Defining and Institutionalizing Proactive Advising: Insights from a National Survey

Proactive advising plays a critical role in enhancing college student success, improving outcomes such as persistence and on-time completion through intentional advisor-advisee relationships (Institute for Education Sciences, 2021; Van Jura & Prieto, 2021). Unfortunately, empirical research on its conceptualization and institutionalization in U.S. higher education remains limited, with most analyses coming from industry reports that primarily offer descriptive or anecdotal recommendations (American Association of State Colleges and Universities, 2021; Tyton Partners & BayView Analytics, 2021, 2022; University Innovation Alliance, 2021). Proposed definitions have varied considerably over time (Varney, 2013), creating disconnects that make it difficult for institutions to implement proactive advising and for advisors to meet NACADA’s core competency expectation of understanding advising approaches and strategies (NACADA, 2017).

These challenges mirror a broader gap in student success research, that of implementation methods (Kinzie & Kuh, 2017). To address these gaps, we led a cluster of advising administrators in reflecting on our institutions’ processes of proactive advising adoption, with funding from the American Association of Public and Land-Grant Universities’ Powered by Publics (APLU PxP) initiative (APLU, n.d.). Examining existing support structures and processes, we identified missing components for full implementation (Brabender, 2017; Chamberlain & Burnside, 2021) and grappled with the possibly divergent views of student success between administrators and advisors (Taylor, 2022). We then surveyed advisors and advising administrators at U.S. four-year institutions (i.e., the types of institutions within the APLU PxP initiative) to ascertain perceptions and states of institutionalization of proactive advising.

Using a mix of descriptive and inferential statistics, multilevel linear regression, and thematic content analyses, we asked:

Academic advising is critical to student success, transcending mere course registration and degree requirement guidance (AASCU, 2021). Effective advising encompasses learning outcomes, pedagogies, role definitions, and relational rapport (Mu & Fosnacht, 2019; NACADA, 2023; National Survey of Student Engagement, 2020).

Originating in the 1970s as intrusive counseling, proactive advising emphasizes early, deliberate interventions by advisors (Cannon, 2013; Earl, 1988; Glennen, 1976; Varney, 2007, 2013). Proactive advising differs from developmental advising (Crookston, 1994; O’Banion, 1994), which focuses on decision-making through backwards design, starting with vocational goals and moving towards major choice, course choice, and scheduling, while emphasizing problem-solving and evaluation skills. Appreciative advising, another advising approach, emphasizes building rapport and valuing students' strengths to design academic and professional goals and strategies for goal attainment (Bloom et al., 2008). Coaching-based advising involves guiding students to evaluate their experiences, identifying effective personal strategies, and exploring options for goal attainment (McClellan & Moser, 2011). Centering advising conversations around the logic or coherence of the broad curriculum with which students are engaging is the goal of Learning-centered advising (Lowenstein, 2005; Rust, 2011). These different approaches to advising do share commonalities, such as ongoing communication and discussion of plans and resources to achieve long-term goals.

Proactive advisors initiate contact and intentional involvement in their students’ affairs (Earl, 1988; Glennen, 1976; Van Jura & Prieto, 2021; Varney, 2013), often reaching out to students identified by faculty-submitted early alerts, progress reports (Kraft-Terry & Kau, 2019; Miller & Murray, 2005; Upcraft & Kramer, 1995), or technology-enabled predictive alerts (Kalamkarian et al., 2017; Valentine & Price, 2023). As part of a holistic case management strategy (Klein, 2012), proactive advising commonly features mandatory, early, and frequent appointments with students (Kirk-Kuwaye & Nishida, 2001; Vander Schee, 2007), offering referrals to other student supports, such as tutoring and peer mentoring. The outreach associated with proactive advising includes ongoing, multi-modal communication (Bettinger & Baker, 2014). These practices increase students’ academic self-efficacy (Kitchen et al., 2021), engagement with advising (Schwebel et al., 2012), grade point averages (Earl, 1986; Fowler & Boylan, 2010; Molina & Abelman, 2000; Robbins et al., 2009), persistence (Bahr, 2008; Bettinger & Baker, 2014; Fowler & Boylan, 2010; Molina & Abelman, 2000; Valentine & Price, 2023), and timely graduation (Scrivener et al., 2015).

Advising models are typically classified by the locus of organizational control (e.g., centralized or decentralized), by the actors delivering academic advising (e.g., professional/primary-role advisors or faculty advisors), and by the forms of accountability for advising outcomes (AASCU, 2021; Habley, 1997; Habley & Morales, 1998; UIA, 2021; Young‐Jones et al., 2013). Centralized advising consolidates advising into one unit, whereas decentralized advising spreads responsibilities across academic schools or departments, enhancing discipline-specific connections but at the expense of coordination (King, 2008). Professional advisors (i.e., primary-role advisors) focus on comprehensive student support, while faculty advisors offer discipline-specific advice alongside their main teaching and research roles (Self, 2008). Jones et al. (2021) noted, however, that these models “vary by campus and can often be legacy approaches that no longer serve the needs of the students” (p. 67). Habley’s (1997) survey found that public institutions trended toward models with special focus advising (e.g., first-year students) and increased reliance on professional advisors, while most private institutions used department faculty as advisors.

Regarding perceived success of various models, studies suggest decentralized and faculty advising models correlate negatively with advising effectiveness (Habley & Morales, 1998). Institutions with integrated supports and clear responsibilities exhibit better student retention and degree completion (Tyton Partners & Babson Survey Research Group, 2019). Decentralized models can hinder consistent proactive advising implementation (Miller & Weiss, 2022; Taylor, 2022; Waddington, 2019). Finally, smaller advising caseloads are commonly associated with more proactive advising approaches, while larger caseloads are seen as barriers (Miller & Weiss, 2022; Scrivener & Au, 2007; Scrivener et al., 2015; Tyton Partners & Bay View Analytics 2022).

Proactive advising is increasingly recognized for enhancing student retention and graduation rates (AASCU, 2021; Burns, 2021, 2022; Brabender, 2017; Tyton Partners & Bay View Analytics, 2022). Indeed, the Department of Education’s What Works Clearinghouse empaneled a group of experts to critically review the evidence supporting the effectiveness of advising, which resulted in a recommendation that institutions of higher education adopt,

In fact, the rising emphasis on proactive advising has spurred consulting firms and technology vendors to promote their solutions (EAB, 2019; McFarlane, 2018; Tyton Partners & Bay View Analytics, 2021), aligning with the literature's focus on data-driven insights (AASCU, 2021; Jones et al., 2021; Pelletier, 2021) and advanced communication tools (Scrivener & Au, 2007; Van Jura & Prieto, 2021; Varney, 2013).

Our study's conceptual framework suggests that proactive advising improves student outcomes, such as GPA, persistence, and timely graduation, although these outcomes are contingent on clear definitions and institutional integration (Brabender, 2017; Chamberlain & Burnside, 2021). We view “institutionalizing” as the systemic integration of specific practices, tools, and strategies into organization structures, processes, and culture to ensure consistent and sustained application. As Kezar & Sam (2013) clarified, institutionalization “moves beyond standard operating procedures to the actual value system of the organization. Members come to a consensus, accept the value of innovation, and see the innovation as normative behavior for the institution” (p. 60). Institutionalizing proactive advising within a college or university is accomplished through three broad sets of tactics:

1. Campus-level implementation strategies that define proactive advising and its goals (AASCU, 2021; Brabender, 2017; Fowler & Boylan, 2010), support its utilization (Institute of Education Sciences, 2021; Scrivener et al., 2015), and train and develop proactive advisors (Tyton Partners & Bay View Analytics, 2022; UIA, 2021).

2. Honing advisors’ proactive advising practices around identifying and encouraging meetings with students in advance of, rather than as reaction to, academic or other difficulty.

3. Ensuring that appropriate technology pervades the advising ecosystem to optimize proactive advising's timely and targeted delivery (Chamberlain & Burnside, 2021).

Finally, we also adopt the position that when students are not successful in college or when academic advisors are not effective in promoting student success, it is a result of institutional problems, not a failure of the student to assimilate or of an advisor to fix the institutional issues (Bensimon, 2007; Museus, 2014; Taylor, 2022).

In collaboration with advising administrators from 13 urban-serving, public, four-year universities within the APLU PxP cluster, we developed the Survey of Proactive Advising (Rust & Chadwick, 2021). This web-based questionnaire was designed based on a review of proactive advising literature and collected data on demographics, advising roles, institutional models, conceptions of proactive advising, and perceptions of its institutionalization. Utilizing a drop-down menu with 2,933 four-year institutions from the Department of Education’s IPEDS for 2019 (National Center for Education Statistics, 2019), respondents consented to have their responses linked to institutional data. Following a pilot phase with cluster colleagues, we refined the survey for clarity.

We identified potential respondents through web scraping and obtained 20,294 email addresses linked to academic advising roles at four-year institutions. Participants were incentivized to participate in this survey, which we conducted from March 16 to April 8, 2021, with a webinar invitation. This protocol received Institutional Review Board approval.

Complete responses were received from 968 individuals at 323 unique, four-year institutions across 48 states, the District of Columbia, and Guam, representing a response rate of 4.8%, assuming all emails were successfully delivered. Academic advisors comprised 64.2% of respondents, while 35.8% were advising administrators (see Table 1 for detailed demographics). The most common advising structure was decentralized (49.6%), followed by coordinated (27.3%), and centralized (23.1%). For most respondents’ institutions, advising was done by primary-role/professional advisors (53.7%). The modal student-to-advisor ratio was 300 to 399 (see Figure 1). We incorporated IPEDS 2019 data for institutional characteristics, detailed in  Appendix A. While the exact population of academic advisors and administrators remains unknown, our data offers a representativeness assessment and indicates a possible over-representation of larger and public institutions and of institutions with lower retention rates.

The survey collected extensive respondent demographic data used in the regression analysis. Key measures on roles and institutions included:

• institution name, which was tied to institutional details from IPEDS data;

• respondent-identified advising role, which enabled differentiation between administrators and academic advisors and accounted for time in role and impacted student populations;

• advising model and structure description for respondent institution (e.g., faculty, primary-role advisors, or split) and organizational structure (decentralized, centralized, or coordinated: multiple common advising efforts or strategies with decentralized delivery and reporting);

• advising caseload average size: respondent selection of estimated student advising caseload size from predefined categories;

• attributes of proactive advising: respondent selection of top five attributes of proactive advising from a list;

• reactions to proposed definition: respondent-rated agreement with the proposed definition of proactive advising, developed by our APLU PxP cluster:

• personal belief in proactive advising;

• institutionalization approaches: respondents indicated the status and importance of various institutionalization approaches (i.e., implementation strategies, advising practices, and technologies);

• composite impression of institutionalization: primary dependent variable in regression analyses, averages three items to gauge respondents' views on proactive advising's institutionalization (validity discussed below in results);

• open comments for respondents to discuss strengths and challenges in institutionalizing proactive advising at their institution.

We employed a concurrent triangulation strategy within our mixed-method design to collect both quantitative and qualitative data, optimizing the strengths and mitigating the limitations of each data type (Bryman, 2006; Creswell & Plano Clark, 2007). Our initial analysis utilized descriptive nd inferential statistics to examine understanding and institutionalization of proactive advising and to identify any discrepancies between advisor and administrator perceptions. Recognizing the risk of inflated Type II error rates due to multiple comparisons, we treated any observed differences as exploratory, informing our decision on whether to include role indicators in the regression analyses. We then used multilevel linear regression to identify predictors of institutionalization of proactive advising and to explore whether these predictors depended on respondent role. Lastly, to enrich our quantitative findings, we conducted a content analysis of open comments to identify themes. Qualitative insights allowed us to navigate potential tensions among our conceptual and paradigmatic lenses (Jones et al., 2014).

To address our first research question, we analyzed the 20 attributes respondents deemed most important in defining proactive advising. The most frequently selected attributes included: developmental, appreciative, intrusive, coaching-based, caseload management, teaching students to anticipate obstacles, focused on specific student demographics, meeting early in the academic term, initiated by the advisor, and student participation on a required basis. We then examined potential differences by advising role (administrator vs. advisor) and gauged reactions to our proposed definition. Table 2 displays the attribute frequency distributions, disaggregated by role. Notably, differences emerged in prioritizing developmental advising (72% of advisors, 64.3% of administrators), intrusive approaches (48.0% of advisors, 58.8% of administrators), focus on at-risk students (15.8% of advisors, 25.1% of administrators), and the preference for opt-in participation (2.1% of advisors, 4.9% of administrators). These potential differences by role supported the inclusion of the role indicator variable in later regression analyses.

Respondents’ reactions to our proposed definition of proactive advising illustrated that almost all respondents (97.9%) agreed/strongly agreed that the definition is accurate; 91.7% agreed/strongly agreed that it is comprehensive; and 93.4% agreed/strongly agreed that it is helpful, with no apparent differences in agreement by role. Forty-nine respondents (5.2%), who expressed some level of disagreement, provided suggestions to improve the definition, including: shorten the definition; use more student strength-centered language and less jargon; and change “institutionally initiated” to “advisor-initiated.” These results informed the revised definition of proactive advising in our discussion.

Addressing our second research question, we examined three sets of institutionalization approaches. As dissatisfaction is often a precursor to organizational change (Rosenbaum et al., 2018) and institutionalization requires members to value innovation (Kezar & Sam, 2013), we focused on the approaches deemed absent but needed. This focus was also important because responses were bi-modal with most respondents deeming practices, implementation strategies, and technologies as either absent-but-needed or present-and-important.

Respondents evaluated thirteen proactive advising practices that have not been happening but need to (see Figure 2). The most frequently cited missing and needed practice was, “Requiring students at risk for departure from institution to meet frequently with an advisor,” selected by 59.5% of respondents. Table 3 shows four practices where advisors and administrators differed in their identification of the practice as absent but needed: “Assigning students to levels of risk for insufficient academic progress” (24.0% of advisors, 32.0% of administrators); “Conducting outreach to students enrolled in high D, F, W or gateway courses” (38.6% of advisors, 45.2% of administrators); “Conducting outreach to students with low activity or low grades in the Learning Management System” (27.4% of advisors, 42.7% of administrators); and “Requiring first year students to meet with an advisor once per academic term (semester/quarter)” (23.5% of advisors, 29.4% of administrators). On average, administrators identified more absent but needed practices (M = 4.23, SD = 2.78) than advisors (M = 3.64, SD = 2.83).

Respondents next assessed eight proactive advising implementation strategies. As Figure 3 shows, the top absent-but-needed strategies (each selected by over 64% of respondents) related to incentivizing advisors through informal recognition, formal performance review, or promotional structures. Table 3 presents two strategies differently selected by advisors and administrators as absent-but-needed: “Defining institutional motivations or goals for implementing proactive advising” (44.3% of advisors, 51.0% of administrators), and “Incentivizing advisors to utilize proactive advising strategies through performance review or promotional structures” (60.9% of advisors, 70.3% of administrators). No differences were observed in total missing strategies by role.

Respondents then evaluated eleven proactive advising technologies. As Figure 4 reveals, the most frequently cited missing but needed technology was “Real-time filtering of student advising caseloads.” Table 3 compares frequencies for all selection choices by role and identifies three potential differences in selection of missing but needed: “Appointment reminders to students by text messaging” (37.7% of advisors, 45.2% of administrators); “Direct text messaging to students (individually or in groups)” (36.1% of advisors, 44.7% of administrators); and “Early alert system based on student activity in the Learning Management System (LMS)” (45.9% of advisors, 54.5% of administrators). No differences were observed in total missing technologies by role.

To address our third research question, we constructed a dependent variable representing respondents' overall perception of the institutionalization of proactive advising at their respective institutions. Our composite measure was derived from averaging three items, which showed decreasing levels of agreement on our 4-point scale:

1. “I believe my institution has been working to implement or improve our use of Proactive Advising” (M = 3.11, SD = .74).

2. “I believe my institution has specific goals related to our use of Proactive Advising” (M = 2.83, SD = .83).

3. “I am satisfied with the current state of Proactive Advising at my institution” (M = 2.52, SD = .82).

These items formed a reliable scale with a Cronbach's Alpha of .84, with inter-item correlations between .569 to .714, indicating measurement of a similar underlying concept. These survey items also exhibited face validity as field administrators had reviewed and refined the survey items, ensuring their relevance and accurate representation of proactive advising institutionalization. We used this composite measure, with an average score of M = 2.82, SD = .69, as our regression analysis’ dependent variable.

In response to the statement, “I believe that Proactive Advising is an important student success strategy,” the average rate of agreement was very high (M = 3.69, SD = 0.50). This predictor is included in the regression analyses given the connection between institutionalization of innovation and valuing of innovation (Kezar & Sam, 2013). We identified no differences of belief between advisors and administrators in the importance of proactive advising.

To discern respondents’ perceptions of institutionalization of proactive advising and account for response clustering by institution, we employed a multilevel modeling approach. This method recognizes the data's nested structure (individuals within institutions) and divides the variance between the two levels (Snijders & Bosker, 2012). Starting with a null model (Model 0 in Table 4), we found significant variability in perceptions across institutions, with 17% of the variance due to within-institution similarities (σ² = .40, τ₀₀ = .08, ICC = .17, R²_marginal = .00, R²_conditional = .17), justifying a multilevel approach.

Individual/Level 1 Models. We then began maximum likelihood estimation of models with individual-level (i.e., Level 1) predictors (Models 1–3 in Table 4). In Model 1, the fixed effect for “Black or African American” race/ethnicity origin indicated a more positive perception compared to the White reference group. Model 2 introduced more respondent details, revealing a slight decline in positive perceptions among younger respondents and those in their roles longer. Model 3 shows the strong influence of “Personal Belief in Proactive Advising” on perceptions, with only age, administrator role, and time in role having significant effects. The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) statistics favored Model 3 over the null model. There was virtually no reduction in the Intraclass Correlation Coefficient (ICC) in these Level 1 models, indicating individual level variables did little to explain variance between institutions.

Standardized Final Model Insights. In the standardized final model presented in Table 6 (σ² = .22, τ₀₀ = .02, ICC = .07, R²_marginal = .51, R²_conditional = .54), the most influential predictor was “missing but needed implementation strategies,” which was negatively associated with impressions of institutionalization (β = −.35, SE = .02, t(634) = −19.34, p < .001). The next largest standardized effect was personal belief in proactive advising, which positively influenced impressions of institutionalization (β = .14, SE = .02, t(634) = 8.71, p < .001). Missing advising practices (β = −.09, SE = .02, t(634) = −4.91, p < .001), missing technologies (β = −.09, SE = .02, t(634) = −3.95, p < .001), and decentralized advising structures (β = −.08, SE = .02, t(634) = −3.77, p < .001) all negatively impacted perceptions with similar magnitudes. The interaction between the administrator role and missing technologies showed a mitigating effect for administrators (β = .07, SE = .03, t(634) = 2.39, p = .02). The administrator role itself had a negative effect (β = −.07, SE = .03, t(634) = −2.68, p = .01).

Assumptions. We checked multilevel modeling assumptions (Snijders & Bosker, 2012) regarding our final standardized model. Using the “ranef()” function from R's lme4 package (Bates et al., 2015; R Core Team, 2023) to extract Level 2 residuals, we found their covariance (0.005) to be nearly zero, confirming their independence. We then ensured the independence between Level 1 and 2 residuals by comparing their aggregated covariances, which was zero, indicating no inter-level dependency. Residual plots against each predictor showed a random dispersion, affirming the independence of Level 1 residuals (see Appendices B and C). QQ-plots for both residual levels suggested an approximately normal distribution with minimal outliers (see Appendices D and E). To address potential multicollinearity, we calculated the Variance Inflation Factor (VIF) for each predictor in two separate linear models, one for each level. All VIF values (see Table 6) were below the 2.5 threshold recommended by Allison (2012), with the highest being 1.66, indicating no significant multicollinearity among predictors at either level.

Of the respondents, 42% (408 individuals) provided substantive comments on the strengths and challenges of institutionalizing proactive advising at their respective institutions. We distilled these responses into seven primary themes.

The most prevalent theme, present in over a third of responses, highlighted the challenges of silos, lack of standardization, and disparities due to decentralization. One advisor noted, “After the students are advised in the 'first-year' advising center, they move on to their colleges, which each do things differently … In many cases, the ball that was once in play gets dropped.” An administrator emphasized, “Each unit operates as if it’s not a part of the University with very [different] rules that are sometimes [contradictory] to University policy and procedures.”

The second most common theme was concerns about the feasibility of proactive advising given large caseloads. An administrator remarked, “the lack of resources to hire adequate numbers of professional/primary-role advisors to keep caseloads low enough to provide ‘high touch’ advising for all undergraduate students has been and continues to be a challenge.” An advisor mentioned, “Caseloads are too large to manage multiple contacts with students that would develop the meaningful relationships required for successful proactive advising approaches.”

Over 60 respondents remarked on the lack of faculty buy-in. Some respondents felt that faculty commitments hindered prioritizing advising. One administrator observed, “faculty advisors who have limited training or expertise in advising [present a challenge] it is not a high priority for them among competing demands.” An advisor suggested a solution, stating, “We also need to generate [faculty] buy-in by providing data that shows some strategies actually have an impact.”

Over 50 respondents mentioned that, while technology was a valuable tool, its effectiveness was contingent upon its reliability, accuracy, and user-friendliness. An advisor expressed frustration, stating, “Advisors are expected to do far, far too much data mining … This is not a failure of technology. The technology is available. It's a failure of intuitive and sensical design and programming.” Another advisor lamented time lost for meaningful conversations with students due to the challenges of navigating inefficient systems.

Over 40 respondents highlighted the need for continuous training. One advisor emphasized, “Relevant or targeted professional development/best practices trainings are needed on a continual basis to ensure advisors are up-to-date with best practices and strategies to best service student needs.”

Over 30 respondents raised one of three common objections to implementing proactive advising: (1) students do not want or value it, (2) it is enabling instead of educating students, and (3) it can lead to an overwhelming number of communications. An advisor shared, “A concern is that too much convenience leads to a lack of patience, empathy, and autonomy … [Texting is causing] duress from ever-increasing emails/online chats/and so many other direct venues to connect.”

A final frequent theme, observed in over two dozen responses, was a desire for clear institutional goals, metrics, and accountability mechanisms related to proactive advising. One administrator mentioned challenges such as, “inconsistent job descriptions of advisors, lack of career ladder and promotions/recognition for advisors, [and] inconsistent expectations…” Another administrator stated, “We need to create more specific metrics and acknowledgements for proactive outreach.”

Our findings suggest proactive advising is perceived as an important strategy for promoting student success across a range of institutions. In our first research question, we uncovered high levels of agreement—among advisors and administrators—with our proposed definition of proactive advising, though improvements were suggested. Based on open comment themes and most frequently selected attributes of proactive advising, we propose this revised definition for proactive advising:

This revision gives more agency to the advisor, while still holding the institution responsible for empowering advisors to adopt proactive advising. It also honors student identities and strengths and expects proactive advising to be common practice.

We observed differences between advisors and administrators in their understanding of proactive advising. Administrators leaned towards intrusive methods, focusing on identifying students at risk of leaving the institution. In contrast, advisors more frequently linked proactive advising with developmental approaches. These differing motivations behind proactive advising—preventing attrition versus individual development—should be acknowledged when institutional leaders are defining proactive advising in their contexts.

Regarding our second research question, advisors and administrators reported different institutionalization approaches (i.e., advising practices, implementation strategies, and technologies) they deemed absent-but-needed at their institutions. Administrators expressed greater interest in strategies that identify students at-risk for attrition and enable intrusive communication to those students. Nevertheless, there was considerable support in the aggregate for requiring students to meet frequently with advisors, for implementation strategies of incentivizing advisors to engage in proactive advising, for harmonizing inconsistent practices across campus, and for technologies that support real-time filtering of student caseloads based on variables that identify students needing intervention.

Our third research question yielded a multilevel model, explaining over half the variance in impressions of institutionalization of proactive advising. The model emphasized the importance of absent-but-needed implementation strategies over specific practices or technologies. While all three approaches showed significant effects, focusing on implementation strategies might yield more substantial progress in institutionalizing proactive advising. We also found a significant interaction between role and the total number of absent-but-needed technologies, indicating that administrators perceive the negative impact of absent-but-needed technologies less strongly than advisors. Importantly, decentralized advising structures were a consistently negative predictor of respondents’ impressions of proactive advising institutionalization, while caseload size and delivery models (faculty vs. professional advisors) were not significant predictors in any model accounting for institutionalization approaches.

Finally, our fourth research question provided richer insights into the strengths and challenges of proactive advising institutionalization. Open-ended responses mostly reinforced our quantitative results, emphasizing the challenges of decentralization. These qualitative insights also suggested that caseload size and faculty roles might be more influential in proactive advising institutionalization than our regression models indicated.

Several limitations need to be considered. The study's sample, derived through web scraping and focusing on individuals with significant roles in advising or advising administration, most likely omitted faculty advisors. Designed by administrators at large, urban, public institutions, the survey may not resonate across institution types, potentially affecting the representativeness of our findings. The reliance on self-reported data introduces the risk of biases in reporting institutional commitment and personal perceptions towards proactive advising. Additionally, conducting the study during the COVID-19 pandemic and the peak advising period (mid-March through early April) might have impacted response rates. Given its cross-sectional design, the study highlights correlations at a specific point in time without implying causation.

Drawing from the insights of our quantitative and qualitative analyses, we identify two key areas of focus for university administrators, like us, aiming to embed proactive advising within their institutions.

Institutionalizing proactive advising necessitates rallying around a shared definition and vision. We recommend forming a dedicated task force of advisors, advising administrators, and campus leaders, to deliberate upon and endorse our proposed definition of proactive advising or a tailored version of it. With a consensus on the definition, the subsequent phase should involve articulating transparent, unified objectives that resonate with the broader institutional mission. It is imperative for advisors and administrators to collaboratively design frameworks that guarantee consistently excellent advising experiences for students. If existing organizational structures in advising pose challenges to maintaining uniformly high standards, the structure should be re-evaluated, not the benchmarks. These efforts should enhance the personal belief of advisors and advising administrators in proactive advising as a key strategy for student success.

A shared vision and objectives, though foundational, will remain aspirational without the institution's commitment to resource allocation. It is paramount for institutional leaders to ensure that staffing is optimized, reflecting strategic caseload sizes that align with the proactive advising model. The technological infrastructure, including data systems, should be reliable, current, and designed to offer clear, actionable insights to its users. Advisors and advising administrators, being the primary users, should be influential stakeholders in the selection or development of these technological tools. New and existing advisors and advising administrators should be systematically introduced to the proactive advising standards through training programs and opportunities for continuous professional growth. Finally, proactive advising practices should be expected, recognized, and rewarded through formal and informal incentive structures including job descriptions, performance reviews, and promotional ladders.

This study provides empirical evidence of high belief in the importance of proactive advising and insights into how it is being implemented at U.S. four-year colleges and universities. We hope that the revised, comprehensive definition of proactive advising and the empirical findings presented serve as a call to action for colleges and universities. We look forward to a future where more institutions have championed a unified vision and objectives for proactive advising and have appropriately allocated resources to institutionalize this important student success strategy.