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This study adds to the body of research that has been conducted in the charitable giving area to help us better understand the relationship between these two aspects. An exploratory analysis of 143 males and 161 females suggests that people are most likely to make their charitable giving decisions based on the reputation of a charity. The results of this study also suggest that some factors have differing influences across demographic groups. Managerial implications are discussed.

Keywords: management of nonprofits, nonprofit marketing, charitable giving, donations to charities, demographics and charitable donations, charitable donor behavior, management of nonprofit foundations. Introduction Many©American universities and health services organizations were built on a foundation of philanthropic giving. In fact, prior to the age of health care insurance, hospitals relied on donations to remain viable. The onset of health insurance took the pressure off philanthropy for many years in the hospital industry as did state funding in our nation’s universities.

However, today in the age of evaporating operating profits, charitable giving is becoming an important funding source and an area of focus for executives in many of our nation’s universities and not-for-profit organizations (Jaklevic, 2000). Statistics show that seven out of 10 people donate money during their lifetime, indicating that charity is big business (Hughes, 2002). Charitable organizations, specifically, have seen a 44 percent increase ($191 billion) since 1990 with some interruptions of late because of the stock market problems (Shinkman, 2001).

Nonetheless, the importance of philanthropic giving is becoming more and more critical to long-term viability. Given this, it seems that the knowledge of what factors influence donor giving would be of great benefit to fund-raisers and development officers alike. For marketing strategies to be effective, marketers must first have detailed information on who their customers are and what motivates their actions. Peltier, Schibrowky, and Schultz (2002) suggest that most organizations have not gained full knowledge of why their donors perform as they do and what can be done to influence those behaviors.

Therefore, in this paper we investigate six factors that influence charitable giving. Specifically, we examine those factors previously identified in the literature to determine which have the largest impact on charitable giving. We also examine to what © Robin L. Snipes, Sharon L. Oswald, 2010. extent the influences of these factors are related to donor demographics. Though the charitable-giving literature shows that demographics are important influencers of charitable giving behavior, little empirical research has been done to better understand this relationship. 1. Theoretical framework 1. 1. Factors that affect donor behavior.

The question as to whether individuals differ systematically in their charitable giving has been a subject of much debate (Eckel and Grossman, 2000; Nelson, 2001; Sell, Griffin and Wilson, 1993). Some research suggests that people are rational beings and are likely not to make large contributions towards a public good (Landesman, 1995). Other studies of public goods have found the opposite to be true (Fischbacher, Gachter, and Fehr, 2000). While average household contributions to philanthropic organizations have grown in recent years, the number of people actually contributing has fallen (Cohen, 2001).

Thus, the question remains, what motivates an individual to make a monetary donation? Past studies have suggested that several factors can impact charitable contributions. Hughes (2002) found that personal experience1 with an organization was a motivating factor for charitable giving (Hughes, 2002). Likewise, in their study of 49 British givers, Radley and Kennedy (1995) found that personal experience with the charity, either directly or indirectly2, was the major reason people elected to donate money.

Nelson (2001) found that women, in particular, were likely to make monetary contributions to organizations with which they felt 1 Personal experience is defined as either being a recipient of the charitable contributions (monetary) or a patient at a not-for-profit facility. 2 Indirect experience is defined as someone who can relate to the work of the not-for-profit organization because of something in their own life (e. g. , cancer patients and the American Cancer Society). 73 Innovative Marketing, Volume 6, Issue 1, 2010 personally or emotionally connected.

This was supported by Shinkman (2001) who suggested that former patients were likely to make monetary donations to hospitals as an expression of gratitude. Hart (1999) reported that the most common reason donors get involved with not-for-profit organizations was to help others in need or to help find a means of saving lives – such as donations to entities like the American Heart Association or the American Cancer Society. This sentiment was echoed by Cohen (2001: 4) who suggested “charity fuels”, adding that more people are seeing the need to help and want to take it upon themselves to do so.

Familiarity with the not-for-profit organization has also been found to be a reason for monetary donations. People become familiar with a not-forprofit organization often because of the involvement by famous people. For example, Lance Armstrong promotes his favorite charities through benefit bike rides. Marketing and advertising campaigns for notfor-profit and charitable organizations have become more prevalent in recent years as a means of trying to familiarize the public with their product or service.

Some studies of donor behavior in not-for-profit companies have suggested that marketing communications affect the donor’s perception of the quality of the services provided by the organization (see Peltier Schibrowsky, and Schultz, 2002). Guy and Patten (1989) suggest that the donation decision process is sequential in nature and proposed three time-ordered stages: (1) potential donors must first become aware others are in need of help and deserve to be helped; (2) benefits of donating must be understood; and (3) potential donors must accept the responsibility for solving this problem or helping the eneficiaries. In the marketing of notfor-profit donations, both stages (1) and (2) above are related to communications strategies. In other words, organizations that do a better job communicating the benefits of their services and developing name awareness should receive more donations. This philosophy was proven true by UCLA Medical Center when it decided to name its flagship facility after former President Ronald Reagan. Thirty percent of those pledging in the campaign were first time donors who wanted to see the building named for the former president (Cohen, 2001).

Related to familiarity is the scope of services and the perceived reputation of a not-for-profit organization. Some previous research on not-forprofit charitable contributions has indicated that donors are motivated in part by the reputation (Andreoni and Scholz, 1998; Peltier, Schibrowsky, 74 and Schultz, 2002)1. Additionally, research in the advertising area has shown that targeted advertising and communications expenditures are highly correlated with perceived quality and reputation of a brand name (see White and Miles, 1996).

This suggests that strategies to increase donor communications and the number and quality of services provided by a healthcare organization could also increase its name awareness and reputation. Another factor that has been studied in the past is employer recommendations of specific charities. Many employers make specific charity recommendations to their employees. For example, United Way campaigns take place annually in companies throughout the US. Employees are asked to give to charities at their place of work.

Many fundraisers feel that large “leadership contributions” can be influential in encouraging more and larger contributions by others (Bakal, 1979). Radley and Kennedy (1995) note that whether people make donations at all, and, if so how much they give, may be affected by social norms. Their own judgments as to what organizations to support and how much to give to that organization may be totally based on what is normative for their group (Macaulay, 1970). Thus, if it is the norm to give to a particular not-for-profit, the individual will not break with the norm.

Hence, employer recommendations may play a significant role in the donation decision-making process. In summary, the following factors have been previously found to contribute to the decisionmaking process for charitable donations to a not-forprofit organization: (1) employer recommendations for charitable contributions; (2) previous assistance from or experience with the not-for-profit organization; (3) the scope of the services provided by the organization; (4) awareness of the organization and its services; (5) reputation of the organization; and (6) advertisements by the organization for needed donations.

However, the question still remains, which of these factors are more influential in the donor decision process? The marketing literature may provide some guidance on which factors will have the greatest impact on charitable giving behavior. As mentioned in the foregoing literature review, previous consumer behavior research has found that brand equity has a large effect on price elasticity and customer loyalty. Given this, we posit the following hypothesis: 1 Reputation here relates to the public perception of whether or not the organization is doing what is expected.

Innovative Marketing, Volume 6, Issue 1, 2010 Hypothesis 1: Favorable name recognition of a charity will have the largest impact on donor giving behavior. 1. 2. Demographic influences on charitable giving behavior. There is growing evidence to suggest that men’s and women’s taste for giving is quite different. For example, Eller (1997) found a considerable difference between men and women in their estate giving, while other studies have found that price and income affect men’s and women’s propensity to give differently.

Researchers from the social and behavioral sciences have long supported the stereotypes that men are more individually oriented, as traditional economic theory would suggest, and thus, less likely to contribute to charitable causes, while women are more socially-oriented and, therefore, more willing to contribute (Eckel and Grossman, 2000). As early as 1965, Rapoport and Chammah examined behavioral differences between the genders in monetary situations and through the years the results have met with mixed reviews.

Nowell and Tinkler (1994) found, all other things being equal, that women made higher contributions. Newman (1996) found that women are more likely than men to donate when they see a need – a sense or urgency. A later study by Eckel and Grossman (2000) indicated that in situations where risk was involved in the monetary situation, there was no significant gender difference; however, when risk was no longer part of the equation, women demonstrated more sociallyoriented and less individually-oriented behaviors. Specifically, women tended to be more generous with their giving than men.

Similar results were found by Chrenka, Gutter and Jasper (2003) who looked only at unmarried men and women who headed households. On the contrary, based on a series of public goods experiments, Brown-Kruse and Hummels (1993) and Sell et al. (1993) found that women contributed less to the public good than did men. Specifically, in the Sell et al. ’s (1993) study, men were found to have contributed 61. 1 percent to the public good as compared to 48. 7 percent for women. In addition, a 2000 Harris Interactive Poll found that men were more motivated by tax benefits to give to charities than were women (Chaker, 2001).

Based on the foregoing literature review the following hypothesis is advanced: Hypothesis 2: There will be gender differences in the impact of each factor on charitable giving decisions. The results of a study by Radley and Kennedy (1995) indicate that other demographic characteristics might also affect donor behavior. Their study showed that donors who were older, married, and in higher income brackets were more likely to be influenced by the factor of “previous assistance from charity” in their charitable giving decision-making.

Respondents in their study commented that their attitudes to charity had changed over the years as they had matured, married, had children and aging parents, and experienced the sickness of relatives or friends. These life events were all seen as making the person more sensitive to the needs of others and to the plight of people in desperate situations. Some spoke of “getting a better perspective”, and of “being less self-interested” as they got older. Others mentioned having more disposable income to donate, though having less time to spare because of increased commitments. Chrenka et al. 2003) likewise found that individuals with greater than a high school degree were more likely to make charitable donations than those with less education. And, in accordance with Radley and Kennedy (1995), Newman (2000) found that older individuals were more likely to contribute than younger individuals primarily because they were in lower income brackets. Previous research in the charitable giving literature leads us to the following hypothesis: Hypothesis 3: The influence of each factor will differ significantly across demographic groups based on age, income, marital status and education. 2. Method 2. 1. Sample.

In order to investigate our hypotheses, a mall intercept survey was conducted containing questions relating to both donor influences and demographics. The mall intercept was held in a Southeastern US town of nearly 300,000 with an average household income of $47,600 (according to the 2000 census). As an incentive to participate in the survey, the respondents were told that they would be given coupons for discounts at mall retailers. This procedure was repeated every day, at various times, for two weeks, generating a total of 304 usable responses for the study. The sample consisted of 143 males and 161 females. . 2. Measures. 2. 2. 1. Factors influencing donations. Based on the foregoing literature review, we asked respondents to rate, on a scale of 1 to 7 (1 = very unlikely and 7 = very likely), the likelihood that each of the following factors would influence their decisions about charitable giving: (1) employer recommendation; (2) previous assistance from or experience with a charity; (3) the scope of the services provided; (4) awareness of the charity and services provided by the charity; (5) reputation of the charity; and (6) advertisements (by charity) for needed donations. 75

Innovative Marketing, Volume 6, Issue 1, 2010 2. 2. 2. Demographics. Five demographic variables previously discussed in the literature (i. e. , age, gender, marital status, household income, education) were included in the survey. The majority of respondents were younger than 40 years old (74. 5%), Caucasian (73. 9%), and reported having at least a college education (80. 8%). Over half were married (51. 9%). 3. Data analysis and results 3. 1. Factors influencing donations – the magnitude examination. Analyses were conducted to explore which factors have the largest influence on consumers’ charitable giving decisions.

As hypothesized, the results show that people are most likely to make their charitable giving decision on the reputation of a charity. This factor had a significantly higher mean score (5. 75) than the others, followed by the scope of services (mean = 5. 26) and awareness of charity (mean = 5. 25). The employers’ recommendation factor seems to have the least influence on people’s charitable giving (mean = 3. 75). The factors of advertisements for needed donations (mean = 4. 11) and previous assistance from charity (mean = 4. 69) have only a moderate influence on charitable giving decisions. 3. 2. Impact of donor demographics on donations.

Several regression analyses with optimal scaling (CATREG) were conducted to investigate the relationships between factors influencing donors’ charitable giving decisions and donor demographics. Using optimal scaling allowed the categorical data to be quantified by assigning numerical values to the categories, resulting in an optimal linear regression equation with the transformed variables. Regression with optimal scaling is also known by the acronym CATREG, for categorical regression with optimal scaling. CATREG extends the standard regression approach by simultaneously scaling nominal, ordinal, and numerical variables.

The procedure quantifies categorical variables such that the quantifications reflect characteristics of the original categories. By doing so, it treats quantified categorical variables in the same way as numerical variables. Using nonlinear transformations it allows variables to be analyzed at a variety of levels to find the best-fitting model. The likelihood of being influenced by each of the six factors was used as the interval dependent variable respectively. Five demographic variables (i. e. , age, gender, marital status, household income, education) were the independent variables.

Thus, the effects of these five variables on each factor were examined. In order to evaluate the models, F tests of significance were used. This statistical analysis first focused on the significance of the whole demographic domain, and then the individual 76 variables that comprise this domain. For each dependent variable, we first conducted a regression analysis with optimal scaling to determine whether demographics (as a whole) influence the dependent variable. A statistically significant result means that demographics are related to the influence of the factor being studied.

Individual regressions were then applied to those individual variables that were not significant in the model to eliminate the possible problem of multicollinearity, which could cause potentially influential variables to be mistakenly considered unimportant (see Table 1 below). Having identified possible demographic variables that were important in explaining the likelihood of being influenced by each factor in charitable giving decision, we then described the group mean effectiveness of these factors using the demographics as classification variables. 3. 2. 1.

Employer recommendations and demographics. The overall demographic regression equation provided a significant model for explaining likelihood of being influenced by “employer recommendation” (F = 3. 004, Sig. = . 007). The aggregate model was only significant for one of the five demographic variables investigated (see Table 1). Age was the primary influential demographic factor. The analysis indicated that older people were slightly more likely to be influenced by employer recommendation in their charitable decision-making than younger people (Beta = 8. 016E-02, F = 1. 720).

None of the insignificant individual demographics in the aggregate model were found to be significant in the individual regression models. 3. 2. 2. Previous assistance from the charity and demographics. The aggregate model of demographics on influence of previous assistance from the charity for charitable giving decision was again statistically significant (F = 2. 137, Sig. = 0. 049). Three of the demographic variables were statistically significant for the model (see Table 1). Level of education seemed to have a large and positive impact on influence of previous assistance from the charity.

A higher likelihood of being influenced by this factor was found in people with higher education (Beta = 0. 114, F = 3. 662). On the other hand, the analysis indicated that females were more likely to be affected by previous assistance from the charity in their charitable decisions (Beta = 9. 859E-02, F = 2. 831) (see Table 1). Marital status was also found to be an influential demographic factor. It was negatively related to the dependent variable (Beta = -0. 134, F = 3. 731), indicating that single people Innovative Marketing, Volume 6, Issue 1, 2010 ere more likely to be influenced by previous assistance on charitable decision-making than non-single respondents. Again, none of the insignificant individual demographics in the aggregate model were found to be significant in the individual regression models (see Table 1). Table 1. Regression with optimal scaling for factors influencing charitable giving Dependent variables Employer recommendation Previous assistance from charity The scope of services provided Awareness of charity Reputation of charity Aggregate model fit F = 3. 004* P = . 007 F = 2. 137* P = . 049) F = 2. 303* P= . 035) F = 1. 977 P=. 069 F = 1. 546 P= . 63 F = 6. 443 P = . 000 Significant independent variables Age Gender Marital status Highest level of education Gender Annual household income Highest level of education Gender Highest level of education Ethic group Age Age Gender Marital status Annual household income Highest level of education Standard coefficient 8. 016E-02* 9. 859E-02* -. 134* . 114* . 100* . 137* -7. 898E-02* . 106* 8. 688E-02* . 104* . 136* -. 171* . 159* . 100* . 150* -. 105* Standard error . 061 . 059 . 069 . 060. .058 . 060 . 058 . 058 . 060 . 058 . 063 . 062 . 055 . 058 . 063 . 056 Fstatistic 1. 720 2. 831 3. 731 3. 662 3. 007 5. 107 1. 864 3. 361 2. 02 3. 172 4. 638 7. 465 8. 230 2. 984 5. 581 3. 594 Advertisements for needed donations Note: * Significant at p < 0. 05. 3. 2. 3. Scope of services provided and demographics. The overall demographic regression equation provided a significant model for explaining likelihood of being influenced by “the scope of services provided” (F = 2. 303, Sig. = . 035). The aggregate model was significant for three of the five demographic variables investigated. The analysis indicated that higher educated people were somewhat less likely to be influenced by the scope of services provided in their charitable decisionmaking (Beta = -7. 98E-02, F = 1. 864). However, people with higher income were much more likely to be influenced by this factor than those with lower income (Beta = 0. 137, F = 5. 107). Additionally, the analysis showed that the scope of service provided was more likely to influence females than males (Beta = 0. 100, F = 3. 007). “Age” and “marital status”, the two insignificant individual demographics in the aggregate model, were found insignificant in the corresponding individual regression models (see Table 1). 3. 2. 4. Awareness of charity and demographics. The ggregate model of demographics on influence of awareness of charity for charitable giving decisions was marginally significant at 90% confidence level (F = 1. 977, Sig. = 0. 069). Two of the demographic variables were statistically significant for the model (see Table 1). The analysis indicated that females were more likely to be affected by awareness in making their charitable decision (Beta = 0. 106, F = 3. 361). Also, higher educated individuals were more likely to be affected by charity awareness (Beta = 8. 69. F = 2. 102). 3. 2. 5. Reputation of charity and demographics.

The overall demographic regression equation provided a non-significant model for explaining likelihood of being influenced by “reputation of charity” (F = 1. 546, Sig. = . 163). (see Table 1). One demographic variable was found significant by checking individual variables. The analysis indicated that older people were more likely to be influenced by reputation of charity in their charitable decisionmaking than younger people (Beta = 0. 136, F = 4. 638). None of the insignificant individual demographics in the aggregate model were found significant in the individual regression models. . 2. 6. Advertisements for needed donations and demographics. The aggregate model of demographics on influence of advertisements for charitable giving decisions was statistically significant (F = 6. 443, Sig. = 0. 000). All demographic variables were statistically significant for the model (see Table 1). For instance, single people were less likely to be influenced by advertisements for needed donations than non-single respondents (Beta = 0. 100, F = 2. 984), and older people are less likely to be influenced by this factor than younger people (Beta = -0. 171, F = 7. 465).

A higher likelihood of being influenced by this factor was found in people with lower education (Beta = -0. 105, F = 3. 594) than in those with higher education. On the other hand, the analysis indicated that females were more likely to be influenced by advertisements in making their charitable decisions (Beta = 0. 159, F = 8. 230). 77 Innovative Marketing, Volume 6, Issue 1, 2010 Table 2 shows a summary of group means by demographic classification. As can be seen by this table, charity reputation has the largest impact on charitable giving across all demographic groups.

However, other factors have differing influences across the demographic groups. For example, advertisements for needed donations were likely to have a larger impact on the following demographics: female, younger, and less educated. Charity awareness and Advertisements for donations Gender: Male Female Age: Young (50) Marital Status: Single Married Divorced/Widowed Education: High School or Less Some College Undergraduate Degree Graduate Degree Income: Lower ($75,001) 3. 8 4. 4 4. 3 3. 9 3. 9 4. 2 4. 0 4. 3 4. 7 4. 1 3. 8 4. 1 4. 2 4. 2 4. 0 Reputation of charity 5. 7 5. 8 5. 7 6. 1 5. 4 5. 7 5. 8 5. 9 5. 8 5. 5. 7 5. 8 5. 5 6. 0 5. 6 scope of services are likely to have a larger impact on the following demographics: female, middle-age and middle-income. Previous assistance from a charity is likely to have a larger impact on the following demographics: female, single, middle-age, and higher education. As can be seen by Table 2, employer recommendations have the smallest impact on charitable giving behavior across all demographic classifications. Previous assistance from 4. 5 4. 8 4. 6 4. 9 4. 3 4. 9 4. 5 4. 5 4. 5 4. 7 4. 6 5. 0 4. 8 4. 7 4. 7 Employer recommendation 3. 6 3. 8 3. 7 3. 8 3. 6 3. 7 3. 7 4. 0 4. 0 3. 3. 8 4. 0 3. 7 3. 9 3. 6 Table 2. Group means by demographic classification Awareness of charity 5. 1 5. 4 5. 2 5. 4 5. 1 5. 2 5. 3 5. 2 5. 3 5. 4 5. 0 5. 3 5. 2 5. 4 5. 1 Scope of services 5. 1 5. 4 5. 2 5. 5 5. 1 5. 2 5. 3 5. 4 5. 5 5. 1 5. 3 5. 4 5. 0 5. 4 5. 2 3. 3. Factor correlations. Pearson correlation coefficients were computed to determine the monotonic relationships that exist among the influences of different factors on charitable giving. The correlation coefficients indicated a significant and positive relationship between the awareness of charity and reputation of charity factors (coefficient = 0. 85), the scope of services provided and awareness of charity factors (coefficient = 0. 521), and the advertisement for needed donations and the awareness of charity factors (coefficient = 0. 443). Since the reputation of a charity was found to have the largest influence on charitable giving, other factors that were highly correlated to charity reputation could also have a large influence on donations through their effect on charity reputation. That means that the awareness of a charity could also have a large positive influence on charitable giving since it is significantly correlated to the reputation of a charity.

Factors that are correlated to charity awareness included advertisements for needed donations and scope of services. Put differently, to effectively influence donor behavior, charitable organizations should look for ways to increase the perceived reputation of the charity, which includes strategies such as increasing advertisements and/or 78 increasing the scope of services provided. These actions should positively affect charity reputation that, in turn, should have a positive impact on donor behavior. Discussion, implications conclusion and managerial

Our results show that the reputation of a non-profit organization is the primary factor on which people base donation decisions. In fact, this factor was found to be most important across all demographic groups. Other factors that were found to be important in the charitable contribution decision were the organization’s “scope of services” and name awareness. Not surprisingly, a correlation analysis revealed a positive and significant relationship between charity awareness, reputation, scope of services and advertisements for needed donations.

These results are consistent with previous research in the advertising area which has found a positive and significant relationship between advertising, name awareness, and brand equity (White and Miles, 1996). Brand equity is the “intangible asset of added value or goodwill that results from the favorable image, impressions of differentiation, and/or the strength of consumer attachment to a company name, brand name, or Innovative Marketing, Volume 6, Issue 1, 2010 trademark” (Belch and Belch, p. 56).

Brand name awareness and brand equity allow a brand to earn greater sales volume and enjoy higher profit margins than it could without the name (see Farris and Reibstein, 1979). One interesting finding in this study was that scope of services factor was found to be positively and significantly correlated with charity reputation. Prior to this study, little research was available on the effect of “scope of services” on charitable contributions. Our results imply that, ceteris paribus, health care organizations that offer a greater number of services will be perceived in higher regard than those with more narrow offerings.

Since adding services tends to broaden the target market, it would make intuitive sense that name awareness would increase as the number of services offered increases. Additionally, charitable organizations with a broader scope of services tend to be the ones that have been around for a while and, hence, have had more time to develop name recognition and awareness through word-of-mouth communications. Based on the results of this study, strategies to increase charitable donations to health care organizations include targeted advertising.

Advertising could increase brand name recognition and equity across all demographic groups, but this research suggests that advertisements are likely to have a larger impact on females, younger donors, and those with less education. Not surprisingly, this study supports previous research in the area of charitable contributions in that females were found to be more likely to be affected by advertising than males. Consistent with some previous researchers, it appears that females are more affected by the marketing strategies of charitable companies.

Table 2 shows higher ratings for females across all factors. Although past research has shown conflicting results, some research on gender differences in charitable giving has supported this finding. For example, Nowell and Tinkler (1994) found, all other things being equal, that women showed higher contributions. Researchers of charitable giving behavior have recommended that marketers of health care References 1. 2. 3. 4. organizations develop “longitudinal” communications strategies (Kestnbaum, Kestnbaum, and Ames, 1998).

Longitudinal communications strategies take advantage of the emerging media technologies to increase company-donor dialogs and nurture long-term interactive relationships. Consistent with our study, research in the area of longitudinal communications reveals that factors such as personal impact, communications frequency, and charity reputation all have an important longterm impact on private donations (see Peltier, Schibrowsky, and Schultz, 2002). This implies that increases in targeted communications and service quality should have a significant long-term positive impact on future donations. As pointed out by Peltier et al. 1998), to “maximize the value of interactive relationships, it is critical to have detailed data on who your customers are, what they are doing, and information on why they are seeking a relationship. ” Despite the profit potential of this approach, however, few health care organizations are truly customer-focused and thus fail to reach the full potential of longitudinal marketing communications. The results of this study show that longitudinal marketing communications, along with a continued emphasis on service quality, continue to be some of the best strategies to increase charitable donations in the health care industry today.

Limitations and directions for future research One limitation of this study is the sample. While taken on separate days, the sample was collected at one location in a mid-sized Southern US town. For all practical purposes, the sample was a convenience sample. The demographics of this study may not be representative of the entire US population, but the results provide a starting point for future research. Future research should perhaps look at a more stratified sample design with multiple locations that may yield differing results.

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The Cuckoo’s Egg: Tracking a Spy Through the Maze of Computer Espionage.

The Cuckoo’s Egg: Tracking a Spy Through the Maze of Computer Espionage..

Read the book The Cuckoo’s Egg: Tracking a Spy Through the Maze of Computer Espionage. When you completed the book, create a report following these instructions: Cite chapters for each question so the graders can look up the references in case your points are unclear. Failing to cite chapters will result in a 4-point deduction. Also, by citing chapters, you are demonstrating you understand where you found the information. This assignment is NONCOLLABORATIVE—please do your own work. Turnitin function will be turned on for this assignment. Rubric Question Task– remember to cite chapters Grading weight 1-3.) In 1-2 pages (a paragraph or so for each item), describe your top 3 security-related takeaways or security insights you noted while reading the book. These insights can be about anything you noted in the story (chocolate chip recipes do not count, though) whether it is about technology, investigative/(pre)forensic techniques—technical or otherwise, preventative, reactive, collaboration (or lack thereof) between entities/organizations/groups, etc 16 points for each insight (total=48 points for this part) 4.) Explain the cuckoo’s egg exploit using the 4 security tenets of confidentiality, integrity, availability, and accountability. 7 points each (total=28 pts for this part) 5-8.) Choose and answer any 4 questions below; 6 points each (total=24 pts for this part) Questions—choose only 4 to answer, and remember to cite chapters: Name 5 default passwords that Cliff saw that every system administrator should have changed immediately upon installing new software that came with default, known passwords. Name 3 passwords Cliff and other system users use (NOT the hacker) and discuss why they are poor; What was the significance of copying the encrypted password file? When Cliff describes how the hacker is getting in, what 2 security design principles were not followed that allowed the cuckoos egg security flaw? What is a problem of a homogenous network? What is the value of software heterogeneity? Explain one way the hacker got passwords What was the one action the hacker always did that alerted cliff that it was the hacker? What program helped Cliff realize he could calculate the location of the hacker? Explain a technology/technique Cliff used to keep the hacker from getting any serious access on certain occasions What is the physical device that Cliff used to apprise him to the hacker’s presence? What is an intercept operator? What were the poor security practices that would have had to occur for the hacker to use the system to connect to another system (before VOIP and wireless routers!)? Where was Dockmaster located? What type of physical structure does the author compare a system to? How did Cliff first record the suspected hackers activity? On what did he record the suspected hackers’ activity? How did Cliff determine the hacker was an individual and not an automated program? How did the Morris worm propagate? Cliff states in the book that viruses are rare (At that point in security history, they were). What does he go on to say that is still true today?

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