This section presents the results of the qualitative survey (4.1) and the quantitative survey (4.2).
4.1 Qualitative results
During the preliminary qualitative phase of the analysis, 30 in-depth interviews were conducted (15 = donors; 15= non-donors). The composition of the sample was balanced for pursuing explanatory power concerning different characteristics of the two distinct groups.
As shown in Appendix II, the sample of donor interviewees is composed of 7 males and eight females and the age range is balanced as follows: <25 (2); 26-35 (7); 37-50 (6). The majority of interviewees are regular donors (9), they donate 3-4 times a year. While the non-donors interviewed are five males and ten females. The highest number of non-donors is found in the age groups <25 (6) and 26-35 (7).
During the analysis, the divergences of subjective interpretation and codification were discussed or reanalyzed to solve the conflicting view  and the results were compared to identify the key common aspects and priorities for both donor and non-donors.
The main output of the qualitative analysis has been summarized by developing a cognitive map for donors and non-donors (Figure 2, 3) and a table explaining donation meaning and motivations (Table II).
The maps showed the three main dimensions that arose from the qualitative interview analysis: i) Service quality, ii) Information and Communication and iii) Inhibitors.
The content analysis revealed that service quality aspects are pivotal for individuals who are engaged in the blood donation process. In particular, the donor respondents take into account waiting times to donate, the cleanliness of transfusion centres and the availability and professionalism of the medical staff (Figure 2) (i.e. “When I donate I pay attention to whether medical staff are friendly and qualified, polite treatment and to tangible aspects such as the cleanliness of the facilities”). Moreover, the non-donors considered the security of transfusion centres and easy access to information about donation (e.g., places and times) as strengths of service quality (i.e."The transfusion centres must guarantee the easy access to the donation centre and the easy-to-find information about places and times. Transfusion centres must also be safe and therefore guarantee hygiene and staff qualified").
Indeed, the qualitative analysis shows that the low propensity to donate among non-donors is justified by intimate psychological factors (i.e., needles, infectious diseases, the sight of blood), the physical characteristics that inhibit donation (e.g., low blood pressure and abnormal blood levels), (i.e. "I can't donate due to my health condition." "The sight of blood is unpleasant and I'm afraid of needles and of infectious disease transmission"), the lack of communication and information about initiatives, the lack of interest and the lack of transparency in the system, which generates insecurity. Respondents argued that there is a low propensity to donate among young people due to the lack of information, disinterest and a loss of moral values. (i.e. “It is necessary to meet young people and take initiatives in schools and universities to sensitize them to blood donation”). For donors, the main obstacles to donation are long queues, the location and accessibility of transfusion centres as well as lack of information and communication about blood donation events and initiatives. (i.e. “In small towns, people are not informed about the importance of donation. Donation initiatives are not advertised. Information is often not provided on the places, days and times to donate.”). Both donors and non-donors suggested promoting communication-related to blood donation events by not only using traditional WOM but also using advertising campaigns on social networks (e-WOM) and educational events in schools and universities. (i.e. "More communication and involvement in donation are needed. Social media platformer should be used to receive and transmit information on blood donation campaigns and requests.". "Given the lack of blood, the advertising campaign should be increased, especially in the summer, given the low number of donors.").
As shown below (Table II), both groups consider blood donation a personal responsibility (21) and a custom of altruism and generosity (18) that creates collective well-being. For donors, the donation is a moral obligation (6). Donors believe in the intrinsic values of donation (7); they donate to help friends/family (6) or for external influences (2) such as meeting new people, having a free check-up or obtaining social recognition among friends/family. The main motivations for not donating are fear (8), which includes fear of needles, the sight of blood, bruising and adverse reactions or the lack of requisites to donate (3). Also, the non-donors do not donate due to the lack of transparency (2), which generates insecurity, or because they are not interested in blood donation (2). Donors are perceived as people with a healthy lifestyle (20), people who are altruistic (12) and people who are responsible (7). Also, the non-donors perceive donors as courageous (3) and religious (2).
Table II. Qualitative results: frequency of recurrent key issues for donors and non-donors.
4.2 Quantitative results
The sample is composed of 260 respondents, divided into donors (N=173) and non-donors (N=87). Next, the results of the collected data from the two questionnaires are shown.
4.2.1 Sample description
An overview of the sample characteristics is shown in Table III.
The sample of donors is composed of 173 respondents, including 87 males (50.3%) and 86 females (49.7%) who belonged to the 18-24 (17.3%), 25-34 (38.7%), 35-44 (18.5%), 45-54 (19.1%), 55-64 (5.8%) and over 65 (0.6%) age ranges. High school is the most common level of education (60.1%) of donors. A total of 57.2% of donors are civil servants, and 24.3% are students.
Regarding the donation career of respondents emerged that:
-33 donors (10 males, 23 females) donate occasionally once a year;
-53 respondents (12 males, 41 females) donate blood two times a year;
-38 respondents (28 males, ten females) donate three times a year;
-49 donors (37 males, 12 females) are regular donors (4 times a year).
The non-donors sample included 87 respondents, of which 65.5% were females, and 34.5% were males. The majority of the non-donor sample (52.9%) is in the 25-34 age range. The other respondents belonged to the following age ranges: 18-24 (23%), 35-44 (13.8%), 45-54 (5.7%), 55-64 (2.3%) and over 65 (2.3%). The majority of the sample had a bachelor's degree (36.8%); 43.7% were civil servants, 34.5% were students, and 10.3% were unemployed.
Table III. Demographic characteristics of the quantitative samples.
The distribution of the respondents across Italian regions (Appendix III) shows that the significant number of respondents are Veneto (25.4%), Piedmont (17.9), Lazio (17.9%) and Puglia (11.6).
Concerning the motivations behind the donation, from the analysis of the open questions, it emerged that the main aspects enticing donors to donate are: personal choice and beliefs (37%), educational activities (17%), to have accompanied relatives and friends to donate (13%), and sensitive campaigns (13%) (Table IV a). While the favourite locations for donating seems to be schools and universities (45%) and ad hoc areas in the city centre (24%). For non-donors, the primary aspect that may encourage them to start donation dating is the needs of blood from friends and family members (54%) followed by sensitive companies (21%) (Table IV b).
Table IV. Quantitative results: frequency of donor's motivations to blood donation and a favourite location for donating (a) and frequency of motivations that could push Non-Donors to donate and favourite location to start donating (b)
4.2.2 Donors’ and non-donors’ internal reliability and validity
Regarding the donor dataset, the internal reliability of each factor was calculated using Cronbach's alpha coefficient , and the construct validity was calculated using Convergent Variance Extracted (AVE) and Composite Reliability (CR). All the data meet the criteria for acceptable reliability and validity: 0.7 for Cronbach's alpha [52, 53,54], 0.5 for AVE and 0.7 for CR . Additionally, for the non-donor dataset, reliability and validity were calculated using the same measures. As shown in Table V, the data meet the criteria for acceptable reliability and validity [52-54].
Table V. Constructs reliability and validity: Cronbach’s alpha, Average Variance Extracted (AVE) and Composite Reliability (CR) for Donors (Group A) and Non-donors (Group B).
4.2.3 Structural Equation Models: a multi-group analysis
The conceptual model was tested with SEM using Mplus 7 software .
The adopted procedure is as follows. First, we separately developed models for Group A, i.e., the donors (N=173), and Group B, i.e., the non-donors (N=87). Then, we used the multi-group analysis to identify the main differences between the two independent samples simultaneously. The invariance between the two samples was tested by using multi-group SEM. The baseline model was fitted to the data on both groups simultaneously, χ2 (df = 715) = 1.326.504, p < .01, CFI = .903, RMSEA = .080 (95% CI = 0.074 0.088), SRMR = .065, supporting the configural invariance hypothesis. Then, constraining the loadings between the groups yielded a nonsignificant increase of the CFI (ΔCFI = .003), providing support for metric invariance. Moreover, constraining the intercepts between the groups, we observed a small decrease in the CFI: (ΔCFI = .003). The model is assumed to be non-invariant if the decrease in CFI is larger than 0.002  compared to the baseline model. We have not considered the difference between the chi-square of nested models considering the strong dependence of the chi-square on the sample size . Thus, the hypothesis of scalar invariance can be accepted.
Hence, a graphical representation of the model is proposed. The robust estimator MLMV was used for continuous variables to correct covariance. Table VI shows the results of the goodness-of-fit parameters. Then, a graphical representation of the measurement models is proposed for both groups.
The results of the SEM goodness-of-fit parameters are presented below (Table VI):
- Root mean square error of approximation (RMSEA=0.073; 90% C.I. = 0.066;0.080): acceptable according to Browne and Cudeck ;
- Critical fit index (CFI=0.915): acceptable according to Bentler ;
- Tucker-Lewis index (TLI =0.901): acceptable according to Tanaka ;
- Standardized root mean square residual (SRMR=0.062): acceptable according to Hu and Bentler .
Table VI. Goodness-of-fit index model for Donors (Group A) and Non-donors (Group B).
The analysis confirms that the χ2 (chi-squared) value is significant with its linked probability value. The χ2 test was statistically significant, which indicates an unsuitable fit, even if, according to several authors, it needs to be compared with other indexes before rejection [58,59,60,61].
The other indicators of goodness of fit can be considered adequate since all the values fall within the thresholds suggested by the literature. The graphical representation of the model is shown for both groups: donors (Figure 4) and non-donors (Figure 5), including only the significant relations between factors (p<0.05).
The observed model of Group A shows that there is the covariance between Subjective Norm and Perceived Behavioural Control (β=0.644), as in the model previously tested by Ajzen .
The observed model of Group B shows that there is the covariance between Subjective Norm and Perceived Behavioural Control (β=0.603), as in the model previously tested by Ajzen , as well as between Perceived Behavioural Control and Communication (β=0.524).
The results of the two groups are summarized in Tables VII. It is possible to notice that the indicators have significant loadings on their assigned constructs. The residual variances are reported in Appendix IV.
Table VII. Factor loadings statistics, Donors (Group A) and Non-donors (Group B).
The main results and the status of the research hypotheses for both groups are summarized in Table VIII.
Table VIII. Status of research hypotheses for Donors (Group A) and Non-donors (Group B).
Regarding the donors, all the proposed hypotheses are supported (p-value <0.005), except for H2, H6 and H7. The observed model in Group A (donors) shows that Attitude (β=0.441) and Perceived Behavioural Control (β=0.553) directly and positively influence Intention (H1, H3). Communication has a strong impact on Attitude (β=1.005) and on Propensity to Generate WOM (β=0.494) (H4, H5). Propensity to Generate WOM is predicted by Intention (β=0.216), Service Quality (β=0.268) and Communication (β=0.494) (H8, H9, H5). However, Subjective Norm (β=-0.031) and Inhibitors (β=-0.025) do not significantly affect Intention to donate (H2, H7), and Communication (β=-0.066) does not significantly affect Inhibitors (H6) (p>0.05). In particular, Subjective Norm does not affect Intention or Inhibitors. In addition, Communication does not influence Inhibitors (β=0.271).
Regarding the non-donors, all the proposed hypotheses are supported (p-value <0.05), except H1 and H7. In particular, the results reveal that Subjective Norms (β=0.346), Perceived Behavioural Control (β=0.410) and Attitude (β=0.052) affect Intention to donate (H2, H3, H1). Concerning the construct Attitude, its p-value can be considered marginally significant (p-value=0.054), and for the principle of conservation, we decided to accept H1. Communication positively influences Attitude (β=1.000) and Inhibitors (β=0.183) (H4, H6). Communication (β=0.505), Intention (β=0.174) and Service Quality (β=0.209) affect Propensity to Generate WOM (H5, H8, H9). However, Inhibitors (β=-0.039) does not affect Intention to donate (p-value=0.493) (H7). In particular, Inhibitors is not a significant antecedent of Intention to donate among non-donor respondents.
Summarizing, the results related to the donor group show that Attitude and Perceived Behavioural Control are antecedents of Intention to donate (again). Moreover, the results reveal that Inhibitors do not influence Intention to donate, which makes sense in the case of donors. Communication and Information, which has no impact on Inhibitors, affects Attitude and Propensity to Generate WOM, and Propensity to Generate WOM is affected by Intention and Service Quality.
While, the case of non-donor Attitude, Subjective norm and Perceived Behavioural Control directly influence Intention to donate (for the first time). Even among non-donors, Information and Communication predict both Attitude and Inhibitors. Regarding Propensity to generate WOM, there are three main predictors: Intention, Service Quality and Communication. The non-donors’ Propensity to generate WOM is affected by their Intention and by the importance they give to Service Quality. Among non-donors, Attitude is also influenced by Communication, and Communication has a positive impact on Inhibitors and Propensity to Generate WOM.