Abstract
This study investigates lifecycle-segmented student voices (voice of the customer; VoC) to inform the redesign and governance of an Information Science curriculum. Using a sequential explanatory mixed-methods design, we analyzed survey data from prospective (n = 988), current (n = 187), and alumni (n = 121) cohorts and integrated semi-structured interviews via a joint-display logic. Confirmatory factor analyses supported a three-factor satisfaction structure (Structure, Support, Brand) for current students and alumni, with weaker convergent evidence for alumni Support. A single-factor expectations model fit prospective/current cohorts but not alumni, indicating greater heterogeneity among graduates. Findings reveal a clear lifecycle-segmented divergence: prospective and current students prioritize employability-oriented features—authentic assessment, portfolio-building, and work-integrated learning (WIL)—while alumni elevate enduring analytical foundations coupled with agile integration of fast-evolving tools. Work-integrated learning emerges as a cross-segment anchor and high-impact practice. We translate these insights into a VoC Alignment Framework and Playbook that (i) fortifies the analytical core, (ii) innovates at the edge through modular, industry-linked offerings, and (iii) cultivates the learning ecosystem by expanding mentoring, WIL capacity, and frictionless support. Cross-segment contrasts are interpreted at the pattern level because multi-group invariance was not established and the alumni expectations model showed poor global fit. The study positions VoC as a governed information asset for decision intelligence in higher education and offers a replicable pathway for curriculum governance in iSchool/Information Science contexts.
Keywords
Introduction
Higher education institutions (HEIs) worldwide—including those in Thailand—are operating under converging structural pressures. Demographically, Thailand's rapidly aging population and sustained declines in fertility have reduced the traditional university-aged cohort: annual births have fallen from over 1 million four decades ago to approximately 510,000 in 2023 (Department of Provincial Administration, 2023), with projections indicating sub-replacement fertility by 2027. Correspondingly, public university admissions declined by 14.40% between 2018 and 2022, leaving over 100,000 vacant undergraduate seats in 2022 (Office of the Permanent Secretary, Ministry of Higher Education, Science, Research and Innovation (OPS-MHESI, 2023). These shifts depress tuition revenues and strain public budgets, exacerbating HEI financial pressures.
Economic uncertainty and the lingering effects of the COVID-19 pandemic compound these challenges. Government data indicate elevated unemployment among recent graduates, with bachelor's degree holders in social sciences and business administration comprising the largest segment of the long-term unemployed (National Statistical Office, 2023). This pattern signals a persistent skills–jobs misalignment. Simultaneously, advances in artificial intelligence and automation are reshaping task requirements across sectors, heightening demand for advanced digital competencies, adaptive problem solving, and work-readiness. Together, these dynamics intensify calls for curricula that are responsive to evolving labor-market needs and resilient to technological change.
These conditions expose the limitations of entrenched pedagogical models. Many programs in Thai universities remain aligned with an “Instruction Paradigm,” privileging content delivery and knowledge recall through didactic teaching, and emphasizing inputs over demonstrable learning. In contrast, the “Learning Paradigm” reframes institutional purpose as producing student learning (Sunra et al., 2024). Building on this shift, we refer to learning paradigms as an umbrella for learner-centric, competency-oriented, and authenticity-focused approaches that incorporate outcome-based education (OBE), work-integrated learning (WIL), and assessment of demonstrable performance. Advancing learning paradigms in practice requires curricula that not only articulate outcomes but also align structures, pedagogies, and supports with the diverse needs and expectations of those who experience and are affected by programs.
The study of previous literature highlights the importance of incorporating student voices and experiential perspectives into curriculum design and instructional practices in Library and Information Science (LIS). For example, Mehra et al. (2011) emphasized how diversity and learner perspectives should be embedded across the LIS curriculum, while Bird et al. (2015) examined the role of internships and experiential learning as crucial channels for aligning student experience with professional readiness. Similarly, Burns (2019) demonstrated how reflective journaling can be used as a tool to capture student voices and enhance dialogue in LIS education.
These studies underscore that while student perspectives have been acknowledged in curriculum-related discourse, they often remain descriptive, fragmented, and focused on isolated interventions. Few attempts systematically compare divergent voices across multiple student groups, nor do they translate such comparisons into governance-ready frameworks for curriculum decision-making.
Voice of the customer (VoC) provides a systematic approach to capturing, analyzing, and translating stakeholder requirements into design specifications (Gaskin et al., 2010). Within HEIs, VoC is increasingly recognized in quality frameworks such as the ASEAN University Network–Quality Assurance (AUN-QA) and the Education Criteria for Performance Excellence (EdPEx), which emphasize understanding student and stakeholder requirements for continuous improvement (ASEAN University Network, 2020; Baldrige Performance Excellence Program, 2021; OPS-MHESI, 2021). At the same time, the “student-as-customer” debate cautions against reductive market analogies. Recent scholarship argues for moving beyond the binary by adopting a pragmatic, context-sensitive stance that uses customer-oriented tools to enhance quality while preserving educational values and co-creation (Calma and Dickson-Deane, 2020; Guilbault, 2018). Consistent with this position, we treat students as beneficiaries and partners while segmenting their voices pragmatically along the student lifecycle—prospective (pre-experience), current (lived experience), and alumni (post-experience)—because these positions entail structurally distinct expectations, evaluative frames, and outcome saliencies.
Despite the prominence of stakeholder feedback in HEIs, the research base exhibits two limitations. First, stakeholder groups are often treated as homogeneous or are aggregated without testing whether constructs operate equivalently across segments. This practice risks conflating segment-specific structures of expectations and satisfaction. Second, few studies link segment-specific needs to segment-appropriate outcomes in a way that yields a transparent, defensible priority hierarchy for curriculum decisions. In particular, there is a paucity of validated, comparative VoC methodologies that combine: (i) rigorous cross-group comparison (e.g. measurement invariance for latent constructs); (ii) outcome-linked prioritization (e.g. enrollment intent for prospective students, satisfaction/recommendation for current students, engagement/advocacy for alumni); and (iii) qualitative triangulation to explain convergence and divergence and to inform implementation.
The Bachelor of Information Science (BiS) programme at the Department of Information Science, Khon Kaen University (iSchool KKU) offers a timely and instructive context for examining divergent student voices in curriculum development. As one of the earliest Information Science initiatives in Southeast Asia and a full member of the global iSchools Organization, the department provides academic pathways at the Bachelor's, Master's, and Doctoral levels. Within this structure, the BiS programme functions as the foundational undergraduate route, integrating information management, data analytics, digital media, and knowledge organization within a competency-based framework aligned with national higher education standards.
Following its transformation under the Next Generation Higher Education Initiative (NextGenHE), the BiS programme entered its 5 year review in 2023 and surfaced distinct expectations across stakeholder groups. Alumni emphasised the importance of analytical foundations and long-term career alignment and current students prioritised portfolio-building, capstone experiences, and timely academic support, while prospective students responded strongly to employability signals, particularly paid WIL or industry placements. Treating these structurally different perspectives as a single set of needs risks generating an incoherent curriculum that underserves all groups. A comparative, evidence-informed VoC approach is therefore required to navigate trade-offs and support coherent curriculum redesign within an OBE framework.
This study focuses specifically on the BiS level because it represents the primary entry point into information professions in Thailand. This is a notable distinction from several international contexts—particularly North America—where professional entry into LIS typically requires a Master's degree. In Thailand and several Asian countries, undergraduate education forms the core of professional preparation. Understanding expectations, priorities, and satisfaction at this level therefore contributes not only to local curriculum reform but also to global conversations about the evolving structure of LIS and iSchool education.
This study addresses three interrelated gaps in the literature: the absence of a comparative framework that systematically captures and reconciles divergent student voices; the limited testing of measurement comparability across student segments; and the lack of translation of VoC findings into governance-ready tools for curriculum decision-making. To fill these gaps, we propose and apply a decision-oriented Comparative VoC framework in the information field (iField) to model and navigate both divergence and convergence across the student lifecycle. Our contributions are threefold: methodologically, we introduce a comparative approach that specifies segment-specific need structures and evaluates their measurement comparability prior to cross-group inference; empirically, we map convergent priorities alongside divergent voices that shape segment-specific priorities and trade-offs within an Information Science program; and practically, we translate these comparative insights into a governance-ready, outcome-linked prioritization to guide curriculum redesign.
These study objectives are to:
Analyze and compare expectations, satisfaction structures, and priorities of prospective, current, and alumni students (VoC segments) in the BiS program, integrating quantitative and qualitative evidence to identify convergence and divergence across the student lifecycle. Integrate comparative VoC insights into an evidence-based framework for curriculum redesign and governance, establishing priorities that balance immediate employability, enduring foundational skills, and the evolving demands of information professions.
Literature review
The iSchool movement and the challenge of divergent customer voices
Over the past two decades, the iSchool movement has reoriented the field from traditional LIS toward an interdisciplinary, human-centered “iField” that integrates people, information, and technology (Lorenz, 2014; Shu and Mongeon, 2016). Often described as boundaryless, this evolution reflects broader societal and technological shifts from the analog era to the digital age, and it prioritizes flexible, impact-driven curricula that draw from adjacent domains—such as data science and human–computer interaction—to produce cross-functional graduates for diverse information-intensive careers (Bonnici and Burnett, 2013; Shah et al., 2021; Wei et al., 2023). While the iField broadens opportunity and relevance, it also complicates program design by expanding the space of possible competencies, learning experiences, and outcome profiles.
This breadth introduces a governance challenge for curriculum decision-making. As programs widen their scope, they serve increasingly heterogeneous stakeholder segments across the student lifecycle. Prospective students, current students, and alumni often hold distinct—and at times conflicting—expectations regarding curricular focus (e.g. foundational vs. specialized), skill emphases (e.g. data-centric vs. user-centric), and outcomes (e.g. employability, credentials, reputation). The literature celebrates the agility of iSchools, yet provides fewer systematic approaches for capturing, comparing, and adjudicating these divergent VoC in ways that are transparent, evidence-based, and actionable for program leadership.
The empirical context of this study is iSchool KKU, whose institutional trajectory mirrors the field's analog-to-digital transition while remaining true to core iField principles that connect people, information, and technology in a human-centered and ethically grounded manner. The program began in 1975 with a B.A. in Library Science, was retitled BA in Library and Information Science in 1978, and became a BA in Information Science in 2002. In 2016 (BE 2559) it was formally retitled the Bachelor of Information Science (BiS). In 2018 the Department of Information Science, Khon Kaen University became Thailand's first—and the 100th worldwide—member of the iSchools Organization (iSchools Organization, 2024), and the BiS program was selected for Thailand's NextGenHE initiative (Khon Kaen University, Faculty of Humanities and Social Sciences, 2024).Today, iSchool KKU offers a complete pathway from bachelor to doctoral levels (BiS–MiS–PhD), reflecting a sustained commitment to evolve with technology and society without losing the iField's integrative identity.
Against this backdrop, the present study addresses a clear gap: while the iSchool movement has expanded curricular horizons, program leaders still require rigorous methods to surface, compare, and reconcile divergent VoC across lifecycle segments. We respond by proposing and testing a comparative VoC approach tailored to iSchool curricula, enabling evidence-based prioritization of competencies and learning experiences that balance stakeholder expectations with strategic program goals.
Aligning with learning paradigms
Concurrent with this disciplinary evolution, higher education has been profoundly influenced by learning paradigms, which advocate for a shift from traditional, teacher-centric models to more flexible, learner-centered, and competency-driven educational experiences (Klaassen, 2024; Saulnier et al., 2008). These paradigms are not merely theoretical ideals; they are institutionalized through national policies like Thailand's NextGenHE initiative and quality assurance frameworks such as AUN-QA and EdPEx, which guide curriculum reform. The core tenets of learning paradigms and their specific relevance to Information Science education are synthesized in Table 1.
Core principles of learning paradigms and their implications for information science curriculum.
AUN-QA, ASEAN University Network–Quality Assurance; WIL, work-integrated learning.
While all principles in Table 1 inform modern curriculum design, the final principle, “stakeholder engagement as a locus of tension,” is particularly salient to the central problem of this research. This tenet highlights a critical implementation gap: while quality frameworks mandate listening to stakeholders, they offer little guidance for adjudicating when their voices—particularly the distinct voices of different VoC segments—diverge or conflict (ASEAN University Network, 2020; OPS-MHESI, 2021). This tension is precisely the problem this study aims to address, positioning the VoC framework as a necessary tool not merely for gathering input, but for systematically navigating the competing demands of different customer segments.
Voice of the customer in higher education curriculum design
The VoC framework offers a systematic method for identifying and prioritizing stakeholders’ needs, transforming their input into actionable requirements for continuous improvement (Griffin and Hauser, 1993; Macnamara, 2020). In higher education, frameworks such as the Baldrige Excellence Framework and EdPEx embed VoC as a core component of quality assurance, compelling institutions to systematically use this information to realign curricula with evolving needs (Baldrige Performance Excellence Program, 2021; OPS-MHESI, 2021). For clarity, this study distinguishes VoC, which refers exclusively to student lifecycle voices (prospective, current, and alumni), from VoS (voice of the stakeholder), which refers to non-student groups like employers or faculty.
However, the direct application of VoC from a corporate to an academic context is not without its challenges, presenting a distinct research gap. Unlike in business, the “customer” in higher education is multifaceted, with needs that evolve significantly across their lifecycle (Guilbault, 2018). Most existing literature on student feedback either treats the student body as a monolithic group (e.g. through general satisfaction surveys) or focuses on a single segment in isolation (Brooman et al., 2015; Carter and Yeo, 2016). This study addresses this gap by proposing and validating a comparative VoC framework designed specifically to deconstruct the student lifecycle into distinct segments (prospective, current, and alumni). This enables institutions to systematically identify, prioritize, and act upon critical points of convergence and divergence in their expectations, providing a structured methodology for adjudicating between conflicting voices.
Conceptual framework
Amid the rapid evolution of the information field (iField) and the adoption of learning paradigms, curriculum design must navigate heterogeneous and sometimes conflicting stakeholder needs (Figure 1). We adopt a comparative (VoC) framework to deconstruct segment-specific needs, compare them systematically, and prioritize actions based on evidence. The framework rests on three principles: (i) distinct VoC segments defined by temporal relation to the program (prospective, current, alumni); (ii) a comparative analysis engine that identifies convergence (foundational cross-segment elements) and divergence (segment-specific priorities and trade-offs); and (iii) evidence-based prioritization that links needs to segment-appropriate outcomes (e.g. enrollment intent, satisfaction), triangulated with qualitative insights where direct quantitative links are limited.
Conceptual framework.
Operationalization and criteria. Needs are measured via expectations (e.g. Q1–Q10) and satisfaction components (Structure, Support, Brand), with outcomes aligned to each segment (prospective: enrollment intent; current: satisfaction/recommendation; alumni: engagement/advocacy). Convergence and divergence are determined using predefined thresholds. For latent constructs, we assess measurement invariance. Prioritization weights combine quantitative predictive strength, qualitative salience, and feasibility/risk to produce transparent tiers (Tier 1/2/3). This process yields key decision artifacts, including a priority map, a trade-off table, and a curriculum action roadmap, with full procedural details specified in the Methodology section.
Finally, the framework is embedded in an iterative annual cycle (collect–analyze–communicate–decide–implement–monitor–update), treating VoC as an institutional information asset governed by clear data and decision processes. This positions the framework as a rigorous, replicable, and decision-oriented lens for curriculum design in iField/learning paradigms contexts.
Research methodology
Research design
This study employed an explanatory sequential mixed methods design (QUAN → qual) (Creswell and Plano Clark, 2017). This approach was strategically chosen to first quantitatively establish the prevalence and priority of stakeholder requirements, and then use a subsequent qualitative phase to explain and contextualize those quantitative patterns. This sequence ensures the qualitative inquiry is purposefully directed at explaining the “why” behind the quantitative “what,” maximizing the explanatory power of the integrated results.
Participants and data collection
The study population comprised three VoC segments: prospective students, current students, and alumni. Data collection proceeded in two distinct phases. All participants were drawn exclusively from the Bachelor of Information Science (BiS) context. The prospective student group consisted of individuals who had expressed interest in applying to the BiS programme through university outreach activities and national admission channels. The current student group comprised only those formally enrolled in the BiS programme at the time of data collection. The alumni group included BiS graduates from the past five years who were reachable through institutional records and alumni networks. Alumni were employed across a wide range of information-related fields—such as data management, digital media, information services, knowledge management, and administrative roles in public and private organisations—with a smaller proportion pursuing further study or working in adjacent domains where information competencies remain relevant. This distribution reflects typical career pathways of BiS graduates and provides an appropriate basis for interpreting their expectations and satisfaction.
Quantitative Phase (QUAN)
A structured online questionnaire was administered to achieve a broad survey of each segment. A census of current students yielded N = 187. Purposive sampling of alumni from recent cohorts with diverse career paths resulted in N = 121. Finally, convenience sampling of prospective students at university events produced a large sample of N = 988. Prospective students were accessed through multiple high-volume recruitment channels—such as university outreach events, school visits, and national admissions activities—resulting in a naturally larger respondent pool than the fixed and comparatively limited groups of enrolled students and contactable alumni. Thus, the difference in sample sizes reflects structural differences in population accessibility rather than intentional oversampling.
Qualitative Phase (qual)
Following the quantitative phase, semi-structured online interviews were conducted with 25 participants—eight alumni, 12 current students, and five prospective students. All interviewees were recruited from among the individuals who had participated in the survey and had indicated willingness to be contacted for follow-up interviews. A maximum-variation purposive sampling strategy was used to ensure representation across demographic, academic, and experiential backgrounds. Alumni interviewees reflected diverse graduation cohorts and employment sectors; current students represented different years of study and academic performance profiles; and prospective students varied in intended study pathways and prior exposure to the programme. The final sample size was guided by thematic saturation.
Data analysis and integration
Quantitative analysis
Data were analyzed using descriptive statistics and confirmatory factor analysis (CFA) in AMOS. The CFA was conducted separately for alumni and current students to validate the hypothesized three-factor model of program satisfaction (structure, support, and brand). Model adequacy was assessed using standard fit indices, including χ²/df, the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the Root Mean Square Error of Approximation (RMSEA), in accordance with established reporting guidance (Schreiber et al., 2006).
Qualitative analysis
Interview transcripts were analyzed using a systematic thematic analysis approach, following the principles outlined by Braun and Clarke (2006). The process involved data familiarization, inductive coding, and theme development, with member checking used to enhance rigor. To maintain confidentiality while ensuring analytical clarity, participants were assigned a unique identifier (e.g. A7) signifying their stakeholder group: A for alumni, C for current student, and P for prospective student. We enhanced credibility through analyst triangulation and a maintained audit trail. Divergent cases were retained in the analysis and are reported to avoid confirmation bias. Researcher reflexivity was documented to surface potential interpretive blind spots.
Mixed methods integration
Following an explanatory QUAN→qual design (Creswell and Plano Clark, 2017), we: (1) used CFA results for satisfaction constructs and descriptive cross-segment expectations to establish the “what”; (2) constructed an internal joint-display matrix to align segments with convergent (✓), divergent (Δ), and absent (×) themes while logging negative cases; and (3) wove illustrative quotes to contextualize the signals (the “why”). For parsimony, the matrix is reported narratively without numeric values.
Ethical considerations
This study received ethical approval from the Center for Ethics in Human Research at Khon Kaen University (approval no. HE663220). All procedures adhered to the ethical principles of institutional guidelines. Participation was voluntary, informed consent was obtained from all participants (including parental consent for minors), and confidentiality was ensured through the de-identification of all data.
Results
The presentation of results is organized around two research objectives. Objective 1 analyzes and compares expectations, satisfaction structures, and priority signals across three VoC segments—prospective, current, and alumni. Because multi-group measurement invariance was not established and the alumni expectations model showed inadequate fit, we report between-segment differences as pattern-level signals, triangulated with qualitative evidence, rather than as statistically comparable estimates. This includes quantitative findings from CFA of the satisfaction constructs (structure, support, brand), descriptive cross-segment analyses of curriculum expectations, and qualitative themes that contextualize convergence and divergence across the student lifecycle. Objective 2 integrates these comparative VoC insights into an evidence-informed framework for curriculum redesign and governance. This is presented through the VoC Alignment Framework and Playbook, which translate empirical signals into actionable principles and strategies for balancing employability, foundational competencies, and evolving industry demands.
Comparative analysis of student voices
Measurement models for student satisfaction
We estimated confirmatory factor models for three satisfaction-related constructs—structure, support, and brand—separately for current students and alumni using ML. Table 2 reports global fit (χ2, d.f., χ2/d.f., CFI, TLI, RMSEA with 90% confidence interval [CI], and the Standardized Root Mean Square Residual [SRMR]), standardized loadings, and reliability/validity indices (composite reliability [CR] and average variance extracted [AVE]). Within each segment, models met conventional thresholds; all standardized loadings were positive and statistically significant, and reliability/convergent validity were generally acceptable. An exception was the alumni support construct (CR = 0.60; AVE = 0.35), which appears less well captured by the present indicators; we therefore avoid item-level or fine-grained inferences for alumni support and triangulate with qualitative accounts (e.g. limited exposure to the simulated enterprise). Because multi-group measurement invariance was not established, we do not compare parameters across segments; any contrasts are described at the pattern level only. No re-ranking or re-weighting of items was performed, and items are presented in their original order.
Results of confirmatory factor analysis for the satisfaction measurement model.
Note: The table reports standardized loadings and reliability indices (CR, AVE). A core set of model fit indices (χ2/d.f., CFI, TLI, RMSEA) was examined and indicated acceptable fit; full indices are available upon request.
Lifecycle-segmented expectation structures
Following the satisfaction models estimated for current students and alumni in Section Measurement Models for Student Satisfaction, we examined whether expectation structures are consistent across lifecycle segments. We estimated the three-construct expectations model separately for prospective students, current students, and alumni. Model fit was acceptable for the prospective and current segments, with all standardized loadings positive and significant. The same specification showed poor global fit for the alumni segment; therefore, we refrain from factor-based interpretation for alumni. Because multi-group measurement invariance was not established, we avoid cross-segment parameter comparisons and interpret patterns within segments only. We use qualitative evidence to contextualize the alumni misfit. Segment-specific results are summarized in Table 3.
Confirmatory factor analysis results and model fit indices by lifecycle segment.
Note: Estimator: ML (AMOS v26.0). aPoor fit based on conventional cutoff criteria (CFI/TLI < 0.90; RMSEA > 0.08). Indices reported in this table: χ2/d.f., CFI, RMSEA. Alumni expectations: Single-factor model exhibited poor fit; loadings are presented for completeness only and are not ranked or interpreted as a unidimensional hierarchy. Sample sizes: prospective (N = 988); current (N = 187); alumni (N = 121). Significance markers were removed for readability; full standard errors and p-values are available upon request. WIL, work-integrated learning.
Qualitative themes contextualizing divergences
Qualitative analysis identified three themes that contextualize and refine interpretation of the quantitative signals. Presented without rank order and illustrated with exemplar quotes, these themes triangulate areas of convergence and divergence across cohorts. Overall, the evidence suggests a lifecycle-segmented divergence rather than a homogeneous pattern (Table 4).
Summary of qualitative themes from participants interviews.
VOC, Voice of the customer; WIL, work-integrated learning.
Theme 1: alumni's dual perspective
Quantitatively, alumni exhibit weaker coherence in a single-factor expectations model, suggesting heterogeneous priorities. Qualitatively, this is not dissatisfaction but a dual perspective: alumni value enduring foundations while recognizing fast-evolving technical demands. This helps explain the alumni-side patterns.
Subtheme 1.1: enduring, transferable foundations
Most alumni (A1, A2, A4, A6, A7, A8) emphasized transferable competencies—analytical reasoning, problem framing, critical thinking, and teamwork. As A5 reflected on a capstone: “Beyond just hard skills, we had to use soft skills like negotiation, communication, and creativity. These fundamentals are a crucial advantage over other new graduates.” This underscores the strength and salience of the analytical core.
Subtheme 1.2: the “learn-on-the-job” gap and cohort exposure
Several alumni (A2, A4, A7) noted that specific tools learned aged quickly, requiring substantial self-learning post-graduation—especially around cloud, AI, and newer data visualization platforms. A3 commented, “Once I started working, I realized the world had moved on … I had to start from scratch.” Alumni also clarified cohort exposure: “In my cohort at iSchool KKU, MobiLib [simulated enterprise] hadn't been set up yet, so I can't say what it's like, but it should be useful for the juniors” (A3). Others prioritized career services and networks over labs post-graduation (A1, A8). These narratives help explain weaker convergence of Support indicators among alumni.
Subtheme 1.3: definition drift of “support”
Alumni tended to define support as career services, alumni networks, and professional mentoring, whereas current students include studio resources and the simulated enterprise. “I studied during COVID-19 and hardly used the lab, but talking with my advisor via Zoom helped compensate” (A4). Another alumnus noted, “Back then, the Studio and MobiLib didn't exist, but I got to produce content at the Learning and Teaching Innovation Center through a connection my lecturer recommended” (A1). This cohort-specific meaning contributes to weaker convergence for alumni on the Support construct.
Subtheme 1.4: the acceleration trade-off (Q8: accelerated path)
Alumni described trade-offs in compressed time-to-degree—less depth, fewer networks, and fewer high-impact experiences (e.g. extended WIL). One alumnus reflected, “Graduating too quickly isn't ideal; it means missing the chance to make the most of four years of student life at KKU” (A7). Taken together, these accounts are consistent with the lower salience of acceleration among alumni: in hindsight, the marginal benefit of graduating sooner may be outweighed by cumulative gains in depth, portfolio quality, and network formation.
Subtheme 1.5: retrospective value of credit accumulation (Q4: pre-university credits)
Alumni highlighted time/cost efficiencies and faster career entry from recognizing prior learning, explaining why this resonates more post-graduation than during study. Many (A1, A2, A3, A4, A5, A8) felt that portions of first-year general education were outdated or redundant with self-directed learning but still required attendance in lecture format. Several suggested using placement tests or recognized certifications (e.g., the International English Language Testing System [IELTS] and the Test of English as a Foreign Language [TOEFL]) and competency recognition to reduce time in basic courses and accelerate entry into practice-oriented learning (A3, A7).
Theme 2: next-generation priorities
In contrast to alumni, both current and prospective students showed a coherent pattern centered on immediate employability and future-oriented skills. Qualitatively, this shared priority clarifies why certain structural features are more salient to these cohorts.
Subtheme 2.1: from theory to portfolio: tangible outcomes
A dominant subtheme was the desire for learning experiences that yield portfolio-ready outputs. Students preferred hands-on projects over traditional lectures, viewing them as direct pathways to internships and jobs. A final-year student (C11) noted: “[Doing] events or capstone projects gives me experience and builds my profile. It's easier when applying for apprenticeships at big companies, and after the internship, there's a chance to get hired immediately.” Similarly, a prospective student (P3) emphasized: “I don't want to learn from traditional lectures that focus on memorization; I want to do real, challenging things.”
Subtheme 2.2: learning the “language” of industry
Current and prospective students converged on the need for technology literacy, creative thinking, and agility, with authentic exposure to private-sector contexts and WIL. As one prospective student (P3) explained their choice of BiS: “I’m interested in digital topics already. When the advisors explained we’d learn about AI, get real practice in private companies, and have diverse career options after graduation, [it solidified my choice].” These convergent expectations form a clear VoC signal for workforce-aligned preparation.
Subtheme 2.3: credentials as safety net and signal (Q5: intermediate diploma)
Participants frequently framed an intermediate diploma as both a safety net (a milestone credential if plans change) and a signaling device to employers—consistent with its higher salience among prospective students. A third-year student shared: “When iSchool KKU took us on a company visit, HR said that if you have a diploma (Dip.) or a High Vocational Certificate (HVC), during your internship they can hire you like a regular employee. If the new curriculum could award a Dip., that would be great” (C10). Prospective students agreed (P1–P5) that earning a credential after each module would be motivating—“It's like a game where you keep accumulating points as you clear each level” (P2).
Subtheme 2.4: perceiving the “module” vs. experiencing the work (Q3: module-based curriculum)
Several current students indicated that, while they value hands-on projects and internships, the competency-focused, module-based curriculum is less salient in day-to-day experience—particularly before capstone/WIL. This suggests students anchor expectations to tangible experiences they can showcase, with structural logic recognized later. “In the first year I didn't know what a module was, but by second year, after doing a real capstone project, I understood it better” (C4).
Theme 3: deconstructing satisfaction
While quantitative analysis (Section Comparative Analysis of Student Voices) validated Lecturer Quality as a key component of satisfaction for current students, the qualitative data unpacks what “quality” means in practice. Satisfaction is driven less by syllabi per se and more by relational mentorship and high-impact WIL experiences.
Subtheme 3.1: redefining “lecturer quality”
For satisfied students, standout faculty acted as mentors/coaches who offered guidance beyond academics. As C4 explained: “What impressed me most was Lecturer V [pseudonym]. Although he can seem strict, he is very attentive and gives good advice not just about our studies, but also about life.” When satisfaction was lower, the issue was often a pedagogical mismatch despite good intentions: “Lecturer Y [pseudonym] is attentive, but still relies on traditional, lecture-based teaching” (C1).
Subtheme 3.2: WIL as a high-impact experience
Work-integrated learning (internships/ traineeships/apprenticeships) was consistently cited as transformative for career discovery and professional validation. A final-year student (C12) shared: “If I hadn't done the WIL, I still wouldn't know what career path I truly like. It was the best experience ever.”
Subtheme 3.3: infrastructure and scheduling
Satisfaction was tempered by logistical and resource constraints rather than core curriculum issues. For example, C9 pointed to infrastructure limits: “I wish the lab had more high-spec computers for projects requiring heavy processing power.” Others cited administrative constraints, such as desired electives not being offered due to limited staffing (C8, C9, C10).
Subtheme 3.4: disconfirming cases and variation
Within segments dominant patterns highlight mentoring, WIL, and hands-on learning, yet meaningful within-segment variation exists. A subset of current students prefer highly structured, lecture-forward delivery; a subset of alumni value acceleration owing to time-sensitive constraints. Recognizing these cases prevents overgeneralization and clarifies dispersion in item salience and satisfaction. “Sometimes I want the lecturer to summarize the content in class, because studying in DigiClass or reading handouts is sometimes hard to understand” (C6). “No exams and focusing on projects is fine, but in team work there are people who don't contribute yet get the same score. There needs to be clear grading criteria” (C11). Alumni offered counterpoints as well: “If you can graduate in three and a half years, it's a good chance to find a job earlier than others” (A5); whereas another noted, “Graduating early didn't help me get a job sooner, because I still had to wait to complete military conscription” (A4).
Integrated patterns of convergence and divergence
Integrating the quantitative and qualitative findings reveals a clear pattern of convergence around a central curriculum element and significant divergence in peripheral priorities. The strongest point of convergence across all three segments was the non-negotiable value placed on WIL. In contrast, significant divergence emerged in the weighting of other priorities. Alumni emphasized enduring analytical foundations and institutional reputation as sources of long-term value. Conversely, prospective and current students prioritized immediate employability, portfolio-building experiences, and flexible learning pathways.
Taken together, the comparative analysis demonstrates both convergence and divergence across student lifecycle segments: while all groups consistently prioritize work-integrated learning as a non-negotiable element of value, alumni emphasize enduring analytical foundations and reputation, whereas current and prospective students emphasize employability, portfolio-building experiences, and flexible pathways. These findings confirm that a one-size-fits-all curriculum is inadequate and justify the need for a differentiated, evidence-based approach.
Framework and governance implications
Development of the VoC alignment framework
Building on the comparative findings from Objective 1, the next step was to consolidate these insights into a practical framework for curriculum governance. The framework was designed to systematically connect lifecycle-segmented student voices to decision-making processes, positioning VoC as a strategic information asset. It rests on three guiding principles:
(1) Fortify the core (alumni perspective)—preserving enduring competencies such as analytical reasoning, critical thinking, and problem-framing, which alumni consistently identified as long-term value.
(2) Innovate at the edge (prospective and current student perspectives)—incorporating flexible modules, industry-linked micro-credentials, and emerging technologies to meet the strong employability and future-oriented skills demands voiced by younger cohorts.
(3) Cultivate the learning ecosystem (cross-segment consensus)—strengthening mentorship, expanding WIL opportunities, and addressing “friction points” in support systems, reflecting shared priorities across all groups.
By aligning empirical patterns with these principles, the framework bridges lifecycle-based divergences and provides a decision-oriented lens for guiding curriculum redesign.
The VoC Alignment Playbook
The framework is operationalized via a VoC Alignment Playbook that translates guiding principles into concrete governance strategies. The playbook sequences actions into near-term improvements and longer-term innovations to balance responsiveness and sustainability. Prioritization is enacted through transparent decision rules rather than numeric weights: multi-segment, convergent signals warrant implementation in the next cycle; segment-specific signals warrant targeted pilots with predefined scale-up criteria; and ambiguous or conflicting signals warrant additional inquiry before any major change. Decisions are logged with an owner, timeline, and success metrics, and reviewed on a scheduled cycle.
(1) Fortify the core—explicitly map analytical and critical-thinking competencies to program learning outcomes; embed authentic assessments that make these enduring skills visible to students and employers; and establish periodic review mechanisms to ensure the intellectual foundation remains intact amid technological shifts.
(2) Innovate at the edge—introduce modular, refreshable units on emerging domains (e.g. AI, data visualization, cloud platforms) that can be updated without destabilizing the core curriculum; expand capacity for WIL placements aligned with emerging professional fields; and provide e-portfolio scaffolding so students can evidence future-ready competencies.
(3) Cultivate the ecosystem—invest in faculty development for mentorship and advising roles; strengthen alumni networks and career services; enhance infrastructure such as laboratories and the simulated enterprise; and use routine VoC pulse checks to monitor and address support-related friction points.
Together, these actions illustrate how empirical insights can be systematically embedded in curriculum governance. The playbook provides academic leaders with a structured, evidence-informed mechanism for balancing stability with innovation, helping the BiS program remain responsive to evolving labor market needs while staying true to its academic mission.
Evidence-based prioritization for curriculum redesign
To move from principles to actionable decisions, the VoC Alignment Framework was operationalized through an evidence-based prioritization process. This process integrates three dimensions of evidence: (i) quantitative predictive strength (e.g. factor salience and CFA loadings); (ii) qualitative salience (themes emphasized across interviews); and (iii) feasibility and risk (institutional capacity, resource implications, and alignment with accreditation/OBE requirements). By combining these indicators, student needs were mapped into the tiered priority structure shown in Table 5.
Tiered priority structure for curriculum redesign.
WIL, work-integrated learning.
This prioritization sequence creates a transparent roadmap for decision-makers, allowing the program to address immediate student demands without sacrificing long-term academic integrity. Importantly, the tiered model supports an annual VoC-to-decision cycle, in which student feedback is continuously collected, analyzed, and translated into curriculum adjustments. By embedding this cycle into governance routines, the BiS program can maintain relevance amid shifting demographics, labor market disruptions, and emerging educational paradigms.
Discussion
Our findings align with the learning-to-learn and outcomes-based paradigms reviewed earlier: prospective and current students prioritize authentic assessment, portfolio evidence, and work-integrated learning, consistent with constructive alignment toward observable performance (Vlachopoulos and Makri, 2024). Work-integrated learning's prominence across segments is also consistent with the high-impact practice literature (Billett, 2009; Jackson and Cook, 2025). At the same time, alumni emphasize durable analytical foundations while asking for agile integration of new tools—nuancing the employability discourse by distinguishing enduring capabilities from transient technologies. This pattern supports stakeholder-sensitive VoC approaches in higher education (Degtjarjova et al., 2018) while adopting a balanced stance in the student-as-customer debate (Guilbault, 2018): institutions should listen to diverse “voices,” but translate them through academic standards and long-horizon capability building.
Extending and reconciling the literature
Prior VoC work in HEIs often aggregates “students” as a single segment and treats satisfaction holistically. Our results show lifecycle segmentation: a three-factor satisfaction structure (structure, support, brand) fits current students and alumni, but the operational meaning of Support differs by cohort (career services/networks for alumni; simulated enterprise/studios for current), signaling construct drift that measurement and design must manage. Expectations appear unidimensional for prospective/current cohorts but heterogeneous for alumni, suggesting that post-experience evaluations invoke broader capability frames than pre-experience expectations—extending employability models by introducing a temporal reframing mechanism. Rather than a “generational divide,” the evidence favors a lifecycle-segmented divergence: frames shift from short-horizon employability cues to long-horizon capability signals as learners transition from study to work.
Integration of evidence (joint display)
Quantitatively, we corroborated a three-factor satisfaction model and observed weak convergent validity for alumni Support; qualitatively, alumni narratives explained this by highlighting networking, mentorship, and reputation effects that are less salient to current students. Conversely, younger cohorts stressed portfolio-ready outputs and authentic assessment. This triangulation strengthens internal coherence between models and themes while offering a practical map for segment-specific interventions. Cross-segment contrasts are interpreted at the pattern level because multi-group measurement invariance was not established; we therefore emphasize convergent themes that are corroborated qualitatively rather than statistical rank orders.
Theoretical and methodological contributions
Theoretically, we reposition VoC in higher education as an information-intensive governance problem: heterogeneous stakeholder signals must be curated, validated, and routed into curriculum decisions. We contribute (i) a lifecycle-segmented account of expectations/satisfaction, (ii) a temporal reframing mechanism, and (iii) a VoC Alignment Framework that balances “fortify the analytical core” with “innovate at the edge.” Methodologically, we show how joint-display integration can reconcile mixed-model tensions and highlight the necessity of testing measurement invariance before cross-segment comparisons—an issue under-addressed in prior HEIs VoC studies.
Implications for curriculum governance
Voice of the customer should be treated as a governed information asset: (1) protect the analytical core through authentic assessments aligned to enduring outcomes; (2) innovate at the edge via modular, industry-linked offerings without destabilizing the core; and (3) cultivate the ecosystem by expanding WIL capacity, mentoring, and frictionless processes. Signals should be translated into tiered actions using a simple priority index (quantitative signal + qualitative salience + feasibility/risk) embedded in an annual evidence-to-decision cadence.
Limitations and future research
Cross-segment contrasts are interpreted at the pattern level because multi-group measurement invariance was not established and the alumni expectations model fit poorly; Likert-type items were estimated via maximum likelihood (ML) without ordinal-appropriate estimators or sensitivity checks; and the study is single-institution, cross-sectional, with specific recruitment channels—limiting generalizability. Future work should implement sequential validation (content/cognitive pretest → exploratory factor analysis [EFA] → multi-group confirmatory factor analysis [CFA] with configural/metric/scalar invariance; test partial invariance or alignment if needed), use ordinal estimators (e.g., weighted least squares mean and variance adjusted [WLSMV]) with robustness checks vs. ML or maximum likelihood robust [MLR] (report comparative fit index [CFI], Root Mean Square Error of Approximation [RMSEA], Standardized Root Mean Square Residual [SRMR], average variance extracted and composite reliability [AVE/CR], and Heterotrait–Monotrait ratio [HTMT]), probe alternative structures (second-order/bifactor) with cross-validation, and estimate segment-specific structural links to behavioral outcomes: prospective—intent to enroll; current—satisfaction/recommendation/persistence; alumni—engagement/advocacy, with preregistered hypotheses and decision thresholds.
Conclusion
This study suggests that student expectations and satisfaction are lifecycle-segmented rather than homogeneous. Prospective and current students converge on employability-oriented priorities—authentic assessment, portfolio-building, and WIL—whereas alumni articulate a dual perspective that couples enduring analytical foundations with agile integration of emerging tools. Quantitatively, a three-factor satisfaction structure (structure, support, brand) is supported for current students and broadly replicated for alumni, with cohort-specific drift in the meaning of Support and weaker convergent validity for that factor among alumni. Expectations appear coherent for prospective/current students but more heterogeneous for alumni. Treating the VoC as a governed information asset can help institutions translate heterogeneous signals into decision intelligence.
Practically, the VoC Alignment Framework offers a replicable pathway for curriculum governance: fortify the analytical core, innovate at the edge via modular industry-linked offerings, and cultivate the learning ecosystem through mentoring, WIL capacity, and frictionless processes. Embedding an annual collect–analyze–decide–implement–monitor cadence, with tiered priorities grounded in quantitative signals and qualitative salience, enables transparent, evidence-informed trade-offs. Theoretically, the study extends VoC from product development to higher education and frames curriculum redesign as an information-intensive management process.
Boundary and transferability
Findings derive from a single program and context, with cross-sectional data, nonprobability sampling for prospective/alumni, Likert-type measures, and incomplete tests of measurement invariance; therefore, cross-segment comparisons should be interpreted cautiously. Transferability is strongest to iSchool/Information Science programs in medium-sized institutions with employability-oriented missions and comparable governance capacity. Adaptation should proceed via local VoC pipelines, small-scale pilots, and validation of instruments before scale-up. Programs that institutionalize these routines may be better positioned to sustain relevance while remaining true to their academic mission.
A key limitation of this study is that it focuses exclusively on an undergraduate Information Science programme. As such, the findings may not be directly transferable to other iSchool contexts where professional preparation is delivered primarily at the master's or doctoral level and where curricular structures, competency expectations, and student pathways differ substantially. Nevertheless, the broader implication of this work remains relevant: the use of lifecycle-segmented VoC insights provides a valuable, adaptable approach to curriculum development. The principles demonstrated here—systematically capturing stakeholder expectations, identifying divergent needs across learner groups, and translating these into coherent curriculum decisions—offer a valuable approach that can be adapted across degree levels and disciplinary domains.
Footnotes
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.