Professional associations in the hospitality, tourism and events (HTE) industry are struggling to keep their members committed and engaged, especially in uncertain times of external crises. This study examines whether pre-crisis perceived benefits from membership lead to increased post-crisis organizational commitment and organizational citizenship, considering the moderating role of organizational social responsibility (OSR) and the mediating effect of perceived indebtedness.
Two sequential studies were conducted. Study 1 was designed to test the effects of perceived benefits on organizational commitment and citizenship and the moderating effects of OSR. Study 1 used a survey of event association members for a more homogeneous sample, thereby increasing the internal validity of the initial findings. Subsequently, Study 2 tested the conceptual model with the addition of a mediator – perceived indebtedness – using the second survey with a larger sample from broader hospitality and tourism associations, thereby increasing the external validity of the findings. Data from both studies were analyzed using partial least squares structural equation modeling.
Study 1 found that perceived benefits (knowledge, networking and self-esteem) enhanced organizational commitment. When moderated by OSR, networking benefits increased organizational citizenship, a more proactive form of reciprocal dedication. Study 2 confirmed perceived indebtedness as a significant mediator.
This paper is among the first to frame organizational commitment and citizenship as reciprocal dedication and to conceptually propose and empirically confirm indebtedness as an explicit mediator, providing robust evidence for the theory of indebtedness. The findings also offer practical implications, suggesting strategic directions for professional associations to effectively communicate their OSR initiatives to members, provide enhanced networking opportunities and cultivate perceived indebtedness.
1. Introduction
Association Laboratory Inc.’s Looking Forward Solutions 2023 report (West, 2023) reveals that while associations’ top strategies focus on navigating the uncertainties of an external crisis, particularly the COVID-19 pandemic, associations continue to be concerned about declining membership. Even in the post-pandemic era, this remains a pressing issue for professional associations in the hospitality, tourism, and events industry (hereafter professional associations). External crises can occur unexpectedly, and without stable membership retention and growth, associations may struggle to maintain their current status or expand.
From a crisis management perspective, crises are categorized as either external or internal based on their root causes (Bhaduri, 2019). Internal crises can arise from service failures, leadership changes, and financial mismanagement, while external crises are triggered by natural disasters, wars, or global economic downturns. This study focuses on external crises, such as the pandemic, which are unpredictable but significantly impact the hospitality, tourism, and events (HTE) industry. Given the prevalent external crises, the urgency of this focus lies in developing effective marketing and management strategies to maintain membership and enhance member engagement in professional associations. For example, event professional associations such as the Events Industry Council (EIC) and the Professional Convention Management Association (PCMA) effectively responded to the challenges posed by the pandemic by developing comprehensive resource pages that offer guidance on risk management, safety protocols, and industry recovery strategies. The EIC has played a pivotal role in unifying the industry’s voice, advocating for support from policymakers during the crisis. Many organizations hosted virtual community conversations and networking opportunities to help members connect and share experiences during the crisis.
Such practices to address the challenges faced by professional associations have been examined in the literature. Recent studies on how organizations in the hospitality and tourism field engage their internal members after external crises, such as the pandemic, highlight the critical role of providing relevant support. Prayag et al. (2024) emphasize that tourism organizations fostered organizational resilience by sharing resources and knowledge through effective communication and vision-sharing, as well as by empowering members and supporting their development. Similarly, Shulga and Busser (2024) stress that reengaging members after crises is facilitated by cultivating a positive ethical climate that prioritizes well-being. Bressan et al. (2023) found that regional business associations in Italy strengthened networks to provide essential social capital to their members during crises. While previous studies provide insights into recovery strategy for member engagement in the post-crisis context, a significant knowledge gap remains regarding proactive strategies to cultivate member commitment before crises occur. Committed members are shown to contribute to the association’s success and give back to the organization (Azim, 2016).
The concept of giving back is not new in social psychology; it gained prominence with the introduction of social exchange theory (Blau, 1968) and the theory of indebtedness (Greenberg, 1980). These conceptual paradigms seek to understand why people engage in such behavior from slightly different perspectives, yet they share common themes: gratitude, a perceived obligation to repay, and reciprocal behavior. Although both gratitude and indebtedness can explain reciprocity, indebtedness has been largely overlooked in the literature (Peng et al., 2018). This study argues that the underlying mechanisms of reciprocal commitment and behaviors among professional association members can be clarified through the theoretical lens of indebtedness.
Unlike for-profit firms, non-profit organizations, including most HTE professional associations, prioritize member and public well-being over financial gain in their social responsibility efforts (Andreini et al., 2014; Jung et al., 2022). For example, the mission of the PCMA is “to drive social and economic progress through business events” (n.d.) while the Global Business Travel Association highlights community building as its integral component of their mission (n.d.). Consequently, the socially responsible actions of non-profits require distinct examination from those of for-profits (Andreini et al., 2014). However, existing literature often uses the term “corporate social responsibility (CSR)” regardless of organization type (e.g. Tang et al., 2024; Yeon et al., 2021). This study addresses this gap by introducing “organizational social responsibility (OSR)” to highlight the social responsibility specific to professional associations, a crucial non-profit sector within HTE.
Our paper extends the theoretical discourse by examining the relationships between professional associations' pre-crsis OSR efforts and members’ long-term commitment and proactive behaviors during and/or after crises through the theoretical lens of indebtedness. The theory of indebtedness posits that perceived indebtedness—a moral obligation to reciprocate—can drive stronger and more enduring reciprocal behaviors than gratitude alone (Greenberg, 1980). To that end, our research incorporates OSR as a moderator and perceived indebtedness as a mediator. We conceptualize OSR as an association’s commitment to member support and broader societal goals. When members perceive robust OSR efforts, they are more likely to reciprocate through organizational citizenship behaviors, driven by perceived indebtedness.
Taken together, the research purposes are: (1) to examine whether professional association members’ (pre-crisis) perceived benefits increase their (post-crisis) organizational commitment and organizational citizenship; (2) to explore whether perceived OSR strengthens the positive effects of perceived benefits on organizational commitment and organizational citizenship; and finally, (3) to test whether explicitly measured “perceived indebtedness” acts as a significant mediator linking perceived benefits to organizational commitment and organizational citizenship. These purposes are achieved through two empirical studies conducted with members of professional associations within the HTE industry.
This paper makes novel contributions to the literature and the HTE industry. First, this research sheds light on “pre-crisis” benefits and post-crisis organizational commitment and organizational citizenship, filling the gap in the literature that has primarily focused on “post-crisis” aspects. Second, drawing on the theory of indebtedness, this study introduces a unique theoretical lens on the organizational commitment and organizational citizenship of professional association members, framing them as the forms of give-back intentions driven by perceived indebtedness. Third, our paper proposes and empirically tests perceived indebtedness as an explicit mediator and OSR as a moderator that reinforces reciprocity. Fourth, this study distinguishes OSR from CSR, highlighting the unique responsibilities of professional associations as nonprofit organizations. Finally, the findings offer practical implications for professional associations in the HTE industry, suggesting a focus on pre-crisis benefits and responsible actions to retain and engage members during unforeseen crises.
2. Theoretical framework and hypotheses development
2.1 Theory of indebtedness
Perceived indebtedness stem from the recognition of the presence of reciprocity in human social interactions (Blau, 1968). Receiving certain types of benefits can elicit both gratitude and indebtedness (Peng et al., 2018). As Greenberg (1980) argued, gratitude and indebtedness go hand in hand; however, indebtedness drives more actions based on the obligation of reciprocity (Mpinganjira, 2019), which requires a long-term commitment that cannot be ensured by business contracts or formal agreements as in corporate settings. Peng et al. (2018) examined indebtedness in social exchange, uncovering a positive association with the perceived obligation for reciprocity. While the association with gratitude was only marginally significant, indebtedness significantly influenced the desire to repay the benefactor. Their study concluded that indebtedness, not gratitude, is pivotal in maintaining social exchange balance. These findings support the notion that indebtedness is key to explaining organizational commitment and organizational citizenship among professional association members.
This study applies the theory of indebtedness to examine give-back intentions of professional association members, departing from traditional CSR studies that often rely on social exchange theory to explain the mechanisms behind CSR outcomes (e.g. Peng et al., 2018; Tang et al., 2024). While previous studies have focused on how external stakeholders (e.g. customers, sponsors, investors) perceive and respond to socially responsible practices within for-profit firms (e.g. hotels, Airbnb and travel agencies), the current research looks at professional association members’ perceptions of OSR activities, specifically exploring their give-back intentions through the theoretical lens of indebtedness.
Social exchange theory posits that social interactions are based on a cost-benefit analysis in which individuals seek to maximize rewards and minimize costs, and individuals often feel gratitude that leads them to reciprocate in order to balance perceived benefits (Blau, 1968). In contrast, the theory of indebtedness extends beyond mere gratitude, emphasizing a moral obligation to reciprocate due to feelings of debt or obligation (Greenberg, 1980). Indebtedness involves a stronger sense of compulsion to repay, driven by the guilt associated with receiving favors, and creates a long-term emotional commitment (Greenberg, 1980). Specifically, indebtedness prompts behaviors not only to restore balance, but also out of a psychological sense of duty. In the present research, the theory of indebtedness provides a more robust framework for explaining proactive member commitment and behaviors driven by a moral obligation to the organization because perceived indebtedness directly impacts long-term commitments and more intense reciprocal actions such as organizational citizenship. It taps into a deeper emotional commitment, making it more applicable to non-profit professional associations where long-term member loyalty is critical.
2.2 Organizational commitment and organizational citizenship
Organizational commitment and organizational citizenship have traditionally been viewed as distinct constructs with a causal relationship. Organizational commitment typically refers to a member’s willingness to demonstrate emotional attachment, involvement, and loyalty to an organization (Azim, 2016). Organizational citizenship refers to a member’s willingness to perform voluntary and discretionary acts beyond their designated roles and duties (Azim, 2016). In the context of professional associations, these two constructs can be understood as different manifestations of a member’s dedication to the association. In essence, they represent varied ways in which members express their attachment and dedication to the association, similar to how employees demonstrate their commitment to their organizations (Bae, 2023; Nishanthi and Kailasapathy, 2018) https://digitalcommons.unl.edu/qicwdumbrella/88/. This perspective shifts the focus from a causal relationship between the two constructs to viewing them as parallel expressions of a member’s bond with their professional association. Similarly, Al-Romeedy and El-Sisi (2023) examined the mediating roles of organizational commitment and organizational citizenship between workplace incivility and employee performance. By framing organizational commitment and organizational citizenship in parallel, the present study provides a more nuanced understanding of how perceived benefits differentially affect these two forms of reciprocal dedication to professional associations. This approach also aligns with recent trends in organizational behavior research, as highlighted by Coyle-Shapiro et al. (2019), who suggest exploring the relationship between psychological constructs and different types of commitments.
2.3 Perceived benefits and organizational commitment
Perceived benefits favorably affect a person’s behavior (Tsujikawa et al., 2016) and refer to a perception of the acquisition of abilities and advantages entailing skills and knowledge that may be applied to one’s personal life, career, or professional development as a consequence of training and education from that person’s organization (Nordhaug, 1989). Social exchange theory (Emerson, 1976) posits that providing professional and personal advantages may enhance members’ commitment to an organization by instilling a sense of obligation to reciprocate for those benefits (Haar and Spell, 2004).
When it comes to organizational commitment, members feel the need to act in a way that demonstrates good attitudes and dedication to balance their relationship with the organization (Kurtessis et al., 2017). Furthermore, organizational support that can benefit members in times of crisis is more important and therefore may have an even greater positive impact on behavioral outcomes (Jung et al., 2022). Professional association members may perceive knowledge benefits, the benefits of training provided by the association to improve skills that can be used in a variety of work environments (Brammer et al., 2007). Learning while being a part of an organization improves members’ knowledge, skills, career prospects, and perceptions of their profession (Hendri, 2019). Ng et al. (2006) found that learning opportunities are associated with higher levels of organizational commitment among members.
Networking benefits describe the perceived interpersonal benefits of developing professional contacts through social connections facilitated by an organization (Zhou et al., 2014). Networking through socialization within the organization is closely related to the commitment of existing members; the greater the commitment, the more opportunities there are for exchange relationships (Nishanthi and Kailasapathy, 2018). Therefore, member interaction and the implementation of well-structured networking programs appear to be related to organizational commitment.
Self-esteem is people’s tendency to feel optimistic about themselves, which allows them to build confidence in their abilities and skills and to adapt quickly to new situations, even in unexpected crises (Baumeister et al., 2003). Various types of assistance and support from an organization can cultivate members’ abilities to enhance their self-esteem (Zhang et al., 2021). Thus, self-esteem benefits are defined as the extent to which members believe that their sense of self-confidence and personal satisfaction are enhanced by their involvement in the organization (Kuo and Feng, 2013).
Members are more likely to commit to an organization when they believe that the organization genuinely cares about its members and their future by providing relevant programs that benefit them (Masiero et al., 2022). Based on the theory of indebtedness and the literature discussed above, it is plausible that a member who perceives greater benefits from an organization will be more likely to demonstrate stronger organizational commitment:
Knowledge benefits (a), networking benefits (b), and self-esteem benefits (c) will increase organizational commitment.
2.4 Perceived benefits and organizational citizenship
Professional association members’ perceived benefits can enhance organizational citizenship, which, along with organizational commitment, represents different expressions of a member’s reciprocal dedication to the association. Organizational citizenship includes cooperating with other members, taking on extra tasks voluntarily, being mindful of how one’s actions affect others, and providing assistance to fellow members (Azim, 2016; Lin et al., 2010). Members who perceive benefits from the organization may develop a willingness to reciprocate through these acts of citizenship, as part of their overall expression of dedication. This perspective suggests that perceived benefits might directly influence members’ organizational citizenship, alongside their commitment, as interrelated aspects of their engagement with the professional association (Al-Romeedy and El-Sisi, 2023). Thus, we hypothesize:
Knowledge benefits (a), networking benefits (b), and self-esteem benefits (c) will increase organizational citizenship.
2.5 Organizational social responsibility and its moderating role
Non-profit organizations place a far greater value on social responsibility than for-profit organizations (Acar et al., 2001). Acar et al. (2001) introduced the concept of OSR, a more inclusive notion than CSR, referring to the responsibility of organizations, including non-profit sectors, associated with the impact of their decisions and activities on society, the environment, and their members. Consistently, Pope et al. (2018) argued that CSR should more accurately be called OSR for non-profit organizations because for-profit corporations and non-profit organizations approach social responsibility differently; the basis of a corporation’s social responsibility tends to be related to profitability, while that of a non-profit organization’s OSR is not. This study therefore introduces perceived OSR, which refers to the extent to which a member perceives an organization as behaving in a way that fulfills its specific mission, broad community stakeholders, and society at large.
Despite the importance of OSR, the development of social responsibility in non-profit organizations is still in its infancy (Pope et al., 2018), which inevitably makes this study rely on the CSR literature to infer the role of OSR in professional associations. Recent CSR literature has demonstrated its importance in catalyzing organizational outcomes, which has led scholars to investigate CSR as a moderator. For instance, Tang et al. (2024) indicated the importance of internal CSR on customer-oriented organizational citizenship behavior. Boğan and Dedeoğlu (2019) found that the relationship between community-focused CSR and employee trust in their employer is moderated by the employees’ self-experienced perceptions of CSR. Accordingly, this study proposes the following hypothesis:
The positive effects of perceived benefits on organizational commitment (a) and organizational citizenship (b) are stronger for members with higher perceived OSR than for members with lower perceived OSR.
2.6 Perceived indebtedness and its mediating role
The concept of indebtedness arises when individuals receive assistance from others, leading to feelings of guilt for potentially burdening them and a subsequent desire to reciprocate the favor (Gao et al., 2021). This study posits that members who perceive benefits from their association are likely to experience feel indebted to the association, which compels them to give back in some way, such as helping other members or volunteering for tasks as requested by the association. Employees who perceive greater benefits are more likely to be committed to the organization and enhance their performance, which ultimately benefits the organization (Islam et al., 2016; Kurtessis et al., 2017). Based on the theory of indebtedness and the literature review, this phenomenon is expected to be driven by perceived indebtedness, resulting in positive behavioral changes:
Perceived indebtedness mediates the effects of perceived benefits on organizational commitment (a) and organizational citizenship (b).
Reflecting the hypotheses developed above, a conceptual model is proposed in Figure 1.
On the left side, three ovals appear in a vertical sequence: “Knowledge Benefits” at the top, “Networking Benefits” in the middle, and “Self-Esteem Benefits” at the bottom. On the right side, two outcome ovals appear in a vertical sequence: “Organizational Commitment” and “Organizational Citizenship”. At the top center is an oval labeled “Organizational Social Responsibility (Study 1)” and at the bottom center is an oval labeled “Perceived Indebtedness (Study 2)”. Arrows labeled “H 4” from each benefit oval lead to “Perceived Indebtedness (Study 2)”. Arrows labeled “H 1 (a)” and “H 2 (a)” from “Knowledge Benefits” lead to “Organizational Commitment” and “Organizational Citizenship”, respectively. Arrows labeled “H 1 (b)” and “H 2 (b)” from “Networking Benefits” lead to “Organizational Commitment” and “Organizational Citizenship”, respectively. Arrows labeled “H 1 (c)” and “H 2 (c)” from “Self-Esteem Benefits” lead to “Organizational Commitment” and “Organizational Citizenship”, respectively. Arrows labeled “H 4” from “Perceived Indebtedness (Study 2)” lead to “Organizational Commitment” and “Organizational Citizenship”. Arrows labeled “H 3” extend from “Organizational Social Responsibility (Study 1)” toward the set of arrows flowing between the benefit ovals and the two outcome ovals.The overarching conceptual model depicts the effects of perceived benefits on organizational commitment and organizational citizenship, moderated by organizational social responsibility and mediated by perceived indebtedness
On the left side, three ovals appear in a vertical sequence: “Knowledge Benefits” at the top, “Networking Benefits” in the middle, and “Self-Esteem Benefits” at the bottom. On the right side, two outcome ovals appear in a vertical sequence: “Organizational Commitment” and “Organizational Citizenship”. At the top center is an oval labeled “Organizational Social Responsibility (Study 1)” and at the bottom center is an oval labeled “Perceived Indebtedness (Study 2)”. Arrows labeled “H 4” from each benefit oval lead to “Perceived Indebtedness (Study 2)”. Arrows labeled “H 1 (a)” and “H 2 (a)” from “Knowledge Benefits” lead to “Organizational Commitment” and “Organizational Citizenship”, respectively. Arrows labeled “H 1 (b)” and “H 2 (b)” from “Networking Benefits” lead to “Organizational Commitment” and “Organizational Citizenship”, respectively. Arrows labeled “H 1 (c)” and “H 2 (c)” from “Self-Esteem Benefits” lead to “Organizational Commitment” and “Organizational Citizenship”, respectively. Arrows labeled “H 4” from “Perceived Indebtedness (Study 2)” lead to “Organizational Commitment” and “Organizational Citizenship”. Arrows labeled “H 3” extend from “Organizational Social Responsibility (Study 1)” toward the set of arrows flowing between the benefit ovals and the two outcome ovals.The overarching conceptual model depicts the effects of perceived benefits on organizational commitment and organizational citizenship, moderated by organizational social responsibility and mediated by perceived indebtedness
3. Empirical studies
3.1 Overview and rationale of two studies
This research consists of two sequential studies designed to achieve distinct research objectives, ultimately providing a comprehensive understanding of the role of perceived benefits, OSR, and perceived indebtedness in determining reciprocal dedication among members of professional associations. The purpose of Study 1 was to establish an initial test of our conceptual model examining the effects of perceived benefits on organizational commitment and organizational citizenship, as well as the moderating effects of OSR. Study 1 used a purposive sample of professional association members from a specific sector (professional “event” associations). This first step allows us to rigorously assess the theoretically grounded model and precisely test the moderating role of OSR with a smaller and more homogeneous sample, reducing variability and increasing the internal validity of the initial findings (Vehovar et al., 2016). Study 2 built on the insights from Study 1 and was designed to address the potential limitations of generalizability. We used a larger and more diverse sample, drawn from a broader range of hospitality and tourism associations, to test the conceptual model. This ensures that the findings are generalizable across different professional associations within the HTE industry, thereby increasing the external validity of the findings (Tsang and Kwan, 1999). Furthermore, Study 2 added a mediator (perceived indebtedness) to the model to empirically extend the theoretical framework, allowing for a more nuanced understanding of the relationships and mechanisms underlying the phenomena, thereby providing an incremental theoretical contribution (Hayes and Rockwood, 2017). In essence, conducting two studies strengthens the robustness, validity, and generalizability of the findings and extends the theoretical contributions.
3.2 Study 1 methods
3.2.1 Data collection and sample
To quantitatively test the proposed model, it was necessary to collect data through an online survey. The questionnaire was created in Qualtrics and distributed to members of professional event associations using purposive sampling, a form of non-probability sampling technique that aims to reduce variation in the selected sample (Vehovar et al., 2016). Respondents accessed the survey through social media pages and online community portals operated by a number of professional event associations.
Of the 113 returned questionnaires, the 60 that were fully completed were used for the final data analysis, which is partial least squares structural equation modeling (PLS-SEM). To meet the 10 times rule for sample size in PLS-SEM (Goodhue et al., 2012), 60 samples are required with six arrows pointing to the endogenous variable in the empirical model. We also assessed the adequacy of the sample size using G* Power software (Faul et al., 2009). According to the minimum values suggested by Cohen (1988) for an medium effect size of 0.35, a statistical power of 95% and a probability of error of 5%, the G* Power calculations showed a minimum sample size of 40. Therefore, the final sample size met and exceeded the minimum requirements. Of the final sample, approximately 38% were between the ages of 18 and 35, another 38% were between the ages of 36 and 54, and 20% were 55 or older. The majority were Caucasian (approximately 77%), followed by Asian (approximately 15%), and Hispanic/Latino (approximately 3%).
3.2.2 Measures and procedure
The main constructs were measured using established scales from the literature, slightly adapted to fit the context of professional event associations (Table 1). The questionnaire began by asking participants to identify the professional association to which they currently belonged. For the remainder of the survey, Qualtrics’ auto-embedding feature allowed each participant to answer the next series of questions with the association they identified, so that participants’ responses were focused on specific associations as a point of reference. The questionnaire asked how participants perceived the particular association prior to the pandemic as a vivid context of external crises, using measures including perceived OSR and perceived benefits. The context of the questionnaire then shifted to the aftermath of the pandemic, and participants were asked to indicate their level of agreement with their willingness to act toward their association.
Measurement items
| Latent variable | Coding | Measurement indicators | Used in | Sources |
|---|---|---|---|---|
| Knowledge benefits | KB1 | XYZa allows me to increase my knowledge about working in the industryb | Study 1, 2 | Kuo and Feng (2013), Nambisan and Baron (2009) |
| KB2 | XYZ helps me solve work-related problems | |||
| KB3 | XYZ helps increase my understanding of new trends and technologies in the industry | |||
| Networking benefits | NB1 | XYZ enables me to connect with other industry professionals | Study 1, 2 | Kuo and Feng (2013), McAlexander et al. (2002), Nambisan and Baron (2009) |
| NB2 | XYZ helps me become familiar with other industry professionals | |||
| NB3 | XYZ helps me develop my social skills | |||
| Self-esteem benefits | SE1 | I can enhance my status and reputation in XYZ. | Study 1, 2 | Kuo and Feng (2013), Nambisan and Baron (2009) |
| SE2 | I can increase my credibility and authority in XYZ. | |||
| SE3 | I feel a sense of satisfaction when I can influence others’ knowledge and skills in XYZ. | |||
| SE4 | I feel a sense of satisfaction when I can influence the professional development of other professionals | |||
| Organizational commitment | OCI1 | I would tell others how proud I am to be a member of XYZ, whenever I have the chance | Study 1, 2 | Allen and Meyer (1990) |
| OCI2 | The way XYZ conducts its day-to-day business inspires me to do everything I can to ensure it is successful | |||
| OCI3 | I would identify with XYZ’s vision in such a way that my commitment remains unwavering, even when conditions become difficult | |||
| Organizational citizenship | OCT1 | I am willing to give my time to help other members | Study 1, 2 | Azim (2016), Saks (2006) |
| OCT2 | I am willing to adjust my work schedule to accommodate other members | |||
| OCT3 | I am willing to give up time to help other members in XYZ. | |||
| OCT4 | I am willing to assist other members with their work-related needs | |||
| Organizational social responsibility | OSR1 | XYZ believes in social commitment | Study 1 | Menon and Kahn (2003), Rim and Ferguson (2020) |
| OSR2 | XYZ is highly involved in giving back to the community | |||
| OSR3 | XYZ is genuinely concerned about the welfare of the public | |||
| Perceived indebtedness | DEBT1 | I feel that I owe XYZ quite a bit because of what it has done for me | Study 2 | Jaros (2007), Meyer et al. (1993) |
| DEBT2 | XYZ deserves my loyalty because of its treatment towards me | |||
| DEBT3 | I feel I would be letting my colleagues down if I wasn’t a member of XYZ |
| Latent variable | Coding | Measurement indicators | Used in | Sources |
|---|---|---|---|---|
| Knowledge benefits | KB1 | XYZa allows me to increase my knowledge about working in the industryb | Study 1, 2 | |
| KB2 | XYZ helps me solve work-related problems | |||
| KB3 | XYZ helps increase my understanding of new trends and technologies in the industry | |||
| Networking benefits | NB1 | XYZ enables me to connect with other industry professionals | Study 1, 2 | |
| NB2 | XYZ helps me become familiar with other industry professionals | |||
| NB3 | XYZ helps me develop my social skills | |||
| Self-esteem benefits | SE1 | I can enhance my status and reputation in XYZ. | Study 1, 2 | |
| SE2 | I can increase my credibility and authority in XYZ. | |||
| SE3 | I feel a sense of satisfaction when I can influence others’ knowledge and skills in XYZ. | |||
| SE4 | I feel a sense of satisfaction when I can influence the professional development of other professionals | |||
| Organizational commitment | OCI1 | I would tell others how proud I am to be a member of XYZ, whenever I have the chance | Study 1, 2 | |
| OCI2 | The way XYZ conducts its day-to-day business inspires me to do everything I can to ensure it is successful | |||
| OCI3 | I would identify with XYZ’s vision in such a way that my commitment remains unwavering, even when conditions become difficult | |||
| Organizational citizenship | OCT1 | I am willing to give my time to help other members | Study 1, 2 | |
| OCT2 | I am willing to adjust my work schedule to accommodate other members | |||
| OCT3 | I am willing to give up time to help other members in XYZ. | |||
| OCT4 | I am willing to assist other members with their work-related needs | |||
| Organizational social responsibility | OSR1 | XYZ believes in social commitment | Study 1 | |
| OSR2 | XYZ is highly involved in giving back to the community | |||
| OSR3 | XYZ is genuinely concerned about the welfare of the public | |||
| Perceived indebtedness | DEBT1 | I feel that I owe XYZ quite a bit because of what it has done for me | Study 2 | |
| DEBT2 | XYZ deserves my loyalty because of its treatment towards me | |||
| DEBT3 | I feel I would be letting my colleagues down if I wasn’t a member of XYZ |
Note(s): aXYZ: The professional association’s name to which each respondent responded. bStudy 1 used the “event” industry and Study 2 uses the “hospitality and tourism” industry
Source(s): Authors’ own work
3.2.3 Data analysis overview and rationale
PLS-SEM was chosen for the primary data analyses and was performed using the SmartPLS 4.0 software, following the guidelines of Hair et al. (2019) and Hair et al. (2021). PLS-SEM was chosen as an appropriate analytical method for the present study over covariance-based SEM (CB-SEM) for the following reasons (Hair et al., 2021): This study seeks to explore the complex relationships between perceived benefits and indebted behaviors. Given the social nature of the research question, where there are latent variables underlying the observed behavior, PLS-SEM, which is a powerful tool for analyzing complex models is particularly well suited for the current study purposes. PLS-SEM is also known to perform better than CB-SEM when dealing with small sample sizes and non-normally distributed data because PLS-SEM generates t-values using nonparametric bootstrapping making its distributional assumptions less stringent than CB-SEM.
Unlike CB-SEM, which uses measurement model testing followed by structural model testing with goodness-of-fit indices as the main criteria for model evaluation, PLS-SEM follows a two-step procedure recommended by Hair et al. (2019) and Hair et al. (2021)—outer model testing followed by inner model testing without examining fit indices. The main model analysis to test H1 and H2 was completed; consequently, partial least squares multigroup analysis (PLS-MGA) within PLS-SEM continued to test the moderating effects of OSR in the model (H3). Previous studies in hospitality/tourism marketing and management using PLS-SEM and PLS-MGA provided valuable references for the present study (e.g. Cruz-Milán, 2019; do Valle and Assaker, 2016; Kim et al., 2020; Nekmahmud et al., 2022; Nguyen and Llosa, 2023).
3.3 Study 1 results
3.3.1 Common method bias testing results
Prior to the main PLS-SEM procedure, common method bias (CMB) was tested because the data collection relied on a self-report survey method in which CMB could threaten the validity of the responses. Following the guidance in the literature (Podsakoff and Organ, 1986), several methods were employed to minimize the potential CMB issues. First, Harman’s single-factor test was conducted (Podsakoff and Organ, 1986). As a result, a five-factor model was suggested; the total cumulative variance was 81.06%, and no single factor accounted for more than 50% of the variance (the largest variance accounted for by any factor was 41.53%). A full collinearity variance inflation factor (FCVIF) test was then performed. The highest VIF value was 1.56, which did not exceed the threshold of 3.30, supporting the absence of CMB for this study (Hair et al., 2019). Taken together, CMB is unlikely to be a critical issue for the data.
3.3.2 Outer model testing results
The first step required by PLS-SEM is the outer model test, which aims to ensure measurement reliability and validity (Table 2). First, internal consistency reliability was demonstrated by the following results (Dijkstra and Henseler, 2015): Cronbach’s alpha ranging from 0.761 to 0.929, estimates of Dijkstra-Henseler’s rho_A estimates ranging from 0.797 to 0.956, and estimates of composite reliability ranging from 0.860 to 0.955, all of which were above the recommended threshold of 0.70. Next, convergent validity was established based on the following results (Hair et al., 2021): the outer loadings ranged from 0.735 to 0.949, all of which were above than the recommended threshold (0.70); the indicator reliability estimates (squared outer loadings) ranged from 0.540 to 0.901, again above than the recommended threshold (0.50); and the average variance extracted estimates (AVEs) ranged from 0.673 to 0.876, all of which were above the recommended threshold (0.50). Finally, discriminant validity (Table 3) was confirmed by that each of the square roots of the AVEs was greater than the correlations between the latent variables (Fornell and Larcker, 1981). Taken together, the results supported the measurement reliability and validity.
Outer model properties
| Item coding | Mean | Standard deviation | Outer loadings | Indicator reliability | Cronbach’s alpha | D-H rho A | Composite reliability | AVE |
|---|---|---|---|---|---|---|---|---|
| Study 1 | ||||||||
| KB1 | 4.583 | 0.614 | 0.735 | 0.540 | 0.761 | 0.797 | 0.860 | 0.673 |
| KB2 | 4.133 | 0.763 | 0.846 | 0.716 | ||||
| KB3 | 4.600 | 0.611 | 0.874 | 0.764 | ||||
| NB1 | 4.667 | 0.650 | 0.890 | 0.792 | 0.822 | 0.835 | 0.893 | 0.736 |
| NB2 | 4.450 | 0.784 | 0.855 | 0.731 | ||||
| NB3 | 3.950 | 0.990 | 0.828 | 0.686 | ||||
| SE1 | 4.150 | 0.853 | 0.884 | 0.781 | 0.903 | 0.920 | 0.932 | 0.775 |
| SE2 | 4.250 | 0.829 | 0.941 | 0.885 | ||||
| SE3 | 4.233 | 0.863 | 0.897 | 0.805 | ||||
| SE4 | 4.250 | 0.698 | 0.794 | 0.630 | ||||
| OCI1 | 3.883 | 0.968 | 0.923 | 0.852 | 0.929 | 0.930 | 0.955 | 0.876 |
| OCI2 | 3.600 | 1.068 | 0.949 | 0.901 | ||||
| OCI3 | 3.650 | 1.014 | 0.935 | 0.874 | ||||
| OCT1 | 4.367 | 0.795 | 0.925 | 0.856 | 0.919 | 0.956 | 0.942 | 0.804 |
| OCT2 | 3.933 | 0.854 | 0.875 | 0.766 | ||||
| OCT3 | 4.183 | 0.719 | 0.914 | 0.835 | ||||
| OCT4 | 4.317 | 0.645 | 0.871 | 0.759 | ||||
| Study 2 | ||||||||
| KB1 | 3.984 | 0.635 | 0.633 | 0.401 | 0.654 | 0.703 | 0.807 | 0.586 |
| KB2 | 3.945 | 0.806 | 0.840 | 0.706 | ||||
| KB3 | 4.022 | 0.630 | 0.807 | 0.651 | ||||
| NB1 | 3.967 | 0.639 | 0.661 | 0.437 | 0.666 | 0.701 | 0.81 | 0.589 |
| NB2 | 4.165 | 0.701 | 0.818 | 0.669 | ||||
| NB3 | 3.984 | 0.769 | 0.814 | 0.663 | ||||
| SE1 | 3.846 | 0.629 | 0.744 | 0.554 | 0.759 | 0.766 | 0.847 | 0.580 |
| SE2 | 3.907 | 0.826 | 0.726 | 0.527 | ||||
| SE3 | 3.901 | 0.766 | 0.784 | 0.615 | ||||
| SE4 | 3.956 | 0.757 | 0.790 | 0.624 | ||||
| OCI1 | 3.637 | 0.773 | 0.776 | 0.602 | 0.723 | 0.725 | 0.844 | 0.644 |
| OCI2 | 3.830 | 0.833 | 0.827 | 0.684 | ||||
| OCI3 | 3.758 | 0.877 | 0.803 | 0.645 | ||||
| OCT1 | 3.934 | 0.718 | 0.713 | 0.508 | 0.764 | 0.776 | 0.849 | 0.586 |
| OCT2 | 3.791 | 1.025 | 0.798 | 0.637 | ||||
| OCT3 | 3.802 | 0.913 | 0.819 | 0.671 | ||||
| OCT4 | 4.022 | 0.793 | 0.727 | 0.529 | ||||
| DEBT1 | 3.420 | 0.924 | 0.894 | 0.799 | 0.834 | 0.835 | 0.901 | 0.751 |
| DEBT2 | 3.680 | 0.904 | 0.854 | 0.729 | ||||
| DEBT3 | 3.360 | 1.171 | 0.852 | 0.726 | ||||
| Item coding | Mean | Standard deviation | Outer loadings | Indicator reliability | Cronbach’s alpha | D-H rho A | Composite reliability | AVE |
|---|---|---|---|---|---|---|---|---|
| Study 1 | ||||||||
| KB1 | 4.583 | 0.614 | 0.735 | 0.540 | 0.761 | 0.797 | 0.860 | 0.673 |
| KB2 | 4.133 | 0.763 | 0.846 | 0.716 | ||||
| KB3 | 4.600 | 0.611 | 0.874 | 0.764 | ||||
| NB1 | 4.667 | 0.650 | 0.890 | 0.792 | 0.822 | 0.835 | 0.893 | 0.736 |
| NB2 | 4.450 | 0.784 | 0.855 | 0.731 | ||||
| NB3 | 3.950 | 0.990 | 0.828 | 0.686 | ||||
| SE1 | 4.150 | 0.853 | 0.884 | 0.781 | 0.903 | 0.920 | 0.932 | 0.775 |
| SE2 | 4.250 | 0.829 | 0.941 | 0.885 | ||||
| SE3 | 4.233 | 0.863 | 0.897 | 0.805 | ||||
| SE4 | 4.250 | 0.698 | 0.794 | 0.630 | ||||
| OCI1 | 3.883 | 0.968 | 0.923 | 0.852 | 0.929 | 0.930 | 0.955 | 0.876 |
| OCI2 | 3.600 | 1.068 | 0.949 | 0.901 | ||||
| OCI3 | 3.650 | 1.014 | 0.935 | 0.874 | ||||
| OCT1 | 4.367 | 0.795 | 0.925 | 0.856 | 0.919 | 0.956 | 0.942 | 0.804 |
| OCT2 | 3.933 | 0.854 | 0.875 | 0.766 | ||||
| OCT3 | 4.183 | 0.719 | 0.914 | 0.835 | ||||
| OCT4 | 4.317 | 0.645 | 0.871 | 0.759 | ||||
| Study 2 | ||||||||
| KB1 | 3.984 | 0.635 | 0.633 | 0.401 | 0.654 | 0.703 | 0.807 | 0.586 |
| KB2 | 3.945 | 0.806 | 0.840 | 0.706 | ||||
| KB3 | 4.022 | 0.630 | 0.807 | 0.651 | ||||
| NB1 | 3.967 | 0.639 | 0.661 | 0.437 | 0.666 | 0.701 | 0.81 | 0.589 |
| NB2 | 4.165 | 0.701 | 0.818 | 0.669 | ||||
| NB3 | 3.984 | 0.769 | 0.814 | 0.663 | ||||
| SE1 | 3.846 | 0.629 | 0.744 | 0.554 | 0.759 | 0.766 | 0.847 | 0.580 |
| SE2 | 3.907 | 0.826 | 0.726 | 0.527 | ||||
| SE3 | 3.901 | 0.766 | 0.784 | 0.615 | ||||
| SE4 | 3.956 | 0.757 | 0.790 | 0.624 | ||||
| OCI1 | 3.637 | 0.773 | 0.776 | 0.602 | 0.723 | 0.725 | 0.844 | 0.644 |
| OCI2 | 3.830 | 0.833 | 0.827 | 0.684 | ||||
| OCI3 | 3.758 | 0.877 | 0.803 | 0.645 | ||||
| OCT1 | 3.934 | 0.718 | 0.713 | 0.508 | 0.764 | 0.776 | 0.849 | 0.586 |
| OCT2 | 3.791 | 1.025 | 0.798 | 0.637 | ||||
| OCT3 | 3.802 | 0.913 | 0.819 | 0.671 | ||||
| OCT4 | 4.022 | 0.793 | 0.727 | 0.529 | ||||
| DEBT1 | 3.420 | 0.924 | 0.894 | 0.799 | 0.834 | 0.835 | 0.901 | 0.751 |
| DEBT2 | 3.680 | 0.904 | 0.854 | 0.729 | ||||
| DEBT3 | 3.360 | 1.171 | 0.852 | 0.726 | ||||
Note(s): KB: Knowledge Benefits; NB: Networking Benefits; SE: Self-Esteem Benefits; OCI: Organizational Commitment; OCT: Organizational Citizenship; DEBT: Perceived Indebtedness
Source(s): Authors’ own work
Discriminant validity matrix
| Study 1 | Knowledge benefits | Networking benefits | Self-esteem benefits | Organizational commitment | Organizational citizenship |
|---|---|---|---|---|---|
| Knowledge benefits | 0.821 | ||||
| Networking benefits | 0.365 | 0.858 | |||
| Self-Esteem benefits | 0.419 | 0.552 | 0.881 | ||
| Organizational commitment | 0.552 | 0.542 | 0.598 | 0.936 | |
| Organizational citizenship | 0.295 | 0.233 | 0.226 | 0.403 | 0.896 |
| Study 1 | Knowledge benefits | Networking benefits | Self-esteem benefits | Organizational commitment | Organizational citizenship |
|---|---|---|---|---|---|
| Knowledge benefits | 0.821 | ||||
| Networking benefits | 0.365 | 0.858 | |||
| Self-Esteem benefits | 0.419 | 0.552 | 0.881 | ||
| Organizational commitment | 0.552 | 0.542 | 0.598 | 0.936 | |
| Organizational citizenship | 0.295 | 0.233 | 0.226 | 0.403 | 0.896 |
| Study 2 | Knowledge benefits | Networking benefits | Self-esteem benefits | Organizational commitment | Organizational citizenship | Perceived indebtedness |
|---|---|---|---|---|---|---|
| Knowledge benefits | 0.765 | |||||
| Networking benefits | 0.722 | 0.768 | ||||
| Self-Esteem benefits | 0.690 | 0.658 | 0.762 | |||
| Organizational commitment | 0.557 | 0.516 | 0.708 | 0.802 | ||
| Organizational citizenship | 0.576 | 0.515 | 0.546 | 0.723 | 0.765 | |
| Perceived indebtedness | 0.524 | 0.362 | 0.571 | 0.711 | 0.563 | 0.867 |
| Study 2 | Knowledge benefits | Networking benefits | Self-esteem benefits | Organizational commitment | Organizational citizenship | Perceived indebtedness |
|---|---|---|---|---|---|---|
| Knowledge benefits | 0.765 | |||||
| Networking benefits | 0.722 | 0.768 | ||||
| Self-Esteem benefits | 0.690 | 0.658 | 0.762 | |||
| Organizational commitment | 0.557 | 0.516 | 0.708 | 0.802 | ||
| Organizational citizenship | 0.576 | 0.515 | 0.546 | 0.723 | 0.765 | |
| Perceived indebtedness | 0.524 | 0.362 | 0.571 | 0.711 | 0.563 | 0.867 |
Note(s): Italicized values on the diagonal line represent squared root of AVE. Others represent correlations between latent variables
Source(s): Authors’ own work
3.3.3 Inner model testing results
The next step in PLS-SEM was to test an inner model, through which the relationships between the latent variables could be examined, enabling the hypothesis testing. Once the inner model was estimated, the inner variance inflation factor (VIF) was examined to identify any potential collinearity issues: VIF values ranged from 1.252 to 1.560, none of which exceeded the threshold of 4.0. The results thus supported that multicollinearity was unlikely to be an issue for the inner model (Hair et al., 2011; Hair et al., 2021). R2 was then examined as one of the criteria used to assess the predictive accuracy of the model. The model captured a higher level of variance in organizational commitment (R2 = 0.507) and a moderate level of variance in organizational citizenship (R2 = 0.108). Predictive relevance (Q2) was also examined using the blindfolding procedure (Henseler et al., 2009). The results indicated a large predictive relevance for organizational commitment in the model (Q2 = 0.427) and a small predictive relevance for organizational citizenship in the model (Q2 = 0.066) (Hair et al., 2014).
To examine the specific relationships among the latent variables, path coefficient estimates were assessed at the level of their absolute values, and their significance was tested by bootstrapping. All three types of perceived benefits significantly increased organizational commitment, supporting H1. Although positive directional effects of perceived benefits on organizational citizenship were detected, none of the effects were statistically significant, so H2 was not supported. The complete path estimates are reported in Table 4.
PLS-SEM path estimates – direct effects
| Path | β | t | p | CI (2.5%, 97.5%) | f2 | Relevant H | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Study 1 | Study 2 | Study 1 | Study 2 | Study 1 | Study 2 | Study 1 | Study 2 | Study 1 | Study 2 | Study 1 | Study 2 | |
| Knowledge benefits → Organizational commitment | 0.327 | −0.048 | 2.760 | 0.614 | 0.006 | 0.539 | (0.111, 0.538) | (−0.204, 0.104) | 0.173 | 0.002 | H1(a) Accepted | H1(a) Rejected |
| Networking benefits → Organizational commitment | 0.242 | 0.124 | 2.278 | 1.450 | 0.023 | 0.147 | (0.033, 0.434) | (−0.049, 0.283) | 0.080 | 0.018 | H1(b) Accepted | H1(b) Rejected |
| Self-Esteem benefits → Organizational commitment | 0.327 | 0.393 | 3.347 | 5.095 | 0.001 | 0.000 | (0.142, 0.522) | (0.242, 0.545) | 0.139 | 0.178 | H1(c) Accepted | H1(c) Accepted |
| Knowledge benefits → Organizational citizenship | 0.225 | 0.199 | 1.775 | 2.047 | 0.077 | 0.041 | (−0.016, 0.467) | (0.012, 0.391) | 0.045 | 0.027 | H2(a) Rejected | H2(a) Accepted |
| Networking benefits → Organizational citizenship | 0.113 | 0.189 | 0.581 | 2.100 | 0.562 | 0.036 | (−0.315, 0.432) | (0.001, 0.357) | 0.010 | 0.027 | H2(b) Rejected | H2(b) Accepted |
| Self-Esteem benefits → Organizational citizenship | 0.069 | 0.092 | 0.397 | 0.949 | 0.692 | 0.343 | (−0.243, 0.412) | (−0.092, 0.298) | 0.003 | 0.006 | H2(c) Rejected | H2(c) Rejected |
| Path | β | t | p | CI (2.5%, 97.5%) | f2 | Relevant H | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Study 1 | Study 2 | Study 1 | Study 2 | Study 1 | Study 2 | Study 1 | Study 2 | Study 1 | Study 2 | Study 1 | Study 2 | |
| Knowledge benefits → Organizational commitment | 0.327 | −0.048 | 2.760 | 0.614 | 0.006 | 0.539 | (0.111, 0.538) | (−0.204, 0.104) | 0.173 | 0.002 | ||
| Networking benefits → Organizational commitment | 0.242 | 0.124 | 2.278 | 1.450 | 0.023 | 0.147 | (0.033, 0.434) | (−0.049, 0.283) | 0.080 | 0.018 | ||
| Self-Esteem benefits → Organizational commitment | 0.327 | 0.393 | 3.347 | 5.095 | 0.001 | 0.000 | (0.142, 0.522) | (0.242, 0.545) | 0.139 | 0.178 | ||
| Knowledge benefits → Organizational citizenship | 0.225 | 0.199 | 1.775 | 2.047 | 0.077 | 0.041 | (−0.016, 0.467) | (0.012, 0.391) | 0.045 | 0.027 | ||
| Networking benefits → Organizational citizenship | 0.113 | 0.189 | 0.581 | 2.100 | 0.562 | 0.036 | (−0.315, 0.432) | (0.001, 0.357) | 0.010 | 0.027 | ||
| Self-Esteem benefits → Organizational citizenship | 0.069 | 0.092 | 0.397 | 0.949 | 0.692 | 0.343 | (−0.243, 0.412) | (−0.092, 0.298) | 0.003 | 0.006 | ||
Source(s): Authors’ own work
3.3.4 PLS-MGA results
A preparation for PLS-MGA was made to examine the moderating effects of OSR in the model, thus testing H3. The full sample was divided into two groups using a median split (the median OSR score was 4.33) and labeled as follows: high OSR (n = 28) and low OSR (n = 32). Before conducting PLS-MGA, measurement invariance was first examined, following Henseler et al.’s (2016) measurement invariance of composite models (MICOM) procedure, a series of rigorous three steps (Table 5). In Step 1, configural invariance was established through the equal specification of modeling across groups, including data treatment, algorithm setting, and optimization criteria. In Step 2, compositional invariance was established: the c values of all composites are close to one, and all of the permutation p-values are insignificant, fully supporting compositional invariance. In Step 3, equal composite means and variances were examined. While most composite variances are equal across the two groups, the composite mean values are unequal, suggesting partial measurement invariance. Given the achievement of configuration and composition invariance, it is sufficient and permissible to proceed with PLS-MGA to compare model path coefficients across groups (Chin et al., 2019; Henseler et al., 2016; Nguyen and Llosa, 2023).
Study 1 measurement invariance of composite models step 2 and 3
| Step 2 compositional invariance assessment results | ||||
|---|---|---|---|---|
| Composite | Original correlation | Correlation permutation mean | 5.0% | Permutation p-value |
| Knowledge benefits | 0.974 | 0.980 | 0.948 | 0.184 |
| Networking benefits | 0.993 | 0.987 | 0.955 | 0.562 |
| Self-Esteem benefits | 0.995 | 0.996 | 0.990 | 0.264 |
| Organizational commitment | 0.999 | 0.999 | 0.998 | 0.228 |
| Organizational citizenship | 0.999 | 0.992 | 0.979 | 0.852 |
| Step 2 compositional invariance assessment results | ||||
|---|---|---|---|---|
| Composite | Original correlation | Correlation permutation mean | 5.0% | Permutation p-value |
| Knowledge benefits | 0.974 | 0.980 | 0.948 | 0.184 |
| Networking benefits | 0.993 | 0.987 | 0.955 | 0.562 |
| Self-Esteem benefits | 0.995 | 0.996 | 0.990 | 0.264 |
| Organizational commitment | 0.999 | 0.999 | 0.998 | 0.228 |
| Organizational citizenship | 0.999 | 0.992 | 0.979 | 0.852 |
| Step 3 composite equal means and variances assessment results | |||||
|---|---|---|---|---|---|
| Composite | Mean – original difference | Mean – permutation mean difference | 5.0% | 95.0% | Permutation p-value |
| Knowledge benefits | 0.670 | −0.001 | −0.438 | 0.452 | 0.002 |
| Networking benefits | 0.753 | 0.022 | −0.390 | 0.482 | 0.004 |
| Self-esteem benefits | 0.716 | 0.016 | −0.403 | 0.451 | 0.000 |
| Organizational commitment | 1.089 | 0.008 | −0.388 | 0.425 | 0.000 |
| Organizational citizenship | 0.676 | 0.009 | −0.436 | 0.471 | 0.008 |
| Step 3 composite equal means and variances assessment results | |||||
|---|---|---|---|---|---|
| Composite | Mean – original difference | Mean – permutation mean difference | 5.0% | 95.0% | Permutation p-value |
| Knowledge benefits | 0.670 | −0.001 | −0.438 | 0.452 | 0.002 |
| Networking benefits | 0.753 | 0.022 | −0.390 | 0.482 | 0.004 |
| Self-esteem benefits | 0.716 | 0.016 | −0.403 | 0.451 | 0.000 |
| Organizational commitment | 1.089 | 0.008 | −0.388 | 0.425 | 0.000 |
| Organizational citizenship | 0.676 | 0.009 | −0.436 | 0.471 | 0.008 |
| Variance – original difference | Variance – permutation mean difference | 5.0% | 95.0% | Permutation p-value | |
|---|---|---|---|---|---|
| Knowledge benefits | −0.526 | −0.009 | −0.811 | 0.744 | 0.142 |
| Networking benefits | −0.812 | −0.031 | −0.878 | 0.767 | 0.068 |
| Self-esteem benefits | −0.710 | −0.025 | −0.656 | 0.586 | 0.040 |
| Organizational commitment | −0.600 | −0.005 | −0.729 | 0.651 | 0.082 |
| Organizational citizenship | −0.860 | −0.051 | −0.820 | 0.752 | 0.044 |
| Variance – original difference | Variance – permutation mean difference | 5.0% | 95.0% | Permutation p-value | |
|---|---|---|---|---|---|
| Knowledge benefits | −0.526 | −0.009 | −0.811 | 0.744 | 0.142 |
| Networking benefits | −0.812 | −0.031 | −0.878 | 0.767 | 0.068 |
| Self-esteem benefits | −0.710 | −0.025 | −0.656 | 0.586 | 0.040 |
| Organizational commitment | −0.600 | −0.005 | −0.729 | 0.651 | 0.082 |
| Organizational citizenship | −0.860 | −0.051 | −0.820 | 0.752 | 0.044 |
Source(s): Authors’ own work
PLS-MGA was then performed using the Welch–Satterthwait test (Cheah et al., 2020). As depicted in Figure 2, the model worked differently in the two groups. The positive effects of networking benefits on both organizational commitment and organizational citizenship were significantly stronger in the high-OSR group than in the low-OSR group (Δβorganizational commitment = −0.626, p < 0.05; Δβorganizational citizenship = −0.977, p < 0.05). Therefore, H3 was partially supported. As an additional finding, the effects of knowledge benefits and self-esteem benefits on organizational commitment were not statistically different in size between the two groups while these effects were significant only for the low-OSR group.
High O S R Group: On the left side, three ovals appear vertically: “Knowledge Benefits” at the top, “Networking Benefits” in the middle, and “Self-Esteem Benefits” at the bottom. On the right side, two ovals appear vertically: “Organizational Commitment” at the top and “Organizational Citizenship” at the bottom. From “Knowledge Benefits”, two dashed arrows extend to the outcomes: A dashed arrow to “Organizational Commitment” labeled “0.212 (n s)”. A dashed arrow to “Organizational Citizenship” labeled “0.338 (n s)”. From “Networking Benefits”, two thick solid arrows extend: A thick solid arrow to “Organizational Commitment” labeled “0.499 asterisk”. A thick solid arrow to “Organizational Citizenship” labeled “0.588 asterisk”. From “Self-Esteem Benefits”, two dashed arrows extend: A dashed arrow to “Organizational Commitment” labeled “0.220 (n s)”. A dashed arrow to “Organizational Citizenship” labeled “0.042 (n s)”. Low O S R Group: On the left side, three ovals appear vertically: “Knowledge Benefits” at the top, “Networking Benefits” in the middle, and “Self-Esteem Benefits” at the bottom. On the right side, two ovals appear vertically: “Organizational Commitment” at the top and “Organizational Citizenship” at the bottom. From “Knowledge Benefits”, two arrows extend to the outcomes: A thick solid arrow to “Organizational Commitment”, labeled “0.504 double asterisk”. A dashed arrow to “Organizational Citizenship” labeled “0.288 (n s)”. From “Networking Benefits”, two dashed arrows extend: To “Organizational Commitment”, labeled “negative 0.126 (n s)”. To “Organizational Citizenship”, labeled “negative 0.389 (n s)”. From “Self-Esteem Benefits”, two arrows extend: A thick solid arrow to “Organizational Commitment”, labeled “0.449 asterisk”. A dashed arrow to “Organizational Commitment”, labeled “negative 0.126 (n s)”. Below the diagram, the note states: “Significant at p less than 0.05 asterisk; Significant at p less than 0.01 double asterisk”.Organizational social responsibility moderates the effects of perceived benefits on organizational commitment and organizational citizenship
High O S R Group: On the left side, three ovals appear vertically: “Knowledge Benefits” at the top, “Networking Benefits” in the middle, and “Self-Esteem Benefits” at the bottom. On the right side, two ovals appear vertically: “Organizational Commitment” at the top and “Organizational Citizenship” at the bottom. From “Knowledge Benefits”, two dashed arrows extend to the outcomes: A dashed arrow to “Organizational Commitment” labeled “0.212 (n s)”. A dashed arrow to “Organizational Citizenship” labeled “0.338 (n s)”. From “Networking Benefits”, two thick solid arrows extend: A thick solid arrow to “Organizational Commitment” labeled “0.499 asterisk”. A thick solid arrow to “Organizational Citizenship” labeled “0.588 asterisk”. From “Self-Esteem Benefits”, two dashed arrows extend: A dashed arrow to “Organizational Commitment” labeled “0.220 (n s)”. A dashed arrow to “Organizational Citizenship” labeled “0.042 (n s)”. Low O S R Group: On the left side, three ovals appear vertically: “Knowledge Benefits” at the top, “Networking Benefits” in the middle, and “Self-Esteem Benefits” at the bottom. On the right side, two ovals appear vertically: “Organizational Commitment” at the top and “Organizational Citizenship” at the bottom. From “Knowledge Benefits”, two arrows extend to the outcomes: A thick solid arrow to “Organizational Commitment”, labeled “0.504 double asterisk”. A dashed arrow to “Organizational Citizenship” labeled “0.288 (n s)”. From “Networking Benefits”, two dashed arrows extend: To “Organizational Commitment”, labeled “negative 0.126 (n s)”. To “Organizational Citizenship”, labeled “negative 0.389 (n s)”. From “Self-Esteem Benefits”, two arrows extend: A thick solid arrow to “Organizational Commitment”, labeled “0.449 asterisk”. A dashed arrow to “Organizational Commitment”, labeled “negative 0.126 (n s)”. Below the diagram, the note states: “Significant at p less than 0.05 asterisk; Significant at p less than 0.01 double asterisk”.Organizational social responsibility moderates the effects of perceived benefits on organizational commitment and organizational citizenship
3.4 Study 2 methods
Building on the findings of Study 1, the primary purpose of Study 2 was to empirically test the mediating role of perceived indebtedness in linking perceived benefits to organizational commitment and organizational citizenship, thereby testing H4. Using samples from diverse hospitality and tourism associations, this phase of the study validates the model in a broader context, while enhancing the generalizability of the findings across the hospitality and tourism industry.
3.4.1 Data collection and sample
The online survey was distributed through Prolific, with a qualifying condition that a respondent must be a member of hospitality and tourism associations. After cleaning, the 182 valid responses were used for the final analysis. To satisfy the 10 times rule (Goodhue et al., 2012) for sample size in PLS-SEM, 80 samples were required since there are eight arrows pointing to the endogenous variable in the empirical model. Furthermore, a sample size adequacy test using G* Power software suggested that a minimum of 146 samples would be required for a medium effect size of 0.15, 95% power, and 5% error (Cohen, 1988). Thus, the final sample size exceeded this requirement. Of the 182 respondents, 73.1% were male, and 25.3% were female. Approximately 70% were between the ages of 21 and 35, approximately 28% were between the ages of 36 and 54, and 2% were 55 or older. The majority of respondents were Caucasian, at approximately 84%, followed by African American at approximately 6.6% and Hispanic or Latino at approximately 3.8%.
3.4.2 Measures and procedure
The same measures from Study 1 were used for perceived benefits and indebted behaviors. The additional variable included for Study 2 was perceived indebtedness, which was measured with three items (see Table 1 for items and the source). The questionnaire was structured to be consistent with the questionnaire used in Study 1.
3.4.3 Data analysis
Similar to Study 1, PLS-SEM was used as the primary data analysis for Study 2. A recommended series of steps were used to ensure rigorous analysis: CMB test, outer model test, inner model test, and mediation effect test. Although the main purpose of Study 2 was to test the mediating effects of perceived indebtedness (H4), the main direct effects of perceived benefits on indebted behaviors (H1 and H2) were also retested to validate the findings of Study 1.
3.5 Study 2 results
3.5.1 Common method bias testing results
Following the same recommended guidelines and procedures reported in Section 3.2.1, the potential CMB were also examined for the newly collected data in Study 2. First, Harman’s single-factor test suggested a six-factor model with the largest variance accounted for by any factor being 40.99%, confirming that no single factor accounted for more than 50% of the variance. Second, based on the FCVIF test, the highest VIF value was 2.26, which did not exceed the threshold of 3.30, providing further evidence for the absence of CMB. Therefore, CMB is unlikely to be a critical issue for the data.
3.5.2 Outer model testing results
Similar to the steps taken in Study 1, the next step in Study 2 was the outer model testing. A PLS algorithm was run to examine the outer model properties (see Table 2 for complete estimates). First, internal consistency reliability was supported by Dijkstra-Henseler’s rho_A estimates ranging from 0.701 to 0.835 and composite reliability estimates ranging from 0.810 to 0.901, all of which were above the recommended threshold of 0.70 (Dijkstra and Henseler, 2015). While most of the Cronbach’s alphas were above 0.70, those of two constructs (knowledge benefits: 0.654; networking benefits: 0.666) were still above 0.60, supporting an acceptable level of reliability (Ursachi et al., 2015).
Next, convergent validity was established based on the following results (Hair et al., 2021): the outer loadings and indicator reliability estimates of all but two items were above the recommended thresholds (0.70 and 0.50, respectively). These two items were only marginally below the thresholds, and all of the AVE estimates including these two items were still higher than the recommended threshold (0.50). Therefore, the results supported an acceptable level of convergent validity. Finally, discriminant validity was confirmed (Fornell and Larcker, 1981): each of the square roots of the AVEs was greater than the correlations between the latent variables (see Table 3). Overall, the results supported the measurement reliability and validity.
3.5.3 Inner model testing results
Next, once the inner model was estimated, the VIF was examined to identify any potential collinearity issues: VIF values ranged from 1.594 to 2.709, none of which exceeded the threshold of 4.0. The results thus supported that multicollinearity was unlikely to be an issue for the inner model (Hair et al., 2011; Hair et al., 2021). Subsequently, R2 was then examined. The model captured a higher level of variance in organizational commitment (R2 = 0.647) and a moderate level of variance in organizational citizenship (R2 = 0.452). Predictive relevance (Q2) was also examined using PLSPredict. The results indicated that there was a large predictive relevance for organizational commitment in the model (Q2 = 0.482) and a moderate predictive relevance for organizational citizenship in the model (Q2 = 0.344) (Hair et al., 2014).
The specific relationships between the latent variables were examined using bootstrapping. As shown in Figure 3 and Table 4, self-esteem benefits significantly increased organizational commitment, supporting H1(c). Meanwhile, knowledge benefits and networking benefits significantly increased organizational citizenship, supporting H2(a) and H2(b).
On the left side, three ovals appear vertically: “Knowledge Benefits” at the top, “Networking Benefits” in the middle, and “Self-Esteem Benefits” at the bottom. At the bottom center, an oval labeled “Perceived Indebtedness” represents the mediating variable. On the right side, two ovals appear vertically: “Organizational Commitment” at the top and “Organizational Citizenship” below it. Multiple arrows connect the constructs with numerical path coefficients and significance labels: From “Knowledge Benefits” to “Organizational Commitment”, a dashed arrow is labeled negative 0.048 (n s). From “Knowledge Benefits” to “Organizational Citizenship”, a solid arrow is labeled 0.199 asterisk. From “Knowledge Benefits” to “Perceived Indebtedness”, a solid arrow is labeled 0.343 double asterisk. From “Networking Benefits” to “Organizational Commitment”, a dashed arrow is labeled 0.124 (n s). From “Networking Benefits” to “Organizational Citizenship”, a solid arrow is labeled 0.189 asterisk. From “Networking Benefits” to “Perceived Indebtedness”, a dashed arrow is labeled negative 0.186 asterisk. From “Self-Esteem Benefits” to “Organizational Commitment”, a solid arrow is labeled 0.393 double asterisk. From “Self-Esteem Benefits” to “Organizational Citizenship”, a dashed arrow is labeled 0.092 (n s). From “Self-Esteem Benefits” to “Perceived Indebtedness”, a solid arrow is labeled 0.456 double asterisk. From “Perceived Indebtedness” to “Organizational Commitment”, a solid arrow is labeled 0.467 double asterisk. From “Perceived Indebtedness” to “Organizational Citizenship”, a solid arrow is labeled 0.338 asterisk. Below the diagram, a note states: “Significant at p less than 0.05 asterisk; Significant at p less than 0.01 double asterisk”.Perceived indebtedness mediates the effects of perceived benefits on organizational commitment and organizational citizenship
On the left side, three ovals appear vertically: “Knowledge Benefits” at the top, “Networking Benefits” in the middle, and “Self-Esteem Benefits” at the bottom. At the bottom center, an oval labeled “Perceived Indebtedness” represents the mediating variable. On the right side, two ovals appear vertically: “Organizational Commitment” at the top and “Organizational Citizenship” below it. Multiple arrows connect the constructs with numerical path coefficients and significance labels: From “Knowledge Benefits” to “Organizational Commitment”, a dashed arrow is labeled negative 0.048 (n s). From “Knowledge Benefits” to “Organizational Citizenship”, a solid arrow is labeled 0.199 asterisk. From “Knowledge Benefits” to “Perceived Indebtedness”, a solid arrow is labeled 0.343 double asterisk. From “Networking Benefits” to “Organizational Commitment”, a dashed arrow is labeled 0.124 (n s). From “Networking Benefits” to “Organizational Citizenship”, a solid arrow is labeled 0.189 asterisk. From “Networking Benefits” to “Perceived Indebtedness”, a dashed arrow is labeled negative 0.186 asterisk. From “Self-Esteem Benefits” to “Organizational Commitment”, a solid arrow is labeled 0.393 double asterisk. From “Self-Esteem Benefits” to “Organizational Citizenship”, a dashed arrow is labeled 0.092 (n s). From “Self-Esteem Benefits” to “Perceived Indebtedness”, a solid arrow is labeled 0.456 double asterisk. From “Perceived Indebtedness” to “Organizational Commitment”, a solid arrow is labeled 0.467 double asterisk. From “Perceived Indebtedness” to “Organizational Citizenship”, a solid arrow is labeled 0.338 asterisk. Below the diagram, a note states: “Significant at p less than 0.05 asterisk; Significant at p less than 0.01 double asterisk”.Perceived indebtedness mediates the effects of perceived benefits on organizational commitment and organizational citizenship
To test the mediating effects of perceived indebtedness (H4), the indirect effects were examined. All of the indirect effects of perceived benefits on indebtedness behaviors were significant. Specifically, both knowledge benefits (βcommitment = 0.160, p < 0.01; βcitizenship = 0.116, p < 0.01) and self-esteem benefits (βcommitment = 0.213, p < 0.01; βcitizenship = 0.154, p < 0.01) indirectly and positively predicted organizational commitment and organizational citizenship, mediated by perceived indebtedness. Interestingly, the significant indirect effects of networking benefits on both organizational commitment (β = −0.087, p = 0.039) and organizational citizenship (β = −0.063, p = 0.048) were negative in direction. However, the results should be interpreted with caution because the effects were only significant at a p-value of 0.05, but not significant at a conservative p-value of 0.01, and the effect sizes were too small. Notably, the results confirm the significant mediating role of perceived indebtedness, linking perceived benefits to indebted behaviors.
4. Discussion and implications
4.1 General discussion
Professional associations in the HTE industry are struggling to keep their members committed and engaged, especially in uncertain times of external crises. This study explored whether pre-crisis perceived benefits from membership lead to increased post-crisis organizational commitment and organizational citizenship, considering the moderating role of OSR through Study 1 and the mediating effect of perceived indebtedness through Study 2. In Study 1, initial tests of the model on a full sample indicated that perceived benefits pertaining to knowledge, networking, and self-esteem increased organizational commitment but not organizational citizenship; however, when taking into account the moderating effects of OSR, networking benefits significantly increased organizational citizenship, a more proactive and deeper level of reciprocal dedication. That is, when members perceive that their professional association is strongly committed to serving the various needs of its members, they are more likely to “repay the favor” by making a commitment to the association and being a good citizen. The findings are consistent with the study of Tang et al. (2024), which showed the moderating role of CSR in bolstering customer-oriented organizational citizenship behavior, and the study of Boğan and Dedeoğlu (2019), which demonstrated the moderating role of CSR in strengthening employee trust. However, given the scarcity of scholarship on social responsibility in non-profit organizations (Pope et al., 2018) and the fact that most previous studies in HTE have focused on for-profit firms (e.g. Tang et al., 2024; Yeon et al., 2021), the present study makes a significant contribution to the literature by demonstrating the important role of OSR as a moderator that can strengthen the positive effects of members’ perceived benefits on their reciprocal dedication.
The effects of perceived benefits on organizational commitment and organizational citizenship found in Study 1 were confirmed in Study 2. The results of Study 2, which was based on a larger sample of hospitality and tourism association members, showcased that benefits related to self-esteem specifically increased organizational commitment, while benefits related to knowledge and networking enhanced organizational citizenship. Although the specifics were somewhat different, the main findings from Study 1 and Study 2 remained consistent. Taken together, a consistent pattern emerged from both studies, suggesting that a member who believes that he or she has received substantial benefits from an association (prior to a crisis) is more likely to give back to the association (after a crisis) through a greater willingness to become more involved in the association and to sacrifice his or her own time and resources on behalf of other members. The findings can further build on the previous studies showing that learning and networking opportunities and caring perceptions are associated with higher levels of organizational commitment (Masiero et al., 2022; Ng et al., 2006; Nishanthi and Kailasapathy, 2018). As a step forward from these previous studies, the present study showcased that such perceived benefits can increase organizational citizenship, taking into account external crisis situations. Different from most recent previous studies on crisis management for hospitality and tourism organizations, which have focused on what organizations can do “after” a crisis occurs such as recovery strategies (Bressan et al., 2023; Prayag et al., 2024; Shulga and Busser, 2024), the present study sheds light on what organizations can do “before” a crisis occurs. Understanding this mechanism not only contributes to the current literature on crisis management, but also enhances the preparedness and resilience of professional associations in the face of future crises.
4.2 Theoretical implications
The results of the present study make significant theoretical contributions. First, by employing the theory of indebtedness to examine members’ organizational commitment and organizational citizenship, our study provides a novel perspective in the HTE literature, which has extensively utilized social exchange theory (e.g. Peng et al., 2018; Tang et al., 2024) to study the similar context. Specifically, our research conceptually proposes and empirically tests perceived indebtedness as an explicit mediator. The findings provide empirical support for the theory of indebtedness in professional association contexts by demonstrating that perceived benefits significantly affect organizational commitment and organizational citizenship through perceived indebtedness. This allows us to suggest that the theory of indebtedness may better explain long-term emotional attachment and stronger reciprocal actions in professional association contexts.
Second, this study introduces the concept of OSR to distinguish non-profit organizations from their for-profit counterparts, where the primary focus of previous research on stakeholder perceptions in for-profit sectors such as hotels (e.g. Boğan and Dedeoğlu, 2019; Chomvilailuk and Butcher, 2021) and Airbnbs (e.g. Chuah et al., 2022; Farmaki et al., 2023). This study, however, shifts the focus to non-profit organizations, specifically HTE professional associations, thereby recognizing the need for distinct measurement instruments to assess member perceptions of OSR. By demonstrating that OSR moderates and reinforces the positive influence of perceived member benefits on reciprocal dedication, this study offers clarification regarding the conceptualization of OSR, differentiating it from CSR and addressing a notable gap within the HTE non-profit literature.
The critical role of professional associations in driving growth and sustainable development is achieved by theorizing change and institutionalizing new norms (Greenwood et al., 2002). While OSR practices such as advocacy, representation, and excellence promotion facilitate the process, a key and often overlooked driver, perceived indebtedness among members, provides association marketing and management executive teams with a fresh perspective on how to cultivate sustainable growth of professional association sector particularly in the HTE industry.
4.3 Practical implications
For members with higher levels of perceived OSR, the present study reveals that perceived benefits—especially networking benefits—not only increase their commitment to the association but also encourage organizational citizenship behaviors. This suggests that the fundamental mission of professional associations, which is to connect people and the community through various gatherings should remain a high priority. Despite the decline in the scale of business events and the number of business travelers post pandemic (West, 2023), professional associations should innovate in providing networking opportunities. Implementing a mix of virtual and in person, leveraging technology to create meaningful connections eve in remote settings could boost retention rates and ensures sustainable growth for the association. Professional associations also should develop a comprehensive communication strategy to effectively convey OSR efforts to members. This could include regular impact reports, member spotlights highlighting OSR participation, and interactive platforms for members to engage with OSR initiatives directly.
The study identifies that for members with lower levels of perceived OSR, knowledge and self-esteem benefits are key drivers of organizational commitment. This finding offers a valuable opportunity for targeted engagement strategies. Association members perceive knowledge benefits from learning about new trends and technologies and about solutions to work-related problems. They also feel a sense of self-esteem through an enhanced reputation and credibility when they have the opportunity to influence others through the association. Therefore, providing members who are less sensitive to OSR with these types of benefits through on-demand offerings or live webinars where members can actively participate as facilitators or speakers, rather than just passively listen, will encourage them to be loyal and committed to the association. Other platforms, such as sub-communities or special interest groups within the association where members can share knowledge, mentor each other, and collaborate on industry specific challenges, can also positively influence member behaviors. Implementing a system of small, manageable volunteer tasks that allow members to contribute their expertise and influence the association’s direction without significant time commitments can help engage members with lower perceived OSR.
The study validates perceived indebtedness as a critical mediator driving members’ reciprocal actions in professional associations. To leverage these findings, professional associations can intentionally design benefit offerings and OSR initiatives to cultivate perceived indebtedness among members. For instance, associations can implement personalized outreach to acknowledge members’ contributions and highlight how the benefits provided by the association have positively impacted their careers or personal growth. Such personalized communication fosters a sense of moral obligation, motivating members to contribute to the association’s success and support fellow members, especially during challenging times. By adopting these strategies, professional associations can enhance member engagement and long-term commitment, ultimately strengthening their organizational resilience in times of crisis.
4.4 Limitations and future studies
As with any study, the research presented here has certain limitations. In this study, the pandemic was used as an example of external crises due to its significant importance to the professional associations and the HTE industry as a whole (Lin et al., 2022). However, presenting the pandemic as an example of an external crisis also has a limitation because it was such an unprecedented event in history, which may not always have the same intensity or severity as other external crises. Therefore, other examples of external crises that affect a non-profit organization, such as economic downturns, natural disasters, or political climates can be used in future studies to generalize the findings of the present study. In addition to external crises, internal crises are also worthy of attention. Therefore, future studies warrant exploring the model presented above in the context of internal crises to examine if and what types of perceived benefits significantly lead to indebted behaviors, which will provide a more strategic understanding of how to manage the negative consequences of both types of crises.
Another limitation of this study is that the results are contingent upon self-reported data, which may induce bias. To prevent potential bias and more thoroughly investigate the mediating effect of perceived indebtedness and the moderating effect of OSR, additional research may employ experimental or mixed-method approaches. In addition, the sample sizes for both studies, especially for Study 1, need to be addressed. It reflects that the studies are not based on the general public, but rather on a specific population—members of professional associations in a particular sector, which is inevitably small. Even though both sets of samples met or exceeded the sample size required for data analysis according to the power analysis, future studies employing larger sample sizes will be beneficial to increase the power and thus re-validate the findings of the present study. Finally, a more comprehensive understanding of the observed relationships could be achieved by investigating additional potential mediators and/or moderators, such as organizational climate.
