E-JOURNAL USER STUDY APPENDIX I: SAMPLE METHODOLOGY I.1. List of Participating Societies Members of 20 professional societies in life sciences were sampled for the Scientific Journal User survey May 22 to June 20, 2001. The following table shows the names of societies and sample sizes per society.
*The number exceeds total sample size (12,465) because some respondents belong to multiple societies I.2. Sampling Methods: The first survey was conducted online; data were collected from a Web-based questionnaire, recorded in the form of TSV files, by a third-party specialist firm called Perseus. The purpose of the survey (as described in greater detail elsewhere) was to uncover the perceptions of scientists about e-journals. Target Population: Life scientists and medical professionals who read life and medical science journals, especially HighWire journals in either paper or e-journal format (both users and nonusers of e-journals) Survey Design:
Sample Population:
APPENDIX II: MODELS AND RESULTS II. 1. Econometric Models for Analyses The study has three dependent variables to (1) e-journal usage; (2) scientists' preferences regarding printed copy versus online editions; (3) scientists' perception about the impact of e-journals on their research activities. Ordered probit regression is used to measure equation (1), e-journals usage, and logistic regressions for equations (2) and (3), preference regarding journal formats and perception about e-journals.
where EJU is a degree of e-journal usage with 5 categories (see Table 2); PREF is a dependent variable, preferences regarding journal format, printed copy vs online versions; and PEJ represents perception about the impact of e-journals on research activities. DEMO1 is a set of demographic characteristics such as AGE, MD, BIOLOGY, MALE, USA-CAN which represent respondents' age, whether a respondent is a medical doctor, whether a respondent's field of research is biology, whether a respondent is male, and an indicator that a respondent resides in either the U.S. or Canada, respectively (refer Table 1); INTERNET is a variable to measure Internet familiarity–weekly hours spent on the Internet; SUBS represents the resources available through personal subscriptions–numbers of journal subscriptions; FACILITY and ACCESS represent available resources such as PC, printers, and Internet access and scientific journal access through institutions; PAPER1, the numbers of papers submitted in the past year, PAPER2, the number of papers published or accepted in the journals during the past year, which measure respondents' research productivity. DEMO2 represents a set of variables in DEMO1 and JOBEXP, job experience measured by years (refer Table 1). P^[EJU] is a predicted probability of e-journal usage from equation (1). EJU variable consists of five categorical values (question 2 in Table 2), while values of both PREF (questions 4 and 5 in Table 3 and Table 4) and PEJ variables (questions 17, 14, 15, and 16 in Table 5, Table 6, and Table 7) are binary. Five categories for EJU are yesterday or today, within last week, within a month, longer than a month, and never. A binary value for PREF and PEJ is "Agree" and "Disagree." The binary value has created by recoding four categories, "Strongly Agree," "Somewhat Agree," "Somewhat Disagree," and "Strongly Disagree," into two categories, "Agree" and "Disagree." Table 2 shows a question on EJU and responses per category. Many different questions were used to analyze preferences regarding formats including reasons of preference. Table 3 and Table 4 describe questions for PREF variable. We measured a predicted probability of whether they prefer online to printed editions. Questions in Table 4 are analyzed condition on their responses to whether they prefer online journals to printed versions (Table 3). Equation (3) has been used to measure respondents' perception about the impact of e-journals on their research activities (questions in Table 5, Table 6, and Table 7). II. 2. How to Read the Regression Tables The signs of the four cut off points (a1, a2, a3, and a4) listed at the top of the column titled P[EJU] in Table 8 change from negative to positive, and their values increase. This first overall test tells us that e-journal usage frequency can be adequately "explained" by the set of independent variables we chose for the ordered probit model: demographic variables (age, occupation, fields of research, country of residence), Internet familiarity, number of subscriptions, number of papers submitted last year, and institutional access to scientific journals (see second column, Table 8). Moving down the column to the detailed results of this regression analysis, we can see that certain groups of people use e-journals more frequently than others. Because 5 = never and 1 = yesterday or today, the EJU variable actually represents how little respondents use e-journals. Thus a positive coefficient for any of the categorical independent variables means that that category uses e-journals less often than the rest of the sample, while a negative coefficient means that that category uses e-journals more often. For continuous variables such as age or the number of subscriptions, if they are positive, then as they increase, e-journal usage frequency decreases; if they are negative, then as they increase, e-journal usage frequency increases. If a variable is not marked with asterisks, then its relationship with e-journal usage frequency was not large enough to be statistically significant. Because all the variables are entered into the regression equation at once, each effect "controls for" all of the other variables. Consequently, we can say that a statistically significant variable has an "independent effect" on a person's e-journal usage frequency, regardless of that person's other characteristics. II. 3. Tables
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