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Soc. Sci. 2019, 8(12), 324;

Knowing the Blood Nondonor to Activate Behaviour
Department of Economics and Business Administration, Faculty of Economics, Business and Tourism, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
Department of Business Economics, Faculty of Legal and Business Sciences, University Rey Juan Carlos, 28032 Madrid, Spain
Author to whom correspondence should be addressed.
Received: 4 October 2019 / Accepted: 26 November 2019 / Published: 28 November 2019


This work is aimed at further developing the study of blood nondonor behaviour through a joint analysis of sociodemographic characteristics, psychological and physical barriers, impure altruism and anticipated emotions, as predictors of intention to donate. To that end, a step-by-step hierarchical regression analysis was applied on a sample of 2383 Spanish nondonors. The study’s results confirm the influence of traditional variables (sociodemographic characteristics, psychological and physical barriers and impure altruism), as well as the power of anticipated emotions, both positive and negative, of donation action and inaction as variables explaining the intention to donate. Another important contribution has been to develop the moderating role of psychological and physical barriers, in addition to impure altruism, on cause–effect relationships between anticipated emotions and intention to donate. A number of practical implications can be derived from this study for transfusion centres responsible for donation promotion.
blood donation; barriers; motivations; anticipated emotions; donation intention; nondonors

1. Introduction

Blood transfusion, along with its derivatives, is an essential service for every healthcare system. Despite its importance in satisfying patients’ medical and surgical needs, there is a worrying imbalance between blood offer and demand (World Health Organization and International Federation of Red Cross and Red Crescent Societies 2010). This imbalance is caused by multiple factors, e.g., the increasing number of procedures requiring blood transfusion, an aging population, the tighter donor screening criteria and the perishable nature of blood (Zucoloto et al. 2019a; Vavić et al. 2012). In addition, even though approximately 40% of the male and female population are eligible to donate blood, only 5–10% donate (Custer et al. 2012; Lacetera and Macis 2010).
Taking into account, on the one hand, that blood is a scarce resource, and on the other, that voluntary, unremunerated donors are the main and preferred source for the donor system (Van der Poel et al. 2002), blood transfusion centres must allocate resources to promoting repetitive and extended donation behaviour and also direct efforts towards recruiting new donors among the population. Although the literature has proven that, in general terms, it is more efficient to retain existing donors than to recruit new donors, there are certain limitations with regard to donor recruitment, which leads transfusion centres to search for other alternatives, such as the number of allowed donations per year, age restrictions and rejection of donors through screening criteria related to health or medical specifications (Godin et al. 2007). The interest in recruiting new donors is about increasing donor pool size and also replacing donors who are no longer suitable with new donors (Wildman and Hollingsworth 2009). However, donor recruitment efforts carried out by blood transfusion centres have not been successful, as most centres have suffered a decrease in new donor recruitment, in spite of the high amount of promotion campaigns (Carter et al. 2011). For this reason, studying variables that determine the behaviour of nondonors (individuals who have never donated blood) is essential for blood transfusion centres (Zucoloto et al. 2019a), with intention to donate being the variable explained in most studies that research this group of people (e.g., Reid and Wood 2008; Robinson et al. 2008).
Traditionally, intention is defined as the intrinsic declaration to carry out a specific action or behaviour (Ajzen 1991), and it is the most immediate determinant of behaviour, and of course of donation behaviour (Bednall et al. 2013). Regardless of traditional constructs addressed in models explaining intention (as a behaviour antecedent), and also the antecedents of intention (mainly TPB1-based models), in the field of blood donation, intention to donate can also be influenced by individuals’ particular characteristics. Principal among them are sociodemographic characteristics (e.g., sex, age, education, place of residence) (Piersma et al. 2017), as well as other intrinsic variables such as donation barriers and motivations to donate (Martín-Santana and Beerli-Palacio 2013). Barriers and motivations are especially relevant because they are two factors that can tip the balance towards donation or nondonation, particularly among nondonors. Although research on these two factors has been widely addressed in donation literature, there are few works where they are jointly studied for nondonors. On the other hand, there is currently a growing research area dealing with anticipated emotions as key components determining the individual’s behaviour, which might be relevant to expanding knowledge on nondonor behaviour.
With the above in mind, this work is aimed at further studying the influence of traditional variables of donor behaviour (sociodemographic characteristics, barriers and motivations) on nondonors’ intention to donate. Additionally, in order to expand the range of variables determining intention, we have also analysed the influence of anticipated emotions. This study is therefore aimed at addressing a research gap in the knowledge about blood nondonor behaviour using a holistic method that incorporates barriers, motivations and sociodemographic characteristics as direct antecedents to the intention to donate. Additionally, this work makes a contribution to the donation field by including the factor of anticipated emotions as an intrinsic variable influencing the decision to donate. What is more, addressing all these variables together is an innovation and may help to define social marketing strategies of blood transfusion centres.

2. Theoretical Background and Hypotheses

In the blood donation field, there are numerous studies that have analysed donation behaviour. Some of these models comprise intention (and not behaviour) as an explained variable (e.g., France et al. 2008; Godin et al. 2005; Lemmens et al. 2009). In the case of nondonors, the only variable that could be explained is intention to donate, given that nondonors have never given blood. It is true that intention is not a perfect measurement because it does not necessarily always translate into behaviour (i.e., a donation) given the existence of situational factors, such as lack of time, health conditions, etc. (Holdershaw et al. 2011). However, some authors have used the intention to donate as a proxy variable of behaviour when real measurements of behaviour cannot be obtained (Polonsky et al. 2013; Schlumpf et al. 2008; France et al. 2007).
Most studies on the intention to donate analyse samples of donors (e.g., Masser et al. 2013; France et al. 2007) or mixed samples of donors and nondonors (e.g., James et al. 2013; Godin et al. 2005). Nevertheless, very few studies have expressly focused on intention (and its antecedents) in nondonors (e.g., Faqah et al. 2015; Reid and Wood 2008; Robinson et al. 2008). That is why it is necessary to study nondonors in particular, which is the aim of this paper, to address a current research gap.
Some authors state that there are certain individual characteristics influencing the intention to donate (Piersma et al. 2017; Robinson et al. 2008). In first place are sociodemographic characteristics (sex, age, education, etc.), which have been used mainly to describe a typical donor (a male adult with high levels of education and income) and to determine how sociodemographic characteristics influence real donation behaviour (Gemelli et al. 2017; Wittock et al. 2017; Zucoloto et al. 2019b). However, when analysing the effect of sociodemographic characteristics on the intention to donate, the existing studies are limited, and their results are inconclusive. The work by Schlumpf et al. (2008) is especially noteworthy, where older individuals (particularly, aged 55 years or more) reported up to 50% less intention to donate than younger subjects, which translates into an inversely proportional relationship between age and intention to donate. Godin et al. (2005) also found an inverse relationship between education and intention, albeit to a reduced extent. Finally, in their study, Nguyen et al. (2008) did not find statistically significant associations between donors’ demographic characteristics and intention to donate, although they did identify a trend towards a greater intention among older donors (aged 40 years or more).
Based on the aforementioned works, a generic, nonparticularised hypothesis is presented, as there is no previous evidence that supports a specific hypothesis for each proposed sociodemographic characteristic. More precisely, this work considered the following: sex, age, highest level of education, employment status, total monthly income and population at place of residence. This broad selection of variables is justified by the fact that donating blood is a high-involvement decision. Therefore, exploring the influence of sociodemographic characteristics on donation intention could contribute to the existing knowledge of the donation behaviour of nondonors.
For these reasons, taking into account the fact that antecedents are limited and inconclusive, the first hypothesis is presented:
Hypothesis 1 (H1).
Sociodemographic characteristics influence the intention to donate.
In second place, barriers and motivations are factors directly related to donor behaviour in general. On one hand, donation barriers are the current difficulties and contexts that an individual must overcome to donate blood (Godin et al. 2007). On the other hand, motivations are the forces or reasons driving individuals to donate (Martín-Santana and Beerli-Palacio 2013). The interaction between barriers and motivations ultimately determines the decision to donate (Hupfer et al. 2005; Gillespie and Hillyer 2002). Particularly for nondonors, studying these variables is crucial because its prevalence in this group is different compared to that of donors. This is confirmed by the results of the meta-analysis carried out by Bednall and Bove (2011), who found statistically significant differences between barriers reported by nondonors in comparison to donors. Out of the 27 barriers for which data were collected from both donor types, 14 were more prevalent among nondonors than among donors. Along the same lines, Shaz et al. (2010) also confirmed in a comparative study that 10 out the 12 barriers analysed were more prevalent among nondonors in comparison to current donors and lapsed donors.
In the case of motivations to donate, the issue is more inconclusive, since these variables have been researched more extensively among donors than among nondonors (Bednall and Bove 2011). The greater focus on finding out why individuals are compelled to remain in the system may be owed to the fact that retaining donors is less costly for transfusion centres than recruiting new ones (Gemelli et al. 2017; Wevers et al. 2014). Previous works analysing nondonor motivation towards potentially donating for the first time have studied mainly mixed samples comprising donors and nondonors (e.g., Hupfer et al. 2005; Shaz et al. 2010; James et al. 2013). One of the most important motivations for both donor types has been altruism (defined as the ultimate selfless desire to help others at a personal cost), although there are other least relevant donation motivations such as the space–time convenience of the donation site, donation incentives, etc. Given that nondonors have never given blood, reporting altruism as a possible motivation could be due to the socially desirable nature of such behaviour (Bednall and Bove 2011). Therefore, if donors and nondonors largely have the same motivations in common, it can be inferred that the difference between being a donor and being a nondonor is either how intensely these factors influence the decision to donate or the greater prevalence of barriers over motivations.
The literature on donor behaviour is characterised by the large variety of barriers and motivations analysed (Bednall and Bove 2011). Among existing barriers, the literature dwells mainly, due to their significance and frequency, on psychological barriers related to the aversion to the donation process and fears associated with it (e.g., needles, seeing blood); and physical barriers, i.e., concerns about suffering negative physical consequences when donating, such as fainting, dizziness, nausea, etc. (France et al. 2014; Kowalsky et al. 2014; Zucoloto et al. 2019b). Regarding nondonors, the prevalence of psychological and physical barriers is especially relevant, as proven in some empirical studies (Shaz et al. 2010; Ngoma et al. 2013; Shaz et al. 2009). This is largely due to nondonors’ lack of experience and of familiarity with the donation process (Bagot et al. 2016; Vavić et al. 2012). In fact, blood transfusion centres usually consider these barriers as being the most decisive on the decision to donate, so eliminating or at least mitigating them is more often than not the focal point of their social marketing campaigns (Newman 2014; Pagliariccio and Marinozzi 2012).
On the other hand, as previously mentioned, the main motivation to donate blood for donors and nondonors has been altruism (Bednall and Bove 2011; Bagot et al. 2016), to the extent that most campaigns to promote donation are based on messages directly appealing to donors’ altruistic and solidary nature (Eamonn Ferguson 2015; Chell and Mortimer 2014). However, although the act of donating meets all the characteristics of an altruistic act (Guiddi et al. 2015; Tey et al. 2019), it can be motivated by more “selfish” reasons (Ferguson 2015). Thus, some authors suggest that individuals donate blood not only to selflessly help other people in need (pure altruism), but also to get positive emotional rewards such as satisfaction, personal merit, etc. (warm-glow). The combination of both dimensions of altruism results in impure altruism (Andreoni 1990), which is closer to the reality of blood donation (Ferguson and Lawrence 2016; Evans and Ferguson 2014). Therefore, new donor recruitment campaigns may be of limited success, because they are too pure (Barreto 2019; Chell and Mortimer 2014) and should actually focus on the impure or “selfish” dimension of altruism. This is not to suggest that donating blood is a selfish act. In spite of looking for a personal benefit, the blood donor is contributing to other people’s well-being while taking a number of risks and costs in terms of time, effort, etc. (Alfieri et al. 2017; Tey et al. 2019).
With regard to the influence of barriers and motivations on intention to donate, previous studies are scarce, although their results are significant. Martín-Santana and Beerli-Palacio (2013) verified that intrinsic and extrinsic inhibitors are antecedents of intention to donate, with this effect being negative. The authors concluded that intention decreased when the donor (1) was afraid of needles, of contracting a disease transmitted by blood transfusion or of feeling unwell; (2) was averse to seeing blood; (3) thought that donating blood required great effort; and (4) could not adjust schedules to the donation venue’s opening times. In parallel, Ferguson et al. (2008), in their Study 2, analysed the predictive capacity of four motivation types on intention to donate: hedonism, benevolence, kinship and altruism. Out of these four variables, only benevolence (pattern in which the donor feels personal reward and the recipient receives a donation) was a statistically significant, and positive, predictor of intention. With a similar approach, Williams et al. (2019) performed a path analysis to assess the effect of the four motivational orientations proposed by the self-determination theory: amotivation (no motivation), external regulation (motivated by external rewards), introjected regulation (motivated to avoid guilt or to enhance one’s ego) and autonomous motivation (intrinsic motivation). In this particular case, amotivation, introjected regulation and autonomous motivation had a direct effect on donation intention (negative in the case of amotivation), and autonomous motivation was the motivational orientation with larger total positive effects. For their part, Schlumpf et al. (2008) designed two logistic regression models, one based on behaviour and the other based on intention to donate as dependent variables. In the latter, social pressure and the responsibility to help others were positive, statistically significant predictors of intention to donate. Finally, Reid and Wood (2008) made a hierarchical regression analysis including 10 possible predictors. Out of these 10 factors, three of them, related to barriers and motivations, were significant: subjective norm (perception of existing social pressure towards donating blood), perceived control (degree of control or confidence that an individual perceives that they have on the act of donating) and temporal barriers (i.e., lack of time).
However, these results cannot be generalised because, with the exception of the study by Reid and Wood (2008), which uses a sample consisting only of nondonors, the rest include individuals who have already donated once. Although the results offered by Reid and Wood (2008) are relevant, they need to be expanded in order to go deeper into nondonor behaviour.
Given the little attention paid to barriers and motivations as antecedents of the intention to donate, and given, in the light of the few existing studies, the positive signs that they might be determining factors of the intention to donate, the following hypotheses are presented:
Hypothesis 2 (H2).
Psychological and physical barriers have a negative influence on the intention to donate.
Hypothesis 3 (H3).
Impure altruism has a positive influence on the intention to donate.
Finally, to expand the existing knowledge on the determining factors of the intention to donate, it is necessary to incorporate new variables that can help to explain it. Based on decision affect theory, Mellers et al. (1999) suggest that, in addition to actual emotions at the moment of decision-making, decisions are influenced by emotions individuals expect to experience in the future, that is, anticipated emotions (AEs), so-called due to their prospective orientation (Bagozzi et al. 2000). Bagozzi et al. (2003, p. 278) have described AEs as “the notion of the hope of success and the fear of failure as antecedents of approach and avoidance behaviours in achievement contexts”. Authors such as Xie et al. (2013) support using AEs as predictors of intention. In their study, they added AEs to the classic TPB model. This new model (model of goal-directed behaviour) provides a more exhaustive, enriching explanation of intention than TPB because it includes an emotional component with AEs. Thus, it addresses the criticism raised against the TPB model, which does not consider the role of affective variables (Conner et al. 2015).
The positive and/or negative influence of AEs on the intention to behave in a certain way has been verified by several authors (e.g., Bagozzi et al. 1998; Mellers and McGraw 2001; Richard et al. 1995; Sandberg and Conner 2008; Steg and Vlek 2009) in a wide range of contexts (e.g., Bagozzi and Dholakia 2006; Beaudry and Pinsonneault 2010; Wang 2011; Kim et al. 2013; Onwezen et al. 2013), including blood donation (Conner et al. 2013). In this particular context, the study of emotions has mainly focused on pre-donation anxiety, usually reported as a psychological barrier (Lemmens et al. 2005), anticipated regret (Faqah et al. 2015) and positive emotions such as satisfaction or pride derived from the act of donating (Ferguson et al. 2008). Except for the aforementioned regret, and despite the importance of AEs in predicting behaviour and the intention to carry it out (Richetin et al. 2011), AEs have been scarcely studied in the field of blood donation, especially positive AEs. The only exception is the work carried out by Conner et al. (2013), who analysed anticipated affective reactions, both positive and negative, as antecedents of the intention to donate. In both cases, the effect on intention was positive, even more so in the case of negative reactions. However, the authors’ study only considered anticipated positive affective reactions of donation and anticipated negative affective reactions of nondonation, without taking into account positive reactions of nondonation and negative reactions of donation.
Thus, in our study, we suggest applying, for the first time in the field of blood donation, a more holistic, integrative approach such as that proposed by Bagozzi et al. (2016), Fong and Wyer (2003) and Zeelenberg et al. (2000). These authors determined positive and negative AEs derived from action and inaction. From this perspective, although most people experience positive emotions towards donation that lead them to donate (e.g., pride or satisfaction), even overcoming barriers (e.g., lack of time or information, fear of needles), others display negative emotions (e.g., regret or anxiety). Additionally, the existence of both positive emotions (e.g., calmness or relief) and negative emotions (e.g., disappointment at oneself or guilt) caused by nondonation is also very relevant, but it has not been researched enough yet.
In view of the above, the following general hypothesis is presented, concerning the influence of anticipated emotions on the intention to donate, in addition to the corresponding four subhypotheses:
Hypothesis 4 (H4).
Anticipated emotions influence the intention to donate.
Hypothesis 4a (H4a).
Positive anticipated emotions towards donation have a positive influence on the intention to donate.
Hypothesis 4b (H4b).
Negative anticipated emotions towards donation have a negative influence on the intention to donate.
Hypothesis 4c (H4c).
Positive anticipated emotions towards nondonation have a negative influence on the intention to donate.
Hypothesis 4d (H4d).
Negative anticipated emotions towards nondonation have a positive influence on the intention to donate.
Although scientific literature has paid little attention to the specific circumstances that may moderate the influence of AEs on decisions, according to Pollai et al. (2011), individuals’ decision processes also depend on specific conditions that may moderate such AEs. As indicated above, the role of barriers and motivations in behaviour research is essential, because the result of the interactions between barriers and motivations determines the decision to donate. Thus, when motivations towards donating blood prevail over barriers, the person decides to donate, and vice versa (Hupfer et al. 2005; Gillespie and Hillyer 2002).
Due to the lack of literature, our research aims at addressing the questions of how current barriers and motivations may moderate the relationship between AEs and the intention to donate for nondonors in the following terms:
Question 1 (Q1).
Do psychological and physical barriers moderate the relationship between anticipated emotions and the intention to donate?
Question 2 (Q2).
Does impure altruism moderate the relationship between anticipated emotions and the intention to donate?

3. Methodology

3.1. Sample Design and Data Collection

Study Population and Data Collection

The study population comprised nondonors (individuals who have never given blood) who were older than 18 years, from both sexes and who lived in Spain. Data were collected with an online self-administered questionnaire from March to September 2018. Since there is no specific nondonor register, the collaboration of 14 of the 17 existing Spanish regional blood transfusion centres and 24 of the 83 Spanish public and private universities was needed in order to reach the study population. These institutions spread the URL of the questionnaire along with an invitation message through their main social media accounts (especially Facebook and Twitter) and their own platforms (e.g., official website, newsletter, blog). Additionally, universities also spread the invitation to the whole university community through their institutional e-mail service. A total of 46 dissemination campaigns (22 actions carried out by blood transfusion centres and 24 carried out by universities) gathered an initial sample of 2584 nondonors. Some questionnaires remained unfinished, however, so the final sample was reduced to 2383 nondonors. For this reason, the questionnaire completion rate was 92.2%. Out of the final sample, 1261 questionnaires (52.9%) came from blood transfusion centres social media, and 1122 (47.1%) came from universities.
The final Spanish nondonor sample (see Table 1) had a higher rate of women (74.3%) than men. Most respondents were younger than 25 years of age (47.7%), had a university degree (60.4%), were employed (57.0%), earned less than 2000 EUR a month (53.2%) and lived in populations with more than 100,000 inhabitants (45.9%).

3.2. Measures

3.2.1. Dependent Variable

Donation intention (DI). A 7-point Likert scale of two items was used for measuring DI (Godin et al. 2014; Masser et al. 2012). In this scale, a response of 1 meant ‘strongly disagree’ and 7 meant ‘strongly agree’.

3.2.2. Independent Variables

Sociodemographic characteristics. A number of questions were included in the questionnaire to elicit information on participants’ sociodemographic characteristics concerning sex, age, highest level of education completed, employment status, total monthly household income and population at place of residence (measured as number of inhabitants). These variables have been analysed in previous studies (Piersma et al. 2017; Wittock et al. 2017; Shehu et al. 2015).
Psychological and physical barriers. A four-item, dichotomous (Yes/No) scale was used to evaluate the existence of psychological and physical barriers among nondonors. These barriers correspond to fears associated with the donation process and the potential negative physical repercussions attached to it. This scale was based on the items found in the literature on blood donation (Alinon et al. 2014; Beerli-Palacio and Martín-Santana 2015; Boenigk and Leipnitz 2016; Charbonneau et al. 2016; Hupfer et al. 2005; James et al. 2013; Polonsky et al. 2015; Shaz et al. 2010; Solomon 2012). The use of a Likert-type scale was discarded to avoid response fatigue biases.
Impure altruism. A 7-item dichotomous scale was used to evaluate respondents’ impure altruism. It was designed based on the different items found in the literature (Beerli-Palacio and Martín-Santana 2015; Charbonneau et al. 2015; Chell and Mortimer 2014; Ferguson 2015; Gonçalez et al. 2013; Hupfer et al. 2005; James et al. 2011; Karacan et al. 2013; Shaz et al. 2010). The use of a Likert-type scale was also discarded for the same previous reason.
Anticipated emotions (AEs). AEs were measured using 7-point Likert scales on which a response of 1 meant ‘strongly disagree’ and 7 meant ‘strongly agree’, following the structure of the four categories proposed by Bagozzi et al. (2016): positive AEs of donation, negative AEs of donation, positive AEs of nondonation and negative AEs of nondonation. The decision to measure positive and negative AEs separately, both towards donation and nondonation, is due to the fact that they are two separate psychological systems instead of opposite ends of the same dimension (Zampetakis et al. 2016). To adapt the scales to the present research context, we based our work on the literature on AEs (e.g., Bagozzi and Dholakia 2006; Perugini and Bagozzi 2001) and on emotions in the context of blood donation (Conner et al. 2013). AEs initially considered in this work were as follows:
  • Positive AEs towards donation: ‘I would feel happy’, ‘I would feel proud’ and ‘I would feel satisfied’.
  • Negative AEs towards donation: ‘I would feel worried’, ‘I would regret it’ and ‘I would be anxious’.
  • Positive AEs towards nondonation: ‘I would be relieved’, ‘I would be convinced of my decision’ and ‘I would feel calm’.
  • Negative AEs towards nondonation: ‘I would be disappointed’, ‘I would feel guilty’ and ‘I would be angry at myself’.
The initial results of confirmatory factorial analysis (CFA) demonstrated that the individual reliability of the item ‘I would be relieved’ was lower than 0.50. For this reason, this item was eliminated.
The Appendix A shows the final items in the scales. All scales were pretested by 10 experts belonging to Spanish blood transfusion centres, who validated their content, and its suitability for the blood donation field.

4. Results

4.1. Analysis of the Validity of the Measurement Scales

Before performing a regression analysis, we needed to determine the one-dimensional nature of the scales designed to measure psychological and physical barriers and impure altruism. According to Debelak and Tran (2013), when variables are dichotomous, the correlation matrix that must be used to perform a principal component analysis (PCA) is the tetrachoric correlation matrix. For this reason, based on this matrix, a PCA was performed for both scales, with results being shown in Table 2 and Table 3. Moreover, the reliability of both scales was determined by calculating Kuder–Richardson’s Formula 20 (KR20), which is the equivalent to Cronbach’s alpha for dichotomous variables (Nunnally and Bernstein 1994).
Once the validity of both scales was proven, two new variables were created, each corresponding to the sum of barriers or motivations that respondents selected in each of the proposed scales, labelled as ‘Barrier’ and ‘Motivation’. Thus, the first variable could range from 0 to 5; and the second one, from 0 to 7, with 0 meaning that the respondent did not present any of the proposed barriers or motivations, and 5 or 7 meaning that they presented all of them. Based on the average values of these variables, the percentage of barrier and motivation prevalence for each respondent was calculated, with mean values being MBarrier = 32.51% and MMotivation = 82.82%. In light of these average values and following Gomes et al.’s (2019) methodology, respondents were classified into two categories: ‘high’ and ‘low’, based on whether their prevalence percentages were above or below such mean values. These variables were labelled as ‘Barrier_Extent’ and ‘Motivation_Extent’. The results indicated that 45.7% present a percentage of psychological and physical barriers above the global average (i.e., high Barrier_Extent), and 68.8% present a percentage of impure altruism above the global average (i.e., high Motivation_Extent).
Concerning the scales designed to measure AEs and DI, a CFA was used, based on the maximum likelihood estimation method, given that we are dealing with range scales. Both scales were validated in a single model. The resulting model was statistically significant (χ2 (55) = 670.290, p = 0.000), although this statistic does depend on sample size. However, other indicators of the overall fit of the model were found within the values recommended by the literature (CFI = 0.962; NFI = 0.959; TLI = 0.946; RMSEA = 0.069). For this reason, the measurement model was considered to appropriately reproduce the covariance matrix under observation. This measurement model presented a satisfactory fit, with a CFI value greater than 0.95 and an RMSEA value of under 0.08 (Mathieu and Taylor 2006). Taking into account the approach of Anderson and Gerbing (1988), as can be seen in Table 4, the model showed an acceptable level of individual reliability; i.e., the relationship between each item and its respective dimension was statistically significant, with t-statistic values that were also significant. Concerning internal consistency, the composite reliability (CR) values were close to or higher than 0.70, and those of variance extracted (AVE) were close or higher than 0.50. Moreover, the results suggested that the measurement model was reliable. Finally, the four AE scales obtained adequate levels of reliability based on Cronbach’s alpha.
Using the results of CFA, five new variables were labelled as ‘positive AEs towards donation’ (AE1–AE3), ‘negative AEs towards donation’ (AE4–AE6), ‘positive AEs towards nondonation’ (AE7–AE8), ‘negative AEs towards nondonation’ (AE9–AE11) and ‘donation intention’ (DI1–DI2). These new variables corresponded to the average values of the items in each scale, which were weighted with the standardised estimators obtained from the CFA.
The existence of multicollinearity was tested in this study. Multicollinearity refers to the correlation among three or more independent variables. This is one of the problems found in regression models, as the predictive power of any single independent variable is reduced by the extent to which it is associated with other independent variables. There are three ways of identifying collinearity: (1) analysing the correlation levels between the continuous variables; (2) analysing the tolerance value, which is defined as the amount of variability of the selected independent variable not explained by the other independent variables; and (3) analysing the values of the variance inflation factor (VIF), which is 1/tolerance. Thus, very small tolerance values and large VIF values denote high collinearity.
The results of Table 5 suggest that the correlation coefficients between the continuous variables used in the regression models are low, and therefore, discriminant validity exists (discriminant validity shows that two measures that are not supposed to be related are in fact unrelated, therefore suggesting that there is no multicollinearity). The tolerance values and the values of VIF corroborate the inexistence of multicollinearity, given that the tolerance values in this study are higher than 0.10 (0.672–0.983) and the VIF values are far from the threshold of 10 (1.018–1.487).

4.2. Hypothesis-Testing

The hypotheses were tested using hierarchical regression analysis by steps (Hair et al. 2010), in which the variables are entered in successive blocks (see Table 6). Thus, firstly, the effect of sociodemographic characteristics (sex, age, highest level of education, employment status, total monthly income and population at place of residence) as independent variables on the dependent variable (DI) was analysed (Model I). Secondly, the ‘Barrier_Extent’ was incorporated to determine its additional effect on DI (Model II). Thirdly, the ‘Motivation_Extent’ was added to study its influence on DI (Model III). In addition, the four variables of AEs (positive AEs towards donation, negative AEs towards donation, positive AEs towards nondonation, negative AEs towards nondonation) were included as independent variables to analyse their predictive power on DI (Model IV). Finally, in order to analyse the potential moderating effect of barriers and motivations on the relationship between AEs and DI, two new models were estimated (Model V and Model VI). It should be noted that the moderating effect is significant if the change in the determination coefficient is significant. In this regard, empirical evidence indicates that an increase of more than 1 percent can be considered significant and therefore indicates the existence of a large moderating effect.
The results of the regression model (see Table 6, Model I) indicate that every sociodemographic variable influenced DI, with the exception that the respondent was working or not. The results suggest that DI is higher when the subject was female (β = −0.078; t = 3.860, p = 0.000), younger (β = −0.109; t = 4.555, p = 0.000), had a lower level of education (β = −0.140; t = 6.544, p = 0.000), lived in places with fewer inhabitants (β = −0.060; t = 2.924, p = 0.003) and had lower income (β = −0.036; t = 1.758, p = 0.079). For these reasons, the results support H1.
In Model II (see Table 6, Model II), when ‘Barrier_Extent’ was incorporated as an independent variable, which classified individuals into two groups (‘high’ or ‘low’) based on the higher or lower prevalence of psychological or physical barriers, significant changes in the determination coefficient were observed (ΔR2 = 0.078; ΔF = 212.103; p = 0.000). Thus, the results showed that the individuals with higher levels of such barriers (β = −0.280; t = 14.564, p = 0.000) had lower DI. Therefore, H2 is accepted.
In Model III (see Table 6, Model III), when ‘Motivation_Extent’ was added as an independent variable, which also grouped individuals into two categories (‘high’ or ‘low’) based on higher or lower levels of impure altruism, significant changes in the determination coefficient were observed (ΔR2 = 0.056; ΔF = 161.877; p = 0.000). The results showed that the more altruistic subjects are (β = 0.240; t = 12.723, p = 0.000), the greater their DI is. For this reason, H3 is accepted.
In Model IV (see Table 6, Model IV), including the four categories of AEs revealed the existence of significant changes in the determination coefficient (ΔR2 = 0.151; ΔF = 134.131; p = 0.000), which indicates how significant the influence of AEs is on DI. The results of this fourth model reveal that the four categories of AEs influence DI. As expected, the direction of the relationships between the different AEs and DI corresponds to what has been established in the literature. Thus, the AEs that motivated the act to donate (positive AEs towards donation, and negative AEs towards nondonation) were positive, whereas AEs that discourage the act to donate (negative AEs towards donation, and positive AEs towards nondonation) were negative. The greater influence on DI corresponded to positive AEs towards donation (β = 0.217; t = 11.020, p = 0.000) and negative AEs towards nondonation (β = 0.173; t = 8.478, p = 0.000). Thus, H4 is accepted.
With Model V (see Table 6, Model V), we tried to analyse the moderating effect of the ‘Barrier_Extent’ variable on the relationship between AEs and DI. The results indicated that this interaction did not bring about significant changes in the determination coefficient, although a slight change was observed in the model (ΔR2 = 0.002; ΔF = 1.961; p = 0.098). To be more precise, the results suggested that a high level of psychological and physical barriers weakened the relationship between positive AEs towards donation and DI (β = −0.230; t = 2.568, p = 0.010). Given that the rest of the interactions were not significant at a significance level of 5%, Q1 does not have sufficient empirical support.
The results of Model VI (see Table 6, Model VI) indicated that the ‘Motivation_Extent’ variable does not have a moderating effect on the relationship between AEs and DI. In fact, no significant changes in the determination coefficient (ΔR2 = 0.001; ΔF = 0.974; p = 0.421) were observed. For that reason, respondents’ level of impure altruism did not moderate the AEs-DI relationship, so Q2 was not empirically supported.
After contrasting the hypothesis, it was deemed appropriate to perform within-group comparisons (t-tests) to find out whether there were differences between the average values of the constructs analysed (DI and AEs) depending on the Barrier_Extent and Motivation_Extent values of the respondents. Firstly, focusing on the variable DI, Table 7 shows that individuals with a low prevalence of psychological and physical barriers (i.e., low Barrier_Extent) had the highest DI levels (M = 4.67). For Motivation_Extent, it is the opposite: The respondents with a high prevalence of impure altruism (i.e., high Motivation_Extent) reported greater levels of DI (M = 4.57).
Secondly, regarding AEs and their relationship with Barrier_Extent, differences can be observed between the two groups in three out of four dimensions of AEs. The fact that no differences have been found in positive AEs of donation (p = 0.105) means that an individual who decided to donate would feel happy, proud and/or satisfied with themselves, regardless of whether they presented psychological and physical barriers or not. On the other hand, as expected, negative AEs of donation and positive AEs of nondonation are stronger in the group of people with greater barriers, while the opposite is true with negative AEs of nondonation.
Lastly, there are differences in all dimensions of AEs according to Motivation_Extent. Thus, positive AEs of donation and negative AEs of nondonation are greater among individuals with greater impure altruism levels, whereas negative AEs of donation and positive AEs of nondonation are higher among the people with lower impure altruism levels.
To summarise, major findings of this work are presented in the following table (see Table 8).

5. Conclusions

The main contribution of this work has been the analysis and evaluation of the joint influence of blood donors’ traditional behaviour variables (sociodemographic characteristics, psychological–physical barriers and impure altruism) and anticipated emotions (AEs) on nondonors’ donation intention. Due to the absence of literature on the variables that might moderate AEs, this work has also contributed by examining the moderating role of psychological and physical barriers, along with impure altruism, on the cause–effect relationships between AEs and the intention to donate.
Therefore, from an academic point of view, we have expanded and made progress in the study on nondonor behaviour by identifying new behaviour variables, both explanatory and moderating, alongside revisions of usual models. At the same time, four scales have been designed and validated to measure the constructs studied in this work: psychological and physical barriers, impure altruism, AEs and intention to donate.
National hemotherapy systems currently abide by European regulations based on using blood from voluntary donors. Therefore, programmes to promote donation focus on raising public awareness to recruit and retain new donors (Carter et al. 2011). A number of practical implications can be drawn from this work, which may help transfusion centres to improve their new donor recruitment measures, in view of their capacity to increase their current donor pool and to replace donors who are no longer suitable. The results allowed us to conclude that an important segment to increasing the donor pool is made up of younger women with lower levels of education and income, who live in places with fewer inhabitants. These results are supported by previous literature (Wittock et al. 2017; Zucoloto et al. 2019a). Additionally, centres must make efforts to reduce the discouraging effect of psychological and physical barriers on the intention to donate, as well as incentivise (impure) altruistic motivations of this nondonor segment (Beerli-Palacio and Martín-Santana 2015; Polonsky et al. 2015).
The duality of positive and negative anticipated emotions, related to both action and inaction, in AE theory, has been particularly useful, since it has been validated that all of them influence the intention to donate and the expected direction. For this reason, necessary empirical evidence has been provided to support the notion that it is necessary to consider the perspective of inaction (positive and negative AEs motivating nondonation), and not only the perspective of action (positive and negative AEs motivating donation) when creating a model for the role of AEs on the intention to donate. In fact, the AEs that most strongly influence the intention to donate do not belong to the same system (action vs. inaction), given that the most influential AEs are positive AEs towards donation and negative AEs towards nondonation. Having said that, in the context of nondonors, it is as important to consider AEs motivating donation (positive AEs towards donation and negative AEs towards nondonation) as AEs discouraging people from donating (positive AEs towards donation and negative AEs towards nondonation). This quadruple model of AEs, as recommended by Bagozzi et al. (2016), has been scarcely used in general terms. It is the isolated positive or negative dimension of AEs that is usually analysed. Belanche Gracia et al. (2017), in their preliminary study, find themselves among the few authors who apply the quadruple model to study the influence of AEs on purchase decisions. The authors obtained relevant theoretical results but did not follow up on them.
For all of the above, transfusion centres should launch campaigns with a dual objective: reinforcing emotions that motivate the intention to donate and eliminating emotions that inhibit it. Thus, their messages should emphasise feelings of happiness, pride and satisfaction associated with donating, and potential feelings of disappointment, guilt and anger at oneself derived from nondonation, as well as highlighting the altruist nature of blood donation. Having said that, given that the concept of altruism corresponds to impure altruism in the field of blood donation (i.e., people donate blood to help others, and also to get an emotional reward), messages should also refer to the personal emotional benefits derived from donation, and they should even be negatively framed (i.e., the loss of these emotional benefits) (Tey et al. 2019).
Furthermore, taking into account that nondonors have no donation experience, another way to influence their AEs, strengthening positive emotions and diminishing negative ones, as well as eliminating barriers and promoting motivations, could be to use AR (Augmented Reality) or VR (Virtual Reality) experiences. These immersive interactions would allow nondonors to face a simulation of blood donation in a controlled environment. Simulated experiences are particularly interesting as a first approach to the emotional and physical dimensions of blood donation. In addition, this technique has been used previously in other related fields to fight against negative emotions, as observed in the study made by Chesham et al. (2018). There, the authors used this approach to address social anxiety, achieving highly positive results.
Another contribution that this study has made in the context of AEs is to introduce new variables moderating the relationship between AEs and the intention to donate, e.g., psychological and physical barriers and impure altruism. In this sense, the results have only verified that high levels of psychological and physical barriers weaken the relationship between positive AEs of donation and the intention to donate. This highlights how necessary it is to invest resources in eliminating discouraging factors, in order to make the most of using positive AEs of donation to influence donation intention among nondonors.
Lastly, it would be recommendable for these campaigns, intent on achieving social marketing objectives, to use new technologies in order to connect with the targeted nondonor segment, which consists mainly of young people (less than 25 years old). To this end, we suggest that transfusion centres enter into collaboration agreements with partners whose target audience coincides with this nondonor group (e.g., universities, cinemas). In this way, they would be able to operate using means and platforms (social networks, media and digital platforms) that impact not only the relevant demographic (Sümnig et al. 2018), but also its largest proportion (Abbasi et al. 2018).
The main limitation of this study is the geographical context where it was carried out (Spain). In order to validate the results, it needs to be replicated in other different contexts. In the same vein, since volunteering is the only way to legally donate blood in Spain, it would be especially interesting, for comparative purposes, that the study be repeated in countries with different blood donation systems (e.g., remunerated or mixed systems). Another limitation could be the methodology that has been used, which could have conditioned the sample’s configuration. The fact that respondents decided to participate in the study requires us to be careful when generalising the results because there might be differences between individuals who decided to participate and those who did not. Additionally, the sample was composed of a majority of young people, presumably due to the origin of the sample (universities and social networks). However, in terms of management, this demographic is interesting. Young university students make up a numerous collective that is logistically very accessible (i.e., campaigns at universities, mobile units that can come closer to them) and very attractive for transfusion centres given their good health and great potential as long-term donors (Eshak et al. 2019; Healy and Murphy 2017; Gomes et al. 2019). In any case, it would be advisable to replicate the study while using other sampling systems which guarantee a sample distribution that is identical to the study population. Finally, it would be advisable to include additional items in the donation intention construct to increase its reliability, which, although it falls within acceptability, could be improved.

Author Contributions

All authors (J.D.M.-S., L.R.-C., E.R.-L. and L.R.-D.) contributed to study conception and design, material preparation, data collection and analysis, and manuscript drafting and revision. All authors read and approved the final manuscript.


This research was funded by the Spanish Ministry of Economy and Competitiveness, grant number ECO2015-64875-R.


The authors would like to thank the 14 blood transfusion centres and the 24 universities, as well as all survey respondents, for participating in the study.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Final Items of the Scales

BARR1General fear and anxiety of donation
BARR2Fear of needles and/or pain
BARR3Fear of seeing blood
BARR4Suffering physical distress (nausea, vomit, dizziness, etc.)
BARR5Suffering wounds in arms due to use of needles (haematoma, irritation, etc.)
MOTIV1Human solidarity, helping someone or saving lives
MOTIV2Personal satisfaction derived from helping others
MOTIV3Since blood cannot be artificially produced, we must all collaborate
MOTIV4Donating blood makes me feel needed and useful to society
MOTIV5Fulfilling the social duty or moral obligation of helping other people
MOTIV6I or my family could need blood in the future
MOTIV7Donating blood requires no effort
Positive anticipated emotions of donation (posAEdon):
AE1If I decided TO DONATE, I would feel happy
AE2If I decided TO DONATE, I would feel proud
AE3If I decided TO DONATE, I would feel satisfied
Negative anticipated emotions of donation (negAEdon):
AE4If I decided TO DONATE, I would feel worried
AE5If I decided TO DONATE, I would regret it
AE6If I decided TO DONATE, I would feel anxious
Positive anticipated emotions of nondonation (posAEnondon):
AE7If I decided NOT TO DONATE, I would be convinced with my decision
AE8If I decided NOT TO DONATE, I would feel calm
Negative anticipated emotions of nondonation (negAEnondon):
AE9If I decided NOT TO DONATE, I would feel disappointed
AE10If I decided NOT TO DONATE, I would feel guilty
AE11If I decided NOT TO DONATE, I would feel angry at myself
DI1I am going to donate in the next four months
DI2I would like to become a regular donor (twice or more times a year)


  1. Abbasi, Rabeeh Ayaz, Onaiza Maqbool, Mubashar Mushtaq, Naif R. Aljohani, Ali Daud, Jalal S. Alowibdi, and Basit Shahzad. 2018. Saving Lives Using Social Media: Analysis of the Role of Twitter for Personal Blood Donation Requests and Dissemination. Telematics and Informatics 35: 892–912. [Google Scholar] [CrossRef]
  2. Ajzen, Icek. 1991. The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes 50: 179–211. [Google Scholar] [CrossRef]
  3. Alfieri, Sara, Maura Pozzi, Elena Marta, Vincenzo Saturni, Giovanni Aresi, and Paolo Guiddi. 2017. ‘Just’ Blood Donors? A Study on the Multi-Affiliations of Blood Donors. Transfusion and Apheresis Science 56: 578–84. [Google Scholar] [CrossRef]
  4. Alinon, Kokou, Koffiwai Gbati, Paul Clay Sorum, and Etienne Mullet. 2014. Emotional-Motivational Barriers to Blood Donation among Togolese Adults: A Structural Approach. Transfusion Medicine 24: 21–26. [Google Scholar] [CrossRef]
  5. Anderson, James, and David W. Gerbing. 1988. Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin 103: 411–23. [Google Scholar] [CrossRef]
  6. Andreoni, James. 1990. Impure Altruism and Donations to Public Goods: A Theory of Warm-Glow Giving. The Economic Journal 100: 464–77. [Google Scholar] [CrossRef]
  7. Bagot, Kathleen L., Andrea L. Murray, and Barbara M. Masser. 2016. How Can We Improve Retention of the First-Time Donor? A Systematic Review of the Current Evidence. Transfusion Medicine Reviews 30: 81–91. [Google Scholar] [CrossRef]
  8. Bagozzi, Richard P., and Utpal M. Dholakia. 2006. Antecedents and Purchase Consequences of Customer Participation in Small Group Brand Communities. International Journal of Research in Marketing 23: 45–61. [Google Scholar] [CrossRef]
  9. Bagozzi, Richard P., Daniel Belanche, Luis V. Casaló, and Carlos Flavián. 2016. The Role of Anticipated Emotions in Purchase Intentions. Psychology & Marketing 33: 629–45. [Google Scholar] [CrossRef]
  10. Bagozzi, Richard P., Hans Baumgartner, and Rik Pieters. 1998. Goal-Directed Emotions. Cognition and Emotion 12: 1–26. [Google Scholar] [CrossRef]
  11. Bagozzi, Richard P., Hans Baumgartner, Rik Pieters, and Marcel Zeelenberg. 2000. The Role of Emotions in Goal-Directed Behavior. In The Why of Consumption. Edited by S. Ratneshwar, David Glen Mick and Cynthia Huffman. London: Routledge Publishing, pp. 36–58. [Google Scholar]
  12. Bagozzi, Richard P., Utpal M. Dholakia, and Suman Basuroy. 2003. How Effortful Decisions Get Enacted: The Motivating Role of Decision Processes, Desires, and Anticipated Emotions. Journal of Behavioral Decision Making 16: 273–95. [Google Scholar] [CrossRef]
  13. Barreto, Ana Margarida. 2019. Personal Traits behind the Intention to Donate Blood. Media & Jornalismo 18: 193–206. [Google Scholar] [CrossRef]
  14. Beaudry, Anne, and Alain Pinsonneault. 2010. The Other Side of Acceptance: Studying the Direct and Indirect Effects of Emotions on Information Technology Use. MIS Quarterly 34: 689–710. [Google Scholar] [CrossRef]
  15. Bednall, Timothy C., and Liliana L. Bove. 2011. Donating Blood: A Meta-Analytic Review of Self-Reported Motivators and Deterrents. Transfusion Medicine Reviews 25: 317–34. [Google Scholar] [CrossRef]
  16. Bednall, Timothy C., Liliana L. Bove, Ali Cheetham, and Andrea L. Murray. 2013. A Systematic Review and Meta-Analysis of Antecedents of Blood Donation Behavior and Intentions. Social Science and Medicine 96: 86–94. [Google Scholar] [CrossRef]
  17. Beerli-Palacio, Asunción, and Josefa D. Martín-Santana. 2015. How to Increase Blood Donation by Social Marketing. International Review on Public and Nonprofit Marketing 12: 253–66. [Google Scholar] [CrossRef]
  18. Belanche Gracia, Daniel, Luis Vicente Casaló Ariño, and Carlos Flavián Blanco. 2017. La Influencia de Las Emociones Anticipadas Sobre Las Decisiones de Compra: Variables Mediadoras y Moderadoras. In XXIX Congreso de Marketing AEMARK 2017. Sevilla: ESIC Editorial, pp. 253–59. Available online: (accessed on 30 September 2019).
  19. Boenigk, Silke, and Sigrun Leipnitz. 2016. Acquiring Potential Blood Donors in Large Cities: A Preference-Based Donor Segmentation Study. Journal of Nonprofit & Public Sector Marketing 28: 364–93. [Google Scholar] [CrossRef]
  20. Carter, Moira C., Jennifer Wilson, Gordon S. Redpath, Paul Hayes, and Carol Mitchell. 2011. Donor Recruitment in the 21st Century: Challenges and Lessons Learned in the First Decade. Transfusion and Apheresis Science 45: 31–43. [Google Scholar] [CrossRef]
  21. Charbonneau, Johanne, Marie-Soleil Cloutier, and Élianne Carrier. 2015. Whole Blood and Apheresis Donors in Quebec, Canada: Demographic Differences and Motivations to Donate. Transfusion and Apheresis Science 53: 320–28. [Google Scholar] [CrossRef]
  22. Charbonneau, Johanne, Marie-Soleil Cloutier, and Élianne Carrier. 2016. Why Do Blood Donors Lapse or Reduce Their Donation’s Frequency? Transfusion Medicine Reviews 30: 1–5. [Google Scholar] [CrossRef]
  23. Chell, Kathleen, and Gary Mortimer. 2014. Investigating Online Recognition for Blood Donor Retention: An Experiential Donor Value Approach. International Journal of Nonprofit and Voluntary Sector Marketing 19: 143–63. [Google Scholar] [CrossRef]
  24. Chesham, Rachel K., John M. Malouff, and Nicola S. Schutte. 2018. Meta-Analysis of the Efficacy of Virtual Reality Exposure Therapy for Social Anxiety. Behaviour Change 35: 152–66. [Google Scholar] [CrossRef]
  25. Conner, Mark, Gaston Godin, Paschal Sheeran, and Marc Germain. 2013. Some Feelings Are More Important: Cognitive Attitudes, Affective Attitudes, Anticipated Affect, and Blood Donation. Health Psychology 32: 264–72. [Google Scholar] [CrossRef] [PubMed]
  26. Conner, Mark, Rosemary McEachan, Natalie Taylor, Jane O’Hara, and Rebecca Lawton. 2015. Role of Affective Attitudes and Anticipated Affective Reactions in Predicting Health Behaviors. Health Psychology 34: 642–52. [Google Scholar] [CrossRef] [PubMed]
  27. Custer, Brian, Karen Schlumpf, Toby L. Simon, Bryan R. Spencer, David J. Wright, and Susan L. Wilkinson. 2012. Demographics of Successful, Unsuccessful and Deferral Visits at Six Blood Centers over a 4-Year Period. Transfusion 52: 712–21. [Google Scholar] [CrossRef] [PubMed]
  28. Debelak, Rudolf, and Ulrich S. Tran. 2013. Principal Component Analysis of Smoothed Tetrachoric Correlation Matrices as a Measure of Dimensionality. Educational and Psychological Measurement 73: 63–77. [Google Scholar] [CrossRef]
  29. Eshak, Ehab S., Eman R. Ghazawy, Tarek A. Abd-El Rahman, and Shaimaa M. Hafez. 2019. Crossing the Wide Gap between Positive Attitude towards Blood Donation and Its Poor Practice among University Students: Can Knowledge and Demographic Characteristics Help?! Journal of Public Health: From Theory to Practice 27: 329–37. [Google Scholar] [CrossRef]
  30. Evans, Roy, and Eamonn Ferguson. 2014. Defining and Measuring Blood Donor Altruism: A Theoretical Approach from Biology, Economics and Psychology. Vox Sanguinis 106: 118–26. [Google Scholar] [CrossRef]
  31. Faqah, Anadil, Bushra Moiz, Fatima Shahid, Mariam Ibrahim, and Ahmed Raheem. 2015. Assessment of Blood Donation Intention among Medical Students in Pakistan—An Application of Theory of Planned Behavior. Transfusion and Apheresis Science 53: 353–59. [Google Scholar] [CrossRef]
  32. Ferguson, Eamonn, and Claire Lawrence. 2016. Blood Donation and Altruism: The Mechanisms of Altruism Approach. ISBT Science Series 11: 148–57. [Google Scholar] [CrossRef]
  33. Ferguson, Eamonn, Kathleen Farrell, and Claire Lawrence. 2008. Blood Donation Is an Act of Benevolence Rather Than Altruism. Health Psychology 27: 327–36. [Google Scholar] [CrossRef] [PubMed]
  34. Ferguson, Eamonn. 2015. Mechanism of Altruism Approach to Blood Donor Recruitment and Retention: A Review and Future Directions. Transfusion Medicine 25: 211–26. [Google Scholar] [CrossRef] [PubMed]
  35. Fong, Candy P. S., and Robert S. Wyer. 2003. Cultural, Social, and Emotional Determinants of Decisions under Uncertainty. Organizational Behavior and Human Decision Processes 90: 304–22. [Google Scholar] [CrossRef]
  36. France, Christopher R., Janis L. France, Bruce W. Carlson, Lina K. Himawan, Kirsten Yunuba Stephens, Terri A. Frame-Brown, Geri A. Venable, and Jay E. Menitove. 2014. Fear of Blood Draws, Vasovagal Reactions, and Retention among High School Donors. Transfusion 54: 918–24. [Google Scholar] [CrossRef] [PubMed]
  37. France, Janis L., Christopher R. France, and Lina K. Himawan. 2007. A Path Analysis of Intention to Redonate among Experienced Blood Donors: An Extension of the Theory of Planned Behavior. Transfusion 47: 1006–13. [Google Scholar] [CrossRef] [PubMed]
  38. France, Janis L., Christopher R. France, and Lina K. Himawan. 2008. Re-Donation Intentions among Experienced Blood Donors: Does Gender Make a Difference? Transfusion and Apheresis Science 38: 159–66. [Google Scholar] [CrossRef]
  39. Gemelli, Carley N., Jane Hayman, and Daniel Waller. 2017. Frequent Whole Blood Donors: Understanding This Population and Predictors of Lapse. Transfusion 57: 108–14. [Google Scholar] [CrossRef]
  40. Gillespie, Theresa W., and Christopher D. Hillyer. 2002. Blood Donors and Factors Impacting the Blood Donation Decision. Transfusion Medicine Reviews 16: 115–30. [Google Scholar] [CrossRef]
  41. Godin, Gaston, Marc Germain, Mark Conner, Gilles Delage, and Paschal Sheeran. 2014. Promoting the Return of Lapsed Blood Donors: A Seven-Arm Randomized Controlled Trial of the Question-Behavior Effect. Health Psychology 33: 646–55. [Google Scholar] [CrossRef]
  42. Godin, Gaston, Mark Conner, Paschal Sheeran, Ariane Bélanger-Gravel, and Marc Germain. 2007. Determinants of Repeated Blood Donation among New and Experienced Blood Donors. Transfusion 47: 1607–15. [Google Scholar] [CrossRef]
  43. Godin, Gaston, Paschal Sheeran, Mark Conner, Marc Germain, Danielle Blondeau, Camille Gagné, Dominique Beaulieu, and Paul H. Naccache. 2005. Factors Explaining the Intention to Give Blood among the General Population. Vox Sanguinis 89: 140–49. [Google Scholar] [CrossRef] [PubMed]
  44. Gomes, Maria José, António J. Nogueira, Celeste Antão, and Cristina Teixeira. 2019. Motivations and Attitudes towards the Act of Blood Donation among Undergraduate Health Science Students. Transfusion and Apheresis Science 58: 147–51. [Google Scholar] [CrossRef] [PubMed]
  45. Gonçalez, Thelma T., Claudia Di Lorenzo Oliveira, Anna Barbara F. Carneiro-Proietti, Elizabeth C. Moreno, Carolina Miranda, Nina Larsen, David J. Wright, Silvana Leão, Paula Loureiro, Cesar de Almeida-Neto, and et al. 2013. Motivation and Social Capital among Prospective Blood Donors in Three Large Blood Centers in Brazil. Transfusion 53: 1291–301. [Google Scholar] [CrossRef] [PubMed]
  46. Guiddi, Paolo, Sara Alfieri, Elena Marta, and Vincenzo Saturni. 2015. New Donors, Loyal Donors, and Regular Donors: Which Motivations Sustain Blood Donation? Transfusion and Apheresis Science 52: 339–44. [Google Scholar] [CrossRef] [PubMed]
  47. Hair, Joseph F., William C. Black, Barry J. Babin, and Rolph Anderson. 2010. Multivariate Data Analysis, 7th ed. New York: Pearson. [Google Scholar]
  48. Healy, John, and Maurice Murphy. 2017. Social Marketing: The Lifeblood of Blood Donation? In The Customer Is NOT Always Right? Marketing Orientationsin a Dynamic Business World. Edited by Colin L. Campbell. Cham: Springer, p. 811. [Google Scholar]
  49. Holdershaw, Judith, Philip Gendall, and Malcolm Wright. 2011. Predicting Blood Donation Behaviour: Further Application of the Theory of Planned Behaviour. Journal of Social Marketing 1: 120–32. [Google Scholar] [CrossRef]
  50. Hupfer, Maureen E., David W. Taylor, and J. A. Letwin. 2005. Understanding Canadian Student Motivations and Beliefs about Giving Blood. Transfusion 45: 149–61. [Google Scholar] [CrossRef] [PubMed]
  51. James, Adelbert B., Derrick G. Demmons, George B. Schreiber, Christopher D. Hillyer, and Beth H. Shaz. 2011. Contribution of Attitudinal Factors to Blood Donation in African American Church Attendees. Transfusion 51: 158–65. [Google Scholar] [CrossRef]
  52. James, Adelbert B., George B. Schreiber, Christopher D. Hillyer, and Beth H. Shaz. 2013. Blood Donations Motivators and Barriers: A Descriptive Study of African American and White Voters. Transfusion and Apheresis Science 48: 87–93. [Google Scholar] [CrossRef]
  53. Karacan, Eda, Guldane Cengiz Seval, Zeynep Aktan, Meltem Ayli, and Refia Palabiyikoglu. 2013. Blood Donors and Factors Impacting the Blood Donation Decision: Motives for Donating Blood in Turkish Sample. Transfusion and Apheresis Science 49: 468–73. [Google Scholar] [CrossRef]
  54. Kim, Yong Joong, David Njite, and Murat Hancer. 2013. Anticipated Emotion in Consumers’ Intentions to Select Eco-Friendly Restaurants: Augmenting the Theory of Planned Behavior. International Journal of Hospitality Management 34: 255–62. [Google Scholar] [CrossRef]
  55. Kowalsky, Jennifer M., Christopher R. France, Janis L. France, Elizabeth A. Whitehouse, and Lina K. Himawan. 2014. Blood Donation Fears Inventory: Development and Validation of a Measure of Fear Specific to the Blood Donation Setting. Transfusion and Apheresis Science 51: 146–51. [Google Scholar] [CrossRef] [PubMed]
  56. Lacetera, Nicola, and Mario Macis. 2010. Do All Material Incentives for Pro-Social Activities Backfire? The Response to Cash and Non-Cash Incentives for Blood Donations. Journal of Economic Psychology 31: 738–48. [Google Scholar] [CrossRef]
  57. Lemmens, Karin P. H., Charles Abraham, Robert A. C. Ruiter, Ingrid J. T. Veldhuizen, Cary J. G. Dehing, Arjan E. R. Bos, and Herman P. Schaalma. 2009. Modelling Antecedents of Blood Donation Motivation among Non-Donors of Varying Age and Education. British Journal of Psychology 100: 71–90. [Google Scholar] [CrossRef] [PubMed]
  58. Lemmens, Karin P. H., Charles Abraham, T. Hoekstra, Robert A. C. Ruiter, Wim de Kort, J. Brug, and Herman P. Schaalma. 2005. Why Don’t Young People Volunteer to Give Blood? An Investigation of the Correlates of Donation Intentions among Young Nondonors. Transfusion 45: 945–55. [Google Scholar] [CrossRef]
  59. Martín-Santana, Josefa D., and Asunción Beerli-Palacio. 2013. Intention of Future Donations: A Study of Donors versus Non-Donors. Transfusion Medicine 23: 77–86. [Google Scholar] [CrossRef]
  60. Masser, Barbara M., Katherine M. White, and Deborah J. Terry. 2013. Beliefs Underlying the Intention to Donate Again among First-Time Blood Donors Who Experience a Mild Adverse Event. Transfusion and Apheresis Science 49: 278–84. [Google Scholar] [CrossRef]
  61. Masser, Barbara M., Timothy C. Bednall, Katherine M. White, and Deborah J. Terry. 2012. Predicting the Retention of First-Time Donors Using an Extended Theory of Planned Behavior. Transfusion 52: 1303–10. [Google Scholar] [CrossRef]
  62. Mathieu, John E., and Scott R. Taylor. 2006. Clarifying Conditions and Decision Points for Mediational Type Inferences in Organizational Behavior. Journal of Organizational Behavior 27: 1031–56. [Google Scholar] [CrossRef]
  63. Mellers, Barbara A., and A. Peter McGraw. 2001. Anticipated Emotions as Guides to Choice. Current Directions in Psychological Science 10: 210–14. [Google Scholar] [CrossRef]
  64. Mellers, Barbara, Alan Schwartz, and Ilana Ritov. 1999. Emotion-Based Choice. Journal of Experimental Psychology: General 128: 332–45. [Google Scholar] [CrossRef]
  65. Newman, Bruce H. 2014. Management of Young Blood Donors. Transfusion Medicine and Hemotherapy 41: 284–95. [Google Scholar] [CrossRef] [PubMed]
  66. Ngoma, Alain M., A. Goto, S. Yamazaki, M. Machida, T. Kanno, K. E. Nollet, H. Ohto, and S. Yasumura. 2013. Barriers and Motivators to Blood Donation among University Students in Japan: Development of a Measurement Tool. Vox Sanguinis 105: 219–24. [Google Scholar] [CrossRef] [PubMed]
  67. Nguyen, Dorothy D., Deborah A. DeVita, Nora Hirschler, and Edward L. Murphy. 2008. Blood Donor Satisfaction and Intention of Future Donation. Transfusion 48: 742–48. [Google Scholar] [CrossRef] [PubMed]
  68. Nunnally, Jum C., and Ira H. Bernstein. 1994. Psychometric Theory, 3rd ed. New York: McGraw Hill, Inc. [Google Scholar]
  69. Onwezen, Marleen C., Gerrit Antonides, and Jos Bartels. 2013. The Norm Activation Model: An Exploration of the Functions of Anticipated Pride and Guilt in pro-Environmental Behaviour. Journal of Economic and Administrative Sciences 39: 141–53. [Google Scholar] [CrossRef]
  70. Pagliariccio, Antonella, and Maria Marinozzi. 2012. Increasing Regular Donors through a Psychological Approach Which Reduces the Onset of Vasovagal Reactions. Transfusion and Apheresis Science 47: 301–4. [Google Scholar] [CrossRef]
  71. Perugini, Marco, and Richard P. Bagozzi. 2001. The Role of Desires and Anticipated Emotions in Goal-Directed Behaviours: Broadening and Deepening the Theory of Planned Behaviour. British Journal of Social Psychology 40: 79–98. [Google Scholar] [CrossRef]
  72. Piersma, Tjeerd W., René Bekkers, Elisabeth F. Klinkenberg, Wim L. A. M. De Kort, and Eva-Maria Merz. 2017. Individual, Contextual and Network Characteristics of Blood Donors and Non-Donors: A Systematic Review of Recent Literature. Blood Transfusion 15: 382–97. [Google Scholar] [CrossRef]
  73. Pollai, Maria, Erik Hoelzl, Luise Hahn, and Alexandra Hahn. 2011. The Influence of Anticipated Emotions on Consumer Decisions Examining the Role of Product Type. Journal of Psychology 219: 238–45. [Google Scholar] [CrossRef]
  74. Polonsky, Michael, Andre M. N. Renzaho, Ahmed Shahriar Ferdous, and Zoe McQuilten. 2013. African Culturally and Linguistically Diverse Communities’ Blood Donation Intentions in Australia: Integrating Knowledge into the Theory of Planned Behavior. Transfusion 53: 1475–86. [Google Scholar] [CrossRef]
  75. Polonsky, Michael, Kate Francis, and Andre M. N. Renzaho. 2015. Is Removing Blood Donation Barriers a Donation Facilitator? Journal of Social Marketing 5: 190–205. [Google Scholar] [CrossRef]
  76. Reid, Mike, and Angela Wood. 2008. An Investigation into Blood Donation Intentions among Non-Donors. International Journal of Nonprofit and Voluntary Sector Marketing 13: 31–43. [Google Scholar] [CrossRef]
  77. Richard, René, Joop van der Pligt, and Nanne Vries. 1995. Anticipated Affective Reactions and Prevention of AIDS. British Journal of Social Psychology 34: 9–21. [Google Scholar] [CrossRef] [PubMed]
  78. Richetin, Juliette, Mark Conner, and Marco Perugini. 2011. Not Doing Is Not the Opposite of Doing: Implications for Attitudinal Models of Behavioral Prediction. Personality and Social Psychology Bulletin 37: 40–54. [Google Scholar] [CrossRef] [PubMed]
  79. Robinson, Natalie G., Barbara M. Masser, Katherine M. White, Melissa K. Hyde, and Deborah J. Terry. 2008. Predicting Intentions to Donate Blood among Nondonors in Australia: An Extended Theory of Planned Behavior. Transfusion 48: 2559–67. [Google Scholar] [CrossRef]
  80. Sandberg, Tracy, and Mark Conner. 2008. Anticipated Regret as an Additional Predictor in the Theory of Planned Behaviour: A Meta-Analysis. British Journal of Social Psychology 47: 589–606. [Google Scholar] [CrossRef]
  81. Schlumpf, Karen S., Simone A. Glynn, George B. Schreiber, David J. Wright, Whitney Randolph Steele, Yongling Tu, Sigurd Hermansen, Martha J. Higgins, George Garratty, and Edward L. Murphy. 2008. Factors Influencing Donor Return. Transfusion 48: 264–72. [Google Scholar] [CrossRef]
  82. Shaz, Beth H., Adelbert B. James, Derrick G. Demmons, George B. Schreiber, and Christopher D. Hillyer. 2010. The African American Church as a Donation Site: Motivations and Barriers. Transfusion 50: 1240–48. [Google Scholar] [CrossRef]
  83. Shaz, Beth H., Derrick G. Demmons, Colleen P. Crittenden, Claudine V. Carnevale, Mark Lee, Miriam Burnett, Kirk Easley, and Christopher D. Hillyer. 2009. Motivators and Barriers to Blood Donation in African American College Students. Transfusion and Apheresis Science 41: 191–97. [Google Scholar] [CrossRef]
  84. Shehu, Edlira, Ann-Christin Langmaack, Elena Felchle, and Michel Clement. 2015. Profiling Donors of Blood, Money, and Time. Nonprofit Management & Leadership 25: 269–95. [Google Scholar] [CrossRef]
  85. Solomon, Guy Douglas. 2012. Segmentation and Communications to Solve the Blood Shortage: An Exploration of the Problem with Recommendations. Voluntas 23: 415–33. [Google Scholar] [CrossRef]
  86. Steg, Linda, and Charles Vlek. 2009. Encouraging Pro-Environmental Behaviour: An Integrative Review and Research Agenda. Journal of Environmental Psychology 29: 309–17. [Google Scholar] [CrossRef]
  87. Sümnig, Ariane, Martin Feig, Andreas Greinacher, and Thomas Thiele. 2018. The Role of Social Media for Blood Donor Motivation and Recruitment. Transfusion 58: 2257–59. [Google Scholar] [CrossRef] [PubMed]
  88. Tey, Yeong Sheng, Poppy Arsil, Mark Brindal, Sook Kuan Lee, and Chi Teen Teoh. 2019. Motivation Structures of Blood Donation: A Means-End Chain Approach. International Journal of Health Economics and Management. [Google Scholar] [CrossRef]
  89. Van der Poel, Cees L., Erhard Seifried, and Wim P. Schaasberg. 2002. Paying for Blood Donations: Still a Risk? Vox Sanguinis 83: 285–93. [Google Scholar] [CrossRef] [PubMed]
  90. Vavić, Nataša, Antonella Pagliariccio, Milica Bulajić, Maria Marinozzi, Glorija Miletić, and Anka Vlatković. 2012. Blood Donor Satisfaction and the Weak Link in the Chain of Donation Process. Transfusion and Apheresis Science 47: 171–77. [Google Scholar] [CrossRef]
  91. Wang, Xiao. 2011. The Role of Anticipated Negative Emotions and Past Behavior in Individuals’ Physical Activity Intentions and Behaviors. Psychology of Sport and Exercise 12: 300–5. [Google Scholar] [CrossRef]
  92. Wevers, Anne, Daniël H. J. Wigboldus, Rick van Baaren, and Ingrid J. T. Veldhuizen. 2014. Return Behavior of Occasional and Multigallon Blood Donors: The Role of Theory of Planned Behavior, Self-Identity, and Organizational Variables. Transfusion 54: 805–13. [Google Scholar] [CrossRef]
  93. Wildman, John, and Bruce Hollingsworth. 2009. Blood Donation and the Nature of Altruism. Journal of Health Economics 28: 492–503. [Google Scholar] [CrossRef]
  94. Williams, Lisa A., Jessie Sun, and Barbara Masser. 2019. Integrating Self-Determination Theory and the Theory of Planned Behaviour to Predict Intention to Donate Blood. Transfusion Medicine 29: 59–64. [Google Scholar] [CrossRef]
  95. Wittock, Nathan, Lesley Hustinx, Piet Bracke, and Veerle Buffel. 2017. Who Donates? Cross-Country and Periodical Variation in Blood Donor Demographics in Europe between 1994 and 2014. Transfusion 57: 2619–28. [Google Scholar] [CrossRef]
  96. World Health Organization, and International Federation of Red Cross and Red Crescent Societies. 2010. Towards 100 % Voluntary Blood Donation A Global Framework for Action. Available online: (accessed on 20 April 2018).
  97. Xie, Chunyan, Richard P. Bagozzi, and Jens Østli. 2013. Cognitive, Emotional, and Sociocultural Processes in Consumption. Psychology & Marketing 30: 12–25. [Google Scholar] [CrossRef]
  98. Zampetakis, Leonidas A., Manolis Lerakis, Konstantinos Kafetsios, and Vassilis Moustakis. 2016. Anticipated Emotions towards New Venture Creation: A Latent Profile Analysis of Early Stage Career Starters. International Journal of Management Education 14: 28–38. [Google Scholar] [CrossRef]
  99. Zeelenberg, Marcel, Wilco W. van Dijk, Antony S. R. Manstead, and Joop van Der Pligt. 2000. On Bad Decisions and Disconfirmed Expectancies: The Psychology of Regret and Disappointment. Cognition and Emotion 14: 521–41. [Google Scholar] [CrossRef]
  100. Zucoloto, Miriane L., Thelma Gonçalez, Brian Custer, Willi McFarland, and Edson Z. Martinez. 2019a. Comparison of the Demographic and Social Profile of Blood Donors and Nondonors in Brazil. Health and Social Care in the Community 27: 330–36. [Google Scholar] [CrossRef] [PubMed]
  101. Zucoloto, Miriane Lucindo, Thelma Gonçalez, Neia Prata Menezes, Willi McFarland, Brian Custer, and Edson Zangiacomi Martinez. 2019b. Fear of Blood, Injections and Fainting as Barriers to Blood Donation in Brazil. Vox Sanguinis 114: 38–46. [Google Scholar] [CrossRef]
TPB: Theory of Planned Behaviour.
Table 1. Sample profile.
Table 1. Sample profile.
Age (Years)
No formal education30.1
Total Monthly Income (EUR)
Population at Place of Residence (Inhabitants)
Table 2. Results of the principal component analysis (PCA) applied to the barrier scale and its frequencies.
Table 2. Results of the principal component analysis (PCA) applied to the barrier scale and its frequencies.
Psychological and Physical BarriersPCA ResultsFrequencies
BARR1General fear and anxiety of donation0.8030.89694639.7
BARR2Fear of needles and/or pain0.7870.88799541.8
BARR3Fear of seeing blood0.7340.85766828.0
BARR4Suffering physical distress (nausea, vomit, dizziness, etc.)0.6210.78887036.5
BARR5Suffering wounds in arms due to use of needles (haematoma, irritation, etc.)0.4250.65239416.5
Total percentage of explained variance67.39
KR-20 of global scale0.759
Table 3. Results of the PCA applied to the motivation scale and its frequencies.
Table 3. Results of the PCA applied to the motivation scale and its frequencies.
Impure AltruismPCA ResultsFrequencies
MOTIV1Human solidarity, helping someone or saving lives0.8500.922232897.7
MOTIV2Personal satisfaction derived from helping others0.7410.861222093.2
MOTIV3Since blood cannot be artificially produced, we must all collaborate0.6690.818200784.2
MOTIV4Donating blood makes me feel needed and useful to society0.5610.749168370.6
MOTIV5Fulfilling the social duty or moral obligation of helping other people0.5460.739192080.6
MOTIV6I or my family could need blood in the future0.4990.706218791.8
MOTIV7Donating blood requires no effort0.3570.598147161.7
Total percentage of explained variance60.33
KR-20 of global scale0.660
Table 4. Confirmatory factorial analysis (CFA) of anticipated emotions (AEs) and donation intention (DI).
Table 4. Confirmatory factorial analysis (CFA) of anticipated emotions (AEs) and donation intention (DI).
Causal RelationshipsStandardised EstimatorstpComposite Reliability
AE1posAEdon0.88767.6030.000CR = 0.933
AE2posAEdon0.90670.7560.000AVE = 0.822
AE3posAEdon0.926 α = 0.932
AE4negAEdon0.807 CR = 0.719
AE5negAEdon0.70123.7160.000AVE = 0.468
AE6negAEdon0.51220.2790.000α = 0.682
AE7posAEnondon 0.80026.5940.000CR = 0.805
AVE = 0.674
AE8posAEnondon0.842 α = 0.805
AE9negAEnondon0.83849.1690.000CR = 0.896
AE10negAEnondon0.89452.9550.000AVE = 0.741
AE11negAEnondon0.850 α = 0.895
DI1DI0.561 CR = 0.666
AVE = 0.509
DI2DI0.83918.0460.000α = 0.639
Table 5. Correlation between variables in the regression models.
Table 5. Correlation between variables in the regression models.
1 posAEdon5.951.32---
2 negAEdon2.672.32−0.265 ***---
3 posAEnondon3.571.82−0.251 ***0.220 ***---
4 negAEnondon 4.411.860.429 ***−0.131 ***−0.448 ***---
5 Intention4.241.730.404 ***−0.298 ***−0.293 ***0.378 ***---
* p < 0.1, ** p < 0.05, *** p < 0.01. Triple dash (---) corresponds to the correlation of each variable with itself, which is always 1.000.
Table 6. Results of the multiple linear hierarchical regression models for donation intention.
Table 6. Results of the multiple linear hierarchical regression models for donation intention.
β Coefficientst ValueBeta Coefficientst ValueBeta Coefficientst ValueBeta Coefficientst ValueBeta Coefficientst ValueBeta Coefficientst Value
Constant 27.651 *** 30.160 *** 25.481 *** 14.165 *** 11.741 *** 11.346 ***
Sex−0.0783.860 ***−0.0844.361 ***−0.0552.914 ***−0.0160.959−0.0150.901−0.0171.001
Age−0.1094.555 ***−0.1305.687 ***−0.1305.841 ***−0.0934.626 ***−0.0934.597 ***−0.0934.619 ***
Education−0.1406.544 ***−0.1225.946 ***−0.0984.891 ***−0.0784.292 ***−0.0804.401 ***−0.0774.233 ***
Income−0.0361.758 *−0.0432.173 *−0.0381.949 *−0.0372.105 **−0.0352.026 **−0.0372.114 **
Population at place of residence−0.0602.924 ***−0.0552.820 ***−0.0583.057 ***−0.0442.527 **−0.0442.549 **−0.0442.538 **
Barrier_Extent −0.28014.564 ***−0.26013.872 ***−0.21212.164 ***−0.0710.670−0.21112.101 ***
Motivation_Extent 0.24012.723 ***0.1096.062 ***0.1096.071 ***0.0510.498
posAEdon 0.21711.020 ***0.26110.057 ***0.2097.105 ***
negAEdon −0.1286.988 ***−0.1084.059 ***−0.1605.415 ***
posAEnondon −0.0763.972 ***−0.0863.446 ***−0.0992.734 **
negAEnondon 0.1738.478 ***0.1405.078 ***0.1935.185 ***
posAEdon*Barrier_Extent −0.2302.568 ***
negAEdon*Barrier_Extent −0.0441.184
posAEnondon*Barrier_Extent 0.0270.579
negAEnondon*Barrier_Extent 0.0961.774 *
posAEdon*Motivation_Extent 0.0300.338
negAEdon*Motivation_Extent 0.0461.343
posAEnondon*Motivation_Extent 0.0380.740
negAEnondon*Motivation_Extent −0.0390.622
Adj. R20.0520.1300.1860.3350.3370.335
F22.763 ***51.556 ***68.420 ***100.616 ***76.075 ***75.702 ***
ΔR2 0.0780.0560.1510.0020.001
ΔF 212.103 ***161.877 ***134.131 ***1.961 *0.974
Note: *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 7. Within-group comparisons of donation intention and AEs.
Table 7. Within-group comparisons of donation intention and AEs.
ConstructsLow Barrier_ExtentHigh Barrier_Extentt
Low Motivation_ExtentHigh Motivation_Extentt
Table 8. Major findings.
Table 8. Major findings.
Hypotheses/Research QuestionsModelResults
H1. Sociodemographic characteristics influence the intention to donateIAccepted
H2. Psychological and physical barriers has a negative influence on the intention to donateIIAccepted
H3. Impure altruism has a positive influence on the intention to donateIIIAccepted
H4. Anticipated emotions influence the intention to donateIVAccepted
H4a. Positive anticipated emotions towards donation have a positive influence on the intention to donateIVAccepted
H4b. Negative anticipated emotions towards donation have a negative influence on the intention to donateIVAccepted
H4c. Positive anticipated emotions towards nondonation have a negative influence on the intention to donateIVAccepted
H4d. Negative anticipated emotions towards nondonation have a positive influence on the intention to donateIVAccepted
Q1. Do psychological and physical barriers moderate the relationship between anticipated emotions and the intention to donate?VNot empirically supported
Q2. Does impure altruism moderate the relationship between anticipated emotions and the intention to donate?VINot empirically supported
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