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Article

An Empirical Test of Educational Donors’ Satisfaction Levels in Donating for Education before and after the COVID-19 Era

by
Huifeng Pan
1 and
Hong-Youl Ha
2,*
1
Department of Economics, Shanghai University of Political Science and Law, Shanghai 201701, China
2
Department of International Trade, Dongguk University-Seoul, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(19), 12874; https://doi.org/10.3390/su141912874
Submission received: 6 September 2022 / Revised: 30 September 2022 / Accepted: 7 October 2022 / Published: 9 October 2022
(This article belongs to the Section Economic and Business Aspects of Sustainability)
Browse Figure
Versions Notes

Abstract

:
Adopting expectancy–disconfirmation theory and the cycle of satisfaction, this study examined the dynamic nature of the satisfaction cycle and identified the moderating roles of satisfaction and adjusted expectations in the context of sustaining educational donations. Using a three-time-lag survey, we showed that the sequential shifts from prior expectations (T1) to satisfaction (T2) to adjusted expectations (T3) were consistent with the expectance–disconfirmation mechanism. We found that the carryover effect between prior and adjusted expectations is significantly powerful. In addition, we found that the two mediators—satisfaction and adjusted expectations—absorb the effect of prior expectations and then transfer that effect to behavioral intentions over time. Therefore, this study provides a theoretical account of the link between prior expectations–satisfaction–behavioral intentions and prior expectations–adjusted expectations–behavioral intentions during donation activities.

1. Introduction

For at least the past 30 years, research on satisfaction has more than sufficiently covered the topics of loyalty, word-of-mouth marketing, and behavioral intentions [1,2,3]; however, most of such studies have been limited to products or services. Satisfaction-based research examining the sustainability of educational donations can provide a solid theoretical framework by employing satisfaction theory in the context of the COVID-19 pandemic era. Regarding the pandemic, behavioral researchers have emphasized the importance of new insights due to the resulting shifts in consumer behavior [4], particularly in the education sector [5,6]. For example, the satisfaction cycle [7] is useful in ascertaining temporal and carryover effects during subsequent consumption periods. However, consideration of how educational donors’ prior expectations change after donating is a fundamental issue, especially from a sustainability perspective. If donors’ satisfaction after donating (T2) is less than expected (T1), they may not donate again in the future.
Furthermore, the positive impact of educational donations can be improved by ensuring sustainability and implementing donation-satisfaction measures. Therefore, this study seeks to understand the satisfaction cycle for the relationships among prior expectations, satisfaction, behavioral intentions, and adjusted expectations. In particular, this study is closely related to the unique current situation of the pandemic. Researchers may wish to expand on their consideration of the change in donation behavior within the satisfaction cycle while maintaining their academic ability to study the evolution of consumer behavior during the COVID-19 pandemic.
After a comprehensive review of the relevant literature, we found that the theoretical framework employed in satisfaction studies mainly reflects two research streams. The first stream is the expectancy–disconfirmation paradigm, which emphasizes the link between prior expectations, performance, and satisfaction [7,8,9]. For example, Oliver [7] highlighted the importance of previous expectations as a determinant of satisfaction levels, and Yi and La [10] emphasized the dynamics of customer expectations using the terms “prior expectations” and “adjusted expectations.”
The second stream focuses on the dynamic nature of marketing constructs [11]. By highlighting the importance of changes in constructs, this stream generates a deeper understanding of the validity of research results. For example, Mittal et al. [12] confirmed that temporal dynamics influence the satisfaction levels of vehicle owners’ behavior, and Johnson et al. [13] analyzed the carryover effects of marketing on mobile users’ behavior. Thus, the marketing and sustainability literature corpus tends to emphasize changes in research constructs over time.
In line with these observations, the first research gap that this study sought to fill was how the expectancy–disconfirmation paradigm acts as a function of donating behaviors in the context of educational funding. Although the satisfaction cycle fully substantiates the paradigm theoretically [7], to the best of our knowledge, empirical research has yet to combine a longitudinal approach (to detect changes that might occur over time) with donors’ revised expectations of satisfaction and behavioral intentions.
The second research gap addressed in this study is the relative dearth of knowledge on research constructs’ dynamic nature. For example, when adjusted expectations improve, decrease, or remain unchanged compared to prior expectations, the following questions emerge: Can prior expectations affect satisfaction and behavioral intentions during subsequent donation periods? Do previous expectations significantly affect adjusted expectations?
We employed a conceptual model for the two mediations—satisfaction and adjusted expectations—to answer these research questions arising from the gaps in the literature. First, extant research underscores the importance of mediator effects, which can explain the variations in behavioral outcomes [13,14]. In this regard, the literature has emphasized that prior expectations affect satisfaction and behavioral intentions and, in turn, require a comprehensive framework [15]. Theoretically, previous expectations affect donor satisfaction during subsequent donation activities, resulting in adjusted expectations. Thus, this study offers a deeper understanding of the satisfaction cycle through the mediating effects of prior expectations, moving from satisfaction to adjusted expectations.
Second, it is essential to develop a comprehensive understanding of the mechanism through which theoretical relationships are established beyond the marketing context [16]. The experience of donating for education helps people manage behavioral directions and adjusts future expectations. Mechanisms that elaborate on how the behavioral intentions of T3 work may establish the fundamental role of an evolutionary stage beyond the marketing context. To this end, our study provides theoretical insight into how the mechanism caused by the temporal differences should relate to the sustainability of educational donations.
In line with this, we extend the satisfaction theory that predicts a new phenomenon using a longitudinal approach. An additional novelty of this study is the ability to account for identifying the new evolutionary mechanism of expectations. These contributions are especially relevant for sustainable educational donation practices. Therefore, the focus of this study is not only on extending the satisfaction theory, but also on strategically pursuing the success of sustainable educational donations.

2. Theoretical Background

The COVID-19 pandemic has changed consumer behavior. During the lockdown period, satisfaction evaluations have changed from analyzing how values impact satisfaction to how new experiences will continuously affect satisfaction. Consistent with our study, a recent McKinsey & Company report highlighted the importance of behavior change, depending on the satisfaction of new experiences [17].
As such, satisfaction is a popular topic in marketing literature. While the definition of satisfaction often depends on the nature of a given study, it has a strong tendency to be assessed by cumulative experience rather than by transactional judgment [7]. In addition, satisfaction is considered a response to an evaluation process [18]. Considering these observations, we define satisfaction as an affective fulfillment response based on a consumer’s cumulative experience with a particular service or product. This definition reflects affective and evaluative responses [7,19], psychological states [18], and overall evaluation over time [12,20].
Drawing upon expectancy–disconfirmation theory and the satisfaction cycle, this study examines a conceptual rationale for the developmental process of customer satisfaction. In doing so, the first step is to ascertain the donors’ (or customers’) expectations of a particular object (e.g., a product or service) before purchasing or experiencing it. Expectations are the beliefs about the experiences associated with a particular object, product, or service [7], and they play an important role in evaluating the procured object or experience, making initial behavioral decisions, and choosing subsequent behaviors [21]. Thus, prior expectations influence initial decision-making, whereas adjusted expectations influence repeated behaviors [9]. Regarding the latter, we define adjusted expectations as a participant’s expectations that have been updated on the basis of their most recent experience.
Expectations result in either confirmation or disconfirmation, depending on whether or not there is a discrepancy between outcomes and prior expectations [1]. If outcomes differ from expectations, people may react favorably or unfavorably. Consistent with assimilation–contrast theory [22], we propose that expectations related to educational donations can be confirmed (i.e., the results of educational donations are as expected) or disconfirmed (i.e., the results of educational donations exceed expectations). Accordingly, our first hypothesis is as follows:
Hypothesis 1 (H1).
Prior expectations (T1) positively affect satisfaction (T2) after donors participate in making educational donations.
After a donor participates in educational donation activities, satisfaction is typically reevaluated by the expectancy–disconfirmation process [7,10]. In H1, if the satisfaction level is positive, then expectations for subsequent events should be adjusted to a level that is either equal to or slightly higher than the prior expectation level. Thus, previous expectations should positively affect adjusted expectations, resulting in a carryover effect. Similarly, if satisfaction with donation activities is positive, educational donors will adjust their expectations for subsequent actions based on their prior expectations. However, because the series of processes in this study are omitted from both the satisfaction cycle and expectancy–disconfirmation theory, our logical approach offers the following additional hypotheses:
Hypothesis 2 (H2).
Prior expectations (T1) positively affect adjusted expectations (T3).
Hypothesis 3 (H3).
Satisfaction (T2) positively affects adjusted expectations (T3).
Although the satisfaction cycle is oriented toward sequential processes, prior expectations may be essential in forming people’s desires and, subsequently, triggering their behavioral intentions [23]. For example, if customers have expectations regarding a particular event, they will be strongly motivated to participate in the future. Furthermore, if satisfaction mediates the relationship between prior expectations and behavioral intentions, the direct influence of prior expectations on intentions may be positive. More specifically, if participants assess their prior expectations regarding an object or service, the heuristic influence on satisfaction judgment may be enhanced, affecting behavioral intentions [24]. Thus, the following hypothesis is proposed:
Hypothesis 4 (H4).
Prior expectations (T1) positively affect behavioral intentions (T3).
Satisfaction is generally formed through accumulated experience, triggering the subsequent behavioral intentions, which is consistent with both expectancy–disconfirmation theory and the cycle of satisfaction. However, regarding educational donations, it is unclear how educational donors’ satisfaction levels lead to continuous donation behaviors or subsequent educational donation intentions. To this end, certain behaviors signal participants’ satisfaction with a particular service [25]. More specifically, if donors are satisfied with an educational donation event, they are more likely to continue donating. Thus, the following hypothesis is presented:
Hypothesis 5 (H5).
Satisfaction (T2) positively affects behavioral intentions (T3).
As noted earlier, expectations are continuously revised as satisfaction accumulates during subsequent episodes [7,26]. That is, expectations can induce the updating of existing satisfaction levels [7] and reinforce donors’ behavioral intentions for future events. An early work by Rust and Oliver [27] argued that newly updated expectations depend on evaluating prior experiences, whch resulting in behavioral intentions in the next period. Thus, our final hypothesis is the following:
Hypothesis 6 (H6).
Adjusted expectations (T3) positively affect behavioral intentions (T3).

3. Methodology

3.1. Data Collection

We collected data on educational donors in Korea. The criteria for our survey participant selection were those who have participated in educational donation activities at least twice but have not yet participated in educational donation activities in 2021. To meet these criteria, we used a sample framework of educational donation participants registered with Donation for Education (www.teacherforkorea.go.kr (accessed on 1 August 2021)). We implemented stratified sampling techniques based on probability sampling. In addition, we collaborated with a local donation center to identify the program schedules of educational donation participants in advance and to contact them both before and after they made educational donations.
Using the Google Survey platform, 326 participants were asked to fill in a survey email and respond to a text message. As shown in Figure 1, we conducted a longitudinal study using three response cycles for each respondent. At T1 (one week before making educational donations), we collected 261 responses, excluding 14 that were unusable (usable responses = 247). At T2 (one week after donating), we contacted the participants (n = 247) who had responded in the T1 period and collected 225 responses, excluding eight due to unfaithful responses and non-participation in educational donations (usable responses = 217). One month later (T3), we administered a final survey for the participants who had provided valid responses in the T2 period; a total of 187 responses were collected, of which 185 were usable (T3) and employed to test our hypotheses. In terms of determining sampling size, as research on educational donations was limited, we relied on statistics based on a sample. According to the recommendation of Wunsch [28], the sample size was set to a minimum of 30 to secure statistical usefulness using normal distribution and to minimize sampling errors as much as possible. The type Ⅰ error method has been widely accepted for calculating the appropriate sample size [29,30].
Among our sample’s respondents, 63.4% (n = 138) were male and 75.5% (n = 162) had more than two years’ experience making educational donations. The largest donation programs were robot technology (27.4%), followed by coding information (20.6%), oriental medicine (14.2%), internet of things (12.8%), electronic vehicle technology (8.1%), and playing musical instruments (6.6%).

3.2. Measures

We selected items used in previous studies to measure the four constructs (Table 1). Prior expectations were measured using three items adapted from Yi and La [10]. Adjusted expectations were measured similarly to prior expectations, but the message “after donation participation” was added. Therefore, these items were not simply cited from Yi and La [10], but were modified to fit this study’s purpose. Both constructs (all six items) used 5-point Likert scales with items ranging from “not at all” (1) to “quite a lot” (5). We measured satisfaction with a single item adapted from Szymanski and Hise [31]. Although only one item was chosen, two types of anchors were used as follows: “very dissatisfied” (1) to “very satisfied” (5) and “very displeased” (1) to “very pleased” (5). Finally, behavioral intentions were measured using three items adapted from Baker and Cromption [32]. These items’ anchors ranged from “strongly disagree” (1) to “strongly agree” (5).

3.3. Model Evaluation

We tested our proposed model using partial-least-squares–structural-equation modeling (PLS-SEM). Because PLS-SEM places no constraints on a sample’s normal distribution, it can be employed to analyze complex models that include many latent and observed variables [33]. In addition, PLS-SEM has the advantage of analyzing models with small sample sizes, without identification problems [34]. Because this study has a complex design with different time lags and a limited final sample (n = 217), we decided that PLS-SEM was the ideal method. In particular, this study used the consistent algorism of PLS-SEM, which addresses measurement error. Finally, we verified the path’s significance by generating 1000 resamples, using the bootstrapping method. We used the Smart-PLS program 4.0 to test our research hypotheses.

4. Results

4.1. Model Estimates

To verify the measurement model, we checked for convergent validity by factor loadings, average variance extracted (AVE), and composite reliability (CR) [33]. As shown in Table 1, all loading values were above the minimum fit level of 0.7, and the AVE values were also exceeded, above the minimum of 0.5. The Cronbach’s alpha values exceeded 0.7 for all constructs, and the CR values also satisfied the cut-off value of 0.7. Thus, we concluded that the convergent validity was acceptable.
Next, we assessed discriminant validity based on Fornell and Larcker’s criterion [35], which states that the correlation value must be less than the square root of AVE. As shown in Table 2, all correlation values met this requirement. As there has been criticism of Fornell and Larcker’s criterion, discriminant validity using the Heterotrait Monotrait Ratio (HTMT) was also required [36]. To this end, we evaluated discriminant validity based on a conservative level of 0.85 (Table 2), and all values were below this level, indicating that discriminant validity was acceptable. R² values (the explanatory power of endogenous variables) for satisfaction, adjusted expectations, and behavioral intentions were 0.15, 0.38, and 0.29, respectively. Finally, the model’s predictive power was sufficient because the standardized root mean square residual (SRMR) was 0.078, which was smaller than the cut-off value of 1.

4.2. Path Results

We analyzed our proposed hypotheses using Smart–PLS 4. H1 evaluated the relationship between prior expectations and satisfaction after donors participated in educational donations. As shown in Table 3, the proposed relationship was both positive and significant (β = 0.23, p < 0.01), indicating that donors’ satisfaction met or exceeded their expectations. H2 tested the relationship between prior and adjusted expectations after making educational donations. We found that the proposed relationship was positively significant (β = 0.44, p < 0.01). That is, donors’ prior expectations positively influenced their adjusted expectations, resulting in an update of prior expectations. These findings supported both H1 and H2.
H3 proposed that the relationship between satisfaction and adjusted expectations was positively significant and, as expected, the relationship was supported (β = 0.34, p < 0.01). Next, H4 proposed that prior expectations positively affect behavioral intentions. Interestingly, the proposed relationship was not supported (β = 0.03, p > 0.05). This suggests that the direct effect of prior expectations on behavioral intentions is diluted over time. The purpose of H5 was to test the relationship between satisfaction and behavioral intentions, which was both positive and significant (β = 0.32, p < 0.01). Finally, H6 stated that adjusted expectations would positively affect behavioral intentions. We found that the relationship was indeed positively significant (β = 0.33, p < 0.01). These findings substantiated both H5 and H6.

4.3. Indirect Effects

Although no indirect effects were discussed in our hypotheses, our model did include five vital indirect effects. As noted earlier, our model covered the important mediating effects of satisfaction and adjusted expectations, through the link between prior expectations–satisfaction–behavioral intentions and prior expectations–adjusted expectations–behavioral intentions. Table 4 shows that the prior expectations–satisfaction–behavioral intentions linkage was statistically significant (β = 0.07, p < 0.05). Notably, another mediation of the prior expectations–adjusted expectations–behavioral intentions linkage was likewise significant (β = 0.14, p < 0.01). Because the direct effect of prior expectations on behavioral intentions was insignificant (H4), these two mediating variables (i.e., satisfaction and adjusted expectations) absorbed the impact of prior expectations and transferred it to behavioral intentions. Thus, a nuanced understanding of these mediators was essential in determining the expectancy–disconfirmation mechanism.

5. Discussion

This study’s main objective was to determine whether the expectancy–disconfirmation paradigm, which emphasizes the link between prior expectations, performance, and satisfaction, works in a sustainable educational donation context. Other objectives were to identify whether there is the possibility of a carryover effect of expectations and to track the two main mediating effects of satisfaction and adjusted expectations over time. Regarding the latter, the moderating roles of satisfaction and adjusted expectations received empirical support; that is, there was no direct effect of prior expectations on behavioral intentions.
Our findings demonstrate that the sequential shifts from prior expectations (T1) to satisfaction (T2) to adjusted expectations (T3) are consistent with the expectance–disconfirmation mechanism. We also found that the carryover effect between prior and adjusted expectations is significantly powerful, indicating that adjusted expectations stem from prior expectations, thereby extending the satisfaction cycle. Based on this study’s key findings, we provide theoretical contributions and practical insights in the following section.

5.1. Theoretical Implications

This study contributes to the literature on satisfaction levels and educational donations by examining the mediating effects of satisfaction and adjusted expectations, which may offer a nuanced understanding of the expectancy–disconfirmation mechanism. Previous studies tended to focus on the direct influence of expectations on satisfaction level or a specific performance factor [7,21,37]. However, both prior expectations–satisfaction–behavioral intentions and prior expectations–adjusted expectations–behavioral intentions linkages are sequentially generated by participants’ updated prior expectations and satisfaction levels.
In addition, our findings are related to aspects of sustainability. Donation satisfaction measures should be measured sustainably, as these approaches can track donors’ satisfaction over time. The two mediating roles indicated that donor satisfaction is predominantly in a sustainable donation context. A deeper understanding of these two mediators provides a more extended and integrated mechanism of educational donation in all phases of satisfaction judgment [38]. In particular, tracking donor satisfaction is important in monitoring the effectiveness of educational donations, fulfilling donors’ expectations, and facilitating their subsequent participation.
Using a longitudinal study, Habel et al. [39] demonstrated that customer expectations do not affect sales growth if the disconfirmation and placebo effects are balanced. Although this study did not focus on placebo effects, the same result (i.e., that prior expectations do not affect behavioral intentions) could be explained by the two mediating roles of satisfaction and adjusted expectations. As discussed above, these two mediators absorb the effect of prior expectations and then transfer that effect to behavioral intentions over time. Therefore, the expectancy–disconfirmation paradigm (i.e., that prior expectations can affect subsequent behavioral intentions based on a specific performance) may require a revised model that reflects mediating effects over time.

5.2. Practical Implications

Revisiting Table 4, this study demonstrates the importance of mediators to fund-raising managers. Both satisfaction and adjusted expectations are crucial in facilitating participation in educational donations. Thus, the question becomes: How can managers effectively manage these constructs? We highlight the importance of donors’ prior expectations to influence education donors successfully. Because participant expectations of donating for education are unstable [40], prior expectations serve as an anchor for educational donation evaluations. In other words, there is a need to effectively manage expectations in advance through video training, participant reviews, and word-of-mouth marketing to encourage those who are hesitant to participate in educational donation activities. However, it must be noted that managing expectations at an acceptable level is key, because having excessive expectations can lead to dissatisfaction or negatively adjusted expectations.
Managers responsible for educational fund-raising want to secure as many potential donors as possible. Creating effective promotions (or word-of-mouth marketing) allows managers to widen the scope of potential donors. For example, by publicly disclosing interviews before and after donations, managers may heighten prospective donors’ expectations and motivation and strengthen subsequent donation activities. Alternatively, managers could depend on the carryover effects of expectations, which this study has identified as effectively managing prospective donors’ expectations, as these effects are based on longitudinal data estimates that are accurately derived from prior expectations. From a perspective that considers the sustainability of educational donations, managing expectations should be carefully planned, controlled, and implemented [41].

5.3. Limitations and Future Research Directions

Although this study provides theoretical and practical implications, it has some limitations. The study’s main focus is on educational donors. However, the overall evaluations of donating for education tend to rely on the roles of participating students. Therefore, future research should reflect the interaction effect between students and donors to update the satisfaction cycle structure, at least in the context of educational donation.
Another limitation is that educational donors’ competency differs. Although we conducted this study based on people who had not yet participated in educational donations in 2021, the differences in the educational capabilities of potential donors inevitably affected their performance. This, in turn, affected satisfaction and adjusted expectations. Thus, we expect our findings to be expounded upon by comparing the effects of satisfaction/dissatisfaction, adjusted expectations (positive or negative), and behavioral intentions based on individual competencies.

6. Conclusions

This study aimed to draw insights from the literature on satisfaction to better understand the expectancy–disconfirmation paradigm and the satisfaction cycle, and to capture the moderating roles of satisfaction and adjusted expectations in the context of sustaining educational donations. Using a three-time-lag survey, we found that the sequential shifts from prior expectations (T1) to satisfaction (T2) to adjusted expectations (T3) were consistent with the expectance–disconfirmation mechanism. As for the role of the two mediators, they absorbed the effect of prior expectations and then transferred it to behavioral intentions over time.
As the COVID-19 pandemic has changed consumer behavior, our findings provide important implications for educational donation literature. From a theoretical perspective, our findings traced how the cycle of satisfaction and expectancy–disconfirmation theory were generated and evolved through sequential events. In practice, our findings emphasized the monitoring of donors’ expectations. Thus, given the focus and outcomes of this research, this study makes a theoretical and practical contribution to this field, where donating and education intersect.
As noted earlier, we recommend additional studies to extend our results. More specifically, further studies should expand the satisfaction theory and provide practical implications for educational donations. We hope that such studies will serve as a cornerstone for sustainable educational donation activities through the evolutionary mechanism of donor expectations presented in this study.

Author Contributions

Conceptualization, H.P. and H.-Y.H.; methodology, H.-Y.H.; software, H.P.; validation, H.P. and H.-Y.H.; formal analysis, H.-Y.H.; investigation, H.P.; resources, H.P.; data curation, H.-Y.H.; writing—original draft preparation, H.-Y.H.; writing—review and editing, H.P.; visualization, H.P.; supervision, H.-Y.H.; project administration, H.P.; funding acquisition, H.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financed by the 2020 Foundation for Young Scholars of Philosophy and Social Science of Shanghai Research on the Enhancement of the Participation Mechanism of Individual Donation Behavior in Education-Targeted Poverty-Alleviation Policy (2020EGL002).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 12874 g001 550
Figure 1. Conceptual model of two mediations.
Figure 1. Conceptual model of two mediations.
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Table
Table 1. Measures and CFA results.
Table 1. Measures and CFA results.
AVELoading
Prior expectations (α = 0.77; CR = 0.87)0.68
I expect the program to motivate me to participate. 0.75
I expect the program to be useful to students. 0.88
How good do you expect the program to be, overall? 0.85
Satisfaction (α = 0.75; CR = 0.86)0.70
Overall, how do you feel about your education donation experience?
Very dissatisfied to very satisfied 0.86
Very displeased to very pleased 0.82
Adjusted expectations (α = 0.80; CR = 0.88)0.71
After participating in the donation program, I expect the program to motivate me to participate further. 0.80
After participating in the donation program, I expect the program to be useful to students. 0.86
After participating in the donation program, how good do you expect the program to be, overall? 0.86
Behavioral intentions (α = 0.73; CR = 0.93)0.77
I will continue attending <the program> if I have time. 0.90
I will say positive things about <the program or educational donation> to other people. 0.89
I will encourage friends and relatives to join <the program>. 0.85
Table
Table 2. Discriminant validity results.
Table 2. Discriminant validity results.
Fornell and Larcker’s Criterion1234
1. Prior expectations0.82
2. Satisfaction0.230.84
3. Adjusted expectations0.510.440.84
4. Behavioral intentions0.210.460.450.88
Heterotrait Monotrait Ration (HTMT)
1. Prior expectations
2. Satisfaction0.28
3. Adjusted expectations0.640.55
4. Behavioral intentions0.210.650.58
Note: Bold numbers indicate the root-squared value of AVE.
Table
Table 3. Path estimates.
Table 3. Path estimates.
PathEstimateT-Valuep-ValueSupport?
H1. PE → SA0.232.7170.007Yes
H2. PE → AE0.447.4750.000Yes
H3. SA → AE0.345.3150.000Yes
H4. PE → BI0.03 (ns)0.4290.668No
H5. SA → BI0.324.1400.000Yes
H6. AE → BI0.334.0920.000Yes
Note: PE = Prior expectations; SA = Satisfaction; AE = Adjusted expectations; BI = Behavioral intentions
Table
Table 4. Specific indirect effects.
Table 4. Specific indirect effects.
PathEstimateT-Valuep-Value
PE → SA → AE0.082.7980.005
PE → SA → BI0.072.4030.017
SA → AE → BI0.113.1580.002
PE → AE → BI0.143.5860.000
PE → SA → AE → BI0.032.1730.030
Note: PE = Prior expectations; SA = Satisfaction; AE = Adjusted expectations; BI = Behavioral intentions.
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Pan, H.; Ha, H.-Y. An Empirical Test of Educational Donors’ Satisfaction Levels in Donating for Education before and after the COVID-19 Era. Sustainability 2022, 14, 12874. https://doi.org/10.3390/su141912874

AMA Style

Pan H, Ha H-Y. An Empirical Test of Educational Donors’ Satisfaction Levels in Donating for Education before and after the COVID-19 Era. Sustainability. 2022; 14(19):12874. https://doi.org/10.3390/su141912874

Chicago/Turabian Style

Pan, Huifeng, and Hong-Youl Ha. 2022. "An Empirical Test of Educational Donors’ Satisfaction Levels in Donating for Education before and after the COVID-19 Era" Sustainability 14, no. 19: 12874. https://doi.org/10.3390/su141912874

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