Timing Circular Regeneration with Adaptive Reuse Potential: A Century of Transformations at the Renoma Department Store, Wroclaw

Abstract

Historic department stores are an underexamined lever for circular, low-carbon urban transition. This study tests whether Langston’s Adaptive Reuse Potential (ARP) can be applied retrospectively and how contextual readiness shapes the timing of interventions. Using the Renoma Department Store in Wroclaw, Poland (1930–2025), we reconstruct five adaptive phases and combine expert scoring of seven obsolescence dimensions (O1–O7) with a Readiness index covering finance, governance/approvals, use commitment, delivery/supply chain, and policy priority. Decision windows are interpreted via a WAIT–PREPARE–GO lens. Results show that peaks in ARP and Readiness aligned with major reinvestments—post-war reconstruction, socialist modernisation, and post-EU-accession renewal—while the original steel frame retained high structural reserves, indicating that timing was driven more by institutional and economic conditions than by technical decay. We propose ARP as an interpretive lens for circular regeneration and show that the Readiness index clarifies feasibility and risk. The combined ARP × Readiness approach yields a replicable, phase-sensitive diagnosis of adaptive capacity and intervention timing, contributing evidence to circular city practice and aligning with New European Bauhaus principles of sustainability, inclusion, and quality of place.

1. Introduction

1.1. Historic Department Stores in Transition

European city centres are undergoing rapid reconfiguration of retail and service ecosystems as consumption models shift and the sector restructures. The rise of e-commerce, new logistics networks, and suburban retail formats has weakened the role of traditional department stores—once emblematic venues of modernity, mass retail, and middle-class consumption [1]. Many such buildings now stand underused, marginally occupied, or fully decommissioned. Yet their prime locations and cultural salience make them high-potential assets for contemporary urban transformation.

Multiple drivers underpin this crisis: service digitalisation and lifestyle change, escalating operating costs for large inner-city premises, investment pressure from residential uses, and the eroding profitability of legacy retail formats [2,3]. The COVID-19 pandemic further accelerated these trends by suppressing footfall and fast-tracking digital sales, leading to a wave of closures and temporary reuses of former department stores [4]. In Germany, these shifts have subsequently informed new municipal and policy frameworks for inner-city transformation [5].

Adaptive reuse has therefore become a critical strategy within circular-economy and climate-mitigation agendas, offering carbon and resource savings while safeguarding urban heritage [6,7,8,9]. However, such projects must balance environmental and economic gains with authenticity and conservation requirements, especially for listed heritage assets [10].

Recent reviews have synthesised research frameworks and methodological approaches in adaptive reuse and heritage transformation, emphasising both the diversity of available tools and the persistent scarcity of longitudinal applications. Li et al. (2021) identified dominant trends in assessment methods—particularly those based on multi-criteria decision-making (MCDM) and preference measurement models (PMM)—while Ranasinghe and Illankoon (2024) highlighted the link between adaptive reuse and the acceleration of the circular economy [11,12]. Both highlight the need for multi-phase, data-driven evaluations of long-lived buildings, which this study directly addresses.

At the European level, recent evidence confirms both the scale of the challenge and the feasibility of reuse. In Germany, for example, many decommissioned department stores have been successfully repurposed into flexible mixed-use facilities through proactive municipal frameworks [13,14,15]. Interim and experimental uses have also served as transitional strategies bridging functional obsolescence and full-scale transformation [4]. Prominent European examples—La Samaritaine (Paris), KaDeWe (Berlin), Galeries Lafayette Haussmann (Paris), and Kaufhaus Tyrol (Innsbruck)—illustrate diverse adaptation models, from comprehensive interior redevelopments to staged modernisations [16,17,18,19].

At the building scale, adaptability depends not only on typological and structural features—such as durable steel frames, modular grids, open-plan layouts, and service reserves—but also on programme, governance, and metropolitan location, which shape investment timing and feasibility [20]. For long-lived, heritage-protected department stores, these enablers—and the institutional constraints that accompany them—remain underexplored from longitudinal, multi-phase perspectives.

At the planning–policy level, integrative frameworks embed heritage within wider sustainability agendas. UNESCO’s Recommendation on the Historic Urban Landscape explicitly links heritage conservation with urban development, participatory governance, and long-term territorial management [21,22]. In Europe, the Circular Economy Action Plan and the New European Bauhaus agenda frame circular refurbishment and quality of place as climate- and society-relevant objectives [23,24], while recent EEA analyses underline renovation as the meeting point of circularity and decarbonisation [25]. For conceptual trade-offs in timing reuse decisions, Campbell’s “planner’s triangle” remains a useful lens to balance environmental protection, economic development, and social equity [26].

Among quantitative decision frameworks, the Adaptive Reuse Potential (ARP) model formalises the timing logic of major interventions by combining two components: an age–position term, which peaks when the remaining and elapsed life of a building are comparable, and an obsolescence term, which penalises constraints across seven dimensions (O1–O7) [27,28]. Earlier work by Conejos, Langston and Smith [29] highlighted the implementation challenges of adaptive reuse strategies for historic buildings, emphasising the need for integrated decision support to reconcile heritage values with sustainability objectives. In practice, ARP has been predominantly applied prospectively—at the design or feasibility stage or for portfolio-level screening of building stock—while related tools such as AdaptSTAR and iconCUR extend this logic toward value-based, risk-oriented, and strategic decision-making frameworks [28,30]. Subsequent developments, including the AdaptSTAR model, further adapted ARP principles to integrate climate-resilient and sustainability criteria within early design decisions [31]. While these frameworks provide structured ex ante support at a given decision point, they do not trace how adaptive capacity and decision conditions evolve across multiple intervention cycles in the life of a single asset. In contrast, the present study repurposes ARP for a retrospective, phase-based reading of one building’s century-long trajectory and couples it with a contextual Readiness index to reconstruct how obsolescence, institutional readiness and policy priorities interacted over time to open and close decision windows (WAIT–PREPARE–GO).

We situate the study within recognised assessment guidance rather than conduct a full circularity evaluation: ISO 20887 (adaptability/disassembly), EN 16883 (value-respecting option appraisal), and ICOMOS HIA (significance-led compatibility), complemented by BS 7913 and the RIBA Plan of Work for decision gates and timing; these serve as conceptual references, not full protocols applied here [32,33,34,35,36].

Yet, this prospective orientation leaves unresolved whether such models can explain multi-phase trajectories in long-lived heritage assets, where adaptive decisions are shaped by both material durability and socio-institutional factors. This study addresses that gap by testing the retrospective applicability of ARP in conjunction with a contextual Readiness Index.

Against this background, the Renoma Department Store in Wrocław, Poland, offers a rare, century-spanning case of repeated adaptation with sustained cultural relevance [37,38,39,40]. Yet systematic, data-driven evaluations of adaptability across multiple transformation phases remain scarce. Where quantitative decision lenses do appear (e.g., ARP, iconCUR, AdaptSTAR), they are typically applied to design-stage feasibility or single-phase assessments rather than to heritage assets with multiple successive interventions [30,31,41,42].

This study therefore positions Renoma as a testbed for assessing the retrospective applicability of Langston’s Adaptive Reuse Potential (ARP) model, complemented by a contextual Readiness Index—an original, author-developed framework indicator capturing governance, policy, and financial conditions shaping intervention timing.

1.2. Research Gap and Aim

While adaptive reuse research has expanded significantly, longitudinal applications of the Adaptive Reuse Potential (ARP) model remain rare. Most studies focus on single-phase feasibility, leaving open the question of whether ARP—when combined with contextual indicators—can explain the timing of adaptive interventions over multiple decades.

This paper addresses that gap through a retrospective, five-phase assessment of the Renoma Department Store (1930–2025), originally built as a flagship Wertheim department store. The study asks:

• Can the ARP model, when applied retrospectively and integrated with a contextual Readiness Index, reconstruct the timing and drivers of major interventions?
• How do morphological enablers—such as the steel frame, modular grid, and open-plan layout—affect obsolescence and adaptive potential?
• How do readiness factors evolve under different socio-political and economic regimes?

To address these questions, the study applies the ARP model retrospectively, reconstructing phase-by-phase adaptive potential from historical evidence and expert elicitation. It is complemented by a contextual Readiness Index capturing external enabling conditions.

Methodologically, the paper tests the retrospective robustness of ARP and introduces the original Readiness Index, which evaluates enabling factors in five domains: Finance (F), Governance and Approvals (G), Use Commitment (U), Delivery and Supply Chain (D), and Policy or Strategic Priority (P).

Both analytical lenses are then synthesised into a GO metric that classifies decision states for each phase as WAIT, PREPARE, or GO. This dual framework enables comparison between predicted and realised interventions, providing a basis to assess both model validity and interpretive strength.

1.3. Contributions & Scope

This research makes four key contributions. First, it advances ARP methodology by testing its retrospective applicability across a century of adaptive cycles and by introducing the Readiness index as a contextual complement. Second, it empirically validates the ARP × Readiness integration by comparing computed decision windows with actual redevelopment phases (e.g., 1947, 1977, 2004–2009, 2018–2025). Third, it contributes to the theoretical framework of flexible heritage, showing how long-life commercial buildings can sustain cultural and economic relevance through iterative adaptation rather than replacement. Finally, it provides a replicable, circularity-oriented diagnostic model for assessing complex heritage assets and informing portfolio-scale adaptive-reuse strategies.

Although department stores form the empirical focus, the protocol is transferable to other large-scale urban assets—such as arcades, post-industrial warehouses, civic halls, and early modern office blocks—where long-life structures and modular morphologies enable reuse.

1.4. Paper Structure

Section 1 introduces the problem, research gap, aim, scope, and contributions. Section 2 details Materials and Methods (case and data; retrospective ARP based on expert questionnaires; exploratory Readiness index based on archival and contextual evidence; GO synthesis and validation and sensitivity; brief note on related frameworks). Section 3 presents the Case Study—Renoma’s transformations (1930–2025), architectural enablers, and phase drivers. Section 4 reports Results (O1–O7 by phase; ARP timeline; decision map; robustness). Section 5 discusses mechanisms, policy/design implications, and limitations/future work. Section 6 concludes by synthesising methodological and theoretical contributions to adaptive reuse, flexible heritage, and circular transformation.

2. Materials and Methods

2.1. Evidence Base and Research Design

The case study focuses on the Renoma Department Store (Wrocław, Poland, 1930–2025), a repeatedly adapted modernist landmark whose successive redevelopments provide a unique longitudinal record of architectural, functional, and policy-driven transformation. Its complex history offers a robust testing ground for retrospective application of the Adaptive Reuse Potential (ARP) and Readiness models.

We adopt a design-based, mixed-methods approach. To support a multidisciplinary readership, Figure 1 summarises the overall evidence-to-decision workflow, from case documentation through ARP and Readiness scoring to the derivation of WAIT–PREPARE–GO decision windows. The evidence base combines archival, morphological/spatial, and contextual sources, together with semi-structured interviews and structured expert questionnaires. Two complementary assessment lenses are applied. First, the Adaptive Reuse Potential (ARP) model [28] is used retrospectively to capture phase-specific obsolescence (O1–O7). In line with the model’s validated practice, O1–O7 scores are derived from expert structured questionnaires informed by professional experience and retrospective involvement; archival and spatial sources serve only as supplementary context and were not used to score ARP directly. Second, we introduce a lean, exploratory Readiness index to gauge near-term implementability across five dimensions (F, G, U, D, P). Readiness is scored by the research team based on the assembled archival, morphological/spatial, and contextual evidence, together with qualitative interview data. To avoid double-counting, Readiness captures event-level enabling conditions, while ARP’s O6/O7 reflect structural legal-political regimes. Taken together, the dual lenses bridge potential and preparedness: ARP identifies the degree of obsolescence, while Readiness translates potential into decision timing; their synthesis is expressed as GO = ARP × Readiness. This structure, elaborated in Section 2.2, Section 2.3 and Section 2.4, ensures methodological transparency and reproducibility.

For clarity, the international standards and guidance cited herein (e.g., [32,33,34,35,36]) are used for conceptual alignment only and are not implemented as full assessment protocols in this study.

Data integration was carried out in a structured spreadsheet model built in Microsoft Excel (Microsoft 365; Microsoft Corporation, Redmond, WA, USA) (see Supplementary Materials File S1), where archival and expert-derived variables were processed into the quantitative indicators EL_b, EL_u, ARP, Readiness, and GO. This ensured full traceability between the evidence base, methodological parameters, and resulting phase-level outcomes.

2.1.1. Archival and Supplementary Sources (Phase-Level)

We reference archival and additional sources at the phase level rather than enumerating every individual item. Depending on availability, these included building documentation (as-built drawings, permits), archival photographs, press articles, technical and conservation reports, and scholarly publications.

For each phase, key source packages were catalogued with provenance and dates, digitised where necessary, and linked to the variables they informed. Their main role was to provide systematic evidence for the Readiness components (F–P), while serving only as contextual support for ARP (O1–O7), which was formally scored through expert questionnaires. A concise phase-level overview is given in

Table 1. Phase-level source packages informing the Readiness and ARP assessments (see Appendix A for extended source inventories).

Ref.	Phase	Period	Archival, Morphological, Spatial, and Contextual Sources/Year(s)	Informs (Readiness F–P/ARP Contextual Support)
PK-P1	P1	1930–1945	Original building projects and competition documentation (1928–1930, H. Dernburg); contemporary press reports; interview with H. Dernburg	Readiness: F, G, U ARP context: O1, O3, O4
PK-P2	P2	1946–1976	Reconstruction and alteration projects (1948–1975, PDT); archival photos; press coverage	Readiness: G, P ARP context: O2, O6
PK-P3	P3	1977–2004	Modernisation projects and technical assessments (1993–2000); conservation study (1998); project documentation (1995–2002); press coverage	Readiness: F, U, D ARP context: O4, O5
PK-P4	P4	2005–2018	Conservation programme (2005); technical assessments (2005); redevelopment and extension projects (2005–2009, MPP, ARUP, Benoy); Adaptation projects (2011–2016, MPP, KMA); press interviews; press coverage	Readiness: G, D, P ARP context: O1, O3
PK-P5	P5	2019–2025	Adaptation projects (2020, MPP); technical and fire safety assessments (2020); conservation expertise (2020); press interviews; scholarly publications (2020s)	Readiness: F, G, U, D, P ARP context: O2, O6, O7

Note: Not all Readiness dimensions (F–P) were evidenced equally in every phase package. Table 1 lists only the dominant evidence anchors, i.e., the components and obsolescence domains (O1–O7) that were directly supported by archival or technical documentation in each phase. Other components were triangulated from secondary or contextual sources (e.g., interviews, policy documents, press coverage) and are reported in the Supplementary Materials Files S2 and S3. Therefore, the distribution shown here does not indicate data gaps but reflects differentiated evidentiary strength across phases.

, indicating the main source types and the variables they informed. Extended source inventories with provenance details are provided in Appendix A, while iconographic items are included as a minimal dataset. Table 1 summarises the dominant archival evidence per phase, indicating which Readiness and ARP dimensions were directly informed by primary documentation.

We compiled a harmonised corpus of plans, permits, photographs, conservation reports, press materials, interviews, and scholarly publications to establish the phase chronology (1930–2025) and extract inputs for spatial, contextual, and decision analyses. Sources cover: (i) the 1927–1930 competition, design and as-built documentation by H. Dernburg; (ii) 1948–1997 socialist-era alterations under PDT/DTC; and (iii) 1998–2025 commercial-era redevelopment by Maćków Pracownia Projektowa. Key materials were catalogued with essential provenance and dates, digitised where necessary, and assigned to phases P1–P5. For each source, we noted the variables it informed (e.g., structural grid, program shifts, façade changes) and linked them to Readiness components. For ARP, these sources served only as supplementary background, since formal O1–O7 scores were derived from expert ratings. Conflicts between sources were resolved through triangulation (cross-checks with interviews and site inspections) and flagged with a confidence score. The resulting dataset primarily supports the Readiness/GO decision lens (Figure 1), while also contextualising the ARP rubric.

2.1.2. Morphological & Spatial Analysis

The morphological analysis focused on archival baseline drawings and a diachronic illustration of the building’s five transformation phases, complemented by comparative aerial views. These materials were selected to capture the core architectural DNA—its robust steel skeleton, rational modular spans, paired light courts, and consolidated service cores—and to trace how structural continuity and envelope durability supported successive reconfigurations. This approach, adapted from diachronic morphological traditions in architecture and urban studies [43,44], provided the necessary spatial evidence to inform the Readiness assessment and contextualise selected ARP dimensions.

2.1.3. Contextual Analysis (Socio-Economic, Political, Cultural)

We reviewed socio-economic, political, and cultural determinants that shaped investment, design, and use decisions across Renoma’s successive phases. Sources included scholarly literature, industry reports, planning and conservation documents, press archives, and economic data. Particular attention was given to systemic transitions: Aryanisation and militarisation during the Third Reich, centrally planned economy and symbolic functions under socialism, reprivatisation and market competition after 1990 (including EU accession in 2004), and post-2020 transformations linked to COVID-19 and digital retail.

These contextual drivers were systematically mapped to Readiness components (e.g., finance, governance/approvals, policy windows) and to ARP only as background framing. This ensured that broader institutional conditions shaping intervention timing were consistently integrated into the assessment of implementability.

2.1.4. Expert Elicitation: Interviews and O1–O7 Questionnaires

We conducted a total of 8 semi-structured interviews in 2025 with architects, conservation and heritage professionals, senior structural engineers with expert-consultant credentials, representatives of the asset manager, and historians familiar with the building’s documented past. All interviews were carried out after institutional ethics approval for this study had been obtained, and followed a common protocol. They were conducted both in person and online and focused on three main themes: (i) decision-making contexts and perceived constraints for major interventions, (ii) design intentions and trade-offs between commercial performance and heritage values, and (iii) ex post evaluations of functional, technical, and governance outcomes. The interviews deepened understanding of decision-making, design, and implementation logics, and complemented the archival, morphological, and contextual analyses—particularly in relation to Readiness components.

Beyond these formal interviews, the reconstruction of phases P1–P5 was also informed by the co-authors’ long-term professional engagement with Renoma since the 1980s, including repeated consultations with designers, structural engineers, former employees and municipal heritage and planning officers. These long-standing contacts were treated as background experiential knowledge rather than as formal interview data, but helped refine the interpretation of decision contexts, especially for the earlier phases. Participation was based on informed consent; quotations were member-checked and anonymised where required.

The interview sample thus comprised eight individuals, who then formed the pool of experts invited to complete the structured O1–O7 questionnaire covering all five adaptive phases (P1–P5). This procedure was independent of the conversational part: interviews generated narrative evidence for Readiness, while the questionnaire provided formal, standardised inputs for ARP scoring (see Supplementary Materials File S2). In practice, this produced a small, purposive panel of three to four domain experts per phase—typically two conservation architects, one architect, and, for the most recent phases (P4–P5), an additional facility manager—each with direct professional involvement in Renoma’s redevelopment or regulatory processes (see Supplementary Materials File S2, Section S2.6). Rather than aiming for statistical representativeness, the panel was constructed to capture the main decision-making perspectives relevant to each phase, and earlier historical phases rely more heavily on documentary reconstruction and therefore carry lower reliability than recent ones.

2.1.5. Data Integration & Phase Delineation (P1–P5)

Evidence from Section 2.1.1, Section 2.1.2, Section 2.1.3 and Section 2.1.4 was consolidated to delineate five phases of Renoma’s transformation (P1–P5). The harmonised dataset provided the basis for scoring ARP factors (via expert questionnaires) and Readiness components (via archival, morphological, contextual, and interview evidence). Detailed phase narratives and imagery are reported in Section 3. Implementation details and the phase-level dataset are provided in Supplementary Materials File S1—Evidence-to-decision workflow dataset.

2.2. Adaptive Reuse Potential (ARP): Retrospective Operationalisation

Following the ARP formulation [27,28], we operationalize the model retrospectively to reconstruct phase-by-phase adaptive potential from historical evidence and expert elicitation, focusing here on parameterisation, scoring rules and adjustments for a listed, repeatedly adapted asset. ARP integrates two components: (i) the age–position term, which peaks when the remaining and elapsed life of a building are comparable, and (ii) an obsolescence term that penalises constraints across seven dimensions (O1–O7). Unlike prospective decision-support tools such as AdaptSTAR model [30,31] or related adaptability indices (e.g., FLEX, SAGA), which typically provide a static or ex ante rating at a single decision point, our application of ARP is explicitly retrospective and phase-based: ARP is computed separately for each historically observed intervention phase (P1–P5) to reconstruct how adaptive reuse potential evolved over time.

The interpretation of obsolescence adopted here follows the conceptual framework proposed by Thomsen and van der Flier [45], who distinguish between physical, functional, economic, technological, and social forms of building decline. This theoretical foundation supports the ARP model’s seven obsolescence indicators and facilitates longitudinal evaluation of adaptive potential over time. We estimate the remaining capacity for adaptation at the end of each phase. Let L_p denote the notional physical life, L_b the elapsed life, and Lu the remaining useful life. We report the following ratios (in percent):

$${EL}_b=100·{\displaystyle \frac{L_b}{L_p}}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}{EL}_u=100·{\displaystyle \frac{L_u}{L_p}}$$

(1)

In Langston’s original specification, the remaining useful life is defined as EL_u = 1 − EL_b, so that the age–position term maps a single notional life cycle and reaches its maximum when roughly half of that reference life has been consumed [27,28]. For a repeatedly modernised heritage asset such as Renoma, this assumption would artificially depress ARP scores after major reinvestments, because structural and functional renewal would not be reflected in the remaining-life term. We therefore decouple EL_u from (1 − EL_b) and estimate it directly as L_u/L_p for each phase, based on the renewed useful life. This adjustment allows partial life-cycle renewals to be represented explicitly: the age–position surface f(EL_u, EL_b) retains its generic shape and still yields the highest values when expended and remaining capacity are of similar magnitude, but successive modernisations shift the building’s trajectory across that surface rather than along a single diagonal path. Consequently, EL_b + EL_u ≠ 1, and the present life-cycle policy should be understood as a heritage-specific variant of ARP rather than a strict replication of the original model. This operational choice also positions ARP alongside related assessment instruments—AdaptSTAR, ARAM, SAGA, and FLEX 3.0—as points of reference for benchmarking adaptability [30,31,46,47,48]. However, these tools inform our understanding of adaptability metrics rather than generating the temporal decision windows that are central to the ARP × Readiness framework.

The baseline design life (L_p) was set at 120 years for the original 1930 structure, reflecting the durable steel construction typical of early twentieth-century commercial buildings. At each major modernisation, L_p was recalibrated proportionally to the intervention scope and the share of renewed structural area. This hybrid lifecycle policy combines a fixed reference lifespan with partial resets, maintaining continuity for the heritage core (1930 wing) while acknowledging the effective capacity added through extensions such as the 2005–2009 east wing.

The baseline value of 120 years aligns with Langston’s recommendations for long-life commercial or institutional buildings [28] and ensures appropriate scaling of the age–position function for a heritage asset with demonstrated long-term usability. These assumptions were further validated through consultations with structural engineers involved in Renoma’s successive redevelopments and through comparative studies on extending the service life of steel structures (e.g., [49]).

O1–O7 follow Langston’s obsolescence rubric, adapted here for retrospective scoring in a heritage context. The evidence inputs supporting each factor are illustrated in Figure 1. These seven factors (physical, economic, functional, technological, social, legal, political) were operationalized through a structured expert questionnaire (anchors in Supplementary Materials File S2), in which items were rated on a three-point scale {0, 0.5, 1}. Archival, morphological, and contextual sources were used by the research team to establish the phase chronology and triangulate interpretations, but the scoring itself was based on the experts’ professional knowledge and retrospective involvement. A panel of 3–4 domain experts per phase independently completed the questionnaires with predefined prompts, and medians across raters were used as consensus; interquartile ranges (IQRs) were computed per factor and phase. Panel members selected only the discrete anchor values {0, 0.5, 1} at the item level; intermediate factor scores (e.g., 0.71 or 0.75) arise from the weighted aggregation of multiple item ratings and the use of medians across experts, rather than from any single rater assigning those precise values. Items deemed historically inapplicable (e.g., ESG reporting in the 1930s) could be marked as N/A; such responses were excluded from aggregation and item weights were renormalized within the factor.

Table 2. Operationalisation of obsolescence factors (O1–O7): constructs and supporting evidence.

Factor	What it Captures (≤15 Words)	Main Evidence Used (Examples; all Factor Scores Derived from Structured Expert Questionnaires Informed by Retrospective Professional Practice and Historical Involvement)
O1 Physical	Structural integrity, durability of frame/envelope, and service pathway condition	As-built drawings, repair packages, structural and MEP reports, site photos
O2 Economic	Relative cost of adaptation vs. rebuild; investment viability and OPEX predictability	Market/benchmark reports, owner/press statements, cost/incentive files
O3 Functional	Layout efficiency, floorplate flexibility, circulation, accessibility, adaptability for mixed uses	Architectural plans/sections, phase overlays, access/egress documentation
O4 Technological	Building systems performance, energy efficiency, infrastructure capacity, and upgradability	MEP inventories, energy/compliance reports, riser mapping
O5 Social	User/public acceptance, heritage identity, and social/public realm effects	Press coverage, consultation records, heritage statements, footfall/public realm data
O6 Legal	Statutory compliance, heritage listing constraints, ownership and tenure clarity	Permits/decisions, listings/conditions, tenure and ownership files
O7 Political	Policy regime, stability, incentives/grants, and political support for reuse	Strategic plans, program priorities, authority statements

summarizes the constructs of O1–O7 and the main types of supporting evidence. The complete O-factor item bank and within-factor weights (O1–O7), as well as detailed aggregation rules (treatment of N/A and rater reconciliation), are provided in Supplementary Materials File S2, while evidence packages are compiled in Appendix A and factor-level scoring results (medians, Q1–Q3, and IQR) are reported in Appendix B. Earlier phase estimates carry lower reliability due to longer temporal distance and incomplete evidence.

For synthesis across dimensions, the seven factor scores were combined with equal weights (αi = 1/7) into the overall obsolescence term:

$$\Sigma O=\sum _{i=1}^7{\alpha }_i{O}_i,\hspace{1em}\hspace{1em}\hspace{1em}\Sigma {\alpha }_i=1$$

(2)

This two-level procedure ensures that salient aspects within each factor (e.g., structural frame in O1, compliance path in O6) are proportionally reflected, while maintaining parity among the seven dimensions in the overall ARP index.

ARP is modelled as a function of age position and obsolescence,

$$ARP=f({EL}_u, {EL}_b) · (1-\Sigma O)\times 100\%$$

(3)

where f(·) follows the standard Langston specification (maximum near EL_u ≈ EL_b). Parameterization and checks are documented in Supplementary Materials File S2. ARP outputs are reported in percent.

Operationalising ARP for a listed, repeatedly adapted asset required three methodological safeguards: (i) a phase-based life policy without wholesale age reset, with counterfactuals tested in sensitivity; (ii) heritage-aware anchors for O1–O7 (e.g., treatment of conservation constraints under O6/O7, loose-fit morphology under O3/O4), supported by evidence packages; and (iii) ex-post validation against the observed timing of subsequent interventions. Because all O1–O7 scores were assigned retrospectively, with experts aware of realised projects, we treat ex-post alignment between modelled trajectories and realised interventions as plausibility checks rather than demonstrations of ex ante predictive power; as discussed in Section 5.4, such retrospective judgements may be subject to hindsight bias. These adjustments preserve the core age-position logic while making the obsolescence rubric reproducible for heritage contexts. Within the broader landscape of adaptive-reuse decision support, our emphasis is distinct: we use ARP’s time-sensitive age-position lens explicitly for temporal gating and pair it with a Readiness index that captures implementability in governance, finance, use commitment, delivery, and policy terms. This complements design-stage adaptability models and spatial/performance-oriented approaches without duplicating their full protocols.

2.3. Readiness Indicator and the GO Metric

Readiness is an author-devised implementability index tailored to retrospective heritage contexts. Unlike ARP—which measures potential (age-position × obsolescence)—Readiness captures the near-term ability to act given institutional, financial, market/use, and execution conditions. Scores are event-anchored (e.g., approvals granted, funding secured, confirmed tenants, contractor mobilisation, program priority) within a phase-bounded window (±2 years; ±3 years for early phases).

Readiness synthesises five recurrent gating elements: F finance, G governance/approvals, U use commitment, D delivery/supply chain, and P policy window/strategic priority. Each component was operationalised through a structured rubric with predefined anchors (see Supplementary Materials File S3). Each dimension is scored on a three-point evidence-based scale {0, 0.5, 1}, where the anchors correspond to clearly defined thresholds of financial, governance, use, delivery and policy readiness, as specified in Supplementary Materials File S3. For each phase, two members of the research team independently applied the rubric, drawing on evidence packages (Appendix A) and reconciling differences in scoring. Formal dispersion statistics (IQR) were not computed for Readiness; instead, each item carries an H/M/L confidence tag reflecting evidence richness and traceability of sources. Concise scoring anchors for F–G–U–D–P and confidence tags (H/M/L) are provided in Supplementary Materials File S3.

Component scores $r_j$ were therefore restricted to these discrete anchors {0, 0.5, 1} and aggregated using baseline weights:

$$w_F = 0.25, { w}_G= 0.25, w_U = 0.20, { w}_D = 0.20, { w}_P = 0.10, \Sigma w_j = 1$$

(4)

Readiness is then

$$R=\Sigma w_j{r}_j\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}GO (\%)=ARP \left(\%\right)\times R$$

(5)

Values such as R = 0.75 thus arise from the weighted combination of these anchored component scores (for example F = 1.0, G = 1.0, U = 0.5, D = 0.5, P = 0.5), rather than from any single fine-grained rating. Unit convention: computationally, GO is stored as a fraction in [0–1]; where denoted [%] it is presented as percent. Decision thresholds are applied to GO as a fraction: WAIT < 0.20, PREPARE 0.20–0.35, GO ≥ 0.35. Decision thresholds are applied to the fraction. For phases with both wings (P4–P5), GO and the Decision are reported only for the combined (OW + NW) scope; wing rows retain lifecycle/obsolescence context only.

The weighting scheme reflects front-end, stage-gate practice in public-investment guidance—where financial closure and institutional coordination are treated as preconditions for activation—and thus prioritizes F and G [36,50,51]. To test sensitivity, we also computed an equal-weight scenario (Scenario C in Supplementary Materials File S4). For phases P1–P3, GO values differ by at most approximately 1 percentage point and no phase changes its decision class (WAIT/PREPARE/GO), indicating that the results are robust to moderate changes in the weighting scheme. These frameworks consistently show that finance and governance conditions exert the strongest influence on activation, justifying the higher weights for F and G in this study. Here R ∈ [0–1]. Readiness is distinct from O1–O7 to avoid double counting: O6/O7 in ARP capture structural legal–political regimes, whereas G/P reflect event-level implementability (permissions granted, program priority). Where documentary evidence pointed to overlap—for instance, statutory changes affecting both O6/O7 and G/P—raters attributed structural regime shifts to ARP and event-level triggers to Readiness, recording notes in the audit trail (Supplementary Materials File S3).

Content validity is ensured by event-anchored rubrics and package-level evidence (Appendix A; Supplementary Materials File S3). Convergent validity is assessed ex post against the observed initiation of major works (procedures/results in Section 4.5; Supplementary Materials File S4). Decision thresholds are applied to GO (%): WAIT < 20%, PREPARE 20–35%, GO ≥ 35%, providing a pragmatic lens to interpret phase-level decision windows.

Although introduced specifically for this study, the Readiness construct is conceptually grounded in established frameworks of project feasibility, governance, and front-end decision-making. It follows the staged logic of assurance and activation described in the OECD Framework for Better Governance (2017), the UK HM Treasury’s Green Book (2022), and the Infrastructure and Projects Authority’s Project Routemap (2022), where financial closure, institutional alignment, and delivery capability form the core preconditions for implementation [50,51,52]. These principles are consistent with the front-end management literature, which emphasises early-stage governance and decision quality as predictors of project success [53,54,55], and align with front-end loading practices widely used in complex infrastructure and building programmes [56]. Within the heritage-reuse domain, Readiness extends this decision-gate logic to the adaptive reuse process, complementing established assessment frameworks (e.g., ARP, AdaptSTAR, ARAM) by translating contextual enablers—finance, governance, use commitment, delivery, and policy—into an implementability index suitable for retrospective, evidence-based evaluation.

2.4. Validation and Sensitivity

We validate the framework ex post by comparing model-implied decision windows with the actual initiation of subsequent major adaptation projects (e.g., the 1977 assessment preceding the late-socialist modernisations and the post-socialist retail transition up to 2004; the 2005–2018 redevelopment preceding the office-led refurbishments of 2019–2024). For each phase, the model’s decision class (WAIT/PREPARE/GO) was compared with the observed initiation of subsequent major interventions, classified as convergent (alignment) or divergent (external trigger). Phase-level alignment is reported as the share of decision windows where the observed intervention fell within the predicted class (WAIT/PREPARE/GO), with discrepancies labelled as external triggers and discussed qualitatively in Section 5. Validation is therefore conducted at the level of decision classes rather than fine-grained score differences: our concern is whether phases are correctly classified as WAIT, PREPARE or GO, not whether small numerical differences in ARP or GO (e.g., 0.41 vs. 0.44) separate distinct states. In line with the semi-quantitative nature of the scales, such fine-grained score differences are not interpreted as hard qualitative thresholds but as belonging to the same decision band. Realised interventions are treated as indicative rather than normatively ‘optimal’, and periods where the model suggests GO but no project occurred are interpreted as potential missed opportunities or the result of external constraints not fully captured in the available evidence.

Robustness tests examined: (i) alternative specifications of the life policy L_p (with and without partial resets at modernisations); (ii) shifts in the obsolescence term ΣO (±0.10 absolute); and (iii) alternative Readiness weightings (equal-weight versus baseline). Detailed numerical outputs for all scenarios (ARP, R, GO and any shifts in WAIT/PREPARE/GO classes) are provided in Supplementary Materials File S4. For the equal-weight scheme (Scenario C), GO values for P1–P3 differ by at most about one percentage point and no phase changes its decision class, indicating robustness to moderate changes in the weighting scheme. In the alternative L_p and ΣO settings, absolute ARP/GO values shift slightly but phase-level WAIT/PREPARE/GO classes remain unchanged (see Supplementary Materials File S4). We also signpost routes for future validation, notably post-occupancy evaluation families (PROBE/POE), which could triangulate functional outcomes in subsequent follow-ups without expanding the present scope [57,58].

3. Case Context: Renoma’s Transformations (1930–2025)

This chapter situates the case within a diachronic morphological reading that follows the protocol outlined in Section 2, combining archival sources, drawing reconstruction, and interview triangulation. The periodisation does not derive from historical chronology alone but from architectural and documentary evidence. It builds on the reconstruction of baseline plans and sections (1930), complemented by archival records, project documentation, and expert testimonies that indicate when major structural, functional, and regulatory shifts occurred—or when original design elements were deliberately preserved. Archival files (including Hermann Dernburg’s competition and as-built drawings), post-war rebuilding projects, late-socialist and post-socialist modernisations, and two major waves of large-scale works after 2005 provided the anchors for identifying five phases (P1–P5)—namely the retail-led redevelopment and extension of 2005–2018 (P4) and the office-led refurbishments of 2019–2024 (P5).

When read through contemporary guidance, this ‘skin-and-frame’ logic can be interpreted as design for adaptability and reversible intervention [32,59,60], while conservation guidance emphasises proportional change and compatible use [35,61]. We draw on these documents as conceptual anchors only, not as full assessment protocols.

The adaptive history of Renoma is therefore structured around five successive transformations, each responding to changing political, economic, and social conditions while building on the resilience of the original structure.

Table 3. Main phases of Renoma’s functional–spatial transformation, 1930–2025: program changes and adaptive strategies. Source: authors’ compilation drawing on phase-level source packages (Table 1).

Phase	Period	Functional–Spatial Changes	Adaptive Strategy
P1. Initial design and wartime transformations	1930–1945	Construction of a modern department store for Wertheim with retail, gastronomy, and offices; partial conversion to administrative/paramilitary use during WWII; severe damage in 1945.	New commercial investment with a “loose-fit” modular structure; open floorplates and courts enabling long-term flexibility; war damage left the frame intact, securing reuse potential.
P2. Post-war socialist reconstruction	1946–1976	Incremental reopening of retail floors, partial use as storage, offices, and education; reliance on original technical systems.	Pragmatic rebuilding and simplified interiors; technical repair of façades; multi-stage spatial and functional recycling under resource constraints; O2 pressure.
P3. Reconfiguration of retail departments	1977–2004	Multiple retail and gastronomy remodels; installation of escalators and lifts; continued use of upper floors for offices and storage.	Program adaptations without altering structural grid; basic façade maintenance; preparatory studies for larger repositioning.
P4. Retail-led mixed-use transition & extension	2005–2018	Major redevelopment and expansion: five-level shopping arcade, new wing with parking, updated interiors, new food court and offices.	Comprehensive transformation based on “research by design”; structural strengthening, façade conservation, and full MEP renewal; alignment with capital market standards; O4 mitigation.
P5. Office-led functional transition	2019–2025	Gradual reduction of retail; growing share of offices, coworking, medical and fitness services; reinterpretation of courts and entrance hall.	Strategic shift toward office and service mix; adaptation to e-commerce and post-pandemic realities; high-standard finishes and enhanced accessibility; O3 flexibility; O6 clarity

provides a comparative overview of these phases, highlighting the main functional–spatial changes and corresponding adaptive strategies. Section 3.2 elaborates on their historical context and implications. Alternative splits, such as separating 1946–1988 and 1989–2004, were considered but rejected, since these political changes did not translate into a decisive functional-spatial restructuring of Renoma.

3.1. Architectural Concept & “Flexible Heritage” Enablers

Dernburg’s design, completed in 1930 for the Wertheim network [62,63], established a structural and environmental “loose-fit” platform (Figure 2 and Figure 3). Conceived on a steel frame with a regular modular grid, the main beam and column spans ranged between 8.0 and 9.2 m, with wide-flange girders reaching 13.8 m across the central bays between the light courts. This configuration produced open floorplates with generous floor-to-floor heights of 4.5–5 m, offering sufficient service headroom for mechanical and life-safety retrofits. Together, these features minimised the risk of premature functional or technical obsolescence and align with what today would be framed as design for adaptability [60,64].

Two glazed light courts brought daylight deep into the plan and stabilised internal orientation; compact circulation cores and rational riser stacks were concentrated so that later service upgrades required only limited invasive works. The ceramic-clad façades and sculptural detailing projected a strong public identity, while the skeletal interior remained fundamentally adaptable. Contemporary press repeatedly highlighted the building’s ceramic ornamentation and the so-called ‘exotic heads’ designed by Ulrich Nitschke and Hans Klakow, which became a recognizable element of Wrocław’s architectural identity [65]. This symbolic layer reinforced public attachment while leaving the internal structural grid free for future reconfiguration.

Figure 2 documents the baseline ground- and second-floor plans, foregrounding the modular grid and courts that underpinned later flexibility, while Figure 3 shows the transverse and longitudinal sections, emphasising the span logic, core positions, and sectional reserves that made subsequent technical upgrades feasible. Figure 4 complements this diachronic reading by illustrating the building in its state after Phase 4. They show how the extension on the eastern plot, realised during the 2005–2009 redevelopment, was consolidated and reconfigured in subsequent refurbishments, enabling the shift toward an office- and service-led program while preserving the original structural DNA. These features collectively resonate with circular strategies of zoning and disassembly, which reduce intervention needs across successive adaptation cycles [8,66].

Figure 2. Original plans of the Wertheim Department Store (1930), designed by Hermann Dernburg: (a) ground floor plan, (b) second floor plan. The drawings illustrate the modular grid, open floorplates, and two glazed light courts that provided both natural lighting and spatial flexibility, underpinning the building’s long-term adaptability. Source: [62] (a) [67] (b) [67].

Figure 2. Original plans of the Wertheim Department Store (1930), designed by Hermann Dernburg: (a) ground floor plan, (b) second floor plan. The drawings illustrate the modular grid, open floorplates, and two glazed light courts that provided both natural lighting and spatial flexibility, underpinning the building’s long-term adaptability. Source: [62] (a) [67] (b) [67].

Figure 3. Sections of the Wertheim Department Store (1930): (a) reconstructed longitudinal section B–B prepared by the authors based on archival documentation. Together, they highlight the steel-frame skeleton, generous floor-to-floor heights, and compact circulation cores, which created reserves for technical upgrades and facilitated future adaptive reuse, (b) original transverse section A–A through the twin light courts. Source: authors’ elaboration based on [62] (a,b).

Figure 3. Sections of the Wertheim Department Store (1930): (a) reconstructed longitudinal section B–B prepared by the authors based on archival documentation. Together, they highlight the steel-frame skeleton, generous floor-to-floor heights, and compact circulation cores, which created reserves for technical upgrades and facilitated future adaptive reuse, (b) original transverse section A–A through the twin light courts. Source: authors’ elaboration based on [62] (a,b).

Figure 4. Renoma Department Store during Phase 4 redevelopment (2005–2009): (a) ground floor plan, (b) longitudinal section B–B through the new wing. The drawings illustrate the reconfigured circulation, mixed-use ground floor, and office-led upper floors, highlighting how the adaptive reuse strategy integrated the reserved eastern plot into the extended complex while sustaining the durable steel frame and façades. Source: authors’ elaboration based on [68].

Figure 4. Renoma Department Store during Phase 4 redevelopment (2005–2009): (a) ground floor plan, (b) longitudinal section B–B through the new wing. The drawings illustrate the reconfigured circulation, mixed-use ground floor, and office-led upper floors, highlighting how the adaptive reuse strategy integrated the reserved eastern plot into the extended complex while sustaining the durable steel frame and façades. Source: authors’ elaboration based on [68].

Importantly, at the time of construction, the adjacent eastern plot was left vacant, deliberately anticipating future expansion (Figure 5a). This provision was not only a technical reserve but also an urban strategy, as the simplified party wall towards ul. Czysta signalled a conscious anticipation of block-level intensification [69]. The potential was eventually realized during the 2005–2009 redevelopment, when the reserved space was integrated into the extension (Figure 4 and Figure 5b). In contemporary terms, this can be read as an early circular strategy of “design for adaptability and extension” [32,41,70], embedding capacity for future intensification without dismantling the original frame. As later conservation and redevelopment accounts emphasised, these original enablers were repeatedly invoked as arguments for preservation rather than replacement, demonstrating how the architectural DNA of 1930 continued to shape decision-making across successive decades [69,71].

This constellation—modular frame, open plan (primarily across the five retail floors), light courts, concentrated cores and risers, and the provision for future extension—proved decisive for the building’s long-term adaptability [73]. The steel structure was bolted rather than welded, which required stricter fire-safety standards, while its load capacities were deliberately oversized to accommodate interchangeable retail and storage uses. It enabled successive program reconfigurations without compromising structural integrity or protected elevations, positioning Renoma as an exemplar of flexible heritage. Figure 6 synthesises this diachronic trajectory, demonstrating how, despite successive reconstructions, the main structural frame and ceramic façades were retained, while interiors and programs evolved within a durable “skin-and-frame” model—an approach increasingly recognised in circular building literature as a key enabler of long-term adaptability [8,41].

3.2. Phased Transformations (P1–P5)

Building on the overview in Table 3, the following narrative elaborates the historical, regulatory, and design contexts of each phase. These accounts illustrate how successive owners, designers, and regulators interpreted and reinterpreted the building’s potential while preserving the core structural and spatial enablers identified in Section 3.1.

The first phase (P1, 1930–1945) established Renoma as a flagship A. Wertheim department store, combining retail, gastronomy, and offices within a modern steel-frame structure. At its opening it offered approximately 36,000 m² of floor space, placing it among the largest commercial complexes in Central Europe [62,63,72]. Beyond shopping, the building housed restaurants and leisure facilities, positioning it as a multifunctional social hub. Retail was also innovatively organised on a partly self-service model, illustrating an early readiness to accommodate changing consumer practices. The open plan and twin courts reflected an advanced design for its time, offering a level of spatial generosity that would later prove crucial for reuse. Aryanisation in 1936 and wartime adaptation for administrative and paramilitary functions marked the beginning of programmatic shifts, as also evidenced by contemporary press coverage in Schlesische Tageszeitung (1936–1941) [74,75,76]. Despite severe wartime damage, the survival of the skeleton frame, principal floorplates meant and structure of facades that the essential DNA of the building endured [77,78].

The second phase (P2, 1945–1977) began with the Polish socialist state’s decision to rebuild the ruin as a Powszechny Dom Towarowy [79,80]. Contemporary accounts also emphasized the symbolic dimension of Renoma’s reconstruction: restoring the ruin as a socialist “people’s department store” was framed as a civic act of renewal as much as a technical intervention [81,82]. This dual role reinforced the political legitimacy of investment despite severe material shortages. The process unfolded incrementally under severe resource constraints: façades were secured, interiors simplified, and retail floors reintroduced step by step. By the mid-1970s, most of the pre-war commercial areas were functioning again. The 1977 heritage listing, repeatedly noted in both professional and popular sources, not only imposed conservation duties but also provided regulatory clarity, stabilising investor expectations and confirming the building’s cultural significance [38,71].

The third phase (P3, 1977–2004) illustrates both late-socialist pragmatism and early market-oriented ambition. Escalators and lifts were added, departments reconfigured, and infrastructure selectively upgraded. Following privatization, new corporate owners viewed Renoma as a strategic asset but lacked the resources for large-scale redevelopment. Preparatory studies and partial modernizations maintained operability while awaiting major investment. The 1997 flood, which devastated the basements but left the pre-war systems recoverable, underscored the robustness of the original construction [67,78,83,84].

The fourth phase (P4, 2005–2018) marked the most extensive redevelopment and extension in Renoma’s history, positioning it among the largest hybrid retail–office complexes in Central Europe at the time. While the core redevelopment works occurred in 2005–2009, the phase extends through 2018 as a retail-led mixed-use operation. Backed by international capital, a €100 million functional program transformed the building into a five-storey shopping arcade integrated with new offices, services, and structured parking, resulting in a total floor area of about 99,500 m², including 31,000 m² of retail and 10,000 m² of offices [68,69,85,86,87,88,89,90,91]. Conservation and innovation went hand in hand: façades were restored, courts reinterpreted by Benoy, and ARUP engineered cutting-edge safety and environmental systems. The introduction of glazed atria, light wells, and multi-level galleries significantly redefined the internal circulation, expanding daylight access while reinforcing the building’s public identity. These interventions, while spatially transformative, remained consistent with the underlying loose-fit frame and thus can be read as extensions rather than ruptures of its adaptive DNA [92,93]. Restoration involved the meticulous cleaning and reinstatement of the ceramic tile cladding and decorative heads, as well as the reopening of the historic Świdnicka entrance, reflecting a negotiated balance between heritage protection and commercial upgrade [38,40,94,95]. The result was a hybrid complex that reasserted Renoma as both a historic landmark and a contemporary retail hub. Renoma mirrors a broader European shift from mono-functional retail to multi-functional service and work hubs, as evidenced by sectoral syntheses and public reports [13,15].

The fifth phase (P5, 2019–2025) reflects structural changes in the retail sector and the competitive pressures of new malls such as Wroclavia. Under Globalworth ownership, Renoma increasingly transitioned toward an office- and service-led functional program, with retail consolidated to the ground floor and basement, while upper floors were converted into offices, coworking, medical, fitness, and event spaces. This transition was explicitly framed by the owner as a strategic response to the decline of traditional department store formats, positioning Renoma as a flexible mixed-use hub [96,97,98]. The 2020–2024 refurbishment, designed in response to the COVID-19 pandemic, combined spatial reconfiguration with a cultural reinterpretation: the courts and entrance hall were remodelled in a contemporary Art Deco spirit, positioning Renoma for a future beyond retail while sustaining its architectural heritage [99,100]. Unlike the 2005–2018 redevelopment, which prioritized contemporary reinterpretations of the historic courts and circulation, the 2020–2024 works placed stronger emphasis on conservation-led solutions. The restored courts and entrance halls more closely recalled the building’s pre-war interiors, with reconstructed finishes and stylistic motifs explicitly referencing the original Art Deco character. This approach blended functional transition with a quasi-reconstructive conservation strategy, positioning Renoma simultaneously as a living commercial asset and a protected heritage landmark.

The five-phase periodization (P1–P5) therefore provides not only a historical narrative but also the analytical framework for assessing Renoma’s adaptive capacity. Each breakpoint coincided with decisive shifts in ownership, regulation, and investment strategy, directly influencing the building’s potential for reuse. For this reason, the ARP dimensions (O1–O7) and readiness indicators (F–P) are evaluated at phase transition ‘decision windows’: 1947, 1977, 2004, 2018, and the current 2025 assessment (each ±2–3 years). The chosen phases (P1–P5) and reference years (1947, 1977, 2004, 2018, 2025) reflect decisive moments when functional-spatial restructuration could be observed or anticipated. Alternative splits—such as distinguishing 1946–1988 and 1989–2004—were considered, but eventually dismissed. Although the political transformation of 1989 introduced a free-market economy, the changes in Renoma’s program occurred gradually, through trials and adjustments, without a clear structural break. In contrast, the reference year 2018 was selected because it encapsulated multiple catalysts of change: the opening of Wroclavia, a major competing retail and leisure complex integrated with the city’s main railway and bus interchange hub, the rapid growth of e-commerce, the Europe-wide decline of traditional department store chains, and the increasing demand for inner-city office and mixed-use developments (including leisure, gastronomy, healthcare, and flexible open-plan layouts) [101]. This combination of pressures made 2018 the critical assessment window (±2–3 years) for the subsequent transition phase. These junctures mark the moments when adaptive opportunities or constraints crystallized in both policy and design terms. Table 1 details the phase-level source packages underpinning this evaluation, while Table 3 synthesizes the resulting trajectory of functional–spatial transformation.

3.3. Key Decision Drivers and Their Spatial Manifestation

Behind each transformation lay a complex interplay of economic, technical, social, and regulatory drivers. These forces not only determined the feasibility and scope of interventions but also explain why Renoma could repeatedly adapt without destructive change to its structural DNA. The identification of phases (P1–P5) therefore reflects not every minor refurbishment but distinct decision windows, where ownership, capital flows, regulations, and spatial enablers converged in ways directly relevant to adaptive reuse potential (O1–O7) and subsequent Readiness assessment.

Economic and financial conditions shaped the timing of major works. In 1947, rebuilding the ruin was framed as a symbolic act of socialist reconstruction, attracting public investment [80]. In the early 2000s, accession to the European Union unlocked international capital, enabling a wholesale redevelopment that local owners had previously been unable to finance [37,40,76]. By contrast, after 2015, the saturation of retail markets and the rise of e-commerce made continued reliance on department store functions untenable [13]. Transitioning to an office- and service-led mix stabilised yields and rebalanced the business model, ensuring financial sustainability. These shifts marked turning points in O2 (economic viability) and O6 (legal/tenure clarity) within the ARP model, while simultaneously triggering F (finance) and G (governance/approvals) in the Readiness lens.

Technical factors provided the backbone of adaptability. The original steel frame, open grid, and twin courts meant that subsequent interventions could proceed without radical demolition [83]. Floor strengthening in the 1990s and early 2000s, together with riser consolidation [85,102] and plant relocation, laid the groundwork for the comprehensive upgrades of the 2005–2009 redevelopment [68,86,87]. The 1997 flood revealed both vulnerabilities and resilience, leading to new strategies for redundancy and basement protection [84,103,104]. By the 2020s, service headroom, rational cores, and an already settled conservation strategy allowed program shifts to proceed largely within the existing envelope. Such conditions directly influenced obsolescence-related dimensions (O3/O4), repeatedly demonstrating the flexibility of the historic spatial structure. By contrast, the most substantial interventions were required in the new wing, where parts of the retail areas and the multi-storey car park were converted into offices.

Social and cultural value also played a critical role. Renoma remained an urban landmark and meeting point throughout its history—from an interwar “palace of consumption” operated as the Wertheim department store [105,106], through the socialist PDT everyday department store [80,82], to the 2000s prestige mall that received multiple architectural awards [92,107,108] and the contemporary mixed-use workplace and services hub [109]. This persistent symbolic presence as a civic reference point—repeatedly noted in press and public discourse [65,109]—underwrote investor confidence and legitimised successive rounds of investment in public-facing spaces such as the entrance hall and courts, shaping assessments of O5 (cultural value) and O6 (urban significance).

Governance and regulation both constrained and enabled adaptation. The 1977 listing imposed conservation duties but also clarified what interventions were permissible, reducing investor risk. Successive waves of building law and fire safety regulation influenced sequencing, from post-war repairs through 1990s escalator additions. A conservation study prepared in the late 1990s provided the formal basis for subsequent protection guidelines [75], while in the 2000s, comprehensive life-safety upgrades were undertaken within a research-by-design framework aligned with new European building regulations. City planning frameworks after 2004 supported large-scale extension and, more recently, program diversification. Far from blocking change, regulation structured a predictable pathway for it—an element captured by O7 (regulatory framework)

Taken together, these drivers converged with the inherent spatial DNA revealed in Section 3.1. Economic cycles provided the capital windows, technical resilience kept interventions proportional, social value justified the preservation of public identity, and regulatory frameworks stabilised the risk environment. These interdependencies set the stage for the adaptive cycles later assessed through the ARP and Readiness framework. Importantly, the analysis draws on a triangulated evidence base—archival and project documentation complemented by contemporary expert testimonies—ensuring that the identification of key drivers is grounded in verifiable sources rather than retrospective interpretation.

4. Results: ARP and Readiness Assessment of the Renoma Department Store

4.1. Structural and Lifecycle Parameters Underlying ARP

Following the hybrid lifecycle policy introduced in Section 2.2, the Adaptive Reuse Potential (ARP) model integrates quantitative indicators that capture the building’s physical ageing and utilisation efficiency. The parameters—elapsed life (L_b), projected physical design life (L_p = 120 years), elapsed-life ratio (EL_b), utilisation efficiency (EL_u), and proximity-to-midlife coefficient (f)—together describe the balance between expended and remaining structural capacity. Their combination establishes the baseline ARP before contextual weighting and expert adjustment.

For the 2009 extension, an independent set of lifecycle parameters (L_p, L_b, EL_b, EL_u) was calculated, reflecting its distinct structural system and later entry into the building’s overall life trajectory. Conversely, the 1930 wing preserves continuity of the original “age-policy”, without full lifecycle reset, but with partial renewal of adaptive capacity after major modernisations (e.g., 1977, 2005–2009). This approach enables consistent longitudinal comparison across both wings, aligning with the retrospective reconstruction of adaptive potential.

The results indicate that the original 1930 steel frame remains far below its theoretical design limit, maintaining high structural reserves (EL_b = 0.79 in 2025) and an almost balanced ratio between elapsed and usable life (EL_b/EL_u ≈ 1.0). This confirms the long-term robustness of the heritage core and its capacity for continued adaptation. In contrast, the 2009 extension—although considerably newer—shows a lower proximity-to-midlife (f = 0.35) due to its shorter operational maturity and limited renewal cycles.

Overall, the comparative lifecycle assessment demonstrates that physical ageing has not constrained Renoma’s adaptability. Structural endurance and partial modernisation policies have sustained the building’s functional lifespan, while subsequent redevelopment phases were driven primarily by economic, regulatory, and institutional factors rather than technical decay. This finding reinforces the central assumption of the ARP framework: that long-life commercial buildings exhibit adaptive resilience when structural capacity is periodically renewed rather than replaced.

4.2. Obsolescence Assessment: Component Scores (O1–O7 Across Phases P1–P5)

Building upon the lifecycle baseline, the expert-based assessment of obsolescence across seven dimensions (O1–O7) provides a quantitative profile of adaptive constraints for each transformation phase (P1–P5). The scoring reflects the retrospective application of Langston’s ARP framework, calibrated against the structural parameters presented in Section 4.1.

Median values were derived from independent assessments by 3–4 experts per phase, with interquartile ranges (IQR) below 0.15 for most dimensions, indicating a high level of inter-rater consistency. The aggregation process followed the standard ARP procedure: individual O1–O7 ratings (0–1 scale) were synthesised into the overall obsolescence index (ΣOi, expressed as the Langston fraction).

Figure 7 visualises the multidimensional obsolescence pattern across phases. The radar chart decomposes the seven ARP obsolescence factors (O1–O7) by adaptive phase (P1–P5), providing a visual complement to the factor-level results reported in

Table 4. Lifecycle and utilisation parameters for the Renoma Department Store, 1947–2025. (Source: author’s computation based on archival construction data and lifecycle model; see Supplementary Materials File S2).

Ref. Year	Built	Lb (Years)	Lp	ELb	ELu	ELb/ELu	Proximity-to-Midlife (f)
1947	1930	17	120	0.14	0.69	0.2	0.45
1977	1930	47	120	0.39	0.66	0.6	0.74
2004	1930	74	120	0.62	0.75	0.82	0.87
2018	1930 *	88	120	0.73	0.62	1.18	0.89
	2009 **	9	120	0.08	0.62	0.12	0.45
2025	1930 *	95	120	0.79	0.79	1.01	1.00
	2009 **	16	120	0.13	0.79	0.17	0.35

* projected physical design life of the existing building’s wing; ** projected physical design life of the new building’s wing.

and Appendix B. It highlights the shift from predominantly technical constraints (O1–O4) in the early phases (P1–P3) toward market- and socially driven obsolescence (O2, O5) in P4, followed by a moderation of overall obsolescence in the current phase (P5).

Table 5. Expert-assessed obsolescence scores (median values) for seven dimensions (O1–O7) across five phases (P1–P5). Source: Authors’ computation based on expert survey protocol (Supplementary Materials File S2).

Decision Window (Ref. Year)	O1 Physical	O2 Economic	O3 Functional	O4 Technological	O5 Social	O6 Legal	O7 Political	Sigma_O (Mean O1–O7)	ΣOi (Langston Fraction)
P1 (1930–1945, ref. 1947)	0.50	0.39	0.06	0.17	0.11	0.39	0.22	0.26	0.37
P2 (1946–1976, ref. 1977)	0.61	0.22	0.11	0.44	0.11	0.22	0.39	0.30	0.42
P3 (1977–2004, ref. 2004)	0.45	0.06	0.06	0.44	0.11	0.17	0.17	0.21	0.29
P4 (2005–2018, ref. 2018)	0.08	0.33	0.06	0.17	0.06	0.06	0.22	0.14	0.48
P5 (2019–2025, ref. 2025)	0.20	0.33	0.11	0.06	0.11	0.17	0.22	0.17	0.24

Note: Median values reported; interquartile ranges (IQR) available in Appendix B. ΣOi (Langston fraction) represents the sum of O1–O7 expressed as a fraction per Langston’s ARP (equivalent to the 0–20 scale normalized to 0–1).

summarises the median scores for all obsolescence dimensions (O1–O7) and the resulting ΣOi index for each decision window.

Overall, the obsolescence patterns confirm that Renoma’s adaptive constraints evolved from predominantly structural and technological factors (P1–P3) to economically and socially driven pressures (P4–P5), while the building’s original framework and modular organization consistently supported successive transformations.

4.3. ARP Scoring Results (O1–O7 Across Phases P1–P5)

Building on the component-level obsolescence assessment (Section 4.2), the aggregated Adaptive Reuse Potential (ARP) score integrates seven dimensions (O1–O7) into a single composite indicator representing each phase’s readiness for transformation. Following Langston’s formulation, the ARP % expresses the residual adaptive capacity after accounting for physical, economic, functional, and contextual obsolescence factors.

The resulting phase-level scores are presented in

Table 6. Adaptive Reuse Potential (ARP) scores across five adaptive phases (P1–P5).

Decision Window (Ref. Year)	Decision Window (Ref. Year)	ARP %	Classification	Trend
P1 (1930–1945, ref. 1947)	~1947	28.39	moderate	increasing
P2 (1946–1976, ref. 1977)	~1977	42.50	moderate	increasing
P3 (1977–2004, ref. 2004)	~2004	61.64	high	increasing
P4 (2005–2018, ref. 2018)	~2018	31.96	moderate	increasing
P5 (2019–2025, ref. 2025)	~2025	44.08	moderate	increasing

Note: Median values derived from expert assessments based on the ARP protocol and obsolescence components (O1–O7) (Supplementary Materials File S3). Source: Authors’ computation.

, indicating that Renoma’s adaptive potential has evolved non-linearly across its nearly century-long lifecycle.

The ARP trajectory demonstrates a fluctuating yet generally upward trend in adaptive potential. Phases P1–P3 show a steady growth in the capacity for reuse, supported by the structural robustness of the original 1930 steel frame and the building’s modular spatial system. The peak in P3 (61.64%) coincides with the completion of the post-socialist restructuring and the onset of privatisation processes, which reactivated dormant adaptability within the structure.

A decline in P4 (31.96%, measured at the 2018 decision window within the 2005–2018 retail-led phase) marks the post-peak situation following the 2005–2009 retail-led redevelopment, when economic obsolescence (O2) and market saturation temporarily reduced reuse potential despite large-scale investment. The subsequent recovery in P5 (44.08%) reflects the diversification of functions after 2018, including office conversion and adaptive leasing models responding to post-retail urban dynamics.

This non-linear evolution confirms that Renoma’s adaptability has been conditioned primarily by external market and policy dynamics rather than intrinsic technical ageing. The pattern also supports Langston’s assumption that adaptive reuse cycles are externally triggered, with each major reinvestment wave coinciding with moments of regulatory or economic transition.

Overall, the ARP% results highlight the continuity between structural resilience and cyclical obsolescence pressures, forming a quantitative foundation for integrating readiness analysis in the following section.

4.4. Phase-Level Readiness Assessment (F–P Dimensions)

Building upon the ARP results, this section assesses phase-level readiness for adaptive intervention (R), conceptualised as the contextual counterpart to the technical potential. The readiness index aggregates five components—Finance (F), Governance (G), Use commitment (U), Delivery (D), and Policy (P)—each representing a specific decision-making domain influencing the feasibility of transformation.

The scoring procedure follows the rubric introduced in Section 2.3, where component weights (F 0.25, G 0.25, U 0.20, D 0.20, P 0.10) reflect hierarchical decision-gate priorities, giving the highest importance to financial closure and governance stability. Assessments were performed by two members of the research team using triangulated archival, policy, and morphological evidence (see Supplementary Materials File S3).

Median values per phase are summarised in

Table 7. Readiness scores for five components (F–G–U–D–P) across five adaptive phases (P1–P5).

Phase	Period	Decision Window (Ref. Year)	F Finance	G Governance & Approvals	U Use Commitment	D Delivery & Supply Chain	P Policy/Strategic Priority	Readiness R (0–1)
P1. Initial design and wartime transformations	1930–1945	~1947	0.50	0.75	0.75	0.50	1.00	0.66
P2. Post-war socialist reconstruction	1946–1976	~1977	0.50	1.00	1.00	0.50	0.75	0.75
P3. Reconfiguration of retail departments	1977–2004	~2004	0.75	0.75	0.75	0.75	0.75	0.75
P4. Retail-led mixed-use transition & extension	2005–2018	~2018	1.00	0.75	0.75	1.00	0.50	0.84
P5. Office-led functional transition	2019–2025	~2025	0.50	1.00	0.50	1.00	0.75	0.75

Note: Median values derived from structured expert rubrics integrating archival, morphological, and policy evidence (Supplementary Materials File S3). Source: Authors’ computation based on the Readiness protocol.

, representing the relative contribution of finance (F), governance/approvals (G), use commitment (U), delivery/supply (D) and policy/strategic priority (P) to overall readiness in each adaptive phase. This table provides the phase-level decomposition of the Readiness index, and the underlying component scores are reported in full in Appendix B and Supplementary Materials File S1.

Figure 8 visualises these patterns in a radar chart, decomposing the Readiness Index into its five components (F–G–U–D–P) for each adaptive phase.

The readiness results reveal distinct trajectories of institutional and financial preparedness that align with Renoma’s transformation history.

During the post-war reconstruction (P2), the readiness score reached 0.75, reflecting the strong state-led governance and policy alignment that enabled large-scale rebuilding. In contrast, P3 (1977–2004) maintained a similar readiness level (0.75) but under evolving ownership structures, indicating an increasingly stable yet still constrained institutional environment during late socialism and early market transition.

The highest readiness (R = 0.84) was observed in P4 (2005–2018), coinciding with the EU accession period, inflow of private capital, and favourable policy frameworks supporting retail-led redevelopment. This phase illustrates the synergy between financial capability, governance stability, and delivery mechanisms, consistent with the “GO” decision window observed later in the integrated ARP × Readiness analysis.

In the current phase (P5), readiness remains moderate (R = 0.75), driven by strong governance and delivery capacity but moderated by financial and use-related uncertainties linked to the post-retail transition. Despite reduced market confidence, institutional support and adaptive leasing models sustain the building’s capacity for transformation.

Overall, the readiness trajectories demonstrate that Renoma’s adaptive interventions were enabled by periods of institutional stability and policy alignment, with the financial and governance components exerting the strongest influence on timing and scope. The next section integrates these readiness scores with the ARP trajectories to analyse decision thresholds and trigger conditions for each adaptive phase.

4.5. Integration of ARP and Readiness Trajectories

This section combines the technical and contextual assessments to examine how Renoma’s adaptive capacity evolved through successive transformation phases (P1–P5). By integrating the Adaptive Reuse Potential (ARP) and Readiness (R) indices, the analysis identifies critical moments when structural capability aligned—or diverged—from institutional and financial preparedness. While ARP captures the technical and functional potential of the building based on obsolescence and lifecycle parameters, Readiness expresses its contextual feasibility, incorporating financial, governance, and policy enablers. The joint examination of both dimensions makes it possible to identify moments of alignment and lag between technical capacity and contextual preparedness, thereby revealing whether particular transformations were technically driven or contextually induced.

An apparently paradoxical case is Phase P4 (2005–2018). This period contains the largest single redevelopment of Renoma (2005–2009), yet the phase-level GO metric is classified as PREPARE because ARP dips due to heightened economic obsolescence (O2) and increased financial exposure. Interviewees emphasised that the decision to proceed with the project was not driven by acute physical failure but by an opportunity to reposition the asset under favourable macroeconomic conditions, accessible finance and a clear strategic ambition to establish Renoma as a flagship property (Appendix A; expert interviews). In ARP × Readiness terms, this corresponds to a high-readiness state at the end of P3 and the beginning of P4, combined with still-manageable physical and functional obsolescence. The model therefore registers a GO-type window in the late P3 assessment, immediately preceding the redevelopment, while the longer P4 window—encompassing implementation and post-redevelopment risk—is classified as PREPARE. Rather than indicating a mismatch between model and history, this pattern illustrates how major interventions may be opportunity-driven and implemented at the boundary between PREPARE and GO, with ARP × Readiness making explicit the distinction between pressure-driven and opportunity-driven adaptive projects.

Table 8. Integrated ARP, Readiness (R), GO and Decision Class per phase, with aggregate states (WAIT/PREPARE/GO). Phase-level Readiness scores for five components (F–G–U–D–P) across five phases (P1–P5). Source: Authors’ computation based on the Readiness protocol and phase-level evidence integration (Supplementary Materials File S3).

Phase/Decision Window (Ref. Year ±2–3)	ARP%	Readiness R (0–1)	Aggregate Readiness (WAIT/PREPARE/GO) GO [%] (=ARP × R)	Decision Class	Evidence Anchors (see Table 1 and Appendix A)
P1 (1930–1945, ref. 1947)	28.39	0.66	18.81	WAIT	PK-1945; reconstruction decrees
P2 (1946–1976, ref. 1977)	42.50	0.75	31.88	PREPARE	PK-1977; heritage listing
P3 (1977–2004, ref. 2004)	61.64	0.75	46.23	GO	PK-1989; privatisation files
P4 (2005–2018, ref. 2018)	31.96	0.84	26.77	PREPARE	PK-2004; EU accession; permits
P5 (2019–2025, ref. 2025)	44.08	0.75	33.06	PREPARE	PK-2015; Globalworth strategy

presents the integrated ARP–Readiness results and corresponding decision states for each phase. Combined scores were computed as GO [%] = ARP × R, while readiness categories (WAIT/PREPARE/GO) reflect phase-level thresholds derived from the Readiness protocol (Supplementary Materials File S3).

The integrated trajectory (Figure 9) reveals a non-linear relationship between technical potential and contextual readiness, suggesting that Renoma’s adaptive cycles were primarily shaped by external economic and policy shifts rather than by structural degradation.

In the first two phases (P1–P2, 1930–1977), readiness grew in parallel with post-war reconstruction and institutional consolidation under the socialist system. Although ARP remained moderate (28–43%), readiness scores between 0.66 and 0.75 indicate a substantial organisational capacity for managed rebuilding. The transition from a WAIT to a PREPARE state corresponds with the post-war reconstruction decrees and the 1977 heritage listing, both marking policy-driven impulses for transformation.

The third phase (P3, 1977–2004) shows the only GO signal in the long-term trajectory, generated by a marked increase in ARP (62%) combined with stable readiness (0.75). This period, covering late-socialist modernisation and the subsequent market transition, was defined by ownership reforms and privatisation, which created both uncertainty and opportunity. The high GO index (46%) reflects a rare moment of convergence between structural capacity and governance maturity, aligning with the preparatory phase preceding the major redevelopment of the early 2000s.

In the following phase (P4, 2005–2018), readiness reached its highest level (0.84), supported by favourable financing, EU accession, and stable governance. Yet, ARP declined to 32%, mirroring rising economic and market obsolescence (ΣO ≈ 0.48). The resulting PREPARE state corresponds with the 2005–2009 retail-led redevelopment, where strong policy and financial drivers outweighed intrinsic technical necessity. The redevelopment thus addressed not only physical obsolescence but, above all, structural repositioning within a changing urban economy.

The current phase (P5, 2019–2025) continues to display moderate readiness (0.75) alongside renewed structural potential (ARP ≈ 44%), reflecting functional diversification toward office uses and more flexible leasing strategies. Despite sustained governance stability, the persistence of a PREPARE signal points to a strategic pause, where adaptive capacity is available but investment timing is influenced by post-retail market fluctuations and broader urban transitions.

Overall, the combined ARP × Readiness trajectories confirm that Renoma’s transformation cycles were triggered primarily by external governance and economic stimuli rather than by intrinsic obsolescence. Phases of high readiness coincide with major reinvestments and regulatory transitions following periods of systemic change—notably the post-war reconstruction (1945–1947), the socialist-era consolidation and heritage listing (1977), and the post-accession redevelopment (2005–2009)—validating the framework’s decision thresholds and demonstrating the long-term relevance of ARP as a diagnostic tool. Conversely, technical potential remained robust across all phases, underscoring the lasting resilience of the 1930 steel frame and its capacity to accommodate successive adaptive strategies under evolving spatial, institutional, and market conditions.

The alignment between predicted decision states and observed redevelopment actions validates the interpretive robustness of the ARP × Readiness framework. Periods identified as GO coincide with the 2004–2009 retail redevelopment (GO signal in the 2004 decision window preceding the 2005–2009 transformation), while PREPARE states correspond to the 2018–2025 diversification phase, and WAIT reflects intervals of maintenance and consolidation.

This convergence between model predictions and actual interventions confirms that adaptive reuse cycles in long-life commercial buildings are predominantly externally triggered rather than physically constrained. Detailed validation results are provided in Supplementary Materials File S4.

5. Discussion

5.1. Adaptive Cycles and Key Drivers

The integrated ARP × Readiness analysis reveals that Renoma’s transformation followed a sequence of adaptive cycles characterised by alternating phases of technical potential and contextual preparedness. Across nearly a century of use, the building did not age linearly but oscillated between states of structural renewal and market or institutional realignment. This cyclical behaviour is consistent with the interpretation that adaptive reuse in long-life commercial buildings emerges less from physical degradation than from shifts in governance, ownership, and socio-economic frameworks. The Phase P4 redevelopment thus appears “paradoxical” only at first sight: it combines a PREPARE classification with the largest intervention because it was primarily opportunity-driven, triggered at the edge of a GO window under high readiness rather than by extreme physical decay (see Section 4.5).

Throughout the five adaptive phases, three dominant drivers can be identified. First, economic and policy stimuli repeatedly initiated major interventions—from post-war reconstruction under state control, through privatisation and EU-accession redevelopment, to current diversification driven by post-retail economies. Second, institutional governance and regulatory alignment consistently determined the feasibility of these transitions: the 1977 heritage listing, 2004 privatisation framework, and subsequent planning permits each correspond to moments of increased readiness (G and P dimensions). Third, spatial and technological robustness—low functional (O3) and technological (O4) obsolescence—enabled each cycle to build upon existing structures rather than replace them, confirming the long-term adaptability of the 1930 steel-frame system. ARP × Readiness scores align with this pattern, showing a recurrent alternation between high technical potential (O3–O4) and governance readiness (G–P) across the five phases.

The pattern that emerges can be interpreted through the lens of flexible heritage: a condition in which architectural and organisational systems maintain capacity for change without losing identity. Renoma’s successive adaptations illustrate how flexibility operates as a cumulative resource—each phase reactivating and extending latent potential embedded in previous interventions. This finding resonates with recent scholarship on adaptive reuse as a dynamic continuum rather than a one-time event, and with the notion of “heritage resilience” as an active process of renewal under shifting socio-economic pressures [8,110].

Methodologically, the study demonstrates the novelty of retrospective ARP application. While most ARP-based assessments are prospective—used to evaluate proposed projects—this research applies the model longitudinally to reconstruct a century of decision contexts. Such retrospective calibration suggests that the ARP × Readiness framework can serve as an interpretive tool for analysing realised transformation cycles and for identifying recurrent decision thresholds in existing heritage portfolios.

Finally, the findings contribute to ongoing debates on circular economy and climate-adaptive regeneration. By documenting how Renoma’s structural system and design modularity have supported repeated reuse, the study documents how extending the lifespan of existing assets can help reduce embodied carbon and material waste while sustaining cultural continuity. This longitudinal evidence of circular adaptability underscores the relevance of integrative frameworks that link lifecycle assessment with socio-institutional readiness—key to advancing sustainable transformation policies within the European built environment.

5.2. Policy and Design Implications

Practically, municipal instruments can encode decision windows by coupling in-centives and approvals to phase-specific ARP×Readiness alignment [36,50,51,52,111]. The Phase-3 transition (~2004) illustrates a ‘GO’-like state (high ARP and Readiness), supporting early, opportunity-driven intervention before physical obsolescence would escalate [7,112]. This proactive timing logic also aligns with European evidence linking circular refurbishment to climate objectives [23,25,113]. Consistent with public-investment guidance, governance and finance are treated as activation preconditions [50,52]. Recent synthesis also underscores coupling adaptive-reuse timing with participatory urban governance and circular-economy objectives [114].

These policy linkages echo the priorities of the Circular Economy Action Plan [23] and the New European Bauhaus Compass [24], and are reflected in the New European Bauhaus Progress Report [115], which annexes the Compass and sets out implementation and funding actions.

Effective adaptive reuse depends on governance frameworks that enable timely coordination and flexible regulatory models, particularly through financial and procedural incentives. As highlighted by Foster and Saleh [116,117], adaptive reuse strategies tend to be more successful when embedded in urban sustainability agendas and supported by public–private cooperation. Similarly, the World Economic Forum’s Adaptive Reuse of Assets: Model Policy [111] emphasises the importance of removing regulatory barriers, aligning land-use planning with reuse objectives, and promoting portfolio-scale adaptation.

In the European context, policy coherence between urban regeneration, circular economy, and cultural heritage management remains critical. Recent studies stress that reuse should be recognised as a core climate-mitigation tool—reducing embodied carbon and construction waste while retaining cultural continuity [23,25,111]. Consequently, adaptive reuse policy should not remain confined to heritage registers but be integrated into broader sustainability frameworks and city-scale investment strategies [116,118]. At municipal and national levels, the ARP × Readiness framework can be embedded into adaptive reuse and urban regeneration toolkits as a timing and prioritisation module, complementing existing policy instruments for heritage-led renewal, circular economy roadmaps and whole-life carbon management.

Implications for Design Practice and Heritage Management

For architects, engineers, and asset managers, the Renoma case underscores the value of modular structure, spatial flexibility, and technological upgradeability as enduring design attributes. Functional (O3) and technological (O4) obsolescence remained consistently low across all phases, indicating that—in this case—the robustness of the original design can sustain multiple adaptive cycles [32,113]. As demonstrated by the Renoma case, the original steel-frame and grid layout have repeatedly accommodated programmatic and technological updates, illustrating how structural robustness enables long-term adaptability in heritage commercial buildings. This observation echoes research on design for adaptability and open building principles, which advocate embedding future-proof flexibility into spatial systems and façade modularity [119], and is consistent with case-based evidence on flexible-building outcomes [64,120].

Design practitioners can therefore employ the ARP × Readiness framework as a decision-support tool, allowing them to map technical indicators (e.g., EL_b, EL_u, f) alongside readiness factors (F–G–U–D–P) to identify optimal intervention windows. This approach aligns with the growing use of lifecycle-based assessment in adaptive reuse planning [28]. Timing interventions when both ARP and readiness are high can enhance cost efficiency and sustainability outcomes while reducing the risk of fragmented renovation.

From a conservation perspective, this supports a shift from ‘intervention when failure occurs’ to ‘intervention when opportunity aligns’. The concept corresponds to the evolving understanding of heritage as a dynamic resource, where cultural and economic value are sustained through managed transformation rather than static preservation [121,122]). Recent research in sustainable heritage practice also frames this as a co-evolutionary mode—a mode of stewardship enabling adaptive capacity while safeguarding identity [123].

Finally, integrating readiness checks (financial closure, governance stability, and stakeholder alignment) into project briefing and management workflows can strengthen the link between design and policy decisions. This integration bridges technical assessments with institutional preparedness, supporting strategic and well-timed interventions in complex heritage redevelopment projects [36,111,120,124]. Together, these insights suggest that adaptive reuse strategies benefit from synchronising technical readiness with institutional and financial timing, forming a practical bridge between building-level adaptability and policy-level resilience.

5.3. Transferability and Portfolio Application

The retrospective application of Langston’s Adaptive Reuse Potential (ARP) model to the Renoma Department Store indicates diagnostic value beyond prospective feasibility studies. While most prior ARP applications focused on forward-looking assessments of underused assets [28,112], this study extends the method’s scope by interpreting a century of completed interventions within a single, dynamically evolving structure. Such a retroactive analysis supports the use of the ARP model as a longitudinal heuristic—capable of mapping adaptation cycles and correlating technical resilience with contextual triggers.

5.3.1. Methodological Transferability

The ARP × Readiness framework appeared sufficiently robust in reconstructing adaptive sequences across heterogeneous datasets (archival, morphological, institutional). This suggests its potential applicability to other complex, long-life urban buildings—including department stores, office buildings, transport hubs, and post-industrial or civic complexes—where multiple adaptation layers accumulate over time [8,120].

For such assets, retrospective ARP analysis may serve as a portfolio diagnostic tool, identifying when and why certain typologies respond effectively to external stimuli. Portfolio-scale use of ARP indicators has already been piloted for office-to-housing conversion [112] and public-sector reuse schemes [120], indicating potential for integration with data-driven asset-management systems. This approach mirrors the longitudinal evaluation of Renoma, demonstrating how asset-level adaptability metrics can inform broader portfolio strategies.

Furthermore, the readiness dimension adds a strategic layer absent from conventional ARP formulations. By embedding governance, finance, and policy variables, it allows comparative ranking of assets not only by physical potential but by decision readiness—a concept transferable to institutional or municipal portfolios [111]. This dual approach aligns with emerging European frameworks linking heritage reuse, carbon reduction, and socio-economic resilience [23,25,117,125]. Transferability will depend on data availability, appropriate local policy proxies for readiness, and scoring consistency across portfolios.

5.3.2. Flexible Heritage and Dynamic Contexts

Renoma’s trajectory illustrates that adaptive potential can persist under shifting ownership, functions, and policy regimes. This resilience underscores a dynamic, values-based view of heritage in which significance is sustained through managed transformation rather than static preservation [121,122]. The retrospective ARP analysis suggests that design attributes—such as modular structural grids and open spatial logic—enable recurring reuse without compromising authenticity, echoing principles of design for adaptability [59]. This pattern aligns with co-evolutionary accounts of heritage reuse, where adaptive capacity emerges from iterative interactions among actors, norms, and spatial systems [123,124].

Transferring this framework to other heritage typologies thus offers not only a technical model but also a conceptual bridge between conservation theory and circular-economy practice [118]. When coupled with readiness metrics, ARP can help stakeholders anticipate feasible intervention windows—shifting adaptive reuse from reactive necessity to planned cycles of sustainable renewal. This trajectory resonates with ‘living/future-oriented’ heritage perspectives that emphasise adaptation while safeguarding identity [126,127].

5.4. Limitations and Future Research

While the retrospective application of the ARP × Readiness framework has yielded novel insights into Renoma’s long-term adaptive dynamics, several methodological and empirical limitations must be acknowledged. The reconstruction of adaptive trajectories over nearly a century inevitably depends on the availability and reliability of historical documentation. Certain periods, notably 1946–1956 and 1980–1990, were characterised by fragmented or inconsistent records, particularly concerning governance and financial decision-making. Although triangulation with archival, morphological and policy evidence (see Section 2.3) mitigated these data gaps, such limitations may have influenced the precision of readiness scoring and may introduce endogeneity between readiness and intervention timing; we therefore refrain from causal claims. In addition, all O1–O7 and readiness scores were assigned retrospectively, with experts aware of the subsequent intervention sequence, so the trajectories are unavoidably susceptible to hindsight bias. This may lead modelled decision windows to align too closely with realised projects and to under-represent missed opportunities, and we therefore interpret the ARP × Readiness outputs as structured reconstructions of decision contexts rather than evidence of ex ante predictive performance. We do not assess whether individual interventions were optimal in a long-term sense; some high-GO periods without realised projects may represent missed opportunities or constraints that cannot be fully reconstructed from available documentation. Given the semi-quantitative nature of the underlying scales, small differences in ARP or R scores should not be over-interpreted; our inferences focus on the relative ordering of phases and their classification into WAIT/PREPARE/GO states. Consequently, the main conclusions (Section 5 and Section 6) should be read as pattern-oriented explanations for this specific case rather than as statistically generalisable estimates or prescriptive rules.

Similar challenges have been noted in other longitudinal studies of adaptive reuse where data completeness and expert interpretation affect model calibration [120,127]. Despite these constraints, the retrospective application of the ARP × Readiness model appeared valuable for uncovering decision-making patterns otherwise inaccessible through conventional historical or design-based analysis.

A further limitation concerns stakeholder perspectives: the empirical design privileges expert and archival sources and does not systematically incorporate user or community stakeholders. As a result, the adaptive trajectories reconstructed here primarily reflect institutional and professional readings of reuse decisions rather than lived experience or local perceptions of value, accessibility and distributional effects. Future applications of the framework should therefore triangulate expert-based assessments with participatory methods to capture how different stakeholder groups experience and evaluate adaptive reuse outcomes.

Another limitation lies in the methodological reinterpretation of Langston’s Adaptive Reuse Potential model, originally developed for prospective assessment [28]. The life-cycle policy adopted here (EL_u estimated independently of 1 − EL_b to represent partial renewals) slightly departs from the original single-life specification and should therefore be read as a heritage-specific variant of ARP rather than a direct replication. Its retrospective adaptation, as applied here, required translating expert judgements and lifecycle metrics into backward-looking parameters. This introduces a degree of uncertainty regarding temporal comparability, yet the consistent results across the five adaptive phases suggest that the model is sufficiently robust when used as a heuristic rather than a predictive tool. Recent methodological syntheses also suggest integrating ARP-type models with multi-criteria decision-making (MCDM) and preference measurement modules (PMM) to enhance transparency in weighting and the reproducibility of adaptive assessments, which could be explored in future research [11,128,129]. Similarly, the inclusion of the Readiness dimension—conceptually justified as the contextual counterpart to technical potential—relies on interpretive weighting of institutional and financial evidence. Future research should operationalise these indicators through measurable proxies, such as policy indices, investment flows or stakeholder-network metrics [117,130].

The study’s single-case design, centred on a large commercial heritage building, limits direct generalisability. Future multi-case research could reduce interpretive uncertainty by pairing ARP × Readiness with standardised indicators [32,113] (ISO 20887:2020; Level(s) Indicator 2.3) and by applying a formal multi-round Delphi protocol with predefined consensus criteria (e.g., IQR/Kendall’s W) to strengthen methodological consistency [131,132]. A complementary avenue is to align evaluation with beyond-GDP well-being and equity metrics emerging in adaptive-reuse policy discourse [114].

Nevertheless, the Renoma Department Store provides an exceptional laboratory for testing adaptive-reuse theory in a context of repeated reinvestment and institutional change. Comparative research across portfolios of department stores, post-industrial or civic buildings could verify whether the WAIT–PREPARE–GO pattern observed here holds across different typologies [112,121]. Such comparative extensions could also draw on emerging longitudinal databases and parametric models of building adaptability, allowing statistical appraisal of ARP-derived trajectories across broader typological samples [12,31]. Furthermore, the specific socio-economic and policy environment of Wrocław—as a post-socialist city undergoing rapid market and ownership transitions—suggests that governance and readiness dynamics may differ in more stable regulatory frameworks. Cross-national analyses could thus explore how political economy and market maturity shape adaptive timing and decision thresholds [23,24].

Future research should deepen the model’s analytical scope by quantifying contextual enablers and inhibitors, integrating environmental and carbon-performance indicators, and testing portfolio-scale applications through multi-criteria decision tools [9,115,128,129,130]. Building upon the longitudinal ARP × Readiness assessment, subsequent work will also extend toward a comprehensive circularity- and morphology-oriented analysis of Renoma. This next phase will examine the spatial and functional evolution of the building through the lens of circular design and use value, tracing how successive transformations have generated spatial, material and operational loops.

Integrating this morphological mapping (cf. [43,44]) with lifecycle and policy indicators would provide a multidimensional model of circular adaptive reuse, bridging architectural transformation with environmental performance [23,113,117]. Such an approach could strengthen evidence for the building’s long-term flexibility and offer replicable insights for circularity assessment in complex heritage assets. In parallel, combining ARP × Readiness with participatory tools—such as user surveys, focus groups or co-design workshops—would help test how expert-based timing assessments align with everyday use patterns, perceived inclusiveness and community notions of value. By addressing these dimensions, future studies may consolidate the ARP × Readiness framework as a longitudinal and multiscalar methodology for evaluating and managing adaptive heritage, connecting empirical urban research with practice-oriented models for circular transformation. The authors plan to continue this line of research by developing a circularity-oriented analytical framework applicable to comparable adaptive-heritage cases, linking morphological mapping with policy and lifecycle data. Future work could also explore conceptual alignments between the Readiness dimension and existing frameworks such as ISO 20887:2020 and the European Commission’s Level(s) indicator (2020), to examine potential pathways toward harmonised metrics for circular built-heritage assessment [32,113].

Overall, the study indicates that retrospective use of the ARP × Readiness framework can reveal systemic decision dynamics and long-term adaptive patterns, offering insights that may lie beyond the reach of conventional single-phase analyses.

6. Conclusions

This study offers a longitudinal reading of the Renoma Department Store as a flexible heritage asset, showing how cyclical obsolescence and adaptation interact over nearly a century and reframing why key interventions occurred when they did—not only whether they were technically feasible. By combining Langston’s Adaptive Reuse Potential (ARP) with a contextual Readiness index and interpreting decision windows through a WAIT–PREPARE–GO lens, we capture how architectural adaptability interacts with changing institutional and market conditions between 1930 and 2025.

These conclusions respond directly to the three research questions set out in Section 1.2. First, with respect to whether a retrospectively applied ARP model, combined with a contextual Readiness index, can reconstruct the timing and drivers of major interventions (RQ1), the integrated ARP × Readiness trajectories capture the observed sequence of post-war reconstruction, socialist modernisation, and post-EU-accession renewal, while also revealing earlier periods of unexploited potential. Second, in relation to how morphological and structural features affect obsolescence and adaptive potential (RQ2), the Renoma case confirms that the original steel frame, light courts, and depth-modulated plan have consistently enabled reconfiguration and mitigated physical obsolescence of the 1930 fabric. Third, concerning how readiness conditions evolved across socio-political regimes (RQ3), the phase-based readiness profiles show shifts in finance, governance, use commitment, delivery capacity, and policy priority from state socialism through post-socialist transition to EU-accession and post-accession contexts, clarifying how these factors shaped the timing of major interventions.

Empirically, peaks in ARP and Readiness coincided with the major reinvestments observed in the record—post-war reconstruction, socialist modernisation, and post-EU-accession renewal—while the original steel frame retained substantial reserves. Put simply, the frame endured; the “go” moments appeared when institutions and capital aligned. This pattern indicates that timing was governed primarily by governance stability, finance, and policy priorities rather than by technical ageing, and it aligns with the observed intervention sequence.

Methodologically, we extend ARP from prospective screening to a retrospective, phase-sensitive diagnostic. The combined ARP × Readiness approach is transparent and replicable: it enables practitioners to map technical potential alongside institutional feasibility and is adaptable to complex building portfolios as well as policy-level analyses that link circular-city objectives with heritage stewardship. As a Central European case, Renoma adds evidence from a post-socialist governance setting to a literature still dominated by Western exemplars.

Implications for practice include treating long-life commercial heritage as “skin-and-frame” assets; timing projects when technical capacity and institutional readiness align; and using readiness checks—finance (F), governance/approvals (G), use commitment (U), delivery/supply chain (D), and policy/strategic priority (P)—to de-risk delivery. For readers concerned with sustainability outcomes, this alignment can help maximise retained value while minimising avoidable whole-life-carbon loss from premature replacement. Future work should test transferability across portfolios and cities, integrate quantitative environmental metrics (e.g., Level(s) adaptability; whole-life carbon), develop a circularity-oriented morphological reading that traces spatial, material, and operational loops, and systematically incorporate user and community stakeholder perspectives into readiness assessments and decision windows.