From Scandal to Redemption: Exploring Word-of-Mouth Effects on Professor Brand Equity

From Scandal to Redemption: Exploring Word-of-Mouth Effects on Professor Brand Equity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Scandal to Redemption: Exploring Word-of-Mouth Effects on Professor Brand Equity Ahmad Juhaidi, Sri Hartini, Noor Hidayati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5853054/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bad news about professor scandals can damage the brand of professors in Indonesia. This study aims to explore the relationship between negative word of mouth (NWOM), positive word of mouth (PWOM), satisfaction (SAT), commitment (COMM), trust, and reputation (REP) to professor brand equity (PBE). This study included 306 students selected through convenience and snowball sampling. The analysis employed the partial least squares structural equation modeling (PLS-SEM) technique. We found that trust affects commitment, and professor reputation affects student trust. Student satisfaction with academic services has a huge effect on commitment, trust, reputation, and PBE. NWOM has no effect on all of these variables, except for PWOM, which affects REP and PBE. Further, in male students, the effect of satisfaction on PBE tends to be greater than that of female students. The findings make a theoretical contribution to the study of professors as a human brand. Marketing Other Business Word of mouth satisfaction professor brand equity Figures Figure 1 Figure 2 1. INTRODUCTION Professors in academia are highly regarded individuals who hold a distinguished position, considered just below the revered status of a prophet. The professor serves as an intellectual leader, embodying the roles of role model, mentor, advocate, guardian, acquirer, and ambassador (Macfarlane, 2011). The career trajectories of professors are linked to their standing within their academic discipline, as evidenced by citations in esteemed publications and their global engagement (Rossier, 2020). A professor is a highly productive scientist over the course of his or her career (Kwiek & Roszka, 2024). Professorship is the pinnacle of academic careers (Mantai & Marrone, 2023). Professors can evolve into personal brands (Jillapalli & Wilcox, 2010). Consequently, the professor brand signifies academic excellence and exceptional ethical standards within society. Professors brand are administering challenges in Indonesia. The fraud controversy concerning the acquisition of professorships led to the withdrawal of eleven professors' credentials (Lavenia Y, 2024; Rizki, 2024; Widodo, 2024). The conferral of professorships to politicians has emerged as a subject of discourse on social media and in mainstream media (Irawan, 2024). The news that has emerged as a national concern is a detrimental form of negative word of mouth (NWOM) that will influence students' perceptions of professors. In marketing research, negative word-of-mouth (NWOM) poses significant risks to brands, including damage to brand reputation and potential boycotts ((Azemi et al., 2020; Bambauer-Sachse & Young, 2024). The study reveals that negative word-of-mouth (NWOM) might tarnish or harm the reputation of professors as brands. However, we lack evidence regarding the impact of word-of-mouth on the brand of the professor following the crisis. Studies on professors have been conducted to understand professors from various points of view. The study, for example, was conducted by Vergara et al., (2024), which concluded that in the post-pandemic, female engineering professors have increased their ICT skills more than male professors. During the pandemic, professors' psychological stress from transitioning to online synchronous teaching was positively associated with vocal symptoms, but this relationship was moderated by their overall stress levels (Besser et al., 2022). In Brazil, public universities, risky drinking, or probable alcohol dependence among professors is associated with being male, younger than 40, tobacco use, existing health issues, teaching in applied social sciences, and the use of hypnotic medications (Pereira et al., 2022). Another study noted that professors had lower levels of satisfaction with teacher and staff coordination than students, still more satisfied with administrative services of academics (Olmos-Gómez et al., 2021).Favorable reputations of professors influence judgment, often resulting in overly high assessments of performance (McNatt, 2022). Professor brand traits—such as teaching quality, competence, and reputation—influence students' attachment and relationships, shaping professor brand equity (Jillapalli & Jillapalli, 2014). Professors' competence, awareness, and teaching quality shape brand equity by boosting student attachment, trust, and loyalty (Ghorashi et al., 2019). Professors who have a high level of professional commitment tend to have a high commitment to the university (Sheikh & Aghaz, 2019). Workload, when combined with bureaucratic university practices, contributes to a low view of work-related well-being among university professors (Pace et al., 2021). Professor support has an effect on perceived organizational support that lowers the intention to quit college (Chambers et al., 2023). The study has not discussed in depth the effects of WOM on human brand professors, especially brand equity professors, post-scandals. Therefore, we identified theoretical and population gaps related to WOM and brand equity professors in Indonesia. In other words, we still need an explanation of how negative word of mouth (NWOM) and positive word of mouth (PWOM) affect professor brand equity in the context of the post-scandal of professorship in Indonesia. This study fills this gap by exploring the effects of NWOM and PWOM on reputation, students trust, and commitment. Furthermore, the effect of these variables on brand equity professors was also tested in this study. Therefore, this study aims to: RA_1: Explore the effects of Negative Word-of-Mouth (NWOM) and Positive Word-of-Mouth (PWOM) on professor reputation, commitment, satisfaction, and trust. RA_2: Verify the impact of professor reputation on commitment, satisfaction, and trust. RA_3: Examine the influence of commitment, satisfaction, and trust on professor brand equity. RA_4: Investigate the role of commitment, satisfaction, and trust as mediators in the relationship between NWOM and PWOM and professor brand equity. This study aims to provide a theoretical contribution to the field of educational marketing, particularly concerning the concept of professors as human brands. Such theoretical insights hold significant relevance, especially within the context of post-scandal scenarios. Furthermore, from a practical perspective, the study is expected to offer valuable recommendations to higher education administrators in formulating policies related to professor academic services toward university quality. 2. LITERATURE REVIEW 2.1. Customer Based Professor Brand Equity (CBPBE) The concept of a professor's brand refers to how students perceive and evaluate a professor (Pass, 2022). Key dimensions of a professor's brand include their performance in shaping the student experience, responsiveness to student needs, and the respect they show toward their students (Berndt & Pantelic, 2020). A strong professor brand often correlates with brand equity, which is evident in students' willingness to pay more or their consistent preference for a particular professor. From the perspective of signaling theory, a brand can be evaluated based on elements of the marketing mix, which serve as indicators of quality. In this context, the value consumers attribute to a product or service, as signaled by the brand, constitutes brand equity (Erdem & Swait, 2001). Accordingly, brand equity (BE) reflects the perceived value of a brand from the consumer's viewpoint (Y. Kim & Nicolau, 2025). Brand equity can be observed through consumer responses to two different brands receiving the same marketing intervention (Verma, 2021). It represents the perceived value of a brand in the eyes of consumers, playing a significant role in shaping consumer decisions, guiding marketing strategies, and influencing collaborations (Piriyakul et al., 2024). Translating this concept to academia, professor brand equity can be viewed as a signal of the professor's brand strength, valued through students' perceptions. In this study, professor brand equity (CBPBE) is reflected in students' willingness to pay more and their consistent desire to study under the same professor. Research in marketing underscores the critical role of brand equity across various domains. For instance, brand equity influences the intention to enroll in online courses (Ray et al., 2021).shapes employee attitudes in organizations (Ertz et al., 2024)., and impacts long-term creditworthiness in companies with strong brand equity (Rahman et al., 2023). Conversely, brand equity alone cannot mitigate the negative effects of poor service(Sahaf & Fazili, 2024). Moreover, it significantly contributes to enhancing brand reputation (Ren et al., 2023). In the academic context, CBPBE holds strategic importance for universities, as professors are integral to shaping institutional reputation. Professors with weak brand equity can adversely impact not only their personal standing but also the overall reputation of their universities. Therefore, understanding the factors that diminish a professor's brand equity is crucial, particularly in cases involving post-scandal professors. 2.3. Brand Commitment Brand commitment is a psychological attachment or dedication to a brand that supports the establishment and maintenance of a consistent, long-term relationship, influencing purchasing decisions and brand usage (Amoroso & Ackaradejruangsri, 2024). Burmann and Zeplin (2005) describe brand commitment as a psychological attachment that drives the desire to engage with a brand. It encompasses three dimensions: calculative, affective, and normative (Khan et al., 2020; Shukla et al., 2016). Osuna Ramírez et al (2017) highlight that commitment is linked to the relationship with the brand and the intention to choose it in the future, indicating that brand commitment is not necessarily influenced by past experiences with the brand. As a form of personal connection, brand commitment is not directly affected by brand experience (J. Singh et al., 2022). This suggests that students’ commitment to a professor can develop even if they have not directly experienced learning under that professor. Previous studies have established the significant impact of brand commitment and satisfaction on brand equity (Iglesias et al., 2019). For instance, Jeon (2017). demonstrated that brand commitment positively influences the brand equity of global brands. In the banking sector, employer commitment has been shown to play a critical role in shaping employer brand equity (Altaf et al., 2022). However, other research indicates that brand commitment does not directly affect brand equity but serves as a mediator for the impact of brand credibility (Mathew et al., 2012). Applying these findings to the context of professor branding, we propose that student commitment to a professor significantly influences professor brand equity (PBE). Based on this, we formulate the following hypothesis: H_1: Commitment significantly influences professor brand equity. 2.4. Brand trust Trust arises from a consumer's perception that a brand is reliable and possesses the competence to meet their needs (Vuong et al., 2024). Cantú Alejandro and Güemes-Castorena (2024)concluded that trust is closely linked to vulnerability and positive expectations. In other words, trust reflects a feeling that a person or brand has the ability to fulfill expectations, especially when one is in a vulnerable state. Mayer et al. (1995) identified three key elements of trust: ability, benevolence, and integrity. (Hoang et al. (2024) expanded on this by including trust propensity, describing trust as encompassing ability (reliability), benevolence (confidence), integrity (honesty), and trustworthiness. In both consumer-brand and interpersonal relationships, trust plays a vital role. For example, celebrity endorsers' credibility significantly impacts consumer commitment and brand equity for endorsed products (Dwivedi & Johnson, 2013; R. P. Singh & Neelotpaul Banerjee, 2021). Trust in smartphone brands has a significant influence on consumer commitment (Erciş et al., 2012; Şahin et al., 2013). Similarly, in the airline industry, trust affects employee commitment (Erkmen & Hancer, 2015). In the higher education sector, trust is a critical factor for student satisfaction (Chen, 2017). For undergraduate and graduate business students, trust significantly influences satisfaction, highlighting the importance of student trust in professors within higher education (Hiatt et al., 2023). Moreover, brand trust impacts enrollment intention(Juhaidi et al., 2025) and the learning experience, enhancing university brand equity (Pinar et al., 2020). Although these studies did not specifically focus on professor branding as their primary object of study, we can conclude that trust significantly influences student commitment and Professor Brand Equity (PBE). In other words, students' trust in a professor enhances their commitment and positively impacts PBE. Based on this foundation, we propose the following hypotheses: H_2 Trust significantly influences students commitment. H_3 Trust significantly influences professor brand equity. 2.5. Reputation Brand reputation refers to the long-term image held by customers. While an image may emerge at specific moments, reputation represents the cumulative perception over an extended period (Han, 1989; Passow et al., 2005; Yousaf & Li, 2015). Vuong et al. (2024) define brand reputation as the perception embedded in the public mind. Accordingly, professor reputation reflects public perceptions of a professor, serving as a competitive advantage. Reputation is associated with three key aspects: awareness, awareness of specific attributes, and an overall positive impression (Y.-F. Liu et al., 2022).Awareness pertains to recognition of a brand's presence, awareness of specific attributes refers to the public's knowledge of a particular brand attribute, and an overall positive impression represents an appreciation of several brand attributes that delight consumers. In the context of this study, professor reputation refers to the professor’s long-term, widely recognized positive image in both academic and moral dimensions among students. Research has demonstrated that reputation significantly influences trust, particularly when consumer experience with the brand is limited (Einwiller, 2003).Reputation also impacts trust, customer satisfaction (Meilatinova, 2021), and customer commitment (Su et al., 2016). Moreover, image, as the precursor to reputation, has been shown to affect brand equity (Faircloth et al., 2001) and customer satisfaction (Tahir et al., 2024). These findings suggest that a professor’s reputation will influence students trust and PBE. Based on this foundation, we propose the following hypotheses: H_4 Professor reputation significantly influences students trust. H_5 Professor reputation significantly influences professor brand equity. 2.2. Satisfaction Satisfaction is a reflection of a customer's positive evaluation of a product or service after use (Nilashi et al., 2023). It represents an emotional response resulting from a positive assessment of the service provided (Su et al., 2016). Tahir et al. (2024) synthesized various literature and concluded that satisfaction is an emotional response characterized by happiness when expectations are met by a product or service. In this study, satisfaction is defined as students' emotional responses as a result of their evaluation of the academic services provided by professors. These emotional responses can be positive (satisfaction) or negative (dissatisfaction, such as sadness or disappointment). Satisfaction plays a critical role in brand perception. Customer satisfaction positively impacts brand equity (Torres & Tribó, 2011). Conversely, other studies have found that brand equity influences global brand satisfaction (González-Mansilla et al., 2019; Tran & Nguyen, 2022). In the banking industry, customer satisfaction positively affects affective commitment (Iglesias et al., 2019). Positive attitudes following the use of a product or service significantly influence brand trust trust (Erciş et al., 2012; Şahin et al., 2013). Similarly, studies have shown that students' experiences significantly influence university reputation (Amado Mateus et al., 2024). These findings suggest that a favorable assessment of academic services offered by professors might elevate their reputation, cultivate students' confidence, reinforce dedication, and raise professor brand equity (PBE). In light of this comprehension, we propose the subsequent hypotheses: H_6: Student satisfaction significantly influences student commitment. H_7: Student satisfaction significantly influences student trust. H_8: Student satisfaction significantly influences professor reputation H_9: Student satisfaction significantly influences professor brand equity. 2.3. Word of Mouth (WOM) Word of mouth (WOM) refers to interaction and the exchange of information, either negative or positive, through informal, non-commercial communication between individuals about a brand or product (Juhaidi et al., 2024; H. Liu et al., 2024). Traditional WOM typically occurs face-to-face, while online or electronic WOM (EWOM) involves consumer communication in the form of opinions, endorsements, or product recommendations delivered via videos or texts shared widely through blogs, social media, or other platforms (Nadroo et al., 2024). In the business sector, EWOM has been shown to influence trust and the intention to visit hotels (Abubakar & Ilkan, 2016). Similarly, WOM impacts trust in electronic products (Abubakar & Ilkan, 2016). Other studies have found that EWOM significantly affects brand image and reputation (Aditi et al., 2023; Castellano & Dutot, 2017). WOM has also been linked to customer satisfaction (Shi et al., 2016), commitment (Patrocínio et al., 2015), and brand equity (Virvilaite et al., 2015). Conversely, negative reviews can damage brand equity (Ahmad & Guzmán, 2021). Hua et al (2024) conclude that both positive and negative WOM affect customer satisfaction. In the higher education sector, Perera et al. (2020) demonstrated that eWOM influences a university's brand equity. These findings indicate that WOM (positive or negative) impacts key factors such as commitment, satisfaction, trust, reputation, and professor brand equity. Based on these insights, we propose the following hypotheses: H_10 Negative WOM (NWOM) significantly and negatively affects student commitment. H_11 NWOM significantly and negatively affects student trust. H_12 NWOM significantly and negatively affects professor reputation. H_13 NWOM significantly and negatively affects professor brand equity. H_14 PWOM significantly affects student commitment. H_15 PWOM significantly affects student trust. H_16 PWOM significantly affects professor reputation. H_17 PWOM significantly affects professor brand equity. 2.7. Gender as moderator Studies in marketing suggest that gender can significantly influence customer behavior. For women, the effect of brand-self connection on brand loyalty is more pronounced compared to men (Gao & Shen, 2024). Similarly, other research has shown that the impact of a dissertation advisor's benevolence on interpersonal trust is stronger among women than men (Qiu et al., 2022). Furthermore, the influence of talent management on faculty members' commitment is significantly higher for women than for men (Antony et al., 2024). In the context of professor branding, we conclude that gender acts as a moderator for the effects of satisfaction, NWOM (negative word-of-mouth), PWOM (positive word-of-mouth), and other exogenous variables. Hence, we formulated hypothesis: H_18 Gender moderates the effect of satisfaction on professor brand equity, such that the influence of satisfaction on professor brand equity varies across different gender groups. H_19 Gender moderates the effect of NWOM on professor brand equity, such that the influence of NWOM on professor brand equity varies across different gender groups. H_20 Gender moderates the effect of PWOM on professor brand equity, such that the influence of PWOM on professor brand equity varies across different gender groups. H_21 Gender moderates the effect of commitment on professor brand equity, such that the influence of commitment on professor brand equity varies across different gender groups. H_22 Gender moderates the effect of trust on professor brand equity, such that the influence of trust on professor brand equity varies across different gender groups. H_23 Gender moderates the effect of reputation on professor brand equity, such that the influence of reputation on professor brand equity varies across different gender groups. 2.8. Theoretical Framework Previous studies have highlighted the relationships among satisfaction (SAT), negative word-of-mouth (NWOM), positive word-of-mouth (PWOM), commitment (COMM), trust, reputation (REP), professor brand equity (PBE), and gender (G). We have developed a theoretical framework to test in this study based on these findings. The framework illustrates the relationships between exogenous, endogenous, and moderating variables, as depicted in Fig. 1 . 3. METHOD 3.1. Research Design This study employed a quantitative exploratory research design using the partial least squares structural equation modeling (PLS-SEM) approach. PLS-SEM was chosen due to its suitability for analyzing latent variables and complex models, as well as its ability to handle single-item indicators (Guenther et al., 2023; Hair et al., 2021). Hair et al. (2022), deemed PLS-SEM more appropriate for exploratory research compared to composite-based SEM (CB-SEM), which focuses on theory testing. 3.2. Participants A total of 441 students responded to the survey. One response was excluded because the respondent filled out the survey twice, leaving 440 valid responses (female = 339, male = 101). Among these, 134 participants had never attended lectures conducted by professors and were removed from the participant list. Consequently, the final sample for this study consisted of 306 participants (237 females and 69 males). The participants were selected using a non-probability sampling method, specifically convenience sampling combined with the snowballing technique. This approach was chosen because it is easier, faster, and more cost-effective. Additionally, the snowballing technique allowed us to reach participants who were otherwise inaccessible. The minimum sample size was determined using power analysis with the G*Power software. With an effect size (f²) of 0.15, an error probability of 0.05, a power level of 0.95, and seven predictor variables (including a moderator variable) as recommended by Uttley (2019), the analysis indicated a minimum sample size of 153 participants. Therefore, the sample size in this study exceeded the minimum recommended by the power analysis. The participants in this study were predominantly from lower-middle socioeconomic backgrounds, with parents whose education levels were below high school. This indicated that the study was well-suited to the context of lower-middle socioeconomic backgrounds. Table 1 presents the characteristics of the study participants. Table 1 Participants characteristic N = 306 Share (%) Gender Female 237 77.45 Male 69 22.55 Father’s education Under high school 124 40.52 High school 97 31.70 Diploma/bachelor 71 23.20 Postgraduate 14 4.58 Mother’s education Under high school 136 44.44 High school 95 31.05 Diploma/bachelor 63 20.59 Postgraduate 12 3.92 Allowance per month (USD) Under 30.92 72 23.53 30.92–49.47 75 24.51 > 49.47–55.65 5 1.63 > 55.65–74.21 76 24.84 > 74.21–92.76 30 9.80 > 92.76-111.31 5 1.63 > 111.31-123.68 28 9.15 > 123.68-154.59 5 1.63 > 154.59-185.51 7 2.29 > 185.51-216.43 1 0.33 > 216.43-309.15 0 0.00 > 309.15 2 0.65 Note : 1 USD = 16171.30 3.3. Measurement We adapted the measurement of word of mouth (WOM) from East et al. (2008) and Mittal et al. (2022). Indicators for trust, commitment, and professor brand equity were adapted from Jillapalli and Jillapalli (2014) and combined with the brand equity measurements from Castro-Gómez et al. (2024). We translated these items into Indonesian and reviewed them with three students to ensure the questionnaire was comprehensible to the respondents. The questionnaire consisted of 31 items, including demographic background (5 items), satisfaction measurement (4 items), negative WOM (NWOM) (3 items), positive WOM (PWOM) (5 items), reputation (3 items), trust (5 items), commitment (5 items), and brand equity (5 items). We created the questionnaire using Google Forms and distributed its link to students directly through WhatsApp messages, both personally and with assistance from lecturers. We requested students to respond and provide written consent to participate as respondents within the questionnaire itself. Additionally, we asked them to share the questionnaire link with their peers. We conducted the data collection between November 26 and December 12, 2024. The data analysis was conducted in two stages. In the first stage, we assessed the reliability and validity (outer model), as well as the model fit. In the second stage, we tested the hypotheses using the bootstrapping process with a subsample of 15,000 and a two-tailed test at a significance level of 0.05. We conducted the data analysis using SmartPLS version 4.0.9.9. 4. RESULT 4.1. Validity and reliability In the initial stage, we tested the reliability and validity of the study constructs. Indicator reliability was assessed using outer loading (OL), while internal consistency reliability was evaluated using Cronbach's alpha (α) and composite reliability (CR). Construct validity was tested through convergent validity, using average variance extracted (AVE), and discriminant validity, using the heterotrait-monotrait ratio (HTMT). The outer loading values for all items exceeded 0.7, except for PW1 and PW2. These two items were removed despite exceeding the threshold value of 0.5 (Hair et al., 2021). The removal of these items addressed the AVE issue and increased the AVE values, ensuring they met the required threshold of 0.5 (AVE > 0.5). The results showed that the Cronbach's alpha (α) and CR values for PWOM were above 0.6. Although these values were in a critical range, they still met the criteria for exploratory studies in PLS-SEM (Hair et al., 2021). The internal consistency reliability assessed using CR (rho-a and rho-c) also confirmed that the items were reliable (CR > 0.7). Thus, we ensured that all constructs were reliable. Furthermore, the convergent validity test further demonstrated that all items were valid, as the AVE values exceeded the threshold of 0.5 (Hair et al., 2019). We also conducted collinearity testing, and the results indicated that all variance inflation factor (VIF) values were below the threshold of 0.5 (Ringle et al., 2023). This finding suggested no collinearity issues. Table 2 presents the results of reliability and convergent validity testing. Table 2 Validity and reliability of construct VIF OL α CR (rho-a) CR (rho-c) AVE SA1 2.175 0.856 0.898 0.901 0.929 0.767 SA2 3.794 0.915 SA3 3.469 0.900 SA4 2.031 0.830 NW1 3.044 0.879 0.894 1.066 0.929 0.814 NW2 2.554 0.883 NW3 2.570 0.942 PW1* 1.625 0.599 0.662 0.683 0.816 0.599 PW2* 1.882 0.597 PW3 1.211 0.663 PW4 1.367 0.794 PW5 1.546 0.853 CO1 2.002 0.815 0.897 0.901 0.925 0.711 CO2 1.846 0.774 CO3 2.950 0.883 CO4 3.309 0.896 CO5 2.387 0.841 TR1 2.114 0.813 0.907 0.909 0.931 0.730 TR2 2.615 0.861 TR3 2.373 0.848 TR4 2.988 0.886 TR5 2.652 0.861 RE1 2.022 0.863 0.837 0.839 0.902 0.754 RE2 2.122 0.885 RE3 1.804 0.857 BE1 1.509 0.669 0.825 0.871 0.870 0.575 BE2 1.825 0.819 BE3 1.987 0.835 BE4 2.366 0.752 BE5 2.331 0.702 Note: * The item was removed due to convergent validity issues; the calculation process was repeated after the item was removed. Additionally, we tested discriminant validity using HTMT values, which were below 0.9, confirming that the constructs met the discriminant validity requirements (Hair et al., 2019). Table 3 displays the HTMT results. Table 3 Discriminant validity the heterotrait-monotrait ratio of correlations (HTMT) COMM G NWOM PWOM PBE REP SAT Trust G x COMM G x REP G x NWOM G x Trust G x PWOM G x SAT COMM G 0.095 NWOM 0.095 0.015 PWOM 0.340 0.108 0.320 PBE 0.597 0.119 0.086 0.457 REP 0.823 0.110 0.095 0.391 0.508 SAT 0.857 0.049 0.072 0.316 0.652 0.78 Trust 0.868 0.061 0.082 0.299 0.536 0.887 0.773 G x COMM 0.018 0.121 0.033 0.037 0.129 0.030 0.046 0.071 G x REP 0.054 0.135 0.034 0.146 0.078 0.023 0.068 0.080 0.688 G x NWOM 0.035 0.005 0.115 0.075 0.047 0.042 0.023 0.040 0.029 0.094 Gx Trust 0.076 0.083 0.016 0.116 0.089 0.088 0.100 0.121 0.753 0.738 0.042 G x PWOM 0.020 0.108 0.057 0.158 0.047 0.111 0.099 0.078 0.229 0.146 0.277 0.131 G x SAT 0.056 0.061 0.027 0.135 0.162 0.061 0.052 0.082 0.743 0.595 0.044 0.623 0.100 Note: COMM = Commitment ; G = Gender; NWOM = Negative WOM; PWOM = Positive WOM; PBE = Professor brand equity; REP = Reputation = REP; SAT = Satisfaction. Discriminant validity threshold: HTMT < 0.9. 4.2. Hypothesis Verification This study confirmed that commitment (COMM) had no significant effect on professor brand equity (PBE). On the other hand, we found that trust significantly influenced COMM. These findings were substantiated by T-statistics (t), P-values (p), and the confidence interval bias-corrected (CIBC) values, which met the established criteria (t > 1.96; p < 0.05; CIBC excluded 0). The CIBC values, ranging from [0.374; 0.569], did not include zero, further supporting this result. Additionally, the path coefficient (β) values above 0.3 indicated a substantial effect of trust on COMM. The path coefficient (β = 0.474) and f² value (f² = 0.400) demonstrated that trust exerted a large effect on COMM. This suggests that students' commitment to the professor increases as their trust in the professor increases. However, student commitment did not lead to an increase in the professor's brand equity. Therefore, we confirmed that H_1 and H_3 were rejected, while H_2 was supported. Furthermore, we found that professor reputation (REP) influenced student trust but did not affect PBE. The effect of REP on trust was substantial (β = 0.556, f² = 0.467). This suggests that as a professor’s reputation increases, students are more likely to trust him. However, this reputation did not lead to an increase in the professor's brand equity among students. Therefore, we supported H_4 and rejected H_5. Testing the variable student satisfaction (SAT) showed that student satisfaction (SAT) had a significant effect on student commitment (COMM), trust, professor reputation (REP), and professor brand equity (PBE). These findings were based on CIBC, T-statistics (t), and P-value (p), which met the required criteria (CIBC excluded 0; t > 1.96; p < 0.05). The path coefficient (β) values above 0.3 indicated a large effect of SAT on COMM, trust, REP, and PBE. The effect size (f²) and statistical power also confirmed the significant impact of SAT. The largest effect of SAT was on reputation (β = 0.647, f² = 0.755), followed by commitment (β = 0.427, f² = 0.323). These findings suggest that students who are satisfied with the professor's academic services are more likely to enhance the professor's reputation, exhibit higher commitment, trust more, and increase the professor's brand equity. Therefore, we supported H_6, H_7, H_8, and H_9. In the next test, we examined the effects of NWOM and PWOM. Our study found that NWOM had no effect on COMM, trust, REP, or PBE. The T-statistic (t 0.05) indicated no significant effect of NWOM on these variables. This was further supported by the CIBC values, which did not include zero [-0.073; 0.071], [-0.090; 0.065], [-0.128; 0.106], and [-0.130; 0.075]. These findings suggest that negative information or issues did not affect the professor’s brand. Therefore, rumors or negative news about scandals did not damage the professor’s brand equity. As a result, H_10, H_11, H_12, and H_13 were rejected. In contrast to NWOM, the effect of PWOM on COMM, trust, REP, and PBE varied. The assessment results demonstrated that PWOM had a positive and significant effect on REP and PBE (CIBC excluded 0, t > 1.96, p < 0.05). This indicates that positive WOM information, whether offline or online, affects the reputation and brand strength of professors among students. While this effect is significant, the path coefficient value is below 0.3 (β < 0.3), suggesting a small influence that could be considered negligible. In contrast, PWOM on COMM and Trust was not significant. Positive information about a professor does not lead to increased students commitment and trust in them. Therefore, hypotheses H_14 and H_15 were rejected, while H_16 and H_17 were supported. The moderating role of gender in this study was not dominant. We found no significant moderating effect, except for the relationship between SAT and PBE where t-values were below 1.96 (t 0.05). Nevertheless, the effect of gender as a moderator was weak, as evidenced by a β value of less than 0.3 and an f² value less than 0.15. This suggests that the influence of SAT on PBE is more significant among male students than female students. In other words, the effect of SAT on PBE differs significantly between female and male students. Therefore, we confirm that hypotheses H_18, H_19, H_20, H_21, and H_22 were rejected, while H_23 was supported. Table 4 presents the results of these tests. Table 4 Hypotheses verification result β f 2 CIBC t p Hypotheses 2.5% 97.5% Commitment -> Professor brand equity 0.144 0.011 -0.023 0.320 1.652 0.099 H_1 was rejected Trust -> Commitment 0.474 0.400 0.374 0.569 9.475 0.000 H_2 was supported Trust -> Professor brand equity 0.152 0.012 -0.012 0.309 1.868 0.062 H_3 was rejected Reputation -> Trust 0.556 0.467 0.452 0.655 10.656 0.000 H_4 was supported Reputation -> Professor brand equity -0.064 0.003 -0.192 0.078 0.930 0.352 H_5 was rejected Satisfaction -> Commitment 0.427 0.323 0.328 0.529 8.387 0.000 H_6 was supported Satisfaction -> Trust 0.324 0.163 0.217 0.431 5.865 0.000 H_7 was supported Satisfaction -> Reputation 0.647 0.755 0.570 0.718 17.058 0.000 H_8 was supported Satisfaction -> Professor brand equity 0.384 0.098 0.223 0.540 4.784 0.000 H_9 was supported Negative WOM -> Commitment 0.003 0.000 -0.073 0.071 0.072 0.943 H_10 was rejected Negative WOM -> Trust -0.011 0.000 -0.090 0.065 0.289 0.772 H_11 was rejected Negative WOM -> Reputation 0.006 0.000 -0.128 0.106 0.110 0.912 H_12 was rejected Negative WOM -> Professor brand equity -0.019 0.001 -0.130 0.075 0.366 0.715 H_13 was rejected Positive WOM -> Commitment 0.049 0.008 -0.021 0.118 1.389 0.165 H_14 was rejected Positive WOM -> Trust -0.005 0.000 -0.082 0.071 0.123 0.902 H_15 was rejected Positive WOM -> Reputation 0.133 0.030 0.034 0.225 2.751 0.006 H_16 was supported Positive WOM -> Professor brand equity 0.224 0.077 0.121 0.326 4.302 0.000 H_17 was supported Gender x Commitment -> Professor brand equity -0.102 0.005 -0.277 0.070 1.148 0.251 H_18 was rejected Gender x Reputation -> Professor brand equity -0.027 0.001 -0.147 0.106 0.419 0.675 H_19 was rejected Gender x Negative WOM -> Professor brand equity -0.041 0.003 -0.129 0.048 0.898 0.369 H_20 was rejected Gender x Trust -> Professor brand equity 0.077 0.003 -0.070 0.238 0.980 0.327 H_21 was rejected Gender x Positive WOM -> Professor brand equity -0.010 0.000 -0.107 0.098 0.196 0.845 H_22 was rejected Gender x Satisfaction -> Professor brand equity 0.192 0.030 0.031 0.345 2.423 0.015 H_23 was supported Note: f 2 > 0.02 small effect size, f 2 > 0.15 medium effect size, f 2 >0.35 large effect size (Cohen, 1988) 4.3. Model Fit The results of testing our proposed model indicate that it's a good fit. The Standardized Root Mean Square Residual (SRMR) value meets the criteria for model fit, being smaller than 0.08 (Ringle et al., 2023). Therefore, no misspecification has occurred, and the model can be considered as fit. Furthermore, The R² value indicates the explanatory power of this study based on the sample used (Hair et al., 2021). The R² values demonstrate that the variables measured in the model successfully explain 71.1% of the variance in COMM, while trust is influenced to 65.1%. On the other hand, the variables in this study explain 47.3% of REP and 45.4% of PBE. Thus, other unmeasured variables may still influence the variables COMM, trust, REP, and PBE. Table 5 and Fig. 2 present the results of the model testing. Table 5 Model Quality R 2 R 2 adjusted SRMR Commitment 0.715 0.711 Moderate 0.076 Reputation 0.479 0.473 Weak Trust 0.655 0.651 Moderate Professor brand equity 0.477 0.454 Weak Note: R 2 > 0.75 = substantial, R 2 > 0.50 = moderate, R 2 > 0.25 = weak (Hair et al., 2021); Model fit threshold SMMR < 0.08 (Ringle et al., 2023).. We also tested the model using the Q 2 predict value, calculated with PLS Predict in SmartPLS version 4.0.9.9. The Q 2 values for all indicators were greater than zero (Q 2 > 0), indicating that the model has predictive power (Hair et al., 2011). Furthermore, we evaluated the model by examining the root mean squared error (RMSE) and the mean absolute error (MAE) of the endogenous variable indicators. The majority of the RMSE values from the PLS-SEM analysis were lower than those from the linear regression model (LM), while the MAE values for the PLS-SEM indicators were comparable to those of the LM. This suggests that the model exhibits medium predictive power (Shmueli et al., 2019). Table 6 presents the results of the PLS-SEM prediction test. Table 6 PLS-SEM predict result. PLS-SEM LM Q² predict RMSE MAE LM_RMSE LM_MAE CO1 0.368 0.573* 0.471 0.575 0.467 CO2 0.347 0.553* 0.400 0.562 0.398 CO3 0.463 0.521* 0.354* 0.536 0.362 CO4 0.432 0.516 0.377* 0.516 0.384 CO5 0.482 0.483 0.338 0.460 0.324 BE1 0.132 0.763* 0.614* 0.770 0.617 BE2 0.402 0.523 0.394 0.511 0.377 BE3 0.329 0.579 0.454 0.565 0.439 BE4 0.146 0.715* 0.584 0.717 0.582 BE5 0.053 0.781 0.632 0.781 0.632 RE1 0.302 0.593 0.428* 0.591 0.442 RE2 0.390 0.499 0.386 0.497 0.378 RE3 0.350 0.551* 0.416* 0.554 0.422 TR1 0.305 0.594* 0.443* 0.604 0.446 TR2 0.331 0.529* 0.379* 0.542 0.391 TR3 0.367 0.553* 0.411 0.566 0.409 TR4 0.366 0.549* 0.418* 0.558 0.431 TR5 0.370 0.553* 0.402* 0.570 0.420 Note : Predictive Q 2 > 0 = Small, > 0,25 = medium, > 0.5 = large (Hair et al., 2019); * Lower prediction error 5. DISCUSSION AND IMPLICATION 5.1. Discussion Our study has demonstrated that reputation has a significant impact on trust, which, in turn, has a substantial effect on student commitment. However, these two variables do not significantly influence PBE These findings support previous studies suggesting a relationship between reputation and brand trust. Reputation improves as information from individuals who trust the brand circulates, thereby promoting loyalty (Chaudhary et al., 2021). Loyalty is defined as a commitment to remain with or continue purchasing a brand (Nadeem et al., 2020). In other words, reputation influences student commitment, which, in turn, enhances loyalty. Moreover, reputation influences students' evaluations of instructors in the higher education (HE) sector. Even when the course content, grading structure, and materials are identical, students rate professors as having better teaching performance than others (McNatt, 2022). This suggests that reputation can lead to biases in student evaluations. Reputation, therefore, plays a critical role not only in academic settings but also in business contexts involving professors. For example, a favorable reputation among professors can increase efficiency and reduce the salary gap between executives and employees (Song et al., 2024). One of our most crucial findings demonstrates that Negative Word-of-Mouth (NWOM) does not affect professors' brand reputation. We found that student satisfaction has a stronger influence on professors' brand reputation. This non-significant effect of NWOM contradicts prior studies that highlight its negative impact on brand reputation. For example, WOM significantly influences trust in services like Google Maps (Gelashvili et al., 2024), and EWOM positively affects online purchase intentions (Kumar et al., 2023). NWOM tends to decrease positive attitudes toward products (Liao et al., 2021)and diminish the perceived value of travel destinations (Gkritzali et al., 2020). However, our study aligns with the findings of Sun et al. (2021), which demonstrated that PWOM affects brand equity, while NWOM does not affect brand equity for smartphone brands. If a brand perceives NWOM as unjust and responds to it with empathy, it doesn't harm its reputation (Allard et al., 2020) Bastos dan Moore, (2021)explain that WOM has a greater impact when it pertains to experiences rather than material factors. WOM about professors is typically centered on material aspects, making it less impactful on the professor's brand. WOM will be more influential when it is based on experiences with the professor. However, a direct response from the professor tends to enhance trust more than news or rumors. Responses from individuals reflect empathy, which is likely to increase trust (Lim et al., 2025). Our findings highlight that student satisfaction has a greater influence on professors' reputations than WOM. We demonstrate that student satisfaction plays a crucial role in shaping reputation, trust, commitment, and PBE. This supports studies in the HE industry that show student satisfaction affects university reputation (Mendoza-Villafaina & López-Mosquera, 2024), positive behavioral intentions, loyalty, and PWOM (Cant et al., 2023). Satisfaction is identified as a key determinant of university reputation (Zeqiri et al., 2023), and satisfaction with university services enhances student trust (S. Singh & Manohar, 2024). In the online service sector, satisfaction also influences trust (J. Kim et al., 2011). Although these studies do not directly address professor reputation, they suggest that satisfaction impacts both reputation and trust. Our findings align with Zenker and Rütter (2014) who concluded that satisfaction is a key factor in the relationship between a brand and individual behavior. In contrast to our findings, Chen (2017) shows that trust and commitment significantly influence student satisfaction, suggesting a reversed causal direction where trust and commitment precede satisfaction, rather than being outcomes of it as in our study. Therefore, if WOM is associated with dissatisfaction with the professor's academic services, it has the potential to negatively impact the professor's brand. In other words, the spread of negative WOM about a student's experience with a professor will impact the professor as a human brand. However, professors are strong brands that cannot be easily tarnished by irrelevant negative WOM unrelated to the professor’s academic engagement with students. 5.2. Implication 5.2.1. Theoretical implication Our findings provide a theoretical contribution to the study of human brands, particularly in the context of professors. From a theoretical perspective, professors possess a brand strength that is largely immune to the effects of NWOM. Students' experiences with the academic services they receive primarily shape the professor's brand. NWOM will only impact the professor’s brand when it pertains to students' academic interactions with the professor. However, NWOM that is unrelated to student experiences does not have a significant influence on the professor’s brand. 5.2.2. Practical implication Our study indicates the significant influence of professors' brand power in students' perceptions. Therefore, we recommend that higher education administrators place greater emphasis on enhancing professors' roles within the academic process, fostering more intensive professor-student relationships. In addition to providing support, we also advise HE administrators to periodically supervise professors' academic activities, as professors may occasionally overlook ethical and academic responsibilities. Second, we encourage professors to provide the best possible academic services to students. This will enhance student satisfaction, which in turn positively affects the reputation, trust, commitment, and brand strength of the professors. This, ultimately, will contribute to the overall excellence of the university. 6. LIMITATION AND FUTURE STUDY The use of convenience and snowball sampling techniques in this study presents limitations in terms of generalizability. Furthermore, the sample only consists of students from two public universities in South Kalimantan. This sample may be biased and may not fully represent the broader population, limiting the generalizability of the study's findings to populations with similar characteristics. A second limitation of this study concerns the variables measured. The study did not examine potential mediating variables, meaning it cannot explain how exogenous variables influence endogenous variables. Therefore, we recommend that future research involve participants from universities across Indonesia or globally, using probability sampling methods. We also suggest that future studies investigate potential mediators, such as loyalty, professor brand attachment, and brand love. This would provide a more comprehensive understanding of the effects of satisfaction and WOM. 7. CONCLUSION The performance of professors is a key element in PBE. Our study demonstrates that national scandals do not affect COMM, trust, REP, or PBE. On the other hand, professor reputation has an impact on trust but does not directly influence PBE. Trust, however, significantly affects commitment. Additionally, our study shows that satisfaction with professor performance plays a dominant role in shaping the professor's brand. Furthermore, gender differences among students do not serve as moderators for the effects of exogenous variables, except for the effect of satisfaction on PBE, which is stronger for male students. This indicates that the academic quality perceived by students is crucial to the professor's brand. Our findings confirm that a professor's brand is robust and resistant to NWOM, even when originating from journalistic news. Long live the professor! Declarations FUNDING: No funding was received to assist with the preparation of this manuscript. INSTITUTIONAL REVIEW BOARD STATEMENT: The Ethical Committee of the Faculty of xxxxxxxxxx, Indonesia (Number: 508a/Un.14/III.1.h/PP.00.9/10/2024), October 3, 2024). Written consent has been obtained from all participants. TRANSPARENCY: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. DATA AVAILABILITY STATEMENT: The raw data required to reproduce the above findings are available and will be provided upon request. COMPETING INTERESTS: Authors state that there is no conflict of interest. CREDIT AUTHORSHIP CONTRIBUTION STATEMENT: xxxx: Conceptualization, Methodology, Software, Supervison, Formal analysis, Writing - original draft, Writing - review & editing. xxxxxx: Data curation, Project administration, Resources. xxxxx: Visualization, Data curation. ACKNOWLEDGEMENT: We express our gratitude to our colleagues and students who assisted in distributing the questionnaire online. References Abubakar, A. M., & Ilkan, M. (2016). 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INTRODUCTION","content":"\u003cp\u003eProfessors in academia are highly regarded individuals who hold a distinguished position, considered just below the revered status of a prophet. The professor serves as an intellectual leader, embodying the roles of role model, mentor, advocate, guardian, acquirer, and ambassador (Macfarlane, 2011). The career trajectories of professors are linked to their standing within their academic discipline, as evidenced by citations in esteemed publications and their global engagement (Rossier, 2020). A professor is a highly productive scientist over the course of his or her career (Kwiek \u0026amp; Roszka, 2024). Professorship is the pinnacle of academic careers (Mantai \u0026amp; Marrone, 2023). Professors can evolve into personal brands (Jillapalli \u0026amp; Wilcox, 2010). Consequently, the professor brand signifies academic excellence and exceptional ethical standards within society.\u003c/p\u003e \u003cp\u003eProfessors brand are administering challenges in Indonesia. The fraud controversy concerning the acquisition of professorships led to the withdrawal of eleven professors' credentials (Lavenia Y, 2024; Rizki, 2024; Widodo, 2024). The conferral of professorships to politicians has emerged as a subject of discourse on social media and in mainstream media (Irawan, 2024). The news that has emerged as a national concern is a detrimental form of negative word of mouth (NWOM) that will influence students' perceptions of professors. In marketing research, negative word-of-mouth (NWOM) poses significant risks to brands, including damage to brand reputation and potential boycotts ((Azemi et al., 2020; Bambauer-Sachse \u0026amp; Young, 2024). The study reveals that negative word-of-mouth (NWOM) might tarnish or harm the reputation of professors as brands. However, we lack evidence regarding the impact of word-of-mouth on the brand of the professor following the crisis.\u003c/p\u003e \u003cp\u003eStudies on professors have been conducted to understand professors from various points of view. The study, for example, was conducted by Vergara et al., (2024), which concluded that in the post-pandemic, female engineering professors have increased their ICT skills more than male professors. During the pandemic, professors' psychological stress from transitioning to online synchronous teaching was positively associated with vocal symptoms, but this relationship was moderated by their overall stress levels (Besser et al., 2022). In Brazil, public universities, risky drinking, or probable alcohol dependence among professors is associated with being male, younger than 40, tobacco use, existing health issues, teaching in applied social sciences, and the use of hypnotic medications (Pereira et al., 2022). Another study noted that professors had lower levels of satisfaction with teacher and staff coordination than students, still more satisfied with administrative services of academics (Olmos-G\u0026oacute;mez et al., 2021).Favorable reputations of professors influence judgment, often resulting in overly high assessments of performance (McNatt, 2022). Professor brand traits\u0026mdash;such as teaching quality, competence, and reputation\u0026mdash;influence students' attachment and relationships, shaping professor brand equity (Jillapalli \u0026amp; Jillapalli, 2014). Professors' competence, awareness, and teaching quality shape brand equity by boosting student attachment, trust, and loyalty (Ghorashi et al., 2019). Professors who have a high level of professional commitment tend to have a high commitment to the university (Sheikh \u0026amp; Aghaz, 2019). Workload, when combined with bureaucratic university practices, contributes to a low view of work-related well-being among university professors (Pace et al., 2021). Professor support has an effect on perceived organizational support that lowers the intention to quit college (Chambers et al., 2023).\u003c/p\u003e \u003cp\u003eThe study has not discussed in depth the effects of WOM on human brand professors, especially brand equity professors, post-scandals. Therefore, we identified theoretical and population gaps related to WOM and brand equity professors in Indonesia. In other words, we still need an explanation of how negative word of mouth (NWOM) and positive word of mouth (PWOM) affect professor brand equity in the context of the post-scandal of professorship in Indonesia. This study fills this gap by exploring the effects of NWOM and PWOM on reputation, students trust, and commitment. Furthermore, the effect of these variables on brand equity professors was also tested in this study.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA_1:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplore the effects of Negative Word-of-Mouth (NWOM) and Positive Word-of-Mouth (PWOM) on professor reputation, commitment, satisfaction, and trust.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA_2:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVerify the impact of professor reputation on commitment, satisfaction, and trust.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA_3:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExamine the influence of commitment, satisfaction, and trust on professor brand equity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA_4:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvestigate the role of commitment, satisfaction, and trust as mediators in the relationship between NWOM and PWOM and professor brand equity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study aims to provide a theoretical contribution to the field of educational marketing, particularly concerning the concept of professors as human brands. Such theoretical insights hold significant relevance, especially within the context of post-scandal scenarios. Furthermore, from a practical perspective, the study is expected to offer valuable recommendations to higher education administrators in formulating policies related to professor academic services toward university quality.\u003c/p\u003e"},{"header":"2. LITERATURE REVIEW","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Customer Based Professor Brand Equity (CBPBE)\u003c/h2\u003e \u003cp\u003eThe concept of a professor's brand refers to how students perceive and evaluate a professor (Pass, 2022). Key dimensions of a professor's brand include their performance in shaping the student experience, responsiveness to student needs, and the respect they show toward their students (Berndt \u0026amp; Pantelic, 2020). A strong professor brand often correlates with brand equity, which is evident in students' willingness to pay more or their consistent preference for a particular professor.\u003c/p\u003e \u003cp\u003eFrom the perspective of signaling theory, a brand can be evaluated based on elements of the marketing mix, which serve as indicators of quality. In this context, the value consumers attribute to a product or service, as signaled by the brand, constitutes brand equity (Erdem \u0026amp; Swait, 2001). Accordingly, brand equity (BE) reflects the perceived value of a brand from the consumer's viewpoint (Y. Kim \u0026amp; Nicolau, 2025). Brand equity can be observed through consumer responses to two different brands receiving the same marketing intervention (Verma, 2021). It represents the perceived value of a brand in the eyes of consumers, playing a significant role in shaping consumer decisions, guiding marketing strategies, and influencing collaborations (Piriyakul et al., 2024).\u003c/p\u003e \u003cp\u003eTranslating this concept to academia, professor brand equity can be viewed as a signal of the professor's brand strength, valued through students' perceptions. In this study, professor brand equity (CBPBE) is reflected in students' willingness to pay more and their consistent desire to study under the same professor.\u003c/p\u003e \u003cp\u003eResearch in marketing underscores the critical role of brand equity across various domains. For instance, brand equity influences the intention to enroll in online courses (Ray et al., 2021).shapes employee attitudes in organizations (Ertz et al., 2024)., and impacts long-term creditworthiness in companies with strong brand equity (Rahman et al., 2023). Conversely, brand equity alone cannot mitigate the negative effects of poor service(Sahaf \u0026amp; Fazili, 2024). Moreover, it significantly contributes to enhancing brand reputation (Ren et al., 2023).\u003c/p\u003e \u003cp\u003eIn the academic context, CBPBE holds strategic importance for universities, as professors are integral to shaping institutional reputation. Professors with weak brand equity can adversely impact not only their personal standing but also the overall reputation of their universities. Therefore, understanding the factors that diminish a professor's brand equity is crucial, particularly in cases involving post-scandal professors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Brand Commitment\u003c/h2\u003e \u003cp\u003eBrand commitment is a psychological attachment or dedication to a brand that supports the establishment and maintenance of a consistent, long-term relationship, influencing purchasing decisions and brand usage (Amoroso \u0026amp; Ackaradejruangsri, 2024). Burmann and Zeplin (2005) describe brand commitment as a psychological attachment that drives the desire to engage with a brand. It encompasses three dimensions: calculative, affective, and normative (Khan et al., 2020; Shukla et al., 2016). Osuna Ram\u0026iacute;rez et al (2017) highlight that commitment is linked to the relationship with the brand and the intention to choose it in the future, indicating that brand commitment is not necessarily influenced by past experiences with the brand. As a form of personal connection, brand commitment is not directly affected by brand experience (J. Singh et al., 2022). This suggests that students\u0026rsquo; commitment to a professor can develop even if they have not directly experienced learning under that professor.\u003c/p\u003e \u003cp\u003ePrevious studies have established the significant impact of brand commitment and satisfaction on brand equity (Iglesias et al., 2019). For instance, Jeon (2017). demonstrated that brand commitment positively influences the brand equity of global brands. In the banking sector, employer commitment has been shown to play a critical role in shaping employer brand equity (Altaf et al., 2022). However, other research indicates that brand commitment does not directly affect brand equity but serves as a mediator for the impact of brand credibility (Mathew et al., 2012). Applying these findings to the context of professor branding, we propose that student commitment to a professor significantly influences professor brand equity (PBE).\u003c/p\u003e \u003cp\u003eBased on this, we formulate the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_1:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommitment significantly influences professor brand equity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Brand trust\u003c/h2\u003e \u003cp\u003eTrust arises from a consumer's perception that a brand is reliable and possesses the competence to meet their needs (Vuong et al., 2024). Cant\u0026uacute; Alejandro and G\u0026uuml;emes-Castorena (2024)concluded that trust is closely linked to vulnerability and positive expectations. In other words, trust reflects a feeling that a person or brand has the ability to fulfill expectations, especially when one is in a vulnerable state. Mayer et al. (1995) identified three key elements of trust: ability, benevolence, and integrity. (Hoang et al. (2024) expanded on this by including trust propensity, describing trust as encompassing ability (reliability), benevolence (confidence), integrity (honesty), and trustworthiness.\u003c/p\u003e \u003cp\u003eIn both consumer-brand and interpersonal relationships, trust plays a vital role. For example, celebrity endorsers' credibility significantly impacts consumer commitment and brand equity for endorsed products (Dwivedi \u0026amp; Johnson, 2013; R. P. Singh \u0026amp; Neelotpaul Banerjee, 2021). Trust in smartphone brands has a significant influence on consumer commitment (Erciş et al., 2012; Şahin et al., 2013). Similarly, in the airline industry, trust affects employee commitment (Erkmen \u0026amp; Hancer, 2015). In the higher education sector, trust is a critical factor for student satisfaction (Chen, 2017). For undergraduate and graduate business students, trust significantly influences satisfaction, highlighting the importance of student trust in professors within higher education (Hiatt et al., 2023). Moreover, brand trust impacts enrollment intention(Juhaidi et al., 2025) and the learning experience, enhancing university brand equity (Pinar et al., 2020).\u003c/p\u003e \u003cp\u003eAlthough these studies did not specifically focus on professor branding as their primary object of study, we can conclude that trust significantly influences student commitment and Professor Brand Equity (PBE). In other words, students' trust in a professor enhances their commitment and positively impacts PBE. Based on this foundation, we propose the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrust significantly influences students commitment.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrust significantly influences professor brand equity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Reputation\u003c/h2\u003e \u003cp\u003eBrand reputation refers to the long-term image held by customers. While an image may emerge at specific moments, reputation represents the cumulative perception over an extended period (Han, 1989; Passow et al., 2005; Yousaf \u0026amp; Li, 2015). Vuong et al. (2024) define brand reputation as the perception embedded in the public mind. Accordingly, professor reputation reflects public perceptions of a professor, serving as a competitive advantage. Reputation is associated with three key aspects: awareness, awareness of specific attributes, and an overall positive impression (Y.-F. Liu et al., 2022).Awareness pertains to recognition of a brand's presence, awareness of specific attributes refers to the public's knowledge of a particular brand attribute, and an overall positive impression represents an appreciation of several brand attributes that delight consumers.\u003c/p\u003e \u003cp\u003eIn the context of this study, professor reputation refers to the professor\u0026rsquo;s long-term, widely recognized positive image in both academic and moral dimensions among students. Research has demonstrated that reputation significantly influences trust, particularly when consumer experience with the brand is limited (Einwiller, 2003).Reputation also impacts trust, customer satisfaction (Meilatinova, 2021), and customer commitment (Su et al., 2016). Moreover, image, as the precursor to reputation, has been shown to affect brand equity (Faircloth et al., 2001) and customer satisfaction (Tahir et al., 2024). These findings suggest that a professor\u0026rsquo;s reputation will influence students trust and PBE. Based on this foundation, we propose the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessor reputation significantly influences students trust.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessor reputation significantly influences professor brand equity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Satisfaction\u003c/h2\u003e \u003cp\u003eSatisfaction is a reflection of a customer's positive evaluation of a product or service after use (Nilashi et al., 2023). It represents an emotional response resulting from a positive assessment of the service provided (Su et al., 2016). Tahir et al. (2024) synthesized various literature and concluded that satisfaction is an emotional response characterized by happiness when expectations are met by a product or service. In this study, satisfaction is defined as students' emotional responses as a result of their evaluation of the academic services provided by professors. These emotional responses can be positive (satisfaction) or negative (dissatisfaction, such as sadness or disappointment).\u003c/p\u003e \u003cp\u003eSatisfaction plays a critical role in brand perception. Customer satisfaction positively impacts brand equity (Torres \u0026amp; Trib\u0026oacute;, 2011). Conversely, other studies have found that brand equity influences global brand satisfaction (Gonz\u0026aacute;lez-Mansilla et al., 2019; Tran \u0026amp; Nguyen, 2022). In the banking industry, customer satisfaction positively affects affective commitment (Iglesias et al., 2019). Positive attitudes following the use of a product or service significantly influence brand trust trust (Erciş et al., 2012; Şahin et al., 2013). Similarly, studies have shown that students' experiences significantly influence university reputation (Amado Mateus et al., 2024).\u003c/p\u003e \u003cp\u003eThese findings suggest that a favorable assessment of academic services offered by professors might elevate their reputation, cultivate students' confidence, reinforce dedication, and raise professor brand equity (PBE). In light of this comprehension, we propose the subsequent hypotheses:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_6:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent satisfaction significantly influences student commitment.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_7:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent satisfaction significantly influences student trust.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_8:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent satisfaction significantly influences professor reputation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_9:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent satisfaction significantly influences professor brand equity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Word of Mouth (WOM)\u003c/h2\u003e \u003cp\u003eWord of mouth (WOM) refers to interaction and the exchange of information, either negative or positive, through informal, non-commercial communication between individuals about a brand or product (Juhaidi et al., 2024; H. Liu et al., 2024). Traditional WOM typically occurs face-to-face, while online or electronic WOM (EWOM) involves consumer communication in the form of opinions, endorsements, or product recommendations delivered via videos or texts shared widely through blogs, social media, or other platforms (Nadroo et al., 2024). In the business sector, EWOM has been shown to influence trust and the intention to visit hotels (Abubakar \u0026amp; Ilkan, 2016). Similarly, WOM impacts trust in electronic products (Abubakar \u0026amp; Ilkan, 2016). Other studies have found that EWOM significantly affects brand image and reputation (Aditi et al., 2023; Castellano \u0026amp; Dutot, 2017). WOM has also been linked to customer satisfaction (Shi et al., 2016), commitment (Patroc\u0026iacute;nio et al., 2015), and brand equity (Virvilaite et al., 2015). Conversely, negative reviews can damage brand equity (Ahmad \u0026amp; Guzm\u0026aacute;n, 2021). Hua et al (2024) conclude that both positive and negative WOM affect customer satisfaction. In the higher education sector, Perera et al. (2020) demonstrated that eWOM influences a university's brand equity.\u003c/p\u003e \u003cp\u003eThese findings indicate that WOM (positive or negative) impacts key factors such as commitment, satisfaction, trust, reputation, and professor brand equity. Based on these insights, we propose the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative WOM (NWOM) significantly and negatively affects student commitment.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNWOM significantly and negatively affects student trust.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNWOM significantly and negatively affects professor reputation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNWOM significantly and negatively affects professor brand equity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePWOM significantly affects student commitment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePWOM significantly affects student trust.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePWOM significantly affects professor reputation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePWOM significantly affects professor brand equity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Gender as moderator\u003c/h2\u003e \u003cp\u003eStudies in marketing suggest that gender can significantly influence customer behavior. For women, the effect of brand-self connection on brand loyalty is more pronounced compared to men (Gao \u0026amp; Shen, 2024). Similarly, other research has shown that the impact of a dissertation advisor's benevolence on interpersonal trust is stronger among women than men (Qiu et al., 2022). Furthermore, the influence of talent management on faculty members' commitment is significantly higher for women than for men (Antony et al., 2024). In the context of professor branding, we conclude that gender acts as a moderator for the effects of satisfaction, NWOM (negative word-of-mouth), PWOM (positive word-of-mouth), and other exogenous variables.\u003c/p\u003e \u003cp\u003eHence, we formulated hypothesis:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender moderates the effect of satisfaction on professor brand equity, such that the influence of satisfaction on professor brand equity varies across different gender groups.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender moderates the effect of NWOM on professor brand equity, such that the influence of NWOM on professor brand equity varies across different gender groups.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender moderates the effect of PWOM on professor brand equity, such that the influence of PWOM on professor brand equity varies across different gender groups.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender moderates the effect of commitment on professor brand equity, such that the influence of commitment on professor brand equity varies across different gender groups.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender moderates the effect of trust on professor brand equity, such that the influence of trust on professor brand equity varies across different gender groups.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender moderates the effect of reputation on professor brand equity, such that the influence of reputation on professor brand equity varies across different gender groups.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Theoretical Framework\u003c/h2\u003e \u003cp\u003ePrevious studies have highlighted the relationships among satisfaction (SAT), negative word-of-mouth (NWOM), positive word-of-mouth (PWOM), commitment (COMM), trust, reputation (REP), professor brand equity (PBE), and gender (G). We have developed a theoretical framework to test in this study based on these findings. The framework illustrates the relationships between exogenous, endogenous, and moderating variables, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. METHOD","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Research Design\u003c/h2\u003e \u003cp\u003eThis study employed a quantitative exploratory research design using the partial least squares structural equation modeling (PLS-SEM) approach. PLS-SEM was chosen due to its suitability for analyzing latent variables and complex models, as well as its ability to handle single-item indicators (Guenther et al., 2023; Hair et al., 2021). Hair et al. (2022), deemed PLS-SEM more appropriate for exploratory research compared to composite-based SEM (CB-SEM), which focuses on theory testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Participants\u003c/h2\u003e \u003cp\u003eA total of 441 students responded to the survey. One response was excluded because the respondent filled out the survey twice, leaving 440 valid responses (female\u0026thinsp;=\u0026thinsp;339, male\u0026thinsp;=\u0026thinsp;101). Among these, 134 participants had never attended lectures conducted by professors and were removed from the participant list. Consequently, the final sample for this study consisted of 306 participants (237 females and 69 males).\u003c/p\u003e \u003cp\u003e The participants were selected using a non-probability sampling method, specifically convenience sampling combined with the snowballing technique. This approach was chosen because it is easier, faster, and more cost-effective. Additionally, the snowballing technique allowed us to reach participants who were otherwise inaccessible.\u003c/p\u003e \u003cp\u003eThe minimum sample size was determined using power analysis with the G*Power software. With an effect size (f\u0026sup2;) of 0.15, an error probability of 0.05, a power level of 0.95, and seven predictor variables (including a moderator variable) as recommended by Uttley (2019), the analysis indicated a minimum sample size of 153 participants. Therefore, the sample size in this study exceeded the minimum recommended by the power analysis.\u003c/p\u003e \u003cp\u003e\u003cimg 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T6obKU+Xys71plDrlcfH06406pn0Kw/kB1f03M9attKXfa4eZ89X8kN4XKrZGEPlBB5PFHZFqxyVN24mP4jfunKq5o7Kz5XCczPiqDStywO5cbfC09BKK+lR2nTFPfc134TiVZWo5vizjlbchtqSFPrkXctszG0Ih+YFfrrKd1QgETNjGFqSnBPMRL799tvkxYYwC2NFiRWHqwIzaJbSu8Flo9cRVxHNmrOMHsIO6MWBI6FK6utmGledOgvRLCKMU+vRVaCmyVdM5kZdCZhzXoSwC45yRSCEEEIIhyFnDYQQQggzJoJACCGEMGNGBQF9ylTVuk9tzgt/BNr0U6JwPvQpXFX86c6hXNzcP3UaM9sWPqfadX3zT/QOBfnoeaOwPd/33a5CuAzQ5tUmNulDaD8tN9J3dci2fxkYFQS0iYb+RvBouUHQus03dsGU/QHC7uD76/v375/aN+JkuQGTwzfY/Gtddvxf22Nm52HXdUH1ehf7JkyBjog0KF887DfeeKN/Ju9T58PcQWBGEFA7Ya+IKZNCBv8bN24sn9rIT9Sh2v5lYetXA5rhUACSslRg/qx7SWly17ILVAIKHyWzOlPCTp2pht0hCbqiMuITNsrAGTOrqw21PN0MhX3N2tlwhZ3oZDaF1koWetCqr8LNWnZIl/Q3qX9sCkQ6cKd2IBBK8ItPRNM5hXB6x8vXlrtRrsM3mRtCbbe2wXAOQYDv2pk98r2/pCzt5MY9qHBQdHR0qrjzwsXMn5kVUZgouT3ECkR4DgOWJGu+wff9C7QDHLP+OnsdMmNQZyBXWbKqhP8SBhhcWXmQueqCZu3UMWbLMp/CW2+9NehGdXKog1G902qI0kU86UDcz6nCADs5aqWlrpLgBzv0TZ35hLAN1DMNhFVddbQ1t1YrNRaF/3HuPwvSyQk6u8ePHy+fng8Cgnvf2vM8MNBkg539QBkyWO+KBw8e9Fd1OhIyaIwIAwgevukP5XremTGDtq8ikJ4pG/AwSKPoJPSaA3eKJ3ryk8F76ha7Y5BeCRdXafOjcFx4PavqqqNJhfoABHr6n/A/zi0IhKuH9lMfekWwKcy+veNBadBD8Ng16vTkt69ArEMCq4QAR/+RcRXCZWDOKwJhPXsVBHxmx3vS1vsbXiWMzazowOs7Z56nLsuGi4UVI8rXB2I6H2bdCBzMtH3lCP2hd3hjZkL/D+DKypEEgvoqo4VeY2gFgbBQxBM//AAg4pJONFwWLtOKgM/WW2MD7W5dP+AwVngfw6rfoU5WvTR0FWGQblZELTmjuo7y1JanPHeD/OpZ7lyPe+H+dh3syi/8ETKv+iA3YXe0Dj+REl6eNf/HzOpWyZSfaNUx9ARl72braPnnda+aoRRf1UNXY27JsymQXrkhL0IIw3h78fYnWvpjbbf2IS0/587ethhGausKIH/0CyGEEI6YvQgCvmTaSWr9slQIIYQQjo8cOhRCCCHMmHw1EEIIIcyYCAIhhBDCjIkgEEIIIcyYCAIhdPAHV9TUjYdCCPuBfWLUHjfdL0DufJ8ZbYzmyvcVCBEEQujJf2ZDuHgYwBEEaI+oTU4f1AZiKO6rECEzVA74Ok0EgXAKl5r57JPrjz/+uDQdRjvtSem0PyRv6blkzn191v2YtK6dA1EepjoLN8cfzS68M5G57DhsRyyzTWYjIYTdsM3pg+DuWjsHpl0PE0EgrPjwww8XL7/8ci8xf/bZZ/1Z+fC73/2uvw7BIKtT/VCc/MdxxAzKSN5Pnz7t7THIYsbJftjXmQYyQx9zHVTUgq1/5R9bkSpMzgdgUNcBI9euXeu3C6ZDYGMrwgMEA8KRO+y4MIA7meF/Oo1wFfBl86quOjl9cAJdxoTQ0wkBv37wwQf9/Q8//MAI/Ws3QPfPY/iWoK60Ba+2/R3akhezTbbelX9cBX4TD8F93Uq02gHC1RbD+OnbWWtr0hDCYaAtel9Qn4egrXt7r8+Ot/nwnKwIhFH8mOkxuka3mklL1fdwWmFosY9DQKb4+dJLLy3vQri6zHlFIKwngkBY8fvf/75/Vw//+Mc/+usUWD7nTz36X4De04+9698Ft2/fXt49P91y3TI+ggmnHfqyIK8GXNjxEwaHTswM4bIxh9MH3Z3fIwR5X5TTBxt0FSGEFVQJFK8JPvvss/6e6zpYhpNblC+9uVldkmOZTmZ12X4IvRpwf335kFcA0kdV3Ezu9Ez8/D6EcFj8VSNtvDKkP9R2c/rgenLWQLh0sOLAnxH58w9/DgwhhLA9eTUQLhUSAoBr/tUfQgjnIysCIYQQwozJikAIIYQwYyIIhBBCCDMmgkAIIYQwYyIIhBBCCDMmgkBY0dp97DLsyc0mSJwh4BBv4r/JpkYcruRpZxOWOaCDmYbyCv2avyHsC9VH1CZfBanNo7zf8gPNpPa92dmlg68GQhBsxOGb81BFhs4IOBbquQK+IckmcfezFjhjgee5QJ6N5dVlqAfh8kP/45sBUe+8PxpCmwbp/BGvzzkzZD1ZEQijdI1y8ezZs/4eKdqlaqhHCYNL5uhVd7KnrYhRmnVyv8kqBH6xbfCdO3eWOovVueRdZ7DUmcYPP/yw+O1vf7t8er7l8hQ8vShttVzzRvekdcxsCM+vodmP62mFx2dVMkfVsNhuWWa4dTpBa/RUyBB2hW//S/8zBdo87V0bjN29e/fUNsOgup29Rxo8lwdCeI6vCGgrXyTqKnEjbav6YB+FHpI4cJWeVzP5w1VgFz1m9ejLjykQxpB99DeZxRIHbaus+3XU8JVe5RPIP+VHnfEMmbVQmVQ/PJ2YyY7sA/EkHOHuMJM9wK2HUcs/XC5UJ1rqmCCeXkfr8xCq56qf3KtdVrPaDkKXX8trCD21w9BA0WqQNCjMaWDcM5DLjRreUAfkfsntNuCOMFoofk4rPooL9xr8v/766/758ePH/fMQhOF+SXm4PA91PGNmLWqnBoRFPARprHlS7QDhyl7NqxqOnl2AC2HXUB+9PdTnMai/1FEU/UKt78LrfXhOXg2EM3QNBQGxV/UoYUd/INNyHCcQdo2vX2LmKv2u0a38k/rkk096M3Go08Bap7DVuMBvfvOb/vrf//63v47RdTpn/Kz5Npa+faR9ip85gnk+zOEYYtqc2h+vBQ7Vp1wFIgiESXBU76effrp6/837ZY4I1YCnRvevf/2rv+o9HO6wx3twQeezyf8AxkAY4f3grvj3v//dXyUA/N///V9/HYJ0XsUjmO/du9cLcBLmfvrpp/56/fr1/houF3M4hlhg5v8byjHEE+gqQgg9LJdRJVCtpXpfekP58jRLxnKDP76EzFKcu/Mlu+rnprTeXbt/Ur7sPUR1M+U/AkC63Z0vO3ra61LlmNkQWqL3MPFHKD+kPF/AzeSOsHn28q/xITxPVwj7QvUR1eqHWvrej9S6W9tEy8+5k0OHwqWHVQEkfCT988BMoxv8F3/5y1+WOscHKw6vXsARzORN19mOvioKIVxO8mogXHpY9qufCl1FJAQA13WvA3YF+dvNsiIEhHBFyYpACCGEMGOyIhBCCCHMmAgCIYQQwoyJIBBCCCHMmAgCoUd712uTID2j6r7zU/E99I8NxUtK+wBcFPwhj3go/wV5v2n+ay+DEC4b1H9vl6h978kRIgiEJeyud3Jystp0hudHjx71Ztsex/v222+v/DgmSN/NshvgIT/Fa8E/8vk8r0Leb5v/Fy3chLANtAO1S75WeeONN5YmYV9EEAhn8B0Eoc7sfbXA713Vma2buYTvfruZZrXS00yB8KYgt3LvkD52A2yZtdD2rJ5WT5/MucpOy7zqg5ux05/wfKlugHjLXEr6/okh+oQhan6rnF3f75U/1R0qhF3DLqH6TJU6R92/aCF9FnRSVwg97DTH7nEodt/Sjlzg9+DP1T672bG7F7t9SU+7eWlnPDfT7nduJrTLmPyvu4a1wI7vtlf9xC/FZyradU9xxb3vtEd4mKOAOBBf5Y3gXmnAzP3AbU0f8a56Q/km9CxzIX3cQ/UHeEaRHsJW/NxdDS8cP5SjyraqY4W24u047I+05rBCgoAGCA1uID3hz7jBrusxePlg7ygcGjpmVfnAiT/+vI7WwKnwBP5t4icojQ5xJX3geSU0YLbUP//5z/7qgzB+TBEE1sVf4brf0HJX04W7mk4gDop7y+8Qdg11LvXsMOTVQDgD7/a7AaNfQt83XWNn9Dyl6lbB5z0g5FCn7HWD6vLuNN1gfiaNrThtEs/W64JtaIXZym+WbIm34LVD/oNwefBXUFUdI3ktcFgiCIQm6/bt33Rff3+3j4DBqXc6Jczf09P4z7N17pRT9rbF/zuB/wgxCE1D0IkhHPipfnq3D5g9fPiwvwe3NwYDdRXSyDc6zxYMAoRLvvLfCNnjyvOtW7f65yGwpzhLkAGdSBiOn8t0+iDkhMAD01WEEPolaKqDFNTlbpaVZe73UrLPcnNdSkZP9770XMNFiVacpuLuPDwtm0tpaX8dxN/TTPoc95N4OzUvPD5upvxDr5W/KC2Vyq4U8XPc3M30qkbK0+9uPH3VDcrTEMKuof7ltcDhyFkDIUyAWTUzFD6rDCGEq0QEgRDWgBDw5MmT5VM3HU6TCSFcISIIhBBCCDMmfxYMIYQQZkwEgRBCCGHGRBAIIYQQZsyoIOD7vbuaupGIvl/eJXy/fZ7vzEObuke+nlGU4zGy6/pFvVKafR8CZ8hs7vVybB+DXcL38CqjH3/8cakbrhLUJZVxbd9jZmN4f+bttzXGubn3CeobhfuJcoibm/mYOWamPTukDtGmeviz4BAY1++F9a3xFPh+edffG+Nf/WY67AaVrcpM348fK/uoX7CNv3Ovl3z3XfdP2Ccvv/zyrzdu3Fg+hasC+2eoHWnPD+0nMGY2hvox2fW6OuaP+kNB2Apfe5zIHXHT3hsKT3t0uD9jZoqL4lbD2CdrXw3U3Z30LMkM6calKklp3PPJFTugyQx7cufSlGacXGUGMtcz5viHvzKrUiH+H+sM9rLgO+g5VZIFL3uXnt2925GSpFslYJWn6on76WXNc6t+yQxFvVGd0mzd/UPV+jOE11f5JYbqpcImjZ536+qnwsKe/EApfTXP3D8Px+0pv/UsBe6mKjFUTipbdnNkB0eZi1r2asvgekpnzdsxus50eReuCt9+++3i7t27/T07c3aD62oHyzGzMdgeG7varhg/8Mthy2zqX22b3cC/vDs7FrqfrV0QtevopmY6dvmgxy8vBYImGEtyEZJoAAnIZwH1GempNbPCngeNPSQq3bub+sw9ekPg95h5GEZ5i6I8vKx1L+lU0irIzCVZLwM3A8oIN9XPKhFzr3ohM4cwWvULMMM+15ZbGNIf85f4KE7OUL10f3DXstMCd8TN87QVV8BPz1+lS+7xayjO0lfc5JayUT6sKyeobR/cL8Gz5wlhoOdhr+Px48e9PdTXX3+91A1jKJ9b6lhRG1C9c8bMKqpXsss99VXwrDpKPqlNEAbPwp+p6+6HP6u9CH8eM6vxrM/75ML+LNhl3PJusfjiiy/6Pc814zkPSH+8Rwzbg8RMebjUzD74XUNYScDs6d9V/NXMEHSOOLN5bcCDOfZkBpQRErHOK5A0zhU8XM0QFa5mtlPoGm1fF3Db1fVez2e2Cm8X9W4I9vHXLIM83eTMA+KvfOPKs2bUWjFBkdfPnj3r9R3aGO7YDVF57SsirGK4Oz+A6Pr168u7/50rMVZOLXSOwo0bN1ZhQnXXKqcx/v73vy9efvnlXv3nP/9Z6oYxLttZA9Rz6qf3OWLMrAV2aAuqv91AvzR5DnmgGTrtc129vorkq4FwBhoFjYXGNgaD0RTG7CEk1I5p0wONhqjLbgz4DEp0CoTTSdpLk/1BJ8QAK2HABaJtwS/yVPnFQNqiLi3SgSKMyB0d6VTOU07VXRXUW8ujY/ztb39b/OlPf+rr6SaC4ZzRq5eWOjZ8oK9beo+ZjUG7U/1joN+0zl111goCVToakpZokLwnHIKZob9/4X2iuHfvXt+ZVemOjlszyxZ6/+jQQXo4YTtqJ4+kTAPU7JnypGzWDWwMRtjzDptZKQ2a665OH6z1q4XeJyrO1Lt9UOvlnTt3+jwYGrCHwI3yhvzjmRUGVlS0siD9KeDOZ0PrBD2xaTmpbHWqIc+CMjpP+5QQ8Yc//GHxxz/+cfHDDz+cESzCWS7LigD1hnqJsF4H+jEzsa7/V12mTerZ6+eDBw9OCQneturYhz/qD1vjovq8Tc0eP3586noQuoowiN5RVKV3Fnq/geo6uf4diZtX99LHHu9hpI9b4W6whxn3hCUUDqqrFEvd58hN2AzyUXmKgk7qXt1DtUNZ1fICPascvJ5IieqnzLyMCUP1QObgYcse1PCkD17vlD7Z0b0rpcHjI6X0CrdT62VLbwziRtg1H6C2O+UNbmCobYH0Zd+fXY9w5S/hDZWTqHkuWmU/pZyG+OCDD3q7gq8H/DlcXobaIPVkzMxRvXW87tY2W/2tbr0tVbe1/Ti1vXjdHjOrbaKmb19cyFkDSG28h97FMmkIxw4zaVbANmlqzFKYLWS2G0LYNwf/jwBCAEsqdIzch3BVYTDnFYFeg01dEscdS6AsS+LeX6uEEMKuyemDIYQQwozJVwMhhBDCjIkgEEIIIcyYCAIhhBDCjIkgEE6hb+BRfGPLH9z0rew6sD/0B1D8G/KHf9WPmW8KcVYa/Nv3MfhDntxUFUI4HPQhantT2y94u/c/5rq+lO8dQL8l/dp/uRn3jv4MLOWoT0NVP90Mtat+71zwZ8EQQN/U6nt0fe+6i29Zd+XPJvDdL2mYir7hFfU5hLBf+G5f3/KrP/Lv7IfAnX/nzz16gH/q04Bn9UVc3Z3bVf8nvD+pfUMr3goDM8VF7mSmvQgumqwIhBXsxd1VzNWuXdrngX3nXYr12TP3voqAcmQXtOe8S8gu/bc+k5OZlGYIVcpvud0Wj3MI4XCwd8Y2JwyC7wjo9+zFoT6NvkpnnQBXnlsQF/pDgZ+tMz0q7AjYCQWrMFru3OwoWAoEIZySVFtI0kUhGSPNStKFKiU76/xumSOBSzoHJO7WDB93LtULl+CnoPhLhXBVoO143XZ1rGi2PHVFwPui+ixqnyXoKwir1Y+A+gbvTxQ/qSEwU5jyR9TniyIrAmFjusbQrxYgZe/qgKAKs3I2nvI9xZHstUrhKxSsNGB3V3SNc3kXwtXgspw1ILSpVjfYTjphcCrM8uu7fmBVQHlRzemL6GOY5av/QY/40VfgDrOWv1pZ3Fc/uSsiCIRTtA7BqNRT7fZFJ50v705DI2S3PjXCXQ/cLNsdawcZwja0/jAndWy4EDB0uNA21NcCLXgt4X2gCwG+3Tf+0D/JLw4Bq8v/ytvL0JdEEAgrnj592jdA/0ctlZnGsEuQnD2MFjQwZvn6TwDgBre1EX788cf9NYTQ5rKsCNC+6YNYdRwSAuiTWrNvH8BbE5qHDx+eeSePP97H4E7/YUIfIeDmzZunhADATj19kOPGQf+Zoo8ayl/1qa14XghdRENY4f8DQPn7NO6lzztHUd+VSfH+S/BuTfp6D4e525fCP6hxQel9ob/z9PBbblCt94JOKy4KK4Swf4barvcjgF6rPXuf4P2TQK/6Vdu9+ibg3s1Q6pvA+x135/2klMfH+0LUMfQzOWsghBBCmDF5NRBCCCHMmAgCIYQQwoyJIBBCCCHMmAgCIYQQwoyJIBB6+DSPT1706YyeUX6AxzFBvPzTn0OhvKn54tslo3b92WUIV53ahlAX0cbnRgSB0MM3uycnJ/23sQx0PD9abtRTv6E9Jqbs/b1rlFcOnRWdGB/hSI1tXBJCaMMeAmpDfIt/qA3M5kwEgXAGNvTQRhnAzFbSOfhqgd+70soCMEi6mWbK2ngDpYGUe/yEOjvwOPH85MmTPq5D5lI+o5CfbCSC4n7qiofHh3AdNjUiPpgp/iGEzWCzMN/Glza3yy2GwwCd1BVCDxtksPEFik0xtNkG+D34c7XPBhlssMHGGdo8Q8gOV6GNO+SHb84h8Mc35QCefYMPgXvXx2/cC234oXC4X7epB3ZxB9r4xOOjvAvhGKFuUmdb6lihvbXad9g9WREIZ2C/7a+++mqj7S+R3IVL8A8ePOivmkmzZSdUv7tG3x/MwXI6swLw1QbOFpD+GJr9+/akXWeyiofoBvaVf107GJ11MDPhlYkODsEu8Q3hsnDZDh0C+ohbt24tn8I+iSAQzsBgzEBXl7+3pZuNnOl86j7idQ9wBnTC72bfvX3eG26L9gB3znsOuAs+IRw7l+nQIchrgcMSQSA0WXds5tRjNTmVi3fn+l8A0Pmse4/OnwARINQRrDtYCMGBzo73i/rDo2A1gXhsC4IRKwj+X4NdCUkhHILLtiJA/3JeYT1sQFcRQjhzEAbwfs6rCO/sZO73UrLP+0jeqXPP+3TpS+ndPLTCFfIDJT/crd7VS/l7ftfHrahu/L8K63B3ntZq5v9HCCFsDu3c23PYLzl0KIQQQpgxeTUQQgghzJgIAiGEEMKMiSAQQgghzJgIAiGEEMKMiSAQQgghzJgIAqGHb+TrRiP+3fxFoXj5OQLnhTMGLuI8AKWlbkZUN3s5hnwP4aJQOxlqo+xJgjntWEjPVdrRdCIIhB424jk5OVntAvjo0aN+I56LbkzEi/jseoexKdsV7xrSUndIpAMjLqRRSoeuhDA3EIrpd8YY21ws7Wg7IgiEJuymJ2FAuwL63v8oZul+gqBO85OZqNI6Zi0JXkpumTlLT3EAzaA9PJ891Bm2u8VPzlFADZlLv+UnV+VDndkP4fGpnRydGjshYuYznBDmiHZAZELSgokJO5UOnfWhdpa2tBkRBMIgfp4+gx/bfkraZmb76quv9mYIDMCWoDK7fft2r8egzkFD2MGMK+6uX7/er0Bcu3at1wfc0cDv3bvXP2umXKGzwB3bEGOOYstfCRC+nSrh+cwAPwkDJTtKJwO20oAivRIG8JP46uhjzMGFiBb4CfKzwtbHyoOp2zaHsCkujFZ1mUBwph1W6E/g6fJsEg4sckE+jBNBIEyCQUozV5Rmto8fP+6voAGXmTIDJjCoM+hqsOWKtP/w4cP+2WfVU2fYws8PYDBVXPSOEYUQQrzXwYBOnH0w5hTG1uCsAR2hwoWlCoIJfn7xxRdLnecnIYZwaC7bWQMtGNhpy/XAMuDVIWnRK0RWBDY5PXXuRBAIg9TZrs+ipbZ5D9c6DXBXEGeEFF+B2JaWYDK0ZDmVfaY9hCGuwoqAhHLirNd7mfXvhggCoQkDKrNpluuZ9SJh0/C0/M6VBrnuz4RyJ6GCK8/7OmecWTqrA5qprzu1kPgw4GMfd/5uESHnPO8amZ346gesi08I++AqrAj4n2r1ek+rAwg63hchNOT0wg3oMjWEM6cAotBzuoZ3yrwb5E6d5tcNpL09N4duVr7SQ/Hseid2OqHC+POf/7zSc4Vd/NUz/ni8iI+bu9+O9FD4IUiD9PHXcTc1b8ZwPxUf5VU1C2HOeHsYahetNlP7mNp2wzg5fTCEEEKYMXk1EEIIIcyYCAIhhBDCjIkgEEIIIcyYCAIhhBDCjIkgEFbwGZ1/Xyw15bwBfU64KR6mPjE8RvheWfE8zyeFm8JnUdqdMIQ5oA3B6h4BvlGYlPcZvldC2sxmRBAIp2DfANS15ba3XDdB+wxMZWgb4WOD75WJ582b7T3O9wXff6NCmAMM4GOHDqlfktJ+IZqsSJ9+ZcoEJjwngkCYTJXIBfo6d4Brlcjdjbs7D1qBcCUhxGcGKM0aXM8V8dfKhM/8N5lV+MrGJu5qXDULIs7Sw2/HzVp2PA2oTYWzEC4KbXzERlwtfJtzXzFg868IzNsTQSCsQIquWwZLj8EHSV2HerBqQGMEzNEHmXujlJSOOjk52cnS+ltvvdXHQf466kxQj+zQIe6BONLRMLvHD3b7I53MNnSgkPycElcGYey5uynCgDoyufPVBmY66BG/ITBXviue+MmuavIT9whnEQbmTRU4XV0WJOyqbtNW66xfacJu7cvCMBEEwiR06pcO9aCRMXBOWX5jkFIDpfFyauB5oaEjmMhfBnnFzVcuWocOyZ7PooUPvBwW5Nsqt0BAwn/SpTA5aEiHLo3BuQPYkztoHahSkZBAvBjkKRe5QwjwWVPrcKgwP1w4ruqyoHovEJzrwUJKE+3KVwzCOBEEwta0BtIKjZHBVA2UQWsXqGPTfxgY8BmUtXKBYIC5VgH2jcJztQ6EKewpT8inTVZLqhAg6CBrXDI7mjdXYUVgE2gXOX1wOhEEwiQ48pdZr2bHzLqRuocGGDoe7LDk7kve+HFeiAMdGFf8Z6BDIOBeS/z6E9Gmh/z4H5U4QplXCFpBaEE4hOfhIPxM6WDJI+zqj4h0XlNXS/CffMWt/jMACBK+iqG8mrJyE64uV2FFgIkHdV1Qz3WwEG3JzRACctLnBnQVIYRTUC1Q3cC01HnO/XIw0dOnT5cmz8G+zDj4R7gb2cG8m0WfMpOq4VYIt7rpBsWl6a+Dhw5JYS47uNO1G9BXz7InMJO+FHqimpFX6/B4SgmPhyvSXstBSlS3no4QjplWO1N/UOu99xPVLHV+M3LoUAhLmHHcvXs3y+ghhFkRQSCEDoQA/amQpX5eMYQQwhyIIBBCCCHMmPxZMIQQQpgxEQRCCCGEGRNBIKzQp2aoTb5nB7ndJby3V3z806AQwtXF2/0m/ZA+20X5ZkL6vNaVf07rG54RtjNm5uHx+aLjaWiZA3qY6VPfiySCQFjB9/L8ZeTmzZtrd9QbYpeVWnsEhBDmAYMjgyjtHkU/5IP6EAz27FHCltu4Y4fNuneG/ETpyyD8JgzpE7YGbfxkPwI3U1xqeFDjed+2QPct1wH3U3YfPRQRBMIZqPwnJyeLhw8fLnWe05KsGfhpcK1Dh7RK4Pb8GarkfF5BQv64tF5nFR6mGq+kc3UCIDvSc3duz90qXOyGEDZHmwTBawOHD1W0kZg2/+Iz4LqzoNpu7Q88DA+bzcKGvh7CbyZMCg931a5vgV5BEMH90dBJKyGsYJMOFBvX+IY52sSHTYAEz+iDzPUsqv6QPWBTkNZGIDXcdWDf4869Nh/BfzbcEW6GO0CPuGiTEunJHuCPxxUz7Lr9TeIcwj6hrqp+VnVMEM/azvx5iNqvcE8bhGrmbZlrbcf+DNh3/yryfyjeXN2t+pUar4skKwLhFCyp3bp1q5d0mdVqeY3Vga5yr7buha7+jG6/OwWfuSNB7+r7fV8W5J5lPC3HkUbBzEHPSOjEh/0EHjx4sJL6cTf1YCHyBEiH51UIF8lV2GJ4DPohluK1IumzbcxIp/oqVgTqasEYtGXlU11NYAWTMLuB/tSZH+S3nulP6T+02kk/1wkH/f2xEEEgrGDApMKqMTHQMSCKXe/drQG6k4j7hkZDPjS+hM/yHvGhUZN238sc1h0shKAUwjGi11ctdVVguV3tkoHe2+4uqK8bXAiYOoHRBIV+Breg60USQSCsYNBHUvWBjgGRCk+jqn8gpHPxmbczZOb/O+CAHQZPSeqbHhA0hm8TjL/MEHRAkEv12NPzG2+80V951owC8/McLBTCMXCZVgR8sG2tutHu6szcwYwJzZ07d/rn2hexAuhCgofhYeOPu8NMEwdWCRnA6b+qEIAbwhT0efQf9HMurDABAl0vlC5CIazeg/l7LkAPxftuvduSwo2DW5nxjky4fjfAnnKrcFGyhx7hSd9VjV8L7Ckc3TseZjUj3ryzI3xPA8iNFPkhhvRDCJsx1j6hpe99k/oWUfuS6pbnltvqzs3oG9wM5f56GlCV2pdeNNliOFw5mDF0jTjv6EMIYQIRBMKVwpfrWbar3++GEEI4TQSBEEIIYcbkz4IhhBDCbFks/j94CRFZsHpAuAAAAABJRU5ErkJggg==\" height=\"179\" width=\"514\"\u003e\u003c/p\u003e \u003cp\u003eThe participants in this study were predominantly from lower-middle socioeconomic backgrounds, with parents whose education levels were below high school. This indicated that the study was well-suited to the context of lower-middle socioeconomic backgrounds. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the characteristics of the study participants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants characteristic\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;306\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShare (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather\u0026rsquo;s education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma/bachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma/bachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllowance per month (USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder 30.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30.92\u0026ndash;49.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;49.47\u0026ndash;55.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;55.65\u0026ndash;74.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;74.21\u0026ndash;92.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;92.76-111.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;111.31-123.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;123.68-154.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;154.59-185.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;185.51-216.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;216.43-309.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;309.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote : 1 USD\u0026thinsp;=\u0026thinsp;16171.30\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Measurement\u003c/h2\u003e \u003cp\u003eWe adapted the measurement of word of mouth (WOM) from East et al. (2008) and Mittal et al. (2022). Indicators for trust, commitment, and professor brand equity were adapted from Jillapalli and Jillapalli (2014) and combined with the brand equity measurements from Castro-G\u0026oacute;mez et al. (2024). We translated these items into Indonesian and reviewed them with three students to ensure the questionnaire was comprehensible to the respondents. The questionnaire consisted of 31 items, including demographic background (5 items), satisfaction measurement (4 items), negative WOM (NWOM) (3 items), positive WOM (PWOM) (5 items), reputation (3 items), trust (5 items), commitment (5 items), and brand equity (5 items).\u003c/p\u003e \u003cp\u003eWe created the questionnaire using Google Forms and distributed its link to students directly through WhatsApp messages, both personally and with assistance from lecturers. We requested students to respond and provide written consent to participate as respondents within the questionnaire itself. Additionally, we asked them to share the questionnaire link with their peers. We conducted the data collection between November 26 and December 12, 2024.\u003c/p\u003e \u003cp\u003eThe data analysis was conducted in two stages. In the first stage, we assessed the reliability and validity (outer model), as well as the model fit. In the second stage, we tested the hypotheses using the bootstrapping process with a subsample of 15,000 and a two-tailed test at a significance level of 0.05. We conducted the data analysis using SmartPLS version 4.0.9.9.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. RESULT","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Validity and reliability\u003c/h2\u003e \u003cp\u003eIn the initial stage, we tested the reliability and validity of the study constructs. Indicator reliability was assessed using outer loading (OL), while internal consistency reliability was evaluated using Cronbach's alpha (α) and composite reliability (CR). Construct validity was tested through convergent validity, using average variance extracted (AVE), and discriminant validity, using the heterotrait-monotrait ratio (HTMT).\u003c/p\u003e \u003cp\u003eThe outer loading values for all items exceeded 0.7, except for PW1 and PW2. These two items were removed despite exceeding the threshold value of 0.5 (Hair et al., 2021). The removal of these items addressed the AVE issue and increased the AVE values, ensuring they met the required threshold of 0.5 (AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.5). The results showed that the Cronbach's alpha (α) and CR values for PWOM were above 0.6. Although these values were in a critical range, they still met the criteria for exploratory studies in PLS-SEM (Hair et al., 2021). The internal consistency reliability assessed using CR (rho-a and rho-c) also confirmed that the items were reliable (CR\u0026thinsp;\u0026gt;\u0026thinsp;0.7). Thus, we ensured that all constructs were reliable.\u003c/p\u003e \u003cp\u003eFurthermore, the convergent validity test further demonstrated that all items were valid, as the AVE values exceeded the threshold of 0.5 (Hair et al., 2019). We also conducted collinearity testing, and the results indicated that all variance inflation factor (VIF) values were below the threshold of 0.5 (Ringle et al., 2023). This finding suggested no collinearity issues. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of reliability and convergent validity testing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidity and reliability of construct\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCR (rho-a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCR (rho-c)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNW3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: * The item was removed due to convergent validity issues; the calculation process was repeated after the item was removed.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we tested discriminant validity using HTMT values, which were below 0.9, confirming that the constructs met the discriminant validity requirements (Hair et al., 2019). Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the HTMT results.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity the heterotrait-monotrait ratio of correlations (HTMT)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNWOM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePWOM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePBE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eREP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTrust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eG x COMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eG x REP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eG x NWOM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eG x Trust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eG x PWOM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eG x SAT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNWOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG x COMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG x REP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG x NWOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGx Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG x PWOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG x SAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eNote: COMM\u0026thinsp;=\u0026thinsp;Commitment ; G\u0026thinsp;=\u0026thinsp;Gender; NWOM\u0026thinsp;=\u0026thinsp;Negative WOM; PWOM\u0026thinsp;=\u0026thinsp;Positive WOM; PBE\u0026thinsp;=\u0026thinsp;Professor brand equity; REP\u0026thinsp;=\u0026thinsp;Reputation\u0026thinsp;=\u0026thinsp;REP; SAT\u0026thinsp;=\u0026thinsp;Satisfaction. Discriminant validity threshold: HTMT\u0026thinsp;\u0026lt;\u0026thinsp;0.9.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Hypothesis Verification\u003c/h2\u003e \u003cp\u003eThis study confirmed that commitment (COMM) had no significant effect on professor brand equity (PBE). On the other hand, we found that trust significantly influenced COMM. These findings were substantiated by T-statistics (t), P-values (p), and the confidence interval bias-corrected (CIBC) values, which met the established criteria (t\u0026thinsp;\u0026gt;\u0026thinsp;1.96; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; CIBC excluded 0). The CIBC values, ranging from [0.374; 0.569], did not include zero, further supporting this result. Additionally, the path coefficient (β) values above 0.3 indicated a substantial effect of trust on COMM. The path coefficient (β\u0026thinsp;=\u0026thinsp;0.474) and f\u0026sup2; value (f\u0026sup2; = 0.400) demonstrated that trust exerted a large effect on COMM. This suggests that students' commitment to the professor increases as their trust in the professor increases. However, student commitment did not lead to an increase in the professor's brand equity. Therefore, we confirmed that H_1 and H_3 were rejected, while H_2 was supported.\u003c/p\u003e \u003cp\u003eFurthermore, we found that professor reputation (REP) influenced student trust but did not affect PBE. The effect of REP on trust was substantial (β\u0026thinsp;=\u0026thinsp;0.556, f\u0026sup2; = 0.467). This suggests that as a professor\u0026rsquo;s reputation increases, students are more likely to trust him. However, this reputation did not lead to an increase in the professor's brand equity among students. Therefore, we supported H_4 and rejected H_5.\u003c/p\u003e \u003cp\u003eTesting the variable student satisfaction (SAT) showed that student satisfaction (SAT) had a significant effect on student commitment (COMM), trust, professor reputation (REP), and professor brand equity (PBE). These findings were based on CIBC, T-statistics (t), and P-value (p), which met the required criteria (CIBC excluded 0; t\u0026thinsp;\u0026gt;\u0026thinsp;1.96; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The path coefficient (β) values above 0.3 indicated a large effect of SAT on COMM, trust, REP, and PBE. The effect size (f\u0026sup2;) and statistical power also confirmed the significant impact of SAT. The largest effect of SAT was on reputation (β\u0026thinsp;=\u0026thinsp;0.647, f\u0026sup2; = 0.755), followed by commitment (β\u0026thinsp;=\u0026thinsp;0.427, f\u0026sup2; = 0.323). These findings suggest that students who are satisfied with the professor's academic services are more likely to enhance the professor's reputation, exhibit higher commitment, trust more, and increase the professor's brand equity. Therefore, we supported H_6, H_7, H_8, and H_9.\u003c/p\u003e \u003cp\u003eIn the next test, we examined the effects of NWOM and PWOM. Our study found that NWOM had no effect on COMM, trust, REP, or PBE. The T-statistic (t\u0026thinsp;\u0026lt;\u0026thinsp;1.96) and P-value (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) indicated no significant effect of NWOM on these variables. This was further supported by the CIBC values, which did not include zero [-0.073; 0.071], [-0.090; 0.065], [-0.128; 0.106], and [-0.130; 0.075]. These findings suggest that negative information or issues did not affect the professor\u0026rsquo;s brand. Therefore, rumors or negative news about scandals did not damage the professor\u0026rsquo;s brand equity. As a result, H_10, H_11, H_12, and H_13 were rejected.\u003c/p\u003e \u003cp\u003eIn contrast to NWOM, the effect of PWOM on COMM, trust, REP, and PBE varied. The assessment results demonstrated that PWOM had a positive and significant effect on REP and PBE (CIBC excluded 0, t\u0026thinsp;\u0026gt;\u0026thinsp;1.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicates that positive WOM information, whether offline or online, affects the reputation and brand strength of professors among students. While this effect is significant, the path coefficient value is below 0.3 (β\u0026thinsp;\u0026lt;\u0026thinsp;0.3), suggesting a small influence that could be considered negligible. In contrast, PWOM on COMM and Trust was not significant. Positive information about a professor does not lead to increased students commitment and trust in them. Therefore, hypotheses H_14 and H_15 were rejected, while H_16 and H_17 were supported.\u003c/p\u003e \u003cp\u003eThe moderating role of gender in this study was not dominant. We found no significant moderating effect, except for the relationship between SAT and PBE where t-values were below 1.96 (t\u0026thinsp;\u0026lt;\u0026thinsp;1.96) and p-values were above 0.05 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Nevertheless, the effect of gender as a moderator was weak, as evidenced by a β value of less than 0.3 and an f\u0026sup2; value less than 0.15. This suggests that the influence of SAT on PBE is more significant among male students than female students. In other words, the effect of SAT on PBE differs significantly between female and male students. Therefore, we confirm that hypotheses H_18, H_19, H_20, H_21, and H_22 were rejected, while H_23 was supported. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of these tests.\u003c/p\u003e \u003c/div\u003e \n \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypotheses verification result\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ef\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCIBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommitment -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_1 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust -\u0026gt; Commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e9.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_2 was supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_3 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReputation -\u0026gt; Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e10.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_4 was supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReputation -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_5 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction -\u0026gt; Commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e8.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_6 was supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction -\u0026gt; Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_7 was supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction -\u0026gt; Reputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e17.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_8 was supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_9 was supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative WOM -\u0026gt; Commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_10 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative WOM -\u0026gt; Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_11 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative WOM -\u0026gt; Reputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_12 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative WOM -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_13 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive WOM -\u0026gt; Commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_14 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive WOM -\u0026gt; Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_15 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive WOM -\u0026gt; Reputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_16 was supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive WOM -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_17 was supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender x Commitment -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_18 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender x Reputation -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_19 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender x Negative WOM -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_20 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender x Trust -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_21 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender x Positive WOM -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_22 was rejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender x Satisfaction -\u0026gt; Professor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH_23 was supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: f\u003csup\u003e2\u003c/sup\u003e \u0026gt; 0.02 small effect size, f\u003csup\u003e2\u003c/sup\u003e \u0026gt; 0.15 medium effect size, f\u003csup\u003e2\u003c/sup\u003e \u0026gt;0.35 large effect size (Cohen, 1988)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Model Fit\u003c/h2\u003e \u003cp\u003eThe results of testing our proposed model indicate that it's a good fit. The Standardized Root Mean Square Residual (SRMR) value meets the criteria for model fit, being smaller than 0.08 (Ringle et al., 2023). Therefore, no misspecification has occurred, and the model can be considered as fit.\u003c/p\u003e \u003cp\u003eFurthermore, The R\u0026sup2; value indicates the explanatory power of this study based on the sample used (Hair et al., 2021). The R\u0026sup2; values demonstrate that the variables measured in the model successfully explain 71.1% of the variance in COMM, while trust is influenced to 65.1%. On the other hand, the variables in this study explain 47.3% of REP and 45.4% of PBE. Thus, other unmeasured variables may still influence the variables COMM, trust, REP, and PBE. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e present the results of the model testing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Quality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessor brand equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.75\u0026thinsp;=\u0026thinsp;substantial, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.50\u0026thinsp;=\u0026thinsp;moderate, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.25\u0026thinsp;=\u0026thinsp;weak (Hair et al., 2021); Model fit threshold SMMR\u0026thinsp;\u0026lt;\u0026thinsp;0.08 (Ringle et al., 2023)..\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also tested the model using the Q\u003csup\u003e2\u003c/sup\u003e predict value, calculated with PLS Predict in SmartPLS version 4.0.9.9. The Q\u003csup\u003e2\u003c/sup\u003e values for all indicators were greater than zero (Q\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0), indicating that the model has predictive power (Hair et al., 2011). Furthermore, we evaluated the model by examining the root mean squared error (RMSE) and the mean absolute error (MAE) of the endogenous variable indicators. The majority of the RMSE values from the PLS-SEM analysis were lower than those from the linear regression model (LM), while the MAE values for the PLS-SEM indicators were comparable to those of the LM. This suggests that the model exhibits medium predictive power (Shmueli et al., 2019). Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the results of the PLS-SEM prediction test.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePLS-SEM predict result.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePLS-SEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ\u0026sup2; predict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLM_RMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLM_MAE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.573*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.553*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.521*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.354*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd 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colname=\"c5\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.763*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.614*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.715*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.428*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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colname=\"c1\"\u003e \u003cp\u003eTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.594*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.443*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.529*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.379*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.553*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.549*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.418*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.553*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.402*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote : Predictive Q\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026thinsp;=\u0026thinsp;Small, \u0026gt; 0,25\u0026thinsp;=\u0026thinsp;medium, \u0026gt; 0.5\u0026thinsp;=\u0026thinsp;large (Hair et al., 2019); * Lower prediction error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003c/div\u003e"},{"header":"5. DISCUSSION AND IMPLICATION","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Discussion\u003c/h2\u003e \u003cp\u003eOur study has demonstrated that reputation has a significant impact on trust, which, in turn, has a substantial effect on student commitment. However, these two variables do not significantly influence PBE These findings support previous studies suggesting a relationship between reputation and brand trust. Reputation improves as information from individuals who trust the brand circulates, thereby promoting loyalty (Chaudhary et al., 2021). Loyalty is defined as a commitment to remain with or continue purchasing a brand (Nadeem et al., 2020). In other words, reputation influences student commitment, which, in turn, enhances loyalty.\u003c/p\u003e \u003cp\u003eMoreover, reputation influences students' evaluations of instructors in the higher education (HE) sector. Even when the course content, grading structure, and materials are identical, students rate professors as having better teaching performance than others (McNatt, 2022). This suggests that reputation can lead to biases in student evaluations. Reputation, therefore, plays a critical role not only in academic settings but also in business contexts involving professors. For example, a favorable reputation among professors can increase efficiency and reduce the salary gap between executives and employees (Song et al., 2024).\u003c/p\u003e \u003cp\u003eOne of our most crucial findings demonstrates that Negative Word-of-Mouth (NWOM) does not affect professors' brand reputation. We found that student satisfaction has a stronger influence on professors' brand reputation. This non-significant effect of NWOM contradicts prior studies that highlight its negative impact on brand reputation. For example, WOM significantly influences trust in services like Google Maps (Gelashvili et al., 2024), and EWOM positively affects online purchase intentions (Kumar et al., 2023). NWOM tends to decrease positive attitudes toward products (Liao et al., 2021)and diminish the perceived value of travel destinations (Gkritzali et al., 2020). However, our study aligns with the findings of Sun et al. (2021), which demonstrated that PWOM affects brand equity, while NWOM does not affect brand equity for smartphone brands. If a brand perceives NWOM as unjust and responds to it with empathy, it doesn't harm its reputation (Allard et al., 2020)\u003c/p\u003e \u003cp\u003eBastos dan Moore, (2021)explain that WOM has a greater impact when it pertains to experiences rather than material factors. WOM about professors is typically centered on material aspects, making it less impactful on the professor's brand. WOM will be more influential when it is based on experiences with the professor. However, a direct response from the professor tends to enhance trust more than news or rumors. Responses from individuals reflect empathy, which is likely to increase trust (Lim et al., 2025).\u003c/p\u003e \u003cp\u003eOur findings highlight that student satisfaction has a greater influence on professors' reputations than WOM. We demonstrate that student satisfaction plays a crucial role in shaping reputation, trust, commitment, and PBE. This supports studies in the HE industry that show student satisfaction affects university reputation (Mendoza-Villafaina \u0026amp; L\u0026oacute;pez-Mosquera, 2024), positive behavioral intentions, loyalty, and PWOM (Cant et al., 2023). Satisfaction is identified as a key determinant of university reputation (Zeqiri et al., 2023), and satisfaction with university services enhances student trust (S. Singh \u0026amp; Manohar, 2024). In the online service sector, satisfaction also influences trust (J. Kim et al., 2011). Although these studies do not directly address professor reputation, they suggest that satisfaction impacts both reputation and trust. Our findings align with Zenker and R\u0026uuml;tter (2014) who concluded that satisfaction is a key factor in the relationship between a brand and individual behavior. In contrast to our findings, Chen (2017) shows that trust and commitment significantly influence student satisfaction, suggesting a reversed causal direction where trust and commitment precede satisfaction, rather than being outcomes of it as in our study.\u003c/p\u003e \u003cp\u003eTherefore, if WOM is associated with dissatisfaction with the professor's academic services, it has the potential to negatively impact the professor's brand. In other words, the spread of negative WOM about a student's experience with a professor will impact the professor as a human brand. However, professors are strong brands that cannot be easily tarnished by irrelevant negative WOM unrelated to the professor\u0026rsquo;s academic engagement with students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Implication\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1. Theoretical implication\u003c/h2\u003e \u003cp\u003eOur findings provide a theoretical contribution to the study of human brands, particularly in the context of professors. From a theoretical perspective, professors possess a brand strength that is largely immune to the effects of NWOM. Students' experiences with the academic services they receive primarily shape the professor's brand. NWOM will only impact the professor\u0026rsquo;s brand when it pertains to students' academic interactions with the professor. However, NWOM that is unrelated to student experiences does not have a significant influence on the professor\u0026rsquo;s brand.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2. Practical implication\u003c/h2\u003e \u003cp\u003eOur study indicates the significant influence of professors' brand power in students' perceptions. Therefore, we recommend that higher education administrators place greater emphasis on enhancing professors' roles within the academic process, fostering more intensive professor-student relationships. In addition to providing support, we also advise HE administrators to periodically supervise professors' academic activities, as professors may occasionally overlook ethical and academic responsibilities.\u003c/p\u003e \u003cp\u003eSecond, we encourage professors to provide the best possible academic services to students. This will enhance student satisfaction, which in turn positively affects the reputation, trust, commitment, and brand strength of the professors. This, ultimately, will contribute to the overall excellence of the university.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. LIMITATION AND FUTURE STUDY","content":"\u003cp\u003eThe use of convenience and snowball sampling techniques in this study presents limitations in terms of generalizability. Furthermore, the sample only consists of students from two public universities in South Kalimantan. This sample may be biased and may not fully represent the broader population, limiting the generalizability of the study's findings to populations with similar characteristics. A second limitation of this study concerns the variables measured. The study did not examine potential mediating variables, meaning it cannot explain how exogenous variables influence endogenous variables.\u003c/p\u003e \u003cp\u003eTherefore, we recommend that future research involve participants from universities across Indonesia or globally, using probability sampling methods. We also suggest that future studies investigate potential mediators, such as loyalty, professor brand attachment, and brand love. This would provide a more comprehensive understanding of the effects of satisfaction and WOM.\u003c/p\u003e"},{"header":"7. CONCLUSION","content":"\u003cp\u003eThe performance of professors is a key element in PBE. Our study demonstrates that national scandals do not affect COMM, trust, REP, or PBE. On the other hand, professor reputation has an impact on trust but does not directly influence PBE. Trust, however, significantly affects commitment. Additionally, our study shows that satisfaction with professor performance plays a dominant role in shaping the professor's brand. Furthermore, gender differences among students do not serve as moderators for the effects of exogenous variables, except for the effect of satisfaction on PBE, which is stronger for male students. This indicates that the academic quality perceived by students is crucial to the professor's brand. Our findings confirm that a professor's brand is robust and resistant to NWOM, even when originating from journalistic news. Long live the professor!\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFUNDING:\u003c/strong\u003e No funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINSTITUTIONAL REVIEW BOARD STATEMENT:\u003c/strong\u003e The Ethical \u0026nbsp; Committee of the Faculty of xxxxxxxxxx, Indonesia (Number: 508a/Un.14/III.1.h/PP.00.9/10/2024), October 3, 2024). Written consent has been obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTRANSPARENCY:\u003c/strong\u003e The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT:\u003c/strong\u003e The raw data required to reproduce the above findings are available and will be provided upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS:\u003c/strong\u003e Authors state that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCREDIT AUTHORSHIP CONTRIBUTION STATEMENT:\u0026nbsp;\u003c/strong\u003exxxx: Conceptualization, Methodology, Software, Supervison, Formal analysis, Writing - original draft, Writing - review \u0026amp; editing. xxxxxx: Data curation, Project administration, Resources. xxxxx: Visualization, Data curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT:\u003c/strong\u003e We express our gratitude to our colleagues and students who assisted in distributing the questionnaire online.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbubakar, A. 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The impact of e-service quality on word of mouth: A higher education context. \u003cem\u003eThe International Journal of Management Education\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), 100850. https://doi.org/10.1016/j.ijme.2023.100850\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universitas Islam Negeri Antasari Banjarmasin","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Word of mouth, satisfaction, professor brand equity","lastPublishedDoi":"10.21203/rs.3.rs-5853054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5853054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBad news about professor scandals can damage the brand of professors in Indonesia. This study aims to explore the relationship between negative word of mouth (NWOM), positive word of mouth (PWOM), satisfaction (SAT), commitment (COMM), trust, and reputation (REP) to professor brand equity (PBE). This study included 306 students selected through convenience and snowball sampling. The analysis employed the partial least squares structural equation modeling (PLS-SEM) technique. We found that trust affects commitment, and professor reputation affects student trust. Student satisfaction with academic services has a huge effect on commitment, trust, reputation, and PBE. NWOM has no effect on all of these variables, except for PWOM, which affects REP and PBE. Further, in male students, the effect of satisfaction on PBE tends to be greater than that of female students. The findings make a theoretical contribution to the study of professors as a human brand.\u003c/p\u003e","manuscriptTitle":"From Scandal to Redemption: Exploring Word-of-Mouth Effects on Professor Brand Equity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-22 06:54:12","doi":"10.21203/rs.3.rs-5853054/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae78c689-f8af-4211-829e-b77aa51aee41","owner":[],"postedDate":"January 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43227516,"name":"Marketing"},{"id":43227517,"name":"Other Business"}],"tags":[],"updatedAt":"2025-01-22T06:54:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-22 06:54:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5853054","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5853054","identity":"rs-5853054","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}