Search Results (443)

Background: Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business and operational perspectives, this study focuses on the acceptance of AVs from the standpoint of e-consumers—individuals who make purchases via digital platforms—in an emerging market context. Methods: Grounded in an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is specifically suited to consumer-focused technology adoption research, this study incorporates five constructs tailored to AV adoption. Structural Equation Modeling (SEM) was applied to survey data collected from 304 e-consumers in Northeast Brazil. Results: The findings reveal that performance expectancy, hedonic motivation, and environmental awareness exert significant positive effects on acceptance and intention to use AVs for LM delivery. Social influence shows a weaker, yet still positive, impact. Importantly, price sensitivity exhibits a minimal effect, suggesting that while consumers are generally cost-conscious, perceived value may outweigh price concerns in early adoption stages. Conclusions: These results offer valuable insights for policymakers and logistics providers aiming to implement consumer-oriented, cost-effective AV solutions in LM delivery, particularly in emerging economies. The findings emphasize the need for strategies that highlight the practical, emotional, and environmental benefits of AVs to foster market acceptance. Full article

To enhance the performance of online food delivery (OFD) services, it is essential to strengthen consumers’ intentions to use OFD apps, which are the core interface of this business. Accordingly, this study aims to identify the cognitive mechanisms that shape consumers’ intentions to use OFD apps and explore strategies to encourage their adoption. To achieve this, the study develops a research model that incorporates segmented dimensions of service convenience as key motivational factors, along with variables from the Technology Acceptance Model (TAM). A survey was conducted among OFD consumers in China, and the proposed research model was empirically tested using data from 478 valid responses. The analysis revealed that all six dimensions of service convenience serve as significant motivational drivers of OFD app usage. Furthermore, the study demonstrates that consumers’ coupon proneness and user-generated online reviews have significant moderating effects that reinforce the mechanism by which consumers adopt and use OFD apps. The findings and implications discussed in this study are expected to provide valuable insights and practical guidance for formulating effective strategies to promote more active consumer engagement with OFD apps in the future. Full article

Micro, Small, and Medium Enterprises (MSMEs) are vital drivers of economic growth in Kenya, yet they face persistent barriers, including limited capacity, financial exclusion, and weak market integration. This study assessed the potential of universities as strategic hubs for MSME capacity building through a collaborative initiative between Egerton University and the KCB Foundation. Using the International Labour Organization’s Start and Improve Your Business (SIYB) methodology, 481 entrepreneurs from Egerton, Njoro, and Gilgil were trained in a business development bootcamp. This study evaluated the training effectiveness, participant demographics, confidence in skill application, networking outcomes, and satisfaction levels. The results showed high participant confidence (over 95% across all regions), strong financial management uptake (85%), and mobile banking adoption (70%). Gilgil led in inclusivity and peer engagement, while Njoro showed stronger gender representation. However, logistical challenges caused 25% absenteeism in rural areas, and only 23% accessed post-training mentorship. These findings underscore the transformative role of HEIs in fostering sustainable entrepreneurship through localized, inclusive, and industry-aligned training. Policy recommendations include hybrid delivery models, tiered curricula for diverse skill levels, and institutionalized mentorship through public–private partnerships. This case demonstrates the value of embedding entrepreneurship support within university mandates to advance national MSME development agendas. Full article

This paper investigates sustainable decision systems within green E-business models by analyzing how different O2O (online-to-offline) fulfillment structures affect emission-reduction efforts and pricing strategies in a two-tier supply chain consisting of a manufacturer and a new retailer. Three practical sales formats—package self-pickup, nearby delivery, and hybrid—are modeled using Stackelberg game frameworks that incorporate key factors such as inconvenience cost, logistics cost, processing fees, and emission-reduction coefficients. Results show that the manufacturer’s emission-reduction decisions and both parties’ pricing strategies are highly sensitive to cost conditions and consumer preferences. Specifically, higher inconvenience and abatement costs consistently reduce profitability and emission efforts; the hybrid model exhibits threshold-dependent advantages over single-mode strategies in terms of carbon efficiency and economic returns; and consumer green preference and distance sensitivity jointly shape optimal channel configurations. Robustness analysis confirms the model’s stability under varying parameter conditions. These insights provide theoretical and practical guidance for firms seeking to develop adaptive, low-carbon fulfillment strategies that align with sustainability goals and market demands. Full article

As sustainability becomes increasingly central to organizational strategy, social economy organizations (SEOs) are rethinking their business models. This study employs stakeholder analysis using the value mapping (VM) tool developed by Short, Rana, Bocken, and Evans for the development of the VOLTO JÁ project. The objective of the VOLTO JÁ project is to operationalize a senior exchange programme between SEOs. The VM approach extends beyond conventional customer value propositions to prioritize sustainability for all stakeholders and identify key drivers of sustainable business model (SBM) innovation. The multi-stakeholder methodology comprises the following elements: (1) sequential focus groups aimed at enhancing sustainable business thinking; (2) semi-structured interviews; and (3) workshop to facilitate qualitative analysis and co-create the VM. The findings are then categorized into four value dimensions: (1) value captured—improved participant well-being, enhanced reputational capital, mitigation of social asymmetries, and affordable service experiences; (2) value lost—underused community assets; (3) value destroyed—institutional and systemic barriers to innovation; and (4) new value opportunities—knowledge sharing, service diversification, and open innovation to foster collaborative networks. The study demonstrates that the application of VM in SEOs supports SBM development by generating strategic insights, enhancing resource efficiency, and fostering the delivery of socially impactful services. Full article

The Architecture, Engineering, and Construction (AEC) industry is plagued by persistent challenges such as low productivity, cost overruns, and frequent project delays. Integrated Project Delivery (IPD) has emerged as a potential solution, offering collaborative approaches to improve project outcomes. This study proposes a two-tiered methodology for evaluating the disruptiveness of innovations in the AEC industry, with a particular focus on IPD as a disruptive innovation. In the first tier, a multidimensional framework is developed to systematically assess the disruptiveness of innovations in the AEC sector. This framework, informed by a thorough literature review and disruptive innovation theory, includes dimensions such as business models, processes, and anticipated outcomes. The second tier applies the framework by analyzing the disruptiveness of IPD. The assessment draws on data from three comprehensive studies, including ethnographic research, interviews, and focus groups, which examine IPD’s impact on different stakeholder groups such as clients, consultants, and contractors. Findings reveal that IPD has the potential to significantly disrupt traditional business models, processes, and project outcomes, particularly at the project level. Notable disruptive characteristics include shifts in collaboration dynamics, redefined project financing models, and improved efficiency. However, several barriers hinder IPD adoption, including resistance to change and misalignment with conventional contractual structures. Expert interviews support these results, indicating that IPD represents a fundamental shift in the AEC industry. This research contributes to the existing body of knowledge by offering a structured framework for assessing the disruptiveness of AEC innovations and demonstrating its practical application. In this way, AEC organizations, projects, and practitioners can better strategize for the adoption of any new disruptive innovation and thus pursue a strategic advantage in the highly competitive industry market. Full article

Data workflows are an important component of modern analytical systems, enabling structured data extraction, transformation, integration, and delivery across diverse applications. Despite their importance, these workflows are often developed using ad hoc approaches, leading to scalability and maintenance challenges. This paper proposes a structured, three-level methodology—conceptual, logical, and physical—for modeling data workflows using Business Process Model and Notation (BPMN). A custom BPMN metamodel is introduced, along with a tool built on BPMN.io, that enforces modeling constraints and supports translation from high-level workflow designs to executable implementations. Logical models are further enriched through blueprint definitions, specified in a formal, implementation-agnostic JSON schema. The methodology is validated through a case study, demonstrating its applicability across ETL and machine learning domains, promoting clarity, reuse, and automation in data pipeline development. Full article

In today’s volatile market environment, supply chain management (SCM) must address complex challenges such as fluctuating demand, fraud, and delivery delays. This study applies machine learning techniques—Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks (RNNs)—to optimize demand forecasting, inventory policies, and risk mitigation within a unified framework. XGBoost achieves high forecasting accuracy (MAE = 0.1571, MAPE = 0.48%), while RNNs excel at fraud detection and late delivery prediction (F1-score ≈ 98%). To evaluate models beyond accuracy, we introduce two novel metrics: Cost–Accuracy Efficiency (CAE) and CAE-ESG, which combine predictive performance with cost-efficiency and ESG alignment. These holistic measures support sustainable model selection aligned with the ISO 14001, GRI, and SASB benchmarks; they also demonstrate that, despite lower accuracy, Random Forest achieves the highest CAE-ESG score due to its low complexity and strong ESG profile. We also apply SHAP analysis to improve model interpretability and demonstrate business impact through enhanced Customer Lifetime Value (CLV) and reduced churn. This research offers a practical, interpretable, and sustainability-aware ML framework for supply chains, enabling more resilient, cost-effective, and responsible decision-making. Full article

With the rapid development of the Internet, businesses in the traditional catering industry are increasingly shifting toward the Online-to-Offline mode, as on-demand food delivery platforms continue to grow rapidly. Within these takeout systems, riders have a role throughout the order fulfillment process. Their behaviors involve multiple key time points, and accurately predicting these critical moments in advance is essential for enhancing both user retention and operational efficiency on such platforms. This paper first proposes a time chain simulation method, which simulates the order fulfillment in segments with an incremental process by combining dynamic and static information in the data. Subsequently, a GRU-Transformer architecture is presented, which is based on the Transformer incorporating the advantages of the Gated Recurrent Unit, thus working in concert with the time chain simulation and enabling efficient parallel prediction before order creation. Extensive experiments conducted on a real-world takeout food order dataset demonstrate that the Mean Squared Error of the prediction results of GRU-Transformer with time chain simulation is reduced by about 9.78% compared to the Transformer. Finally, according to the temporal inconsistency analysis, it can be seen that GRU-Transformer with time chain simulation still has a stable performance during peak periods, which is valuable for the intelligent takeout system. Full article

Energy efficiency (EE) remains an underexploited opportunity in many developing economies, where a complex interplay of policy, institutional, and market-related challenges limit its implementation at scale. This study explores the structural, economic, and policy-related constraints affecting the EE market in Jordan, a country with a high dependence on imported energy. Using a multi-framework approach, we apply the political, economic, social, technological, environmental, and legal (PESTEL) framework to categorize these barriers, complemented by Brown’s business model (BM) typology to enhance the analytical depth. Primary data were collected through semi-structured interviews with key market actors. The findings highlight issues such as economic volatility, regulatory fragmentation, and the structural biases associated with donor-driven interventions, which contribute to an uneven and loosely regulated market environment in which businesses face significant scaling challenges. This study reflects on international experience to explore how strategies from other contexts might inform markets’ adaptation in emerging economies. This study concludes with targeted policy recommendations aimed at clarifying regulatory pathways and supporting more effective market delivery. This research contributes to ongoing policy discourse by highlighting how context-specific BM innovations might help address systemic barriers, while potentially supporting national energy goals. Full article

The categories for call center conversation data are valuably used for reporting business results and marketing analysis. However, they typically lack clear patterns and suffer from severe imbalance in the number of instances across categories. The call center conversation categories used in this study are Payment, Exchange, Return, Delivery, Service, and After-sales service (AS), with a significant imbalance where Service accounts for 26% of the total data and AS only 2%. To address these challenges, this study proposes a model that ensembles meta-information generated through Named Entity Recognition (NER) with machine learning inference results. Utilizing KoBERT (Korean Bidirectional Encoder Representations from Transformers) as our base model, we employed Easy Data Augmentation (EDA) to augment data in categories with insufficient instances. Through the training of nine models, encompassing KoBERT category probability weights and a CatBoost (Categorical Boosting) model that ensembles meta-information derived from named entities, we ultimately improved the F1 score from the baseline of 0.9117 to 0.9331, demonstrating a solution that circumvents the need for expensive LLMs (Large Language Models) or high-performance GPUs (Graphic Process Units). This improvement is particularly significant considering that, when focusing solely on the category with a 2% data proportion, our model achieved an F1 score of 0.9509, representing a 4.6% increase over the baseline. Full article

The rapid expansion of online grocery shopping has intensified the need for strategically located distribution hubs that ensure efficient and sustainable operations. Traditional location models emphasize economic and logistical factors but often neglect energy efficiency and environmental sustainability. This paper proposes a hybrid decision-making model that integrates the analytic hierarchy process (AHP) and the spherical fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS) to address the complexities of delivery hub location selection. The AHP is used to determine the relative importance of key decision-making criteria, including cost, accessibility, infrastructure, competition, and sustainability, while SFTOPSIS ranks the candidate locations based on their proximity to the ideal solution. Spherical fuzzy sets allow for a more nuanced treatment of uncertainty, improving decision-making accuracy in dynamic environments. The results demonstrate that this hybrid approach effectively manages incomplete and uncertain data, delivering a robust ranking of candidate locations. By incorporating sustainability as a key factor, this study provides a structured and adaptive framework for businesses to optimize logistics operations in the post-pandemic landscape. The proposed methodology not only enhances decision-making in location selection but contributes to the development of more resilient and sustainable supply chain strategies. Full article

This study investigates the evolution of customer-centric innovations within modern business ecosystems through the lens of servitization, a concept gaining momentum in contemporary service delivery frameworks. Recognizing the limited exploration of servitization beyond manufacturing, particularly in the context of value-added services, this research employs a multiple case study methodology focused on the tea sector in India and Nepal. Drawing on seven diverse entrepreneurial cases and supported by a thematic analysis, the study identifies nine critical factors influencing successful servitization, including knowledge gaps, procurement strategies, market segmentation, and customer engagement. Central to this investigation is the transformative role of structured training interventions, exemplified by the Chaya School of Tea, which catalyzed innovation and performance improvements among participating businesses. The findings highlight how digital tools, customer education, and strategic planning contribute to product–service integration, yielding enhanced quality, operational efficiency, and sustainable growth. This research contributes to theory by refining the concept of “servitization of services” as a strategic approach for empowering ecosystems through complementary offerings that transcend traditional service delivery. This work provides both conceptual and empirical insights into how service firms, particularly in under-researched sectors, can leverage servitization to drive long-term competitiveness and ecosystem-wide value creation. Full article

Data generated by Internet of Things devices enable the design of new business models and services, improving user experience and satisfaction. This data also serve as an essential information source for many fields, including disaster management, bio-surveillance, smart cities, and smart health. However, personal data are also collected in this context, introducing new challenges concerning data privacy protection, such as profiling, localization and tracking, linkage, and identification. This dilemma is further complicated by the “privacy paradox”, where users compromise privacy for service convenience. Hence, this paper reviews the literature on data privacy in the IoT, particularly emphasizing Personal Data Store (PDS)-based approaches as a promising class of user-centric solutions. PDS represents a user-centric approach to decentralizing data management, enhancing privacy by granting individuals control over their data. Addressing privacy solutions involves a triad of user privacy awareness, technology support, and ways to regulate data processing. Our discussion aims to advance the understanding of IoT privacy issues while emphasizing the potential of PDS to balance privacy protection and service delivery. Full article

This paper proposes a Human-AI collaborative framework for cybersecurity consulting tailored to the needs of small businesses, designed and implemented within a Master of Cybersecurity capstone program. The framework outlines a structured four-stage development model that integrates students into real-world consulting tasks while aligning with academic and industry objectives. Human–AI collaboration is embedded throughout the process, combining generative AI tools and domain-specific AI agents with human expertise to support the design, delivery, and refinement of consulting resources. The four stages include (1) AI agent development; (2) cybersecurity roadmap creation; (3) resource development; and (4) industry application. Each stage supports both development-oriented outputs—such as templates, training materials, and client deliverables—and research-oriented projects that explore design practices, collaboration models, and consulting strategies. This dual-track structure enables iterative learning and improvement while addressing educational standards and the evolving cybersecurity landscape for small businesses. This framework provides a scalable foundation for capstone-based consulting initiatives that bridge academic learning and industry impact through Human–AI collaboration. Full article