) | 949 | 3.05 (1.92) | –1.98 (947) | .048 | –0.13 |
|
If some medicines have been prescribed, I look for information about them on the Internet. | 440 | 4.25 (2.04) | 515 | 3.99 (2.01) | 955 | 4.11 (2.03) | –2.01 (953) | .045 | –0.13 |
|
If the patient is informed, the physician allows more time for the treatment. | 427 | 3.32 (1.92) | 502 | 3.42 (1.88) | 929 | 3.37 (1.90) | 0.75 (927) | .46 | 0.05 |
|
The physician is more likely to prescribe a requested medicine, if the patient is informed. | 418 | 3.66 (1.98) | 500 | 3.58 (1.92) | 918 | 3.61 (1.95) | –0.62 (916) | .54 | –0.04 |
a 1=strongly disagree, 7=strongly agree.
As shown in
An EFA of the 5 items measuring the underlying situational and involvement influences on Internet health information searching lead to a 2-factor solution explaining 78.88% of variance (see Table D in
Gender differences of weighted means of factor sum scores for situational and normative variables influencing Internet health information searching on an aggregate level.
Factors | Female |
Male |
Total |
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|
Hedges’ |
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n | Mean (SD) | n | Mean (SD) | n | Mean (SD) |
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|
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Situational influences on Internet health information searching | 434 | 5.54 (1.23) | 512 | 5.35 (1.27) | 946 | 5.44 (1.25) | –2.25 (944) | .03 | –0.15 |
Normative influences on Internet health information searching | 384 | 3.48 (1.88) | 460 | 3.33 (1.85) | 844 | 3.40 (1.86) | –1.20 (842) | .23 | –0.08 |
Women seemed to be caught in a crossfire of situational, but not normative influences, to a greater extent than men which reinforced the usage of the Internet for health-related information searches. The factor including situational influences had a higher mean score for women than for men (
For the purpose of establishing whether there are gender differences in the present virtual patient-physician relationship, several unrelated
In reference to the future behavioral intention of using the Internet for communication with the GP, male respondents were more prone to replace personal communication with the GP and treatment by the Internet (see
Gender differences for future intention to replace personal communication and treatment by the Internet.
Variables | Female |
Male |
Total |
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|
Hedges’ |
||||
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n | Mean (SD) | n | Mean (SD) | n | Mean (SD) |
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|
|
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Intention of using the Internet more often in the future for communicating with the GPa | 438 | 4.05 (2.31) | 513 | 4.66 (2.17) | 951 | 4.38 (2.54) | 4.15 (905) | <.001 | 0.27 | |
Importance of being able to use online treatment as wellb | 436 | 3.47 (1.99) | 512 | 3.71 (2.04) | 948 | 3.60 (2.02) | 1.88 (946) | .06 | 0.12 | |
Willingness to pay a certain amount additionally for online-treatmentc | 436 | 2.15 (1.74) | 515 | 2.42 (1.87) | 951 | 2.30 (1.81) | 2.24 (941) | .03 | 0.15 | |
|
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Fixing of personal appointments | 440 | 6.21 (1.56) | 514 | 6.41 (1.26) | 954 | 6.32 (1.41) | 2.13 (841) | .03 | 0.14 |
|
Preliminary advice | 436 | 4.75 (2.19) | 511 | 4.86 (2.10) | 947 | 4.81 (2.14) | 0.80 (945) | .42 | 0.05 |
|
Writing of prescriptions | 439 | 5.60 (1.97) | 512 | 5.68 (1.83) | 951 | 5.64 (1.90) | 0.60 (902) | .55 | 0.04 |
|
Doctor’s notes/certificates of health | 434 | 4.58 (2.32) | 504 | 4.67 (2.27) | 938 | 4.63 (2.29) | 0.62 (936) | .54 | 0.04 |
|
Referrals to other doctors | 438 | 5.99 (1.66) | 513 | 5.86 (1.69) | 951 | 5.92 (1.68) | –1.17 (949) | .24 | –0.08 |
|
Discussion of “normal” test results | 437 | 5.07 (2.17) | 514 | 4.95 (2.15) | 951 | 5.01 (2.15) | –0.87 (949) | .39 | –0.06 |
|
Discussion of “critical” test results | 437 | 2.61 (2.01) | 512 | 2.85 (2.05) | 949 | 2.74 (2.04) | 1.85 (947) | .07 | 0.12 |
|
Follow-up checks after treatment | 436 | 3.13 (2.13) | 506 | 3.29 (2.05) | 942 | 3.21 (2.09) | 1.21 (940) | .23 | 0.08 |
|
Supervision of chronically ill people | 435 | 3.90 (2.16) | 510 | 4.25 (2.17) | 945 | 4.09 (2.17) | 2.45 (943) | .01 | 0.16 |
|
Secondary effects of drugs | 438 | 4.74 (2.14) | 512 | 5.00 (2.00) | 950 | 4.88 (2.07) | 1.95 (902) | .052 | 0.13 |
|
Routine treatments (eg, sore throat, head cold) | 436 | 3.96 (2.25) | 510 | 4.31 (2.15) | 946 | 4.15 (2.20) | 2.45 (944) | .014 | 0.16 |
|
Psychotherapy | 435 | 2.42 (2.00) | 505 | 2.48 (1.95) | 940 | 2.45 (1.97) | 0.50 (938) | .62 | 0.03 |
|
Mental health problems | 438 | 2.59 (2.03) | 504 | 2.74 (1.95) | 942 | 2.67 (1.99) | 1.21 (940) | .23 | 0.08 |
|
Acute disorders (eg, chest pains) | 438 | 2.42 (2.04) | 505 | 2.56 (2.01) | 941 | 2.50 (2.03) | 1.11 (939) | .27 | 0.07 |
a 1=highly unlikely, 7=very likely.
b 1=not important at all, 7=very important.
c 1=I would not be willing at all, 7=I would be willing.
In reviewing the literature, only scarce empirical evidence was found on the underlying emotional, motivational, normative and situational, attitudinal, cognitive, and personal involvement variables, which may explain gender differences in Internet health-related information searching and on gender differences in the virtual patient-physician relationship. Therefore, the aim of the current investigation was to shed light on gender differences in these areas.
In order to do justice to the large sample size, we added the effect size Hedge’s
The effect sizes in our study are mostly small, but exceeded the limit of 0.1 as suggested by Bortz and Döring [
In reference to behavioral variables the study is by trend in-line with studies reporting that women are more frequent users of the Internet for health-related information searches [
The current study found that there are no differences between the female and male respondents in their feelings toward the Internet and other Web-based apps in general.
The next question in this research was whether women and men differ in their motivations to use the Internet for health-related information searching. The most interesting finding was that women use the Internet for health-related information searching to a higher degree than men for social reasons and for pleasure. They evaluate it as a more useful medium and they perceive the gained information as more useful than men do. When looking at the differences on the level of the items, the Internet is attractive for women because it is an efficient method of searching (easy, quick, always available, capable of enhancing search success) because of its social dimensions (offering different formats, getting in contact with other people easily) and its entertainment potential. These results can be explained from a social role perspective. Due to the multitasking agenda of women, especially those of middle age, who play key roles as health managers and family caregivers [
With regard to the question of how situational involvement differs between women and men in relation to health-related information searching on the Internet, this study found that situational influences are predominantly important for women, and to a smaller extent for men, whenever they use the Internet for health-related information searching. Surprisingly, normative influences seem to make no contribution to gender differences in usage of the Internet for health-related information searching. A possible explanation for this might be that women, especially middle-aged women, sometimes work part-time because of their manifold roles and therefore have only limited access to and limited time for the Internet. This may cause a higher dependency on situational circumstances and a higher situational involvement with the Internet and Web 2.0. Nevertheless, this explanation must be interpreted with caution, because there are many middle-aged women who work full time in spite of possible manifold roles. Therefore, this interpretation cannot be extrapolated to all women; hence, there is room for many other complementary root cause analyses.
From an attitudinal perspective, the results are consistent with those of other studies revealing that women show higher nutrition and health awareness across different countries and settings (eg, [
This study found that women are more reluctant to visit a physician than men. This result is contrary to a recent study from Smith et al [
At present, men report a higher frequency of communicating online with the GP and they are also more willing than women are to replace personal communication with the GP and treatment by the Internet in the future. Men can imagine fostering the virtual patient-physician relationship in the areas of making personal appointments, the supervision of chronically ill people, and for routine treatments (eg, sore throat, head cold). Additionally, they are more willing to pay a certain amount of extra money for online treatment. We see 2 main explanations for these findings. First, and as outlined previously, women perceive themselves as less digitally literate than men and, therefore, may feel a higher level of unease with regard to replacing the relatively intimate personal face-to-face GP consultation by a virtual one, which is probably rated as being less intimate. Secondly, from a social role perspective, women visit GPs not only for themselves, but also in their role as caregiver to their children. Hence, the replacement of a personal consultation by a virtual consultation may be perceived as being even more difficult if women are acting on behalf of someone else, especially their own children.
Hence, the replacement of the personal dimension through the Internet may be more difficult for women than it is for men. Reduced willingness to pay additionally for online treatment may also be explained by women’s smaller amount of disposable income. Comparing the household net income of the female and the male subsample, in-line with the census data, it was shown that the household net income was higher for the male subsample. Therefore, it may be more affordable for men to pay a certain amount of extra money for online treatment.
The study is not without limitations. There is the possibility of selection bias among respondents, although random selection out of the database was held to minimize its likelihood. The recruitment rate of 64% for this online panel sample also indicates that selection bias among respondents is probably low. A demographic comparison showed that our sample reflects the German online population relatively well. However, in the subsample of male respondents, the age category of older men (45 years and older) was overrepresented and there were also more respondents with higher education than in the general online population for both of the subgroups. Future studies may try to make use of a larger randomized sample of the average online population.
The questionnaire was very comprehensive because of the many variables that were addressed, which might raise the issue of fatigue among the respondents. However, the exact duration of the survey completion was automatically measured and saved in a control variable offering the possibility to control for answer duration and to exclude participants with an extremely short answer time from the analysis. In addition, data were also analyzed for inconsistent answer patterns (eg, flatliners, contradictions). Several multi-item scales were aggregated using EFAs. However, such data treatment for the sake of complexity reduction always leads to a loss of variance of the individual items. Our measurement of daily Internet use by asking respondents for their average usage may have been challenging for participants, especially for individuals with an intermittent usage pattern. An alternative would have been to ask respondents for their duration of Internet usage in the previous week (or month). However, such alternative measurement faces the problem that the previous week (month) might not be representative of the average duration. The construct digital literacy may face a special problem for a gender-specific research focus. The problem is that men and women perceive digital competence differently with men being, in general, more self-confident in this area and women facing less self-ascribed digital affinity. These interpretations may follow differences in self-identity as has been elaborated previously. For this reason, the results conveying gender differences for the construct digital literacy were interpreted as differences in perceived digital competence from a gender identity perspective.
Our study can be categorized as being exploratory in nature, delivering some pioneer knowledge in investigating reasons for gender differences in health-related information search behavior and the virtual patient-physician relationship. Although the
Another limitation of our study is that gender differences are likely to be bounded to the respective cultural background, especially when they are interpreted from a social role perspective. Although we believe that the findings are generalizable beyond the German population to a certain extent (eg, to other German-speaking countries), comparable studies in other countries would bring forward the generalizability of our results.
It would also be interesting to investigate the research questions and validate our results on gender differences by using other methods of inquiry, samples, and countries in the future.
The first implication that can be derived from our study is one from a more general gender perspective. Results from this survey are mostly in-line with previous studies demonstrating that women ascribe themselves a lower degree of digital competence than men. The current study delivers an additional argument from the health sector, namely that the government might want to be more proactive in enabling and encouraging women to be interested in technology and in technical devices from an early age.
Our study delivered the interesting finding that women have a higher social motive for health-related information searches and value the enjoyment of Internet health information searching to a higher degree than men do. Hence, measures to increase the pleasure of health information searching may be especially beneficial to women. This may be interesting for government institutions (eg, for health consciousness campaigns), but it is also of interest to the pharmaceutical industry wanting to promote their products. For instance, advergames targeted at female virtual players could be a means to reinforce health consciousness (educational advergames) or brand knowledge and brand awareness of pharmaceutical products or dietary supplements [
The lower health and nutrition awareness of men could be interesting for GPs, for the government, for the insurance industry, and for entrepreneurs developing apps. Men have a shorter life expectancy, which may be influenced to a certain degree by their lower health and nutrition awareness. Because men have a higher tendency to use apps for health-related information searching, men could be an interesting target group for health-promoting apps and/or fitness apps, which have been booming in recent years. These apps could also be interesting for the insurance industry and the government, which is confronted with ever-increasing expenditures in the health sector.
The fact that men are also more interested in fostering the virtual patient-physician relationship may be of special interest for GPs. For example, if a GP wants to reduce waiting times and operate more efficiently (eg, through Internet communication for administrative purposes), men may be more easily convinced than women.
Aside from gender, there are several areas for GPs in which the virtual patient-physician relationship could be reinforced: the fixing of personal appointments, referrals to other doctors, the writing of prescriptions, discussions of normal test results, and doctor’s notes/certificates of health. If a GP intends to foster her/his customer orientation, she/he may think about reducing waiting times by offering more online services in the preceding areas. An important step here would be to clarify the legal framework conditions for implementing an enhancement of the virtual patient-physician relationship. Yet it will be necessary to segment the patient base according to their individual disposition toward fostering the virtual patient-physician relationship, which may be influenced by gender.
Extract of the questionnaire and justification of items.
Additional tables (A-F) and methodological details of exploratory factor analyses 1- 4.
exploratory factor analysis
Gesellschaft für Konsumforschung
general practitioner
technology acceptance model
theory of planned behavior
theory of reasoned action
The authors are grateful to Martina Moick for her contribution in developing the questionnaire and to GfK HealthCare Nuremberg, Germany, in particular Dr Susanna Meyer and Norbert Schell, for their contributions and for collecting the data for this analysis.
None declared.