Digital transformation increasingly depends on enterprise integration and infrastructure platforms that connect supply chains, workforces, service providers, and business partners. However, these platforms are still commonly assessed through internal measures such as system availability, defect rates, deployment consistency, and operating costs, leaving their wider contribution to organizational resilience insufficiently examined. This study investigates how integration and infrastructure governance practices support resilience during large-scale enterprise transformation. A multiple-case study was conducted across five transformation programs in manufacturing, food and agriculture, and financial services. Governance practices and documented program outcomes were examined through structured evidence mapping across four dimensions: supply continuity, operational dependability, transition disruption, and business continuity across extended stakeholder networks. The analysis found that standardized integration patterns, lifecycle controls, and Integration Center of Excellence (CoE) governance were associated with fewer integration defects and sustained reliability across business-critical operations. Phase-gate migration governance, dependency mapping, validation cycles, and rollback provisions supported cloud migrations and provider transitions without reported service degradation. Hybrid cloud architecture, disaster recovery (DR) design, and multi-vendor coordination also maintained service continuity across partner and customer networks, while platform modernization produced substantial cost savings and improved operational visibility. The findings indicate that enterprise integration and infrastructure governance function as organizational capabilities rather than secondary technical controls. The study presents a practice-derived framework that connects governance mechanisms with resilience outcomes and supports investment decisions concerning digital transformation in operationally critical industries.
Rapidly changing consumer preferences, consumption occasions, service experiences, and promotional practices require coffee businesses to continuously identify and evaluate emerging innovation opportunities. However, converting large volumes of unstructured social media discourse into actionable knowledge remains a major challenge, particularly for businesses with limited market research capabilities. This study investigates how social listening can support technology-enabled market sensing and product innovation in the coffee industry. Four Twitter/X corpora collected during 2023 were analyzed, comprising a global coffee corpus of 172,017 tweets and three brand-specific corpora relating to Kopi Kenangan, Starbucks, and Kopi Janji Jiwa. A Biterm Topic Model (BTM) was applied to identify latent consumer preference structures, while topic prevalence and sentiment-derived satisfaction were integrated through importance–satisfaction mapping to distinguish market-supported innovation opportunities. The global corpus yielded 13 preference topics, five of which represented opportunities associated with work-related benefits, coffee-shop experiences, drinking enjoyment, caffeine and sleep, and morning consumption. The analysis identified three opportunity topics among eight preference topics for Kopi Kenangan, five among 12 topics for Starbucks, and ten among 15 topics for Kopi Janji Jiwa. Cross-brand comparison showed that innovation opportunities extended beyond beverage attributes to include service experience, complementary products, pricing, promotions, social influence, customer segments, and collaboration channels. The findings demonstrate that social listening can function as a decision-oriented innovation mechanism rather than merely a descriptive monitoring tool. The proposed framework advances data-driven innovation research by linking digital consumer discourse to structured opportunity identification and provides a practical approach to market sensing, innovation prioritization, and low-cost experimentation for resource-constrained coffee businesses.
Omnichannel retailers increasingly manage promotional coupons across interconnected email, mobile, web, point-of-sale (POS), and partner systems. When offer definitions, eligibility rules, and redemption logic are distributed among independently operated channel systems, inconsistencies in promotional execution, fragmented audit trails, and synchronization delays can emerge as the channel environment expands. This study investigates this architectural fragmentation and develops a centralized coupon lifecycle model for consistent promotional management across heterogeneous retail channels. A conceptual architecture was developed by drawing on enterprise application architecture, microservices design principles, and event-driven integration patterns. The resulting model separated coupon management into two coordinated layers: a declarative content layer for centralized offer definition and an orchestration layer for real-time eligibility evaluation, conflict resolution, and cross-channel execution. The architectural analysis showed that this separation established a common authority for promotional decision-making and reduced the structural conditions associated with duplicated rules, inconsistent channel execution, and fragmented decision records. The proposed architecture also provided a unified decision-logging structure that can support cross-channel auditability and provide coherent behavioral data for subsequent personalization models. These findings indicate that coupon lifecycle management is more effectively treated as an enterprise digital architecture problem than as a collection of channel-specific promotional functions. The proposed model provides a reusable architectural framework for retailers seeking to strengthen promotional governance, maintain execution consistency as channel complexity grows, and establish a structured data foundation for technology-enabled personalization.
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