Augmented Reality (AR) has emerged as a transformative tool in consumer–product interactions, particularly in food packaging. However, a systematic approach to designing AR-enhanced packaging for Ready-to-Cook (RTC) products and its impact on consumer behavior remains underexplored. This study employed a mixed-method approach based on the ADDIE model. The research was conducted in three phases: (1) qualitative interviews with seven experts to develop the AR-Enhanced Packaging Design Framework and the RTC AR Content Model; (2) the design and development of a Pad Thai RTC packaging prototype; and (3) a quantitative evaluation involving 400 consumers using an integrated framework combining the AIDA model, willingness to pay, and brand experience. The study validated two frameworks: (1) the AR-Enhanced Packaging Design Framework, comprising six dimensions across structural and graphic elements; and (2) the RTC AR Content Model, which identifies cooking methods and food history as the most engaging content types. Experimental results showed that AR significantly enhanced attention (M = 4.48) and interest (M = 4.27), with moderate effects on desire (M = 3.92) and action (M = 3.78). A majority of participants (76.25%) expressed a willingness to pay a premium, averaging 18.42%, for AR-enhanced packaging. Correlation analysis revealed a strong relationship between brand experience and purchase intention (r = 0.71, p < 0.01). These findings demonstrate that the integration of AR into RTC packaging enhances consumer experience and commercial value. The proposed frameworks provide practical guidelines for implementing AR in food packaging design.
Although robo-advisors are rapidly reshaping the retail investment landscape, the factors driving sustained user engagement remain poorly understood. Rather than relying on traditional survey-based methods grounded in generic technology adoption frameworks, which frequently fail to capture organic user priorities, this study takes a different approach. We analysed 122,788 user reviews of three leading robo-advisor apps using a hybrid pipeline that combines BERTopic with the DEMATEL-based Analytic Network Process (DANP). BERTopic surfaced fourteen perception constructs grounded in users' own words, ranging from ease of use and financial benefit to authentication friction and intrusive advertising. DEMATEL mapped the expert-perceived influence pathways linking these constructs, classifying nine as influential causes and five as dependent effects, and revealing a feedback loop in which trust both shaped and was shaped by usability. DANP integrated these interdependencies into global priority weights. Perceived Ease of Use (8.20%) and Perceived Financial Risk and Trust Concerns (7.87%) emerged as structural pillars of adoption, while Account Management Quality, Interface and System Performance, and Perceived Financial Benefit served as convergence points where upstream design and trust signals translate into perceived outcomes. The findings reframe trust as endogenous rather than antecedent, offering providers and regulators a defensible, behaviourally grounded ranking of where attention is most warranted in algorithmic financial services.
National Innovation Systems (NIS) theories were formed in a period of relative international stability, with the functioning of such systems, and the open flows in and out of a given system, and between them, being largely assumed. These systems are now being reconfigured within a geopolitical context marked by strategic competition. They are increasingly driven not by market efficiency, but by the strategic and security interests of the state. Systems must balance the need to protect national security with the desire to foster innovation and promote economic growth, facing complex trade-offs, and understanding the growing significance of the innovation of technologies for state power. NIS theories must place more emphasis on strategic orientation in this context. This article develops a new framework for NIS interaction in the context of international relations (IR), and an overarching framework for the complex interaction between competing and Strategic National Innovation Systems (SNIS) in the international system of states. The SNIS framework addresses modes of control that regulate system inputs and outputs. This includes capital (including investment), export and import, labour, and information (including data) controls. Addressing third states within a bipolar dynamic, it brings in concepts such as balancing, bandwagoning, buck-passing, and hedging, to address the strategic orientation of any given SNIS in the rivalrous context of the international system. This framework can therefore be applied not only to a hegemonic innovation system, or that of a rising power, but to a hegemon-aligned third power, a rising-power aligned power, or a non-aligned third power.
As universities transition beyond their traditional Third Mission, they increasingly participate in complex, Quintuple and N-tuple helix open innovation ecosystems, balancing technology commercialization with pressing sustainability demands. This study investigates 301 universities within the European Economic Area (EEA) to analyze how academic innovation outputs (Horizon 2020 participation, patents, spin-offs) and multi-dimensional sustainability profiles relate to regional economic and labor market outcomes. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), the empirical results demonstrate that while regional scientific and human capital exhibits a strong positive association with regional GDP and employment, a direct conversion of technological innovation outputs into economic growth is absent at the aggregate level, potentially pointing to a “Regional Innovation Paradox”. Furthermore, environmental sustainability performance is positively linked to innovation capacity, whereas social sustainability outcomes appear structurally decoupled in the short run. Finally, Multi-Group Analysis (MGA) reveals critical spatial asymmetries; the economic returns of academic excellence are concentrated in high-density metropolitan hubs with robust absorptive capacity, whereas in low-density peripheral regions, universities primarily stabilize local economies by functioning as critical anchor institutions.
Digital transformation in emerging economies increasingly unfolds under conditions of dependence on externally controlled platforms, infrastructures, standards, and data ecosystems. Although digital sovereignty has become a prominent policy objective, the institutional processes through which public organizations move from digital dependency toward greater governance autonomy remain insufficiently theorized. To address this gap, this study develops a conceptual framework through a concept-driven hermeneutic narrative review to explain how digital open innovation can support the development of digital sovereignty. The framework conceptualizes digital sovereignty as an institutional governance capability that emerges through a sequential process rather than as a static policy objective. Specifically, digital open innovation provides access to external knowledge, institutional learning capacity converts that knowledge into organizational capability, and knowledge localization adapts those capabilities to domestic legal, administrative, and societal contexts. Digital sovereignty is achieved when these localized capabilities enable governments to govern digital systems in accordance with national priorities. The framework also identifies exnovation as a boundary condition that strengthens this pathway by facilitating the retirement of obsolete routines, legacy systems, and dependency-producing arrangements. The study presents a mechanism-based explanation of how public organizations can move from digital dependency toward digital sovereignty through learning, localization, and institutional renewal. The proposed framework contributes to the literature on digital governance and open innovation by integrating previously fragmented perspectives and provides a foundation for future empirical research and policy development in emerging economies.
This study examines the novel role of adhocracy culture, alongside servant leadership, in fostering work engagement and, subsequently, innovative service behaviour and knowledge-sharing behaviour among employees in Indian start-ups. Using data collected from 387 employees of Indian start-ups, the study employs Structural Equation Modelling in AMOS to demonstrate that servant leadership and adhocracy culture indirectly enhance innovative service behaviour and knowledge-sharing behaviour through work engagement. The findings underscore the mediating role of work engagement in translating leadership and organizational culture into positive employee behaviours. The study offers practical implications for policymakers and start-up managers by highlighting the importance of fostering servant leadership and an adhocracy culture to enhance employee engagement, proactive service behaviour, creativity, and knowledge sharing. The study contributes to the literature by identifying adhocracy culture as a novel antecedent of work engagement in the start-up context and by providing a context-specific framework that captures the dynamic nature of start-up organizations. In doing so, it extends existing research by explaining how work engagement serves as a mechanism through which servant leadership and adhocracy culture promote innovative service behaviour and knowledge-sharing behaviour, offering insights that may inform future cross-sector and cross-contextual research.
Artificial intelligence (AI) is transforming recruitment and selection, yet limited research has examined how organisations communicate AI use in job advertisements. Drawing on signalling theory, this study investigates how global technology firms present AI as an attraction-oriented signal and disclose its possible involvement in selection processes. Using a qualitative exploratory design, the study analysed 100 LinkedIn job advertisements from ten globally operating technology firms. The findings show that AI was highly visible in attraction-oriented communication, where it signalled innovation capability, future-readiness, technical prestige, career development, work transformation, and responsible technological identity. By contrast, selection-process disclosure was limited and uneven. Within the sampled advertisements, most did not explain whether AI might be used for application screening, shortlisting, candidate ranking, interview evaluation, or hiring decision support. Where disclosure appeared, it was generally broad and non-specific; only one advertisement provided stage-specific information together with retained human decision authority and applicant-rights information. The study conceptualises this patterned imbalance as AI signalling asymmetry, whereby promotional AI visibility exceeds procedural AI transparency. It extends recruitment signalling theory by shifting attention from signal presence to the location, specificity, and consistency of signals, and offers practical implications for transparent, ethical, and applicant-centred AI disclosure.
Although digital transformation is widely framed as a strategic imperative, its competitive returns remain uneven in fragmented logistics ecosystems. This study addresses that puzzle by integrating the Dynamic Capabilities View and the Attention-Based View into a capability-conversion model for freight forwarding SMEs. In the model, technology capability, strategic agility, and network capability are linked to competitive advantage not directly but through absorptive capacity, which contributes to competitive advantage both directly and through digital transformation. The results position absorptive capacity as the central cognitive-organizational mechanism linking these upstream capabilities to competitive advantage. Entrepreneurial orientation operates as an asymmetric boundary condition: it is associated with a stronger link between absorptive capacity and competitive advantage yet a weaker link between digital transformation and competitive advantage, a pattern consistent with an attention-based account in which a strong entrepreneurial posture may draw managerial attention toward exploration at the expense of execution discipline. The study refines dynamic-capability reasoning, extends attention-based explanations of competitive advantage, and interprets both through open-innovation dynamics under ecosystem fragmentation.
The integration of Deep Learning (DL), a subset of Artificial Intelligence (AI) and Machine Learning (ML), into the financial sector has accelerated over the past decade, transforming a wide range of financial services and research domains. Uncertainty, volatility, and systemic risks are becoming increasingly frequent in financial systems; therefore, ensuring reliable risk assessment is crucial for accurate decision-making, economic stability, and effective regulation. Non-linear connections in financial data are frequently difficult for traditional statistical models to capture, but DL offers versatile tools for modeling extremely complicated risk dynamics. By integrating open and proprietary data in collaborative settings, DL-based risk assessment supports the development of open innovation ecosystems, enabling banks and fintech organizations to strengthen their capacity to address increasingly complex, interconnected, and rapidly evolving financial markets. This paper provides a comprehensive review of recent advances in DL-based risk assessment and management by systematically analyzing the articles published between 2019 and 2026 across different domains, such as Bankruptcy Prediction, Bond Rating, Business Failure Prediction, Loan-insurance Underwriting, and Mortgage Choice Decision. Moreover, it highlights the applications within each domain, along with the limitations of integrating DL into risk management, and how it could improve the prediction, detection, and mitigation across organizations. The results indicate that DL is most frequently utilized in risk management, while its application in asset pricing remains relatively limited.
Traditional performing arts communities in the Global South face a structural paradox: as custodians of intangible cultural heritage (ICH), they are systematically excluded from the digital ecosystems through which cultural value is produced and circulated. This study theorises hyperlocal media innovation as an open innovation process, reporting findings from a three-month participatory action research (PAR) intervention with the Bali Buja community (Paguyuban Peduli Budaya Jawa), a Javanese karawitan association in Klaten, Central Java, Indonesia. A five-stage programme — socialisation, digital literacy training, professional technology deployment, mentoring, and sustainability planning — was evaluated through a convergent mixed-methods design integrating verified YouTube Studio analytics and pre/post digital literacy assessments. During the programme period, average impressions per video increased significantly (+44.6%; Mann–Whitney U = 152, p = .020, r = .46). In the subsequent four-month autonomous period (January–April 2026), the Community Media Team achieved + 716% average views per video (U = 272, p = .002) and + 701% average impressions per video (p < .001, r = .75). The channel now records 3129 subscribers and 28,300 views in the most recent 90-day period, with 79% organic traffic acquisition. Digital literacy gains were verified across five competency dimensions. These findings provide preliminary empirical support for a three-pathway open innovation model (inbound–outbound–coupled) for ICH communities, proposing the Community Media Team as a replicable endogenous open innovation intermediary and extending open innovation theory into cultural heritage and social innovation domains.
With the rapid growth of online stores, electronic word-of-mouth (eWOM) has become a key influence on consumer purchase decisions. This study examines how textual cues (information quality) and non-textual cues (popularity heuristics, performance visual heuristics, user-generated pictures, and emojis) affect purchase intentions in digital bookstores. Using a between-subjects experiment across five conditions, the results showed that perceived information quality plays a fundamental and significant positive role in predicting purchase intention within the control group. Moreover, combining information quality with non-textual cues, especially user-generated pictures, popularity heuristics, and performance visual heuristics, has a notable effect in enhancing purchase intention compared to information quality alone. However, emojis did not produce a significant effect, suggesting that purely decorative cues lack sufficient diagnostic value in high-involvement purchase contexts. Regarding moderation, consumers’ prior online shopping experience plays a significant but cue-specific moderating role. Rather than uniformly amplifying cue effects, experience significantly reduces reliance on popularity heuristics and user-generated pictures, leaves the effect of performance visual heuristics unchanged, and exerts only a marginal influence on emojis. This pattern reflects an “expertise compensation” mechanism within the proposed Diagnostic Multimodal Processing (DMP) framework. The findings confirm the importance of a multidimensional approach in designing eWOM messages and emphasize the a strategically selected combination of textual and non-textual cues based on their diagnostic value. The DMP framework explains cue effects via social proof, authenticity, and processing efficiency, offering practical guidelines for bookstore recommendation advertising.
This study explores the impact of entrepreneurial leadership and digital entrepreneurial ecosystems on the performance of SMEs in Pakistan's manufacturing industries. Based on digital entrepreneurship and open innovation perspectives, a sequential cognitive-behavioral mechanism is proposed in this study. It links digital entrepreneurial mindset and entrepreneurial agility to the transformation of leadership and ecosystem support into better performance in the context of environmental uncertainty. A total of 385 manufacturing SMEs' founders or managers have been selected from Punjab, Pakistan, for data collection and analyzed using PLS-SEM. The results indicate that entrepreneurial leadership and digital entrepreneurial ecosystems have a significant positive influence on digital entrepreneurial mindset, which in turn positively impacts the entrepreneurial agility and SME performance. Entrepreneurial agility is more effective in enhancing SME performance in high environmental uncertainty. The study contributes to the literature by offering an understanding of the digital entrepreneurial mindset as the cognitive mechanism and entrepreneurial agility as the behavioral mechanism between external support of the ecosystem in the form of government, policy, and funding, and the performance of SMEs. In practical terms, the results indicate that the SMEs need to form digital thinking and agile skills to respond in an effective way to the uncertain and technologized markets.
The low-altitude economy (LAE)-encompassing commercial unmanned aerial vehicles, aerial logistics, and low-altitude digital infrastructure-functions as a platform technology with significant implications for regional entrepreneurship and sustainable development. This study examines whether and how LAE infrastructure expands the structural openness conditions of regional innovation ecosystems, thereby stimulating entrepreneurial entry and survival. Using a balanced panel of 63 Vietnamese provinces over 2018–2023 (N = 378), we construct a novel Low-Altitude Economy Intensity Index (LAEI) from administrative drone registration records and employ a Spatial Durbin Model as the primary estimator, complemented by System GMM, staggered Difference-in-Differences, and 1,000-iteration placebo tests. Results show that a one-standard-deviation increase in LAEI raises new enterprise formation by 7.3% and three-year enterprise survival rates by 4.1 %age points. Spatial spillovers account for 38% of total effects, substantially exceeding conventional infrastructure benchmarks and confirming cross-regional platform externalities. Effects are 2.3 times larger in geographically remote provinces, evidencing a leapfrog development mechanism. The study introduces airspace-mediated market access as a novel theoretical construct and derives six evidence-calibrated policy instruments for sustainable LAE ecosystem governance. These findings extend open innovation theory to physical-digital infrastructure and provide new evidence on how platform technologies reshape the spatial architecture of entrepreneurial ecosystems.
Monitoring progress toward the Sustainable Development Goals (SDGs) requires data infrastructures capable of integrating heterogeneous sustainability datasets and transforming them into actionable insights. However, many existing sustainability data systems remain fragmented, relying on isolated analytical tools and static datasets, which limit cross-domain integration and hinder the timely monitoring of sustainability indicators. This study proposes an open and scalable data architecture for sustainability intelligence that integrates modern data engineering practices with real-time analytical capabilities. The framework combines a Medallion-based data warehouse for structured data management, a star schema for multidimensional analytical querying, and an event-driven streaming pipeline that enables continuous monitoring of sustainability indicators. By integrating batch-based historical analytics with streaming-based incremental processing, the architecture supports both long-term sustainability assessment and near real-time detection of environmental anomalies. The framework is demonstrated using environmental indicators related to SDG 7 (Affordable and Clean Energy), SDG 13 (Climate Action), and SDG 15 (Life on Land), incorporating global datasets on carbon emissions, fossil fuel consumption, renewable energy generation, and forest area. Empirical evaluation through system performance benchmarking across six streaming load levels demonstrates that the architecture maintains sub-2000 ms end-to-end latency, stable anomaly detection rates between 4.60% and 4.93%, and scalable throughput up to moderate ingestion loads, providing quantitative evidence of the framework's operational reliability for near real-time sustainability analytics. Overall, the proposed architecture provides a practical foundation for data-driven sustainability intelligence systems that support policy evaluation and evidence-based SDG monitoring.
Government expenditure is frequently viewed as a central policy tool for advancing sustainable development, yet its relationship with Sustainable Development Goals (SDGs) may depend on the broader structural and financial conditions in which it operates. This study examines the association between government expenditure and SDG progress, accounting for the conditioning roles of financial development and economic development in a cross-national panel from 2000 to 2024. The analysis employs complementary panel estimators that allow for cross-sectional dependence, slope heterogeneity, and regime-dependent relationships. The results show that government expenditure is positively associated with SDG progress on average, but this association is conditional and nonlinear rather than uniform across countries. Financial development strengthens the association between government expenditure and SDG achievement, while higher levels of economic development also make this association more favorable. The threshold analysis further reveals statistically significant nonlinear dynamics around an estimated aggregate-development threshold of 26.208 in logged terms: in low-development regimes, government expenditure and its interaction with financial development are less favorably associated with SDG progress, whereas in high-development regimes, government expenditure is more favorably associated with SDG progress when financial development is relatively stronger. The findings indicate that countries may need to sequence fiscal expansion with financial-sector deepening and economic development, rather than treating public expenditure as a standalone instrument for sustainability.
Digital intelligence transformation is increasingly essential for organizations seeking to remain competitive in artificial intelligence–driven environments. Despite its strategic importance, limited research has examined the employee-level factors that determine whether such transformations gain internal support. Grounded in social cognitive theory, this research proposes and validates an integrative framework that examines how employees’ personal competencies, perceptions of the team learning environment, and ethical apprehensions interact to influence their willingness to engage in digital intelligence transformation. Using three-wave survey data from 316 employees working in organizations undergoing digital intelligence transformation, the results show that employees’ confidence in their ability to perform transformation-related tasks is a key driver of supportive intention. Skills in using artificial intelligence digital assistants and team ambidexterity both enhance this confidence, which in turn strengthens employees’ willingness to support the transformation. However, ethical anxiety related to artificial intelligence weakens the positive effect of skill development on confidence and reduces the overall indirect influence on supportive intention. These findings highlight the central role of employee self-beliefs and ethical concerns in shaping successful digital intelligence transformation.
Manufacturing firms in resource-intensive emerging economies, such as Bangladesh, face extensive environmental pressure related to resource consumption, waste generation, environmental damages and buyers’ sustainability demands. Accordingly, green entrepreneurship (GEN) and circular economy practices (CEP) provide vital pathways to enhance the environmental performance (EP) among these firms. This research examines how GEN and CEP influence sustainable green innovation (SGI) and EP, and answers whether innovation leadership (ILE) and environmental dynamism (END) shape the strengths of these influences. This work draws on the green dynamic capability view (GDCV), developing an integrated framework through the operation of dynamic capabilities into Teece (2007) classical core microfoundations of sensing (SEN), seizing (SEI), and transforming (TRA). The study analyzes survey data from 275 managerial professionals from Bangladeshi manufacturing firms through partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fs-QCA). The findings suggest that GEN positively influences both CEP and SGI, whereas CEP also enhances SGI. Besides, SGI is significantly associates in improving EP. In addition, END strengthens the relationship between CEP and SGI, while ILE has a significant negative moderating effect on the relationship between GEN and SGI. The outcomes of this research are valuable for managers in Bangladesh to implement linear and conditional strategies to enhance firms’ environmental performance. The results suggest that managers should promote green entrepreneurial initiatives, implement circular economy practices in daily operational activities, and transform circular routines into clean products and processes to facilitate environmental performance at firms. For policymakers, this study highlights the need for relevant policy actions in this context.
Environmental, Social, and Governance (ESG) performance is now a core part of corporate stability, but traditional financial measures are no longer sufficient to assess value amidst rising environmental and social pressures. ESG data remains complex and is often criticized for being opaque or prone to greenwashing. This methodological challenge is exacerbated by the fact that standard deep learning models do not always outperform tree ensembles on structured data, leaving a gap in reliable forecasting. This study develops the Deep-Tree Fusion framework to address these issues by synergistically integrating tree-based logic with differentiable deep learning. The study uses a long-term dataset of 11,000 firm-years spanning 2015-2025. The architecture follows a hierarchical design that first extracts non-linear structures from tabular data and then refines them through a specialized fusion network. Time-based integrity is maintained by sorting data chronologically to avoid the biases of random sampling. Empirical findings demonstrate that DTF achieves a superior predictive fit (R2 = 0.97866) and significantly outperforms 10 conventional baseline models. Practically, this work provides a reliable diagnostic tool for investors and policymakers to measure sustainability-adjusted value and manage long-term risks. The framework is robust across various industries and maintains stable performance despite market volatility.
This study examines the relationships among financial development (FD), innovation (INN), foreign direct investment (FDI), economic growth (EG), and renewable energy (RE) consumption in the Republic of South Africa over the period 1990–2025. The purpose is to identify key macroeconomic drivers of RE transition while accounting for potential nonlinear and asymmetric dynamics. Using annual time-series data from internationally recognized sources, the analysis employs the Nonlinear Autoregressive Distributed Lag (NARDL) model, complemented by robustness checks using ARDL, FMOLS, and DOLS estimators. The results confirm the existence of a long-run cointegrating relationship among the variables. FD and EG are found to have positive and statistically significant effects on RE consumption, both in the long and short run. In contrast, INN and FDI exhibit positive but statistically insignificant coefficients, indicating that their effects are not empirically robust within the sample period. The nonlinear analysis further reveals asymmetric dynamics: negative shocks in FD and EG exert stronger adverse effects than positive shocks generate benefits. Diagnostic and robustness tests support the model's validity and reliability, though caution is warranted given potential issues with residual autocorrelation and the use of aggregate proxies for FDI and INN. These limitations may mask underlying sectoral heterogeneity, particularly between green and non-green investment and INN activities. The findings suggest that strengthening financial systems and maintaining stable EG are critical for advancing RE adoption in South Africa. However, the limited role of aggregate FDI and INN highlights the need for more targeted policies that promote green investment and energy-specific technological development. In general, the study provides incremental, policy-relevant evidence by clarifying the asymmetric, statistically robust determinants of RE transition in an emerging economy context.
The aim of this study is to investigate how companies disclose quality of employment (QoE) themes in corporate non-financial reports, and how this disclosure is examined in relation to HR KPI Disclosure Maturity and Open Innovation (OI) Reporting Context. The methodological basis of the study is a qualitative and comparative content analysis of the non-financial sections of annual and sustainability reports, supplemented by scoring procedures for comparing disclosure patterns across eight large foreign-owned retail chains. The analysis focuses on the structure of QoE theme disclosure, the systematic nature of HR-related indicator disclosure, and the co-occurrence of OI signals with more developed employment-related disclosure. The results show that QoE is rarely presented as a clearly defined and integrated category of corporate disclosure. More often, employment quality is disclosed through separate themes distributed across different sections of corporate reports. Companies with more developed QoE disclosure tend to present HR-related indicators more systematically. At the same time, OI signals are interpreted as part of a broader reporting pattern rather than as evidence of causal effects. This study contributes to the literature on employment quality and corporate non-financial reporting by demonstrating how QoE disclosure, HR KPI Disclosure Maturity, and OI Reporting Context can be analysed together at the level of corporate disclosure.
